Conference Program of WCICA 2012

WCICA 2012 

The 10 th World Congress on 

Intelligent Control and Automation 

July 6-8, 2012, Beijing, China 

Conference Program 

Sponsored by 

Academy of Mathematics and Systems Science, Chinese Academy of Sciences 

IEEE Robotics and Automation Society 

IEEE Control Systems Society 

National Natural Science Foundation of China 

Chinese Association of Automation 

Chinese Association of Artificial Intelligence 

IEEE RACS Hong Kong Chapter 

IEEE Control Systems Society Beijing Chapter 

IEEE Control Systems Society Singapore Chapter

WCICA 2012 CD-ROM Proceedings 

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Copyright c○ 2012 by IEEE.


Welcome 

Welcome 

It is our great honor to welcome you to the 10th World Congress on Intelligent Control and Automation 

(WCICA 2012), which takes place at Beijing International Convention Center in Beijing, China from July 6 

to 8, 2012. 

WCICA 2012 is the tenth biennial event of the WCICA congress series. The congress has received 

1244 submissions from 24 countries and regions. After a rigorous full-paper peer-review process, 956 

papers were accepted for oral or poster presentation at the conference in 70 technical sessions including 

22 invited sessions. We should mention that, due to the large number of paper submissions, the Technical 

Program Committee worked extremely hard to review all submitted papers rigorously to ensure a high 

quality technical program. 

The congress program is highlighted by five distinguished plenary speakers: Prof. Jie Huang from 

Chinese University of Hong Kong, China; Prof. Francis J. Doyle III from University of California at Santa 

Barbara, USA; Prof. Leon Chua from University of California at Berkeley, USA; Prof. Tim C. Lueth from 

Technical University of Munich, Germany; and Prof. Hong Chen from Jilin University, China. 

In addition, the congress program features two invited panel sessions including the panel session 

on “Beyond control” and the panel session on “Future robotics and automation”, all with world leading 

researchers as panelists. Five pre-conference tutorial workshops are organized to introduce the hot topics 

to young researchers. 

We wish to express our appreciation and gratitude to all the individuals who have contributed to 

WCICA 2012 in a variety of ways. Special thanks are extended to our colleagues in the technical program 

committee and reviewers for great efforts in putting up the high quality technical program, which is vital 

to the success of this congress, and also to the members in the organizing committee and our volunteer 

students who have dedicated their time and efforts in planning, promoting, organizing and helping the 

conference. Last but not least, our special thanks go to distinguished plenary speakers, invited panelists 

and speakers, as well as all the authors for contributing their latest research work to the conference, 

and to the participants and the exhibitors in making the 10th World Congress on Intelligent Control and 

Automation (WCICA 2012) a memorable event. 

Finally, if your travel plans permit, we encourage you to stay beyond the conference to enjoy visiting 

Beijing and the rest of China. We hope that your visit and stay in China will be rewarding both professionally 

and personally. 

Daizhan CHENG 

General Chair 

Lihua XIE 

General Co-Chair 

Ji-Feng ZHANG 

Program Chair 

Yiguang HONG 

Organizing Chair 

i

Conference Program WCICA 2012 

Contents 

Welcome . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i 

Sponsorships . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 

Organizing Committee . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 

Program Committee . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 

General Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 

Instruction for Oral and Poster Presentations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 

Transportation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 

Floor Plan of BICC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 

Plenary Lectures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 

Plenary Panel Sessions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 

Pre-conference Workshops . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 

Best Paper Awards and Finalists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 

Program Schedule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 

Timetable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 

Technical Program . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 

Book of Abstracts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225 

Chair/Co-Chair Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 249 

ii


Sponsorships 

Sponsorships 

Sponsor 

Academy of Mathematics and Systems Science, 

Chinese Academy of Sciences 

http://www.amss.ac.cn/ 

Technical Co-Sponsors 

IEEE Robotics and Automation Society 

http://www.ieee-ras.org/ 

IEEE Control Systems Society 

http://www.ieeecss.org/ 

National Natural Science Foundation of China 

http://www.nsfc.gov.cn/ 

Chinese Association of Automation 

http://www.caa.org.cn 

Chinese Association of Artificial Intelligence 

http://www.caai.cn/ 

IEEE RACS Hong Kong Chapter 

http://www2.acae.cuhk.edu.hk/~ieee-racs-hk/ 

IEEE Control Systems Society Beijing Chapter 

http://icbc.amss.ac.cn/ 

IEEE Control Systems Society Singapore Chapter 

http://uav.ece.nus.edu.sg/~ieee/ 

1


Corporate Sponsor 

Shanghai xPartner Robotics Co., Ltd. 

http://www.xpartner.cn/ 

Exhibitors 

ALDEBARAN ROBOTICS 

http://www.aldebaran-robotics.com/ 

Shanghai GaiTech Scientific Instrument Co., Ltd. 

http://www.gaitech.net/ 

Quanser Inc. 

http://www.quanser.com/ 

深圳市中科鸥鹏智能科技有限公司 

http://www.szopen.cn/ 

Leeman China 

http://www.leemanchina.com/ 

Dynamic Measurement System-DMS (INTERNATIONAL) CO. 

http://www.dms-test.com/ 

Science Press 

http://www.sciencep.com/ 

Sunrise Auto Safety Technology & Sunrise Instruments 

http://www.dummysensor.com/ 

CRC Press 

http://www.crcpress.com/ 

2


Organizing Committee 

Organizing Committee 

Honorary Chairs 

Han-Fu CHEN, China Lei GUO, China Yu Chi HO, USA 

Jian SONG, China Tzyh Jong TARN, USA 

General Chair 

Daizhan CHENG, China 

General Co-Chair 

Lihua XIE, Singapore 


Ji-Feng ZHANG, China 

Program Co-Chairs 

Ben M. CHEN, Singapore 

Yunhui LIU, HK, China 

Regional Program Chairs 

Tongwen CHEN, Canada Zhengtao DING, UK Minyue FU, Australia 

Shuzhi Sam GE, Singapore Xiaoming HU, Sweden Kangzhi LIU, Japan 

Hong QIAO, China 

Organizing Chair 

Yiguang HONG, China 

Organizing Co-Chairs 

Jie CHEN, China 

Zhongping JIANG, USA 

Invited Session Chairs 

Shuang CONG, China Jie HUANG, HK, China Derong Liu, China 

Poster Session Chairs 

Shaoyuan LI, China 

Wei WANG, China 

Tutorial Workshop Chairs 

Peter B. LUH, USA 

Tielong SHEN, Japan 

Panel Discussion Chairs 

Gary G. FENG, HK, China 

Q.-H. Max MENG, HK, China 

Award Committee Chairs 

Guangren DUAN, China 

Zongli LIN, USA 

Publication Chairs 

Jason GU, Canada 

Qianchuan ZHAO, China 

3


Finance Chair 

Haitao FANG, China 

Exibits Chair 

Xiaorui ZHU, China 

Webmaster 

Hongsheng QI, China 

General Secretary 

Yanlong ZHAO, China 

4


Program Committee 



Ji-Feng ZHANG, China 

Program Co-Chairs 

Ben M. CHEN, Singapore 

Yunhui LIU, HK, China 

Regional Program Chairs 

Tongwen CHEN, Canada Zhengtao DING, UK Minyue FU, Australia 

Shuzhi Sam GE, Singapore Xiaoming HU, Sweden Kangzhi LIU, Japan 

Hong QIAO, China 

International Program Committee Members 

Fengshuang Bai, China Lubomir Bakule, Czech Republic Jacky Baltes, Canada 

Zdenek Binder, France Sergio Bittanti, Italy Zixing Cai, China 

Longhan Cao, China C.W. Chan, Hong Kong, China Ben M. Chen, Singapore 

Chin-Yin Chen, Taiwan, China Dongyi Chen, China Hong Chen, China 

Jian Chen, United States Jie Chen, China Jie Chen, United States 

Kaiquan Chen, China Minyou Chen, China Qingwei Chen, China 

Tongwen Chen, Canada Weidong Chen, China Wenhua Chen, United Kingdom 

Xinkai Chen, Japan Xuebo Chen, China Yangquan Chen, United States 

Yuguang Chen, China Zengqiang Chen, China Zhiyong Chen, Australia 

Peng Cheng, China Guoyang Cheng, China Ronghu Chi, China 

Jyhhorng Chou, Taiwan, China Delin Chu, Singapore Xin Dai, China 

Mingcong Deng, Japan Baocang Ding, China Xilun Ding, China 

Zhengtao Ding, United Kingdom Junping Du, China Xiaoping Fan, China 

Huajing Fang, China Yongchun Fang, China Minrui Fei, China 

George A. Fodor, Sweden Jun Fu, China Minyue Fu, Australia 

Huijun Gao, China Dongbing Gu, United Kingdom Jason Gu, Canada 

Xingsheng Gu, China Zhenyu Gu, China Zhihong Guan, China 

Ge Guo, China Shuxiang Guo, Japan Yi Guo, United States 

Liqun Han, China Qinglong Han, Australia Qi Hao, Canada 

Xiaohong Hao, China Huiguang He, China Xiqin He, China 

Youquan He, China Yasuhisa Hirata, Japan Xuezhang Hou, United States 

Yuexian Hou, China Zengguang Hou, China Chunfei Hsu, Taiwan, China 

Baogang Hu, China Chao Hu, China Xiaoming Hu, Sweden 

Hsing-Hsin Huang, Taiwan, China Shyh-Chour Huang, Taiwan, China Wei Huang, China 

Xinhan Huang, China Zhijian Ji, China Qingshan Jia, China 

Bin Jiang, China Zhao-Hui Jiang, Japan Zhongping Jiang, United States 

Yi Jin, China Wei Kang, United States Yu Kang, China 

Weiyao Lan, China Ziqiang Lang, United Kingdom Chinghung Lee, Taiwan, China 

Tichung Lee, Taiwan, China Baopu Li, Hong Kong, China Bin Li, China 

Chuandong Li, China Fanchang Li, Congo Hanxiong Li, Hong Kong, China 

Jingshan Li, United States Ping Li, China Shaoyuan Li, China 

Shengping Li, China Tao Li, China Xiaoan Li, China 

Xiaoli Li, China Yangmin Li, China Yaohua Li, China 

Yibin Li, China Zetao Li, China Zhijun Li, China 

Shan Liang, China Jingsheng Liao, China Chong Lin, China 

Hai Lin, Singapore Zongli Lin, United States Chao Liu, France 

Chaotao Liu, China Derong Liu , China Han Liu, China 

5


Hong Liu, China Hugh H. T. Liu, Canada Jingtai Liu, China 

Lu Liu, United Kingdom Min Liu, China Rong Liu, China 

Shixin Liu, China Tung-Kuan Liu, Taiwan, China Xiangjie Liu, China 

Xiaohua Liu, China Xiaoping Liu, Canada Yungang Liu, China 

Yunhui Liu, Hong Kong, China Zhixin Liu, China Yunjiang Lou, China 

Jinhu Lu, China Xiao Lu, China Zhao Lu, United States 

Peter Luh, United States Kai Yew Lum, Singapore Cui-Qin Ma, China 

Hongbin Ma, China Lei Ma, China Tao Mei, China 

Duoqian Miao, China Mehrdad Moallem, Canada Mohamed M’saad, France 

Yugang Niu, China Lorenzo Ntogramatzidis, Australia Ya-Jun Pan, Canada 

Gang Peng, China Jun Peng, China Kemao Peng, Singapore 

Geoff Pond, Canada Bo Qi, China Chunjiang Qian, United States 

Fucai Qian, China Qi Qian, China Shiyin Qin, China 

Jianbin Qiu, China Jianlong Qiu, China Guang Ren, China 

Jianghong Ren, China Qiuqi Ruan, China Imre Rudas, Hungary 

Yahaya Md. Sam, Malaysia Hideyuki Sawada, Japan Robert Schmid, Australia 

Nariman Sepehri, Canada Keyong Shao, China Tielong Shen, Japan 

Weiming Shen, Canada Jie Sheng, China Weihua Sheng, United States 

Ling Shi, Hong Kong, China Peng Shi, Hong Kong, China Yang Shi, Canada 

Vasile Sima, Romania Quanjun Song, China Rui Song, China 

Zhihuan Song, China Chun-Yi Su, Canada Hongye Su, China 

Jianbo Su, China Rong Su, Singapore Weizhou Su, China 

Yuxin Su, China Changyin Sun, China Dihua Sun, China 

Dong Sun, Hong Kong, China Fengchi Sun, China Fuchun Sun, China 

Guo-Hua Sun, China Jitao Sun, China Qu Sun, China 

Weijie Sun, China Yimin Sun, China Zengqi Sun, China 

Zhendong Sun, China Min Tan, China Jindong Tan, United States 

Xiaobo Tan, United States Ying Tan, Australia Lixin Tang, China 

Guohui Tian, China Kuo-Yang Tu, Taiwan, China Yaqing Tu, China 

Gancho Vachkov, Japan Sandor M. Veres, United Kingdom Bing Wang, China 

Bingchang Wang, China Changjun Wang, China Cong Wang, China 

Dan Wang, China Dianhui Wang, Australia Fuli Wang, China 

Guoyin Wang, China Hong Wang, United Kingdom Jian Wang, China 

Jiandong Wang, China Jiang Wang, China Jing Wang, China 

Kerwin Wang, Taiwan, China Lin Wang, China Liuping Wang, Australia 

Long Wang, China Peijin Wang, China Wei Wang, China 

Wenjune Wang, Taiwan, China Xiaofan Wang, China Xiaona Wang, Hong Kong, China 

Xin Wang, China Xingyu Wang, China Ying Wang, United States 

Yong Wang, China Youqing Wang, China Yuecao Wang, China 

Zhelong Wang, China Zidong Wang, United Kingdom Qiang Wei, China 

Guihua Wen, China Ai-Guo Wu, China Hansheng Wu, Japan 

Jonathan Wu, Canada Min Wu, China Wen-Yen Wu, Taiwan, China 

Ying Wu, China Yuqiang Wu, China Bin Xi, China 

Yuanqing Xia, China Bin Xian, China Cheng Xiang, Singapore 

Jiwei Xiao, China Lihua Xie, Singapore Wei Xie, China 

Xuejun Xie, China Xin Xin, Japan Bugong Xu, China 

Jinxue Xu, China Shengyuan Xu, China Dingyu Xue, China 

Bingru Yang, China Bo Yang, China Chunhua Yang, China 

Guowei Yang, China Hongyong Yang, China Hui Yang, China 

Shanlin Yang, China Simon Yang, Canada Xiaoli Yang, United States 

Xudong Ye, China Hyun Joong Yoon, Korea Keyou You, Singapore 

Chunmei Yu, China Jingjun Yu, China Li Yu, China 

Wen Yu, Mexico Xianchuan Yu, China Yong Yu, Japan 

Decheng Yuan, China Dong Yue, China Constantin Bala Zamfirescu, Romania 

6



Lilin Zang, China Chenghui Zhang, China Chengjin Zhang, China 

Cishen Zhang, Australia Dabo Zhang, China Dongjun Zhang, China 

Guofeng Zhang, China Guoshan Zhang, China Hua Zhang, China 

Huaguang Zhang, China Huanshui Zhang, China Jiangfeng Zhang, South Africa 

Ji-Feng Zhang, China Jingxin Zhang, Australia Li-Mei Zhang, China 

Mingjun Zhang, United States Qijun Zhang, Canada Qingling Zhang, China 

Qizhi Zhang, China Rubo Zhang, China Tianping Zhang, China 

Weicun Zhang, China Weihai Zhang, China Yachong Zhang, China 

Yanduo Zhang, China Yi Zhang, China Yu Zhang, United States 

Yunfeng Zhang, Singapore Zhuhong Zhang, China Jun Zhao, China 

Ping Zhao, China Qianchuan Zhao, China Xin Zhao, China 

Mingguo Zhao, China Taixiong Zheng, China Yuan Fang Zheng, United States 

Qing-Chang Zhong, United Kingdom Huiyu Zhou, United Kingdom Daqi Zhu, China 

Yongli Zhu, China 

Qun Zong, China 

7


General Information 

Conference Venue 

Beijing International Convention Center (BICC) 

Address: No.8 North Si Huan Zhong Road, Chaoyang District, Beijing 100101, China 

Tel.: +86-10-84985588 

Home Page: http://www.bicc.com.cn 

Official Languages 

The official languages of the WCICA 2012 conference are both English and Chinese. 

Conference Secretariat 

Ms. Yuan SHEN 

Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China 

Email: wcica2012@amss.ac.cn 

Tel.: +86-10-62532161 

Fax: +86-10-62587343 

Conference Registration 

The WCICA 2012 Registration Desk is located at the lobby of BICC. The conference registration desk 

working schedule is as follows: 

Thursday, July 5, 2012: 08:00-20:00 

Friday, July 6, 2012: 08:00-18:00 

Saturday, July 7, 2012: 08:30-18:00 

Sunday, July 8, 2012: 08:30-12:00 

Exhibition 

Exhibition will be held from Friday, July 6 to Sunday, July 8, 2012 with the following schedules: 

Friday: 8:30-17:30 

Saturday: 8:30-17:30 

Sunday: 8:30-17:30 

Venue: Exhibition area on the 3rd floor of BICC 

Dietary Needs 

Conference delegates and partners with special dietary need are invited to inform the conference 

secretariat at the WCICA 2012 Conference Registration Desk. 

8


Instruction for Oral and Poster Presentations 

Instruction for Oral and Poster Presentations 

Oral Presentation: 

∙ Oral Presentation Time: 20 minutes (including discussion). 

∙ Each speaker is required to meet his/her session chairs in the corresponding session rooms 10 minutes 

before the session starts and copy the slide file (PPT or PDF) to the computer. 

∙ Please note that each session room will be equipped with a LCD projector, screen, pointer device, 

microphone, and a laptop or desktop computer with general presentation software such as Microsoft 

PowerPoint and Adobe Reader preinstalled. Please make sure that your files are compatible and 

readable with our operation system by using commonly used fonts and symbols. If you plan to use 

your own computer, please try the connection and make sure it works before your presentation. 

Poster Presentation: 

∙ The author should print the poster slide file by yourself in advance and take it with you to the conference 

site. 

∙ The conference will provide an exhibition board (width 0.9m, height 1.5m) for each poster paper. The 

boards will be arranged in order of the paper in the final program. Tape and other materials will 

be provided on site, and volunteer-assistants will give necessary help. Posters are required to be 

condensed and attractive. The characters should be large enough so that they are visible from 1 

meter apart. 

∙ Please note that during your poster session, the author should stay by your poster paper to explain 

and discuss your paper with visiting delegates. The members of the Evaluation Committee of WCICA 

2012 Best Poster Award will also be there to inspect poster papers. 

9


Transportation 

The main airport in Beijing is the Beijing Capital International Airport (BCIA), offering direct flights 

to all major cities in China as well as direct international flights to and from all major cities all over the 

world. Three main railway stations in Beijing are the Beijing Railway Station, Beijing South Railway 

Station, and Beijing West Railway Station. The Beijing Subway connects almost all important areas of 

Beijing city. 

By plane 

The city is served by the Beijing Capital International Airport (BCIA), offering direct flights to all 

major cities in China as well as direct international flights to and from all major cities all over the world. 

Beijing Capital International Airport is located 20km northeast of Beijing. Airport shuttles are available at 

BCIA. Passengers going to urban area can enjoy round trips between BCIA and Xidan / Beijing Railway 

Station / Gongzhufen / Fangzhuang / Zhongguancun / Wangjing / Beijing West Railway Station / Shangdi 

/ etc conveniently by taking airport shuttle. Single trip of airport shuttle is RMB 16 per person. Airport 

Express Railway connects Beijing Capital International Airport with the Dongzhimen station of Subway 

Line 2 and Line 13. Airport Express Railway has 4 stops: Dongzhimen, Sanyuanqiao, Terminal 3, and 

Terminal 2. Single trip of Airport Express Railway is RMB 25 per person. Also you can take a taxi to 

or from the airport. Please refer to the signs inside the terminal building. The minimum charge is RMB 

10 covering the first 3km. The rest will be charged at RMB 2/km; If the speed is below 12km/h, an extra 

expense for 1km will be charged every 5 minutes; While the taxi is waiting as the passengers require, extra 

an expense for 1km will be charged every 5 minutes; The minimum charge will be RMB 11 from 23:00pm 

to 5:00am, and also a rise of 20% per kilometer; The information above is subject to change by the Taxi 

Management Department. 

By train 

Train service is provided through three main train stations, the Beijing Railway Station, the Beijing 

West Railway Station, and the Beijing South Railway Station. Passengers can take Subway Line 2 at the 

Beijing Railway Station, Subway Line 4 at the Beijing South Railway Station, and Subway Line 9 at the 

Beijing West Railway Station. Passengers can take a taxi by referring the signs outside the Beijing Railway 

Station and Beijing West Railway Station and the signs inside the Beijing South Railway Station. 

By subway 

The Beijing Subway is an extensive underground rail system in Beijing. Presently, Subway Lines 1, 2, 

4, 5, 8, 10, 13, the Airport Express and the Batong Line are in service. 

Transportation to Beijing International Convention Center 

Beijing International Convention Center is located in the flourishing Asian Olympic Village area, on 

the No. 4 ring road, along the capital axis line. It is next to the Beijing National Stadium called the “Bird’s 

Nest”, and the Aquatic Center called the “Water Cube”. Passengers from Beijing Capital International 

Airport or Beijing (Beijing West, Beijing South) Railway Station can take a taxi and then get off at No.8 

BeiChenDongLu, Chaoyang District (refer to Fig. 1). Or you can first take Beijing Subway, then get off 

at Olympic Sports Center Station of Line 8 and then walk to Beijing International Convention Center in 

about 5 minutes using the map (refer to Fig. 2). 

10


Transportation 

Fig. 1: Location of BICC 

Fig. 2: Direction for walking from Olympic Sports Center Station of Line 8 to BICC 

11


Floor Plan of BICC 

Plenary talks and technical sessions are held at Beijing International Convention Center on both the 

second floor (Level 2) and the third floor (Level 3) from July 6 to 8, 2012. 

Conference Room 305 is used for plenary talks and plenary panel sessions. Room 203A, 203B, 203C, 

203D, 203E on the second floor, and Room 302, 303, 310, 311A, 311B, 311C on the third floor are used 

for oral sessions. Exhibition area on the second floor is used for poster sessions. 

Fig. 3: Second Floor (Level 2) of BICC 

12


Floor Plan of BICC 

Exhibition area on the third floor is used for company and publisher exhibition. 

Room 310 serves as the meeting area for the WCICA 2012 Awards Committee. 

Fig. 4: Third Floor (Level 3) of BICC 

13


Plenary Lectures 

Plenary Lecture I 

8:30-9:30, Friday, July 6, 2012 

Room 305, Third Floor, Beijing International Convention Center 

Zone Model Predictive Control of an Artificial Pancreas 

Professor Francis J. Doyle III 

University of California at Santa Barbara 

USA 

Chair: Prof. Lihua Xie (Nanyang Technological University, Singapore) 

Abstract 

Type 1 diabetes mellitus (T1DM) is a chronic autoimmune disease affecting approximately 25 million 

individuals in the world, and is the 4th leading cause of global death by disease. Current treatment requires 

either multiple daily insulin injections or continuous subcutaneous (SC) insulin infusion (CSII) delivered via 

an insulin infusion pump. Both treatment modes necessitate frequent blood glucose measurements to 

determine the daily insulin requirements for maintaining near-normal blood glucose levels. More than 30 

years ago, the idea of an artificial endocrine pancreas for patients with type 1 diabetes mellitus (T1DM) 

was envisioned. The closed-loop concept consisted of an insulin syringe, a blood glucose analyzer, and a 

transmitter. In the ensuing years, a number of theoretical research studies were performed with numerical 

simulations to demonstrate the relevance of advanced control design to the artificial pancreas, with delivery 

algorithms ranging from simple PID, to H-infinity, to model predictive control. 

Our algorithmic studies have focused on model predictive control, including safety constraints to prevent 

over-dosing, and multi-parametric implementation for regulatory review. Our latest work has focused 

on soft output constraints using “zones” to emulate the medical outcome metrics. A recent extension of 

that work will be described in this talk, consisting of a multipartite zone model predictive controller (Multi- 

Zone-MPC). 

14



Multi-Zone-MPC provides different tunings for the MPC weights based on four regions of glycemia: 

hypoglycemia, normoglycemia, elevated glycemia, and hyperglycemia. Defining these four zones provides 

richer control tunings that result in safe and effective control. 

Our latest clinical investigations will be reviewed to demonstrate the medical-relevance of such an 

approach to a feedback-controlled artificial pancreas. 

This presentation is based on work coauthored with Eyal Dassau, Rebecca Harvey, Matt Percival, 

Benny Grosman, Howard Zisser, Dale Seborg, and Lois Jovanovic. 

Short Biography 

Dr. FRANCIS J. DOYLE III is the Associate Dean for Research in the College of Engineering at UC, 

Santa Barbara and he is the Director of the Army Institute for Collaborative Biotechnologies. He holds the 

Duncan and Suzanne Mellichamp Chair in Process Control in the Department of Chemical Engineering, 

as well as appointments in the Electrical Engineering Department, and the Biomolecular Science and Engineering 

Program. He received his B.S.E. from Princeton (1985), C.P.G.S. from Cambridge (1986), and 

Ph.D. from Caltech (1991), all in Chemical Engineering. Prior to his appointment at UCSB, he has held 

faculty appointments at Purdue University and the University of Delaware, and held visiting positions at 

DuPont, Weyerhaeuser, and Stuttgart University. He is the recipient of several research awards (including 

the NSF National Young Investigator, ONR Young Investigator, Humboldt Research Fellowship) as well as 

teaching awards (including the Purdue Potter Award, the ASEE Ray Fahien Award, and the ASEE Chemstations 

Lectureship Award). He is a Fellow of multiple professional societies including IEEE, IFAC, AIMBE, 

and AAAS. He served as the editor-in-chief of the IEEE Transactions on Control Systems Technology from 

2004-2009, and has held Associate Editor positions with the Journal of Process Control, the SIAM Journal 

on Applied Dynamical Systems, and Royal Society’s Interface. In 2005, he was awarded the Computing in 

Chemical Engineering Award from the American Institute of Chemical Engineers for his innovative work in 

systems biology. His research interests are in systems biology, network science, modeling and analysis of 

circadian rhythms, drug delivery for diabetes, model-based control, and control of particulate processes. 

15


Plenary Lecture II 



Applying Model Predictive Control in Automotive 

Professor Hong Chen 

Jilin University 

China 

Chair: Prof. Tielong Shen (Sophia University, Japan) 

Abstract 

The basis of model predictive control (MPC) is the on-line solution of a constrained optimization problem 

updated by the actual state. The obtained control is injected into the system until the next sampling 

time, while the procedure is repeated whenever new measurements are available. Due to its ability to 

handle nonlinearity, to include various types of models predicting the future dynamics, to take time-domain 

constraints into account explicitly and to coordinate multiple performance requirements in the sense of optimization, 

MPC has become an attractive feedback strategy for designing control systems in automotive. 

The talk will discuss some aspects of applying MPC in automotive through some selected examples. 


Prof. Hong Chen received the B.S and M.S. degrees in process control from the Zhejiang University, 

China, in 1983 and 1986, respectively, and the Ph.D. degree from the University of Stuttgart, Germany, 

in 1997. From 1993 to 1997, she was a research assistant at the Institute for System Dynamics and 

Control Engineering, University of Stuttgart, Germany. Since 1999, she has been a Professor at the 

Jilin University, where she serves currently as Tang Aoqing Professor. Her current research interests 

include model predictive control, optimal and robust control, nonlinear control and applications in process 

engineering and mechatronic systems. 

She is now serving as a member of Technical Committee on Control Theory of CAA, Technical Committee 

on Process Control of CAA, IEEE CDC Operating Committee, and IFAC TC Automotive Control. 

16



Plenary Lecture III 



Surgical Robotics: Different Successful Concepts 

in the Past and in Future 

Professor Tim C. Lueth 

Technical University of Munich 

Germany 

Chair: Prof. Max Q.-H. Meng (Chinese University of Hong Kong, China) 

Abstract 

Since 20 years, surgical navigation and robotics are two important technologies to improve the state of 

the art in medical treatment. To know where an instrument is located relative to a region of interest (organ, 

vessel, bone structure) inside of the body is of great importance to achieve a preplanned postoperative 

situation. To guide and to move an instrument by a robot is more complex but is definitely required for 

almost all kind of surgery. 

While surgical navigation became a standard in many medical disciplines, surgical robotics is still at 

it’s beginning. Todays great commercial success of only one company with just one robotics approach 

(Telemanipulation), should not mislead to an interpretation that surgical robotics is now successful. There 

are still more problems than solutions. Also the visibility of this company is not typical for medical device 

companies. In the talk, several navigation and robotics systems are presented, that were developed within 

Germany during the past 15 years with different success. All of them skipped the barrier from idea to 

clinical use to the market. A collection of videos shows the robots use. 

Nevertheless, there are different mechanisms that are important to consider if a medical robot should 

be successful. These mechanisms are discussed and also the rules for researcher to design robots as 

medical device from the very beginning. Also some in-between solutions such as “Navigated Control” are 

presented to explain when a robot is useful and why sometimes a different solution is more successful. 

17


In future we will see, surgical robots and medical instruments that are patient specific printed on 

demand based on generative manufacturing methods such as Selective Laser Sintering of biocompatible 

materials. 


Prof. Tim C. Lueth received his degree in electrical engineering from the Darmstadt University of 

Technology, Darmstadt, Germany in 1989. He received the Ph.D. degree in robotics and habilitation in 

computer science from the University of Karlsruhe in 1993 and 1997, respectively. In 1994-1995, he 

was a Visiting Researcher at the MITI-AIST Electrotechnical Laboratory in Tsukuba, Japan. In 1997, 

Lueth became Professor for surgical navigation and robotics at the medical school Charit 茅 of the Humboldt 

University at Berlin. In 2001, he became the Director for Mechatronic Medical Technology at the 

Fraunhofer-Institute for Production Systems and Design Technology IPK. Since 2005, Lueth works as Professor, 

Chair, and Director of the Institute of Micro Technology and Medical Device Technology (MIMED) 

of the Technical University Munich, Germany. In 2006, he received a professor status at the University 

of Toronto, Canada. The European Patent Office elected him in 2007 as TOP-3 inventor in the category 

“lifetime achievement” for his patent activities in the area of surgical robotics and navigation. He received 

several national and international awards for his research on medical devices. In 2010, Lueth became 

elected Member of “acatech” the German National Academy for Science and Technology. Lueth is active 

Member of the IEEE R&A Chapter and the IEEE EMB Chapter. 

18



Plenary Lecture IV 

8:30-9:30, Saturday, July 7, 2012 


Cooperative Output Regulation of Multi-Agent Systems 

Professor Jie Huang 

Chinese University of Hong Kong 

China 

Chair: Prof. Ben M. Chen (National University of Singapore, Singapore) 

Abstract 

In this talk, we will describe the cooperative output regulation problem for multi-agent systems. The 

formulation of this problem generalizes the leader-following consensus problem in the sense that the problem 

simultaneously addresses asymptotic tracking and disturbance rejection, accounts for large model 

uncertainty, and accommodates a general leader system. Like the full information output regulation problem, 

this problem can be handled by the feedforward control approach and the internal model approach. 

These two approaches can also be used to handle other control problems of multi-agent systems such as 

formation, rendezvous, flocking. We will also briefly overview some recent results on this problem, and 

point out some possible future research topics on this problem. 


Prof. Jie Huang studied Power Engineering at Fuzhou University from 1977 to 1979 and Circuits and 

Systems at Nanjing University of Science and Technology (NUST) from 1979 to 1982. He got his Master’s 

degree from NUST in 1982 and was a faculty member there from 1982 to 1986. He completed his Ph.D. 

study in automatic control at the Johns Hopkins University in 1990 and subsequently held a post-doctoral 

fellow position there until July 1991. From August 1991 to July 1995, he worked in industry in USA. 

In September 1995, he joined the Department of Mechanical and Automation Engineering, the Chinese 

University of Hong Kong, and is now a professor and the director of Applied Control and Computing 

Laboratory there. He served as a Science Advisor to the Leisure and Cultural Services Department of 

19


Hong Kong Special Administrative Region, and Honorary Advisor to Hong Kong Science Museum. His 

research interests include control theory and applications, robotics and automation, neural networks and 

systems biology, and guidance and control of flight vehicles. He authored two books and numerous papers. 

He received China State Natural Science Award, Class II, in 2010, Croucher Senior Research Fellowship 

award in 2006, and the best paper award (with Zhiyong Chen) of the Eighth International Conference on 

Control, Automation, Robotics, and Vision in 2004. He is CAA Fellow, FAC Fellow, and IEEE Fellow. 

Dr. Jie Huang is/was editor, associate editor, guest editor of several journals. He was Distinguished 

Lecturer of IEEE Control Systems Society from 2005 to 2008, an appointed member of the Board of 

Governors of IEEE Control Systems Society from 2006 to 2007. He served as general chair, program 

chair in numerous international Conferences. He is Vice Chair of Technical Committee on Control Theory, 

Chinese Association of Automation. 

20



Plenary Lecture V 

9:50-11:50, Saturday, July 7, 2012 


Memristor: Past, Present, and Future 

Professor Leon O. Chua 

University of California at Berkeley, USA 

TUM Distinguished Affiliated Professor Techniche Universitat Munchen 

Chair: Prof. Tzyh Jong Tarn (Washington University, USA) 

Abstract 

Although the memristor was first postulated axiomatically in 1971, and although a proof of principle 

had been published via a bulky circuit using operational amplifiers, it was the hp publication of a nano 

memristor in the 1 May 2008 issue of Nature that had triggered worldwide attention, with the number 

of publications on memristor applications exploding at an exponential rate. This 2-hour lecture presents 

the circuit and system theoretic concept of the memristor and shows why it had generated so much unprecedented 

interest in both academia and industry. Among other things, it will illustrate why memristors 

will replace flash memories and DRAMs in the near future, and why brains are made of memristors. It 

will also show how action potentials (spikes) from the Hodgkin-Huxley axon model are generated by the 

sub-critical Hopf bifurcation nonlinear dynamics of the sodium and potassium memristor Hodgkin-Huxley 

neuron model operating near the edge of chaos. 


Prof. Leon O. Chua, received the MSEE from Massachusetts Institute of Technology in 1961, and 

the Ph.D. from University of Illinois, Urban-Champaign in 1964. After that he was Assistant and Associate 

Professor at Purdue University until 1970. He became a Professor of Electrical Engineering and Computer 

Sciences at University of California at Berkeley since 1971. 

Dr. Chua is known as a pioneer in 3 major research areas, namely, nonlinear circuits, chaos, and 

cellular neural networks. His work in these areas has been recognized internationally through numerous 

21


major awards, including 12 honorary doctorates from major universities in Europe and Japan, and 7 USA 

patents. He was elected as Fellow of IEEE in 1974, a foreign member of the European Academy of 

Sciences (Academia Europea) in 1997, and a foreign member of the Hungarian Academy of Sciences in 

2007. He was honored with many prestigious awards and prizes, including the IEEE Browder J. Thompson 

Memorial Prize Award in 1972, the Frederick Emmons Terman Award in 1974, the IEEE W. R. G. Baker 

Prize Award in 1978, the Frederick Emmons Award in 1974, twice winner of the IEEE M.E.Van Valkenburg 

Award (1995 and 1998), the IEEE Neural Networks Pioneer Award in 2000, the first IEEE Gustav Kirchhoff 

Award in 2005, the IEEE Vitold Belevitch Award in 2007, and the Guggenheim Fellow award in 2010 and 

a Leverhulme Trust Visiting Professorship in 2011. 

Dr. Chua is a Recipient of the top 15 most cited authors Award in 2002 from all fields of engineering 

published during the 10-year period 1991 to 2001, from the Current Contents (ISI) database. 

Dr. Chua has authored more than 500 papers and 8 books. He is widely recognized as the father of 

nonlinear circuit theory and cellular neural networks (CNN). In 1971, he postulated a fourth basic circuit 

element call the memristor, which has recently attracted worldwide interests from industry and academia 

after hp published a physical nano memristor device in Nature in 2008. Dr. Chua also invented a fiveelement 

electronic circuit for generating chaotic signals. Widely known as the Chua Circuit, it is used by 

many researchers to design secure communications systems based on chaos and has become a standard 

paradigm for teaching chaos in textbooks on nonlinear dynamics. 

Dr. Chua is the editor of the International Journal of Bifurcation and Chaos. 

22


Plenary Panel Sessions 


Panel Session I 

8:30-10:10, Sunday, July 8, 2012 


Beyond Control 

Chair: Gary G. Feng, City University of Hong Kong, China 

Ben M. Chen 

National Univ. of 

Singapore, Singapore 

Li-Chen Fu 

National Taiwan Univ., 

China 

Xiaoming Hu 

Royal Institute of 

Technology, Sweden 

Zhong-Ping Jiang 

Polytechnic Inst. of New 

York Univ., USA 

WCICA 2012 proudly presents the plenary panel session on Beyond Control. We are honored to 

be able to invite four prominent professors in the field of control to be the panelists. The objective of 

the plenary panel session is to provide an opportunity for researchers, especially young researchers, to 

interact with world renowned experts in control and seek their views on control and more on “beyond 

control”. In particular they are going to share with us their views on control and its interaction with other 

fields such as bio, communications, robotics, automation, and energy systems. They will also share with 

us current and possible future developments of the fields as well as how to develop a research career and 

how to publish high quality papers. 

Ben M. Chen was born in Fujian, China, received his B.S. in mathematics and computer science from 

Xiamen University, China, in 1983, M.S. in electrical engineering from Gonzaga University, USA, in 1988, 

and PhD in electrical and computer engineering from Washington State University, USA, in 1991. He 

was a software engineer from 1983 to 1986 in South-China Computer Corporation, Guangzhou, China, 

and was an assistant professor from 1992 to 1993 at State University of New York at Stony Brook, USA. 

Since 1993, he has been with Department of Electrical and Computer Engineering, National University of 

Singapore, where he is currently a full professor. His current research interests are in systems and control, 

unmanned aerial systems, and financial market modeling. 

23


Dr. Chen is an IEEE Fellow. He is the author/co-author of 8 research monographs including Loop 

Transfer Recovery:Analysis and Design (Springer, London, 1993), H 2 Optimal Control (Prentice Hall, London, 

1995), H ∞ Control and Its Applications (Springer, New York, 1st Edition, 1998; Revised Edition, 2000; 

Chinese Edition published by Science Press, Beijing, 2010), Hard Disk Drive Servo Systems (Springer, 

New York, 1st Edition, 2002; 2nd Edition, 2006), Linear Systems Theory: A Structural Decomposition 

Approach (Birkhäuser, Boston, 2004; Chinese Edition published by Tsinghua University Press, Beijing, 

2008), and Unmanned Rotorcraft Systems (Springer, New York, 2011; Chinese Edition to be published by 

Tsinghua University Press). 

He has served on the editorial boards of a number of journals including IEEE Transactions on Automatic 

Control, Systems & Control Letters, Automatica, and Journal of Control Theory and Applications. He 

was the recipient of Best Poster Paper Award, 2nd Asian Control Conference, Seoul, Korea (1997); University 

Researcher Award, National University of Singapore (2000); IES Prestigious Engineering Achievement 

Award, Institution of Engineers, Singapore (2001); Temasek Young Investigator Award, Defence Science 

& Technology Agency, Singapore (2003); Best Industrial Control Application Prize, 5th Asian Control Conference, 

Melbourne, Australia (2004); Best Application Paper Award, 7th Asian Control Conference, Hong 

Kong (2009), and Best Application Paper Award, 8th World Congress on Intelligent Control and Automation, 

Jinan, China (2010). 

Li-Chen Fu was born in Taipei, Taiwan, China in 1959. He received the B.S. degree from National Taiwan 

University in 1981, and the M.S. and Ph.D. degrees from the University of California, Berkeley, in 1985 

and 1987, respectively. Since 1987 till now, he has been on the faculty and currently is a professor of 

both Dept. of Electrical Engineering (EE) and Dept. of Computer Science (CS) & Information Engineering 

of National Taiwan University, and currently is serving as the Deputy Dean of College of EECS. He has 

also served as the Secretary General of National Taiwan University from 2005 to 2008, and in 2007 was 

awarded Lifetime Distinguished Professorship from his university and Irving T. Ho’s Chair Professorship 

from Irving T. Ho Foundation, respectively. His areas of research interest include robotics, rehabilitation, 

smart home, precision motion control, visual detection and tracking, human computer interaction, and 

intelligent transportation system. 

Dr. Fu has been extremely active and highly regarded in his technical field. Domestically, he has 

served as President of Chinese Automatic Control Society from 2004 to 2007 and BoG members of Chinese 

Automatic Control Society (CACS) and Chinese Institute of Automation Engineers both for more than 

12 years. In his international service, he has been heavily involved in the IEEE technical affairs. For example, 

he was the founding Chair of the local chapter of IEEE Robotics and Automation Society, and served 

as the 2nd-term Chair of the local chapter of IEEE Control Systems Society, both more than a decade 

ago. He was also appointed as AdCom member of IEEE Robotics and Automation Society before, and 

his most recent term is from 2004/1 2005/12. Besides that, he served as the Program Chair of 2003 IEEE 

International Conference on Robotics (ICRA) and Automation as well as the Program Chair of 2004 IEEE 

Conference on Control Applications (CCA), where both conferences have been highly appreciated by the 

international community. In terms of the editorial work, he has served as Associate Editor of Automatica 

from 1996 to 1999. Starting from 1999, he started a new international control journal, called Asian Journal 

of Control, and became an Editor-in-Chief of the journal till now. Due to his profound academic reputation, 

he was appointed as Vice-President for Publication of Asian Control Association (ACA) since 2006. 

Dr. Fu has received numerous recognitions for his outstanding performance in research and education 

during his about twenty year technical career. Domestically, he has received Outstanding Research 

Award during 1990-1993 and Distinguished Research Awards at the years of 1995, 1998, and 2000 from 

National Science Council, respectively, the Outstanding Youth Medal in 1991, the Outstanding Engineering 

Professor Award in 1995 from Chinese Institute of Engineers, the Best Teaching Award in 1994 from 

Ministry of Education, The Ten Outstanding Young Persons Award in 1999, the Outstanding Control En- 

24



gineering Award from Chinese Automatic Control Society (CACS) in 2000, the Lee Kuo-Ding Medal from 

Chinese Institute of Information and Computing Machinery in 2000, the Industry-Academia Collaboration 

Award from in 2004, the TECO Technology Award in 2005, and Distinguished Research Fellowship from 

National Science Council during 2001-2007. Internationally, he has been awarded IEEE Fellow since 

2004, and has been elected to be a Distinguished Lecturer for IEEE Robotics and Automation Society 

during 2004-2005 and 2007. 

Xiaoming Hu was born in Chengdu, China. He received the B.S. degree from University of Science and 

Technology of China in 1983. He received the M.S. and PhD degrees from Arizona State University in 1986 

and 1989 respectively, under the guidance of Professor Christopher I. Byrnes. He served as a research 

assistant at the Institute of Automation, the Chinese Academy of Sciences, from 1983 to 1984. From 1989 

to 1990 he was a Gustafsson Postdoctoral Fellow at the Royal Institute of Technology, Stockholm, where 

he is a full professor of Optimization and Systems Theory since October 2003, and a vice director of the 

Center for Autonomous Systems. He is also a member of the executive committee of ACCESS Linnaeus 

Centre, established at KTH in 2006 through a ten-year 100 Million SEK grant from the Swedish Research 

Council. He held an S.S. Chern guest professorship at Nankai University, and is now also a member of 

the International Research Team on Complex Systems of the Chinese Academy of Sciences. 

Xiaoming Hu has led or participated in many research projects sponsored by EU, the Swedish Research 

Council, the Swedish Strategic Research Foundation, the Swedish Defense Materiel Administration, 

and the Swedish National Space Board. His main research interests are in complex and networked 

systems, active sensing and perception, control of multi-agent systems, nonlinear observer design, and 

mobile manipulation. He has published more than 100 technical papers in journals and international 

conference proceedings. Xiaoming Hu has been involved in the organization or program committee of 

many international conferences. In particular, he was one of the initiators of the very successful bilateral 

conference-Chinese-Swedish Control Conference, which has so-far been held five times in Sweden and 

China alternately. 

Zhong-Ping JIANG (M’94, SM’02, F’08) received the B.Sc. degree in mathematics from the University 

of Wuhan, Wuhan, China, in 1988, the M.Sc. degree in statistics from the University of Paris XI, France, 

in 1989, and the Ph.D. degree in automatic control and mathematics from the Ecole des Mines de Paris, 

France, in 1993. 

Currently he is a Full Professor of Electrical and Computer Engineering at the Polytechnic Institute 

of New York University (formerly called Polytechnic University), and an affiliated Changjiang Chair Professor 

at Beijing University. His main research interests include stability theory, the theory of robust and 

adaptive nonlinear control, and their applications to underactuated mechanical systems, communication 

networks, multi-agent systems, smart grids and systems physiology. He is coauthor of the book Stability 

and Stabilization of Nonlinear Systems (with I. Karafyllis, Springer 2011). 

An IEEE Fellow, Dr. Jiang is a Subject Editor for the International Journal of Robust and Nonlinear 

Control and has served as an Associate Editor for several journals including Systems & Control Letters, 

IEEE Transactions on Automatic Control, European Journal of Control and J. Control Theory and Applications. 

Dr. Jiang is a recipient of the prestigious Queen Elizabeth II Fellowship Award from the Australian 

Research Council, the CAREER Award from the U.S. National Science Foundation, and the Young Investigator 

Award from the NSF of China. He received the Best Theory Paper Award (with Y. Wang) at the 2008 

World Congress on Intelligent Control and Automation, and with T. Liu and D.J. Hill, the Guan Zhao-Zhi 

Best Paper Award at the 2011 Chinese Control Conference. 

25


Panel Session II 

10:30-12:10, Sunday, July 8, 2012 


Future Robotics and Automation 

Chair: Max Q.-H. Meng, Chinese University of Hong Kong, China 

Tianyou Chai 

Northeast University, 

China 

Kazuhiro Kosuge 

Tohoku University, 

Japan 

Tim C. Lueth 

Technical Univ. of 

Munich, Germany 

Peter B. Luh 

University of 

Connecticut, USA 

Yangsheng Xu 

Chinese Univ. of 

Hong Kong, China 

WCICA 2012 proudly presents the plenary panel session on Future Robotics and Automation. We 

are honored to have invited eight epic international leaders in the field of robotics and automation to be 

the panelists. The objective of the plenary panel session is to provide an opportunity for researchers, 

especially young researchers, to interact with world class leading authorities in robotics and automation 

and seek their views on future robotics and automation. In particular they are going to share with us their 

views on the topic and its active interaction with other fields such as bio, communications, control, and 

energy systems. They will also share with us how to develop a successful research career and how to 

publish high quality papers. 

Max Q.-H. Meng received his Ph.D. degree in Electrical and Computer Engineering from the University of 

Victoria, Canada, in 1992, following his Master’s degree from Beijing Institute of Technology in 1988. He 

has been a Professor of Electronic Engineering at the Chinese University of Hong Kong since 2002, after 

working for 10 years in the Department of Electrical and Computer Engineering at the University of Alberta 

in Canada as the Director of the ART (Advanced Robotics and Teleoperation) Lab, holding the positions of 

Assistant Professor (1994), Associate Professor (1998), and Professor (2000), respectively. He was jointly 

appointed as an Overseas Outstanding Scholar Chair Professor of the Chinese Academy of Sciences and 

the Dean of the School of Control Science and Engineering at Shandong University in China. He is currently 

jointly appointed as a Distinguished Provincial Chair Professor of Henan University of Science and 

Technology and the Honorary Dean of the School of Control Science and Engineering and One Thousand 

Talent Distinguished Professor at Shandong University in China. His research interests include robotics 

26



and active medical devices, tele-medicine and healthcare, bio-sensors and sensor networks, network enabled 

systems and services, and adaptive and intelligent systems. He has published some 400 journal 

and conference papers and book chapters and led more than 30 funded research projects to completion 

as Principal Investigator. He has served as an editor of the IEEE/ASME Transactions on Mechatronics 

and an associate editor of the IEEE Transactions on Fuzzy Systems, and is currently a technical editor 

of Advanced Robotics, Journal of Advanced Computational Intelligence and Intelligent Informatics, and 

International Journal of Information Acquisition. He served as an Associate VP for Conferences of the 

IEEE Robotics and Automation Society (2004-2007), an AdCom member of the IEEE Neural Network 

Council/Society (2003-2006), and a member of the IEEE/ASME Transactions on Mechatronics Management 

Committee (2001-2006). He was the General Chair of IEEE CIRA 2001, IROS 2005, AIM 2008, and 

WCICA 2010 conferences. He is a recipient of the IEEE Third Millennium Medal award and he is a Fellow 

of IEEE. 

Tianyou Chai received the Ph.D. degree in control theory and engineering from Northeastern University, 

Shenyang, China, in 1985. He has been with the Research Center of Automation, Northeastern University 

since 1985, where he became a Professor in 1988 and a Chair Professor in 2004. He is the founder and 

Director of the Center of Automation, which became a National Engineering and Technology Research 

Center in 1997. He has made a number of important contributions in control technologies and applications. 

He has published two monographs, 84 peer reviewed international journal papers and around 219 

international conference papers. He has been invited to deliver more than 20 plenary speeches in international 

conferences of IFAC and IEEE. His current research interests include adaptive control, intelligent 

decoupling control, integrated plant control and system, and the development of control technologies with 

applications to various industrial processes. 

Prof. Chai is a member of Chinese Academy of Engineering, an academician of International Eurasian 

Academy of Sciences, IEEE Fellow and IFAC Fellow. He is a distinguished visiting fellow of The Royal 

Academy of Engineering (UK) and an invitation fellow of Japan Society for the Promotion of Science 

(JSPS). For his contributions, he has won three prestigious awards of National Science and Technology 

Progress, the 2002 Technological Science Progress Award from Ho Leung Ho Lee Foundation, the 2007 

Industry Award for Excellence in Transitional Control Research from IEEE Control Systems Society and 

the 2010 Yang Jia-Chi Science and Technology Award from Chinese Association of Automation. 

Kazuhiro Kosuge is a Professor in the Department of Bioengineering and Robotics. He received the B.S., 

M.S., and Ph.D. degrees in control engineering from Tokyo Institute of Technology in 1978, 1980, and 1988 

respectively. From 1980 through 1982, he was with DENSO Co., Ltd. From 1982 through 1990, he was a 

Research Associate at Tokyo Institute of Technology. From 1990 to 1995, He was an Associate Professor 

at Nagoya University. From 1995, he has been at Tohoku University. For more than 25 years, he has been 

doing research on various robotics problems. He received the JSME Awards for the best papers from the 

Japan Society of Mechanical Engineers in 2002 and 2005, the RSJ Award for the best papers from the 

Robotics Society of Japan in 2005, the Best Paper Award of IROS’97, etc. He is an IEEE Fellow, a JSME 

Fellow, a SICE Fellow and a RSJ Fellow. He served as President, IEEE Robotics and Automation Society 

for 2010 and 2011. 

Tim C. Lueth received his degree in electrical engineering from the Darmstadt University of Technology, 

Darmstadt, Germany in 1989. He received the Ph.D. degree in robotics and habilitation in computer 

science from the University of Karlsruhe in 1993 and 1997, respectively. In 1994-1995, he was a Visiting 

Researcher at the MITI-AIST Electrotechnical Laboratory in Tsukuba, Japan. In 1997, Lueth became 

Professor for surgical navigation and robotics at the medical school Charité of the Humboldt University at 

Berlin. In 2001, he became the Director for Mechatronic Medical Technology at the Fraunhofer-Institute 

for Production Systems and Design Technology IPK. Since 2005, Lueth works as Professor, Chair, and 

27


Director of the Institute of Micro Technology and Medical Device Technology (MIMED) of the Technical 

University Munich, Germany. In 2006, he received a professor status at the University of Toronto, Canada. 

The European Patent Office elected him in 2007 as TOP-3 inventor in the category “lifetime achievement” 

for his patent activities in the area of surgical robotics and navigation. He received several national and 

international awards for his research on medical devices. In 2010, Lueth became elected Member of 

“acatech,” the German National Academy for Science and Technology. Lueth is active Member of the 

IEEE R&A Chapter and the IEEE EMB Chapter. 

Peter B. Luh received his B.S. from National Taiwan University, M.S. from M.I.T., and Ph.D. from Harvard. 

He has been with the Department of ECE, University of Connecticut since 1980, and currently is the S- 

NET Professor of Communications & Information Technologies. He served as the Head of the Department 

from 2006 to 2009. He is also a member of the Chair Professors Group in the Department of Automation, 

Tsinghua University, Beijing. Professor Luh is a Fellow of IEEE, a member of IEEE Periodicals Committee, 

and the Senior Advisor on Automation for the Robotics and Automation Society. He was VP Publication 

Activities for IEEE Robotics and Automation Society, the Editor-in-Chief of IEEE Transactions on Robotics 

and Automation, and the founding Editor-in-Chief of the IEEE Transactions on Automation Science and 

Engineering. His interests include planning, scheduling, and coordination of design, manufacturing, and 

service activities; HVAC fault detection and diagnosis; optimized energy management and emergency 

guidance for green and safe buildings; robust wind integration to the grid; design of auction methods 

for electricity markets; electricity load and price forecasting with demand management; decision-making 

under uncertain, distributed, or antagonistic environments; mathematical optimization for large-scale problems; 

and engineering and socio-economic applications. 

Yangsheng Xu is Pro-Vice-Chancellor of The Chinese University of Hong Kong and Professor of Automation 

and Computer-Aided Engineering. He studied Engineering at Zhejiang University, China and received 

his BEng and MEng degrees in 1982 and 1984 respectively. He pursued further studies at the University 

of Pennsylvania and received his Ph.D. in 1989. He then worked on robotics research at Carnegie Mellon 

University from 1989 to 1997, and joined CUHK in 1997, serving as chairman of the Department of 

Mechanical and Automation Engineering from 1997 to 2004, and Assistant to the Vice-Chancellor from 

2006 to 2008 and Associate Pro-Vice-Chancellor from 2008 to 2011. He is currently Associate Director of 

Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences. 

Professor Xu’s research includes robotics, control and dynamics. More recently, he has been interested 

in service and space robotics, man-machine interface and intelligent electric vehicles. He has published 

5 books and over 300 papers in journals and international conferences. He was elected as Fellow of IEEE, 

Fellow of Hong Kong Academy of Engineering Science, Academician of Chinese Academy of Engineering, 

Academician of International Eurasian Academy of Sciences, and Corresponding Member of International 

Academy of Astronautics. 

Professor Xu has actively participated in various professional activities. He served as an Associate 

Editor of IEEE Transactions on Robotics and Automation from 1997 to 2000, a Board Director of Hong 

Kong Productivity Council from 2003 to 2006, and an Advisor of Chinese Hi-tech Program on Aerospace 

Committee from 2004 to 2006. He is currently a Council Member of the Chinese Society of Automation 

and an Advisor of the Decision and Advisory Committee of Shenzhen Municipal Government. 

28


Pre-conference Workshops 


Workshop I 

9:00-12:00, Thursday, July 5, 2012 

FIT Lecture Hall, Tsinghua University 

Modeling and Control of Engines and Drivelines 

Chair: Lars Eriksson, Linköping University, Sweden 

Abstract 

Engine control plays an important role in the development of clean and efficient vehicles, since it 

has a direct impact on emissions and fuel consumption. The short course will give a survey of the basic 

controllers that are used in an engine control system and that interacts with driveline, and three different 

subjects will be covered. Firstly, attention will be given to the torque based structure. This is a structured 

design methodology for transferring the drivers demand through the driveline to the engine actuators. Secondly, 

basic air and fuel control for engines will covered, giving insight into the feedback and feedforward 

control loops for air and fuel and their importance for achieving a clean vehicle. Thirdly, the ignition control 

will be covered, highlighting the control loops for best engine efficiency and safe engine operation. Then, 

two case studies will be presented. Focusing the attention on improving the performance of gasoline 

engines during the transient operation stages, the first one will introduce model-based design methods 

for the control issues of starting speed and air-fuel ratio under fuel injection path changing. Theoretical 

demonstrations for the proposed control schemes as well as experimental validations will be shown. In the 

second one, the principle and operation strategy on the whole operation range of a gasoline HCCI engine 

equipped with variable valve actuation system will be introduced and some control challenges and counter 

measures on a multi-cylinder engine will be discussed and analyzed. 

Description of Workshop 

Mini-lecture: Modeling and Control of Engines and Drivelines (90 Minutes) 

Lecture: Prof. Lars Eriksson, Linköping University, Sweden 

Case study I: Model-based control design method of gasoline engines (45 Minutes) 

Lecture: Dr. Jiangyan Zhang, Sophia University, Japan 

Case study II: Multi-cylinder HCCI engine control through combustion observation (45 Minutes) 

Lecture: Dr. Kang Song, Tianjin University, China 

Biography of Chair 

Lars Eriksson has been an Associate Professor at Linköping University since 2003. He 

was born in Edsbyn, Sweden in 1970 and received the M.Sc. degree in Electrical Engineering 

1995 and the PhD degree in Vehicular Systems in May 1999 both from Link 枚 ping 

University. Since then he has been employed at Vehicular Systems first as Assistant Professor 

and now as Associate Professor. During 2000 and 2001 he spent one year as a 

post doc in the Measurement and Control group at Swiss Federal Institute of Technology, 

ETH Zurich. 

29


Lars Eriksson is currently managing the engine control laboratory at vehicular systems. His research 

interests are modeling, simulation, and control of internal combustion engines for vehicle propulsion in 

general, but with a focus on downsizing and supercharging concepts for improved fuel economy. His 

contributions are foremost on engine control and control oriented modeling of combustion engines, but 

the research interest spans a broader area ranging from energy efficient vehicle propulsion to control of 

in-cylinder processes. 

Lars Eriksson is also active as a teacher, where he considers the transfer of research results to 

knowledge and courses for engineers to be an important task. Throughout his career he has been giving 

lectures on modeling and control of engines and drivelines both for students at universities as well as 

practicing engineers in industry. 

30



Workshop II 


FIT Lecture Hall, Tsinghua University 

Demand Side Applications in Smart Grid System Architecture 

Chair: Hiroaki Nishi, Keio University, Japan 

Abstract 

Firstly, current status around Smart Grid in Japan is introduced and Smart Community Alliance Japan 

(JSCA), the biggest and government driven alliance is explained. Next, cluster energy management system 

(CEMS) is explained. CEMS is a unit of smart grid which considers commmunity area and is a prime 

model of management, infrastructure, sharing and other elements of energy concerns. As a practical experiment, 

Nagasaki EV&ITS project conducted by Nagasaki local government, Keio University and 460 

companies are explained. In this project 130 EV and associated infrastructure including EV quick charger, 

DSRC communication environment and specially designed car navigation system are intensively introduced 

in a small island. We implemented a building energy management system with EV environment 

in Goto port terminal as a smart island project of Nagasaki EV&ITS project. CO2 emission elimination, 

electric power regulation, and other practical results are shown by using these experimental environments. 

Actually, vehicle to grid (V2G), HVAC control under considering environmental affinity, behavior and task 

management by using RFID and other ICT based management systems are shown. All results are led by 

using real environment. These technologies become the key components to construct smart grid. 

There are many proposals about an area of smart grid. Mainly, these proposals are discussing from 

supply side and most of all was accomplished only by simulation of grids / information infrastructure / 

market. Namely, practical experiment or system design was not discussed. We would like to show the 

system architecture concerning smart grid from the view point of demand side, and to give a practical 

results acquired by using real experimental environment. 


Mini-Course: Green by ICT and its Applications, presented (90 Minutes) 

Lecture: Prof. Hiroaki Nishi, Keio University, Japan 

1. Current status around Green by IT 

∙ Japan’s energy usage profiles 

∙ Smart grid projects in Japan (Smart Community Alliance Japan) 

∙ Cluster/Community Energy Management Systems 

2. Network Infrastructure for Green by IT 

∙ KNIVES system architecture 

∙ Energy management sensor network 

∙ HVAC control with considering environmental affinity 

∙ Low cost acquisition of environmental affinity 

3. Applications 

∙ Nagasaki EV&ITS project 

∙ Green Society Life Infrastructures Project 

∙ Campus EMS project 

31


4. Standards 

∙ Current status of standardization around smart grid in IEEE 

∙ IEEE-SA EV & Smart Grid 

Case Studies: Practice of ICT Technology (90 Minutes) 

Lectures: TianMeng Shin, Masaru Ihara, and Kyosuke Funami, Keio University, Japan 

1. Nagasaki EV&ITS project 

∙ HVAC control experiment 

∙ Electric power demand leveling by using EV battery 

2. Green Society Life Infrastructures Project 

∙ CEMS construction 

∙ Comparison experiment of Air Conditioner control Green Society Life Infrastructures Project 


Hiroaki Nishi received his Ph.D degree from Keio University, and currently he is serving 

as Associate Professor of Keio University, Japan and Visiting Associate Professor 

of National Institute of Informatics. 

Dr. Nishi is selected as IEEE-SA Japanese ambassador. He joins IEEE P2030 

Standards Committee and a member of IEEE-SA ITS & Smart Grid Vision Project. 

He is also a member of IEEE SA EVWPT (EV Wireless Power Transfer) Standard 

Committee and IEEE Industrial Electronics Society (IES) Building Automation Control 

& Management (BACM) Technical Committee. He organizes Energy and IT special session in IEEE IES 

Industrial Informatics (INDIN) and Annual Conference of the IEEE IES (IECON) since 2006. He also 

manages several committee of Japanese ministry. 

32



Workshop III 


Room 106, Postgraduate Building, Beijing Institute of Technology 

Control System Design: From Concepts to Practice, a Historical, 

Humanistic, Practical, and Rational Account 

Chair: Zhiqiang Gao, Cleveland State University, USA 

Abstract 

This is an abbreviated short course on history, concepts and practice of controls for students, professionals, 

and practicing engineers who aspire to become a creative problem solver through better understanding 

of the historical and human side of this profession as it evolved from 1780s. Fundamental 

concepts and problem solving skills are grasped through the lens of history, as we witness how the ingenious 

minds rose to the occasion and made the arts and crafts of controls effective, intelligible, and maybe 

even reproducible. Today, just as two hundred years ago, great solutions for the complex problems in the 

real world are not to be found in a few rules of thumb or a few textbook formulas, but through human 

ingenuity and free thinking. This course is designed to inspire, to unlock the minds, to question what has 

always been done, and to see in history what it takes to be a truly innovative engineering problem solver, 

operating within a given framework and beyond. 


Lecture: Prof. Zhiqiang Gao, Cleveland State University, USA 

I. The Concepts of Control: A Two Hundred Year Evolution 

∙ What concepts, where did they come from, what questions did they answer? 

∙ Feedback: consistent performance in a system made of inconsistent parts 

∙ Feedforward? - the art of anticipation and active control 

∙ Feedback and Feedforward: the twin evolution from 1788 to present 

∙ The story of active disturbance rejection and the story of an enduring idea: from Jean-Victor Poncelet, 

to Grigoriy Shipanov, and to Jingqing Han 

II. The Three Paradigms In Control System Design 

∙ A control problem is a disturbance rejection problem 

∙ Three paradigms: industrial, academic and disturbance rejection 

∙ The inherent uncertainty in physical process and its centrality in control design 

∙ What we need to know about a physical process in order to control it: effective action without perfect 

information 

∙ Dealing with uncertainties 

– PID tuning 

– Modeling and Robust Control 

– Estimating/mitigating 

∙ Anticipatory vs. reactive control 

III. Why the Basic Concepts and Skills Matter 

∙ The language of our understanding: diff. equation, transfer function, state space 

∙ Vocabulary of engineers: PID, feedforward, bandwidth, error budget ... 

∙ What we want: performance-stability and aggressiveness-energy trade-offs 

33


∙ Design choices: finding the right tool for a given problem 

∙ Analysis: stability is not “yes or no” question, but one of “how much” 

∙ Robust control: how much uncertainties are out there, how much can we handle? 

IV. Active vs. Passive Disturbance Rejection 

∙ The concept of total disturbance, including internal dynamics and external forces 

∙ The 2nd degree of freedom: the disturbance rejection inner loop 

∙ Real time disturbance estimation 

∙ Feedback Design: linear and discrete time optimal solutions 

∙ Parameterization and one parameter tuning 

∙ Digital implementation and algorithm 

∙ Applications 

V. Advanced Problem Solving in Control Practice 

∙ What makes it so interesting? 

∙ Bridging the theory-practice gap: problem solving strategy and process 

– Spotting a control problem/issue 

– Formulating it: characterizing the uncertainty, internal and external 

– Evaluating design choices: how to deal with the uncertainty 

– Design and simulation validation 

– Analysis: characteristics of the solution such as stability margins 

– Implementation and tuning 

– Communicating your ideas to others 

∙ Case studies 

∙ Toward the future of control theory and practice: continuous skill development 


Zhiqiang Gao received his Ph.D. in Electrical Engineering from the University of Notre 

Dame in 1990. Since then, he and his research group at CSU have collaborated extensively 

with NASA and industry and conducted multi-million dollar research and development 

projects. Employing an experimental science philosophy to research and a 

humanistic touch to teaching, Dr. Gao and his team of researchers bring creative solutions 

to real world problems and vitality of thinking to young minds. Working with Prof. 

Jingqing Han since 1995, Dr. Gao helped nurturing active disturbance rejection control 

(ADRC) from its conceptual stage to a maturing and emerging industrial control technology. Recently, the 

CSU spinoff company, LineStream Technologies, of which Dr. Gao is a founder, successfully implemented 

the ADRC technology across ten production lines at Parker Hannifin, a Fortune 100 company, with a third 

party quantification of energy saving well above 50%, along with significant quality improvement in product 

qualities. Furthermore, Texas Instrument, an industry giant, announced in July 2011 that it has signed a 

long term, global licensing agreement to embed ADRC algorithms in its chips. 

Throughout his career, Dr. Gao has been giving lectures on basic and advanced control concepts 

to students and practicing engineers across boundaries of academia and industry, at various universities, 

research labs, and companies such as NASA, Rockwell Automation, Energizer, Intel, Texas Instrument, 

Honeywell, Procter & Gamble, Kimberly Clark, Allied Signal. He was also invited to give a talk in July 

2010 at the Institute of Control Science, Russian Academy of Sciences, to which the origin of the ADRC 

conception is traced. This workshop is based on a short course on advanced control he recently taught 

at the Polytechnic University of Turin, Tsinghua University, Chinese Academy of Sciences, and Tianjin 

University. 

34



Workshop IV 



Robotics and Control: The applications and development of robotics in 

the fields of deep-ocean exploration and aerospace manufacturing 

Chair: Hong Qiao, Institute of Automation, Chinese Academy of Sciences, China 

Abstract 

Recently, robotic systems have played important roles in the fields of deep-ocean exploration and 

aerospace manufacturing. For example, (a) the underwater robot with the ability of dynamic perception 

and understanding can smoothly accomplish humanoid intelligent behaviors in underwater detection; (b) 

the flexible industrial robot is always required to assemble numerous fragile optical components of large 

weight, large size, and different specifications for building the large laser equipments; (c) the industrial 

robot with broad moving ranges and heavy loads need to operate the aircraft metal components with 

complex surfaces of high precision and accuracy. 

By analyzing the technological bottleneck in the above fields, and exploring the basic and critical scientific 

problems, this workshop is expected to provide some useful information for the further development 

of robot in these special applications. 


Lecture: Prof. Yongjie Pang, Harbin Engineering University, China 

Prof. Hong Qiao, Institute of Automation, Chinese Academy of Sciences, China 

Yuancheng Zhou, ABB Engineering (Shanghai) Ltd., China 

List of topics: 

I. The technology of the dynamic perception and intelligent understanding of underwater robots in changeful 

hydrological environment 

II. Humanoid interaction and cooperation of robots and its potential applications in precision manufacturing 

industry 

III. ABB’s robotics solution in China aerospace industry 


Hong Qiao, awarded the Distinguished Young Scholar from the National Natural Science 

Funds of China, currently serves as the “One Hundred Talents Program” Professor of 

Chinese Academy of Sciences. She was a Research Assistant Professor and Assistant 

Professor with City University of Hong Kong from 1997 to 2002. From 2002 to 2004, she 

was a Lecturer with the School of Informatics, University of Manchester, and had been 

awarded as Outstanding Young Researcher. In 2004, she joined the Chinese Academy 

of Sciences through the “Overseas Talented Researchers Plan”. 

Professor Hong Qiao has published more than 60 papers in international journals, including over 20 

papers in IEEE Transactions, ASME, Pattern Recognition, and has also published more than 60 papers in 

international conference proceedings. More than 40 of her journal papers she is with the first or second 

author’s identity. She is currently the Associate Editor of the IEEE Transactions on Systems, Man, and 

Cybernetics-Part B. She is also the Deputy Editor-in-Chief of Acta Automatica Sincia. 

Professor Hong Qiao has taken charge of or participated in 9 national projects since she came back 

35


from abroad, and she has also undertaken or participated in 4 corporation projects. In industrial applications, 

Professor Hong Qiao and her research team have developed an autonomous assembling industry 

robot system. In robotic assembly and dynamic vision, her team and Atech Automotive of Chery have 

signed a strategic cooperation framework agreement for further cooperation. Moreover, the autonomous 

grinding robotic system developed by Professor Hong Qiao’s team and their corporation partners has been 

supported by a key project from a famous aircraft manufacturer of Chinese. 

36



Workshop V 



Unmanned Rotorcraft Systems 

Chair: Ben M. Chen, National University of Singapore, Singapore 

Abstract 

In recent years, research and development of unmanned systems have gained much attention in the 

academic and military communities worldwide. Topics like unmanned aircraft, underwater explorers, satellites, 

and intelligent robotics are widely investigated as they have potential applications in the military and 

civilian domains. They are developed to be capable of working autonomously without the interference of 

a human pilot. The challenge is that they need to deal with various situations that arise in very complicated 

and uncertain environments, such as unexpected obstacles, enemies attacking and device failures. 

Besides, they are required to communicate with technical personnel in the ground station. Consideration 

of a wide range of factors needs to be taken. Control systems for the unmanned vehicles are required 

to integrate not only basic input-output control laws but also high-level functionalities for decision making 

and task scheduling. Software systems for unmanned vehicles are required to perform tasks from 

hardware driving to the management of device operations, and from traditional input-output control law 

implementation to task scheduling and event management. 

The proposed workshop aims to explore the research and development of fully functional miniature 

rotorcraft unmanned aerial vehicles (UAV), and provides a complete treatment of their design. The unmanned 

system is an integration of advanced technologies developed in communications, computing, and 

control areas. It is a useful testing ground for trialing and implementing modern control techniques despite 

the challenges introduced by the limitations on direct scalability between the systems of a small-scale rotorcraft 

and those of its full scale counterpart. In this workshop, we intend to cover all the relevant topics 

related to fully-functional UAVs, which include hardware platform construction, real-time software system 

integration, ground control software systems, aerodynamic modeling, automatic flight control, flight formation 

of multiple UAVs, and vision-based tracking and navigation systems. Topics on the development of 

micro aerial vehicles, and navigation systems for indoor and outdoor cluttered environments will also be 

highlighted. 

It is a full day workshop with a total of 5 to 6 lecturing hours. 


Lecture: Ben M. Chen, Guowei Cai, Feng Lin, Xiangxu Dong, National University of Singapore 

Biao Wang, Nanjing University of Aeronautics and Astronautics 

List of topics: 

∙ Introduction to unmanned system 

∙ Systematic UAV platform construction 

∙ Software systems integration 

∙ Aerodynamic modeling 

∙ Automatic flight control system design 

∙ Flight formation of multiple UAVs 

∙ Vision-based tracking and navigation 

37



Ben M. Chen was born in Fujian, China, received his B.S. in mathematics and computer 

science from Xiamen University, China, in 1983, M.S. in electrical engineering 

from Gonzaga University, USA, in 1988, and PhD in electrical and computer engineering 

from Washington State University, USA, in 1991. He was a software engineer from 

1983 to 1986 in South-China Computer Corporation, Guangzhou, China, and was an 

assistant professor from 1992 to 1993 at State University of New York at Stony Brook, 

USA. Since 1993, he has been with Department of Electrical and Computer Engineering, 

National University of Singapore, where he is currently a full professor. His current research interests 

are in systems and control, unmanned aerial systems, and financial market modeling. 

Dr. Chen is an IEEE Fellow. He is the author/co-author of 8 research monographs including Loop 

Transfer Recovery:Analysis and Design (Springer, London, 1993), H 2 Optimal Control (Prentice Hall, London, 

1995), H ∞ Control and Its Applications (Springer, New York, 1st Edition, 1998; Revised Edition, 2000; 

Chinese Edition published by Science Press, Beijing, 2010), Hard Disk Drive Servo Systems (Springer, 

New York, 1st Edition, 2002; 2nd Edition, 2006), Linear Systems Theory: A Structural Decomposition 

Approach (Birkhäuser, Boston, 2004; Chinese Edition published by Tsinghua University Press, Beijing, 

2008), and Unmanned Rotorcraft Systems (Springer, New York, 2011; Chinese Edition to be published by 

Tsinghua University Press). 

He has served on the editorial boards of a number of journals including IEEE Transactions on Automatic 

Control, Systems & Control Letters, Automatica, and Journal of Control Theory and Applications. He 

was the recipient of Best Poster Paper Award, 2nd Asian Control Conference, Seoul, Korea (1997); University 

Researcher Award, National University of Singapore (2000); IES Prestigious Engineering Achievement 

Award, Institution of Engineers, Singapore (2001); Temasek Young Investigator Award, Defence Science 

& Technology Agency, Singapore (2003); Best Industrial Control Application Prize, 5th Asian Control Conference, 

Melbourne, Australia (2004); Best Application Paper Award, 7th Asian Control Conference, Hong 

Kong (2009), and Best Application Paper Award, 8th World Congress on Intelligent Control and Automation, 

Jinan, China (2010). 

38


Best Paper Awards and Finalists 

Best Paper Awards and Finalists 

He-Pan-Qing-Yi Best Paper Award 

Jiliang Luo, and Kenzo Nonami, Approach for Transforming Linear Constraints on Petri Nets, IEEE Transactions 

on Automatic Control, VOL. 56, NO. 12, 2011 2751-2765. 

Best Theoretical Paper Award Finalists 

1) A New Optimal Control Method for Discrete-Time Nonlinear Systems with Approximation Error 

by Qinglai Wei, Derong Liu 

2) Emergence of Flocks with Local Interactions 

by Ge Chen, Zhixin Liu 

3) Cooperative Robust Output Regulation of Linear Uncertain Multi-Agent Systems 

by Youfeng Su, Jie Huang 

4) Stochastic Approximation Based PCA and Its Application to Identification of EIV Systems 

by Wen-Xiao Zhao, Han-Fu Chen 

Best Application Paper Award Finalists 

1) Bilateral Teleoperation of Force/Motion for a Robotic Manipulator with Random Delays 

by Yu Kang, Zhijun Li, Dihua Zhai, and Xiaoqing Cao 

2) Mathematical Model Building and Optimization Control of Horizontal Continuous Heat Treatment Furnace 

by Ling-Yan Hu, Xing-Cheng Wang 

3) Occluded Object Grasping Based on Robot Stereo Vision 

by Chuan Lin, Yen-Lun Chen, Weidong Hao, and Xinyu Wu 

4) Intelligent Switching Control for Cement Raw Meal Calcination Process 

by Jinghui Qiao, Tianyou Chai 

Best Student Paper Award Finalists 

1) Reaching Optimal Consensus for Multi-agent Systems Based on Approximate Projection 

by Youcheng Lou, Guodong Shi, Karl Henrik Johansson, and Yiguang Hong 

2) Quantum Multi-Channel Decoupling 

by Pei-Lan Liu, Jr-Shin Li, and Tzyh-Jong Tarn 

3) Robust Altitude Control of an Unmanned Autonomous Helicopter Using Backstepping 

by Tushar K. Roy, Matt Garratt, H. R. Pota, and H. Teimoori 

4) Function Perturbation Impact on the Topological Structure of Boolean Networks 

by Haitao Li, Yuzhen Wang, and Zhenbin Liu 

Best Paper Award in Biomedical & Bio-system Related Areas Finalists 

1) Epidemic Spreading on Complex Networks with Weighted Adaptive Strategy 

by Yinzuo Zhou, Xiaofan Wang, and Jie Zhou 

2) Identification of overlapping communities in protein interaction networks using multi-scale local information 

expansion 

by Huijia Li, Zhi-Ping Liu, Luonan Chen, and Xiang-Sun Zhang 

3) Colored Petri Nets to Model Gene Mutation Classification 

by Jinliang Yang, Rui Gao, Max Q.-H. Meng, and Tzyh-Jong Tarn 

39


4) Core Module Network Construction for Breast Cancer Metastasis 

by Ruoting Yang, Bernie J. Daigle Jr, Linda R. Petzold, and Francis J. Doyle III 

AIAG (Automotive Industry Action Group) Best Paper Award on Supply Chain Related Topics Finalists 

1) Design of Entry Trajectory Tracking Law for a Hypersonic Vehicle via Inversion Control 

by Zhiqiang Pu, Xiangmin Tan, Guoliang Fan, and Jianqiang Yi 

2) Finite-Horizon Neural Optimal Tracking Control for a Class of Nonlinear Systems with Unknown Dynamics 

by Ding Wang, Derong Liu, and Hongliang Li 

3) Error Modeling and Analysis in Dynamic Wafer Handling 

by Hongtai Cheng, Heping Chen, Ben Mooring, and Harold Stern 

4) Planning Expected-time Optimal Paths for Target Search by Robot 

by Botao Zhang, Shirong Liu 

SUPCON Best Paper Award on Industrial Automation Finalists 

1) Optimal Operation Strategies for Batch Distillation by Using A Fast Adaptive Simulated Annealing 

Algorithm 

by Lin Wang, Zhonghao Pu, and Sufang Wen 

2) PID Control of Glucose Concentration in Subjects with Type 1 Diabetes based on a Simplified Model: 

An In Silico Trial 

by Peng Li, Lei Yu, Liquan Guo, Jixiang Dong, Ji Hu, and Qiang Fang 

3) Parameter Convergence Analysis in Adaptive Disturbance Rejection Problem of Rigid Spacecraft 

by Zhiyong Chen, Jie Huang 

4) H 2 Performance Limitation of a Class of Nonlinear Non-minimum Phase Systems 

by Di Lu, Guoliang Fan, and Jianqiang Yi 

Best Poster Paper Award 

To be evaluated on site. 

40

41 

WCICA 2012 Program Schedule 

July 6-8, 2012, Beijing, China 

July 5 (Thursday) July 6 (Friday) July 7 (Saturday) July 8 (Sunday) 

Registration: 

Lobby of BICC / 北京国际会议中心大堂 

Time: 

July 5 8:00-22:00 

July 6 8:00-22:00 

Other time: contact the conference secretariat 

8:30- 9:30 

9:30- 9:50 

9:50-10:50 

10:50- 11:50 

Plenary Talk by: Francis J. Doyle 

Tea Break 

Plenary Talk by: Hong Chen 

Plenary Talk by: Tim C. Lueth 

8:30- 9:30 

9:30- 9:50 

9:50- 11:50 

Plenary Talk by: Jie Huang 

Tea Break 

Plenary Talk by: Leon Chua 

8:30- 10:10 

10:10- 10:30 

10:30- 12:10 

Panel S1: Beyond Control 

Tea Break 

Panel S2: Future Robotics and 

Automation 

Mobile phones: 

+86-13910962012 (Ms. Yuan Shen) 

11:50-13:30 Lunch 11:50-13:30 Lunch 12:10-13:30 Lunch 

+86-13810226116 (Prof. Yanlong Zhao) 

13:30-15:30 Oral Sessions FrA01-11 13:30-15:30 Oral Sessions SaA01-11 13:30-15:30 Oral Sessions SuA01-11 

15:30-15:50 Tea Break 

15:30-15:50 Tea Break 

15:30-15:50 Tea Break 

Workshop 1 by Lars Eriksson: 9:00-12:00 

15:50-17:50 Oral Sessions FrB01-11 15:50-17:50 Oral Sessions SaB01-11 15:50-17:50 Oral Sessions SuB01-11 

Workshop 2 by Hiroaki Nishi: 13:30-16:30 

Workshop 3 by Zhiqiang Gao: 9:00-12:00 

13:30-14:50 Poster Session PFrA 

13:30-14:50 Poster Session PSaA 

Workshop 4 by Hong Qiao: 13:30-16:30 

15:00-16:20 Poster Session PFrB 

15:00-16:20 Poster Session PSaB 

Workshop 5 by Ben M. Chen: 9:00-16:30 

16:30-17:50 Poster Session PFrC 

16:30-17:50 Poster Session PSaC 

Workshops 1 and 2 are at Tsinghua Univ., and 

the others at Beijing Institute of Technology 18:30-20:00 Dinner 18:30-20:00 Dinner 

18:00-20:00 Welcome Reception 

18:30-21:00 Banquet with Performance 

20:00-22:00 WCICA Award Committee 

Meeting at Room 310 of BICC 

Program Schedule 

WCICA 2012 Program Schedule


Timetable 

Time/Room 

9:00-12:00 

13:30-16:30 

18:00-20:00 

8:30-9:30 

9:30-9:50 

9:50-10:50 

10:50-11:50 

11:50-13:30 

Plenary Lecture I: Zone Model Predictive Control of an Artificial Pancreas , Prof. Francis J. Doyle III, Room 305, Chair: Prof. Lihua Xie 

Tea Break 

Plenary Lecture II: Applying Model Predictive Control in Automotive , Prof. Hong Chen, Room 305, Chair: Prof. Tielong Shen 

Plenary Lecture III: Surgical Robotics: Different Successful Concepts in the Past and in Future , Prof. Tim C. Lueth, Room 305, Chair: Prof. Max Q.-H. Meng 

Lunch, Grand Ballroom, 2nd Floor of BICC 

Time/Room Room 203A Room 203B Room 203C Room 203D Room 203E Room 302 Room 303 Room 310 Room 311A Room 311B Room 311C 

FrA01 FrA02 FrA03 FrA04 FrA05 FrA06 FrA07 FrA08 FrA09 FrA10 FrA11 

13:30-15:30 

15:30-15:50 

15:50-17:50 

Intelligent Computational 

Control & Intelligence & 

Automation (I) Applications (I) 

Artificial 

Intelligence 

Nonlinear 

Control 

Control Theory 

(I) 

Modelling 

Robotics (I) 

Inv: Appl. of 

Semi-tensor 

Product in 

Information & 

Control 

Inv: Coordination 

of MAS 

and Networked 

Systems 

Inv: Control 

over networks: 

an information 

flow point of 

view 

Inv: Intelligent 

Optimization & 

Evolutionary 

Computation 

(I) 

Tea Break 

FrB01 FrB02 FrB03 FrB04 FrB05 FrB06 FrB07 FrB08 FrB09 FrB10 FrB11 

Intelligent 

Control & 

Automation (II) 

Computational 

Intelligence & 

Applications 

(II) 

Control Design 

Slide Mode 

Control 

Control Theory 

(II) 

Identification 

Robotics (II) 

Inv: Datadriven 

Control 

System Design 

and Analysis 

Inv: Nonlinear 

and Networked 

Systems 

Inv: 

Nonsmooth 

Control of 

Nonlinear 

Systems 


Optimization & 

Evolutionary 

Computation 

(II) 

13:30-14:50 

15:00-16:20 

16:30-17:50 

18:30-20:00 

Poster Session PFrA, Exhibition Area, 2nd floor of BICC 

Poster Session PFrB, Exhibition Area, 2nd floor of BICC 

Poster Session PFrC, Exhibition Area, 2nd floor of BICC 

Dinner, Grand Ballroom, 2nd Floor of BICC 

Saturday, July 7, 2012, Beijing International Convention Center (BICC) 

8:30-9:30 

9:30-9:50 

9:50-11:50 

11:50-13:30 

Plenary Lecture IV: Cooperative Output Regulation of Multi-Agent Systems , Prof. Jie Huang, Room 305, Chair: Prof. Ben M. Chen 

Tea Break 

Plenary Lecture V: Memristor: Past, Present, and Future , Prof. Leon O. Chua, Room 305, Chair: Prof. Tzyh-Jong Tarn 



SaA01 SaA02 SaA03 SaA04 SaA05 SaA06 SaA07 SaA08 SaA09 SaA10 SaA11 

13:30-15:30 

15:30-15:50 

15:50-17:50 

Networked 

Control 

Robust Control 

(I) 

Adaptive 

Control & 

Learning 

Control 

Systems 

Engineering & 

Engineering 

Optimization 

Control 

Engineering (I) 

Fault 

Diagnosis 

Advanced 

Control Award: Theory 

Algorithms & (I) 

Applications (I) 

Award: 

Application (I) 

Inv: Guidance 

and Control of 

Flight Vehicles 

Inv: Complex 

Networks and 

MAS 

Tea Break 

SaB01 SaB02 SaB03 SaB04 SaB05 SaB06 SaB07 SaB08 SaB09 SaB10 SaB11 

Optimal 

Control and 

Optimization 

Robust Control 

Machine Vision 

(II) 

Industrial 

Automation & 

On-line 

Monitoring 

Control 

Engineering (II) 

Sesor 

Networks 

Advanced 

Control 

Algorithms & 

Appl. (II) 

Award: Theory 

(II) & Inv: 

Control, 

Informatics, & 

Systems 

Biology 

Award: 

Application (II) 

Inv: Modeling 

and Control: 

Challenges 

from 

Automotive 

Industry 

Inv: Control of 

High- 

Performance 

Engineering 

Systems 

13:30-14:50 

15:00-16:20 

16:30-17:50 

18:30-20:00 

20:00-22:00 

Poster Session PSaA, Exhibition Area, 2nd floor of BICC 

Poster Session PSaB, Exhibition Area, 2nd floor of BICC 

Poster Session PSaC, Exhibition Area, 2nd floor of BICC 

Dinner, Grand Ballroom, 2nd Floor of BICC 

WCICA 2012 Award Committee Meeting, Room 310 of BICC 

Sunday, July 8, 2012, Beijing International Convention Center (BICC) 

8:30-10:10 

10:10-10:30 

10:30-12:10 

12:10-13:30 

Plenary Panel Session I: Beyond Control , Room 305, Chair: Prof. Gary G. Feng 

Tea Break 

Plenary Panel Session II: Future Robotics and Automation , Room 305, Chair: Prof. Max Q.-H. Meng 



SuA01 SuA02 SuA03 SuA04 SuA05 SuA06 SuA07 SuA08 SuA09 SuA10 SuA11 

13:30-15:30 

15:30-15:50 

15:50-17:50 

Stability and 

Stabilization (I) 

Multi-Agent 

Systems (I) 

Image 

Processing 

Intelligent 

Managenment 

and Decision 

Making 

Robotics (III) 

Pattern 

Recognition 

Advanced 

Control 

Algorithms & 

Appl. (III) 

Inv: Appl. of 

Semi-tensor 

Product to 

Control 

Inv: Control 

problems for 

stochastic 

systems 


information 

processing 

Inv: Quantum 

Control and 

Quantum 

Information 

Tea Break 

SuB01 SuB02 SuB03 SuB04 SuB05 SuB06 SuB07 SuB08 SuB09 SuB10 SuB11 

Stability and 

Stabilization 

(II) 

Multi-Agent 

Systems (II) 

Signal 

Processing 

Welcome Reception, Grand Ballroom, 2nd Floor of BICC 

Complex 

Systems 

Thursday, July 5, 2012 

FIT Lecture Hall, Tsinghua University Room 106, Postgraduate Building, Beijing Institute of TechnologyRoom 206, Postgraduate Building, Beijing Institute of Technology 

Workshop I, Chair: Prof. Lars Eriksson 

Workshop II, Chair: Prof. Hiroaki Nishi 

Workshop III, Chair: Prof. Zhiqiang Gao 

Workshop IV, Chair: Prof. Hong Qiao 

Friday, July 6, 2012, Beijing International Convention Center (BICC) 

Biosystems 

Inv: Robot 

Sensing and 

Control 

Inv: 

Disturbance 

Rejection 

Biomedical 

Engineering 

Inv: Databased 

control, 

modeling and 

optimization 

18:30-21:00 Banquet with Award Ceremony and Performance, Convention Hall 1, 2nd Floor of BICC 

Workshop V, Chair: Prof. Ben M. Chen 

Workshop V, Chair: Prof. Ben M. Chen 

Inv: Wireless 

Sensor 

Networks 

Inv: 

Hypersonic 

flight control 

42


Technical Program 

Technical Program 

Friday, July 6, 2012 

PL-1 8:30-9:30 Room 305 


Chair: Xie, Lihua 

Nanyang Technological University, Singapore 

◮ PL-1 8:30-9:30 


Doyle, Francis 


PL-2 9:50-10:50 Room 305 


Chair: Shen, Tielong 

Sophia University, Japan 

◮ PL-2 9:50-10:50 


Chen, Hong 

Jilin University, China 

PL-3 10:50-11:50 Room 305 


Chair: Meng, Max, Q.-H. 

Chinese University of Hong Kong, China 

◮ PL-3 10:50-11:50 

Surgical Robotics: Different Successful Concepts in the Past and in 

Future 

Lueth, Tim C. 

Technical University of Munich, Germany 

FrA01 13:30–15:30 Room 203A 

Intelligent Control and Automation (I) 

Chair: Wang, Peijin 

Co-Chair: Dang, Zhaohui 

Yantai Univ. 

National Univ. of Defense Tech. 

◮ FrA01-1 13:30–13:50 

Development of a Reconfigurable Robot’s Turning Method with Line 

Configuration, pp.61–66 

Chang, Jian 

Wu, Chengdong 

Shang, Hong 

Li, Bin 

Shenyang Inst. of Automation (SIA), Chinese 

Acad. of Sci. 

northeastern Univ. 

organization 

Shenyang Inst. of Automation, Chinese Acad. of 

Sci. 

◮ FrA01-2 13:50–14:10 

Study on the Structure of Human Body Simulated Controller , pp.10–14 

Wang, Peijin 

Yantai Univ. 

◮ FrA01-3 14:10–14:30 

Attitude Control of 3-DOF Helicopter based on Iterative Learning Control, 

pp.128–132 

Zhang, Xining 

Liu, Chenglin 

Liu, Fei 

Jiangnan Univ. 


Inst. of Automation,Southern Yangtze Univ. 

◮ FrA01-4 14:30–14:50 

Dancing Behavior Modeling and Logic Control Simulation of Twowheeled 

Robot based on Stateflow, pp.89–92 

Yu, Jianjun 

College of Electronic Information & Control 

Engineering, Beijing Univ. of Tech. 

Yang, Qiong 

Sun, Liang 

Wang, Guanwei 

Beijing Univ. of Tech. 

Inst. of Electric Information & Control Engineering 


◮ FrA01-5 14:50–15:10 

Stable flocking of mobile agents in mixed absolute and relative navigations 

context with communication , pp.67–73 

Dang, Zhaohui 


◮ FrA01-6 15:10–15:30 

A Modified Fruit-Fly Optimization Algorithm Aided PID Controller Designing, 

pp.233–238 

Liu, Yi 

Wang, Xuejie 

Li, Yanjun 

ZheJiang Univ. City College 

Zhejiang Univ. City College 


FrA02 13:30–15:30 Room 203B 

Computational Intelligence and Applications (I) 

Chair: Zhang, Xuncai 

Co-Chair: Wu, Xiuli 

Zhengzhou Univ. of Light Industry 

Univ. of Sci. & Tech. Beijing 

◮ FrA02-1 13:30–13:50 

An Increasing on Knowledge of MAS Trained by Boltzmann Machine 

Algorithm based Sugarscape CA Using a Synergy of Communication 

and Cooperation bet Agents , pp.484–489 

Nourafza, Nasim 

Setayeshi, Saeed 

Islamic Azad Univ. ,Najafabad Branch 

Amirkabir Univ. of Technolog 

◮ FrA02-2 13:50–14:10 

Data Collection Based on Mobile Agent in Wireless Sensor Networks, 

pp.392–396 

Li, Tongying 

National Astronomical Observatories / Nanjing Inst. 

of Astronomical Optics & Tech., Chinese Acad. of 

Sci., Nanjing 

◮ FrA02-3 14:10–14:30 

3D DNA Self-Assembly for Maximum Clique Problem, pp.438–443 

Zhang, Xuncai 

Fan, Rui 

Wang, Yanfeng 

Cui, Guangzhao 





◮ FrA02-4 14:30–14:50 

A Novel Content Based and Social Network Aided Online Spam Short 

Message Filter, pp.444–449 

Yu, Yang 

Chen, Yuzhong 

Fuzhou Univ. 

Fuzhou Univ. 

◮ FrA02-5 14:50–15:10 

Solving the Flexible Job-shop Scheduling Problem with Quantuminspired 

Algorithm, pp.538–543 

Wu, Xiuli 


◮ FrA02-6 15:10–15:30 

Max-Min Ant System for Bus Transit Multi-depot Vehicle Scheduling 

Problem with Route Time Constraints, pp.555–560 

Hao, Xiao Ni 

Jin, Wen Zhou 

Wei, Ming 

South China Univ. of Tech. 



FrA03 13:30–15:30 Room 203C 

Artificial Intelligence 

Chair: Feng, Xin 

Co-Chair: Han, Deqiang 

Marquette Univ. 

Xi’an Jiaotong Univ. 

◮ FrA03-1 13:30–13:50 

Hierarchical Proportional Redistribution principle for uncertainty reduction 

and bba approximation, pp.664–671 

Dezert, Jean 

Han, Deqiang 

Liu, Zhunga 

Tacnet, Jean-marc 

ONERA 


NW Polytech. Univ 

Cemagref-ETGR 

◮ FrA03-2 13:50–14:10 

An algorithm based on piecewise slope transformation distance for 

short time series similarity measure, pp.691–695 

Li, Huimin 

Fang, Liying 



43


Wang, Pu 

Liu, Jingwei 

Beijing Univ. of Tech., China 


◮ FrA03-3 14:10–14:30 

Pruning-Included Weights and Structure Determination of 2-Input Neuronet 

Using Chebyshev Polynomials of Class 1, pp.700–705 

Zhang, Yunong 

Yin, YongHua 

Yu, Xiaotian 

Guo, Dongsheng 

Xiao, Lin 

Sun Yat-sen Univ. 



Sun Yat-Sen Univ. 


◮ FrA03-4 14:30–14:50 

The Research on Cross-Media Information Retrieval System Based on 

Food Safety Emergencies, pp.706–710 

Han, Pengcheng 

Du, Junping 

Lee, JangMyung 

Univ. of Posts & Telecommunicatios 

School of Computer Sicence & Tech., Beijing Univ. 

of Posts & Telecommunications 

Pusan National Univ. 

◮ FrA03-5 14:50–15:10 

Neural Networks Based Autonomous Learning for a Desktop Robot, 

pp.739–742 

Dai, Lizhen 

Ruan, Xiaogang 

Wang, Guanwei 

Yu, Jianjun 






◮ FrA03-6 15:10–15:30 

Predictive Temporal Patterns Detection in Multivariate Dynamic Data 

System, pp.803–808 

Zhang, Wenjing 

Feng, Xin 



FrA04 13:30–15:30 Room 203D 

Nonlinear Control 

Chair: Wang, Xingxuan 

Co-Chair: Huang, Chaodong 

Fudan Univ. 

Chinese Acad. of Sci. 

◮ FrA04-1 13:30–13:50 

Estimate Error Analysis of the Nonlinear Third Order Extended State 

Observer, pp.1621–1627 

ZHANG, Yuan-wen 

YANG, Le-ping 

Zhu, Yanwei 

National Univ. of defense Tech. 

National Univ. of defence Tech. 


◮ FrA04-2 13:50–14:10 

Adaptive Sliding Mode Control with Nonlinear Disturbance Observer for 

Uncertain Nonlinear System Based on Backstepping Method, pp.1609– 

1614 

Qiao, Jihong 

Wang, Hongyan 

Li, Zihao 

Beijing Tech. & Business Univ. 

Acad. of Armored Force Engineering 


◮ FrA04-3 14:10–14:30 

Control of a Class of Nonlinear Uncertain Systems by Combining State 

Observers and Parameter Estimators, pp.2054–2059 

Huang, Chaodong 

Guo, Lei 



◮ FrA04-4 14:30–14:50 

Adaptive Control for A Class of Nonlinear Uncertain Dynamical Systems 

With Time-varying, pp.2171–2176 

Zhang, Jie 

Wang, Xingxuan 

Fudan Univ. 

Fudan Univ. 

◮ FrA04-5 14:50–15:10 

On the Modeling of a Nonlinear Plate and a Nonlinear Shell, pp.1585– 

1590 

Li, Shun 

Yao, Pengfei 

Acadamy of Mathematics & Sys. Sci., Chinese 

Acadamy of Sci. 


◮ FrA04-6 15:10–15:30 

Multiple Models Adaptive Control Based on Cluster-Optimization for a 

Class of Nonlinear System, pp.1367–1371 

Huang, Miao 

WANG, Xin 

Wang, Zhenlei 

Qian, Feng 

East China Univ. of Sci. & Tech. 

Shanghai Jiao Tong Univ. 



FrA05 13:30–15:30 Room 203E 

Control Theory (I) 

Chair: ZHOU, Shiliang 

North China Electric Power Univ. 

Co-Chair: ROY, TUSHAR KANTI UNSW, Canberra, ACT 2600 

◮ FrA05-1 13:30–13:50 

Robust Maneuver Control with Disturbance Attenuation for Flexible S- 

pacecraft, pp.1269–1275 

ZHOU, Duan 

Guo, Yu 

Chen, Qingwei 

Hu, Weili 

Nanjing Univ. of Sci. & Tech. 



Nanjing Univ. of Sci. &Tech. 

◮ FrA05-2 13:50–14:10 

Hover Flight Control of a Small Helicopter Using Robust Backstepping 

and PID , pp.1688–1693 

ROY, TUSHAR KANTI UNSW, Canberra, ACT 2600 

◮ FrA05-3 14:10–14:30 

Research on Robust Control Allocation for the Advanced Configuration 

Aircraft, pp.1722–1726 

Zou, Jingfeng 

Yang, Lingyu 

Zhang, Jing 

Shen, Gongzhang 

beihang Univ. 

Beijing Univ. of Aeronautics & Astronautics 

Beihang Univ. 


◮ FrA05-4 14:30–14:50 

Low Order Structured Weight Optimization for H∞Loop Shaping Design 

Procedure, pp.2257–2262 

Liu, Yuyan 

ZHOU, Shiliang 



◮ FrA05-5 14:50–15:10 

Research on Terminal Guidance Method in Glide Attack Phase of Aerodynamic 

Missile, pp.1150–1155 

Zhang, Zhi-kai 

Guo, Qing 

Univ. of Electronic Sci. & Tech. of China 


◮ FrA05-6 15:10–15:30 

Robust Diving Control of AUV with L2 Disturbance Attenuation Method, 

pp.1356–1360 

WANG, Hongjian 

Chen, Ziyin 

BIAN, Xinqian 

Jia, Heming 

Harbin Engineering Univ. 



Northeast Forest Univ. 

FrA06 13:30–15:30 Room 302 

Modeling 

Chair: INOUSSA, GARBA 

Central South Univ. 

Co-Chair: Liu, Xi Beijing Univ. of Civil Engineering & Architecture 

◮ FrA06-1 13:30–13:50 

Research on Multi-zone VAV Air Conditioning System Modeling, 

pp.2968–2972 

Liu, Xi 

Beijing Univ. of Civil Engineering & Architecture 

◮ FrA06-2 13:50–14:10 

Modeling for flying boats in regular wave, pp.3019–3024 

Zhu, Yinggu 

Fan, Guoliang 

Yi, Jianqiang 

Inst. of Automation, Chinese Acad. of Sci. 


Inst. of Automation, Chinese Acad. of Sci.,China 

◮ FrA06-3 14:10–14:30 

EBE-based Parallel Finite Element Analysis of Electric Field in Aluminum 

Reduction Cell, pp.2939–2943 

Zhao, Xianyong 

Chen, Xiaofang 

Gui, Weihua 


Central south Univ. 


44


Technical Program: Friday Sessions 

◮ FrA06-4 14:30–14:50 

Quality Model for Integrated Security Monitoring and Control in Water 

Distribution Systems, pp.3107–3112 

Zubowicz, Tomasz 

Arminski, Krzysztof 

Brdys, Mietek 

Gdansk Univ. of Tech. 



◮ FrA06-5 14:50–15:10 

Model Establishment and Simulation for Finite Length Cylindrical Surface 

Heat and Moisture Transfer outside the Borehole, pp.3242–3247 

Zhang, Yating 

Jiang, Dawei 

Zhang, Ye 




◮ FrA06-6 15:10–15:30 

Marine Vehicle Modeling and Tracking Using Wavelet Type Nets Model, 

pp.3118–3125 

INOUSSA, GARBA 

Peng, Hui 



FrA07 13:30–15:30 Room 303 

Robotics (I) 

Chair: Wang, Zhiying 

Co-Chair: Yi, Yang 

Harbin Inst. of Tech. Shenzhen Graduate 

School 

Zhejiang Univ. 

◮ FrA07-1 13:30–13:50 

An energy-based position control and asymptotic stability analysis for 

manipulator handling a flexible payload, pp.3617–3622 

Liu, Shuyang 

Wang, Zhi-qian 

Qiao, Yanfeng 

Xie, Mujun 

Li, Yuanchun 

Jilin Univ. 

Changchun Inst. of Optics,Fine Mechanics & 

Physics,Chinese Acad. of Sci. 


Changchun Univ. of Tech. 

Jilin Univ. 

◮ FrA07-2 13:50–14:10 

A Novel Navigation Method, Optimal for Sloped Terrain, pp.3623–3628 

Chen, Haotian 

Sun, Fengchi 

Song, Meng 

Li, Shulun 

Huang, Yalou 

Nankai Univ. 

Nankai Univ. 

Nankai Univ. 

Nankai Univ. 

Nankai Univ. 

◮ FrA07-3 14:10–14:30 

Path planning for mobile robots with leg/wheel hybrid locomotion system 

on outdoor terrain, pp.3669–3674 

Wang, Zhiying 

Ding, Xilun 

Harbin Inst. of Tech. Shenzhen Graduate School 

BeiHang Univ., China 

◮ FrA07-4 14:30–14:50 

Wasp Swarm algorithm on Terrain Coverage in Obstacle Environments, 

pp.3589–3594 

Zhang, Guo-you 

Taiyuan Univ. of Sci. & Tech. 

◮ FrA07-5 14:50–15:10 

The Research of Shape-shifting Robot Skid-turning with Line Configuration, 

pp.3800–3804 

Chang, Jian 

Wu, Chengdong 

Li, Bin 

Shang, Hong 





Sci. 

organization 

◮ FrA07-6 15:10–15:30 

Stable Running on A Kneed Biped Robot with Only Hip-joint Actuation, 

pp.3846–3851 

Yi, Yang 

Lin, Zhiyun 

Yan, Gangfeng 




FrA08 13:30–15:30 Room 310 

Invited Session: Applications of Semi-tensor Product in Information and 

Control 

Chair: Wang, Yuzhen 

Co-Chair: Chu, Tianguang 

Shandong Univ. 

Peking Univ. 

◮ FrA08-1 13:30–13:50 

On Definition and Construction of Lyapunov Functions for Boolean Networks, 

pp.1247–1252 

Wang, Yuzhen 

Li, Haitao 


Shandong U 

◮ FrA08-2 13:50–14:10 

Finding all controllers for disturbance decoupling of Boolean control networks, 

pp.1344–1349 

Yang, Meng 

Chu, Tianguang 

Peking Univ. 

Peking Univ. 

◮ FrA08-3 14:10–14:30 

New algorithm for finding fixed points and cycles of Boolean network, 

pp.2691–2694 

Suo, Jinghui 

Sun, Jitao 

Tongji Univ. 

Tongji Univ. 

◮ FrA08-4 14:30–14:50 

Matrix Approach to Simulation and Bisimulation Analysis of Finite Automata, 

pp.2716–2721 

Xu, Xiangru 

Hong, Yiguang 

Lin, Hai 

Acad. of Mathematics & Sys. Sci., CAS 


Univ. of Notre Dame 

◮ FrA08-5 14:50–15:10 

Approximation of Boolean Networks, pp.2280–2285 

Cheng, Daizhan 

Zhao, Yun-Bo 

Zhao, Yin 


Univ. of Glasgow 

Acad. of mathematics & Sys. Sci., CAS 

FrA09 13:30–15:30 Room 311A 

Invited Session: Coordination of Multi-Agent Systems and Networked 

Systems With Constraints and Uncertainties 

Chair: Chen, Zengqiang 

Co-Chair: Cheng, Long 

Nankai Univ. 


◮ FrA09-1 13:30–13:50 

Discarded Consensus of Multi-Agent Systems with State Constraint, 

pp.3460–3464 

Liu, Zhongxin 

Chen, Zengqiang 

NanKai Univ. 

Nankai Univ. 

◮ FrA09-2 13:50–14:10 

Control Design for Flexible Hierarchical Formation of Multiple Robots, 

pp.3595–3600 

Tang, Yutao 

Hong, Yiguang 



◮ FrA09-3 14:10–14:30 

Performance Analysis for Multi-Agent Coordination With Partial Measurable 

States Over Digital Networks, pp.863–868 

Li, Tao 

Acad. of Mathematics & Sys. Sci., Chinese Acad. 

of Sci. 

Xie, Lihua 

Nanyang Technological Univ. 

◮ FrA09-4 14:30–14:50 

A Sampled-Data Based Average Consensus Protocol for Double- 

Integrator Multi-Agent Systems with Switching Topologies and Communication 

Noises, pp.886–891 

Cheng, Long 

Wang, Yunpeng 

Hou, Zengguang 

Tan, Min 

Cao, Zhiqiang 



Inst. of Automation Chinese Acad. of Sci. 


Inst. of Automation,Chinese Acad. of Sci. 

◮ FrA09-5 14:50–15:10 

Distributed Tracking Control for Linear Multi-Agent Systems with a 

Leader of Bounded Input Using Output Information, pp.1756–1761 

Li, Zhongkui 

Beijing Inst. of Tech. 

45


Liu, Xiangdong 


◮ FrA09-6 15:10–15:30 

A High Level Decentralized Tracking Algorithm for Three Manipulators 

subject to Motion Constraints, pp.1920–1924 

Wang, Lin 

Markdahl, Johan 

Hu, Xiaoming 

Kragic, Danica 


KTH 

Royal Inst. of Tech. 


FrA10 13:30–15:30 Room 311B 

Invited Session: Control over networks: an information flow point of 

view 

Chair: You, Keyou 

Co-Chair: Chen, Michael Z. Q. 


The Univ. of Hong Kong 

◮ FrA10-1 13:30–13:50 

Distributed Containment Control of Uncertain Linear Multi-Agent Systems, 

pp.869–874 

Wen, Guanghui 

Duan, Zhisheng 

Zhao, Yu 

Chen, Guanrong 

Peking Univ. 

Peking Univ. 

Peking U 

City Univ. of Hong Kong 

◮ FrA10-2 13:50–14:10 

Adaptive Group Consensus of Coupled Harmonic Oscillators with Multiple 

Leaders, pp.3475–3480 

Su, Housheng 

Chen, Michael Z. Q. 

Wang, Xiaofan 

Valeyev, Najl 

Huazhong Univ. of Sci. & Tech. 


Shanghai Jiaotong Univ. 

Univ. of Exeter 

◮ FrA10-3 14:10–14:30 

Pinning Control of General Multi-agent Systems, pp.1930–1935 

Yu, Wenwu 


Lu, Jinhu 

Southeast Univ. 



◮ FrA10-4 14:30–14:50 

Decentralized Adaptive Control for a Class of Semi-parametric Uncertain 

Multi-agent Systems, pp.2060–2065 

Ma, Hongbin 

Zhao, Yali 

Fu, Mengyin 

Yang, Chenguang 




Univ. of Plymouth 

◮ FrA10-5 14:50–15:10 

State Estimation for a Class of Nonlinear Systems with Multi-package 

Transmission, pp.2221–2226 

Wang, Xiaofeng 

He, Xiao 

Wang, Zidong 

Tsinghua Univ. 



◮ FrA10-6 15:10–15:30 

Kalman Filtering with Scheduled Measurements - Part I: Estimation 

Framework, pp.2251–2256 

You, Keyou 

Xie, Lihua 



FrA11 13:30–15:30 Room 311C 

Invited Session: Intelligent Optimization and Evolutionary Computation 

(I) 

Chair: Chen, Jie 

Co-Chair: Wang, Ling 



◮ FrA11-1 13:30–13:50 

Online Route Planning for UAV Based on Model Predictive Control and 

Particle Swarm Optimization Algorithm, pp.397–401 

Peng, Zhihong 

Li, Bo 

Chen, Xiaotian 

Wu, Jin Ping 



Sci. & Tech. on Complex Land Sys. Simulation 

Laboratory 


◮ FrA11-2 13:50–14:10 

Surrogate Models for User’s Evaluations base on Weighted Support 

Vector Machine in IGAs, pp.144–149 

Yang, Lei 

Gong, Dunwei 

Sun, Xiaoyan 

Sun, Jing 

China Univ. of Mining & Tech. 




◮ FrA11-3 14:10–14:30 

A Hybrid Algorithm Based on Simplex Search and Differential Evolution 

for Hybrid Flow-shop Scheduling , pp.643–648 

Xu, Ye 

Wang, Ling 

Wang, Shengyao 




◮ FrA11-4 14:30–14:50 

A Compact Estimation of Distribution Algorithm for Solving Hybrid Flowshop 

Scheduling Problem, pp.649–653 


Wang, Ling 

Xu, Ye 




◮ FrA11-5 14:50–15:10 

A Memetic PSO based KNN Regression Method for Cycle Time Prediction 

in a Wafer Fab, pp.474–478 

Ni, Jiacheng 

Tongji univ 

FrB01 15:50–17:50 Room 203A 

Intelligent Control and Automation (II) 

Chair: Zhang, Tianping 

Co-Chair: Li, Dazi 

Yangzhou Univ. 

Beijing Univ. of Chemical Tech. 

◮ FrB01-1 15:50–16:10 

Adaptive Dynamic Surface Control of Nonlinear Systems with Perturbed 

Uncertainties in Strict-Feedback Form, pp.24–29 

Zhang, Tianping 

Shi, Xiaocheng 

Yang, Yuequan 

Gao, Huating 



Yangzhou Univ., China 


◮ FrB01-2 16:10–16:30 

A Resource-Allocating Network Based on Local Conditions and it’s application 

in Prediction of nonlinear systems, pp.326–330 

Qi, Wenyuan 

Li, Dazi 



◮ FrB01-3 16:30–16:50 

Fuzzy Guaranteed Cost Control Design for Uncertain Chaotic System 

with polytopic uncertainty , pp.180–184 

Liu, Yuyan 


Hao, Zulong 




◮ FrB01-4 16:50–17:10 

Diffusion Source Localization with a Water-proof Boundary, pp.164– 

169 

Luan, Fan 

Chai, Li 

Yang, Jun 

Wuhan Univ. of Sci. & Tech. 


Engineering Research Center of Metallurgical 

Automation & Measurement Tech.,Ministry of 

EducationInsitution 

◮ FrB01-5 17:10–17:30 

Hierarchical Coordination Control Method for Near Space Flight Vehicle 

with Aero and Direct Force Actuators, pp.255–259 

Chao, Tao 

Wang, Songyan 

Yang, Ming 

Harbin Inst. of Tech. 

HIt 

HIT 

◮ FrB01-6 17:30–17:50 

Kalman Filter Restraining Outliers for Short Baseline System, pp.322– 

325 

Qiao, Gang 

Li, Zhuang 

Sun, Zongxin 

Nie, Donghu 

Haerbin Engineering Univ. 


heu 

heu 

46



Cui, Haiyue 


FrB02 15:50–17:50 Room 203B 

Computational Intelligence and Applications (II) 

Chair: Mo, Hong 

Co-Chair: Wang, Ning 

Changsha Univ. of Sci. & Tech. 

Dalian Maritime Univ. 

◮ FrB02-1 15:50–16:10 

Online Self-constructing Fuzzy Neural Identification for Ship Motion Dynamics 

Based on MMG Model, pp.458–463 

Wang, Ning 

Niu, Xiaobing 

Liu, Yudong 




◮ FrB02-2 16:10–16:30 

A Probabilistic Fuzzy Controller with Operant Learning for Robot Navigation, 

pp.368–373 

Gao, Yuanyuan 


Song, Hongjun 


Beijing Univ. of Techbnology 


◮ FrB02-3 16:30–16:50 

The Optimization of Fuzzy Rules Based on Hybrid Estimation of Distribution 

Algorithms, pp.561–565 

Luo, Xiong 

Bai, Xue 



◮ FrB02-4 16:50–17:10 

On the Definition of Type-2 Fuzzy Sets, pp.601–605 

Mo, Hong 

Zhou, Min 



◮ FrB02-5 17:10–17:30 

Estimation of Hand Force from Surface Electromyography Signals using 

Artificial Neural Network, pp.584–589 

Srinivasan, Haritha 

Sauvik, Das Gupta 

Sheng, Weihua 

Chen, Heping 

Oklahoma State Univ. 



Texas State Univ. 

◮ FrB02-6 17:30–17:50 

Supervisor Design with Petri Nets for Asymmetrical System, pp.628– 

632 

FENG, Aixiang 

LUO, Xiong-lin 

China Univ. of Petroleum 


FrB03 15:50–17:50 Room 203C 


Chair: Wang, Guo-sheng 

Co-Chair: XIE, Wei 



◮ FrB03-1 15:50–16:10 

Quadratically Stabilizing Observer-based Controller Design for LPV 

Plant , pp.1073–1076 

XIE, Wei 


◮ FrB03-2 16:10–16:30 

A Closed-loop Evaluation for Regulatory Control Structure of Multivariable 

System, pp.1083–1088 


Ren, Li-hong 



◮ FrB03-3 16:30–16:50 

Design of Parametric Finite Time Functional Observers in Linear Timeinvariant 

Systems, pp.1156–1159 

Wang, Guo-sheng 

Lv, Hong-tao 

Lv, Qiang 




◮ FrB03-4 16:50–17:10 

Observer Design for Wave Equations with van der Pol Type Boundary 

Conditions, pp.1471–1476 

Li, Liangliang 

Huang, Yu 

Xiao, MingQing 

Zhongkai Univ. of Agriculture & Engineering 

Zhongshan (Sun Yat-Sen) Univ. 

Southern Illinois Univ. 

◮ FrB03-5 17:10–17:30 

The Estimation of the Solutions Matrix of the Perturbed Discrete Time 

Algebraic Riccati Equation, pp.1172–1175 

Bi, Haiyun 

Chen, Dongyan 

Anhui Polytechnic Univ. 

Harbin Univ. of Sci. & Tech. 

◮ FrB03-6 17:30–17:50 

Admissibility Conditions for Linear Singular Delta Operator Systems: 

Analysis and Synthesis, pp.1870–1875 

Mao, Qing-tang 

Dong, Xin-zhuang 

Tian, Wan-hu 

Qingdao Univ. 

Qingdao Univ. 

Qingdao Univ. 

FrB04 15:50–17:50 Room 203D 

Slide Mode Control 

Chair: Liu, Shirong 

Co-Chair: SHI, Juan 

Hangzhou Dianzi Univ. 

Victoria Univ. 

◮ FrB04-1 15:50–16:10 

Nonlinear Attitude Control of a 3D Rigid Pendulum using Hierarchical 

Sliding Mode Techniques, pp.1524–1528 

Zou, Kui 

GE, Xinsheng 

Beijing Information Sci. & Tech. 

Beijing Information Sci. & Tech. Univ. 

◮ FrB04-2 16:10–16:30 

Integral Sliding Mode Control of Z-source Inverter for Motor Drive System 

of Electric Vehicles, pp.1196–1200 

Sun, Qu 

Wang, Yongyu 


Univ. of Beijing Post & Telecommunication 

◮ FrB04-3 16:30–16:50 

On Hierarchical Sliding Mode Control of Underactuated TORA System, 

pp.1785–1789 

Bao, Yuqing 

Li, Junyuan 

Xie, Jihua 

Gao, Bingtuan 





◮ FrB04-4 16:50–17:10 

Dynamic Output Feedback Sliding Mode Control for Magnetic Bearing 

System Stabilization, pp.1547–1552 

SHI, Juan 

Yan, Yan 

Yu, Xinghuo 



RMIT Univ. 

◮ FrB04-5 17:10–17:30 

Second Order Sliding Mode Control of Pan-tilt Joint in Modular Manipulator, 

pp.2188–2193 

Liu, Shirong 

Chen, Jianye 



◮ FrB04-6 17:30–17:50 

Sliding Mode Variable Structure Control Based on Exact Linearization 

Mode of Nonlinear System, pp.2878–2881 

Wang, Ya-hui 

Xia, Zhifeng 

Zhang, Tong 


Department of Automation Engineering Beijing 

Univ. of Civil & Architecture Engineering 

Beijing Univ. of Architecture & Civil Engineering 

FrB05 15:50–17:50 Room 203E 

Control Theory (II) 

Chair: Han, Qing-Long 

Co-Chair: Ren, Li-hong 

Central Queensland Univ., Australia 


◮ FrB05-1 15:50–16:10 

A New Variable Pairing Method and Its Application in Non-square Multivariable 


Ren, Li-hong 




◮ FrB05-2 16:10–16:30 

Synchronization for Lur’e Type Complex Dynamical Networks with 

Time-Varying Delay Based on Linear Feedback Controller, pp.1389– 

1394 

Duan, Wenyong 

Cai, Chenxiao 

Nanjing Univ. of Sci. & Tech., China 

nanjing Univ. of Sci. & Tech. 

47


Zou, Yun 


◮ FrB05-3 16:30–16:50 

Distributed Estimation For Discrete Time Systems With Time-Varying 

Observation Delay, pp.1881–1886 

Wang, Wei 

Han, Chunyan 

Feng, Shulin 


Univ. of JInan 


◮ FrB05-4 16:50–17:10 

Global Stabilization for a Class of Integrator Chain with Bounded Control, 

pp.2245–2250 

Fang, Haijun 

MKS Instrument 

◮ FrB05-5 17:10–17:30 

Estimation of the domain of attraction for asymmetric saturated linear 

systems via Polyhedral Lyapunov Functions, pp.1253–1258 

Zhao, Guanglei 

Wang, Jingcheng 

Shanghai JiaoTong Univ. 

electronic & electric engineering 

◮ FrB05-6 17:30–17:50 

Output feedback stabilization of polytopic-type uncertain discrete systems 

with interval-like time-varying state and input delays, pp.2119– 

2124 

Ge, Xiaohua 

Jiang, Xiefu 

Han, Qing-Long 

Central Queensland Univ. 

hangzhou dianzi Univ. 


FrB06 15:50–17:50 Room 302 


Chair: Yang, Hua 

Co-Chair: Chen, Xi 

Ocean Univ. of China 


◮ FrB06-1 15:50–16:10 

Recursive Identification for Wiener-Hammerstein Systems Using Instrumental 

Variable, pp.3043–3048 

Chen, Xi 

Fang, Hai-Tao 



◮ FrB06-2 16:10–16:30 

Roll and Pitch Model Identification for Miniature Unmanned Helicopter 

Based on Subspace Method, pp.3059–3063 

Bai, Meng 

Li, Minhua 

Shandong Univ. of Sci. & Tech. 


◮ FrB06-3 16:30–16:50 

Blind Identification of Multi-Rate Sampled Plants, pp.3220–3225 

Yu, Chengpu 

Zhang, Cishen 

Xie, Lihua 


Swinburne Univ. of Tech. 


◮ FrB06-4 16:50–17:10 

Data-driven Subspace Approach to MIMO Minimum Variance Control 

Performance Assessment, pp.3157–3161 

Yang, Hua 

Li, Shaoyuan 



◮ FrB06-5 17:10–17:30 

Adaptive Generalized Function Lag Projective Synchronization and Parameter 

Identification of a Class of Hyperchaotic Systems with Fully 

Uncertain Parameters and Disturbance, pp.3265–3269 

Chai, Xiuli 

Wu, Xiangjun 

Guo, Junyan 

Henan Univ. 

Henan Univ. 

Inst. of Image Processing & Pattern Recognition 

◮ FrB06-6 17:30–17:50 

Yaw Dynamic Model Identification for Miniature Unmanned Helicopter, 

pp.3162–3166 

Li, Minhua 

Bai, Meng 



FrB07 15:50–18:10 Room 303 

Robotics (II) 

Chair: Xian, Bin 

Co-Chair: ILYAS, MUHAMMAD 

Tianjin Univ. 

Beihang Univ. 

◮ FrB07-1 15:50–16:10 

Modeling and Variable Structure Control of a Vehicle Flexible Manipulator, 

pp.3657–3662 

Xu, Yongjun 

Qiao, Yanfeng 


Liu, Keping 

Li, Yuanchun 

Jilin Univ. 





Jilin Univ. 

◮ FrB07-2 16:10–16:30 

Data Acquisition and Processing of the Onboard Sensor System of a 

Quadrotor Unmanned Aerial Vehicle, pp.3705–3710 

Guo, Fang 

Xian, Bin 

Li, Haotao 

Huang, Guoping 

Zhao, Tingting 

tianjin Univ. 

Tianjin Univ. 

Tianjin Univ. 

automation 

Tianjin Univ. 

◮ FrB07-3 16:30–16:50 

Research on the Ground Control System of Quadrotor Unmanned Aerial 

Vehicles, pp.3776–3781 


Xian, Bin 

Yang, Yungao 

Guo, Fang 


Tianjin Univ. 

Tianjin Univ. 

Tianjin Univ. 

tianjin Univ. 

automation 

◮ FrB07-4 16:50–17:10 

SINS Initial Alignment Using Wavelet De-noising Method for Aircraft 

Navigation, pp.3921–3926 

ILYAS, MUHAMMAD 

YANG, Yunchun 

Ren, Zhang 

Beihang Univ. 

NavTech. 

Beihang Univ. 

◮ FrB07-5 17:10–17:30 

Orientation Coordination of Multiple Robots based on a Nonlinear Consensus 


Xing, Guansheng 

Sun, Hexu 

Chen, Haiyong 

Liu, Ning 

Hebei Univ. of Tech. 



hebei Univ. of Tech. 

◮ FrB07-6 17:30–17:50 

Octree-based Spherical hierarchical model for Collision detection, 

pp.3870–3875 

Ouyang, Fan 

Zhang, Tie 



◮ FrB07-7 17:50–18:10 

Evaluation of Task Realizability for Robot Systems in Representation 

Space, pp.3816–3821 

Wu, Ban 

Su, Jianbo 



FrB08 15:50–17:50 Room 310 

Invited Session: Data-driven Control System Design and Analysis 

Chair: Li, Shaoyuan 

Co-Chair: Li, Kang 


Queen’s Univ. Belfast 

◮ FrB08-1 15:50–16:10 

Model-Based Predictive Control for Distributed Parameter Systems 

Based on Local Modeling Approach, pp.1287–1292 

Wang, Mengling 

Zhang, Yang 

Shi, Hongbo 


Shanghai Urban & Rural Construction & 

Transportation Committee 


◮ FrB08-2 16:10–16:30 

Nonlinear Dynamic Process Monitoring Based on DLLE-SVDD, 

pp.3131–3136 

Ma, Yuxin 


Shi, Hongbo 




48



◮ FrB08-3 16:30–16:50 

Fuzzy Clustering Based Spatiotemporal Fuzzy Logic Controller Design, 

pp.3167–3172 

Zhang, Xianxia 

LI, Jiajia 

Jiang, Ye 

Su, Baili 

Qi, Chenkun 

Zou, Tao 

Shanghai Univ. 



Qufu Normal Univ. 


Zhejiang Univ. of Tech. 

◮ FrB08-4 16:50–17:10 

A Regression Approach to LS-SVM and Sparse Realization based on 

Fast Subset Selection, pp.612–617 

Zhang, Jingjing 

Li, Kang 



◮ FrB08-5 17:10–17:30 

Input Selection for Dynamic RBF Models in Process Monitoring, 

pp.3037–3042 

LIU, Xueqin 

Li, Kang 

Li, Shaoyuan 

Fei, Minrui 





FrB09 15:50–17:50 Room 311A 

Invited Session: Nonlinear and Networked Systems 

Chair: Wang, Yuan 

Co-Chair: Xie, Lihua 

Florida Atlantic Univ. 


◮ FrB09-1 15:50–16:10 

Persistent awareness coverage for networked mobile sensors with 

awareness loss, pp.4291–4296 

Song, Cheng Univ. of Sci. & Tech. of China & City Univ. of Hong 

Kong Joint Advanced Research Center 

Feng, Gang 

WANG, Yong 


Univ. of Sci. & Tech. of China 

◮ FrB09-2 16:10–16:30 

Control of Discrete-Time Periodic Linear Systems with Input Saturation 

via Multi-Step Periodic Invariant Set, pp.1372–1377 

Zhou, Bin 

Li, Dewei 

Lin, Zongli 



Univ. of Virginia 

◮ FrB09-3 16:30–16:50 

Simultaneous Stabilization and Synchronization of Non-Identical Linear 

Multi-Agent Systems with Switching Topology, pp.976–981 

Hu, Guoqiang 

Xie, Lihua 



◮ FrB09-4 16:50–17:10 

Remarks on Integral-ISS for Systems with Delays, pp.2227–2232 

Tiwari, Shanaz 

Wang, Yuan 

Jiang, Zhongping 



Polytechnic Univ. 

◮ FrB09-5 17:10–17:30 

Optimal Control of Finite-valued Networks, pp.2274–2279 


Zhao, Yin 

Liu, Jiangbo 



Bradley Univ. 

◮ FrB09-6 17:30–17:50 

Robust Flight Control System Design for an Indoor Miniature Coaxial 

Helicopter, pp.2918–2924 

Wang, Biao 

Wang, Fei 

Chen, Ben M. 

Lee, Tong Heng 

Nanjing Univ. of Aeronautics & Astronautics 

National Univ. of Singapore 



FrB10 15:50–17:50 Room 311B 

Invited Session: Nonsmooth Control of Nonlinear Systems 

Chair: Li, Shihua 

Co-Chair: Wang, Jiankui 

Southeast Univ., China 

Tianjin Univ. 

◮ FrB10-1 15:50–16:10 

Finite-time observer for a class of time-varying nonlinear systems, 

pp.2647–2652 

Du, Haibo 

Qian, Chunjiang 

Yang, Shizhong 

Li, Shihua 


Univ. of Texas at San Antonio 



◮ FrB10-2 16:10–16:30 

Non-Smooth Robust Nonlinear Control for Robotic Manipulators, 

pp.1447–1452 

Zhao, Dongya 

Xie, Lihua 

Zhu, Quan-Min 



Univ. of the West of England 

◮ FrB10-3 16:30–16:50 

Finite-time Robust Stabilization of Dynamic Feedback Nonholonomic 

Mobile Robots Based on Visual Servoing with Input Saturation, 

pp.3686–3691 

Chen, Hua 

Wang, Chaoli 

Zhang, Dongkai 

Yang, Fang 

Hohai Univ., Changzhou Campus 

Univ. of Shanghai for Sci. & Tech. 



◮ FrB10-4 16:50–17:10 

Stabilizing the Attitude of a Flexible Spacecraft with Fast Convergence, 

pp.1676–1681 

Ding, Shihong 

Zheng, Wei Xing 

Jiangsu Univ. 

Univ. of Western Sydney 

◮ FrB10-5 17:10–17:30 

Finite-Time Consensus Problem for Multiple Non-holonomic Mobile A- 

gents, pp.1739–1744 

Wang, Jiankui 

Qiu, Zhihui 

Zhang, Guoshan 

Yang, Weichao 

Tianjin Univ. 

Tianjin Univ. 

Tianjin Univ. 

Tianjin Univ. 

◮ FrB10-6 17:30–17:50 

Flux Estimation of Induction Motors Using High-order Terminal Sliding- 

Mode Observer, pp.1860–1863 

Feng, Yong 

Zhou, Minghao 

Shi, Hongyu 

Yu, Xinghuo 




RMIT Univ. 

FrB11 15:50–17:50 Room 311C 


(II) 





◮ FrB11-1 15:50–16:10 

Three-dimensional Deployment Optimization of Sensor Network Based 

on An Improved Particle Swarm Optimization Algorithm, pp.4395–4400 

Lian, Xiaoyan 

Zhang, Juan 

Chen, Chen 

Deng, Fang 





◮ FrB11-2 16:10–16:30 

Optimization of a 3-PRS parallel manipulator based on interval analysis, 

pp.2452–2456 

Zhang, Xu 

Fang, Hao 



◮ FrB11-3 16:30–16:50 

A Differential Evolution Algorithm with Two Speed-up Methods for NF- 

SSP with SDSTs and RDs, pp.490–495 

Qian, Bin 

Du, Puze 

Hu, Rong 

Che, Guolin 

Kunming Univ. of Sci. & Tech. 



kmust 

◮ FrB11-4 16:50–17:10 

49


Implementation of Control Algorithm for Three-Dimensional Pursuer 

Convoy, pp.2005–2010 

Feng, Shulin 

Wang, Wei 

Zhang, Huanshui 




◮ FrB11-5 17:10–17:30 

Design of Networked Air Defense Fire Control Simulation System 

based on BOM, pp.3214–3219 

Chen, Shi-jun 

Zhang, Juan 

Chen, Chen 

School of Automation 



◮ FrB11-6 17:30–17:50 

An ant colony algorithm for permutation flow shop problem, pp.596–600 

Shang, Ke 

State Key Laboratory for Manufacturing Sys. 

Engineering, Xi’an Jiaotong Univ. 

Feng, Zuren 

Ke, Liangjun 

Chair: Duan, Guang-Ren 

Co-Chair: Huo, Wei 

Poster Session PFrA 

July 6, 13:30-14:50 


Sys. engineering Inst. 



◁ PFrA-01 

Research on Indoor Location Technology Based on RSSI and Back 

Propagation Neural Network , pp.80–83 

Zhang, Huiqing 

Shi, Xiaowei 



◁ PFrA-02 

AUTO DRAVCNC PROCESSING DATA INQUIRY SYSTEM, pp.196– 

198 

Liu, Hongjun 

Men, Xiyao 

Shenyang Aerospace Univ. 


◁ PFrA-03 

Further Understanding of the Meaning of Fuzzy Multi-stage Conditional 

Statements, pp.251–254 

Yu, Fusheng 

Wang, Xiao 

Beijing Normal Univ. 


◁ PFrA-04 

Architecture and Cooperation Mechanism of MAS for Information Integration 

Emergency-Service Oriented, pp.341–345 

Yu, Haixin 

Chen, Jie 

Zhang, Juan 




◁ PFrA-05 

Migration & Competition-based Particle Swarm Optimization for Parameter 

Estimation, pp.590–595 

Ren, Ziwu 

◁ PFrA-06 

Research on the Machine Tool Selection of RMS, pp.623–627 

Zhou, Fengxu 

Li, Aiping 

Xu, Liyun 

Xie, Nan 

Soochow Univ. 

Tongji Univ. 

tongji Univ. 

tongji Univ. 

Tongji Univ. 

◁ PFrA-07 

A Hybrid Method for Short-term Load Forecasting in Power System, 

pp.696–699 

Zhu, Xianghe 

Qi, Huan 

Huang, Xuncheng 

Sun, Suqin 

Huazhong Univ.of Sci. & Tech.Wuchang Branch 

huazhong Univ. of Sci. & Tech. 

Electric Power of HeNan 


◁ PFrA-08 

Group Decision-Making Based Case Retrieval and Its Application, 

pp.773–778 

Zhang, Chun-xiao 

YAN, Aijun 



Zhao, Hui 

Wang, Pu 



◁ PFrA-09 

Attribute Reduction Method Using Water-Filling Principle for Case- 

Based Reasoning, pp.779–782 

Zhao, Hui 

YAN, Aijun 


Wang, Pu 





◁ PFrA-10 

Application of Self-organizing Feature Map Neural Network Based on 

Data Clustering, pp.797–802 

Hu, Xiang 

Yang, Yun 

Zhang, Lihong 

Xiang, Tao 

Hong, Chengqiu 

Zheng, Xiaotong 



Tinghua Univ. 




◁ PFrA-11 

Flow Rate Control and Resource Allocation Policy with Security Requirements 

in OFDMA Networks, pp.1020–1025 

Zhu, Xingzheng 

Yue, Jianting 

Yang, Bo 

Guan, Xinping 

SHANGHAI JIAOTONG Univ. 




◁ PFrA-12 

Augmented Dimension Algorithm Based on Sequential Detection for 

Maneuvering Target Tracking, pp.1323–1327 

Pan, Baogui 

Peng, Dongliang 

Shao, Genfu 

Inst. of Information & Control 



◁ PFrA-13 

Consensus of multi-agent systems with time delay based on nonlinear 

algorithm, pp.1628–1633 

Liang, You-ming 

Liu, Chenglin 

Liu, Fei 

Inst. of Automation, Jiangnan Univ. 


Jiangnan Univ., China 

◁ PFrA-14 

Structured Singular Value Synthesis Based Low Power Level Steam 

Generator Water Level Controller Design , pp.1661–1665 


Liu, Yuyan 

Hao, Zulong 




◁ PFrA-15 

The study of a new Call Admission Control method Based on selfsimilar 

traffic, pp.15–19 

Hu, Yanpu 

Yang, WenFu 

Wang, Yong 

Dong, Ying 

JiLin Univ. .china 

School of Communication Engineering, Jilin Univ., 

Changchun, China 




◁ PFrA-16 

Parameter optimization of PID controller based on Complex System 

Genetic Algorithm in Electro-hydraulic Servo Control System, pp.30– 

35 

Yu, Jue 

Zhuang, Jian 

Yu, Dehong 




◁ PFrA-17 

The Direct Adaptive Fuzzy Robust Control for Satellite Attitude Control, 

pp.36–41 

Guan, Ping 

Zhang, Wei 

Liu, Xiao He 

Beijing Institution of Machinery 


Information Sci. & Tech. Univ., Beijing 

50



Xue, Li 


◁ PFrA-18 

The Adaptive Fuzzy Sliding mode control of Hypersonic Vehicle, pp.51– 

56 

Guan, Ping 

Xue, Li 

Liu, Xiao He 

Wang, Sai 




Beijing informationSci.&Tech. Univ. 

◁ PFrA-19 

Adaptive fuzzy and H∞control of robotic manipulators with uncertainties, 

pp.74–79 

Liu, Yuliang 

Yang, Li 

Duan, Hailong 

Tianjin Univ. of Tech. & Education 


,Tianjin Univ. of Tech. & Education 

◁ PFrA-20 

Minimum Time Dynamic Optimization Using Double-layer Optimization 


Guo, Xuan 

Du, Wenli 

Qi, Rongbin 

Qian, Feng 


automation Inst. 



◁ PFrA-21 

Design of Low-cost acquisition system for multi-channel Analog Signal 

for PLC and its Application in Temperature Control System, pp.93–98 

Li, Ruonan 

Du, Xiuxia 

Li, Pingkang 

Beijing Jiaotong Univ. 



◁ PFrA-22 

Study on the Optimal Passive Creeping Control of the Snake-like Robot, 

pp.99–103 

Zhang, Danfeng 

Wu, Chengdong 

Li, Bin 

Shenyang Inst. of Automation & Graduate Univ. 

of Chinese Acad. of Sci. 



Sci. 

◁ PFrA-23 

Takagi-Sugeno Fuzzy PD Controller for a 3-DOF Stabilized Platform, 

pp.108–112 

Leghmizi, Said 

Harbin engineering Univ. 

◁ PFrA-24 

Repetitive Adaptive Control Based on Sliding Filter Observer in Servo 

Turntable, pp.116–121 

Gan, Ming Gang 

Ma, Hui-xia 


Bejing Inst. of Tech. 

◁ PFrA-25 

Iterative Learning Control Algorithm with Self-adaptive Steps, pp.122– 

127 

WANG, Yan 

Beihang Univ. 

◁ PFrA-26 

Real-Time Simulation of Hydraulic Turbine Governing System Based 

on ARM, pp.170–173 

GUO, Jiang 

Wu, Nian 

Gu, Kaikai 

Bai, Weimin 

Zeng, Bing 

Liu, Yajin 

Wuhan Univ. 

Wuhan Univ. 

Wuhan Univ. 

Wuhan Univ. 

Wuhan Univ. 

Wuhan Univ. 

◁ PFrA-27 

Periodicity Analysis of Uncertain Neural Networks With Multiple Time- 

Varying Delays, pp.174–179 

Lou, Xuyang 

Ye, Qian 

Feng, Wei 

Cui, Baotong 

◁ PFrA-28 





Solar Sail Trajectory Optimization Based on Improved Imperialist Competitive 


WANG, Yong 

Zhu, Min 

WEI, Yiheng 

Zhang, Yang 




Shanghai Inst. of Satellite Engineering 

◁ PFrA-29 

Indirect Adaptive Fuzzy Sliding Mode Control for Uncertain Multi-link 

Robots, pp.199–203 

Mu, Xiaojiang 

Ge, Li 

shenzhen Inst. of information Tech. 

Shenzhen Inst. of Information Tech. 

◁ PFrA-30 

Fuzzy Control for Hyperchaotic Systems via Nonuniform Sampling Approach, 

pp.214–219 

Yang, Dedong 

Wang, Jinhuan 

Sun, Hexu 

Yang, Peng 




Heibei Univ. of Tech. 

◁ PFrA-31 

Identification and Prediction of Nonlinear Multi-parameter Based on 

Least Squares Support Vector Machine, pp.224–228 

Hou, Yuan-bin 

Li, Ning 

Xi’an Univ. of Sci. & Tech. 


◁ PFrA-32 

Complex Dynamical Behaviors Analysis of a Voltage-controlled Memristive 


Sun, Junwei 

Shen, Yi 


Huazhong Univ. of Sci. & Tech., China 

◁ PFrA-33 

Design and Simulation for Dual-Stage Actuator Systems with Multi-rate 

and Friction Compensation Controller, pp.245–250 

Wei, Dongfang 

Yang, Jun 





◁ PFrA-34 

Research on Commutation fluctuation Self-Adaptive Control Suppression 

Strategy for Brushless DC Motor, pp.265–269 

Wang, Weihua 

Huang, Haibo 

Hubei Univ. of Automotive Tech. 

Hubei Automobile Industries Inst. 

◁ PFrA-35 

The Fuzzy Human-Simulated Intelligent Control for Hot-Rolling Strip 

Width, pp.270–274 

Tian, Jianyan 

Zhang, Guanyu 

Taiyuan Univ. of Tech. 


◁ PFrA-36 

On-Ramp Local Control with Neural Network Method, pp.286–289 

Wang, Hao 

Xu, Jinxue 



◁ PFrA-37 

A Wide Range of Course-changing Control Algorithm for Marine Vessel, 

pp.295–299 

Jia, Baozhu 

ZHANG, Gui-chen 



◁ PFrA-38 

Rotary Kiln Combustion Working Condition Recognition Based on 

Flame Image Texture Features and LVQ Neural Network, pp.305–309 

Wang, Jie-sheng 

Ren, Xiudong 

Univ. of Sci. & Tech. Liaoning 


◁ PFrA-39 

Autonomous Navigation Research for Mobile Robot, pp.331–335 

Cai, Jian Xian 

Yu, Ruihong 

Cheng, Lina 

Inst. of Disaster Prevention 



51


◁ PFrA-40 

A New Stability Condition of Neural Networks with Time-Varying Delay, 

pp.336–340 

Chen, Yun 




◁ PFrA-41 

The Study of Intelligent Space Environment Application and ManagementBased 

on Wireless Networkt, pp.424–428 

Duan, Ping 


◁ PFrA-42 

The Research on Eruption Peak Mutation model of Lake and Reservoir 

Alga Bloom, pp.2949–2952 

Zhao, Xiaoping 

Wang, Xiaoyi 

beijing Tech. & bussiness Univ. 

Beijing Tech. & business Univ. 

◁ PFrA-43 

Active fault-tolerant control for satellite system via learning unknown 

input observer, pp.2965–2967 

Jia, Qingxian 

Guan, Yu 

Zhang, Yingchun 

Jiang, Yu 

Shen, Yi 



research centor of satellite Tech. 



◁ PFrA-44 

The Method of Multi-sources Fault Diagnosis in Gas Turbine & Compressor 

Unit Based on SDG and Bayes Theory, pp.2973–2976 

SONG, Yong-jie 

XU, Bao-chang 

China Univ. of Petroleum (Beijing) 

China Univ. of Petroleum(Bejing) 

◁ PFrA-45 

Research on Banknote Image Orientation Based on Least Square, 

pp.2983–2987 

Zhang, Ying 

Univ. of Anshan 

◁ PFrA-46 

Dynamic Characteristics for Evaporator in Organic Rankine Cycle, 

pp.2994–2998 

Hou, Guolian 

Li, Yanbin 

Zhang, Jianhua 

Zhou, Yeli 



North China Electric Power Univ., Beijing 


◁ PFrA-47 

An improved fuzzy identification method based on Sigmoid data transfer 

function, pp.2999–3003 

Liu, Fucai 

Wang, Shu’en 

Dou, Jinmei 

Yanshan Univ. 

Yanshan Univ. 

Yanshan Univ. 

◁ PFrA-48 

Nonlinear System Modeling and Fault Detection Method Using Set 

Membership Estimation and T-S Fuzzy Model, pp.3031–3036 

Chai, Wei 


◁ PFrA-49 

Sample Selection and Training of Self-Organizing Map Neural Network 

in Multiple Models Approximation, pp.3053–3058 

Gao, Dayuan 

Zhu, Hai 

Liu, Xijing 

Wang, Chao 

◁ PFrA-50 

A Rough T-S Fuzzy Model, pp.3072–3076 

Wang, Li 

Zhou, Xianzhong 

Shen, Jie 

Navy submarine Acad. 

navy submarine Acad. 

Navy Submarine Acad. 


Nanjing Universtiy of Tech. 

Nanjing Univ. 


◁ PFrA-51 

Dynamic Fault Tree Analysis based Fault Diagnosis System of Power 

Transformer, pp.3077–3081 

GUO, Jiang 

Shi, Lei 

Wuhan Univ. 

Wuhan Univ. 

Zhang, Kefei 

Gu, Kaikai 

Bai, Weimin 

Zeng, Bing 

Liu, Yajin 

Wuhan Univ. 

Wuhan Univ. 

Wuhan Univ. 

Wuhan Univ. 

Wuhan Univ. 

◁ PFrA-52 

Aging and Life Management Methods of Pressurizer Based on PDCA 

Cycle in Nuclear Power Station, pp.3082–3086 

GUO, Jiang 

Bai, Weimin 

Feng, Zhihui 

Gu, Kaikai 

Zeng, Bing 

Liu, Yajin 

Wuhan Univ. 

Wuhan Univ. 

Wuhan Univ. 

Wuhan Univ. 

Wuhan Univ. 

Wuhan Univ. 

◁ PFrA-53 

Frequency-domain Parameter Identification of Nonlinear Generator 

Excitation System Based on Improved Particle Filtering Algorithm, 

pp.3087–3090 

Liu, Ruilan 

Liu, Wei 

Nanjing Univ. of Post &Telecomomunication 

Guangxi Power Grid Electric Power Research Inst. 

◁ PFrA-54 

Nonlinear torsional vibration dynamics of rolling mill’s drive system 

under spindle angle parametric excitation, pp.3091–3095 

Shi, Peiming 

Li, Jizhao 

Zhao, Dongwei 

Liu, Bin 

Han, Dongying 

Yanshan Univ. 

Yanshan Univ. 

Yanshan Univ. 

Yanshan Univ. 

Yanshan Univ. 

◁ PFrA-55 

Fuzzy Identification of the Steam Multivariable Temperature System 

Based on Improved GK Clustering Algorithm, pp.3096–3101 

Li, Ruonan 

Du, Xiuxia 

Li, Pingkang 




◁ PFrA-56 

Pitch-controlled Wind Turbine Synchronized Cutting-in Control and 

Modeling-Simulation, pp.3113–3117 

Xiao, Yunqi 

Lv, Yuegang 



◁ PFrA-57 

Turbine Machine Fault Diagnosis Using Modified Redundant Second 

Generation Wavelet Packet Transform, pp.3126–3130 

Li, Ning 

Zhou, Rui 

Shanghai Second Polytechnic Univ. 

China Ship Development & Design Center 

◁ PFrA-58 

Study on Time Registration method for Photoelectric TheodoliteData 

Fusion, pp.3137–3139 

YANG, Hong Tao 

GAO, Hui-bin 


Changchun Inst. of Optics, Fine Mechanics & 


◁ PFrA-59 

Parameter Identifiability of Quantized Linear Systems, pp.3140–3145 

Shen, Ying 

Zhang, Hui 



◁ PFrA-60 

Nonlinear Process Fault Diagnosis based on Slow Feature Analysis, 

pp.3152–3156 

Deng, Xiaogang 

Tian, Xue-Min 

Hu, Xiangyang 



Hekou Production Factory of Shengli Oilfield 

◁ PFrA-61 

Fault Diagnosis of Hydraulic Variable Pitch for Wind Turbine Based on 

Qualitative and Quantitative Analysis, pp.3181–3185 

Han, Xiaojuan 

Zhang, Hao 

North China Electrical Power Univ. 


52



Chen, Yueyan 

Zhang, Xilin 

Wang, Chengmin 


Changchun Power Supply Company 


◁ PFrA-62 

Modeling and Control Simulation for Force Couple Leveling System of 

Hydraulic Press, pp.3186–3190 

Du, Chunyan 


Jia, Chao 

Tianjin Univ. 


tianjin Univ. of Tech. 

◁ PFrA-63 

Research on the Application and Compensation for Startup Process of 

FOG Based on RBF Neural Network, pp.3195–3199 

SHEN, Jun 

MIAO, Ling-juan 

GUO, ZIWEI 


Beijing Inst. of Tech., 


◁ PFrA-64 

Short-Term Wind Speed Prediction Model of LS-SVM Based on Genetic 


Han, Xiaojuan 

Chen, Fang 

Cao, Hui 

Li, Xiangjun 

Zhang, Xilin 



China Space Tech. Acad. 

China Electric Power Research Inst. 


◁ PFrA-65 

Fault Diagnosis Based on Genetic Algorithm for Optimization of EBF 

Neural Network , pp.3205–3207 

Wang, Ya-hui 

Huo, Yifeng 



◁ PFrA-66 

Nonsingular Terminal Neural Network Sliding Mode Control for Multilink 

Robots Based on Backstepping, pp.20–23 

Xu, Chuanzhong 

Wang, Yongchu 

Univ. of Huaquao 


◁ PFrA-67 

A Vehicle License Plate Location and Correction Method Based On the 

Characteristics of License Plate, pp.42–46 

Fang, Jun 


◁ PFrA-68 

Routing Algorithm Based on Swarm Intelligence, pp.47–50 

Lv, Yong 

Zhu, Yaodong 

JiaXing Univ. 

JiaXing Univ. 

◁ PFrA-69 

Fuzzy control strategy based on the Particle Swarm Optimization Algorithms, 

pp.57–60 

Han, Shaoze 

WuHan Digital Engineering Inst. 

◁ PFrA-70 

Research on Fleet Networked Air Defense Sensor Cooperative Control, 

pp.113–115 

Zhuan, Yifu 

Pan, Changpeng 

Chen, Rong 

Navy 91550 unit. Dalian 

department of command engineering 

502unit,NAEI 

department of command engineering 502unit,NAEI 

◁ PFrA-71 

Adaptive Visual Servo Control of UAV Ground-Target-Autonomous- 

Tracking System , pp.133–137 

Chen, LongSheng 

NanChang HangKong Univ. 

◁ PFrA-72 

Simulation Research on maximum power point tracking based on perturbation 

and observation, pp.150–153 

Zhou, Jianping 

Shanghai Univ. of Electric Power 

◁ PFrA-73 

Non-fragile Guaranteed-cost H1 Control for a Class of Nonlinear 

Sampled-data System, pp.154–158 

Wang, Shi-gang 

Heilongjiang Univ. 

◁ PFrA-74 

Adaptive Proportional Guidance Law for Reentry Vehicles with Impact 

Angle and Terminal Velocity Constraints, pp.159–163 

Xie, Daocheng 

Wang, Zhongwei 

national Univ. of defense Tech. 


◁ PFrA-75 

Adaptive Fuzzy Path Following Control for a Nonholonomic Mobile 


Shi, Wuxi 

Tianjin Polytechnic Univ. 

◁ PFrA-76 

Study on a Sliding Mode Variable Structure Vector Control of Induction 

Motor Drives, pp.209–213 

Liu, Huan 

Cui, Han 

Shenyang Univ. of Chemical Tech. 


◁ PFrA-77 

The Application of PowerGREP in Corpus Processing for Foreign Language 

Teaching, pp.220–223 

Liu, Huan 

Cui, Han 



◁ PFrA-78 

Multi-objective Optimization of Airport Gate Assignment , pp.260–264 

Liu, Changyou 

Liang, Yutao 

Civil Aviation Univ. of China 

China Aviation Univ. of China 

◁ PFrA-79 

Indirect Position Detection of SRM Based on Genetic Algorithm, 

pp.275–279 

Xiao, Li 

Sun, Hexu 


Hebei Univ. of Technolog 

◁ PFrA-80 

Dynamic Path Optimization Method Based on Ant Colony Algorithm and 

Group Decision-making, pp.300–304 

Huang, Yan Guo 


◁ PFrA-81 

The Design of Video Image’s Storage Converter Based on FPGA, 

pp.346–349 

Liu, Qingqian 

Wang, Bo 

Zheng, Zhihui 




◁ PFrA-82 

A Dynamic Algorithm for Community Detection in Social Networks, 

pp.350–354 

Kong, Bing 

Chen, Hongmei 

Liu, WeiYi 

Zhou, Lihua 

◁ PFrA-83 

Offensive and defensive strategy of web crawler, pp.355–358 

Jiang, Yuanshu 

Guo, Liyong 

Yunnan Univ. 

Yunnan Univ. 

Yunnan Univ. 

Yunnan Univ. 



◁ PFrA-84 

Estimate Method to Solve the Time Lag of Sintering Forecast, pp.359– 

361 

Zhang, Tao 

Hebei United Univ. 

◁ PFrA-85 

Genetic Algorithm with Three Dimensional Chromosome for Large S- 

cale Scheduling Problems, pp.362–367 

Wang, Yong Ming 

Zhao, Guang Zhou 

Yin, Hong Li 



Yunnan Normal Univ. 

◁ PFrA-86 

Application of Ontology Intelligence Search Engine based on Ant 

Colony Algorithm, pp.374–378 

Zhang, Ruihua 

Yang, Tianqi 

Jinan Univ. 

Jinan Univ. 

53


◁ PFrA-87 

Wireless order system Based on WinCE, pp.379–383 

Xiao, Jianan 

Wang, Nana 

Beijing Univ. of Posts & Telecommunications 


◁ PFrA-88 

The Hot Topics Evaluation and Key Issues in Online News about Emergency 

Events, pp.407–412 

Chen, Liping 

Du, Junping 

Shimohara, Katsunori 

Song, Maoqiang 




Doshisha Univ. 


◁ PFrA-89 

Intelligent Test Paper Generation System Based on Slicing Processing, 

pp.506–511 

Ma, Fengning 

Dong, Yao 

Shi, Jin 

Zhang, Ying 

Tianjin Univ. 



Tianjin Univ. 

◁ PFrA-90 

An improved Transfer Learning Algorithm for Document categorization 

Based on data sets reconstruct, pp.575–578 

Sun, Wei 

Qian, Xu 

China Univ. of Mining & Tech.(Beijing) 

School of Mechanical Electronic & Information 

Engineering, China Univ. of Mining & Tech. 

(Beijing) 

◁ PFrA-91 

A New Ant Colony Optimization with Global Exploring Capability and 

Rapid Convergence, pp.579–583 

Deng, Xiangyang 

Yu, Wenlong 

Zhang, Limin 

Naval Aeronautical & Astronautical Univ. 



◁ PFrA-92 

Study on Control Strategy for Vehicle Braking Force on Low Adhesive 

Cornering Road, pp.618–622 

Song, Dandan 

Yang, Tao 

Henan Communication Vocational Tech. 


◁ PFrA-93 

A Novel Prototype Architecture for Equipment Tele-control and Simulation, 

pp.633–637 



Yin, Hong Li 




◁ PFrA-94 

Keep the Geometries: Image Segmentation by K-MSVC with Random 

Region Grouping and Propagation, pp.672–679 

LIN, Yining 

Wei, Wei 

DAI, Yuanming 


College of Electrical Engineering, Zhejiang Univ. 


◁ PFrA-95 

Routing in Wireless Sensor Networks Using Swarm Intelligence, 

pp.680–684 

Lv, Yong 

Chair: Lin, Zongli 

Co-Chair: Hou, Zhongsheng 

Poster Session PFrB 

July 6, 15:00-16:20 

JiaXing Univ. 



◁ PFrB-01 

Reliable H ∞ filtering in low-frequency domain for networked control 

systems subject to packet loss and quantization, pp.1670–1675 

Wang, Heng 

Long, Yue 

Ye, Dan 

◁ PFrB-02 


Northeastern Univ. 


An Included Angle Gridding Algorithm for Multimodel Decomposition of 

Hammerstein Systems with Input Multiplicity, pp.1745–1749 

Du, Jingjing 

Zhang, Xinliang 

Song, Chunyue 

Henan Polytechnic Univ. 



◁ PFrB-03 

Nonlinear Iterated Mappings based Model and Tracking Control of 

Boost Converter, pp.1838–1843 

Bin, Yang 

Dong, Haolong 

Feng, Nenglian 

Univ. of Wisconsin Milwaukee 

BJUT 

BJUT 

◁ PFrB-04 

Improved results on asymptotic stability criteria for neutral neural network, 

pp.1893–1898 

Wu, Xueli 

Li, Yang 

Hou, Lifeng 

Wang, Yuehua 

Hebei Univ. of Sci. & Tech. 



Hebei Chemical & Pharmaceutical Tech. College 

◁ PFrB-05 

Improved results on exponential stability criteria for neutral neural network, 

pp.1899–1903 

Zhao, Zhe 

Zhen, Ran 

Wei, Shaoqing 

Wang, Yuehua 





◁ PFrB-06 

Parallel Rail Transit System Based on ACP Approach, pp.2048–2053 

Dong, Xisong 

XIONG, Gang 

Dong, Fan 

Lv, Yisheng 

Sun, Xubin 

Inst. of Automation, Chinese Acad. of Sci., 


CAISA 

Institue of Automation, Chinese Acad. of Sci. 


◁ PFrB-07 

Working Process Simulation of Roll Forming Machine Based on Virtual 

Reality Technology, pp.2072–2075 

Wang, Dong 

Cao, Yuchao 

Zhang, Tuo 

Qin, Shoutong 

zhengzhou Univ. 

Zhengzhou Univ. 



◁ PFrB-08 

An Agricultural Irrigation System Based on GIS and RFID, pp.2076– 

2080 

Wang, Hui 

Li, Juan 

Zhao, You-gang 

Jiang, Zhongmin 

Qingdao Agricultural Univ. 




◁ PFrB-09 

Output Feedback Organically-Structured Control of Expanded Power 

System based on Parameter Optimization, pp.2085–2090 

Li, Xiaohua 

Xu, Yanying 



◁ PFrB-10 

A Unified Control Design for A Class of Discrete-Time Markov Jump 

Systems: A Game Approach, pp.2130–2135 

Hou, Ting 

Zhang, Weihai 

Ma, Hongji 




◁ PFrB-11 

Based on (w,z) parameter attitude stability control of axisymmetric 3D 

pendulum, pp.2160–2164 

Lv, Wenjun 

GE, Xinsheng 



◁ PFrB-12 

Bus Rapid transit (BRT) Parallel System Based on ACP Approach, 

pp.2485–2490 

54



Dong, Xisong 

XIONG, Gang 

Dong, Fan 

Zhu, Fenghua 



CAISA 


◁ PFrB-13 

Analysis and Optimum Design of Loop Filter in GNSS Receiver, 

pp.2491–2496 

Jiang, Yi 

Zheng, Kai 



◁ PFrB-14 

Optimization Target Resetting Distributed Model Predictive Control for 

Accelerated Cooling Process , pp.2764–2769 

Zheng, Yi 

Li, Shaoyuan 

Wang, Xiaobo 

GE-Global Research (Shanghai) 


BAOSHAN IRON & STEEL CO.,LTD. Research 

Inst. 

◁ PFrB-15 

A New Solution to Weapon-Target Assignment Problem, pp.384–387 

Wang, Rui 

Xi’an Hi-Tech. Inst. 

Wang, Zhengyuan 11 

Liu, Guoqing 

The Second Artillery Engineering College 

Liu, Lingxia 11 

Wang, Guohua 11 

Zhang, Xinyu 11 

◁ PFrB-16 

Study on the Signal Control Problem with Pedestrians Non-complying, 

pp.388–391 

LIU, QIN 


◁ PFrB-17 

Blind Signal Detection Directly Using Functional Networks , pp.402–406 

RUAN, Xiu-kai 

Wenzhou Univ. 

◁ PFrB-18 

Automated Simulation of Flapper and Foil System, pp.413–417 

Hu, Ying 

kunming Univ. of Sci. & Tech. 

◁ PFrB-19 

A Novel Swarm Intelligence Optimization Inspired by Evolution Process 

of A Bacterial Colony, pp.450–453 

Li, Ming 

Southwest Forestry Univ. 

◁ PFrB-20 

Application of Neural network Model to Guangxi Ensemble Precipitation 

Prediction, pp.454–457 

Nong, Mengsong 

Nanjing Univ. of information Sci. & Tech. 

◁ PFrB-21 

Hybrid Particle Swarm Algorithm with Application to Distributed Generation 

Planning, pp.464–467 

Wu, Haitao 

Huang, Fuzhen 


Shang Univ. of Electrical Power 

◁ PFrB-22 

Evolutionary game analysis on opportunistic behavior of purchasing alliance 

with Con t ract mechanism, pp.468–473 

Xiong, Weiqing 

NingBo Univ. 

◁ PFrB-23 

A Control Method of Substrate Feeding about Lysine Fermentation, 

pp.479–483 

Ding, Shenping 

Wu, Weirong 

Wang, Bo 

Suzhou Industrial Park Vocational Technical Inst. 

suzhou industrial park Inst. of vocational Tech. 

JinagSu Univ. 

◁ PFrB-24 

Defect Recognition of Cold Rolled Plate Shape Based on RBF-BP Neural 

Network, pp.496–500 

Li, Xiaohua 

Zhang, Junjie 



◁ PFrB-25 

Research on Intelligent Control Methods of Iron Ore Rotary Kiln Pellets 

Sintering Process, pp.501–505 


Ren, Xiudong 

◁ PFrB-26 

Non-Stationary Type-2 Fuzzy Sets, pp.512–517 

Zhao, Liang 



Henan Univ. of Tech. 

◁ PFrB-27 

A Discrete Artificial Bee Colony Algorithm for the Blocking Flow Shop 


Deng, Guanlong 

Cui, Zhe 

Gu, Xingsheng 



East China Univ. of Sci. & Tech., China 

◁ PFrB-28 

Application of Fuzzy Clustering Model in Groundwater Runoff Zones 

Classification and Indicator Correlation Analysis, pp.528–531 

DOAN, THANH NGHI 

Yu, Xuefeng 

Zhang, Zhongyuan 

heilongjiang Univ. 



◁ PFrB-29 

A Method of UUV Path Planning with Biased Extension in Ocean Flows, 

pp.532–537 

Yan, Zheping 

Zhao, Yufei 

Zhang, Honghan 




◁ PFrB-30 

Research of PSO Algorithm with Variable Constraints in Process System, 

pp.544–548 

Ding, Qiang 

Chen, Hong 

Jiang, Aipeng 

Wang, Chun-lin 

Lin, Weiwei 


Tourism College of Zhejiang 


Hang Zhou Dian Zi Ke Ji Da Xue 


◁ PFrB-31 

A New Approach to Solve the Mission Assignment Problem for Cooperative 

UCAVs Using Immune Particle Swarm Optimizations, pp.549–554 

WANG, GUODONG 

Deng, Zhidong 

Shenyang aircraft design & research Inst. 

Tsinghua Universtiy, China 

◁ PFrB-32 

a Novel Model for Selecting Parameters of SVM with RBF Kernel, 

pp.566–569 

YAN, Zhi-gang 

China Univ. of mining & Tech. 

◁ PFrB-33 

Fundamental Issues of Networked Decoupling Control Systems, 

pp.827–830 

Huang, Congzhi 

Bai, Yan 

North China Electric Power Univ.,Beijing, 

P.R.China 

north china electric power Univ. 

◁ PFrB-34 

Networked Real-time Controller Based on PC/104, pp.831–834 

An, Bao-ran 


◁ PFrB-35 

Co-design-based H∞Control for Wireless Networked Control Systems, 

pp.835–839 

LI, Jinna 

Liu, Dan 

Li, Minghui 

Yu, Haibin 


Sci. 

Shenyang Univ. of Chemical Tethnology 



Sci., China 

◁ PFrB-36 

Distributed Output Regulation of Switching Multi-agent Systems Subjectto 

Input Saturation, pp.840–845 

Wang, Xiaoli 

Ni, Wei 

Yang, Jie 

Harbin Inst. of Tech. at Weihai 

Nanchang Univ. 

Chinese Acad. of 

55


◁ PFrB-37 

Kernel Based Nonlinear Regression for Internet Round Trip Time-delay 


Yang, Yanhua Shenyang Inst. of Automation, Chinese Acad. of 

Sci. 

◁ PFrB-38 

XCP Bandwidth Compensation Algorithm Based on Sliding Mode Control 

in Time Vary Network, pp.857–862 

YIn, Fengjie 

liaoning Univ. 

◁ PFrB-39 

H-infinity State Estimation for Networked Systems with Markov Interval 

Delay, pp.881–885 

Zhang, Yong 

Liu, Zhenxing 

Zhou, Lei 


WuHan Univ. of Sci. & Tech. 

Nantong Univ. 

◁ PFrB-40 

Simulation Research of Networked Control System Based on Ethernet 

and Matlab, pp.898–902 

Peng, Daogang 

Lin, Jiajun 

Zhang, Hao 

LI, Hui 





◁ PFrB-41 

The Study of Wireless Sensor Network Energy, pp.433–437 

Duan, Ping 


◁ PFrB-42 

Research and Application of Whole-process Virtual Simulation for Tunnel 

Shield System, pp.3208–3213 

Han, Yanling 

No.999, Huchenghuan Road,College of 

information, Shanghai Ocean Univ., Pudong New 

Area, Shanghai 

Cao, Shouqi 

Shanghai Ocean Univ. 

◁ PFrB-43 

Using fuzzy theory and information entropy to detect leakage for 

pipelines, pp.3232–3235 

Chen, Zhigang 


◁ PFrB-44 

Model Identification for Closed-loop Multivariable Processes Based on 

Min-max Critical Frequency Search, pp.3260–3264 

Luo, Yunhui 

Liu, Hongbo 

Cai, Wen-Jian 

Jia, Lei 


shandong Univ. 



◁ PFrB-45 

An Adaptive Observer for Actuator and Sensor Fault Diagnosis in Linear 

Time-Varying Systems, pp.3281–3285 

GAO, Fei 

JIANG, Guangwen 

ZHANG, Zebang 

SONG, Jingyu 

Sys. Engineer Research Institude 

CSSC 

CSSC 

CSSC 

◁ PFrB-46 

The Bi-Ramp Type Demand and Price Discount Inventory Model for 

Deteriorating Items, pp.3298–3304 

Zhou, Youjun 

LiuZhou Teachers College 

◁ PFrB-47 

A Data-Driven Based Adaptive Fault Diagnosis Scheme for Nonlinear 

Stochastic Distribution Systems via 2-step Neural Networks and Descriptor 

Model, pp.3311–3315 

Zhang, Yumin 

Liu, Yunlong 

Guo, Lei 

Beihang Univ 

Beihang Univ. 

Beihang Univ. 

◁ PFrB-48 

Study on Periodic Feeding Control for a Semi-batch Polymerization Process, 

pp.3316–3321 

Zhao, Rongchang 


Cao, Liulin 

Wang, Jing 

BUCT 

Beijing Univ. of Chemical Tech., China 

◁ PFrB-49 

Key Technology of Network Monitoring and Early Warning System for 

Wharf Mooring, pp.3336–3339 

Qiu, Zhanzhi 

Sun, Lei 

Liu, Yongchao 

Dalian Jiaotong Univ. 



◁ PFrB-50 

Power Reliable Analysis of Coalmine Emergency Monitoring System in 

Catastrophic Environment, pp.3345–3350 

Ma, Fengying 

Shandong Inst. of Light Industry 

◁ PFrB-51 

Distributed Storage and Prediction Method for Mooring Monitoring System, 

pp.3351–3354 

Sun, Lei 

Qiu, Zhanzhi 



◁ PFrB-52 

Energy consumption monitoring of the steam pipe network based on 

affinity propagation clustering, pp.3364–3368 

You, Xiazhu 

Du, Wenli 

Zhao, Liang 

Qian, Feng 





◁ PFrB-53 

Individual Pitch Control of Large-scale Wind Turbine Based on Load 

Calculation, pp.3384–3388 

Gao, Feng 


◁ PFrB-54 

Adaptive decoupling control systems based on SVM for large supercritical 

CFB boilers combustion system, pp.3401–3406 

Liu, Han 

Xi’an Univ. of Tech. 

◁ PFrB-55 

Research on Control Method Combined with Load Coordinate for Dry 

Desulfurization of Slurry Fluidized Bed Boiler, pp.3407–3411 

Jiang, Aipeng 

Ding, Qiang 

Lin, Weiwei 




◁ PFrB-56 

Research on combustion control and heat efficiency’s online computing 

of slime fluidized bed boiler, pp.3412–3416 

Jiang, Aipeng 

Lin, Weiwei 

Ding, Qiang 




◁ PFrB-57 

Simulation Experiment Platform for Optimal Control of the Raw Slurry 

Blending Process in the Alumina Production, pp.3443–3447 

Guo, Wanli School of Electrical Engineering, Liaoning Univ. of 

Tech. 

Bai, Rui 

Liaoning Univ. of Tech. 

◁ PFrB-58 

Shielding Properties Analysis of underground Methane Sensor based 

on Finite Element Simulation and Electromagnetic Measurement, 

pp.3448–3453 

Ma, Fengying 

Ma, Fengying 


Shandong Polytechnic Univ. 

◁ PFrB-59 

Social Learning with Bounded Confidence, pp.3485–3490 

Liu, Qipeng 




◁ PFrB-60 

The Network Structure of Optimal Synchronizability for Bounded Regions 

Case, pp.3497–3502 

Wang, Lifu 


56



Liu, Yunjing 

Wu, Zhaoxia 

Kong, Zhi 

Wang, Xingang 

North-east Univ. at Qinhuangdao 

Northeastern Univ. At Qinhuangdao 



◁ PFrB-61 

Impact of Evacuee Behavior on Evacuation Clearance Time, pp.3520– 

3525 

Lv, Yisheng 

Zhu, Fenghua 

XIONG, Gang 

Yao, Qingming 

Chen, Songhang 

Ye, Peijun 







◁ PFrB-62 

Sensitivity Analysis in Equilibrium Property of Photosynthetic Carbon 

Metabolism, pp.3549–3554 

Xia, Chao 

Guo, Jin 

Zhao, Yanlong 

Lin, Zhiwei 





◁ PFrB-63 

Mobile Robot Active Observation and Mapping Based on Factored 

Method, pp.3577–3582 

Yuan, Jing 

Huang, Yalou 

Sun, Fengchi 

Huang, Shuzi 

Chen, Huan 

Nankai Univ. 

Nankai Univ. 

Nankai 

Nankai Univ. 

Nankai Univ. 

◁ PFrB-64 

A Method of Map Building for Robots in Unknown Indoor Environments, 

pp.3642–3647 

Liu, Shuhua 

Northeast Normal Univ. 

◁ PFrB-65 

Research on Robot Motion Control Based on Local Weighted kNN-TD 

Reinforcement Learning, pp.3648–3651 

Han, Fei 

College of Information Engineering, Yangzhou 

Univ. 

Jin, Lu 

Yang, Yuequan 

Cao, Zhiqiang 



Univ. 




◁ PFrB-66 

An Algorithm for Sub-optimal Attribute Reduction in Decision Table 

Based on Neighborhood Rough Set Model, pp.685–690 

Liu, Zunren 

Wu, Gengfeng 

Yu, Zhongqing 

Shanghai Univ.; Qingdao Univ. 


qingdao Univ. 

◁ PFrB-67 

Traffic Signal Timing and Phase Optimization for Intersection Based on 

IPSO, pp.711–714 

JIN, Chengjun 


◁ PFrB-68 

RNA secondary structure prediction algorithm based on combinatorial 

optimization algorithm and SVMs method, pp.715–719 

He, Jingyuan 

Chongqing Univ. 

◁ PFrB-69 

Stigmergy Agent and Swarm Intelligence based Multi-agent System, 

pp.720–724 

Duan, Junhua 

Zhu, Yi-an 

Huang, Shujuan 

Northwestern Polytechnical Univ. 



◁ PFrB-70 

Research on the Fuzziness of Rough Set Model based on Compatible 

Relation over Double Universes, pp.783–787 

Zhan, Zhenyu 

Liu, Wenqi 

Shi, Mengting 




◁ PFrB-71 

A Concept Architecture Model with Autonomous Coordination for Flying 

Formation, pp.793–796 

Liu, Zilin 

Logistic Engineering Univ. 

◁ PFrB-72 

Stability Analysis of Local Swarms in an Environment with a Quadratic 

Profile, pp.809–814 

Wang, Liangshun 

Fang, Huajing 



◁ PFrB-73 

Web Design and Implementation for Remote Control, pp.920–924 

Chen, Wanmi 

Chen, Yucheng 



◁ PFrB-74 

Intervening a group of mobile agents in three dimension, pp.970–975 

Li, Xuejing 


◁ PFrB-75 

Guaranteed Cost Control for Uncertain Networked Switched Fuzzy 

Time-Delay Systems , pp.982–986 

Liu, Yi 

Yao, Lu 

Sun, Li-Ying 

Tianjin Inst. of Urban Construction 

Neusoft Group (Dalian) Co., Ltd 


◁ PFrB-76 

Research and Implementation of Security System in Intelligent Residential 

District, pp.987–990 

Zhang, Guofeng 

Gao, Yan 

Xie, Changwu 



BIT 

◁ PFrB-77 

Networked Control Systems Based on New Smith Predictor and Internal 

Model Control, pp.1014–1019 

Du, Feng 

Li, Jinyu 

Ren, Jia 

Zhang, Yu 

Zhou, Hui 

Guo, Cheng 

Hainan Univ. 

Hainan Univ. 

Hainan Univ. 

Hainan Univ. 

Hainan Univ. 

Hainan Univ. 

◁ PFrB-78 

Stability and Stabilization of Singular Systems: Strict LMI Sufficient 


Zhang, XueFeng 


◁ PFrB-79 

New Delay-dependent Absolute Stability for Uncertain Lur’e System 

with Interval Delay, pp.1062–1066 

Li, Peiran 

Bao, Zhejing 

Yan, Wenjun 



CEE, Zhejiang Univ. 

◁ PFrB-80 

Decentralized Finite-Time Stabilization of Large-Scale Interconnected 

Nonlinear Systems, pp.1067–1072 

FU, Qin 

Univ. of Sci. & Tech. of Suzhou 

◁ PFrB-81 

The Modeling and Simulation on Sliding Mode Control Applied in the 

Double Inverted Pendulum System, pp.1089–1091 

Li, Zhongjuan 

Zhang, Xinzheng 

Chen, Guohai 

Guo, Yuguang 

Guangdong Univ. of Tech. 


guangdong Univ. of Tech. 


◁ PFrB-82 

Adaptive Hydraulic Active Suspension Control for Traversing Rough 

Terrain, pp.1130–1134 

Shi, Mina 

Jilin Univ. 

57


◁ PFrB-83 

Further Results On Delay-dependent Stability and Robust Stability of 

Linear System with Interval Time-varying Delay, pp.1166–1171 

Wang, Jian-an School of Electronics Information Engineering, 


Yin, Zhi-gang 

Jiujiang Univ. 

◁ PFrB-84 

Construction on Traction Converter System of EMU, pp.1188–1191 

Sun, Yannan 

Li, Changxian 

Li, Xiumei 




◁ PFrB-85 

Stability and stabilization for quadratic systems with state saturation 

nonlinearities, pp.1212–1217 

Fu, Chen 

Xu, Shengyuan 



◁ PFrB-86 

Design and Simulation of Large Civil Aircraft’s Thrust Management 

System, pp.1224–1229 

Zheng, Yi 

Wang, Xiaoyan 

Yang, Sen 

Wang, Xinmin 





◁ PFrB-87 

Optimize the PID parameters based on improvment of immune algorithm, 

pp.1259–1262 

Lu, Jinyun 

Pan, Haipeng 

Zhejiang Sci-Tech Univ. 


◁ PFrB-88 

Delay-dependent Robust Stability for Markovian Jump Singular Systems 

with Disturbances, pp.1340–1343 

Cui, Wenxia 

Donghua Univ. 

◁ PFrB-89 

A Robust Adaptive Control for Trajectory Tracking of Underactuated 

Surface Ships, pp.1395–1399 

Liu, Yang 

GUO, Chen 



◁ PFrB-90 

Static Output Feedback Pole Assignment based on the Regional Attractors, 

pp.1400–1405 

Yang, Lingling 

Zhang, Yun 

Chen, Zhenfeng 

ZENG, Qi-jie 





◁ PFrB-91 

Consistency of Signal Coordinate Control Models under Different Traffic 


Lu, Kai 

Liu, Jiuming 

Wang, Shiming 

Liu, Shijie 

Xu, Jian-min 






◁ PFrB-92 

Based on the heating furnace temperature Fuzzy-PID control method 

research, pp.1553–1557 

Cui, Guimei 

Xiao, Peng 

Li, Yani 

Inner Mongolia Univ. 



◁ PFrB-93 

Application on Non-destructive Inspection of Terahertz Wave, pp.1570– 

1573 

Yan, Fang 

Univ. Of Sci. & Tech. Of The Inner Mongol 

◁ PFrB-94 

Inverse Kinematics Solution of Deep Space Probes Mast and Windsurfing, 

pp.1580–1584 

Song, Pei 

Ju, Hehua 

Li, Hui 

Beijing Univ. Of Tech. 



◁ PFrB-95 

Research on PID Control Technique for Chaotic Ship Steering based on 

Dynamic Chaos Particle Swarm Optimization Algorithm, pp.1639–1643 

Huang, Qian 


Co-Chair: Zhao, Qianchuan 

Poster Session PFrC 

July 6, 16:30-17:50 

Dalian Naval Acad. 



◁ PFrC-01 

Particle-swarm optimization algorithm for model predictive control of MI- 

MO with constraints, pp.2576–2581 

Wang, Shubin 


China Univ. of Petroleum, Beijing 


◁ PFrC-02 

Dynamic Optimization of Polymer Flooding for High-salinity Reservoir 

Based on Maximum Principle, pp.2601–2606 

Lei, Yang 

Li, Shurong 

Zhang, Xiaodong 

Guo, Lanlei 


China Univ. of Petroleum(East China) 


Sinopec Shengli Oilfield Company 

◁ PFrC-03 

Numerical Solution to Optimal Control of Switched Systems with Statedependent 

Switchings, pp.2788–2793 

Ji, Qin 

Song, Chunyue 



◁ PFrC-04 

Fuzzy Guaranteed Cost Control for a Class of Nonlinear Systems via 

Observer-based Output Feedback, pp.2582–2587 

He, Guannan 

Ji, Jing 



◁ PFrC-05 

Intersection Analysis of Input and Output Constraints in Model Predictive 

Control and On-line Adjustment of Soft Constraints, pp.2543–2549 

ZHOU, Xiaolong 

Wang, Shubin 





◁ PFrC-06 

New Active Queue Management Scheme Based on Statistical Analysis, 

pp.2562–2565 

Xu, Qin 

Sun, Jinsheng 



◁ PFrC-07 

Non-fragile Robust Design for a Control System of Air Fuel Ratio, 

pp.2607–2610 

Heng, Qinghai 

Lu, Jing 

Lu, Yang 




◁ PFrC-08 

MLD-based Predictive Control of Energy Management for Hybrid Electric 

Bus, pp.2806–2811 

Kou, Zhitao 

Song, Chunyue 

Pan, Zheng 




◁ PFrC-09 

Application Research on Data-based Third-order Linear Extended S- 

tate Observer Filter for Hot Strip Width, pp.2903–2907 

Wang, Lijun 

Li, Qing 

Li, Jiangyun 

Tong, Chaonan 

Yin, Yixin 

Dong, Jie 





Univ. of Sci. & Tech. Beijing, China 


58



◁ PFrC-10 

Multi-Agent Failure Prediction based on Data Assimilation Theory, 

pp.3146–3151 

Huang, Xun 

YAN, Junwei 

Liu, Min 

Tongji Univ. 

Univ. of TongJi 

Tongji Univ. 

◁ PFrC-11 

Fault Diagnosis Observer via Hybrid Fuzzy and Importance Sampling 

Schemes , pp.3254–3259 

AL-BAYATI, AHMAD HUSSAIN 

Wang, Hong 

THE Univ. OF MANCHESTER 

Manchester M60 1QD,U.K 

◁ PFrC-12 

Study and Design of Digital Simulation System for Pumped Storage 

Hydropower Units, pp.3068–3071 

Chen, Qijuan 

Wuhan Univ. 

◁ PFrC-13 

Amplitude Envelope Analysis for Feature Extraction of Direct-Driven 

Wind Turbine Bearing Failure, pp.3173–3176 

Xin, Weidong 

Liu, Yibing 

He, Ying 

Su, Boxian 





◁ PFrC-14 

Application of Over-complete ICA in Separating Turbine Vibration 

Sources, pp.3177–3180 

An, Hongwen 

Liu, Yibing 

Yan, Keguo 

Wang, Yu 

Yang, Huan 






◁ PFrC-15 

The PSO-LSSVM Model for Predicting the Failure Depth of Coal Seam 

Floor, pp.570–574 

YAN, Zhi-gang 


◁ PFrC-16 

Singularity Avoidance in Single Gimbal CMG Using the Theory of Potential 

Functions, pp.1103–1108 

Chakravorty, Jhelum 

Indian Inst. of Tech. Bombay 

◁ PFrC-17 

Hypersonic Vehicle control based on Integral Sliding Mode Method, 

pp.1762–1766 

Liu, Helong 

Zong, Qun 

Tian, Bailing 

Wang, Jie 

Tianjin Univ. 

School of Electrical Engineering & 

Automation,Tianjin Univ. 

Tianjin Univ. 

Tianjin Univ. 

◁ PFrC-18 

Integrated Optimal Algorithm for Flexible Job Shop Scheduling with 

Due-date Window , pp.638–642 

Song, Juan 

Ningxia Univ. 

◁ PFrC-19 

Job-shop Scheduling Optimization Design Based on An Improved GA, 

pp.654–659 

Zhao, Zixiang 


Bing, Zhigang 

Tianjin Univ. 

Tianjin Univ. 


◁ PFrC-20 

Medium and Long-term Electric Load Forecasting based on Chaos 

SVM, pp.660–663 

Wang, Deji 

staff development of CNTC 

◁ PFrC-21 

The Motion Planning in the Automatic Generation of Mobile Phone 3D 

Animation, pp.725–731 

Qiu, Zhenzhen 


◁ PFrC-22 

Plot planning of 3D animation based to Semantic Web in Chinese text, 

pp.732–738 

Nie, Junlian 


◁ PFrC-23 

An Improved Adaptive Harmony Search Algorithm, pp.743–747 

Kong, Zhi 

Wang, Lifu 

Wu, Zhaoxia 

Qi, Shiqing 

Zou, Dexuan 




Northestern Univ. at Qinhuangdao 

Jiangsu Normal Univ. 

◁ PFrC-24 

Parameter Acquirement Methods for Rule-based Model of Virtual Plant 

Based on Optimal Algorithms, pp.748–752 

Ding, Weilong 

Xu, Lifeng 

Hu, Chen 

Zhang, Yuping 

Zhejiang Universy of Tech. 

hangzhoiu 

hangzhou 

China National Rice Research Inst. 

◁ PFrC-25 

A Study on Obstacle Avoidance for Mobile Robot Based on Fuzzy Logic 

Control and Adaptive Rotation, pp.753–757 

Wen, Sufang 

Wang, Lin 

Inner Mongolia Univ. of Tech. 


◁ PFrC-26 

Application and research of Multi-label Naïve Bayes Classifier , pp.764– 

768 

Qin, Feng 

Tang, Xianjuan 

Cheng, Zekai 

Anhui Univ. of Tech. 



◁ PFrC-27 

Research on Personality Behavior in Intelligent Virtual Environment, 

pp.769–772 

Shi, Lin 

Li, Zhigang 

Zhang, Jiajie 


Tang Shan College 

Hebei Polytechnic Univ. 

◁ PFrC-28 

Algorithm Research for Gene-sequence-based 3D Model Reconfiguration 

, pp.788–792 

NING, Shu-Rong 

Yuan, Shengyun 

Lin, Rui 

Zhu, Yuan 


Univ. of Sci. &Tech. Beijing 

univsrsity of Sci. & Tech. beijing 


◁ PFrC-29 

Design Methods of General Fuzzy Systems as Function Approximators, 

pp.815–819 

Wang, Xiuhong 

Yang, Zhenguang 

Gao, Qian 

Wei, Xinjiang 

Ludong Univ. 

Ludong Univ. 

Ludong Univ. 

Ludong Univ. 

◁ PFrC-30 

Fault Diagnosis Method of the Locomotive Brake Based on Wavelet 

Analysis, pp.820–822 

DING, Jian-bo 


◁ PFrC-31 

Intelligent Search Algorithm for Modern Tobacco Agriculture, pp.823– 

826 

Wang, Deji 


◁ PFrC-32 

Distributed Control for Synchronization of Networked Lagrange Systems 

via Estimated State Feedback, pp.903–908 

Chen, Gang 

Lin, Qing 



◁ PFrC-33 

Configurations and Modelling of Networked Feedforward-Feedback 

Control Systems, pp.909–913 

59



Bai, Yan 


P.R.China 


◁ PFrC-34 

MIMO network control systems with asynchronous sampling based on 

dividing the time slice, pp.925–930 

Fan, Weihua 

Chen, Xiaodu 

Xie, Ronghua 

Chen, Qingwei 





◁ PFrC-35 

Constrained Kalman Filtering With Observation Losses, pp.937–941 

Luo, Zhen 

Fang, Huajing 



◁ PFrC-36 

Information consensus for multi-agent systems via nonlinear protocols, 

pp.942–945 

Zhai, Junyong 


Xu, Shouhuai 



The Univ. of Texas at San Antonio 

◁ PFrC-37 

Studied on Anti-interference Based on Dynamic Surface Control, 

pp.996–998 

Gao, Qian 

He, Naibao 

Huaihai Inst. of Techology 


◁ PFrC-38 

Estimation of CAN Bus Reliability Based on Real-time Performance, 

pp.999–1004 

Chen, Xi 

Jin, Wen 

Zhang, Huiqun 

Lv, Weijie 

Tianjin Univ. 

Tianjin Univ. 

Tianjin Univ. 

Tianjin Univ. 

◁ PFrC-39 

Synchronization for a class of Complex Dynamical Networks, pp.1010– 

1013 

Li, Changbin 

He, Yi 

Wu, Aiguo 

Tianjin Univ. 

Tianjin Univ. 

Tianjin Univ. 

◁ PFrC-40 

Networked H infty Filtering for T-S Fuzzy Systems Based on Event- 

Triggered Scheme, pp.1037–1042 

Wang, Qi 

Jia, Xinchun 

Chi, Xiaobo 

He, Dezhi 

Ma, Weiwei 

Shanxi Univ. 

Shanxi Univ. 

Shanxi Univ. 

Shanxi Univ. 

Shanxi Univ. 

◁ PFrC-41 

Quantized Feedback Stabilization of a class of Networked Control Systems 

with Delay and Packet Dropout, pp.1043–1047 

Ren, Jun 

Zhou, Chuan 

Guo, Yu 

Chen, Qingwei 

Nanjing Univ. of Sci. & Tchnology 




◁ PFrC-42 

Short-term Load Forecasting Based on Least Square Support Vector 

Machine Combined with Fuzzy Control, pp.1048–1051 

Gao, Rong 

ludong daxue 

◁ PFrC-43 

Development on Gas Leak Detection and Location System Based on 

ZigBee, pp.429–432 

Duan, Ping 

Ding, Chengjun 



◁ PFrC-44 

A Hierarchical Iterative Closest Point Algorithm for Simultaneous Localization 

and Mapping of Mobile Robot, pp.3652–3656 

Zhang, Qizhi 

Zhou, Yali 

Beijing Information Sci. & Tech. Univ., China 


◁ PFrC-45 

A Solution of Inverse Kinematics for 7-DOF Manipulators and Its Application, 

pp.3711–3717 

Huang, Qiulan 

Wu, Jun 

Xiong, Rong 




◁ PFrC-46 

Acquisition of the Horse Movement Trajectory and Its Reproduction in 

the 6-DOF Parellel Robot Horse, pp.3718–3723 

Xiao, Jinzhuang 

Li, Pengfei 

Wang, Hongrui 

Wang, Liling 

Hebei Univ. 

Hebei Univ. 

Hebei Univ. 

Hebei Univ. 

◁ PFrC-47 

A self-localization method based on omnidirectional vision and MTi for 

soccer robots, pp.3731–3736 

Xiong, Dan 

Lu, Huimin 

Zheng, Zhiqiang 




◁ PFrC-48 

Modeling and Simulation of Omni-directional Lower Limbs Rehabilitation 

Training Robot, pp.3737–3740 

Jiang, Ying 

Bai, Baodong 

Wang, Shuoyu 

Shenyang Univ. of Tech. 


Kochi Univ. of Tech. 

◁ PFrC-49 

A Decentralized Adaptive Controller Design for Lower Extremity Rehabilitation 

Robot, pp.3753–3757 

Mi, Wenjun 

Wu, Zhizheng 

Qian, Jinwu 




◁ PFrC-50 

A Fuzzy-Model-Based Gravity Center Adjustment and Inclination Control 

for Stair-climbing wheelchair, pp.3764–3769 

Wang, Dongxiao 

Gao, Xueshan 

DUAN, Xingguang 

Zhang, Weimin 

HUANG, Qiang 

Liu, Yun-Hui 

Beijing Inst. of Tech. (BIT) 





The Chinese Univ. of Hong Kong 

◁ PFrC-51 

Autonomic Mission Planning for Lunar Rovers in Complex Environment, 

pp.3782–3787 

Xu, Hongxia 


◁ PFrC-52 

A Cognizing Topology Map Building in A Wide-Range Indoor Structural 

Environment, pp.3794–3799 

Wu, Hao 

Tian, Guohui 

Zhou, Fengyu 



Univ. of Shandong 

◁ PFrC-53 

Design and Analysis of A Light Wall-Climbing Robot, pp.3805–3809 

Ke, Zibo 

Zhou, Yulan 

Yu, Zhiqiang 

Yantai Univ. 

Yantai Univ. 

Univ. of Yantai 

◁ PFrC-54 

A Novel Semi-Autonomous Throwbot for Reconnaissance Application, 

pp.3822–3827 

Li, Yue 


◁ PFrC-55 

The Design of a New Upper Limb Rehabilitation Robot System Based 

on Multi-source Data Fusion, pp.3840–3845 

Meng, Fancheng 


60



◁ PFrC-56 

Determining the Feasible Set of Motion States in Target Tracking Using 

Extended Set-Membership Filter, pp.3876–3880 

Qiu, Quan Beijing Research Center of Intelligent Equipment 

for Agriculture 

Zheng, Wengang 

Han, Jianda 

Beijing Research Center of Intelligent Equipment 


Shengyang Inst. of Automation,CAS,China 

◁ PFrC-57 

Research on Dive Plane Trajectory Tracking Control Method of AUV 

Under Current Disturbance, pp.3887–3891 

Yan, Zheping 

Deng, Chao 

Zhou, Jiajia 

Zhao, Yufei 


Haerbin Engneeing Univ. 



◁ PFrC-58 

Damping Control of Rehabilitation Robot for Upper Limbs, pp.3898– 

3902 

Li, Qingling 

Song, Yu 

China Univ. of Mining & Tech., Beijing 


◁ PFrC-59 

Human-machine Interfaces based on Electromyography and Kinectapplied 

to Teleoperation of a Mobile Humanoid Robot, pp.3903–3908 

Wang, Baocheng 


Xie, Qing 



the Ruijin Hospital, Shanghai Jiao Tong Univ. 

◁ PFrC-60 

The development of an Exoskeleton Robot forCo-Manipulation of Human 

Upper Limb Movement, pp.3909–3914 

Ye, WenJun 

School of Naval Architecture, Ocean & Civil 

Engineering 


Xie, Qing 


Ruijin Hospital Luwan branch 

◁ PFrC-61 

Distributed Control for Mobile Robots Using PSD Sensors, pp.3943– 

3947 

Ge, Bin 

Zou, Xuan 

Yasuda, Gen Ichi 

Dalian Univ. 

Dalian Univ. 

Nagasaki Inst. of Applied Sci. 

◁ PFrC-62 

Research and Design of Expert System for Urban Transit Train Signal 

System, pp.3983–3986 

Zou, Hui 


◁ PFrC-63 

Distributed containment control of networked fractional-order systemswith 

multiple leaders, pp.3987–3992 

Liu, Xueliang 

Xu, Bugong 

SCUT 


◁ PFrC-64 

Multiple UAVs Collision Avoidance Trajectory Coordination Using Distributed 

Receding Horizon Optimization , pp.3999–4003 

Peng, Hui 

PLA Univ. of Sci. & Tech. 

◁ PFrC-65 

Research on the Spatial Diffusion of Industrial Clusters based on WH 

Migration Model, pp.4016–4019 

Li, Munan 

South China Univ. ot Tech. 

◁ PFrC-66 

Design and Research of Time-Varying Delay Teleoperation, pp.1666– 

1669 

Cui, Yan 

Ju, He-Hua 

Cao, Ping 

Dong, Guocheng 



INNER MONGOLIA MENGDA NEW ENERGY 

CHEMICAL INDUSTRY BASE DEVELOPMENT 

CO.,LTD 


◁ PFrC-67 

Simulation Study on Low Voltage Operation of DFIG During Grid Three 

Symmetric Short Circuit Fault, pp.1936–1941 

Wang, Ping 

Liang, Fujun 

Qi, Shengbiao 

Tianjin Univ. 

Tianjin Univ. 

Tianjin Univ. 

◁ PFrC-68 

Finite-Time Stabilization for a Class of Distributed Parameter Systems, 

pp.1948–1952 

FU, Qin 


◁ PFrC-69 

On initial alignment of large azimuth misalignment for SINS on the static 

base in Krein space, pp.1964–1968 

FENG, JIN 

YU, FEI 

ZHANG, PENG-Yu 

Zou, Meikui 





◁ PFrC-70 

Research on QFT Controller Design for LOS Stabilization System of 

Opto-electronic Load for UAV, pp.1982–1986 

Xu, Dexin 


HE, Kunpeng 

harbin engineering Univ. 

Wei, Yanhui 


Zhao, Jiangpeng Troops 92854 

◁ PFrC-71 

Chaos Phenomenon in the DC-DC Switching Converters, pp.2039– 

2043 

Zhang, Yumei 

Qin, Hong 

Qu, Yanhua 

Wu, Jianhua 

Shenyang Inst. of Engineering 




◁ PFrC-72 

Adaptive Integral-Type Sliding Mode Control for a Class of Uncertain 


Sun, Li-Ying 


◁ PFrC-73 

Exponential Synchronization of a Class of Chaotic Neural Network, 

pp.2194–2197 

Qiao, Zongmin 

Zhang, Pei 

Hefei Normal Univ. 

anhui Univ. 

◁ PFrC-74 

Adaptive Observer Design for a Class of MIMO Nonlinear Systems, 

pp.2198–2203 

ZHAO, Lili 

Li, Ping 

LI, Xiuliang 




◁ PFrC-75 

A hybrid genetic algorithm/fuzzy dynamic programming approach to 

two-machine flowshop problems, pp.2399–2402 

Zhang, Hong 

Li, Jun 

Zhang, Desheng 

shandong Inst. of commerce & Tech. 



◁ PFrC-76 

Parameters Optimization and Dynamic Characteristic Analysis of Maglev 

Spindle Control System, pp.2403–2406 

Deng, Liang 

Song, Fangzhen 

SONG, Bo 

Univ. of Jinan 



◁ PFrC-77 

Simulation Design and Application of Attack-Defense System in the Sea 

Based on HLA, pp.2417–2420 


Chen, Jie 


502unit,NAEI 

department of automatic control engineering 

301unit,NAEI 

61


Yan, Jiangang 

Institution department of command engineering 

502unit,NAEI 

◁ PFrC-78 

Study of An Improved Algorithm for Arterial Traffic Coordinated Control 

Considering The Effect of Turning-Traffic Flow, pp.2421–2425 

Liu, Shijie 

LIU, Xiao-He 

Liu, Jiuming 

Xie, Qiuyan 

Xu, Jian-min 


First Aviation College of the Air Force in Xinyang, 

Henan Province 




◁ PFrC-79 

Hybrid Load Forecasting Method Based on Fuzzy Support Vector Machine 

and Linear Extrapolation, pp.2431–2435 

Jiang, Xin 

Liu, Xiaohua 

Gao, Rong 

Ludong Univ. 

Ludong Univ. 

ludong Univ. 

◁ PFrC-80 

Study on On-ramp Control Strategy of Urban Freeway Based on Fuzzy 

Control, pp.2512–2516 

Wang, Shiming 

Xu, Jianmin 

Luo, Qiang 




◁ PFrC-81 

The Solution of The Fuzzy Volterra Integral Equations of The Second 

Kind, pp.2556–2561 

Gong, Huarong 

Yantai Engineering Tech. College 

◁ PFrC-82 

Intelligence Fusion Based Control Strategy of Purifying System for 

Drinking Source Water, pp.2592–2596 

Wang, Jing 

Bi, Jianchao 

Chongqing College of Electronic Engineering 


◁ PFrC-83 

Sliding Mode Control based on Particle Filter for Position Tracking System, 

pp.2597–2600 

DAI, Wen-zhan 

HE, Li Ming 

Yang, Aiping 

zhejiang Sci-Tech Univ. 


Zhejiang Univ. of Finance & Economics 

◁ PFrC-84 

The aircraft flutter model parametric identification based on frequency 

domain global optimization algorithm, pp.2611–2617 

Yao, Jie 

jingdezhen ceramic insitute 

◁ PFrC-85 

PID Controller Parameters Optimization for the Main Steam Temperature 

System Based on Immune Algorithm-Particle Swarm Optimization, 

pp.2618–2623 

Yuan, GuiLi 

Zhu, Lei 



◁ PFrC-86 

Research on sampling period of discrete variable structure controller 

Based on the Two-wheeled self-balanced Vehicle, pp.2653–2656 

Meng, Xiang-zhong 

Liu, Xin-wen 

Li, Jing 

Air Defence Forces Acad. 


Zhengzhou Lighting Management Bureau 

Li, Da-yu 

SU, Yongzhen 


air defense Acad. 

◁ PFrC-87 

One Nearspace Hypersonic Aircraft Dynamic Surface Adative Backstepping 

Control Design Based on Nonlinear Neumatic/ Thrust Vectoring 

Composite Control, pp.2669–2673 

Dai, Shaowu department of automatic control engineering 

301unit,NAEI 

Chen, Jie 

Song, Chao 


301unit,NAEI 

School of Automation Sci. & Electrical Engineering, 

Beijing Univ. of Aeronautics & Astronautics, 

◁ PFrC-88 

The Control Theory of Permanent Magnet Synchronous Motor Based 

on Anti-windup Control, pp.2801–2805 

Ma, Jingcong 

Zheng, Yang 

Dalian Maritime Univ., China 


◁ PFrC-89 

Two-Degree-of-Freedom Control Scheme for Cascade Control Systems, 

pp.2812–2816 

Zhang, Jinggang 

Zhao, Zhicheng 


taiyuan Univ. of Sci. & Tech. 

◁ PFrC-90 

Research on the controller of Two-wheeled self-balanced Vehicle 

Based on the sensitivity analysis, pp.2833–2837 


Li, Yanzhao 

Liu, Xin-wen 

Xie, FangMing 

Wang, Qiang 


China Nuclear Power Engineering 

Co.,Ltd.,Zhengzhou Branch 


Air Defense Forces Acad. 


◁ PFrC-91 

Modified Backstepping Control for Time-Delay System of SVC , 

pp.2861–2864 

Zhang, Rui 

Sun, Li-Ying 

Zhang, Bo-Qi 




◁ PFrC-92 

Recursive identification of parameters in the minimum variance control, 

pp.2870–2877 

Yao, Jie 


◁ PFrC-93 

The Predictive Control for Nonlinear systems Based on Dynamic Approximate 

Hammerstein model, pp.2887–2890 

Hu, Zhiqiang 


◁ PFrC-94 

Fault Diagnosis of Underwater Vehicle with Neural Network, pp.2931– 

2934 

WANG, Jianguo 


◁ PFrC-95 

Design of FDI system for the nonlinear Control System, pp.2935–2938 

Zhou, Jing 

Zhu, Xun 

Jianghan Univ. 

jianghan Univ. 

62


Technical Program: Saturday Sessions 

Saturday, July 7, 2012 

PL-4 8:30-9:30 Room 305 


Chair: Chen, Ben M. 

National University of Singapore 

◮ PL-4 8:30-9:30 


Huang, Jie 


PL-5 9:50-11:50 Room 305 


Chair: Tarn, Tzyh-Jong 

Washington University, USA 

◮ PL-5 9:50-11:50 


Chua, Leon O. 


SaA01 13:30–15:30 Room 203A 

Networked Control 

Chair: Wang, Wei 

Co-Chair: Zhai, Feng 

Information school, Renmin Univ. of China 


◮ SaA01-1 13:30–13:50 

The Optimization Method for a Kind of Uncertain Networked Systems 

Based on the Stripping Principle and Optimal Control, pp.964–969 

Wang, Wei 


◮ SaA01-2 13:50–14:10 

Uplink Dead Zone Analysis for Co-Channel Two-Tier Femtocell Network, 

pp.991–995 

Han, Qiaoni 

Guan, Xinping 

Ma, Kai 

Liu, Zhixin 

Yanshan Univ. 

Yanshan Univ. 

Yanshan Univ. 

Inst. of Electrical Engineering,Yanshan Univ. 

◮ SaA01-3 14:10–14:30 

Security Protection Technology of Intelligent Electricity Selling Terminal, 

pp.1005–1009 

Zhai, Feng 

Zhao, Bing 

Cen, Wei 




◮ SaA01-4 14:30–14:50 

Improved delay-dependent stability criteria for networked control systems, 

pp.892–897 

Song, Yan 




◮ SaA01-5 14:50–15:10 

Stability Analysis and Control of Bifurcation in a TCP Fluid Flow Model 

of Wireless Networks, pp.1026–1030 

Liu, Feng 

Wang, Hua O. 

Guan, Zhi-Hong 

China Univ. of GeoSci. 

Boston Univ. 


◮ SaA01-6 15:10–15:30 

Stabilization of Wireless Networked Control Systems with Packet Loss 

and Impulse Disturbance, pp.1031–1036 

QU, Feng-Lin 




Yuan, Fushun 

Anyang Normal Univ. 

Zhan, Xisheng Huazhong Univ. of Sci. & Tech./Hubei Normal Univ. 

SaA02 13:30–15:30 Room 203B 

Robust Control (I) 

Chair: Wang, Yijing School of Electrical Engineering & Automation 

Co-Chair: Sun, Weiwei 


◮ SaA02-1 13:30–13:50 

Robust H∞control of linear systems with intervalnon-differentiable 

time-varying delays, pp.1507–1512 

Emharuethai, Chanikan 

Niamsup, Piyapong 

Chiang Mai Univ. 


◮ SaA02-2 13:50–14:10 

Robust stability for uncertain switched systems with interval timevarying 

delay, pp.1305–1310 

La-inchua, Teerapong 




◮ SaA02-3 14:10–14:30 

Robust Stability Analysis of Hamiltonian Systems with Time-Varying 

Delays and Uncertainties, pp.1832–1837 

Sun, Weiwei 

Wang, Pan 



◮ SaA02-4 14:30–14:50 

Robust Finite-Time Stochastic Stability Analysis and Control Synthesis 

of Uncertain Discrete-Time Markovian Jump Linear Systems, pp.1925– 

1929 

Zuo, Zhiqiang 

Li, Hongchao 

Wang, Yijing 

Liu, Yi 

Tianjin Univ. 

Tianjin Univ. 

School of Electrical Engineering & Automation 

Tianjin Univ. 

◮ SaA02-5 14:50–15:10 

Robust Exponential Delay-Dependent Stability Criteria for Neutral Systems 

with Constant delay, pp.1698–1703 

Warakorn, Sudsanguan 

Rojsiraphisal, Thaned 

Burapha Univ. 

Faculty of Sci., Chiang Mai Univ. 

◮ SaA02-6 15:10–15:30 

Robust H ∞ Fuzzy Control for Nonlinear Discrete-Time Systems by 

Nonquadratic Lyapunov Function Approach, pp.1710–1715 

Horng, Wen-Ren 

Fang, Chun-Hsiung 

Kaohsiung Univ of Applied Sci. 

he serves as the President of National 

Kaohsiung Univ. of Applied Sci. 

SaA03 13:30–15:30 Room 203C 

Adaptive Control and Learning Control 

Chair: Guo, Jing 

Co-Chair: Wei, Airong 

Beijing Inst. of Aerospace Testing Tech. 

shandong uniersity 

◮ SaA03-1 13:30–13:50 

Adaptive Control for Clutch Engagement on Starting up of Vehicle, 

pp.1230–1234 

Dong, Yuehong 

Jiao, Xiaohong 

Chen, Dongzhi 

Yanshan Univ. 

Yanshan Univ. 

Yanshan Univ. 

◮ SaA03-2 13:50–14:10 

Adaptive dynamic friction compensation control of electrohydraulic servo 

system, pp.1682–1687 

Guo, Jing 

Yang, Jizhi 



◮ SaA03-3 14:10–14:30 

Adaptive Simultaneous Stabilization of Two Port-Controlled Hamiltonian 

Systems Subject to Actuator Saturation, pp.1767–1772 

Wei, Airong 

Wang, Yuzhen 

Hu, Xiaoming 




◮ SaA03-4 14:30–14:50 

The adaptive fuzzy PID control study of active vibration isolation system, 

pp.1120–1123 

Lin, Yan 

Liu, Wending 

Beijing Forestry Univ. 


◮ SaA03-5 14:50–15:10 

Iterative learning control for linear switched systems with arbitrary 

switched rules, pp.1182–1187 

Bu, Xuhui 

Yu, Fashan 

Hou, Zhongsheng 




63


◮ SaA03-6 15:10–15:30 

Iterative Learning Control Based on Modified Steepest Descent Control 

For OutputTracking of Nonlinear Non-minimum PhaseSystems, 

pp.1361–1366 

Naiborhu, Janson 

Institut Teknologi Bandung 

SaA04 13:30–15:30 Room 203D 

Systems Engineering and Engineering Optimization 

Chair: Jia, Qing-Shan 

Co-Chair: Bai, Danyu 


Shenyang Unniversity of Chemical Tech. 

◮ SaA04-1 13:30–13:50 

New Approximation Algorithms for Two-machine Flow Shop Total Completion 

Time Problem, pp.2388–2392 

Bai, Danyu 


◮ SaA04-2 13:50–14:10 

Optimal Scheduling of Storage Devices for Building Energy Saving, 

pp.2393–2398 

Xu, Zhanbo 

Jia, Qing-Shan 

Guan, Xiaohong 

Wu, Jiang 

Wang, Dai 

Chen, Siyun 



Xi’an Jiao Tong Univ. 




◮ SaA04-3 14:10–14:30 

Optimal DTC Strategy of PMSM in Electric Vehicle, pp.2447–2451 

Zhai, Li 


◮ SaA04-4 14:30–14:50 

An evacuation algorithm for large buildings, pp.2497–2502 


Liu, Huan 

Fudan Univ. 

Fudan Univ. 

◮ SaA04-5 14:50–15:10 

A Gradient Information Based Real Time Pricing Mechanism for Microgrid 

in Energy Intensive Enterprise, pp.2473–2478 

Wang, Zhaojie 

Gao, Feng 

Zhai, Qiaozhu 

Wu, Jiang 


Zhang, Hao 

Liu, Kun 

Pan, Li 

Dong, Mingyu 

xi’an jiaotong Univ. 

Xi’an Jiaotong Uni 




xian jiaotong Univ. 


Corporate Tech., Siemens Ltd 


◮ SaA04-6 15:10–15:30 

Online Strategy for Scheduling A Hydroelectric Station, pp.2479–2484 

Ru, Hai 

Gao, Feng 


Zheng, Fei Feng 





SaA05 13:30–15:30 Room 203E 

Control Engineering (I) 

Chair: Hua, Changchun 

Co-Chair: Wang, Fei 

yanshan Univ. 

Jilin Univ. 

◮ SaA05-1 13:30–13:50 

PD Control for Teleoperation System with Delayed and Quantized Communication 

Channel, pp.2318–2323 

Yang, Xian 

Hua, Changchun 

Yan, Jing 

Guan, Xinping 

Inst. of Electrical Engineering, Yanshan Univ. 

yanshan Univ. 

Yanshan Univ. 


◮ SaA05-2 13:50–14:10 

LQR Control for a Self-balancing Unicycle robot, pp.1424–1429 

Gong, Daoxiong 

Pan, Qi 

Zuo, Guoyu 

Deng, Wenbo 



beijing Univ. of Tech. 


◮ SaA05-3 14:10–14:30 

Modeling and Control for an In-plane Morphing Wing, pp.1430–1435 

Shi, Rongqi 

Song, Jianmei 



◮ SaA05-4 14:30–14:50 

Dynamics Modeling and Real-time Fault-tolerant Control of A Rotor 

Aerial Robot, pp.1144–1149 

Hou, Xuyang 




◮ SaA05-5 14:50–15:10 

Stability Control of Vehicle with Tire Blowout Using Differential Flatness 

Based MPC Method, pp.2066–2071 

Guo, Hongyan 

Wang, Fei 

Chen, Hong 

Guo, Dapeng 

Jilin Univ. 

Jilin Univ. 

Jilin Univ. 

FAW - Volkswagen Automotive CO., LTD 

◮ SaA05-6 15:10–15:30 

High Stabilization Control of Sensorless Brushless DC Motor On High 

speed, pp.2022–2026 

Lv, Haotun 

Wu, Zhong 

Beihang Univ. 


SaA06 13:30–15:30 Room 302 

Fault Diagnosis 

Chair: Zhang, Ke Nanjing Univ. of Aeronauitcs & Astronautics 

Co-Chair: Yao, Lina 


◮ SaA06-1 13:30–13:50 

On Design of Fault Detection Filter for Nonlinear Markovian Jump Systems 

with Sensor Saturations, pp.2953–2958 

Dong, Hongli 

Wang, Zidong 

Liang, Jinling 

Gao, Huijun 

Northeast Petroleum Univ. 




◮ SaA06-2 13:50–14:10 

Reduced-Order Fault Estimation Observer Design for Discrete-Time 


Zhang, Ke 

Jiang, Bin 

Shi, Peng 

Shumsky, Alexey 

Nanjing Univ. of Aeronauitcs & Astronautics 


Univ. of Glamorgan 

Far Eastern Federal Univ. 

◮ SaA06-3 14:10–14:30 

Design and Implementation of Fault Diagnosis Expert System for Missile 

Circuit, pp.2977–2982 

Qiu, Sihai 


Liu, Zhongxin 

Nankai Univ. 

Nankai Univ. 

NanKai Univ. 

◮ SaA06-4 14:30–14:50 

Fault diagnosis for non-Gaussian time-delayed stochastic distribution 

control system, pp.2988–2993 

Yao, Lina 


◮ SaA06-5 14:50–15:10 

Data-based Fault-tolerant Control of the Semiconductor Manufacturing 

Process based on K-Nearest Neighbor Nonparametric Regression, 

pp.3008–3012 

Luo, Ming 

Zheng, Ying 

Liu, Shujie 




◮ SaA06-6 15:10–15:30 

Fault Tolerant Control for a Robust Nonlinear Robotics via Optimal 

H∞Controller, pp.3248–3253 


Wang, Hong 



SaA07 13:30–15:30 Room 303 

Advanced Control Algorithms and Applications (I) 

Chair: Heng, Qinghai 

Co-Chair: WANG, Xin 



64



◮ SaA07-1 13:30–13:50 

Multiple Models Direct Adaptive Controller Using Dimension-By- 

Dimension Technology, pp.2630–2635 

WANG, Xin 

ZHENG, Yihui 

Li, Lixue 

Yang, Hui 




East China Jiaotong Univ. 

◮ SaA07-2 13:50–14:10 

Robust Control of Hydro Turbine Speed Governor, pp.2680–2684 

Heng, Qinghai 

Lu, Jing 

Lu, Yang 




◮ SaA07-3 14:10–14:30 

H ∞ Loop Shaping Based Low Order Structured Robust Controller Design, 

pp.2710–2715 


Liu, Yuyan 



◮ SaA07-4 14:30–14:50 

Model-free Subspace-based Control Scheme for Grid-Connected Voltage 

Source Inverters, pp.2674–2679 

Chen, Jianmin 

Yang, Fuwen 





◮ SaA07-5 14:50–15:10 

Research on Global Motion Estimation Based on Adaptive SIFT Algorithm, 

pp.2758–2763 


An, Jiancheng 

Cao, Luguang 




◮ SaA07-6 15:10–15:30 

An Exemplar Test Problem on Parameter Convergence Analysis of 

Temporal Difference Algorithms, pp.2925–2930 

Brown, Martin 

Tutsoy, Onder 

The Univ. of Manchester 


SaA08 13:30–15:30 Room 310 

Award: Theory (I) 


Co-Chair: Zhang, Huanshui 



◮ SaA08-1 13:30–13:50 

A New Optimal Control Method for Discrete-Time Nonlinear Systems 

with Approximation Error, pp.185–190 

Wei, Qinglai 

Liu, Derong 

Inst. of Automation 

CASIA 

◮ SaA08-2 13:50–14:10 

Emergence of Flocks with Local Interactions, pp.3515–3519 

Chen, Ge 

Liu, Zhi-Xin 



◮ SaA08-3 14:10–14:30 

Cooperative Robust Output Regulation of Linear Uncertain Multi-Agent 


Su, Youfeng 

Huang, Jie 


Chinese Univ. of Hong Kong, China 

◮ SaA08-4 14:30–14:50 

Stochastic Approximation Based PCA and Its Application to Identification 

of EIV Systems, pp.3276–3280 

Zhao, Wen-Xiao 

Chen, Han-Fu 



◮ SaA08-5 14:50–15:10 

Reaching Optimal Consensus for Multi-agent Systems Based on Approximate 

Projection, pp.2794–2800 

Lou, Youcheng Acad. of Mathematics & Sys. Sci., Chinese Acad. 


Shi, Guodong 

Johansson, Karl Henrik 

KTH Royal Inst. of Tech. 


Hong, Yiguang 


◮ SaA08-6 15:10–15:30 

Quantum Multi-Channel Decoupling, pp.2286–2290 

Liu, Pei-Lan 

Li, Jr-Shin 

Tarn, Tzyh-Jong 

Washington Univ. in St. Louis 


Washington Univ., St. Louis, MO 

SaA09 13:30–15:30 Room 311A 

Award: Application (I) 


Co-Chair: Chen, Hong 


Jilin Univ. 

◮ SaA09-1 13:30–13:50 

Bilateral Teleoperation of Force/Motion for aRobotic Manipulator with 

Communication RandomDelays, pp.3915–3920 

Kang, Yu 

Li, Zhijun 

Zhai, Dihua 

Cao, Xiaoqing 



Uiversity of Sci. & Tech. of China 

Shanghai Jiao Tong Univ 

◮ SaA09-2 13:50–14:10 

Mathematical Model Building and Optimization Control of Horizontal 

Continuous Heat Treatment Furnace, pp.2412–2416 

Hu, Lingyan 

Wang, Xingcheng 

College of Information Engineering,Dalian Univ. 

Dalian Maritime Unversity 

◮ SaA09-3 14:10–14:30 

Occluded Object Grasping Based on Robot Stereo Vision, pp.3698– 

3704 

Lin, Chuan 

Chen, Yen-Lun 

Hao, Weidong 

Wu, Xinyu 

Guilin Univ. of Electronic Tech. 

Shenzhen Inst. of Advanced Tech., Chinese Acad. 



Shenzhen Inst.s of Advacned Tech., CAS 

◮ SaA09-4 14:30–14:50 

Intelligent Switching Control for Cement Raw Meal Calcination Process, 

pp.280–285 

Qiao, Jinghui 

Chai, Tian-you 



◮ SaA09-5 14:50–15:10 

Design of Entry Trajectory Tracking Law for a Hypersonic Vehicle via 

Inversion Control, pp.1092–1097 

Pu, Zhiqiang 

Tan, Xiangmin 

Fan, Guoliang 

Yi, Jian-qiang 




Inst. of automation, chinese Acad. of Sci. 

◮ SaA09-6 15:10–15:30 

Finite-Horizon Neural Optimal Tracking Control for a Class of Nonlinear 

Systems with Unknown Dynamics, pp.138–143 

Wang, Ding 

Liu, Derong 

Li, Hongliang 


CASIA 

chinese Acad. of Sci. 

SaA10 13:30–15:30 Room 311B 

Invited Session: Guidance and Control of Flight Vehicles: Theory and 

Application 

Chair: He, Fenghua 

Co-Chair: Liu, Fuchun 



◮ SaA10-1 13:30–13:50 

Square Division Method for Attitude Dynamic Analysis of Modular S- 

pace Station, pp.1350–1355 

Guo, Jian 

Guo, Yang 

Zhang, Peng 

Yao, Yu 

Zhao, Hui 

Yang, Baoqing 







◮ SaA10-2 13:50–14:10 

Consensus of Second-Order Multi-Agent Systems with Disturbance 

65


Generated by Nonlinear Exosystem, pp.1574–1579 

Zhang, Xuxi 




◮ SaA10-3 14:10–14:30 

Optimal Sweep-based Persistent Surveillance Using Multiple Unmanned 

Aerial Vehicles with Level of Interest, pp.2441–2446 

Yao, Yu 

Zhang, Peng 

Liu, Hugh 

He, Fenghua 



Univ. of Toronto 


◮ SaA10-4 14:30–14:50 

Robust Analysis of Different Guidance Laws of Terminal Guidance System 

under Model Uncertainties, pp.1716–1721 

Wang, Xingdan 

Yao, Yu 

Yang, Baoqing 

Guo, Jian 





◮ SaA10-5 14:50–15:10 

Application of Extended Kalman Filter to Unmanned Helicopter Navigation, 

pp.2291–2295 

Liu, Fuchun 

Zhang, Qian 


South Chian Univ. of Tech 

SaA11 13:30–15:30 Room 311C 

Invited Session: Complex Networks and Multi-Agent Systems: Some 

Recent Advances 

Chair: Lu, Jinhu 

Co-Chair: Yu, Xinghuo 


RMIT Univ. 

◮ SaA11-1 13:30–13:50 

Neuronal Network Control for Robust Entrainment to Natural Oscillations 

- a Case Study, pp.4995–5000 

Chen, Zhiyong 

Zhang, Hai-Tao 

The Univ. of Newcastle 

Huazhong Univ. Sci. Eng 

◮ SaA11-2 13:50–14:10 

On the Lyapunov Exponent of Consensus Algorithm, pp.931–936 

Chen, Yao 

Lu, Jinhu 

Dong, Hairong 

Yu, Xinghuo 

Inst. of Sys. Sci. 



RMIT Univ. 

◮ SaA11-3 14:10–14:30 

Consensus decision-making of animal groups in motion, pp.952–957 

Liu, Zhi-Xin 


◮ SaA11-4 14:30–14:50 

Pinning Synchronization Criterion for Impulsive Dynamical Networks, 

pp.1976–1981 

Yu, Wenwu 


◮ SaA11-5 14:50–15:10 

Modelling Complex Software Systems via Weighted Networks, 

pp.3533–3537 

WANG, Beiyang 

Lu, Jinhu 

Wuhan Univ. 


SaB01 15:50–17:50 Room 203A 

Optimal Control and Optimization 

Chair: Chen, Michael Z. Q. 

Co-Chair: Han, Chunyan 



◮ SaB01-1 15:50–16:10 

Multi-objective optimization for a conventional suspension structure, 

pp.1235–1240 

Hu, Yinlong 




◮ SaB01-2 16:10–16:30 

Optimal control of quantum systems under different manipulation conditions, 

pp.2091–2096 

Wei, Hua 

Zhou, Wei 

NUDT 

NUDT 

Dai, Yi 

Zou, Fengxing 

Zhang, Ming 

NUDT 

NUDT 

NUDT 

◮ SaB01-3 16:30–16:50 

Optimal Filtering on Continuous-time systems with Markovian Communication 

Delays and packet dropouts, pp.2027–2032 

Han, Chunyan 

Wang, Wei 





◮ SaB01-4 16:50–17:10 

FFSM Trajectory Optimization via Multiphase Gauss Pseudospectral 

Method, pp.1615–1620 

Duan, Guang-Ren 

Li, Shi 



◮ SaB01-5 17:10–17:30 

A novel Varible Gain Unscented kalman filter and its application in the 

Integrated Navigation system, pp.1160–1165 

Zhang, Limin 


Zhang, Xinghui 

college of information technicail Sci. 

Nankai Univ. 

tianjin Univ. of Tech. & education 

◮ SaB01-6 17:30–17:50 

Comparison of Nonlinear Filtering Approach in Tightly-coupled GP- 

S/INS Navigation System, pp.1176–1181 

Nie, Qi 

Beijing Aerospace Automatic Control Inst. 

SaB02 15:50–17:50 Room 203B 

Robust Control (II) 

Chair: Brdys, Mietek 

Co-Chair: Zhu, Jiandong 


Nanjing Normal Univ. 

◮ SaB02-1 15:50–16:10 

Robust Adaptive Neural Network Control for Strict-Feedback Nonlinear 

Systems with Uncertainties, pp.1328–1333 

SUN, Gang 

Wang, Dan 

Peng, Zhouhua 

Lan, Weiyao 

Wang, Hao 

Wang, Ning 




Xiamen Univeristy 



◮ SaB02-2 16:10–16:30 

Design Robust Gain-Scheduling Multi-Objective Controller based on Iterative 

LMI, pp.1558–1563 

Li, Wen Qiang 

Cao, Wenjing 

SU, Tao 

LI, Lian 

Naval Aeronautical Engineering Inst. 



Control Enginering 

◮ SaB02-3 16:30–16:50 

Optimised Robust Placement of Hard Quality Sensors for Robust Monitoring 

of Quality in Drinking Water Distribution Systems, pp.1109–1114 

Langowski, Rafal 

Brdys, Mietek 

Qi, Ruiyun 




◮ SaB02-4 16:50–17:10 

Cooling Control of Aluminum Plate with a Peltier Device Thermal 

Process by Using a Robust Right Coprime Factorization Approach, 

pp.1115–1119 

WANG, Dong-yun 

Zhang, Lei 

Zhongyuan Univ. of Tech. 


◮ SaB02-5 17:10–17:30 

Sliding Mode Control for Robust Consensus of Linear Multi-agent Systems, 

pp.1378–1382 

Zhao, Ni 

Zhu, Jiandong 



◮ SaB02-6 17:30–17:50 

Robust Altitude Control for a Small Helicopter by Considering the 

Ground Effect Compensation, pp.1796–1800 


66



SaB03 15:50–17:50 Room 203C 


Chair: Tong, Guofeng 

Co-Chair: YIN, Ziqiang 


Inst. of Oceanographic Instrumention 

Shandong Acad. of Sci. 

◮ SaB03-1 15:50–16:10 

Research on Structured Light 3D Vision in the Remanufacturing System 

based on Robotic Arc Welding, pp.4527–4531 

YIN, Ziqiang Inst. of Oceanographic Instrumention Shandong 


◮ SaB03-2 16:10–16:30 

An Omni-directional vSLAM based on Spherical Camera Model and 3D 

Modeling, pp.4551–4556 

Tong, Guofeng 

Wu, Zizhang 

Weng, Ninglong 

Hou, Wenbo 



ISE 

NEU 

◮ SaB03-3 16:30–16:50 

Tracking the Rotating Targets in Aerial Videos , pp.4574–4578 

Dong, Qiang 

Liu, Aidong 


Huazhong Research Inst. of Electro-Optical Tech. 

◮ SaB03-4 16:50–17:10 

UAV Image Denoising Using Adaptive Dual-Tree Discrete Wavelet 

Packets Based on Estimate the Distributing of the Noise , pp.4649– 

4654 

Liu, Fang 


◮ SaB03-5 17:10–17:30 

A Perception-motivated Image Interpolation Algorithm , pp.4754–4759 

Zi, Lingling 

Du, Junping 

Liang, Meiyu 







◮ SaB03-6 17:30–17:50 

The Study on Infrared Image Mosaic Application Using Immune Memory 

Clonal Selection Algorithm, pp.4831–4836 

Dong, Lin 

Fu, Dongmei 

Yu, Xiao 

Yang, Tao 


Univ. of Sci. & Tech.,Beijing 


Univ. of Sci. & Tech., Beijing 

SaB04 15:50–17:50 Room 203D 

Industrial Automation and On-line Monitoring 

Chair: Li, Pingkang 

Co-Chair: Wang, Zaiying 



◮ SaB04-1 15:50–16:10 

A Dynamic Relative Gain Array Based on Model Predictive Control, 

pp.3340–3344 

JIANG, Huirong 




◮ SaB04-2 16:10–16:30 

Study on the Temperature Dual Control of CSTR with Coil Cooling and 

Jacket Cooling and Application, pp.3359–3363 

Wang, Zaiying 


◮ SaB04-3 16:30–16:50 

Saturated Output Feedback High-Gain Control for the Water-level of U- 

Tube Steam Generators, pp.3378–3383 

Dong, Zhe 


◮ SaB04-4 16:50–17:10 

Incremental PCA based online model updating for multivariate process 

monitoring, pp.3422–3427 

Hou, Ranran 

Wang, Huangang 

Xiao, Yingchao 

Xu, Wenli 





◮ SaB04-5 17:10–17:30 

An Approach to Rainfall Simulator Automation and Performance Evaluation, 

pp.3428–3433 

Cai, Jun 

Li, Pingkang 

Wang, Peng 

Beijing Jiao Tong Univ. 


Beijing jiaotong Univ. 

◮ SaB04-6 17:30–17:50 

A Vison-based Grasping Strategy for the Mineral Sorting, pp.3454– 

3459 

Zhang, Yuren 

Qiao, Hong 

Su, Jianhua 

Huang, Kaiqi 

Fukuda, Toshio 

Inst. of automation, Chinese Acad. of Sci. 

Inst. of Automation, Chinese Acad. of Sci., China 


Jiangxi Univ. of Sci. & Tech. 

Nagoya Univ. 

SaB05 15:50–17:50 Room 203E 

Control Engineering (II) 

Chair: Xiao, MingQing 

Co-Chair: Zhang, Weidong 



◮ SaB05-1 15:50–16:10 

Energy-Balancing-Based Control Design for Power Systems, pp.2364– 

2369 

Sun, Yajie 

Ding, Zhengtao 

Wang, Hong 


Univ. of Manchester 


◮ SaB05-2 16:10–16:30 

Local Observer for Axial Flow Aeroengine Compressors, pp.2233–2238 

Gao, Xuejun 

Huang, Tingwen 

Liu, Jun 



Texas A& M Univ. 



◮ SaB05-3 16:30–16:50 

Missile Controlled by Aero-Fin and Divert Thrusters Using H2 Decoupling 

Analytical Design, pp.1383–1388 

Xue, Mantian 

Zhang, Weidong 

Jia, Xiaohong 

Jia, Jie 

McMaster Univ. 


Luoyang Photoelectric Tech. 

Nanchang Hangkong Univ. 

◮ SaB05-4 16:50–17:10 

Constrained Model Predictive Control for Backing-up Tractor-Trailer 

System, pp.2165–2170 

Bin, Yang 


◮ SaB05-5 17:10–17:30 

Adaptive position tracking control for Bilateral teleoperation with constant 

time delay, pp.2324–2328 


Yang, Yana 

Liu, Xiaoping 

yanshan Univ. 

Inst. of Electrical Engineering 

Carleton Univ., Canada 

◮ SaB05-6 17:30–17:50 

MLP-based Nonlinear Modelling for Energy Saving in Forming Section 

of Paper Machines, pp.2358–2363 

Ding, Jinliang 


Afshar, Puya 

Wang, Hong 





SaB06 15:50–17:50 Room 302 

Sesor Networks 

Chair: Chen, Cailian 

Co-Chair: Liu, Zhigang 



◮ SaB06-1 15:50–16:10 

Critical Transmission Range for Connectivity in Aeronautical Ad-hoc 

Networks, pp.4446–4451 

Yan, Jianshu 

Song, Ge 

SJTU 

Univ. of Michigan-Shanghai Jiao Tong Univ. Joint 

Inst. 

67


Lee, Hua 

Hua, Cunqing 

Chen, Cailian 

Guan, Xinping 

SJTU 

SJTU 



◮ SaB06-2 16:10–16:30 

An analysis of energy balanced deployment strategy for strip-based 

wireless sensor networks, pp.4472–4477 

Wang, Zhengjie 

Zhao, Xiaoguang 

Qian, Xu 


Inst. of Automation,the Chinese Acad. of Sci. 



(Beijing) 

◮ SaB06-3 16:30–16:50 

Auction Based Task Assignment for Pursuit-Evasion Game in Wireless 

Sensor Network, pp.4435–4440 

Du, Rong 

Chen, Cailian 

Yang, Bo 

Guan, Xinping 





◮ SaB06-4 16:50–17:10 

Distortion Analysis for Delay Tolerant Data Collection for High-speed 

Wireless Sensor and Actor Networks, pp.4452–4457 

Liu, Yaxiong 

Chen, Cailian 

Yu, Hangchen 

Guan, Xinping 





◮ SaB06-5 17:10–17:30 

Interacting Multiple Sensor Unscented Kalman Filter, pp.4409–4413 

Liu, Zhigang 

Wang, Jinkuan 



◮ SaB06-6 17:30–17:50 

A measurements fusion filter for the multi-sensor system with correlated 

noises, pp.4458–4462 

Li, Shengwei 

Feng, Xiaoliang 

Lu, Yazhou 

Henan Univ. 

Hohai Univ. 

Henan Univ. 

SaB07 15:50–17:50 Room 303 

Advanced Control Algorithms and Applications (II) 

Chair: Sam, Yahaya 

Co-Chair: Guo, Wei 

Universiti Tech. of Malaysia 

Nanjing Univ. of Information Sci. & Tech. 

◮ SaB07-1 15:50–16:10 

A Novel Model Algorithmic Controller with Fractional Order PID Structure, 

pp.2517–2522 

Guo, Wei 

Song, Ying 

Zhou, Li 

Deng, Ling 





◮ SaB07-2 16:10–16:30 

Path Planning based Quadtree Representation for Mobile Robot Using 

Hybrid-Simulated Annealing and Ant Colony optimization Algorithm, 

pp.2537–2542 

Zhang, Qi 

Ma, Jiachen 

Liu, Qiang 

Harbin Inst. of Tech. Harbin 



◮ SaB07-3 16:30–16:50 

Point-to-Point Trajectory Tracking with Two-Degree-of-Freedom Robust 

Control for a Non-minimum Phase Electro-hydraulic System, pp.2661– 

2668 

Ghazali, Rozaimi 

Sam, Yahaya 

Universiti Tun Hussein Onn Malaysia 


◮ SaB07-4 16:50–17:10 

Multi-grade resin quality adaptive estimation for gas-phase polyethylene 

process, pp.2838–2843 

Zhao, Zhong 

Hu, Chuan 



Liu, Yang 


◮ SaB07-5 17:10–17:30 

A RLS Run-to-Run Control Approach for Semiconductor Manufacturing 

Process, pp.2642–2646 

Liu, Shujie 

Zheng, Ying 

Luo, Ming 

Wang, Yanwei 





◮ SaB07-6 17:30–17:50 

Active Control of Periodic Impulsive Noise in a Non-minimum Phase 

System Using Repetitive Control Algorithm, pp.2770–2775 

Zhou, Yali 

Yin, Yixin 

Zhang, Qizhi 


Univ. of Sci. & Tech. beijing 


SaB08 15:50–18:30 Room 310 

Award: Theory (II) & Invited Session: Control, Informatics, and Systems 

Biology 

Chair: Luh, Peter B. 

Co-Chair: Yang, Ruoting 

Univ. of Connecticut,USA 

UNIV OF CA @ SANTA BARBARA 

◮ SaB08-1 15:50–16:10 

Robust Altitude Control of an Unmanned Autonomous Helicopter Using 

Backstepping , pp.1650–1654 


◮ SaB08-2 16:10–16:30 

Function Perturbation Impact on the Topological Structure of Boolean 


Li, Haitao 

Wang, Yuzhen 

Liu, Zhenbin 

Shandong U 



◮ SaB08-3 16:30–16:50 

Epidemic Spreading on Complex Networks with Weighted Adaptive S- 

trategy, pp.3491–3496 

Zhou, Yinzuo 

Zhou, Jie 





◮ SaB08-4 16:50–17:10 

Identification of overlapping communities in protein interaction networks 

using multi-scale local information expansion, pp.5071–5076 

Li, Huijia 

Liu, Zhi-Ping 

Chen, Luonan 

Zhang, Xiang-Sun 





◮ SaB08-5 17:10–17:30 

Colored Petri Nets to Model Gene Mutation Classification, pp.5077– 

5082 

Yang, Jinliang 

Gao, Rui 

Meng, Max, Q.-H. 






◮ SaB08-6 17:30–17:50 

Core Module Network Construction for Breast Cancer Metastasis, 

pp.5083–5089 

Yang, Ruoting 

Daigle, Bernie 

Petzold, Linda 



Univ. of California Santa Barbara 



◮ SaB08-7 17:50–18:10 

Closed-Loop Blood Glucose Control Using Dual Subcutaneous Infusion 

of Insulin and Glucagon Based on Switching PID Controller, pp.5023– 

5029 

Gao, Xiaoteng 

Wang, Youqing 



◮ SaB08-8 18:10–18:30 

Study on Some Modeling Problems in the process of Gene Expression 

with Finite State Machine, pp.5066–5070 

68



Gao, Rui 


SaB09 15:50–17:50 Room 311A 

Award: Application (II) 


Co-Chair: Yang, Chunhua 


Central South Univ., China 

◮ SaB09-1 15:50–16:10 

Error Modeling and Analysis in Dynamic Wafer Handling, pp.3977– 

3982 

Cheng, Hongtai 

Chen, Heping 

Mooring, Ben 

Stern, Harold 



Lam Research Corporation 


◮ SaB09-2 16:10–16:30 

Planning Expected-time Optimal Paths for Target Search by Robot, 

pp.3881–3886 

Zhang, Botao 

Liu, Shirong 



◮ SaB09-3 16:30–16:50 

Optimal Operation Strategies for Batch Distillation by Using A Fast 

Adaptive Simulated Annealing Algorithm, pp.2426–2430 

Wang, Lin 

Pu, Zhonghao 

Wen, Sufang 




◮ SaB09-4 16:50–17:10 

PID Control of Glucose Concentration in Subjects with Type 1 Diabetes 

based on a Simplified Model: An In Silico Trial, pp.5051–5055 

Li, Peng 

Yu, Lei 

Guo, Liquan 

Dong, Jixiang 

Hu, Ji 

Fang, Qiang 

SIBET 

Suzhou Inst. of Biomedical Engineering & 

Tech.,CAS 

SIBET 

The Second Affliated Hospital Suzhou Univ. 


SIBET 

◮ SaB09-5 17:10–17:30 

Parameter Convergence Analysis in Adaptive Disturbance Rejection 

Problem of Rigid Spacecraft, pp.1418–1423 

Chen, Zhiyong 

Huang, Jie 



◮ SaB09-6 17:30–17:50 

H2 Performance Limitation of a Class of Nonlinear Non-minimum 

Phase Systems, pp.1317–1322 

Lu, Di 

Fan, Guoliang 





SaB10 15:50–17:50 Room 311B 

Invited Session: Modeling and Control: Challenges from Automotive 

Industry 


Co-Chair: OHATA, AKIRA 

Sophia Univ. 

Toyota Motor Corporation 

◮ SaB10-1 15:50–16:20 

Benchmark Problem for Nonlinear Identification of Automotive Engine, 

pp.3305–3310 

OHATA, AKIRA 


◮ SaB10-2 16:20–16:50 

JSAE-SICE Benchmark problem II: Fuel Consumption Optimization of 

Commuter Vehicle Using Hybrid Powertrain, pp.606–611 

Yasui, Yuji 

Honda R&D CO., Ltd 

◮ SaB10-3 16:50–17:20 

Integrated Vehicle Dynamics Control of an In-Wheel-Motor Vehicle, 

pp.2335–2339 

Hirano, Yutaka 


◮ SaB10-4 17:20–17:50 

SICE Benchmark Problem of Engine Control and a Challenging Results, 

pp.2340–2345 

Shen, Tielong 

Zhang, Jiangyan 

Sophia Univ. 

Sophia Univ. 

SaB11 15:50–17:50 Room 311C 

Invited Session: Control of High-Performance Engineering Systems 

Chair: Pang, Chee Khiang 


◮ SaB11-1 15:50–16:10 

Neural-Network Based AUV Path Planning in Estuary Environments, 

pp.3724–3730 

Li, Shuai 

Guo, Yi 

Stevens Inst. of Tech. 


◮ SaB11-2 16:10–16:30 

Leader-follower and communication based formation control of multirobots 

, pp.229–232 

Wang, Zhuping 

Mao, Ying 

Chen, Guangmou 

Chen, Qijun 

Tongji Univ. 

Tongji Univ. 

Tongji Univ. 

Tongji Univ. 

◮ SaB11-3 16:30–16:50 

Tracking Control for Nonholonomic Mobile Robots with Visual Servoing 

Feedback , pp.3864–3869 

Wang, Baolei 

Wang, Chaoli 



◮ SaB11-4 16:50–17:10 

Consensus Output Regulation Without State Estimation, pp.2306–2311 



◮ SaB11-5 17:10–17:30 

design of decoders based on memristors, pp.5116–5121 

Zhou, Jing 


◮ SaB11-6 17:30–17:50 

The Charging and Discharging Characteristics of Memcapacitor Storage 

with Applications, pp.5139–5143 

He, Pengfei 

Wang, Lidan 

Duan, Shukai 

Chair: Zhao, Qianchuan 

Poster Session PSaA 

July 7, 13:30-14:50 

Southwest Univ. 




◁ PSaA-01 

Research of Dynamic Texture Mapping Technology realized by Multisoftware 

collaboratively, pp.3191–3194 

Wang, Dong 

Zhang, Tuo 

Cao, Yuchao 

Li, Dalei 





◁ PSaA-02 

Prediction of Flooding Velocity in Packed Tower Using Least Squares 

Support Vector Machine, pp.3226–3231 

Li, Changli 

Liu, Yi 

Yang, Jie 

Gao, Zengliang 



Zhejiang Univ. of Technolog 


◁ PSaA-03 

Development and Research on Multi-National Banknote Sorting System, 

pp.3331–3335 

Zhang, Ying 


◁ PSaA-04 

Application of PLC and Touch-screen in The Power Window Control 

System, pp.3355–3358 

Li, Binglin 

Tao, Quan 



◁ PSaA-05 

Optimization of Power Plant DCS Control Based on Data Communication, 

pp.3434–3437 

Fu, Junjie 

DATANG INTERNATIONAL TUOKETUO POWER 

69


Zhao, Zhigang 

Long, Junfeng 

Cai, Jun 

Li, Pingkang 





◁ PSaA-06 

QP-based SMP Scheme for Robots with Pseudoinverse Method Compared 

and Singularities Discussed, pp.3583–3588 

Zhang, Yunong 

Xiao, Lin 



◁ PSaA-07 

Research of the Robot for the Elderly Based on Star Positioning, 

pp.3639–3641 

Tian, Liguo 

Li, Meng 

Chen, Zhiliang 

Duan, Haillong 





◁ PSaA-08 

Lateral Stabilization of a Single Wheel Robot Applying Electromagnetic 

Force, pp.3675–3680 


Zhu, Xiaoqing 

Li, Yalei 

Wei, Ruoyan 





◁ PSaA-09 

Modeling and Control for UW-Car in Rough Terrain, pp.3747–3752 

Mao, Lifei 

Huang, Jian 

Ding, Feng 


Matsuno, Takayuki 




Nagoya Univ. 

Toyama Prefectural Univ. 

◁ PSaA-10 

A Novel Pursuit Strategy for Fast Evader in Indoor Pursuit-Evasion 

Games, pp.3834–3839 

Fan, Xinyi 

Zhu, Shanying 

Du, Rong 

Chen, Cailian 

Guan, Xinping 






◁ PSaA-11 

Research and Development of Comprehensive Monitoring and Management 

Platform for Substation, pp.3948–3953 

Wang, Hua 

Yan, Gangfeng 

1.Zhejiang Univ. 2.Zhejiang Univ. of Sci. & Tech. 

college of electrical engineering 

◁ PSaA-12 

Design on Logistics Management Automation System for Waterway Integrated 

Port, pp.3954–3957 

Ao, Chaohua 

Wu, Qian 

Yang, Xiaoyi 

Chongqing Industry Polytechnic College 

Chongqing Univ. of Tech. 

Chongqing Normal Univ. 

◁ PSaA-13 

Study on the Continuous Direction Control for the Snake-like Robot, 

pp.1276–1281 


Wu, Chengdong 

Li, Bin 





Sci. 

◁ PSaA-14 

A Game Theoretical Bandwidth Allocation Mechanism for Cloud 

Robotics, pp.3828–3833 

Wang, Lujia 




◁ PSaA-15 

Multivariable Generalized Predictive Control Based on Receding Feedback 

Correction in Binary Distillation Process, pp.1098–1102 

Li, Chaochun 

Tao, Lili 

Cheng, Hui 

Qi, Rongbin 

Qian, Feng 






◁ PSaA-16 

Nonlinear PID Synchro Control on Broaching Machine with Dual Cylinder, 

pp.1124–1129 

Ni, Jing 

Shao, Bin 

Chen, Guojin 

hang zhou dianzi univeristy 



◁ PSaA-17 

Controllability and Observability of Matrix Riccati Type Differential Systems, 

pp.1135–1138 

Xu, Dengguo 

Chuxiong Normal Univ. 

◁ PSaA-18 

Robust Non-fragile Control for Non-linear Singular Discrete Systems 

with Time-delay, pp.1139–1143 

Fu, Xingjian 

Liu, Xiao He 

Hou, Ming 

Li, Yingchun 




North China Inst. of Astronautic Engineering 

◁ PSaA-19 

The Analysis and Design of Diphasic Pacemaker Pulse System Based 

on Microcontroller, pp.1192–1195 

Xu, Longjian 

Zhang, Houwu 

Yao, Kaixue 

Guizhou Univ. 

Guizhou Univ. 

Guizhou Univ. 

◁ PSaA-20 

Design And Implementation of a Constant Selling Oil Automatic System 

Based on Fuzzy PID Algorithm, pp.1207–1211 

WANG, LI 

ZHAO, ZHENG 



◁ PSaA-21 

Research on the Brittleness Problem of Power System with Wind 

Turbine Generator System Based on the Cellular Automata Theory, 

pp.1218–1223 

Guo, Jian 

Yang, Yong 



◁ PSaA-22 

Bifurcation and stability study in coupling nonlinear rotating machinery’ 

s systemunder the influence of time-varying stiffness, pp.1263–1268 

Liu, Shuang 

Li, Yanshu 

He, Minjia 

Wang, Bohua 

Liu, Bin 

Yanshan Univ. 

Yanshan Univ. 

Yanshan Univ. 

Yanshan Univ. 

Yanshan Univ. 

◁ PSaA-23 

Performance Bound of Parallel Cascade Control System Based on 

Minimum Variance and Generalized Minimum Variance Benchmarking, 

pp.1334–1339 

Guo, Jian Wen 

Du, Wenli 

Qian, Feng 




◁ PSaA-24 

Robust Formation Control of a class of Multi-agent Systems by Output 

Regulation Approach, pp.1412–1417 

Sun, Weijie 

Qiao, Yupeng 

Suo, Xudong 

south china Univ. of Tech. 



◁ PSaA-25 

A fractional-order multi-scroll hyperchaotic Chua system and its synchronization, 

pp.1436–1441 

Xi, Huiling 

Guangzhou Univ. of Tech.;North Univ. of China 

70



◁ PSaA-26 

UKF based Robust Attitude Control for Helicopter, pp.1442–1446 

Song, Qi Shenyang Inst. of Automation, Chinese Acad. of 

Sci. 

Han, Jianda 

JIANG, Zhe 



◁ PSaA-27 

Global asymptotic stability of uncertain nonlinear system with input constraint, 

pp.1483–1488 

Zhou, Yingjiang 

Sun, Changyin 

Wang, Li 

Zhang, Jingmei 

southeast Univ. 




◁ PSaA-28 

Multiperiodicity and Attractivity Analysis for a Class of High-order 

Cohen-Grossberg Neural Networks, pp.1489–1494 

Sheng, Li 

Gao, Ming 

China Univ. of Petroleum (East China) 


◁ PSaA-29 

Delay-dependent Stability for Uncertain Stochastic Neural Networks 

with Distributed Delays, pp.1495–1500 

Gao, Ming 

Sheng, Li 



◁ PSaA-30 

Cutting-in Control with Smooth Switching Strategy for Doubly-fed Wind 

Power Generator, pp.1513–1517 

Xiao, Yunqi 

Lv, Yuegang 



◁ PSaA-31 

Fuzzy Energy-to-Peak Control for Nonlinear Markovian Jump Systems, 

pp.1518–1523 

Shen, Hao 

Wang, Jing 


Zhang, Handong 





◁ PSaA-32 

Cascade Temperature Control for Bench-Scale Batch Reactor –An Application 

of Predictive Functional Control Technique, pp.1564–1569 

Song, Yiming 

Northwest Univ. 

◁ PSaA-33 

Asymptotic Rejection of General Periodic Disturbances with Time- 

Varying Gains, pp.1591–1596 

Chen, Pengnian 

Qin, Huashu 

China Jiliang Univ. 


◁ PSaA-34 

Inverse optimal design of a class of stochastic nonlinear systems with 

uncontrollable linearization, pp.1597–1602 

WANG, Qiangde 

WEI, Chunling 



◁ PSaA-35 

Stability of a multi-state repairable system with two repair distributions, 

pp.1603–1608 

Zheng, Fu 

Bohai Univ. 

◁ PSaA-36 

Nonlinear Sliding Mode Formation Control for Underactuated Surface 

Vessels, pp.1655–1660 

Meng, Wei 

GUO, Chen 

Liu, Yang 

dalian maritime Univ. 



◁ PSaA-37 

Robust H-infinity Control for Switched Singular Systems withLinear 

Fractional Uncertainties: a Strict LMI Approach, pp.1704–1709 

Lin, Jinxing 

Gao, Zhifeng 

Gai, Shaoyan 

Nanjing Univ. of Posts & Telecommunications 



◁ PSaA-38 

Optimal Control for Markov Jump Systems with Constrained Control Input, 

pp.2142–2147 

Wu, Yanrui 

Wu, Youli 

Fang, Yangwang 

Hu, Shiguo 

shaan xi Univ. of Sci. & Tech. 

Air Force Engineering Univ. Engineering College 

Air Force engineering Univ. 

air force engineering Univ. 

◁ PSaA-39 

Fractional-order QFT Controllers for unstable plants based on Automatic 

Loop Shaping, pp.2148–2153 

Meng, Li 

◁ PSaA-40 

TC on PVTOL, pp.2154–2159 

Zhang, Yunfan 

Li, Donghai 

Lao, Dazhong 





◁ PSaA-41 

Co-design of static quantized scheduling strategy and state feedback 

control for networked control systems, pp.2182–2187 

Du, Mingli 

Zhou, Chuan 

Wu, Yifei 

Chen, Qingwei 





◁ PSaA-42 

Support Vector Machine Generalized Predictive Control for Turboshaft 

Engine, pp.2296–2301 

Xiao, Lingfei 

Zhu, Yue 


Nanjing Agricultural Univ. 

◁ PSaA-43 

Stock Turning Point Recognition using Multiple Model Algorithm with 

Multiple Types of Features, pp.4020–4025 

Qin, Xiaoyu 

Peng, Qinke 

Xi’an jiaotong Univ. 


◁ PSaA-44 

Improvement and application of the Delphi method, pp.4026–4029 

Ji, Wen 

Wang, Jianhui 

Fang, Xiaoke 

Gu, Shusheng 




Northeastern Univ., China 

◁ PSaA-45 

A Study on the System Structure of Multi-UCAV Cooperation Mission 


Ye, Wen 

Naval Aeronautical & Astronautical Univ 

◁ PSaA-46 

Design and Development of the Encoding Software in the Identification 

System of the Hydropower Plant, pp.4035–4039 

GUO, Jiang 

Feng, Zhihui 

Gu, Kaikai 

Bai, Weimin 

Zeng, Bing 

Liu, Yajin 

Wuhan Univ. 

Wuhan Univ. 

Wuhan Univ. 

Wuhan Univ. 

Wuhan Univ. 

Wuhan Univ. 

◁ PSaA-47 

Life Cycle Management Support System of Nuclear Power Plant Based 

on Economic Analysis, pp.4040–4044 

GUO, Jiang 

Gu, Kaikai 

Feng, Zhihui 

Zeng, Bing 

Bai, Weimin 

Liu, Yajin 

Wuhan Univ. 

Wuhan Univ. 

Wuhan Univ. 

Wuhan Univ. 

Wuhan Univ. 

Wuhan Univ. 

◁ PSaA-48 

Design and Development of J2EE-Based Identification System for Power 

Plants, pp.4045–4049 

Wu, Zhifang 


71


GUO, Jiang 

Sun, Yu 

Gu, Kaikai 

Zeng, Bing 

Bai, Weimin 

Wuhan Univ. 

Wuhan Univ. 

Wuhan Univ. 

Wuhan Univ. 

Wuhan Univ. 

◁ PSaA-49 

Improved LS-SVM Based Classifier Design and Its Application, 

pp.4050–4054 

Wang, Peng 

YAN, Aijun 



◁ PSaA-50 

Multi-criteria Decision Making Approach Based on Interval Value Vague 

Sets, pp.4061–4064 

Shi, Yu 

Hu, Xiaodong 

Wang, Weiping 

Shandong Jiao Tong Univ. 

Air Force Command College(AFCC) 

Univ. of International Relations 

◁ PSaA-51 

ACP based 3D Emergency Drills System for petrochemical plants, 

pp.4065–4070 

Jing, Sifeng 

Cheng, Changjian 

XIONG, Gang 

LIU, Xiwei 

Shang, Xiuqin 

Wang, Weixing 


parallel management 


Inst. of Automation, CAS 



◁ PSaA-52 

Fault Recovery Based Mission Scheduling of AUV for Oceanographic 

Survey, pp.4071–4076 

Yan, Zheping 

Zhao, Yufei 

Chen, Tao 

Jiang, Ling 





◁ PSaA-53 

Descending rolling horizon procedure for equipment replacement problem, 

pp.4082–4087 

Wang, Bing 

Liu, Dongpo 

Liu, Jing 


Beijing Sciample Tech. Co.,Ltd 


◁ PSaA-54 

Distributed Generation System Design Based on Energy Storage Converter 

Technology, pp.4088–4092 

Han, Xiaojuan 

Cui, Xiwang 

Zhang, Xilin 

Yan, Tao 

Du, Zhanzhan 








◁ PSaA-55 

Data-driven artificial system of parallel emergency management for 

petrochemical Plant, pp.4103–4107 

Shang, Xiuqin 

XIONG, Gang 


LIU, Xiwei 





◁ PSaA-56 

A PSO Algorithm Based on Group History Experience, pp.4108–4112 

Yan, Zheping 

Li, Benyin 

Deng, Chao 




◁ PSaA-57 

A Novel Two-subpopulation Particle Swarm Optimization, pp.4113– 

4117 

Yan, Zheping 

Deng, Chao 

Zhou, Jiajia 




Chi, Dongnan 


◁ PSaA-58 

Intelligence Decision Supporting Algorithms of Production Planning 

based on Hopfield network , pp.4122–4125 

SU, Jinlong 

Tongji Univ. 

◁ PSaA-59 

Project Development Management System of Financial Equipment Enterprises 

Based on PDM , pp.4135–4140 

Cui, Wenhua 

Liu, Xiaobing 


liaoning Sci.&Tech. Univ. 

Dalian Univ. of Tech. 


◁ PSaA-60 

Steady-State Identification with Gross Errors for Industrial Process U- 

nits, pp.4151–4154 

Tao, Lili 

Li, Chaochun 

Kong, Xiangdong 

Qian, Feng 





◁ PSaA-61 

Parameters Estimation of BLDC Motor for Energy Storage System, 

pp.4190–4194 

Wu, Junfeng 

Changchun Inst. of Optics,Fine Mechanies & 

Physics 

◁ PSaA-62 

The Design and Implementation of Metro Carriage LED Controller Software 

Based on Ethernet, pp.4201–4205 

Liu, Quanli 

Bian, Mingkun 

Wang, Wei 




◁ PSaA-63 

Conducted EMI Measurement and its Suppression Measure of Elevator 

System, pp.4226–4229 

Wen, Xiao-Qin 


◁ PSaA-64 

Modeling of Belt Conveyor System based on Least Square Support 

Vector Machine, pp.2944–2948 

Chen, Wei 

Li, Xin 

◁ PSaA-65 

Polynomial process algebra, pp.3004–3007 

Liu, Bai 

Hefei Univ. of Tech. 

hfut 


◁ PSaA-66 

Fuzzy Systems-Based Adaptive Fault-Tolerant Dynamic Surface Control 

for a class of High-order Nonlinear Systems with Actuator Fault, 

pp.3013–3018 

Shen, Qikun 

Jiang, Bin 





◁ PSaA-67 

A Green Wave Band Model Considering Variable Queue Clearance 

Time, pp.3025–3030 

Wang, Shi Ming 

Xu, Jian-min 

Yang, GuiGen 

Chen, ChangJia 



Public Security Office of Guangdong Province 


◁ PSaA-68 

Key Technology Research on Fault Detection and Diagnosis for Brokenwing 

Small Unmanned Aerial Vehicle, pp.3049–3052 

Huang, Junjie 


◁ PSaA-69 

Electric load forecasting based on improved LS-SVM algorithm, 

pp.3064–3067 

Yan, Gang 

Tang, Gao-hui 

Xiong, Ji-ming 


Hunan Univ. of finance & economics 


72



◁ PSaA-70 

Identification of dynamic parameters and friction coefficients for a 

heavy-duty hydraulic manipulator, pp.3102–3106 

Li, Bo 

Yan, Jun 

Guo, Gang 

Wang, Haito 

Zhang, Meijun 



Engineering Inst. of Corps of Engineer, PLA Univ. 

of Sci. & Tech. 





◁ PSaA-71 

Classifying imbalanced dataset based on minority detection, pp.3236– 

3241 

Liu, Tong 


◁ PSaA-72 

PID Fault Tolerant Control System Design with Multi-performance indices 

Constraints, pp.3286–3291 

Feng, Zhimin 

Zhang, Gang 

Ningbo Univ. 

ningbo Univ. 

◁ PSaA-73 

Fault Diagnosis of Induction Motor Rotor Based on BP Neural Network 

and D-S Evidence Theory, pp.3292–3297 

Zhang, Lieping 

WANG, Shoufeng 

GuiLin Univ. of Tech. 

Guilin Univ. of Tech. 

◁ PSaA-74 

Generalized Canalizing Mapping and Its Applications, pp.2376–2381 

Qi, Hongsheng 


◁ PSaA-75 

Design and Realization of S7-200 as Modbus-RTU Master Station, 

pp.3322–3325 

Si, Wenhui 

Wei, Jianping 

Shandong Jiaotong Univ. 

Shandong Jianzhu Univ. 

◁ PSaA-76 

Experimental Validation of a Trajectory Planning Method with Continuous 

Acceleration Implemented on a DSP-Based Motion Controller, 

pp.3326–3330 

Qiang, Yanhui 

Inst. of Automation, Chinese Academay of Sci. 

◁ PSaA-77 

Cascade PID control of desuperheating water system of supercritical 

power generation unit once-through boiler, pp.3369–3371 

Du, Ronghua 

Zhang, Pengtao 

Wang, Hongbin 

Guo, Ruijun 

Inner Mongolia Electric Power Sci. Research Inst. 




◁ PSaA-78 

AC electric power steering system modeling with Feed-forward fuzzy 

control algorithm, pp.3372–3377 

Hu, Jianfeng 

Duan, Jianming 



◁ PSaA-79 

Adaptive Digitally Controlled Flyback Converter Based on Current and 

Phase Margin Estimation, pp.3389–3394 

Yuan, Youchen 

Shao, Zhiwei 

Qingdao Univ. of Sci. & Tech. 


◁ PSaA-80 

Harmonic Compensation Rectifier with Smart Control Technology, 

pp.3395–3400 

Yuan, Youchen 


◁ PSaA-81 

A Vision-based Fault Diagnosis System for Heliostat in A Central Receiver 

Solar Power Plant, pp.3417–3421 

SONG, YANG 

Huang, Wenjun 



Zhu, Xuemei 

Nanjing normal Univ. 

◁ PSaA-82 

Sensorless Estimation and Simulation of PMSM Based on Highfrequency 

Signal Injection, pp.3438–3442 

Wu, Maogang 

Chen, Xing 


HangZhou DianZi Univ. 

◁ PSaA-83 

Adaptive Synchronization of Uncertain and Delayed Chaotic Systems 

with Its Circuit Realization, pp.3465–3470 

Jin, Xiao-zheng 

Che, Weiwei 

Wang, Dan 

Shenyang Univ. 



◁ PSaA-84 

Effect of Complex Network Structure on Synchronizability, pp.3481– 

3484 

Wang, Dan 

Li, Yanping 





◁ PSaA-85 

The Site Selection of Distribution Center Based on Linear Programming 

Transportation Method, pp.3538–3542 

Liu, Xiaohui 

Beihua Univ. 

◁ PSaA-86 

Soft Tissue Deformation ANSYS Simulation of Robot-Assisted Percutaneous 

Surgery, pp.3561–3566 

Zhao, Chengtao 

Zeng, Qingjun 

Liu, Haixia 

Jiangsu Univ. of Sci. & Tech. 



◁ PSaA-87 

Adaptive Neural Network Control of an Aerial Work Platform’s arm, 

pp.3567–3570 

Jia, Pengxiao 


◁ PSaA-88 

PSO-based Parameters Opatimization of Multi-Robots Formation Control 

in Unknown Enviroment , pp.3571–3576 

Liu, Qiang 

Ma, Jiachen 



◁ PSaA-89 

A Novel Inverse Dynamics Control Strategy with Different Phases for 

the Quadruped Robot, pp.3601–3606 

Li, Bin 

Shao, Xuesong 

Wang, Wei 

Guo, Yajuan 


Jiangsu Electric Power Company 





◁ PSaA-90 

Formation Control for Multiple Robots in Uncertain Environments, 

pp.3607–3610 

Li, Li 

Yang, Li 

Li, Bing 




◁ PSaA-91 

Research On Teleoperation for DFFSR Without Time Delay Based on 

Virtual Reality, pp.3611–3616 

Li, Huazhong 


◁ PSaA-92 

Research and Design of Multi-agent Model Structure for Embedded 

Robot Dog , pp.3629–3633 

Min, Huasong 

Huang, Lei 

Gan, Xun 

Wuhan Univ. of Sci. & Tech.,Hubei province 



◁ PSaA-93 

Improved S-Plane Control for Underwater Vehicles, pp.3634–3638 

WANG, Jianguo 


73


◁ PSaA-94 

Position and Singularity Analysis of 4-SPS/PS Parallel Manipulator, 

pp.3663–3668 

Zhou, Jiehua 

PENG, Xiafu 

Chair: Hou, Zhongsheng 

Poster Session PSaB 

July 7, 15:00-16:20 

xiamen Univ. 

Xiamen Univ. 


◁ PSaB-01 

Design of Intelligent Control System for Electric Vehicle Road Train, 

pp.3958–3961 

Cheng, Liangliang 

Xu, Yong 


Guilin Univ. of Electronic Sci. & Tech. 

◁ PSaB-02 

Magnetic Field Analysis of Permanent Magnet Array for Planar Motor 

Based on Equivalent Magnetic Charge Method, pp.3966–3970 

Huang, Rui 

Feng, Jian 



◁ PSaB-03 

SIFT-feature-based Accuracy Measurement Method for Microoperation 

Stage , pp.4184–4189 

Zhang, Ailong 

Inst. of Robotics & Automatic Information 

Sys.,Nankai Univ. 

Zhao, Xin 

Sun, Mingzhu 

Lu, Guizhang 

Nankai Univ. 

Nankai Univ. 

Nankai Univ. 

◁ PSaB-04 

Design and Implementation of a Synchronized Three-phase Electricity 

Acquisition Module, pp.4215–4219 

Fan, Yuling 

Zhang, Lijun 

Zhuan, Xiangtao 

Wuhan Univ. 

Wuhan Univ. 

Wuhan Univ. 

◁ PSaB-05 

Three-Dimensional Stage Self-Calibration: A General Theory Framework, 

pp.4220–4225 

Zhu, Yu 

Hu, Chuxiong 

Hu, Jinchun 




◁ PSaB-06 

Coherent integration weak target detection algorithm based on short 

time sliding window, pp.4264–4266 

Guo, Yunfei 

Zhang, Yunlong 

Xue, Anke 




◁ PSaB-07 

Frequency Estimation of Multisection Signals with Same Frequency and 

Length based on Spectrum Correlation, pp.4283–4286 

Shen, Yanlin 

Tu, Yaqing 

XIAO, WEI 

Su, Dan 

Logistical Engineering Univ. 

lLogistical Engineering Univ. 

Logistical Engineering Univ., Chongqing, P.R.C 


◁ PSaB-08 

A Dynamometer Design and Analysis for Measurement the Cutting 

Forces on Turning Based on Optical Fiber Bragg Grating Sensor, 

pp.4287–4290 

Liu, Mingyao 

Tao, Xiaoliang 

◁ PSaB-09 

On Binary Switching Finite Networks, pp.4347–4349 

Yu, Tao 

Zhou, Xingxing 

Xu, Changqing 

Wuhan Univ. of Tech. 


Zhejiang A&F Univ. 


Suzhou Univ. of Sci. & Tech.; Zhejiang A&F Univ. 

◁ PSaB-10 

A new estimate method for linear constrained systems, pp.4463–4466 

Wen, Chuanbo 

Cai, Yunze 

Xu, Xiaoming 

Shanghai Dianji Univ. 


Shanghai Jiao Tong Univesity; 

◁ PSaB-11 

Subscriber Location in CDMA Wireless Networks, pp.4390–4394 

Ali, Zahid 

Memon, Qurban 

◁ PSaB-12 

A Soft Sensor Method Based on Integrated PCA, pp.4258–4263 

Shao, Weiming 

Tian, Xue-Min 

KFUPM 

UAE Univ. 

China Univ. of Petrolieum 


◁ PSaB-13 

Selection of Effective Singular Values based on least Squares Error 

Spectrum, pp.4429–4434 

GAO, Xue-jin 

Cheng, Li 

LIU, Guang-sheng 




◁ PSaB-14 

Will scene information help realistic action recognition, pp.4532–4535 

Chen, Xian-gan 

Liu, Juan 

Liu, Haihua 

Wuhan Univ. 

WuHan Univ. 

South-Central Univ. for Nationalities 

◁ PSaB-15 

Position Control of an Electric Clutch Actuator, pp.2776–2781 

Wang, Bin State Key Laboratory of Automotive Simulation & 

Control, Jilin Univ. 

Gao, Bingzhao 

Chen, Hong 

Zhang, Zhenwei 

Yue, Hanqi 

Jilin Univ. 

Jilin Univ. 

Jilin Univ. 

State Key Laboratory of Automotive Simulation & 


◁ PSaB-16 

Stability Analysis of a Class of Hybrid Systems, pp.3971–3976 

He, Fenghua 

Yao, Yu 

Zhang, Shuqi 

Zhang, Peng 



HIT 


◁ PSaB-17 

Application Research of Neural Networks in Fruit and Vegetable Harvesting 

Robot, pp.1790–1795 

Wu, Weirong 




◁ PSaB-18 

Guaranteed Cost Control for Polynomial Fuzzy time-delay Systems by 

Sum-of-Squares Approach, pp.1806–1811 

Li, Weihong 

Wang, Weiqun 



◁ PSaB-19 

Fault Detection and Isolation for Switched Linear Systems: A Switched 

Lyapunov Function Approach, pp.1812–1817 

Zhao, Xingang Shenyang Inst. of Automation, Chinese Acad. of 

Sci. 

Li, Jian 

Ye, Dan 



◁ PSaB-20 

The Stability Analysis of Nonlinear Time Variant System and the Application 

Based on Mathematical Simulation, pp.1844–1847 

Zhang, Shuzheng 

Zhou, Fengqi 

Jiang, DeSheng 

Lu, Hongli 

Northwestern Polytechnical Univ. Xian 

northwestern polytechnical Univ. 

Unit 95948 PLA 


◁ PSaB-21 

Longitudinal Control of Intelligent Vehicle Based on Hybrid Automata 

Model, pp.1848–1853 

Ge, Yanrong 


74



Chen, Yangzhou 

Zhang, Guoxiang 



◁ PSaB-22 

Dynamic Graph Hybrid System: a Modeling Method for Complex Networks 

with Application to Urban Traffic, pp.1864–1869 


He, Zhonghe 

Shi, Jianjun 

Han, Xingguang 





◁ PSaB-23 

Distributed Collaborative Processing Based on Task Allocation for Wireless 

Sensor and Actuator Networks, pp.1887–1892 

Mo, Lei 

Xu, Bugong 



◁ PSaB-24 

A New Control Method and Stability Analysis of Linear System with 

Variable Time-delay, pp.1904–1909 

Sheng, Jie 

Li, Shuang 

Ji, Haibo 

Unversity of Sci. & Tech. of China 

USTC 


◁ PSaB-25 

Globally Exponential Synchronization of 4D Four-wing Hyperchaotic 


Liu, Xuezhen 


◁ PSaB-26 

Active disturbance rejection control for the yaw tracking for unmanned 

helicopter, pp.1915–1919 

JIANG, Zhe 


◁ PSaB-27 

ADRC Controller Used in Dynamic Positioning System of a Rescue 

Ship, pp.1942–1947 

Lei, Zhengling 

GUO, Chen 

Liu, Yang 




◁ PSaB-28 

Indirect Adaptive Fuzzy Controller Design for Vertical Direct-driven Servo 

System, pp.1953–1957 

Xu, Xing 

Cai, Tao 

Wang, GuangHui 

Xu, Zhishu 





◁ PSaB-29 

Passivity-Preserving Model Reduction for Descriptor Systems with H ∞ 

Norm Error Bounds, pp.1958–1963 

Wang, Jing - 

◁ PSaB-30 

Simulation Testing Method of V/STOL Flight Control Strategy, pp.1969– 

1975 

Tian, Ye 

He, Yang 

Li, Xinyuan 

Zhu, Jihong 

Tsinghua 

Tsinghua 

Tsinghua 


◁ PSaB-31 

The Robust Adaptive Control of Arc Furnace System Based on Backstepping 

Method, pp.1987–1992 

Liu, Xiao-he 

Gao, Nan 


School of Automation, Beijing Information Sci. & 

Tech. Univ. 

◁ PSaB-32 

Energy-based Robust Nonlinear Control of Multiple Static Var Compensators 

in Power System, pp.1993–1998 

Ding, Qingqing 

LIU, Yanhong 

Li, Jianyong 




◁ PSaB-33 

Improved Robust H∞Filtering for Polytopic Delta Operator Systems, 

pp.2011–2016 

Zhang, Ying 

Zhang, Rui 



◁ PSaB-34 

Robust Nonlinear Model Predictive Control Algorithm Based on Reduced 

Precision Solution Criteria, pp.2033–2038 

Wan, Jiaona 

Zhang, Tiejun 

Wang, Kexin 

Fang, Xueyi 

SHAO, Zhijiang 

Research Inst. of Highway ministry of Transport 

Research Inst. of Highway, Ministry of Transport 

zhejiang Univ. 



◁ PSaB-35 

Control based on variable coefficient of a nonlinear Broer-Kaup system, 

pp.2044–2047 

Ma, Yulan Beijing Tech. & Business Univ., Beijing 100048 

Xiong, Lingchun 

beijing Tech. & business Univ. 

Wang, Wei 


◁ PSaB-36 

Average Dwell-Time Approach to Stabilization of Networked Systemswith 

Actuator Saturation, pp.2097–2102 

Zhou, Rujuan 

Zhang, Xiaomei 

Nantong Univ. 

Nantong Univ. 

◁ PSaB-37 

Development of intelligent control systems on disaster prevention and 

energy saving of underground space, pp.2103–2108 

He, Yi 

Li, Changbin 

Wu, Aiguo 

Tianjin Univ. 

Tianjin Univ. 

Tianjin Univ. 

◁ PSaB-38 

Research on Parameter Design of Tank Stabilizers of Ship, pp.2109– 

2112 


Liang, Guolong 

Jin, Hongzhang 

Zhao, Yufei 

Zhang, Xun 






◁ PSaB-39 

Research on Trajectory Scheduling and Control Method of UUV for Terrain 

Survey Mission, pp.2113–2118 

Chen, Tao 

Yan, Zheping 


Zhao, Yufei 





◁ PSaB-40 

Fractional order QFT Controller for Nonlinear Systems, pp.2136–2141 

Meng, Li 


◁ PSaB-41 

Data-based Predictive Control for Networked Control Systems, 

pp.2302–2305 

Wang, Yan 

Ji, Zhicheng 


Southern Yangtze Univ. 

◁ PSaB-42 

Cross-Layer Energy Efficiency Design in Wireless Sensor Networks, 

pp.2312–2317 

Tang, Xiufang 

Wang, Yan 

Institution Electrical Automation 


◁ PSaB-43 

A New Method of Evolving Hardware Design Based on IIC Bus and 

AT24C02, pp.104–107 

Li, Kangshun 

Chen, Yan 

Liu, Hezhuan 

◁ PSaB-44 


South China Agricultural Univ. 


75


Collaborative Monitoring of Underground Gas disaster Based on Fuzzy 

Information Fusion, pp.4230–4234 

Ma, Fengying 

Ma, Fengying 



◁ PSaB-45 

The Prototype IEEE 1451.4 applied in the IOT, pp.4241–4244 

LI, ZHI 

Qin, Chang-ming 

Zhang, Huo 

guilin universuity of electronic & Tech. 

the Guilin Univ. of electronic & Tech. 


◁ PSaB-46 

A Cooperative Framework for Target Tracking in Wireless Sensor Networks 

, pp.4249–4254 

Li, Xun 

Wang, Jianwen 



◁ PSaB-47 

Distributed Luenberger Observers for Linear Systems, pp.4267–4271 

Ni, Wei 

Wang, Xiaoli 

Yang, Jie 

Chun, Xiong 





◁ PSaB-48 

Distributed Extended Kalman Filter based on Consensus Filter for Wireless 


Long, Hui 

Qu, Zhihua 

Fan, Xiaoping 

Liu, Shaoqiang 





◁ PSaB-49 

Improved CPHD Filtering With Unknown Clutter Rate, pp.4326–4331 

Zheng, Xuetao 

Song, Liping 

Xidian Univ. 

Xidian Univ. 

◁ PSaB-50 

Reduced-rank Space-Time Adaptive Processing to Radar Measure Data, 

pp.4332–4336 

Wen, Xiao-Qin 


◁ PSaB-51 

Large Current Measurements Using a Fibre Optics Current Sensor, 

pp.4337–4340 

Wang, Li 


◁ PSaB-52 

An Optimal Parking Space Search Model Based on Fuzzy Multiple Attribute 

Decision Making, pp.4350–4355 

Yu, Shouyuan Shenzhen Inst.s of Advanced Tech.,Chinese Acad. 


LI, Baopu 

Zhang, Qi 



Shenzhen Inst.s of Advanced Tech. 


◁ PSaB-53 

A New Method to Eliminate Negative Frequency Interference Based on 

Wavelet Transformation and Grey Correlation Theory, pp.4356–4361 

Mao, Yuwen 

Tu, Yaqing 

Yang, Huiyue 



Univ. of logistical enginering 

◁ PSaB-54 

Analysis of Phase Difference Tracking Methods for Signal of Coriolis 

Mass Flowmeter, pp.4368–4373 

Yi, Peng 

Tu, Yaqing 

Xie, Min 

Shen, Ting’ao 

The Logistical Engineering Univ. 




◁ PSaB-55 

A multi-scale analysis approach to selecting signals related with combustion 

status, pp.4374–4377 

Hao, Zulong 


◁ PSaB-56 

An Actuator Real-time Placement Algorithm Based on Regular Hexagonal 

Grid for Wireless Sensor and Actuator Networks, pp.4378–4384 

LIU, Xinhua 


◁ PSaB-57 

Improved Pattern Amendment Inversion Algorithm for Dust Fast Realtime 

Measurement, pp.4423–4428 

Ma, Fengying 


◁ PSaB-58 

Fault-tolerant Data Aggregating using Median Filtering in Cluster-based 

Homogeneous WSN, pp.4441–4445 


Tan, Zongkai 

Fan, Xiaoping 

Long, Hui 

Li, Yongzhou 

Liu, Limin 





automatication institution 

Central South Univ., Changsha 

◁ PSaB-59 

A Street Lamp Clustered-control System Based on Wireless Sensor 

and Actuator Networks, pp.4484–4489 

LIU, Xinhua 


◁ PSaB-60 

Research on blind detection of image splicing based on run length matrix 

combined properties, pp.4545–4550 

Liu, Han 


◁ PSaB-61 

CRM System Design of Financial Equipment Enterprises Based on XM- 

L , pp.4141–4146 

Cui, Wenhua 

Liu, Xiaobing 





◁ PSaB-62 

An improved particle swarm optimization based on wolves’ activities 

circle, pp.4557–4562 

Wei, Bin 


◁ PSaB-63 

A New Color Image Sharing Scheme with Natural Shadows , pp.4568– 

4573 

Chiu, Pei-Ling 

Lee, Kai-Hui 

Peng, K.-W. 

Cheng, S.-Y. 

Ming Chuan Univ. 




◁ PSaB-64 

Research and Application of Image Enhancement, pp.4596–4600 

Zhang, Ying 


◁ PSaB-65 

A particle filter tracking algorithm based on adaptive feature fusion s- 


XU, Fen 

North-china Univ. of Tech. 

◁ PSaB-66 

Solving Integrative Matching Model of Inventory in Continuous Casting 

and Hot Rolling Processes by Improved Genetic Algorithm, pp.5133– 

5138 

Li, Haitao 


◁ PSaB-67 

Dynamic Model and Balancing Control for Two-Wheeled Self-Balancing 

Mobile Robot on the Slopes, pp.3681–3685 

Peng, Kui 


Zuo, Guoyu 




◁ PSaB-68 

A Novel Scenario of Task Planning for Multi-robot Collaboration System, 

pp.3770–3775 

Shi, Zhiguo 


76



Hu, Kaihang 

Tu, Jun 

WEI, Junming 

WANG, Zhiliang 

Univ.of sci.&Tech.Beijing 


Australian National Univ. 


◁ PSaB-69 

Residual Vibration Suppression Using Off-line Learning Input Shaping 

Method for a Flexible Joint Robot, pp.3858–3863 

Qiang, Yanhui 


◁ PSaB-70 

Airfoil design and aerodynamic force testing for flapping-wing Micro Air 


Xia, Wen 

Su, Gang 

Shengyang Ligong Univ. 

Shenyang Inst. of Automation Chinese Acad. of 

Sci. 

◁ PSaB-71 

Development of Flapping-wing Micro Air Vehicle in Asia, pp.3939–3942 

Zhang, Weiping 


◁ PSaB-72 

Comparison of Several Communication Methods between Upper Computer 

and CompactRIO, pp.3962–3965 

Chen, Wanmi 

Su, JinXia 



◁ PSaB-73 

An Approach for Real-time Urban Traffic State Estimation by Fusing 

Multisource Traffic Data, pp.4077–4081 

Zhang, Ning 

Xu, Jianmin 

Lin, Peiqun 

Zhang, Minjie 





◁ PSaB-74 

Dynamic Game Model with Strategic Element on Cooperation and Confront, 

pp.4098–4102 

Dai, Yeming 

GAO, Hongwei 

WANG, Guirong 

qingdao Univ. 

Qingdao Univ. 

Qingdao Univ. 

◁ PSaB-75 

Dynamic Group Method Based on Health Management Applied to Mission 

Tasking for Multi UAVs, pp.4118–4121 

Hu, Wei 

Liu, Gui 

Shenyang aerospace Univ. 


◁ PSaB-76 

The application of RSA digital signature algorithm in the Power Dispatching 

work ticket Transmission system, pp.4132–4134 

Kang, Chaohai 


◁ PSaB-77 

Particle swarm optimization algorithm in the quantitative analysis of N- 

ear Infrared Spectroscopy, pp.4147–4150 

Ma, Bibo 

China Agricultural Univ. 

◁ PSaB-78 

The research of a programmable and high precision gas pressure generator, 

pp.4155–4159 

Li, Xuezhe 

North China Inst. of Sci. & Tech. 

◁ PSaB-79 

Research on the Orientation Method of HMD Based on Image Processing, 

pp.4160–4162 

Wu, Weiling 

naval aviation engineer Inst. 

◁ PSaB-80 

Development of Portable Plant Components Measurement Instrument 

Based on Near-infrared Spectroscopy, pp.4163–4167 

Ma, Hao 


◁ PSaB-81 

High Precision Attitude Estimation Algorithm Using Three Star Trackers, 

pp.4168–4173 

Chen, Binglong 

Harbin Inst. of Technonlogy 

Geng, Yunhai 

Yang, Xu 



◁ PSaB-82 

Application of Blind Sources Separation in Plant Leaves Classification, 

pp.4174–4179 

WU, Ying 

GUO, Tian-tai 

JIANG, Jie-wei 




◁ PSaB-83 

Defect depth automation measurement based on image processing for 

TOFD parallel scanning, pp.4180–4183 

Shan, Mingguang 

Liu, Shengchun 



◁ PSaB-84 

The Discussion of Semi-structured Sample Modeling on Expanding 

2.5D Method, pp.4195–4200 

Yu, Shun 

Zhang, Tie 



◁ PSaB-85 

A Single Chip Multi-functional DDS Waveform Generator based on FP- 

GA with SOPC Design Flow, pp.4206–4210 

Ruan, Yue 

Tang, Ying 

Yao, Wen-ji 

Wang, Zhang-quan 

Xu, Sen 

Zhejiang Shuren Univ. 





◁ PSaB-86 

Pulse Monitoring System Based on Feedback Algorithm, pp.4211– 

4214 

An, Peng 

Zeng, Ming 

Ningbo Univ. of Tech. 


◁ PSaB-87 

The Smulation and Model of Twisted Lever-arm Effect in Transferalignment 

of Stradown Inertial Navigation, pp.4235–4240 

Pan, Shuang 

Navial Submarine Acad. 

◁ PSaB-88 

Monitoring System for Forest Fire Based on Wireless Sensor Network, 

pp.4245–4248 

Zhu, Yingli 

Jiangxi Sci. &Tech. Normal Univ., 

◁ PSaB-89 

Noisy Blind Source Separation Based on Adaptive Noise Removal, 

pp.4255–4257 

Tang, Hui 


◁ PSaB-90 

Research on Wireless Communication Based on GPRS in Greenhouse, 

pp.4272–4276 

Shi, Bing 

Changzhou Univ. 

◁ PSaB-91 

A robust and optimal search tactic using multiple looks, pp.4297–4304 

Nguyen, Bao 

Bourque, François-Alex 

Defence R&D Canada 

Department of National Defence 

◁ PSaB-92 

Design and Implementation on SOAP-Based Things Management Protocol 

for Internet of Things, pp.4305–4308 

Dai, Guiping 

Beijng Univ. of Tech. 

◁ PSaB-93 

Study on A Real-time Optimal Multi-sensor Asynchronous Data Fusion 


Qi, Guoqing 


◁ PSaB-94 

The Research of Environmental Monitor System in Brewage House of 

Alcohol Factory Based on WSN, pp.4401–4404 

Zhang, Kan 

Wang, Weihong 

BeiHang Univ. 

Beihang Univ. 

77


Chair: Huo, Wei 

Poster Session PSaC 

July 7, 16:30-17:50 


◁ PSaC-01 

Recognition of Crude Drugs Based on SVM, pp.4688–4690 

Ming, Zhiyuan 

Yunnan Univ. of Nationalities 

◁ PSaC-02 

Composite Materials Evaluation by the Wavelet Transform and Fuzzy 

Pattern Recognition, pp.4701–4704 

Gao, Zhiqi 


◁ PSaC-03 

A Fast Stereo Matching Algorithm Used in Target Recognition of Mobile 

Robot, pp.4771–4774 

Yu, Naigong 

Lin, Jia 

Huang, Can 






◁ PSaC-04 

Smelting Process Smoke Detection using Multivariate Image Analysis, 

pp.4865–4868 

Zhang, Hongwei 

Song, Zhihuan 



◁ PSaC-05 

Optical Flow Estimation with Parameterized Data Term and Warping, 

pp.4633–4637 

Xu, Jintao 

Feng, Zuren 

Lu, Na 




◁ PSaC-06 

Hardware Design of The Wireless Automatic Meter Reading System 

Based on GPRS, pp.4536–4540 

Zhang, Ying 


◁ PSaC-07 

ANALYSIS OF MULTI-BIOMETRIC ENCRYPTION AT FEA-TURE- 

LEVEL FUSION, pp.4563–4567 

Fu, Bo 


◁ PSaC-08 

Classification Network of Gastric Cancer Construction based on Genetic 

Algorithms and Bayesian Network, pp.4676–4681 

He, Yiheng 

BeiJing Univ. of Tech. 

◁ PSaC-09 

Distributional Clustering Using Nonnegative Matrix Factorization, 

pp.4705–4711 

Zhu, Zhenfeng 

Ye, Yangdong 



◁ PSaC-10 

Error Concealment in JPEG2000 Coded Images, pp.4760–4765 

Bou Matar, Amjad 

Memon, Qurban 

UAE Univ. 

UAE Univ. 

◁ PSaC-11 

An Alternative Clustering Algorithm based on IB Method, pp.4791–4796 

Lei, Yang 

Ye, Yangdong 

Lou, Zhengzheng 




◁ PSaC-12 

Bifurcation Control Design for Simplified HH Neuron Model: A Physiological 

Approach, pp.4953–4956 

Li, Huiyan 

Wang, Jiang 

Jin, Qitao 

Deng, Bin 

Wei, Xile 

Che, Yan-Qiu 

◁ PSaC-13 


Tianjin Univ. 

Tianjin Univ. 

tianjin Univ. 

Tianjin Univ. 


Change excitability of Morris-Lecar Model via Physiological Bifurcation 


Li, Huiyan 

Wang, Jiang 

Jin, Qitao 

Deng, Bin 

Wei, Xile 

Che, Yan-Qiu 


Tianjin Univ. 

Tianjin Univ. 

tianjin Univ. 

Tianjin Univ. 


◁ PSaC-14 

Realization of Quantum Hadamard Gate Based on Lyapunov Method, 

pp.5096–5101 

Wen, Jie 

Cong, Shuang 

Zou, Xubo 




◁ PSaC-15 

Cooperative Game Theoretical Research for Aircraft Deicing Operation 

Scheduling, pp.2407–2411 

Xing, Zhiwei 

Lian, Guan 

Civil Aviation Univ. Of China 


◁ PSaC-16 

A Kind of Robust Controller for Uncertain Linear System LQ Tracking 

Problem, pp.2436–2440 

Yang, Yang 

GUO, Chen 

Shen, Zhipeng 

Du, Jialu 



dalian Maritime Univ. 


◁ PSaC-17 

H ∞ Model Reduction for Positive 2-D Discrete Systems in Roesser 

Model, pp.1733–1738 

Wang, Cuihong 

Shanxi Normal Univ. 

◁ PSaC-18 

Optimal Feedback Reentry Guidance of Hypersonic Vehicle Based on 

Improved Gauss Pseudospectral Method, pp.2457–2462 

Sun, Yong 




◁ PSaC-19 

The stability analysis of the machine drive system, pp.2463–2467 

Zhang, Huiduan 


◁ PSaC-20 

Studied on Anti-interference Control Based on the Disturbance Observer, 

pp.2503–2505 

He, Naibao 

Gao, Qian 



◁ PSaC-21 

Control Three-Phase Star-Connected Switch Three-Level Rectifier 

Considering Alternative Performance Indices, pp.2506–2511 

Zhang, Shaoru 

Hebei Normal Univ. 

◁ PSaC-22 

Nonlinear MPC for Attitude System of Miniature satellite using Multiple 

MEMS Actuators, pp.2523–2528 

Jiang, Yu 


Jin, Jing 




◁ PSaC-23 

Structural Properties of Multi-Agent Linear Systems with Applications 

to Leader-Following Consensus, pp.2550–2555 

Ni, Wei 

Wang, Xiaoli 

Yang, Jie 

Zhao, Ping 





◁ PSaC-24 

Prediction Model of Sintering Burden Based on Information Entropy and 

Chaos PSO Algorithm, pp.2566–2569 

Qin, Ling 

Wuhan Polytechnic Univ. 

78



◁ PSaC-25 

Establishment and Optimization of Prediction Model for Recovery Rate 

of Alloying Elements, pp.2588–2591 

Fang, Xiaoke 

Yu, Liye 

Zhang, Wenle 

Wang, Jianhui 


State Key Laboratory of Hybrid Process Industry 

Automation Sys. & Equipment Tech. 



◁ PSaC-26 

Robust adaptive control for a class of switched nonlinear systems with 

unmodeled dynamics, pp.2636–2641 

Zhu, Baicheng 


An, Yao 




◁ PSaC-27 

A New Wavelet Coefficients Correlation Denoising Method Applied in 

Fault Detection, pp.2657–2660 

Xiao, Qian 


◁ PSaC-28 

Trajectory tracking control for mobile robot based on the fuzzy sliding 

mode, pp.2706–2709 

Xie, Mujun 

LI, Li-ting 






◁ PSaC-29 

Path Following of Underactuated UUV Based on Backstepping, 

pp.2734–2739 

Yan, Zheping 

Chi, Dongnan 

Jia, Heming 

Zhou, Jiajia 





◁ PSaC-30 

Feedback Linearization Robust Control of Arc Furnace Electrode Regulator 

System Based on dSPACE Simulation, pp.2740–2745 

Liu, Xiao-he 

Gao, Yuan 



◁ PSaC-31 

Decentralized Controller Design Based On 3-Order Active-disturbancerejection-control, 

pp.2746–2751 

Tian, Lingling 

Li, Donghai 

Huang, Chun E 

Beihang Univ. 



◁ PSaC-32 

Nonlinear robust control with high gain observer for governor of hydroturbine 

generator sets, pp.2752–2757 

Liu, Song 

Li, Donghai 

Huang, Chun E 

Guodian United Power Co. Ltd 



◁ PSaC-33 

Receding horizon tracking control for nonlinear discrete-time systems, 

pp.2817–2821 

Wang, Hai-Hong 

Hu, Nai-Ping 

QingDao Univ. of Sci. & Tech. 


◁ PSaC-34 

Hazard Detection and Avoidance for Planetary Landing Based on Lyapunov 

Control Method, pp.2822–2826 

Zhu, Shengying 

CUI, Pingyuan 

Hu, Haijing 




◁ PSaC-35 

Energy-Shaping and Passivity-based Control of Three-Phase PWM 

Rectifiers, pp.2844–2848 

Yu, Haisheng 

Qingdao Univ. 

◁ PSaC-36 

An AQM Scheme Based on Adaptive Weight Cascaded PID Controller, 

pp.2849–2854 

Du, Fei 

Sun, Jinsheng 



◁ PSaC-37 

An AQM Algorithm Based on Variable Structure PID Controller, 

pp.2855–2860 

Du, Fei 

Sun, Jinsheng 



◁ PSaC-38 

Compressor Active Surge Controller Design based on Uncertainty and 

Disturbance Estimator, pp.2908–2912 

Xiao, Lingfei 

Zhu, Yue 



◁ PSaC-39 

Data-based Dynamic Characteristic Modeling and Tracking Control for 

High-speed Train, pp.2913–2917 

Gao, Shigen 

Qi, Shuhu 

Dong, Hairong 

Ning, Bin 

Li, Li 

BJTU 




Beijing MTR 

◁ PSaC-40 

M-Nearest Neighbor Selection for Two-Phase Test Sample Representation 

in Face Recognition, pp.4661–4666 

Ma, Xin Jun 

Wu, Ning 

Liang, TianCai 




◁ PSaC-41 

Affine Motion Segmentation from Feature Point Trajectories using Rank 

Minimization, pp.4667–4670 

YANG, Min 


◁ PSaC-42 

A Simple String Matching Method for Shape Recognition, pp.4696– 

4700 

Wu, Wen-Yen 

I-Shou Univ. 

◁ PSaC-43 

Pose Detection of Partly Covered Target in the Micro-Vision System, 

pp.4721–4725 

Su, Jin 

Huang, Xinhan 

Wang, Min 


Huazhong Univ. 0f Sci. & Tech. 


◁ PSaC-44 

Air-Ground Vehicle Detection using Local Feature Learning and Saliency 

Region Detection, pp.4726–4731 

Xu, Qinghan 

JIN, Lizuo 

Fei, Shumin 

Jie, Feiran 




Sci. & Tech. on Electro-optic Control Laboratory 

◁ PSaC-45 

An Indoor Quadrotor Locating and Object-Following Algorithm using 

Monocular Vision, pp.4747–4753 

Chen, Xiaolong 

Tang, Qiang 

Che, Jun 

Flight Automatic Control Research Inst. 



◁ PSaC-46 

Facial Expression Recognition in Video Sequences, pp.4766–4770 

Wan, Chuan 

Tian, Yantao 

Liu, Shuaishi 

Jilin Univ. 

Jilin Univ. 

Jilin Univ. 

◁ PSaC-47 

Sequence Detection of Planetary Surface Craters From DEM Data, 

pp.4775–4779 

Yu, Zhengshi 


79



CUI, Pingyuan 



◁ PSaC-48 

A video tracking method based on Niche Particle Swarm Algorithm- 

Particle Filter, pp.4780–4783 

Li, Xin 

Chen, Wenjie 

Shang, Zengguang 



The Chinese people’s liberation army 

◁ PSaC-49 

An Efficient Approach of 3D Ear Recognition, pp.4784–4790 

Wang, Kai 

Mu, Zhichun 

He, Zhijun 


School of Automation & Electrical Engineering, 


China Nuclear Power Engineering Co.,Ltd 

◁ PSaC-50 

A new method on Solving Correlation Dimension of Chaotic Timeseries, 

pp.4820–4824 

Qiao, Meiying 

Ma, Xiaoping 

◁ PSaC-51 

3D Ear Modeling Based on SFS, pp.4837–4841 

Liu, Cong 

Mu, Zhichun 

Wang, Kai 

Zeng, Hui 

China Univ. Mining & Tech. 







◁ PSaC-52 

A Target Detection Method in Dynamic Scene Based on Harris Algorithm 

with Sub-block Threshold, pp.4842–4847 

Lu, Jinghua 

LEI, Yinghui 

Chen, Jie 

Zhang, Juan 





◁ PSaC-53 

Robust Visual Tracking with Classifier-like Appearance Model and Entropy 


Song, Yu 

Li, Qingling 

Yan, Deli 

Kang, Yifei 




beijing jiaotong Univ. 

◁ PSaC-54 

Abnormal Detection based on Gait Analysis, pp.4859–4864 

Wang, Chao 

Wu, Xinyu 

Li, NanNan 

Chen, Yen-Lun 

Inst. of Advanced Integration Tech. 


shenzhen Inst. of advanced Tech. of chinese Acad. 




◁ PSaC-55 

An Improved Kernelized Discriminative Canonical Correlation Analysis 

and Its Application to Gait Recognition, pp.4869–4874 

WANG, KEJUN 

YAN, TAO 



◁ PSaC-56 

Head Detection Based on 21HT and Circle Existence Model, pp.4875– 

4880 

Zhao, Min 

Sun, Dihua 

Tang, Yi 

He, Hengpan 




ChongQing Univ. 

◁ PSaC-57 

A Three Dimension Reconstruction method on a kind of Micro and Thin 

Laser Seam, pp.4881–4886 

Wang, Liwei 


Chen, Haiyong 

Sun, Hexu 





◁ PSaC-58 

A Novel 3D Ear Reconstruction Method Using a Single Image, 

pp.4891–4896 

Li, Chen 

Mu, Zhichun 

Zhang, Feng 

Wang, Shuai 




The First Research Inst. of Ministry of Public 

Security of P.R.C 


◁ PSaC-59 

Spherical Terrain Matching for SLAM in Planet Exploration, pp.4907– 

4911 

Pan, Haining 

CUI, Pingyuan 

Wang, Huan 

◁ PSaC-60 

Good Resolutions for Hough Transform, pp.4916–4920 

Tu, Chunling 

Van Wyk, Barend 

Du, Shengzhi 




Tshwane Univ. of Tech. 


Univ. of South Africa 

◁ PSaC-61 

Integrating Method Improving HT-Butterfly Based Segment Detection, 

pp.4921–4925 

Du, Shengzhi 

Tu, Chunling 



◁ PSaC-62 

A new method of texture synthesis based on gradient structure information 

searches along a spiral path, pp.4931–4935 

Tan, Yongqian 

Pu, Yuanyuan 

Qian, Wenhua 

Xu, Dan 

Yunnan Univ. 

Yunnan Univ. 

Yunnan Univ. 

Yunnan Univ. 

◁ PSaC-63 

Mechanical Model of Wheat and Vibration Analysis, pp.4965–4968 

Xu, Qicheng 

Sun, Changchun 

Sun, Yazhen 

shenyang jianzhu Univ. 

Shenyang Jianzhu Univ. 


◁ PSaC-64 

IMPROVED DEMPSTER AND SHAFER THEORY TO FUSE REGION 

AND EDGE BASED LEVEL SET FOR ENDOCARDIAL CONTOUR DE- 

TECTION, pp.5013–5018 

Gu, Jason 

Dalhousie Univ. 

◁ PSaC-65 

Noise Reduction of sEMG in SVD Based on Neural Network, pp.5035– 

5039 

Zhang, Li 

Li, Yang 

Xu, Zhuojun 

Tian, Yantao 

Jilin Univ. 

Jilin Univ. NangLing, school of communication 

engineering 

Jilin Univ. 

Jilin Univ. 

◁ PSaC-66 

A New Method for Perturbation Experimental Design in Gene Regulatory 

Network Identification, pp.5090–5095 

Wang, Xin Acad. of Mathematics & Sys. Sci., Chinese Acad. 


◁ PSaC-67 

A wireless data acquisition system designed for greenhouse based on 

ZigBee, pp.4405–4408 

Li, Peng 

Wu, Yunjie 

◁ PSaC-68 

Beihang Univ. 

Beihang Univ. 

80



An Improved Median Filter Algorithm Based On Light Sensor, pp.4414– 

4417 

Yu, Jianjun 

Wang, Guanwei 

Yang, Qiong 





◁ PSaC-69 

The Anti-interference analysis and design for Mode S reply Communication 

of Integrated TCAS, pp.4467–4471 

Huang, Yi 

Shi, ZhongKe 



◁ PSaC-70 

Remarks on Outliers in Time Series of Stock Prices Based on Density, 

pp.4523–4526 

ZHAO, Qingjiang 

Kunming Univ. 

◁ PSaC-71 

Support Vector Machine Based Aircraft Ground Icing Type Classification 

Forecast, pp.4541–4544 

Xing, Zhiwei 

Zhang, Hui 



◁ PSaC-72 

Fusion Algorithm of Infrared and Visible Images Based on Local Energy 

Using NSCT, pp.4579–4582 

DAI, Wen-zhan 

TAN, Libo 

Yang, Aiping 


Inst. of Automatic Control, Zhejiang Sci-Tech Univ. 


◁ PSaC-73 

Fingerprint Orientation Reconstruction from Minutiae Points, pp.4583– 

4587 

Chu, Zhuang 

Yuan, Guosen 

Zhang, Xiyu 

Han, Lin 

Henan Mechanical & Electrical Engineering 

College 


College 

CNNC CHINA NUCLEAR POWER ENGINEERING 

Co 

Xin Xiang Medical Univ. 

◁ PSaC-74 

Image fusion via wavelet transform based on local contrast, pp.4588– 

4591 

Zhang, Daosong 

Pan, Haipeng 



◁ PSaC-75 

An Algorithm for Human Eye Location in Complex Background Based 

on Image Processing, pp.4592–4595 

Wu, Minjie 

Mu, Ping’an 

Dai, Shuguang 

Zhang, Cai-Yan 


College of Optical & Electronics Engineering 



◁ PSaC-76 

Adaptive Fuzzy Apporach to Background Modeling using PSO and 

KLMS, pp.4601–4607 

Li, Zilong 


◁ PSaC-77 

Optimal Motion Control for IBVS of Robot, pp.4608–4611 

Gao, Cheng 

Univ. 

◁ PSaC-78 

Machine-Vision Based Preceding Vehicle Detection Algorithm: A Review, 

pp.4617–4622 

Zhou, Jun-jing 

Duan, JianMin 

Yu, Hongxiao 




◁ PSaC-79 

Traffic Sign recognition Using Dual Tree-Complex Wavelet Transform 

and 2D Independent Component Analysis, pp.4623–4627 

Gu, Mingqin 


Cai, Zi-xing 


◁ PSaC-80 

Feature Detection and Matching for Traffic Sign Images, pp.4628–4632 

Li, Lei-Min 

LI, Li 

Tong, Ru-qiang 

Li, Pei-xi 

Southwest Univ. of Sci. & Tech. 

Changchun Univ. of Sci. & Tech. 

School of Information Engineering , Southwest 

Univ. of Sci. & Tech. 


◁ PSaC-81 

Machine Vision Based Localization of Intelligent Vehicle, pp.4638–4643 

Wang, Fei 

Duan, Jianmin 

ZHENG, Banggui 




◁ PSaC-82 

Divide and Conquer Strategy for Spectral Clustering, pp.4644–4648 

Jia, Zhixian 

Xinjiang Univ. of Finance & Economics 

◁ PSaC-83 

Improved Algorithm for the k-means Clustering, pp.4717–4720 

ZHANG, SHENG 

Wang, Shouqiang 

Shandong Jiaotong Univerisity 

ShanDong JiaoTong Univ. 

◁ PSaC-84 

The Motion Control of Mobile Self-Balancing Robot Base on Vision- 

Teleoperation, pp.4737–4740 


Li, Jun 

Xu, Feng 

Yu, Naigong 

Beijing Univ. of Techology 




◁ PSaC-85 

A Complex Number Algorithm of Image-Hiding, pp.4815–4819 

Wang, Hongxin 

Harbin Commerce Univ. 

◁ PSaC-86 

A Blind Watermarking Algorithm Based on Singular Value Decomposition 

and Quantification, pp.4887–4890 

Chen, Gang 

Ma, Huijie 

Chen, Ning 

Jianghan Univ.,Wuhan ,430056,China 



◁ PSaC-87 

An Imaging Method for 360-Degree Panoramic Bird-Eye View, 

pp.4902–4906 

Yu, Chunxuan 

Fang, Xiang 

Tang, Shuangze 

Wu, Mengzhou 





◁ PSaC-88 

Transmission Rate Calculating and Analysis of Gear System Based On 

ECT, pp.4912–4915 

Li, Junjun 

SHI, Shiying 

Liu, Houyi 

shandong jiaotong Univ. 

Shan-dong Communication Univ. Shan-dong, JN, 

250023 


◁ PSaC-89 

A Microarray Image Gridding Method Based on Image Projection Difference 

Sequences Analysis and Local Extrema Searching, pp.4961– 

4964 

Liu, Jun 

Shenyang Ligong Univ. 

◁ PSaC-90 

Optimal Control Strategy for an Multi-regional Epidemic Model, 

pp.5007–5012 

Sang, Zi 

Qiu, Zhipeng 

Kong, Qingkai 

Zou, Yun 

Nanjing Univ. of Sci. & Technoledge 

Nanjing Univiersity of Sci. & Tech. 



◁ PSaC-91 

Design of a 5mW Capacitive Accelerometer based on MEMS and C- 

81


MOS Technology, pp.5102–5107 

Ruan, Yue 

Ji, Lujun 

Tang, Ying 

Yao, Wen-ji 


Xu, Sen 







◁ PSaC-92 

Attitude And Altitude Instrument Based On DSP, pp.5108–5111 

Xu, Peng 

Li, Baokui 

Geng, Qingbo 

Fei, Qing 





◁ PSaC-93 

Study of Strong Tracking Augmented Unscented Kalman Filter in Integrated 

Navigation System., pp.5112–5115 

Xu, Dexin 

Wang, Lu 

Li, Guangchun 

Ma, Tao 





◁ PSaC-94 

Research of Fault Diagnosis Based on Matching Pursuit and Biomimetic 


Wang, Xiaozhe 

Wang, Jinping 



82


Technical Program: Sunday Sessions 

Sunday, July 8, 2012 

PD-1 8:30-10:10 Room 305 

Plenary Panel Session 1 

Chair: Feng, Gang 

City University of Hong Kong, China 

◮ PD-1 8:30-10:10 


Chen, Ben M. 

Fu, Li-Chen 

Hu, Xiaoming 

Jiang, Zhong-Ping 


National Taiwan Univ. 

Royal Institute of Technology 

Polytechnic Inst. of New York Univ. 

PD-2 10:30-12:10 Room 305 




◮ PD-2 10:30-12:10 


Chai, Tianyou 

Kosuge, Kazuhiro 

Lueth, Tim C. 

Luh, Peter B. 

Xu, Yangsheng 

Northeast Univ., China 

Tohoku Univ. Japan 

Technical Univ. of Munich, Germany 

Univ. of Connecticut, USA 


SuA01 13:30–15:30 Room 203A 

Stability and Stabilization (I) 

Chair: Guo, Yuqian 

Co-Chair: Chen, Ning 



◮ SuA01-1 13:30–13:50 

Stability Analysis for Continuous-time Three-dimensional Systems with 

State Saturation, pp.1282–1286 

Chen, Dongyan 

Ding, Yanhui 

Shi, Yujing 



School of Applied Sci., Harbin Univ. of Sci. & Tech. 

◮ SuA01-2 13:50–14:10 

Exponential stabilization of neutral-type neural networks withinterval 

non-differentiable and distributed time-varying delays, pp.1293–1298 

Weera, Wajaree 




◮ SuA01-3 14:10–14:30 

Finite-time stabilization for a class of nonlinear switched time-delay systems, 

pp.1826–1831 

Wang, Ruihua 

Zong, Guangdeng 

Hou, Linlin 




◮ SuA01-4 14:30–14:50 

The stability analysis of neutral neural network systems with distributed 

delays, pp.1876–1880 

Wu, Xueli 

Li, Yang 

An, Hui 

Wang, Yuehua 





◮ SuA01-5 14:50–15:10 

Quadratic Stability of Reset Control Systems with Delays, pp.2268– 

2273 

Guo, Yuqian 

Xie, Lihua 



◮ SuA01-6 15:10–15:30 

Parametric Stabilization of Large-Scale Nonlinear Systems, pp.2125– 

2129 

Chen, Ning 

Shen, Xiaoyu 

Gui, Weihua 




SuA02 13:30–15:30 Room 203B 

Multi-Agent Systems (I) 

Chair: Cai, Yunze 

Co-Chair: Zhang, Zhenning 



◮ SuA02-1 13:30–13:50 

Multi-agent Competitive Control Systems, pp.2263–2267 

Zhang, Zhenning 




◮ SuA02-2 13:50–14:10 

Flocking of Multi-Agents Based on Consensus Protocol and Pinning 


He, Chenlong 

Feng, Zuren 

Ren, Zhigang 




◮ SuA02-3 14:10–14:30 

Distributed Event-triggered Tracking Control of Multi-Agent Systems 

with Active Leader, pp.1453–1458 

Zhang, Yanqiong Acad. of Mathematics & Sys. Sci., Chinese 


Hong, Yiguang 


◮ SuA02-4 14:30–14:50 

On Necessary and Sufficient Conditions of the Consensusabilityfor 

Second-order Discrete Multi-agent Systems, pp.1727–1732 

Zhu, Jiandong 

Sun, Xue 



◮ SuA02-5 14:50–15:10 

Distributed Filtering Basing Consensus for the Local Strongly Coupled 


Cai, Yunze 

Wang, Hua O. 

Xu, Xiaoming 


Boston Univ. 


◮ SuA02-6 15:10–15:30 

Decentralized Sweep Algorithm of Multi-agent Systems with Adaptive 

Workload Assignment, pp.4320–4325 

Zhai, Chao 

Hong, Yiguang 



SuA03 13:30–15:30 Room 203C 

Image Processing 

Chair: Yang, Wankou 

Co-Chair: Yu, Xiao 



◮ SuA03-1 13:30–13:50 

Compression of MR-Thermometry Images Using Reduced-Order 

Karhunen-Lo‘eve Basis, pp.4655–4660 

Niu, Ran 

GE Global Research (Shanghai) 

◮ SuA03-2 13:50–14:10 

Local Linear Regression Classifier for Image Recognition, pp.4732– 

4736 

Yang, Wankou 

Sun, Changyin 

Ricanek, Karl 

XIA, Jianwei 



UNC Wilmingtong 

Liaocheng Univ. 

◮ SuA03-3 14:10–14:30 

Key Frames-Based Video Super-Resolution Using Adaptive Overlapped 

Block Motion Compensation, pp.4712–4716 

Ge, Jing 

Zhang, Boyang 

Liu, Ju 




◮ SuA03-4 14:30–14:50 

Fast Monotonic Blind Deconvolution Algorithm for Constrained TV 

Based Image Restoration, pp.4682–4687 

Liu, Haiying 

Lu, W.-S. 

Fu, Yanan 

Cheng, Yu 

Yan, Tingfang 

Li, Teng 

Shandong Unversity 

Univ. of victoria 





83




◮ SuA03-5 14:50–15:10 

Application of Artificial Immune Algorithm in Image Segmentation 

Based on Immune Field, pp.4691–4695 

Yu, Xiao 

Fu, Dongmei 

Yang, Tao 




◮ SuA03-6 15:10–15:30 

A Mixed Edge Based Text Detection Method by Applying Image Complexity 


Li, Minhua 

Bai, Meng 



SuA04 13:30–15:30 Room 203D 

Intelligent Managenment and Decision Making 

Chair: Wang, Ya-hui Beijing Univ. of Civil Engineering & Architecture 

Co-Chair: Dong, Xisong Inst. of Automation, Chinese Acad. of Sci., 

◮ SuA04-1 13:30–13:50 

Aided Decision-Making System of Public Transport Management for 

Guangzhou Asian Games, pp.3993–3998 

Dong, Xisong 

XIONG, Gang 

Dong, Fan 

Zhu, Fenghua 

LIU, Sheng 



CAISA 



◮ SuA04-2 13:50–14:10 

Service Oriented Resource Configuration Estimation and Optimization 

in Cloud Computing–an Artificial Enterprise Method, pp.4004–4009 

LIU, Sheng 

Zhu, Fenghua 

Zhao, Hongxia 

YAO, Jian-shi 




The Chinese People’s Liberation Army 

◮ SuA04-3 14:10–14:30 

Convergence and Consensus Analysis of Multi-Choice Deffuant- 

Weisbuch Models, pp.4010–4015 

Zhang, Jiangbo 

Hong, Yiguang 

Acad. of Mathematic & Sys. Sci., C.A.S 


◮ SuA04-4 14:30–14:50 

An Efficient Surrogate Optimization Method for Solving Linear Mixed- 

Integer Problems with Cross-Coupling Constraints, pp.4055–4060 

Bragin, Mikhail 

Luh, Peter B. 

Yan, Joseph 

Univ. of Connecticut 


Southern California Edison 

◮ SuA04-5 14:50–15:10 

The Study on the Characteristics of Emergency Classification and 

Grading about the Electromechanical Equipment Integration Project, 

pp.4093–4097 

Wang, Ya-hui 

Hao, Xue-Jun 

Wang, Chaogang 

Wang, Nan 




Prudent machinery Co., LTD 

◮ SuA04-6 15:10–15:30 

Service Composition Execution Optimization based on State Transition 

Matrix For Cloud Computing, pp.4126–4131 

LIU, Sheng 

XIONG, Gang 

Zhao, Hongxia 

Dong, Xisong 

YAO, Jian-shi 






SuA05 13:30–15:30 Room 203E 


Chair: Zhu, Xiaorui 

Co-Chair: Wang, Fan 


Shenzhen Inst.s of Advanced Tech., Chinese 


◮ SuA05-1 13:30–13:50 

Attractor Design and Prediction-based Adaption for a Robot Waltz 

Dancer in Physical Human-Robot Interaction, pp.3810–3815 

Wang, Hongbo 


Tohoku Univ. 

Tohoku Univ. 

◮ SuA05-2 13:50–14:10 

Walking Control for Compass-like Biped Robot with Underactuated Ankle, 

pp.3852–3857 

Tang, Chong 

Yan, Gangfeng 

Lin, Zhiyun 




◮ SuA05-3 14:10–14:30 

A Real-Time Human Imitation System, pp.3692–3697 

Wang, Fan Shenzhen Inst.s of Advanced Tech., Chinese Acad. 


Tang, Cheng 

Ou, Yongsheng 

Xu, Yangsheng 

Shenzhen Inst.s of Advanced Tech., Chinese Acad. 


Chinese Acad. of Sci., Shenzhen Inst.s of 

Advanced Tech. 


◮ SuA05-4 14:30–14:50 

Automatic Violin Player, pp.3892–3897 

Huang, Hsing-Hisn 

Minghsin Univ. of Sci. & Tech. 

◮ SuA05-5 14:50–15:10 

Mechanical Design of a Slider-Crank Centered Robotic Dolphin, 

pp.3741–3746 

Wei, Changming 

Yu, Junzhi 



◮ SuA05-6 15:10–15:30 

Vision-based Unscented FastSLAM for Mobile Robot, pp.3758–3763 

Qiu, Chunxin 

Zhu, Xiaorui 

Zhao, Xiaobing 




SuA06 13:30–15:50 Room 302 

Pattern Recognition 

Chair: Zheng, Suiwu 

Co-Chair: Li, Kun 



◮ SuA06-1 13:30–13:50 

A Comparative Study of Endoscopic Polyp Detection by Textural Features, 

pp.4671–4675 

LI, Baopu 


◮ SuA06-2 13:50–14:10 

Simplified Minimum Enclosing Ball based Fast Incremental Support 

Vector Machine (SVM) Algorithm for Person Detection and Tracking, 

pp.4936–4941 

Zheng, Suiwu 

Qiao, Hong 

Jia, Lihao 





Nagoya Univ. 

◮ SuA06-3 14:10–14:30 

Fast Eye Localization Based on a New Haar-like Feature, pp.4825– 

4830 

Chen, Yefei 

Su, Jianbo 



◮ SuA06-4 14:30–14:50 

Detection of Pedestrian Crossing from Focus to Spread, pp.4897–4901 

Wang, Caifeng 

Liao, Fucheng 

Ma, Chao 



Capital Univ. of Economics & Business 

◮ SuA06-5 14:50–15:10 

Robot Aided Object Segmentation without Prior Knowledge, pp.4797– 

4802 

Li, Kun 


Chen, Xijun 




84



◮ SuA06-6 15:10–15:30 

Adaptive Switching Anisotropic Diffusion Model for Universal Noise Removal, 

pp.4803–4808 

Wang, Wei 

Lu, Peizhong 

Fudan Univ. 

Fudan Univ. 

◮ SuA06-7 15:30–15:50 

The Recognition of EEG With CSSD and SVM, pp.4741–4746 

Li, Mingai 

Lu, Chanchan 



SuA07 13:30–15:50 Room 303 

Advanced Control Algorithms and Applications (III) 

Chair: Zou, Yuanyuan 

Co-Chair: LIU, Jinkun 


Beihang Univ. 

◮ SuA07-1 13:30–13:50 

On-line Squaring of Non-square Hard Constraints of Input Variable by 

Coordinate Alternating in Model Predictive Control, pp.2529–2536 


Wang, Shubin 



◮ SuA07-2 13:50–14:10 

Sliding Mode Control with Extended State Observer for the Boiler 

Steam Pressure of Fuel-steam Pressure System, pp.2570–2575 

CUI, Zhiqiang 

LIU, Jizhen 

LIU, Jinkun 

China Power Investment Corporation 


Beihang Univ. 

◮ SuA07-3 14:10–14:30 

A predictive Energy Management Strategy for Hybrid Electric Bus 

Based on Greedy Algorithm, pp.2782–2787 

Pan, Zheng 

Song, Chunyue 



◮ SuA07-4 14:30–14:50 

Predictive control design subject to multiple missing measurements, 

pp.2701–2705 

Zou, Yuanyuan 

Niu, Yugang 



◮ SuA07-5 14:50–15:10 

An approach of constraint boundaries tuning based on shadow price for 

two-layered predictive control, pp.2685–2690 

Zou, Tao 

Xiang, Weilong 

Ding, Baocang 

Li, Shaoyuan 





◮ SuA07-6 15:10–15:30 

Extended robust iterative learning control design for industrial batch 

processes with uncertain perturbations, pp.2728–2733 

Liu, Tao 

Shao, Cheng 


dalian Univ. of Tech. 

◮ SuA07-7 15:30–15:50 

Discrete-time Stochastic Iterative Learning Control: A Brief Survey, 

pp.2624–2629 

Shen, Dong 

XIONG, Gang 



SuA08 13:30–15:30 Room 310 

Invited Session: Applications of Semi-tensor Product to Control 

Chair: Feng, Jun-e 

Co-Chair: Lv, Hongli 



◮ SuA08-1 13:30–13:50 

Generalized Reversibility of Cellular Automata with Boundaries, 

pp.418–423 

Zhang, Kuize 

Zhang, Lijun 

College of Automation, Harbin Engineering Univ. 


◮ SuA08-2 13:50–14:10 

Solving a Class of Fuzzy Relation Inequalities via Semi-tensor Product, 

pp.1465–1470 

Fan, Hongbiao 


Feng, Jun-e 

Zhang, Lequn 



◮ SuA08-3 14:10–14:30 

Model-Input-State Matrix of Switched Boolean Control Networks and Its 

Applications, pp.1477–1482 

Zhang, Lequn 

Feng, Jun-e 



◮ SuA08-4 14:30–14:50 

Algebraic method to pseudo-Boolean function and its application in 

pseudo-Boolean optimization, pp.2468–2472 

Li, Zhiqiang 

Song, Jinli 

Xiao, Huimin 

Henan Univ. of Economics & Law 



◮ SuA08-5 14:50–15:10 

Model Construction of Fuzzy Relation Matrices and Application in Intelligent 

Environmental Comfort Systems, pp.2239–2244 

Lv, Hongli 

Feng, Jun-e 





◮ SuA08-6 15:10–15:30 

Reachability and Controllability of BCNs Avoiding States Set, pp.2329– 

2334 

Li, Zhiqiang 

Song, Jinli 



SuA09 13:30–15:30 Room 311A 

Invited Session: control problems for stochastic systems 

Chair: Zhang, Huanshui 

Co-Chair: Wang, Guangchen 



◮ SuA09-1 13:30–13:50 

Optimal Control for Stochastic Discrete-time Systems with Multiple 

Input-delays, pp.1529–1534 

Wang, Hongxia 


Wang, Xuan 

Harbin Insititute of Tech. 



◮ SuA09-2 13:50–14:10 

Output feedback control for high-order stochastic nonlinear time-delay 

systems, pp.1541–1546 

Liu, Liang 

Xie, Xue-Jun 



◮ SuA09-3 14:10–14:30 

On detectability and observability of discrete-time stochastic Markov 

jump systems with state-dependent noise, pp.1644–1649 

Zhang, Weihai 

Tan, Cheng 



◮ SuA09-4 14:30–14:50 

Partial information LQ optimal control of backward stochastic differential 

equations, pp.1694–1697 

Wang, Guangchen 

Wu, Zhen 

Xiong, Jie 



Univ. of Tennessee 

◮ SuA09-5 14:50–15:10 

Nonsmooth Adaptive Control Design for Uncertain Stochastic Nonlinear 


Zhang, Jian 

Liu, Yungang 



SuA10 13:30–15:30 Room 311B 

Invited Session: Intelligent information processing 

Chair: Wang, Biao 

Co-Chair: Duan, Haibin 


Beihang Univ. 

◮ SuA10-1 13:30–13:50 

Similarity Matching Algorithm for Ontology-Based Similarity Matching 

Algorithm for Ontology-Based, pp.758–763 

Gao, Qian 


◮ SuA10-2 13:50–14:10 

85


Hybrid Artificial Bee Colony and Particle Swarm Optimization Approach 

to Protein Secondary Structure Prediction, pp.5040–5044 

LI, Mengwei 

Duan, Haibin 

Shi, Dalong 

Beihang Univ. 

Beihang Univ. 

Beihang Univ. 

◮ SuA10-3 14:10–14:30 

Static H∞Loop-Shaping Control for Unmanned Helicopter, pp.2882– 

2886 

Tang, Jie 

Wei, Chen 

Yang, Fan 

Beihang Univ. 


Univ. of Beihang 

◮ SuA10-4 14:30–14:50 

A New Approach for Long-term Person Tracking, pp.4926–4930 

Fu, Deqian 

Jhang, Seong Tai 

Linyi Unitersity 

The Univ. of Suwon 

◮ SuA10-5 14:50–15:10 

Research on the Strategy Method for the Final Translation Phase of 

Space Autonomous Rendezvous, pp.290–294 

Li, Nan 

Liu, Zhenghua 

Li, Huifeng 

Zong, Xiaoxiao 

Beihang Univ. 


Beihang Univ. 

Beihang Univ. 

◮ SuA10-6 15:10–15:30 

Design and Mathematical Modeling of a 4-Standard-Propeller (4SP) 

Quadrotor, pp.3270–3275 

Phang, Swee King 


Cai, Chenxiao Inst. of Automation, Nanjing Univ. of Sci. & Tech. 

Chen, Ben M. 




SuA11 13:30–15:50 Room 311C 

Invited Session: Quantum Control and Quantum Information 

Chair: Xi, Zairong 

Co-Chair: Cong, Shuang 



◮ SuA11-1 13:30–13:50 

An Efficient Scheme for Multi-party Quantum State Sharing via GHZ 

Channels, pp.5122–5126 

Jiang, Min 

Dong, Daoyi 

Soochow Univ. 

Inst. of Sys. Sci., CAS 

◮ SuA11-2 13:50–14:10 

Transition graph-based control of quantum eigenstates, pp.1750–1755 

Kuang, Sen 

Cong, Shuang 



◮ SuA11-3 14:10–14:30 

Implicit Lyapunov Control of Multi-Control Hamiltonian Systems Based 

on State Distance, pp.5127–5132 

Meng, Fangfang 

Cong, Shuang 

Kuang, Sen 




◮ SuA11-4 14:30–14:50 

quantum discord dynamics of two atoms under different quantum feedbacks, 

pp.1634–1638 

Wang, Hui 

Xi, Zairong 



◮ SuA11-5 14:50–15:10 

Quantum chaotic communication, pp.1854–1859 

Zhang, Jing 

Wu, Rebing 

Li, Chunwen 




Dept. Automation, Tsinghua Univ., 


◮ SuA11-6 15:10–15:30 

Optimal Control of Quantum Discord in a Common Environment, 

pp.1999–2004 

Song, Hongting 

Pan, Yu 

Acad. of Mathematics & Sys. Sci. 


Cui, Wei 

Xi, Zairong 



◮ SuA11-7 15:30–15:50 

Coherent Quantum Feedback Rejection of Non-Markovian Noises, 

pp.2209–2214 

Xue, ShiBei 

Wu, Rebing 

Zhang, Jing 




SuB01 15:50–17:50 Room 203A 

Stability and Stabilization (II) 

Chair: Zhou, Yingjiang 

Co-Chair: Dong, Zhe 



◮ SuB01-1 15:50–16:10 

Delay-dependent exponential stabilization for nonlinear systems with 

interval discrete and distributed time-varying delays via intermittent 

control, pp.1077–1082 

Botmart, Thongchai 

Srinakharinwirot Univ. 

◮ SuB01-2 16:10–16:30 

Shifted-Ectropy Based Self-Stability Analysis Method for General Thermodynamic 

Systems and Its Application, pp.1406–1411 

Dong, Zhe 


◮ SuB01-3 16:30–16:50 

The Stability of Linear Discrete Time Delay Systems Over a Finite Time 

Interval: New Results, pp.1459–1464 

Debeljkovic, Dragutin Univ. of Belgrade, School of Mechanical 

Engineering 

Stojanovic, Sreten 

Dimitrijevic, Nebojsa 

Popov, Dejan 

Univ. of Nis, Faculty of Tech. 

Univ. of Belgrade, Faculty of Mechanical Eng 

Univ. of Belgrade, School of Mechanical 

Engineering 

◮ SuB01-4 16:50–17:10 

Stability Analysis for A Class of Distributed ParameterSwitched Systems 

with Time-varying, pp.2017–2021 

Bao, Leping 

Fei, Shumin 

Zhai, Junyong 




◮ SuB01-5 17:10–17:30 

Global asymptotic stability of uncertain nonlinear system with state and 

input constraint, pp.2695–2700 


Sun, Changyin 

Wang, Li 






◮ SuB01-6 17:30–17:50 

On Non-Lyapunov Stability of Linear Discrete Time Delay Systems: 

LMIs Approach, pp.1535–1540 


Engineering 



Popov, Dejan 




Engineering 

SuB02 15:50–17:50 Room 203B 

Multi-Agent Systems (II) 

Chair: Thunberg, Johan 

Co-Chair: Wang, Hanlei 

Optimization & Sys. theory 

Beijing Inst. of Control Engineering 

◮ SuB02-1 15:50–16:10 

Passivity Based Synchronization of Multiple Robotic Agents with Uncertain 

Kinematics and Dynamics, pp.846–851 

Wang, Hanlei 


◮ SuB02-2 16:10–16:30 

Exponential synchronization of complex dynamical systems with delay 

and derivative coupling via impulse, pp.875–880 

Zhu, Yakun 

Yanshan Univ. 

86



Luo, Xiaoyuan 

Li, Shaobao 

Guan, Xinping 

Yanshan Univ. 

Yanshan Univ. 

Yanshan Univ. 

◮ SuB02-3 16:30–16:50 

Consensus Control of Networked Nonlinear Systems, pp.914–919 

Peng, Junmin 

Ye, Xudong 

Zhou, Wei 




◮ SuB02-4 16:50–17:10 

Distributed Attitude Synchronization Control of Multi-Agent Systems 

with Time-Varying Topologies, pp.946–951 

Song, Wenjun Acad. of Mathematics & Sys. Sci., Chinese Acad. 


Thunberg, Johan 

Hong, Yiguang 

Hu, Xiaoming 




◮ SuB02-5 17:10–17:30 

Convergence Analysis for Quantized Consensus of Multi-agent Systems 

with Directed Topologies, pp.1201–1206 

Li, Dequan Anhui Univ. of Sci. & Tech.; Shanghai Jiaotong 

Univ. 

Liu, Qipeng 




◮ SuB02-6 17:30–17:50 




Song, Wenjun 

Hu, Xiaoming 





SuB03 15:50–17:50 Room 203C 

Signal Processing 

Chair: Jen, Fu-Hua 

Co-Chair: Zhou, Zhenwei 



◮ SuB03-1 15:50–16:10 

A method for parameters estimation of multiple sinusoids signal based 

on ANFs and SGA, pp.4277–4282 

Li, Ming 

Tu, Yaqing 

Su, Dan 




◮ SuB03-2 16:10–16:30 

Application of an Adaptive Sequential Kalman Filter to SINS/GPS Navigation 

Data Fusion, pp.4309–4314 

Bai, Meng 

Li, Minhua 



◮ SuB03-3 16:30–16:50 

Distributed Estimation for Time-Varying Target in Noisy Environment, 

pp.4341–4346 

Zhou, Zhenwei 

Fang, Hai-Tao 

Hong, Yiguang 




◮ SuB03-4 16:50–17:10 

A Frequency Estimation Algorithm based on Spectrum Correlation of 

Multi-section Sinusoids with the Known Frequency-Ratio, pp.4385– 

4389 

XIAO, WEI 

Tu, Yaqing 

Su, Dan 

Shen, Yanlin 

Zhang, Lei 





Zhuozhou Comprehensive Storehouse 

◮ SuB03-5 17:10–17:30 

Covariance Intersection Fusion Wiener Signal Estimator for Timedelayed 

System, pp.4418–4422 

Gao, Yuan 


Deng, Zili 


◮ SuB03-6 17:30–17:50 

Building an Autonomous Line Tracing Car with PID Algorithm, pp.4478– 

4483 

Jen, Fu-Hua 


SuB04 15:50–17:50 Room 203D 

Complex Systems 

Chair: Han, Jing 

Co-Chair: Xiang, Ji 



◮ SuB04-1 15:50–16:10 

Synchronized Output Regulation of Heterogeneous Networked Systems 

via Error Feedback, pp.3503–3508 

Xiang, Ji 

Li, Yanjun 

Wei, Wei 




◮ SuB04-2 16:10–16:30 

Dynamic Configuration of Service based Processes in Cloud Computing 

using Linear Programming, pp.3509–3514 

Du, Yanhua 


◮ SuB04-3 16:30–16:50 

“Knowing More Is Less” in Combinatorial Games , pp.3526–3532 

Han, Jing 

Han, Huawei 

Wang, Xin 


Inst. of Sys. Sci., Acad. of Mathematics & Sys. 

Sci., Chinese Acad. of Sci. 



◮ SuB04-4 16:50–17:10 

Convergence of Adaptive Linear Stochastic Differential Games: 

Nonzero-sum Case, pp.3543–3548 

Li, Yan 

Guo, Lei 



◮ SuB04-5 17:10–17:30 

A New Safety Certification Method for High-risk Flight Testing Subjects, 

pp.3555–3560 

Liu, Dongliang 

Xu, Haojun 

Zhou, Li 

Pei, Binbin 

Air Force Engineering Univ. 




◮ SuB04-6 17:30–17:50 

Design on Integrated Monitoring System for Main Waterway Station, 

pp.3471–3474 

Yuling, Pei 

Wu, Qian 

Yang, Xiaoyi 




SuB05 15:50–17:50 Room 203E 

Biosystems 

Chair: Zhao, Chunhui 

Co-Chair: Li, Jr-Shin 



◮ SuB05-1 15:50–16:10 

Automatic Sleep Stage Classification Based on ECG and EEG Features 

for Day Time Short Nap Evaluation, pp.4974–4977 

Yu, Shanshan 

Chen, Xi 

Wang, Bei 

Wang, Xing-yu 





◮ SuB05-2 16:10–16:30 

Optimal Control in Molecular-level Gene Manipulation, pp.4978–4983 

Yu, Juanyi 

Li, Jr-Shin 



◮ SuB05-3 16:30–16:50 

Multivariate Statistical Analysis Methods to Investigate Interindividual 

Glucose Dynamics for Subjects with Type 1 Diabetes Mellitus, pp.4989– 

4994 

Zhao, Chunhui 


87


Sun, Youxian 

Gao, Furong 


Hong Kong Univ. of Sci. & Tech. 

◮ SuB05-4 16:50–17:10 

Dynamic Feature Extraction of Epileptic EEG Using Recurrence Quantification 


Chen, Lanlan 

Zhang, Jian 

Zou, Junzhong 




◮ SuB05-5 17:10–17:30 

Key-frame Selection in WCE Video Based on Shot Detection, pp.5030– 

5034 

Fu, Yanan 

Liu, Haiying 

Cheng, Yu 

Yan, Tingfang 

Li, Teng 








◮ SuB05-6 17:30–17:50 

Patient Data Tracking in a Collaborative Healthcare, pp.5045–5050 

Memon, Qurban 

Khoja, Shakeel 

UAE Univ. 

IBA, Pakistan 

SuB06 16:10–17:50 Room 302 

Invited Session: Robot Sensing and Control 

Chair: Liu, Yun-Hui 


◮ SuB06-1 16:10–16:30 

Design of an Optimal Flight Control System with Integral Augmented 

Compensator for a Nonlinear UAV Helicopter, pp.3927–3932 

Tang, Yirui 

Li, Yangmin 

Univ. of Macau 


◮ SuB06-2 16:30–16:50 

A New Algorithm for Estimating 3D Structure and Robot Motion Using 

Visual Tracking and IMU/Compass, pp.4942–4947 

WANG, Kai 

Liu, Yun-Hui 



◮ SuB06-3 16:50–17:10 

Real-Time Bird Detection Based on Background Subtraction, pp.4507– 

4510 

Shakeri, Moein 

Zhang, Hong 

Univ. of Alberta 


◮ SuB06-4 17:10–17:30 

Brain-Driven Micro-Biomanipulation with Sensing Feedback, pp.4517– 

4522 

Luo, Yudong 

Shen, Yantao 

Univ. of Nevada, Reno 


SuB07 16:10–17:50 Room 303 

Invited Session: Disturbance Rejection: Formulation, Methodology, and 

Applications 

Chair: Gao, Zhiqiang 

Cleveland State Univ. 

◮ SuB07-1 16:10–16:30 

An Energy Saving, Factory-Validated Disturbance Decoupling Control 

Design for Extrusion Processes, pp.2891–2896 

Zheng, Qing 

Gao, Zhiqiang 

Gannon Univ. 


◮ SuB07-2 16:30–16:50 

On Model-free Accommodation of Actuator Nonlinearities, pp.2897– 

2902 

Zhao, Shen 

Zheng, Qinling 

Gao, Zhiqiang 




◮ SuB07-3 16:50–17:10 

Disturbance-Observer-Based Dynamic Inversion Tracking Control for a 

Hypersonic Vehicle, pp.2346–2351 

WANG, NA 

YANG, JIAN 

Beihang Univ. 

Beihang Univ. 

Guo, Lei 

Beihang Univ. 

◮ SuB07-4 17:10–17:30 

Tracking Trajectory of Heterogenous Multi-Agent Systems with Disturbance 

Observer Based Control, pp.2352–2357 

Yang, Hong-yong 

Guo, Lei 

Han, Chao 

Ludong Univ. 

Beihang Univ. 

BeiHang Univ. 

◮ SuB07-5 17:30–17:50 

Active Disturbance Rejection Control for the Gimbal Servo System of 

Magnetically Suspended Single Gimbal Control Momentum Gyroscope, 

pp.2865–2869 

Wei, Kongming 

Wu, Zhong 



SuB08 15:50–17:50 Room 310 

Biomedical Engineering 

Chair: Wang, Jiang 

Co-Chair: LI, Baopu 

Tianjin Univ. 


◮ SuB08-1 15:50–16:10 

Aeration control of activated sludge wastewater treatment process using 

optimal control, pp.4969–4973 

Wu, Jie 

East China Univ. of Sci. & technoledge 

◮ SuB08-2 16:10–16:30 

UKF-based state feedback control of abnormal neural oscillations in 

demyelination symptom, pp.4984–4988 

Jin, Qitao 

Wang, Jiang 

Li, Huiyan 

Deng, Bin 

Wei, Xile 

Che, Yan-Qiu 

Dong, Feng 

Tianjin Univ. 

Tianjin Univ. 


tianjin Univ. 

Tianjin Univ. 


Tianjin Univ. 

◮ SuB08-3 16:30–16:50 

Research on digital pulse oximeter based on optical frequency converter, 

pp.5056–5060 

Ni, Pingqiang 

LI, Baopu 



◮ SuB08-4 16:50–17:10 

Synchronization between Outputs of Neurons and Neuron Populations 

with Discrete Control Algorithm Basing on Least-square Method, 

pp.5001–5006 

Jia, Chenhui 

Wang, Jiang 

Deng, Bin 

Wei, Xile 

Che, Yan-Qiu 

Dong, Feng 

Tianjin Univ. 

Tianjin Univ. 

tianjin Univ. 

Tianjin Univ. 


Tianjin Univ. 

◮ SuB08-5 17:10–17:30 

Solving Graph Vertex Coloring Problem with Microfluidic DNA Computer, 

pp.5061–5065 

Niu, Ying 

Zhang, Xuncai 





◮ SuB08-6 17:30–17:50 

Action potential initial mechanism control of a minimum model response 

to constant and sinusoidal stimulus, pp.4948–4952 

Li, Huiyan 

Wang, Jiang 

Jin, Qitao 

Deng, Bin 

Wei, Xile 

Che, Yan-Qiu 


Tianjin Univ. 

Tianjin Univ. 

tianjin Univ. 

Tianjin Univ. 


SuB09 15:50–17:50 Room 311A 

Invited Session: Data-based control, modeling and optimization 

Chair: Liu, Derong 

Co-Chair: He, Haibo 


Univ. of Rhode Island 

88



◮ SuB09-1 15:50–16:10 

Data-Based Approach for the Control of a Class of Nonlinear Affine 


Wang, Zhuo 

Liu, Derong 

Univ. of Illinois at Chicago 

CASIA 

◮ SuB09-2 16:10–16:30 

Data-Driven Learning and Control with Multiple Critic Networks, 

pp.523–527 

He, Haibo 

Ni, Zhen 

Zhao, Dong-bin 



Inst. of automation 

◮ SuB09-3 16:30–16:50 

Data-driven Model Free Adaptive Control for Block-Connected Systems, 

pp.2827–2832 

Zhu, Yuanming 


Jin, Shangtai 




◮ SuB09-4 16:50–17:10 

Integration of Fuzzy Controller with AdaptiveDynamic Programming, 

pp.310–315 

Zhu, Yuanheng 


He, Haibo 




◮ SuB09-5 17:10–17:30 

Learning Control of a Bioreactor System Using Kernel-based Heuristic 

Dynamic Programming, pp.316–321 

Lian, Chuanqiang 

Xu, Xin 



SuB10 15:50–17:50 Room 311B 

Invited Session: Wireless Sensor Networks 

Chair: Xiao, Wendong 

Co-Chair: Wu, Jian Kang 


Graduate Univ., Chinese Acad. of Sci. 

◮ SuB10-1 15:50–16:10 

A comparison study on TDOA based localization algorithms for sensor 

networks, pp.4490–4495 

Qu, Xiaomei 

Xie, Lihua 



◮ SuB10-2 16:10–16:30 

Robust Stabilization of Networked Control Systems with Multiple-packet 

Transmission via Jump System Approach, pp.4496–4501 

Ding, Nan 

Yu, Mei 

Yuan, Xiaodan 

Li, Jie 

Yu, Junyan 





Univ. of Electronic Sci. ang Tech. in China 

◮ SuB10-3 16:30–16:50 

Distributed fusion filter for multi-rate multi-sensor systems with packet 

dropouts, pp.4502–4506 

Ma, Jing 

Sun, Shuli 



◮ SuB10-4 16:50–17:10 

Self-learning sensor scheduling for target tracking in wireless sensor 

networks based on adaptive dynamic programming, pp.1056–1061 

Xiao, Wendong 

Song, Ruizhuo 



◮ SuB10-5 17:10–17:30 

A New Approach to Quantized Stabilization of Stochastic System with 

Multiplicative Noise, pp.2382–2387 

Wei, Li 




◮ SuB10-6 17:30–17:50 

Outlier Detection in Heart Rate Signal using Activity information, 

pp.4511–4516 

Yang, Yuanjing 

Ji, Lianying 

Wu, Jian Kang 

Graduated Univ. of Chinese Acad. of Sci. 

Graduate Univ. of Chinese Acad. of Sci. 


SuB11 16:10–17:50 Room 311C 

Invited Session: Hypersonic flight control 

Chair: Chen, Mou 


NUAA 

Beihang Univ. 

◮ SuB11-1 16:10–16:30 

Modeling and Control for Near-Space Vehicles With Oblique Wing, 

pp.1773–1778 

Pang, Jie 

Rong, Mei 

Chen, Mou 

NUAA 


NUAA 

◮ SuB11-2 16:30–16:50 

Attitude Quaternion Control in the Final Approach Phase of Rendezvous 

and Docking by Sliding Mode Control, pp.2177–2181 


Liu, Zhenghua 

Ren, Yan 

Li, Nan 

Beihang Univ. 


Beihang Univ. 

Beihang Univ. 

◮ SuB11-3 16:50–17:10 

Design of 3-D Discrete Sliding Mode Variable Structure Guidance Law 

for Air Missile, pp.2204–2208 

Chang, Le 

Liu, Zhenghua 


Beihang Univ. 


Beihang Univ. 

◮ SuB11-4 17:10–17:30 

Nonlinear Flight Control Design Using Sliding Mode Disturbance 

Observer-Based Constraint Backstepping, pp.1818–1825 

Zhang, Chao 

Chen, Zongji 

Wei, Chen 




◮ SuB11-5 17:30–17:50 

Neural Adaptive Back Stepping Flight Controller for a Ducted Fan UAV, 

pp.2370–2375 

Wang, Jianliang 

Sundaram, Suresh 

RAJASHEKARAN, ARUNESHWARAN 


NanYang Technological Univ. 

Nanyang Technologial 

Univ. 

89


90


Book of Abstracts 

Book of Abstracts 

Friday, July 6, 2012 

PL-1 8:30-9:30 Room 305 


Chair: Xie, Lihua 

Nanyang Technological University, Singapore 

◮ PL-1 8:30-9:30 




Type 1 diabetes mellitus (T1DM) is a chronic autoimmune disease affecting 

approximately 25 million individuals in the world, and is the 4th 

leading cause of global death by disease. Current treatment requires 

either multiple daily insulin injections or continuous subcutaneous (SC) 

insulin infusion (CSII) delivered via an insulin infusion pump. Both 

treatment modes necessitate frequent blood glucose measurements to 

determine the daily insulin requirements for maintaining near-normal 

blood glucose levels. More than 30 years ago, the idea of an artificial 

endocrine pancreas for patients with type 1 diabetes mellitus (T1DM) 

was envisioned. The closed-loop concept consisted of an insulin syringe, 

a blood glucose analyzer, and a transmitter. In the ensuing 

years, a number of theoretical research studies were performed with 

numerical simulations to demonstrate the relevance of advanced control 

design to the artificial pancreas, with delivery algorithms ranging 

from simple PID, to H-infinity, to model predictive control. 

Our algorithmic studies have focused on model predictive control, including 

safety constraints to prevent over-dosing, and multi-parametric 

implementation for regulatory review. Our latest work has focused on 

soft output constraints using “zones” to emulate the medical outcome 

metrics. A recent extension of that work will be described in this talk, 

consisting of a multipartite zone model predictive controller (Multi-Zone- 

MPC). 

Multi-Zone-MPC provides different tunings for the MPC weights based 

on four regions of glycemia: hypoglycemia, normoglycemia, elevated g- 

lycemia, and hyperglycemia. Defining these four zones provides richer 

control tunings that result in safe and effective control. 

Our latest clinical investigations will be reviewed to demonstrate the 

medical-relevance of such an approach to a feedback-controlled artificial 

pancreas. 

This presentation is based on work coauthored with Eyal Dassau, Rebecca 

Harvey, Matt Percival, Benny Grosman, Howard Zisser, Dale Seborg, 

and Lois Jovanovic. 

PL-2 9:50-10:50 Room 305 



Sophia University, Japan 

◮ PL-2 9:50-10:50 


Chen, Hong 

Jilin University, China 

The basis of model predictive control (MPC) is the on-line solution of 

a constrained optimization problem updated by the actual state. The 

obtained control is injected into the system until the next sampling 

time, while the procedure is repeated whenever new measurements 

are available. Due to its ability to handle nonlinearity, to include various 

types of models predicting the future dynamics, to take time-domain 

constraints into account explicitly and to coordinate multiple performance 

requirements in the sense of optimization, MPC has become an 

attractive feedback strategy for designing control systems in automotive. 

The talk will discuss some aspects of applying MPC in automotive 

through some selected examples. 

PL-3 10:50-11:50 Room 305 




◮ PL-3 10:50-11:50 

Surgical Robotics: Different Successful Concepts in the Past and in 

Future 

Lueth, Tim C. 

Technical University of Munich, Germany 

Since 20 years, surgical navigation and robotics are two important technologies 

to improve the state of the art in medical treatment. To know 

where an instrument is located relative to a region of interest (organ, 

vessel, bone structure) inside of the body is of great importance to 

achieve a preplanned postoperative situation. To guide and to move 

an instrument by a robot is more complex but is definitely required for 

almost all kind of surgery. 

While surgical navigation became a standard in many medical disciplines, 

surgical robotics is still at it’s beginning. Todays great commercial 

success of only one company with just one robotics approach 

(Telemanipulation), should not mislead to an interpretation that surgical 

robotics is now successful. There are still more problems than solutions. 

Also the visibility of this company is not typical for medical device 

companies. In the talk, several navigation and robotics systems are p- 

resented, that were developed within Germany during the past 15 years 

with different success. All of them skipped the barrier from idea to clinical 

use to the market. A collection of videos shows the robots use. 

Nevertheless, there are different mechanisms that are important to consider 

if a medical robot should be successful. These mechanisms are 

discussed and also the rules for researcher to design robots as medical 

device from the very beginning. Also some in-between solutions such 

as “Navigated Control” are presented to explain when a robot is useful 

and why sometimes a different solution is more successful. 

In future we will see, surgical robots and medical instruments that are 

patient specific printed on demand based on generative manufacturing 

methods such as Selective Laser Sintering of biocompatible materials. 

FrA01 13:30–15:30 Room 203A 

Intelligent Control and Automation (I) 

Chair: Wang, Peijin 

Co-Chair: Dang, Zhaohui 

Yantai Univ. 


◮ FrA01-1 13:30–13:50 

Development of a Reconfigurable Robot’s Turning Method with Line 

Configuration, pp.61–66 

Chang, Jian 

Wu, Chengdong 

Shang, Hong 

Li, Bin 




organization 


Sci. 

The shape-shifting robot is one of the kinds of the robot and it can 

change it’s configuration according to the environment. A method is 

proposed to solve the shortcomings of the traditional method of robot’s 

chain turning, which can shorten the time and radius of turning. The 

turning resistance moment can be also reduced. The mathematical 

model is built and the resistance torque and required force is computed 

By the analysis of experiment, the current of jaw motor does not exceed 

the limit load of the motor and it is more quick and smooth comparing to 

the link-turning. The validity and enforceability of the turning is proved 

by simulations and experiments. 

◮ FrA01-2 13:50–14:10 

91


Study on the Structure of Human Body Simulated Controller , pp.10–14 

Wang, Peijin 

Yantai Univ. 

Wang, Xuejie 

Li, Yanjun 



According to object oriented methodology, all of the controller object, 

sensor object and actor object are intelligent in a human body simulated 

control system. The above three intelligent control objects can 

co-operate with each other to keep the controlled system safety and 

reliability although one of them does not work. The above three intelligent 

objects consist of human body simulated controller, each of them 

is analyzed simply in this paper. The human body simulated controller 

has been used in a real control system, it proves that the controller is 

best, and the control result is best. 

◮ FrA01-3 14:10–14:30 

Attitude Control of 3-DOF Helicopter based on Iterative Learning Control, 

pp.128–132 

Zhang, Xining 

Liu, Chenglin 

Liu, Fei 



Inst. of Automation,Southern Yangtze Univ. 

Tracking control problems of the elevation and travel angles are investigated 

for 3-DOF (three degree of freedom) helicopter in this paper. By 

combining the normal PID controller based on LQR (linear quadratic 

regulator) and PID-type Iterative Learning Control, a new mixed controller 

is proposed for 3-DOF helicopter to achieve higher tracking accuracy. 

Compared with the normal PID controller based on LQR, the 

mixed controller can achieve better asymptotic converging effect for the 

control of the elevation and travel angles of 3-DOF helicopter. By the 

relatively small times of iteration, the system achieves quite high tracing 

accuracy under the mixed controller. 

◮ FrA01-4 14:30–14:50 

Dancing Behavior Modeling and Logic Control Simulation of Twowheeled 

Robot based on Stateflow, pp.89–92 

Yu, Jianjun 



Yang, Qiong 

Sun, Liang 

Wang, Guanwei 


Inst. of Electric Information & Control Engineering 


Using FSM (Finite State Machine) theory as the foundation, based on 

the MATLAB / Stateflow graphical behavior description method, realizes 

the wheeled robot dance behavior modeling, design of the wheeled mobile 

robot behavior control logic. The simulation results show that the 

simulation model can be constructed, simulated robot dance behavior 

of the actual process; the logic control method can realize the wheeled 

robot dance planning. And the modular design of concise and clear, 

design and debugging time is greatly reduced, cost is reduced, operation 

controllability enhancement, It can provide the theoretical basis for 

wheeled robot dance planning. 

◮ FrA01-5 14:50–15:10 

Stable flocking of mobile agents in mixed absolute and relative navigations 

context with communication , pp.67–73 

Dang, Zhaohui 


In this paper, we studied the flocking motion of multiple agents in mixed 

absolute/relative navigation condition with communication. The difference 

between navigation and communication in the role taken part in 

the neighbourhood relations was firstly pointed out. We gave a criterion 

which can judge whether a communication neighbourhood graph 

with mixed absolute/relative navigation devices is connected. We also 

proposed an algorithm to achieve the absolute navigation using the 

communication. Based on above preliminaries, we design a control law 

to stabilize the flocking motion of multiple agents whose dynamics are 

nonlinear with noise. The simulation in three different scenarios was 

performed and the corresponding results illustrated the effectiveness of 

the control law. 

◮ FrA01-6 15:10–15:30 

A Modified Fruit-Fly Optimization Algorithm Aided PID Controller Designing, 

pp.233–238 

Liu, Yi 

ZheJiang Univ. City College 

Fruit Fly Optimization Algorithm (FOA) is one of the newest intelligent 

optimization algorithms. Attracted by its simple implement procedure 

with effective searching capability, our work is to popularize this algorithm 

to tackle some practical optimization applications requesting 

real-time performance. However, the updating strategy of FOA is with 

strong randomness, thus bringing in some blindness searching in solution 

updating, which will result in slow convergence rate and premature. 

Therefore, a modified FOA (MFOA) based on PSO and SA was 

proposed in this paper to improve the performance of basic FOA. Besides, 

Chaos funtion was used to enhance the stochastic and ergodic 

features of initial solution so as to improve the diversity of initial population 

in MFOA. PSO is introduced to reduce the blindness searching in 

solution updating. SA is used as a local search to improve the convergence 

rate. Finally, in order to verify the efficiency of MFOA algorithm, 

two common functions and a practical high-order AVR system with PID 

controller were tested in simulation. Experimental results revealed the 

encouraging performance of our proposed algorithm. 

FrA02 13:30–15:30 Room 203B 

Computational Intelligence and Applications (I) 

Chair: Zhang, Xuncai 

Co-Chair: Wu, Xiuli 



◮ FrA02-1 13:30–13:50 

An Increasing on Knowledge of MAS Trained by Boltzmann Machine 

Algorithm based Sugarscape CA Using a Synergy of Communication 

and Cooperation bet Agents , pp.484–489 

Nourafza, Nasim 

Setayeshi, Saeed 

Islamic Azad Univ. ,Najafabad Branch 

Amirkabir Univ. of Technolog 

Sugarscape model is a multi-agent environment that is used for modeling 

and organizing processes such as social, political and economic. 

After the previous studies which were concerned with the production 

of a learned multi-agent model based on Boltzmann Machine learning 

algorithm and also the evaluation of the learning of a learned system 

in sugarscape, the purpose of this study is to evaluate the learning 

done after adding the two parameters of communication and cooperation 

to the sugarscape learned model. Thus a cellular learned multiagent 

model with use of Boltzmann Machine learning algorithm based 

on sugarscape model was considered. In this model, each agent has 

been allocated with a parameter that indicates the knowledge of the 

agent. Once all agents reach sugar peaks it means that all agents 

have become knowledgeable and the model has converged. After that 

the two parameters of communication and cooperation are added to 

the given model and for each one of the models the number of agents 

present in sugar peaks after the model had reached convergence per 

the specific number of agents has been measured. After analyzing the 

resulting diagram it was concluded that after the convergence of the 

model, the average number of knowledgeable agents in learned model 

with communication and cooperation is higher than the number of 

knowledgeable agents in learned model without use of communication 

and cooperation. Therefore communication and cooperation of the a- 

gents causes to incre 

◮ FrA02-2 13:50–14:10 

Data Collection Based on Mobile Agent in Wireless Sensor Networks, 

pp.392–396 

Li, Tongying 

National Astronomical Observatories / Nanjing Inst. 

of Astronomical Optics & Tech., Chinese Acad. of 

Sci., Nanjing 

Environment Changes due to temperature, humidity, wind loading and 

so on can affect the performance of a large telescope. The real-time 

information of environment change must be got in order to exert control, 

reduce or eliminate the adverse effects of environment changes on 

the large telescope. A novel method of environment monitoring for a 

large telescope based on wireless sensor networks has therefore been 

proposed to realize environment data acquisition, on-line-detection and 

92


Book of Abstracts: Friday Sessions 

data analyzing in this paper, which provides the basis for the adjustment 

of the large telescope to improve its control performance and image 

quality. 

◮ FrA02-3 14:10–14:30 

3D DNA Self-Assembly for Maximum Clique Problem, pp.438–443 

Zhang, Xuncai 

Fan, Rui 

Wang, Yanfeng 






DNA self-assembly technology has brought novel inspirations to the 

development of DNA computing. At present there are many diversified 

computational models to solve various NP problems, which are very 

useful of solving some complex NP problems. In this paper, 3D self 

- assembly model is presented to solve the maximum clique problem. 

With the capacity of DNA molecules in massive parallel computation, 

the model can simulate a non-deterministic algorithm and solve this 

problem. In this model, the number of distinct tiles used is a constant - 

15, computation time is θ(n2), and computation space is θ(n3). Our 

work makes a significant attempt to explore the computational power of 

3D DNA self - assembly. 

◮ FrA02-4 14:30–14:50 

A Novel Content Based and Social Network Aided Online Spam Short 

Message Filter, pp.444–449 

Yu, Yang 

Chen, Yuzhong 

Fuzhou Univ. 

Fuzhou Univ. 

With the rapid development of mobile SMS (short message service), 

spam messages have grown explosively which trouble our daily life seriously 

and lead to the loss of telecom operators. In this paper, an 

online spam filter based on the analysis of two criteria of content representations 

and relationship between the senders and receivers in social 

network is proposed. A Naïve Bayesian classifier is used to build 

up the filter including both the content features and social network features. 

We use the data provided by a partner telecom operator to do the 

experiments. The results show that our model is effective and satisfies 

all the requirements of our partner and will be deployed recently. 

◮ FrA02-5 14:50–15:10 

Solving the Flexible Job-shop Scheduling Problem with Quantuminspired 


Wu, Xiuli 


The flexible job shop scheduling problem (FJSP) is typically NP hard. 

A quantum inspired algorithm is proposed to solve the FJSP. Firstly, 

the FJSP is formulated. Secondly, the detail of the quantum inspired 

algorithm is designed, including the quantum chromosome encoding 

and decoding mechanism, the updating method with the rotation gate 

matrix. The elitist strategy is integrated to speed up the convergence. 

The niche technology is combined to avoid trapping into the local optimization. 

Finally, some benchmark instances are tested to verify the 

performance of the proposed algorithm. The results shows that the 

proposed algorithm outperform the compared algorithms. 

◮ FrA02-6 15:10–15:30 

Max-Min Ant System for Bus Transit Multi-depot Vehicle Scheduling 

Problem with Route Time Constraints, pp.555–560 

Hao, Xiao Ni 

Jin, Wen Zhou 

Wei, Ming 




The bus transit vehicle scheduling problem (VSP), in which a given set 

of scheduled trips have to be assigned to vehicles stationed at different 

depots, minimizing the capital cost and the overall operational cost, has 

caused great concern to the bus transit companies. Considering the real 

world operational restrictions, this paper researches the bus transit 

multi-depot vehicle scheduling problem with route time constraints and 

depot capability restrictions, and puts forward a model with comprehensive 

objective to minimize the number of required vehicles, travel time 

along deadheading trips and the waiting time at the starting stations of 

service trips for VSP satisfying a set of constraints. This problem is NPhard, 

and therefore its solution is obtained by a Max-Min ant system. 

This article describes steps of the whole algorithm in detail, especially 

construction of solutions and pheromone updating rule. Finally, an 

example was analyzed to demonstrate that the correctness of the application 

of the MMAS, and it prove to be more efficient and effective in 

solving this problem compared with the ACS. 

FrA03 13:30–15:30 Room 203C 

Artificial Intelligence 

Chair: Feng, Xin 

Co-Chair: Han, Deqiang 



◮ FrA03-1 13:30–13:50 

Hierarchical Proportional Redistribution principle for uncertainty reduction 

and bba approximation, pp.664–671 

Dezert, Jean 

Han, Deqiang 

Liu, Zhunga 

Tacnet, Jean-marc 

ONERA 


NW Polytech. Univ 

Cemagref-ETGR 

Dempster-Shafer evidence theory is very important in the fields of information 

fusion and decision making. However, it always brings high 

computational cost when the frames of discernments to deal with become 

large. To reduce the heavy computational load involved in many 

rules of combinations, the approximation of a general belief function is 

needed. In this paper we present a new general principle for uncertainty 

reduction based on hierarchical proportional redistribution (HPR) 

method which allows to approximate any general basic belief assignment 

(bba) at a given level of non-specificity, up to the ultimate level 1 

corresponding to a Bayesian bba. The level of non-specificity can be 

adjusted by the users. Some experiments are provided to illustrate our 

proposed HPR method. 

◮ FrA03-2 13:50–14:10 

An algorithm based on piecewise slope transformation distance for 

short time series similarity measure, pp.691–695 

Li, Huimin 

Fang, Liying 

Wang, Pu 

Liu, Jingwei 





Abstract - Aiming at the irregular and uneven feature of medicine time 

series data, an novel algorithm based on piecewise slope transformation 

distance for short time series similarity measure is proposed. We 

firstly do some preprocess based on algorithm for key points selected, 

make the data curve to zigzag shape, then, we measure the distance 

between two curves based on piecewise slope transformation algorithm. 

By experiments, conclusion can be made that this new approach 

can measure distance rapidly and correctly, especially appropriate to 

short time series data. 

◮ FrA03-3 14:10–14:30 

Pruning-Included Weights and Structure Determination of 2-Input Neuronet 

Using Chebyshev Polynomials of Class 1, pp.700–705 

Zhang, Yunong 

Yin, YongHua 

Yu, Xiaotian 

Guo, Dongsheng 

Xiao, Lin 




Sun Yat-Sen Univ. 


A new type of feed-forward 2-input neuronet using Chebyshev polynomials 

of Class 1 (2INCP1) is constructed and investigated in this 

paper. In addition, with the weights-direct-determination method exploited 

to obtain the optimal weights from hidden layer to output layer 

directly (i.e., just in one step), a new structure-automatic-determination 

method called weights-and-structure-determination (WASD) algorithm 

is proposed to determine the optimal number of hidden-layer neurons 

of the 2INCP1. Such a WASD algorithm includes a procedure of pruning 

the proposed neuronet (after the net grows up). Numerical results 

further substantiate the efficacy of the 2INCP1 equipped with 

the so-called WASD algorithm. 

◮ FrA03-4 14:30–14:50 

93


The Research on Cross-Media Information Retrieval System Based on 

Food Safety Emergencies, pp.706–710 

Han, Pengcheng 

Du, Junping 


Univ. of Posts & Telecommunicatios 




In this paper, we design and implement a cross-media information retrieval 

system based on the area of food safety emergencies. The system 

collects Internet information using topic crawler, establishes data 

index on cross-media information and makes fast retrieval by sort labeling. 

The system supports image semantic retrieval and expansion 

retrieval based on Ontology. The cross-media retrieval provides a new 

technology for the research of emergencies field, and meets unique 

retrieval needs by the largest extend. 

◮ FrA03-5 14:50–15:10 

Neural Networks Based Autonomous Learning for a Desktop Robot, 

pp.739–742 

Dai, Lizhen 


Wang, Guanwei 

Yu, Jianjun 






A method of realizing desktop robot’s negative phototaxis through a 

neural network is presented. The biology is characteristic of biologic 

phototaxis and negative phototaxis. Can a machine be endowed with 

such a characteristic? This is the question we study in this paper. A randomly 

generated network is used as the main computational unit. Only 

the weights of the output units from this network are adjusted during the 

training phase. Learning samples are collected according to the energy 

function. It will be shown that this simple type of a biological realistic 

neural network is able to simulate robot controllers like that incorporated 

in desktop robots. The experiments are presented illustrating the 

stage-like study emerging with this learning mode. 

◮ FrA03-6 15:10–15:30 

Predictive Temporal Patterns Detection in Multivariate Dynamic Data 


Zhang, Wenjing 

Feng, Xin 



In this paper we present a method for detecting multivariate temporal 

patterns that are characteristic and predictive of significant events in 

a multivariate dynamic data system. A new hybrid RPS-GMM method 

is applied to identify patterns. This method constructs phase space 

embedding by using individual embedding of each variable sequences. 

We employ discriminative approach by applying Gaussian Mixture Model 

(GMM) to the multivariate sequence data to cluster multidimensional 

data into three categories of signals, e.g. normal, patterns and events. 

An optimization method is applied to the objective function to search an 

optimal classifier to identify temporal patterns that are predictive of future 

events. We performed two experimental applications using chaotic 

time series and Sludge Volume Index (SVI) series related to the Sludge 

Bulking problem. Experiments show that the new approach presented 

here significantly outperforms the original RPS framework and neural 

network method. 

FrA04 13:30–15:30 Room 203D 

Nonlinear Control 

Chair: Wang, Xingxuan 

Co-Chair: Huang, Chaodong 

Fudan Univ. 


◮ FrA04-1 13:30–13:50 

Estimate Error Analysis of the Nonlinear Third Order Extended State 

Observer, pp.1621–1627 

ZHANG, Yuan-wen 

YANG, Le-ping 

Zhu, Yanwei 

National Univ. of defense Tech. 

National Univ. of defence Tech. 


Nonlinear extended state observer not only estimate all the state of 

uncertain plant, but also the real-time inner and outer disturbance, independent 

to plant model and having better robust capability. However, 

the estimate error analysis of more than second order observer hasn’t 

been depth researched. Based on the estimate error theory of second 

order observer and some proper hypothesis, this paper firstly decoupled 

the third order observer into two second order observers, then the 

performance of estimate error is analyzed using continuous piece-wise 

smooth Lyapunov function theory and a tuning rule is put forward. And, 

based on the analysis of effect of observer parameters, an optimization 

rule is used with the tuning process. Theoretic analysis and simulation 

results indicate that the proposed estimate error analysis method 

is proper, and the tuning law and optimization rule of observer parameter 

are feasible. 

◮ FrA04-2 13:50–14:10 

Adaptive Sliding Mode Control with Nonlinear Disturbance Observer for 

Uncertain Nonlinear System Based on Backstepping Method, pp.1609– 

1614 

Qiao, Jihong 

Wang, Hongyan 

Li, Zihao 




A chattering reduction sliding mode control (SMC) via backstepping 

scheme is proposed for a class of mismatched uncertain nonlinear systems. 

The most significant property of SMC system is its robustness, 

but SMC has some difficulties to handling mismatched uncertainties. 

Backstepping method doesn’t need matching conditions. The method 

of combination of SMC and backstepping become effective in solving 

the mismatched uncertainties. The robust of the systems is guaranteed. 

But chattering caused by using SMC is not good for system. A 

nonlinear disturbance observer is used to estimate disturbance. Whole 

disturbance of the closed-loop systems is reduced. The chattering of 

sliding controller is reduced clearly. The proposed method is validated 

by simulation. 

◮ FrA04-3 14:10–14:30 

Control of a Class of Nonlinear Uncertain Systems by Combining State 

Observers and Parameter Estimators, pp.2054–2059 

Huang, Chaodong 

Guo, Lei 



The main purpose of this paper is to study the control problem for 

a class of SISO affine nonlinear systems with unknown dynamics by 

combining the extended state observer (ESO) technique and the projected 

gradient estimator. While ESO can be used to estimate the total 

uncertainties, the projected gradient algorithm is used to estimate 

the nonparametric uncertainties treated as time-varying parameters. 

This method improves the traditional active disturbance rejection control 

(ADRC) technique. It overcomes the difficulty that the traditional 

ADRC needs to have a “good”estimate for the uncertainties in the 

input channel. Closed loop stability is proven and the control performance 

is also analyzed. 

◮ FrA04-4 14:30–14:50 

Adaptive Control for A Class of Nonlinear Uncertain Dynamical Systems 

With Time-varying, pp.2171–2176 

Zhang, Jie 


Fudan Univ. 

Fudan Univ. 

This paper present a nonlinear adaptive control framework for a class 

of nonlinear uncertain dynamical systems with time-varying that guarantees 

ultimately bounded of the closed-loop systems. In particular, we 

develop both full-state feedback control law and output feedback law. 

In addition, we consider a expanded condition. By the analysis using a 

Lyapunov function, we show that the framework guarantees ultimately 

bounded of the closed-loop systems. An illustrative numerical example 

is provided to demonstrate the efficacy of the proposed framework. 

◮ FrA04-5 14:50–15:10 

On the Modeling of a Nonlinear Plate and a Nonlinear Shell, pp.1585– 

1590 

Li, Shun 

Acadamy of Mathematics & Sys. Sci., Chinese 

Acadamy of Sci. 

94



Yao, Pengfei 


We consider modeling of a nonlinear thin plate and a nonlinear thin 

shell under the following assumptions: (a) the materials are nonlinear; 

(b) the deflections are small (linear strain displacement relations). For 

a plate with a planar middle surface, we consider the bending of the 

plate to establish the strain energy, the equilibrium equations, and the 

motion equations. For a shell with a curved middle surface in R 3 , we 

derive a nonlinear model where a deformation in three-dimensions is 

concerned. 

◮ FrA04-6 15:10–15:30 

Multiple Models Adaptive Control Based on Cluster-Optimization for a 

Class of Nonlinear System, pp.1367–1371 

Huang, Miao 

WANG, Xin 

Wang, Zhenlei 

Qian, Feng 





For a class of nonlinear discrete time system with fast time-varying 

or jumping parameters, a multiple models adaptive controller (MMAC) 

based on cluster-optimization is proposed. Based on the input-output 

data, the sample data are classified into several clusters by the fuzzy 

kernel clustering adaptive algorithm. Then the local models can be constructed 

corresponding clusters by the least square method. To improve 

the transient response during the change of the working points, besides 

the distance, the directional derivative of system is computed also. It is 

utilized to identify the system trend of changing working point. Before 

the changing occurs, new weighted models are developed by the corresponding 

local models, indicated by the system directional derivative. 

Meanwhile the distance between the data and the centre of clusters are 

used to find the weighted coefficients. So a better approach ability can 

be got than that designed only by the distance. The simulation results 

show that the proposed controller is superior to that of the conventional 

multiple models controller. 

FrA05 13:30–15:30 Room 203E 

Control Theory (I) 

Chair: ZHOU, Shiliang 


Co-Chair: ROY, TUSHAR KANTI UNSW, Canberra, ACT 2600 

◮ FrA05-1 13:30–13:50 

Robust Maneuver Control with Disturbance Attenuation for Flexible S- 

pacecraft, pp.1269–1275 

ZHOU, Duan 

Guo, Yu 

Chen, Qingwei 

Hu, Weili 




Nanjing Univ. of Sci. &Tech. 

Disturbance attenuation and model parametric uncertainties must be 

solved in large angle attitude maneuvering control for flexible spacecraft. 

A robust attitude maneuvering controller with disturbance attenuation 

inspired by the model of cell membrane discharge is proposed for 

flexible spacecraft, considering external disturbances and parametric 

uncertainties. The control strategy is designed to make the closed-loop 

system to be able to suppress interference and to be globally asymptotically 

stable. Also, it is considered particularly to solve the problem 

of jump of control quantity which will make the pointing accuracy and 

stability become poor in large angle maneuver of flexible spacecraft. 

When the external disturbances and the estimate errors of parametric 

uncertainties are bounded, the ability of disturbance attenuation and 

the stability of the controller proposed are proved through Lyapunov 

analysis. The simulation results demonstrate the effectiveness of the 

robust controller proposed. 

◮ FrA05-2 13:50–14:10 

Hover Flight Control of a Small Helicopter Using Robust Backstepping 

and PID , pp.1688–1693 


In this paper, a robust control strategy applying on a small helicopter 

is proposed. The controller is designed using the backstepping approach 

based on Lyapunov function. In control design, a hierarchical 

inner-outer loop based structure is proposed to control the hover flight 

in the presence of external wind gusts. The outer loop employs robust 

backstepping controller to control the translational trajectory, while the 

inner loop (attitude control) controller is designed by means of PID controller 

that allow the stabilization of the attitude of a small helicopter. 

This new method combines the advantages of both robust backstepping 

and PID, particularly it is simple and easy to implement and tune 

in future real flight test. Finally, a computer simulation is conducted to 

show the hover flight control performance of the proposed controller in 

a gusty environment. 

◮ FrA05-3 14:10–14:30 

Research on Robust Control Allocation for the Advanced Configuration 

Aircraft, pp.1722–1726 

Zou, Jingfeng 

Yang, Lingyu 

Zhang, Jing 

Shen, Gongzhang 

beihang Univ. 


Beihang Univ. 


Abstract: The accuracy of control allocation algorithms rely on control 

effectiveness matrix. To address this issue, this paper focuses on the 

robustness of control allocation. Several existing robust control allocation 

algorithms are analyzed at first. Then performance evaluation 

criteria for robust control allocation, which are designed for flight control, 

are proposed based on performance requirements of flight control 

system. Comparisons among existing robust control allocation algorithms 

are conducted via simulations. And robustness of existing robust 

control allocation algorithms are evaluated according to the proposed 

robustness evaluation criteria. 

◮ FrA05-4 14:30–14:50 

Low Order Structured Weight Optimization for H∞Loop Shaping Design 

Procedure, pp.2257–2262 

Liu, Yuyan 




A low order structured weight optimization algorithm for H∞loop shaping 

design procedure is proposed, which can be used for single input 

single output plant H∞controller design. This work is build on the 

method given by Lanzon[3], where special structured decision variables 

are defined according to low order structured weight, and additional linear 

matrix inequalities are given which place constraints on parameters 

of weight. Instead of fit transfer function using complex cepstrum algorithm, 

parameters of weight can be derived directly by solving corresponding 

LMIs. Numerical example shows that lower computational 

cost is spent and desired control performance is obtained using the 

proposed approach. 

◮ FrA05-5 14:50–15:10 

Research on Terminal Guidance Method in Glide Attack Phase of Aerodynamic 

Missile, pp.1150–1155 

Zhang, Zhi-kai 

Guo, Qing 



On basis of trimming glide and horizontal flight modes, the Miniderivative 

linearization model of aeromarine aerodynamic missile is 

constructed. As the mathematical model of missile plant, inertia component 

and rudder are combined, the PID parameters and lead/lag correction 

network is designed by frequency domain method to ensure the 

stability margin of attitude inner loop and the maneuverability of outer 

loop. The ballistic obliquity and height of missile are controlled in longitudinal 

plane. At the same time, the anticipant overload command is 

gained by revised proportional navigation law in lateral plane. Simulation 

results show that this terminal guidance method in glide attack 

phase can realize efficiently aeromarine horizontal flight in longitudinal 

plane and tracking target warship quickly with little miss distance. 

◮ FrA05-6 15:10–15:30 

Robust Diving Control of AUV with L2 Disturbance Attenuation Method, 

pp.1356–1360 

WANG, Hongjian 

Chen, Ziyin 



95


BIAN, Xinqian 

Jia, Heming 


Northeast Forest Univ. 

Towards the diving control of an autonomous underwater vehicle (AUV), 

a robust controller built upon L2 disturbance attenuation method with 

recursive backstepping technique is addressed. External disturbances 

are eliminated through L2 controller. A robust controller incorporated 

with switching control scheme is proposed against the parameter uncertainties 

with guaranteed stability. Different from conventional parameter 

adaptive learning method, the decreasing problem of learning precision 

can be avoided due to the fast varying of uncertain parameters, and parameter 

switching law can be obtained through the Lyapunov stability 

theory. The proposed controller yields asymptotical convergence to the 

given depth for the vehicle. Finally, simulation experiments are employed 

to illustrate the effectiveness of the proposed control scheme. 

FrA06 13:30–15:30 Room 302 

Modeling 

Chair: INOUSSA, GARBA 


Co-Chair: Liu, Xi Beijing Univ. of Civil Engineering & Architecture 

◮ FrA06-1 13:30–13:50 

Research on Multi-zone VAV Air Conditioning System Modeling, 

pp.2968–2972 

Liu, Xi 


Variable air volume systems are nonlinear, time-varying and multivariable 

with large time delay. Model predictive control can achieve satisfactory 

stability and energy-saving, the performance of which depends 

on precision and generalization capability of the predictive model. To 

overcome difficulties in modeling by mechanism, this paper proposes a 

modeling method of multi-zone VAV systems based on neural networks. 

The factors influencing on the sensible cooling load and coupling between 

zones are analyzed and consequently the structure of the neural 

network model is determined. In order to fully demonstrate the dynamic 

characteristics of the VAV system, neural network training samples 

cover all the VAV dynamic range. To increase generalization capability, 

Bayesian regularization algorithm is used to train the network. Experimental 

results show that the neural network predictive model has 

satisfactory accuracy and good generalization performance. 

◮ FrA06-2 13:50–14:10 

Modeling for flying boats in regular wave, pp.3019–3024 

Zhu, Yinggu 

Fan, Guoliang 

Yi, Jianqiang 



Inst. of Automation, Chinese Acad. of Sci.,China 

Although flying boats have been studied for over one hundred yeas, 

simulation of flying boats in waves and controllers designing for flying 

boats are still new research fields. After analyzing the forces acting 

on a flying boat, the flying boat is modeled in this paper as a special 

airplane. The forces from water are discussed and calculated by the 

2-dimensional (2D) strip theory which is widely used in the force approximation 

of planing crafts. And the model is validated by experiment 

data, which shows the mathematical model has an acceptable performance 

on the motion-prediction of the flying boat in water with regular 

waves. 

◮ FrA06-3 14:10–14:30 

EBE-based Parallel Finite Element Analysis of Electric Field in Aluminum 

Reduction Cell, pp.2939–2943 

Zhao, Xianyong 

Chen, Xiaofang 

Gui, Weihua 


Central south Univ. 


Finite element analysis for large-scale complicated structure such as 

aluminum reduction cell makes higher demand on memory capacity 

and calculation speed, which often results in failure or inefficiency of 

traditional serial computation for such large-scale problems. Finite element 

EBE-PCG algorithm is proposed on the basis of EBE(Element- 

By-Element) idea and Jacobi preconditioned conjugate gradient(PCG) 

method. The main difficulties in parallel implementation of this algorithm 

are discussed and solved, such as data organization of grid model, 

mixed elements processing and data communication. Subsequently, 

parallel program of finite element analysis based on EBE-PCG is developed 

using C language and MPI standard library, and then applied 

to numeric simulation of electric field distribution in aluminum reduction 

cell. The computational accuracy of parallel program developed is verified 

through comparison with commercial finite element software AN- 

SYS. Experiment results show that the method is of very high parallel 

efficiency and can greatly shorten the calculation time, which indicates 

the effectiveness of EBE’s use in parallel computation of large-scale 

complicated structures. 

◮ FrA06-4 14:30–14:50 

Quality Model for Integrated Security Monitoring and Control in Water 

Distribution Systems, pp.3107–3112 

Zubowicz, Tomasz 

Arminski, Krzysztof 

Brdys, Mietek 




This article addresses the problem of drinking water distribution system 

(DWDS) security in the terms of water quality which in the era of terrorist 

threat is of high importance to the public. The contribution of this 

paper is the development of the so called security module to extend 

a multi-species water quality model. This gives an insight to the situation 

in DWDS not only under normal operational conditions but also 

in case of a malicious attack on water quality. Moreover the security 

inputs are formally introduced to the model structure. This module 

enables simulation of both bacterial and/or chemical water contamination 

in DWDS environment. Previously defined inputs are utilised in 

proposed experiments by an attacking agent to influence the DWDS 

quality. The purpose of model development is to enable water scientists 

and water authorities to simulate the contamination propagation 

pathways and mechanisms throughout the network without omitting the 

’natural’water chemistry effects. A simple simulation example for 

the exemplary DWDS illustrates the model performance for two distinct 

contamination scenarios. 

◮ FrA06-5 14:50–15:10 

Model Establishment and Simulation for Finite Length Cylindrical Surface 

Heat and Moisture Transfer outside the Borehole, pp.3242–3247 

Zhang, Yating 

Jiang, Dawei 

Zhang, Ye 




Basing on the cylindrical heat transfer model of vertical ground source 

heat exchanger, and according to the mass conservation and energy 

conservation theorem, established a finite length cylindrical heat and 

moisture transfer model for soil layer around the borehole. Simulated 

the non-steady-state heat and moisture transfer of soil layers around 

the heat exchanger under summer operating conditions by using COM- 

SOL multi-physics coupling analysis software, and analyzed the influence 

of different soil thermal properties and initial moisture contents in 

temperature field and moisture field of soil layers exerted by the heat 

and moisture transfer. 

◮ FrA06-6 15:10–15:30 

Marine Vehicle Modeling and Tracking Using Wavelet Type Nets Model, 

pp.3118–3125 

INOUSSA, GARBA 

Peng, Hui 



The main objective of this paper is to address the tracking control problem 

of a ship moving with a constant velocity along a desired path. 

To this end, a combination of mathematical model (MM) and statistical 

model, namely the expanded exponential weight wavelet network 

based ARX (E-EW-WNN-ARX) model is proposed as internal predictor 

of a model predictive controller (MPC). Firstly, the EW-WNN-ARX 

model is used to describe the ship motion between the difference of 

heading angle deviation and the rudder angle of the ship. To represent 

the ship’s motion nonlinearity, the rolling angle is used as the EW- 

WNN-ARX model index to allow the model parameters to vary with the 

ship moving state. The EW-WNN-ARX model is identified off-line by 

96



using previously observed real data. Then The difference of heading 

angle deviation are expanded and integrated into mathematical model 

(MM) characterizing the position tracking error of the ship to form the 

E-EW-WNN-ARX-MM model which is used to develop a state space 

model that represent the ship tracking motion behavior. Finally, the i- 

dentified ship’s state-space type tracking motion model is used as 

predictor of a model predictive controller to steer ship moving forward 

with constant velocity along a predefined reference path. The effectiveness 

of the proposed modeling and control methods are demonstrated 

by the tracking control simulation in which the modeling data was obtained 

from the Shioji-maru experimental ship of Tokyo University of 

Marine Science and Technology of Japan. 

FrA07 13:30–15:30 Room 303 

Robotics (I) 

Chair: Wang, Zhiying 

Co-Chair: Yi, Yang 

Harbin Inst. of Tech. Shenzhen Graduate 

School 


◮ FrA07-1 13:30–13:50 

An energy-based position control and asymptotic stability analysis for 

manipulator handling a flexible payload, pp.3617–3622 

Liu, Shuyang 


Qiao, Yanfeng 

Xie, Mujun 

Li, Yuanchun 

Jilin Univ. 





Jilin Univ. 

Abstract - In this paper, an exact dynamic model of manipulator handling 

a flexible payload is derived using Hamiton’ s principle. We 

present a Lyapunov-based positioning controller for manipulator handling 

a flexible payload. On the basis of the distributed parameter model, 

the boundry feedback controller is constructed by applying the Lyapunov 

method. Using the LaSalle’s invariance principle and the characteristic 

of the differential operator, we prove the asymptotic stability in 

the neighbor-hood of the desired states of the closed-loop system. The 

effectiveness of the control strategy proposed is supported by some 

simulations. 

◮ FrA07-2 13:50–14:10 

A Novel Navigation Method, Optimal for Sloped Terrain, pp.3623–3628 

Chen, Haotian 

Sun, Fengchi 

Song, Meng 

Li, Shulun 

Huang, Yalou 

Nankai Univ. 

Nankai Univ. 

Nankai Univ. 

Nankai Univ. 

Nankai Univ. 

Terrain with large areas of inclines is very common for mobile robot. 

Although sloped terrain has special property, available research does 

not pay special attention to slopes, which leads to less efficiency, weak 

adaptability and low operability in such environments. On the other 

hand, people usually have their own values and simply use their own 

experience to deal with slopes in their daily life which suggests an approach 

to robot navigation problem in sloped terrain. In this paper, we 

present a navigation method that consists of a formula for computing 

the directional-dependent cost related to slopes and a path planning algorithm 

for grid maps with slope cost. This paper also provides several 

experimental results in line with human values including zigzag paths 

and spiral paths. 

◮ FrA07-3 14:10–14:30 

Path planning for mobile robots with leg/wheel hybrid locomotion system 

on outdoor terrain, pp.3669–3674 

Wang, Zhiying 

Ding, Xilun 


BeiHang Univ., China 

As the increasing of the population of leg/wheel robots in field trip, a typical 

path planning method is needed. This paper proposed a multi-layer 

strategy based on fuzzy logic for a multi leg/wheel hybrid mobile robot. 

Three main fuzzy logic controllers are proposed for adjusting direction, 

velocity and selecting locomotion type. Simulations were made step by 

step in Matlab. Positive results proved that our method is a possible 

solution. 

◮ FrA07-4 14:30–14:50 

Wasp Swarm algorithm on Terrain Coverage in Obstacle Environments, 

pp.3589–3594 

Zhang, Guo-you 


Terrain coverage algorithm based on response threshold of wasp swarm 

in swarm robotics is inspired of the division labor of wasp swarm. 

Robots decide their moving directions by sensing their local environments, 

and finish the task of terrain coverage cooperatively. In this 

paper, we introduce the problem model of terrain coverage in swarm 

robotics, the response threshold model of wasp swarm, and describe 

the moving strategy of swarm robots based on response threshold model 

in obstacle environments in detail. The coverage performance of the 

algorithm is analyzed through various shapes of obstacle in simulation 

experiments, such as the rate of cell coverage, duplicated coverage 

number of cell and coverage time. Simulation results show the method 

is effective and feasible. 

◮ FrA07-5 14:50–15:10 

The Research of Shape-shifting Robot Skid-turning with Line Configuration, 

pp.3800–3804 

Chang, Jian 

Wu, Chengdong 

Li, Bin 

Shang, Hong 





Sci. 

organization 

The shape-shifting robot AMOEBA-I has the ability to pass the narrow 

and small ruins space with the line configuration, but there are many 

restricts in use due to hard turning and large turning radius. In order 

to reduce the turning radius and the time of turning and enhance 

the compatibility of complex environment of robot, this article proposed 

a method that can make the robot turn more quickly and effectively. 

Through the establishment of mathematical model, the kinematics and 

mechanical properties of three modules are analyzed and the evaluation 

criterion is proposed for the performance of robot turning. At last, 

the validity and enforceability of the turning is proved by simulations and 

experiments. 

◮ FrA07-6 15:10–15:30 

Stable Running on A Kneed Biped Robot with Only Hip-joint Actuation, 

pp.3846–3851 

Yi, Yang 

Lin, Zhiyun 

Yan, Gangfeng 




Passive dynamic locomotion is well-known for its fabulous advantages 

in energy efficiency. On level ground passive dynamic running (P- 

DR) has been found based on planar compass biped, with energypreserved 

strategy applied. This paper extends this PDR motion on 

flat to a compliant kneed biped robot. The robot consists of two identical 

legs, each of which has a massive thigh and a massless shank. 

The knee joints are elastic with torque springs, so that energy dissipation 

is avoided at landing. Passive gaits have first been demonstrated 

on this kneed biped robot, yet they are not stable. Note that there is 

only one actuation at the hip joint. The robot thus has three degrees of 

underactuation, which increases the difficulty of control design. We derive 

an event-based control law to stabilize the system, and add energy 

shaping part to enlarge the basin of attraction and also provide additional 

robustness. The effectiveness of our approach has been verified 

by numerical simulations. 

FrA08 13:30–15:30 Room 310 

Invited Session: Applications of Semi-tensor Product in Information and 

Control 

Chair: Wang, Yuzhen 

Co-Chair: Chu, Tianguang 


Peking Univ. 

◮ FrA08-1 13:30–13:50 

97


On Definition and Construction of Lyapunov Functions for Boolean Networks, 

pp.1247–1252 

Wang, Yuzhen 

Li, Haitao 


Shandong U 

This paper investigates how to define and construct a Lyapunov function 

for Boolean networks, and presents a number of new results based 

on the semi-tensor product of matrices. A proper form of pseudo- 

Boolean functions is found, and the concept of (strict-)Lyapunov functions 

is thus given. It is shown that a pseudo-Boolean function in 

the proper form can play the role of Lyapunov functions for Boolean 

networks, based on which some Lyapunov-based stability results are 

obtained. Then, we study how to construct a Lyapunov function for 

Boolean networks, and propose a definition-based method. The study 

of illustrative examples shows that the new results/method presented in 

this paper work very well. 

◮ FrA08-2 13:50–14:10 

Finding all controllers for disturbance decoupling of Boolean control networks, 

pp.1344–1349 

Yang, Meng 

Chu, Tianguang 

Peking Univ. 

Peking Univ. 

We investigate a type of disturbance decoupling problem (DDP) of 

Boolean control networks. Using semi-tensor product of matrices, the 

dynamics of Boolean control network is expressed in its algebraic form. 

All the necessary arguments of the functions of outputs are called 

output-friendly coordinates. In order to estimate the solvability of D- 

DP, we give a necessary and sufficient condition of the output-friendly 

coordinates being always in a known invariant subspace. Then it is 

computationally feasible to construct all the valid feedback control matrices. 

The logical function of each controller can be recovered from the 

obtained feedback control matrix. We further discuss the constraints of 

the selection of the invariant subspace. Examples are provided to show 

the effectiveness of the proposed method. 

◮ FrA08-3 14:10–14:30 

New algorithm for finding fixed points and cycles of Boolean network, 

pp.2691–2694 

Suo, Jinghui 

Sun, Jitao 

Tongji Univ. 

Tongji Univ. 

Boolean networks have been successfully used in modeling biological 

systems. The fixed points, cycles and transient states that lead to them 

play an essential role to describe the structure of Boolean networks. 

In this paper we propose an algorithm to get all the fixed points and 

cycles of Boolean network by using the results of semi-tensor product 

and permutation. At last, an example is given to illustrate the efficiency 

of the obtained results. 

◮ FrA08-4 14:30–14:50 

Matrix Approach to Simulation and Bisimulation Analysis of Finite Automata, 

pp.2716–2721 

Xu, Xiangru 

Hong, Yiguang 

Lin, Hai 



Univ. of Notre Dame 

In this paper, the simulation and bisimulation relations of finite automata 

are investigated using matrix-based method. To solve the problem, 

composition operations of automata are studied first and then the algorithms 

and criteria are provided to check the simulation and bisimulation 

relations of two automata. An illustrative example is also provided. 

◮ FrA08-5 14:50–15:10 

Approximation of Boolean Networks, pp.2280–2285 


Zhao, Yun-Bo 

Zhao, Yin 


Univ. of Glasgow 


The problem of approximation to large-scale Boolean networks is considered. 

First, we assume a large-scale Boolean network is aggregated 

into several sub-networks. Using the outputs(or inputs) of each 

sub-network as new state variables, a new simplified time-varying network 

is obtained. Then a time-invariant Boolean network is used to 

approximate each subsystem. Observed data are used to find the best 

approximating dynamic models. Finally, the aggregation method is investigated. 

FrA09 13:30–15:30 Room 311A 

Invited Session: Coordination of Multi-Agent Systems and Networked 

Systems With Constraints and Uncertainties 

Chair: Chen, Zengqiang 

Co-Chair: Cheng, Long 

Nankai Univ. 


◮ FrA09-1 13:30–13:50 

Discarded Consensus of Multi-Agent Systems with State Constraint, 

pp.3460–3464 

Liu, Zhongxin 


NanKai Univ. 

Nankai Univ. 

This work addresses the consensus problem of multi-agent systems 

(MAS) with state constraint, while very few researchers consider such 

constraint in their study of consensus. A simple distributed algorithm 

named discarded consensus algorithm is proposed for weighted directed 

multi-agent systems. The proposed method can ensure the MAS 

reaching consensus while keeping the state estimate of each agent 

within its own constraint if the digraph is strongly connected and the 

initial state of each agent is within its constraint. Network of agents with 

both fixed and switching communication topologies are investigated in 

this paper. The convergence of the presented algorithms is analyzed 

theoretically and numerical simulations are provided to demonstrate the 

effectiveness of the theoretic results. 

◮ FrA09-2 13:50–14:10 

Control Design for Flexible Hierarchical Formation of Multiple Robots, 

pp.3595–3600 

Tang, Yutao 

Hong, Yiguang 



In this paper, we consider flexible formation and related control design 

of multi-robot systems. A hierarchical structure is provided for flexible 

formation of unicycle robots. With the virtual hierarchy defined in light 

of interconnection graphs, the formation and related parameters can be 

changed in different levels of the whole structure. The formation control 

design is given from a leader-following viewpoint. Stability analysis is 

also provided after that. 

◮ FrA09-3 14:10–14:30 

Performance Analysis for Multi-Agent Coordination With Partial Measurable 

States Over Digital Networks, pp.863–868 

Li, Tao 



Xie, Lihua 


In this paper, we consider the performance of a class of distributed 

coordination algorithms of discrete-time second-order multi-agent systems 

with partially measurable states and a limited communication data 

rate. The distributed coordinated control law is based on an encodingdecoding 

scheme which integrates the state observation with encoding/decoding. 

The convergence time, the selection of controller parameters 

and the performance limit are discussed. We give upper bounds 

of the convergence time in terms of precision, control and network parameters. 

We develop a linear approximation of the spectral radius of 

the closed-loop matrix with respect to the control gains and the algebraic 

connectivity of the communication graph, by which we show that for a 

connected network, 2-bit quantizers suffice for the exponential asymptotic 

synchronization of the states of the agents. Furthermore, it is 

shown that as the number of agents increases, the asymptotic convergence 

rate can be approximated as a function of the number of agents, 

the number of quantization levels (communication data rate) and the 

ratio of the algebraic connectivity to the spectral radius of the Laplacian 

matrix of the communication graph. 

◮ FrA09-4 14:30–14:50 

A Sampled-Data Based Average Consensus Protocol for Double- 

Integrator Multi-Agent Systems with Switching Topologies and Commu- 

98



nication Noises, pp.886–891 

Cheng, Long 

Wang, Yunpeng 

Hou, Zengguang 

Tan, Min 

Cao, Zhiqiang 






A distributed sampled-data based protocol is proposed for the average 

consensus of double-integrator multi-agent systems with switching 

topologies and communication noises. The proposed protocol uses information 

from two aspects: the agent’s own state and the relative s- 

tates between the agent and its neighbors. Due to the existence of communication 

noises, the relative states cannot be obtained accurately. To 

deal with this difficulty, a time-varying consensus gain is employed to attenuate 

the noise effect. Under the proposed protocol, it is proved that 

sufficient conditions for ensuring mean square average consensus are: 

the consensus gain satisfies the stochastic approximation type condition 

and the communication topology graph at each sampling instant is 

a balanced graph with a spanning tree. Finally, a simulation example is 

provided to demonstrate the effectiveness of the proposed consensus 

protocol. 

◮ FrA09-5 14:50–15:10 

Distributed Tracking Control for Linear Multi-Agent Systems with a 

Leader of Bounded Input Using Output Information, pp.1756–1761 

Li, Zhongkui 

Liu, Xiangdong 



This paper considers the distributed tracking control problem of multiagent 

systems with general linear dynamics and a leader whose control 

input is nonzero and available to only a subset of followers. Based on 

the relative output information of neighboring agents, two distributed 

observer-based controllers with, respectively, static and adaptive coupling 

gains, are designed for each follower to ensure that the states 

of the followers asymptotically approach the state of the leader, if the 

communication graph among the followers is undirected, the leader has 

directed paths to all followers, and the leader’s control input is bounded. 

A sufficient condition for the existence of the distributed controllers 

is that each agent is stabilizable and detectable. Simulation examples 

are given to illustrate the theoretical results. 

◮ FrA09-6 15:10–15:30 

A High Level Decentralized Tracking Algorithm for Three Manipulators 

subject to Motion Constraints, pp.1920–1924 

Wang, Lin 

Markdahl, Johan 

Hu, Xiaoming 

Kragic, Danica 


KTH 



This paper considers a tracking problem for three manipulators grasping 

a rigid object. The control objective is to coordinate the movements 

of the manipulators using local information in order to align the object 

attitude with a desired rest attitude and the object position with a time 

parameterized reference trajectory. The object rigidity is modelled as 

a constraint on the motion of the end-effectors saying that the distance 

between any pair of end-effectors must be constant in time. The control 

law consists of a rotational part and a translational part. The translational 

part also incorporates a linear observer of the reference trajectory. 

We prove stability and illustrate the system dynamics by simulation. 

FrA10 13:30–15:30 Room 311B 

Invited Session: Control over networks: an information flow point of 

view 

Chair: You, Keyou 

Co-Chair: Chen, Michael Z. Q. 



◮ FrA10-1 13:30–13:50 

Distributed Containment Control of Uncertain Linear Multi-Agent Systems, 

pp.869–874 

Wen, Guanghui 

Duan, Zhisheng 

Zhao, Yu 

Peking Univ. 

Peking Univ. 

Peking U 



This paper studies the containment control problem for uncertain linear 

multi-agent systems where the agents have identical nominal dynamics 

but subject to different norm-bounded parameter uncertainties. A new 

kind of distributed control protocol based only on the relative states of 

neighboring agents is constructed for achieving containment. By using 

tools from nonsmooth analysis and algebraic theory, it is proved that 

the states of the followers will asymptotically converge to a convex hull 

formed by those of the leaders if for each follower there exists at least 

one leader that has a directed path to that follower. Finally, a simulation 

example is given to verify the effectiveness of the theoretical results. 

◮ FrA10-2 13:50–14:10 

Adaptive Group Consensus of Coupled Harmonic Oscillators with Multiple 

Leaders, pp.3475–3480 

Su, Housheng 



Valeyev, Najl 




Univ. of Exeter 

In this paper, we investigate the group consensus of coupled harmonic 

oscillators with multiple leaders in an undirected fixed network. Unlike 

many existing algorithms for group consensus of multi-agent systems or 

cluster synchronization of complex dynamical networks, which require 

global information of the underlying network such as the eigenvalues of 

the coupling matrix or centralized control protocols, we propose a novel 

decentralized adaptive group consensus algorithm for coupled harmonic 

oscillators. By using the decentralized adaptive group consensus 

algorithm and without using any global information of the underlying 

network, all agents in the same group asymptotically synchronize with 

the corresponding leader even when only one agent in each group has 

access to the information of the corresponding leader. Numerical simulation 

results are presented to illustrate the theoretical results. 

◮ FrA10-3 14:10–14:30 

Pinning Control of General Multi-agent Systems, pp.1930–1935 

Yu, Wenwu 


Lu, Jinhu 




This paper studies synchronization via pinning control on general multiagent 

systems with strongly connected topologies. A criterion for reaching 

network synchronization on strongly connected networks is given. 

It is found that the vertices with very small in-degrees should be pinned 

first and the vertices with very large out-degrees may be pinned from 

the reformulated lower-order condition. Finally, a simulation example is 

given to verify the proposed pinning scheme. 

◮ FrA10-4 14:30–14:50 

Decentralized Adaptive Control for a Class of Semi-parametric Uncertain 

Multi-agent Systems, pp.2060–2065 

Ma, Hongbin 

Zhao, Yali 

Fu, Mengyin 






Decentralized adaptive control for a discrete-time multi-agent semiparametric 

uncertain dynamical system, where each agent is coupled 

with other agents in both parametric and nonparametric ways, is studied 

in this paper. Each agent can only use its history information and local 

information on its neighborhood agents to design its control law aimed 

at achieving its own local goal, i.e. tracking a local signal sequence. 

To deal with the co-existing parametric and nonparametric uncertainties 

occurred in the local couplings and the internal dynamics, an idea 

of information concentration is adopted with the key idea of nearestneighbor-estimation, 

which makes it possible to compensate well the 

challenging nonparametric uncertainties. With such ideas, decentralized 

adaptive control laws are designed based on the ”certainty equivalence” 

principle, and the simulations as well as preliminary theoretical 

discussions show that the closed-loop system for the whole multi-agent 

system is stable under some mild conditions on the a priori knowledge 

99


on the uncertain couplings. 

◮ FrA10-5 14:50–15:10 

State Estimation for a Class of Nonlinear Systems with Multi-package 

Transmission, pp.2221–2226 

Wang, Xiaofeng 

He, Xiao 

Wang, Zidong 




In this paper, the state estimation problem for a class of nonlinear systems 

with multiple channels and correlated noises is studied in the 

framework of Extended Kalman Filter (EKF). In networked systems, 

when sensors are distributed in a large spatial area and multiple channels 

are employed to transfer data from different sensors, measurements 

may be lost at different rates. A diagonal matrix is utilized to 

describe this phenomenon and an unbiased optimal nonlinear filter is 

constructed in the least mean square sense. An illustrative example is 

provided and the comparison of the results between our method and 

the EKF shows the effectiveness of the proposed approach. 

◮ FrA10-6 15:10–15:30 

Kalman Filtering with Scheduled Measurements - Part I: Estimation 

Framework, pp.2251–2256 

You, Keyou 

Xie, Lihua 



This paper proposes an estimation framework under scheduled measurements 

for linear discrete-time stochastic systems. Both controllable 

and uncontrollable schedulers are considered. Under a controllable 

scheduler, only the normalized measurement innovation greater than a 

threshold will be communicated to the estimator. While under an uncontrollable 

scheduler, the time duration between consecutive sensor communications 

is triggered by an independent and identically distributed 

process. For both types of scheduler, recursive estimators that achieve 

the minimum mean square estimation error are derived, respectively. 

Moreover, necessary and sufficient conditions for stability of the mean 

square estimation error are provided. 

FrA11 13:30–15:30 Room 311C 


(I) 





◮ FrA11-1 13:30–13:50 

Online Route Planning for UAV Based on Model Predictive Control and 

Particle Swarm Optimization Algorithm, pp.397–401 

Peng, Zhihong 

Li, Bo 

Chen, Xiaotian 

Wu, Jin Ping 



Sci. & Tech. on Complex Land Sys. Simulation 

Laboratory 


Based on the model predictive control (MPC) and particle swarm optimization 

(PSO) algorithm, an online three-dimension route planning 

algorithm is proposed in this paper for UAV under the partially known 

task environment with appearing threats. By using the preplanningonline 

route tracking pattern, a reference route is planned in advance 

according to the known environment information. During the flight, the 

UAV tracks the reference route and detects the information of the environment 

and threats. Based on the MPC and PSO algorithm, the online 

route planning can be achieved by means of route prediction and receding 

horizon optimization. In such a case, UAV can avoid the known and 

appearing threats successfully. Compared to the traditional online route 

planning algorithm, the proposed method, by making use of the partially 

known information, can reduce the complexity, and meanwhile improve 

the real-time and the feasibility of the planning route. Simulation results 

demonstrate the effectiveness of the proposed algorithm. 

◮ FrA11-2 13:50–14:10 

Surrogate Models for User’s Evaluations base on Weighted Support 

Vector Machine in IGAs, pp.144–149 

Yang, Lei 


Gong, Dunwei 

Sun, Xiaoyan 

Sun, Jing 




Interactive genetic algorithms (IGAs) are effective methods of tackling 

optimization problems involving qualitative indices by incorporating a 

user’s evaluations into traditional genetic algorithms. The problem 

of user fatigue resulting from the user’s evaluations, however, has a 

negative influence on the performance of these algorithms. Substituting 

the user’s evaluations with various surrogate models is beneficial 

to alleviate user fatigue. Previous studies, however, have not taken full 

advantage of information provided by samples obtained earlier when 

constructing or updating these models. We focus on the issue of user 

fatigue in this study, and present a novel method of effectively alleviating 

user fatigue by substituting the user’s evaluations with a weighted 

support vector machine (WSVM) and by incorporating it with the mechanism 

of transfer learning. The proposed method is applied to the fashion 

evolutionary design system and compared with previous effective 

IGAs. The experimental results confirm the advantage of the proposed 

method in both alleviating user fatigue and improving the precision of 

the surrogate model. 

◮ FrA11-3 14:10–14:30 

A Hybrid Algorithm Based on Simplex Search and Differential Evolution 

for Hybrid Flow-shop Scheduling , pp.643–648 

Xu, Ye 

Wang, Ling 





An effective hybrid algorithm by merging the searching mechanisms 

of Nelder-Mead (NM) simplex method and differential evolution (DE) is 

proposed to solve the hybrid flow-shop problem (HFSP) in this paper. 

By using a special encoding, the NM and DE methods can be used 

to solve permutation based combinatorial optimization problems. By 

combining the DE based global search and NM method based local 

search, the exploration and exploitation abilities are enhanced and well 

balanced for solving the HFSP. Numerical testing results and comparisons 

show that the proposed algorithm is effective, efficient and robust 

in solving the HFSP. 

◮ FrA11-4 14:30–14:50 

A Compact Estimation of Distribution Algorithm for Solving Hybrid Flowshop 



Wang, Ling 

Xu, Ye 




According to the characteristics of the hybrid flow-shop scheduling 

problem (HFSP), the permutation based encoding and decoding 

schemes are designed and a probability model for describing the distribution 

of the solution space is built to propose a compact estimation of 

distribution algorithm (cEDA) in this paper. The algorithm uses only t- 

wo individuals by sampling based on the probability model and updates 

the parameters of the probability model with the selected individual. 

The cEDA is efficient and easy to implement due to its low complexity 

and comparatively few parameters. Simulation results based on some 

widely-used instances and comparisons with some existing algorithms 

demonstrate the effectiveness and efficiency of the proposed compact 

estimation of distribution algorithm. The influence of the key parameter 

on the performance is investigated as well. 

◮ FrA11-5 14:50–15:10 

A Memetic PSO based KNN Regression Method for Cycle Time Prediction 

in a Wafer Fab, pp.474–478 

Ni, Jiacheng 

Tongji univ 

In this paper, cycle time prediction of wafer lots is studied. A memetic 

algorithm called GSMPSO by combining the PSO with a Gaussian 

mutation operator and a Simulated Annealing (SA)-based local search 

operator is developed to weight the features for K Nearest Neighbors 

(KNN) regression. The GSMPSO-KNN regression method is adopted 

to predict the cycle time of wafer lots. The experiment result demon- 

100



strates that a more accurate result can be obtained by the proposed 

method compared with some other prediction methods. The critical 

factors affecting the cycle time of wafer lots can also be extracted by 

the proposed method. 

FrB01 15:50–17:50 Room 203A 

Intelligent Control and Automation (II) 

Chair: Zhang, Tianping 

Co-Chair: Li, Dazi 



◮ FrB01-1 15:50–16:10 

Adaptive Dynamic Surface Control of Nonlinear Systems with Perturbed 

Uncertainties in Strict-Feedback Form, pp.24–29 


Shi, Xiaocheng 

Yang, Yuequan 

Gao, Huating 





Based on the approximation capability of radial basis neural networks 

and the integral-type Lyapunov function, adaptive dynamic surface control(DSC) 

is investigated for a class of strict-feedback nonlinear systems 

with unknown virtual control gain functions. The main advantages of 

the proposed scheme are that only one parameter is adjusted in the 

whole backstepping design by using Young’s inequality and dynamic 

surface control, and the computational burden is effectively alleviated. 

By theoretical analysis, the closed-loop control system is proved to be 

semi-globally uniformly ultimately bounded, with arbitrary small tracking 

error by appropriately choosing design constants. Simulation results 

demonstrate the effectiveness of the proposed method. 

◮ FrB01-2 16:10–16:30 

A Resource-Allocating Network Based on Local Conditions and it’s application 

in Prediction of nonlinear systems, pp.326–330 

Qi, Wenyuan 

Li, Dazi 



In this paper, a resource-allocating network based on local conditions 

(RAN-LC) is proposed to avoid the existing problems of RAN. This 

method gets the initial hidden nodes by using K-means clustering algorithm 

and the characteristics of activation function, and it utilizes new 

Novelty Criterion based on local conditions instead of the old one to 

keep the network neat and efficient. Moreover, it adopts Multi-patterns 

to enhance the generalization ability of network in the state of parameters 

adjustment. The simulation results show that this method can 

generate network quickly and more reasonable. The network generated 

finally has good performance and also works well in the prediction 

of nonlinear systems. 

◮ FrB01-3 16:30–16:50 

Fuzzy Guaranteed Cost Control Design for Uncertain Chaotic System 

with polytopic uncertainty , pp.180–184 

Liu, Yuyan 


Hao, Zulong 




Fuzzy guaranteed cost controller design method for uncertain chaotic 

system stability control is addressed. Uncertain chaotic system is expressed 

by T-S fuzzy model with polytopic uncertainty, and then sufficient 

condition for global T-S fuzzy system stability and solving algorithm 

for state feedback fuzzy guaranteed cost controller are given. Simulation 

is made for uncertain Lorenz chaotic system with fixed parameter 

or time-varying parameters, and simulation results show control system 

designed by the given method has good control performance and 

robust stability. 

◮ FrB01-4 16:50–17:10 

Diffusion Source Localization with a Water-proof Boundary, pp.164– 

169 

Luan, Fan 

Chai, Li 



Yang, Jun 




The source localization problem is studied for a pollution source close 

to a water-proof boundary. An approximation method using the elementary 

analytic function is applied to the density attenuation model, which 

provides easy and fast localization algorithm. Two different algorithms 

are proposed for the pollution source localization based on the information 

sent by wireless sensor networks. While one algorithm, which 

is based on time and concentration, can localize the source with small 

delay, the other, which is based only on concentration of the steady 

state, can provide better localization accuracy and robustness. Simulation 

results are given to demonstrate the effectiveness of the proposed 

algorithms. 

◮ FrB01-5 17:10–17:30 

Hierarchical Coordination Control Method for Near Space Flight Vehicle 

with Aero and Direct Force Actuators, pp.255–259 

Chao, Tao 

Wang, Songyan 

Yang, Ming 


HIt 

HIT 

Proposed an attitude control method based on hybrid dynamic system 

theory and hierarchical coordination control for near space vehicle with 

both aero and direct force actuators, called blended actuators vehicle 

(BAV). The mathematical model of BAV was derived and the hybrid 

dynamic system characteristics of the vehicle were analyzed. A hierarchical 

control framework with two level controllers for BAV was presented 

and described as hybrid automaton. Design method for the two 

level controllers was given. The usefulness and effectiveness of the 

proposed hierarchical coordination control method were demonstrated 

through numerical simulation. The results showed that the vehicle accomplished 

guidance and control mission successfully under the control 

of the designed controllers, though there were modeling uncertainty 

and disturbances. The hierarchical coordination control method is a 

useful way to design attitude controllers for BAV. 

◮ FrB01-6 17:30–17:50 

Kalman Filter Restraining Outliers for Short Baseline System, pp.322– 

325 

Qiao, Gang 

Li, Zhuang 

Sun, Zongxin 

Nie, Donghu 

Cui, Haiyue 



heu 

heu 


In short baseline underwater acoustic positioning system, the outliers 

will be included in measurement data because of noise and multi-path 

channel environment. In this paper, we introduce a modified 

Kalman filter that can perform robust based on innovation variance, 

this method solves the problem of filtering divergence effectively 

by modifying the supplement matrix. Finally the processing result of 

the experiment data in lake is given. The experiment result indicates 

that using the method can be efficiently a smooth processing to the 

localization result. 

FrB02 15:50–17:50 Room 203B 

Computational Intelligence and Applications (II) 

Chair: Mo, Hong 

Co-Chair: Wang, Ning 



◮ FrB02-1 15:50–16:10 

Online Self-constructing Fuzzy Neural Identification for Ship Motion Dynamics 

Based on MMG Model, pp.458–463 

Wang, Ning 

Niu, Xiaobing 

Liu, Yudong 




In this paper, an online self-constructing fuzzy neural identification for 

MMG ship motion model is clearly identified by using the promising 

Generalized Ellipsoidal Function Based Fuzzy Neural Network (GEBF- 

FNN) method. Nonlinear differential equations of MMG-type ship mo- 

101


tion dynamics are used to establish the reference model implicating essential 

nonlinearities for GEBF-FNN based ship motion model (GEBF- 

FNN-SMM) identification. The GEBF-FNN-SMM starts without fuzzy 

rules and online recruits efficient fuzzy rules via rule node generation 

criteria and parameter estimation. The resultant GEBF-FNN-SMM reasonably 

captures essential dynamics since the checking process validates 

the prediction performance with high accuracy. Finally, in order to 

demonstrate that the GEBF-FNN-SMM scheme is effective, simulation 

studies are conducted on zig-zag maneuvers. Moreover, comprehensive 

comparisons are carefully presented. Simulation results indicate 

that the GEBF-FNN-SMM achieves promising performance in terms of 

approximation and prediction. 

◮ FrB02-2 16:10–16:30 

A Probabilistic Fuzzy Controller with Operant Learning for Robot Navigation, 

pp.368–373 

Gao, Yuanyuan 


Song, Hongjun 




Fuzzy logic system (FLS) promises an efficient way for obstacle avoidance. 

However, it is difficult to maintain the correctness, consistency, 

and completeness of a fuzzy rule base tuned by a human expert. In 

this paper, a novel approach termed probabilistic fuzzy controller with 

operant learning (PFCOL) for robot navigation is presented. Operant 

learning (OL) is a form animal learning way. The key feature of this 

approach is that it combines a probabilistic stage and a stochastic perturbation 

generator module into FLS to handle problems. At last, the 

ultimate output is determined by these two uncertain stages. This imitates 

animal learning method of generating stochastic behavior in the 

complex and uncertain environment. The simulation results show that 

the proposed PFCOL method can automatically generate approximate 

actor to adapt complex circumstances. Through studies on obstacle 

avoidance and goal seeking tasks by a mobile robot verify the approach 

is superior in generating efficient fuzzy inference systems. 

◮ FrB02-3 16:30–16:50 

The Optimization of Fuzzy Rules Based on Hybrid Estimation of Distribution 

Algorithms, pp.561–565 

Luo, Xiong 

Bai, Xue 



Optimization of fuzzy rules based on numerical data is an important issue 

in the optimization design of fuzzy system. In this paper, based on 

an improved estimation of distribution algorithm, an optimization learning 

method COR MUMDA for fuzzy rules is proposed. This method can 

generate fuzzy rules directly from numerical data. The method learn 

fuzzy rules mainly based on MUMDA (multi-group univariate marginal 

distribution estimation algorithm). Unlike the general estimation of distribution 

algorithms, MUMDA can increase the diversity of the population 

and avoid sticking at local optima. In addition, the elite genetic 

strategy is used to generate the next population. In this way, it reduces 

the possibility of losing the optimal solutions. To verify the efficiency of 

this algorithm, the simulation experiments are performed. The comparative 

results of three classic examples are given. 

◮ FrB02-4 16:50–17:10 

On the Definition of Type-2 Fuzzy Sets, pp.601–605 

Mo, Hong 

Zhou, Min 



This paper introduces four kinds of definition of type-2 fuzzy sets(T2 

FS), and provides their difference and connection,then presents a new 

definition of type-2 fuzzy set to make it to be understood more easily, 

In finial, we modify the definition and formula of footprint of uncertainty 

(FOU) of T2 FS and give the relation of FOU and T2 FS. 

◮ FrB02-5 17:10–17:30 

Estimation of Hand Force from Surface Electromyography Signals using 

Artificial Neural Network, pp.584–589 

Srinivasan, Haritha 

Sauvik, Das Gupta 



Sheng, Weihua 

Chen, Heping 



Haptic technology has many real world applications such as rehabilitation 

robotics, telepresence surgery, gaming, virtual reality and humanrobot 

interaction. Force plays an important role in the above mentioned 

haptic applications. In this paper, we propose a method to estimate 

force from surface Electromyography (SEMG) signals using Artificial 

Neural Network (ANN). The haptic device is modeled to act as a virtual 

spring. The neural network is trained with EMG data from wrist flexion 

action as input and force values from the haptic device as target. The 

results shown in this paper illustrate the neural network performance in 

estimating the force values in real-time. 

◮ FrB02-6 17:30–17:50 

Supervisor Design with Petri Nets for Asymmetrical System, pp.628– 

632 

FENG, Aixiang 




Asymmetrical processes are common nonlinear systems, where the 

switching between two different operating modes depends on whether 

the system input or output is increasing or decreasing. The existing 

intelligent control methods for asymmetical system can’t explain the relationship 

between continuous and discrete part of the system .With 

the idea of hybrid systems, a supervisory control method is developed 

to distinguish asymmetry phenomena of the thermal process of a furnace. 

In order to ensure the stability of arriving at the setting point, a 

supervisory controller modelled by the extended Controlled Petri nets 

is designed to track errors and the direction of the output.Then the 

system can be switched to an appropriate mode , where the tracking 

determines the switch.Test result verifes the validity of this method. 

FrB03 15:50–17:50 Room 203C 


Chair: Wang, Guo-sheng 

Co-Chair: XIE, Wei 



◮ FrB03-1 15:50–16:10 

Quadratically Stabilizing Observer-based Controller Design for LPV 

Plant , pp.1073–1076 

XIE, Wei 


This paper presents a new quadratic stability-preserving state-space 

realization and quadratically stabilizing observer-based controller design 

for Linear Parameter Varying (LPV) plant, which is combined with 

a set of local vertex LTI plants. A two-step procedure is taken to design 

a quadratically stabilizing observer-based controller for LPV plant. First, 

according to the stabilizabilities of local LTI plants, state-feedback gain 

matrices and Lyapunov matrices could be solved for these local plants 

with linear matrix inequality technique, independently. With these Lyapunov 

matrices, a proper state space realization of LPV plant is provided. 

Second, based on the state space realization, a quadratically 

stabilizing observer-based controller is obtained. 

◮ FrB03-2 16:10–16:30 

A Closed-loop Evaluation for Regulatory Control Structure of Multivariable 

System, pp.1083–1088 


Ren, Li-hong 



In multivariable processes, control structure selection represented as 

the pairing of manipulated variables and controlled variables is a major 

concern during the design of multi-loop regulatory PID control system. 

Each pairing method has its application scope, and it is necessary to 

analyze and evaluate the closed-loop application effect of the system 

pairing. Based on the dynamic transmission ratio between manipulated 

variables and controlled variables under the closed-loop state, the 

degree of coupling for each loop is calculated for the system pairing, 

and the closed-loop evaluation which is achieved by the determination 

of whether the degree of coupling is within the threshold is proposed 

to determine whether the result is satisfied. This method can not only 

be used to compare several different pairing results during the design 

102



of control system, but also to evaluate the pairing which is currently 

applied and analyze whether another variables pairing is needed. 

◮ FrB03-3 16:30–16:50 

Design of Parametric Finite Time Functional Observers in Linear Timeinvariant 


Wang, Guo-sheng 

Lv, Hong-tao 

Lv, Qiang 




A design problem of the parametric finite time functional observers for 

the linear time-invariant systems is investigated. The aim is to design 

the parametric form of the functional observers which can estimate directly 

the linear functions of the state for the linear time-invariant systems 

in a predefined finite time. Based on the parametric solutions for 

a class of Sylvester matrix equation, this paper presents the parametric 

expressions of all the gain matrices for the finite time functional observers, 

in which the free parameters can offer all the design degrees 

of freedom for control system design. Finally, a numerical example and 

its simulation results show the simplicity and effectiveness of the proposed 

design method of parametric finite time functional observers in 

the linear time-invariant systems. 

◮ FrB03-4 16:50–17:10 

Observer Design for Wave Equations with van der Pol Type Boundary 


Li, Liangliang 

Huang, Yu 


Zhongkai Univ. of Agriculture & Engineering 

Zhongshan (Sun Yat-Sen) Univ. 


In this paper, we study the observer design problem for onedimensional 

wave equation with van der Pol type boundary condition, 

whose dynamics presents spatiotemporal chaotic behaviors. By introducing 

a linear error feedback on the boundary, we construct an observer 

via method of characteristic. The main approach is to construct two 

one-dimensional mappings which can characterize the evolutionary dynamics 

of the system as well as the observer, and the convergence of 

error dynamics is obtained in terms of these two mappings. The range 

of the feedback gain is identified. Numerical simulation is provided to 

illustrate the theoretical outcomes. 

◮ FrB03-5 17:10–17:30 

The Estimation of the Solutions Matrix of the Perturbed Discrete Time 

Algebraic Riccati Equation, pp.1172–1175 

Bi, Haiyun 

Chen, Dongyan 

Anhui Polytechnic Univ. 


In this paper, the estimation problem of the solution matrix about the 

perturbed discrete time algebraic Riccati equation (PDTARE) is discussed. 

The estimation of upper and lower bounds of the solution 

matrix to the equation under a certain uncertainty assumption are p- 

resented by applying the matrix calculation property, and the estimation 

results are given by a matrix inequality and a discrete time algebra 

Riccati equations(DTARE). Finally, the effectiveness of above results is 

shown by an example. 

◮ FrB03-6 17:30–17:50 

Admissibility Conditions for Linear Singular Delta Operator Systems: 

Analysis and Synthesis, pp.1870–1875 

Mao, Qing-tang 

Dong, Xin-zhuang 

Tian, Wan-hu 

Qingdao Univ. 

Qingdao Univ. 

Qingdao Univ. 

This paper mainly deals with the problems of admissibility analysis and 

admissible control for linear singular delta operator systems. By introducing 

the delta operator into the discrete model of a linear singular 

continuous system, the delta operator model is set up which tends to 

the corresponding continuous system when the sampling period tends 

to zero. Necessary and sufficient admissibility conditions are proposed 

for linear singular delta operator systems. Based on these results, the 

problem of admissible control is also considered and an explicit expression 

of a desired state feedback controller is given. Finally, examples 

are provided to demonstrate the results in this paper. 

FrB04 15:50–17:50 Room 203D 

Slide Mode Control 

Chair: Liu, Shirong 

Co-Chair: SHI, Juan 



◮ FrB04-1 15:50–16:10 

Nonlinear Attitude Control of a 3D Rigid Pendulum using Hierarchical 

Sliding Mode Techniques, pp.1524–1528 

Zou, Kui 

GE, Xinsheng 

Beijing Information Sci. & Tech. 


This paper studies the attitude control problem of a 3D rigid pendulum. 

The 3D rigid pendulum is a reduced model of Geo-stationary(GEO) 

spacecraft. It consists of a rigid body supported by a fixed and frictionless 

pivot with three rotational degrees, acted on by a constant gravitational 

force. An improved hierarchical sliding mode method is proposed 

for nonlinear attitude control of the 3D rigid pendulum at arbitrary position. 

This method divides the system into two subsystems, and then we 

use Lyapunov law to obtain the total control input which contains equivalent 

control components of each subsystem, we can guarantee that 

each subsystem accesses into their own sliding planes. The asymptotic 

stability of all sliding planes is also proved theoretically, and simulation 

results show the controller’s validity. 

◮ FrB04-2 16:10–16:30 

Integral Sliding Mode Control of Z-source Inverter for Motor Drive System 

of Electric Vehicles, pp.1196–1200 

Sun, Qu 

Wang, Yongyu 


Univ. of Beijing Post & Telecommunication 

In order to overcome the disadvantages of non-minimum phase system, 

which the Z-source inverter has when it is used in the motor drive system 

of electric vehicles, an integral sliding-mode controller based on 

equivalent control is proposed. With the reaching condition imposed, 

the controller can guarantee the stability of closed-loop system and the 

robustness of system output. Based on the pseudo-output, which is defined 

by measured capacitor voltage and inductor current on Z-source 

network, a constant capacitor voltage control strategy for Z-source inverter 

is given. The simulation results show that the closed-loop control 

system for Z-source inverter can achieve better global robustness with 

zero steady error, and that the capacitor voltage on Z-source network 

can be stabilized so that good performances of motor drive system can 

be effectively realized. 

◮ FrB04-3 16:30–16:50 

On Hierarchical Sliding Mode Control of Underactuated TORA System, 

pp.1785–1789 

Bao, Yuqing 

Li, Junyuan 

Xie, Jihua 

Gao, Bingtuan 





Two different hierarchical sliding mode controllers are designed for an 

underactuated TORA (Translational Oscillator with Rotational Actuator) 

system. Firstly, the dynamics of TORA system is separated into two 

subsystems according to the motion degrees of freedom. Each subsystem 

contains two state variables, which are chosen to construct a 

sub-sliding mode surface. And the total sliding mode surface is defined 

as a function of the two sub-sliding mode surfaces. For the two methods 

of hierarchical sliding mode control, the first method is to define the 

total sliding mode surface as a piecewise linear function of the two subsliding 

surfaces. The coefficient of the function needs to be frequently 

switched to guarantee the system stability. The second method is to define 

the total sliding mode surface as a continuous liner function of the 

two sub-sliding mode surfaces. The coefficient sign of the continuous 

liner function is determined by analyzing the model. Finally, simulation 

results demonstrate the feasibility of the two controllers, and show that 

the second controller has better performance. 

◮ FrB04-4 16:50–17:10 

Dynamic Output Feedback Sliding Mode Control for Magnetic Bearing 

System Stabilization, pp.1547–1552 

103


SHI, Juan 

Yan, Yan 

Yu, Xinghuo 



RMIT Univ. 

In this paper, a simplex type dynamic output feedback variable structure 

control (VSC) for the stabilization of a nonminimum phase open-loop 

unstable MBC500 magnetic bearing system is presented. An observer 

type sliding mode controller is designed for the unstable nonminimum 

phase system. The performance of the designed sliding mode controller 

is compared with those of a conventional controller, a controller 

designed based on analytical interpolation approach, and a fuzzy logic 

controller (FLC). Simulation results shown that the designed observer 

type sliding mode controller has superior performance than the other 

control approaches as it provides the best transient and steady-state 

responses with less control efforts. 

◮ FrB04-5 17:10–17:30 

Second Order Sliding Mode Control of Pan-tilt Joint in Modular Manipulator, 

pp.2188–2193 

Liu, Shirong 

Chen, Jianye 



In this paper, a new approach for the robust control of pan-tilt joint in 

modular manipulator is proposed. A MIMO second order sliding mode 

controller is designed by some particular properties of pan-tilt joint. 

The proposed approach can eliminate the high frequency chatter and 

improve the control robustness and accuracy as the same as classical 

sliding mode. The designed controller also works well even under 

the lower frequency control, and such relaxes the limit to controlled 

plants. Simulations have shown the effectiveness of the proposed control 

method in the trajectory tracking of pan-tilt joint. 

◮ FrB04-6 17:30–17:50 

Sliding Mode Variable Structure Control Based on Exact Linearization 

Mode of Nonlinear System, pp.2878–2881 

Wang, Ya-hui 

Xia, Zhifeng 

Zhang, Tong 


Department of Automation Engineering Beijing 

Univ. of Civil & Architecture Engineering 

Beijing Univ. of Architecture & Civil Engineering 

Exact linearization is a traditional method for nonlinear system in the 

nonlinear system theory, the basic idea of which is to linearize the w- 

hole or parts of a nonlinear system in the way of nonlinear coordinate 

transformation and nonlinear state feedback. The input and output of 

the nonlinear system can be expressed as a linearized model that can 

be designed using linear system design method. In order to enhance 

the robustness of the designed system, the robust control strategy is 

adopted by the system that is linearized via feedback. As a result, 

the control law is composed of feedback linearization control and robust 

control. Sliding mode variable structure control is a robust control 

method which is adopted in this paper as a robust control strategy for 

the exact linearized system. 

FrB05 15:50–17:50 Room 203E 

Control Theory (II) 

Chair: Han, Qing-Long 

Co-Chair: Ren, Li-hong 



◮ FrB05-1 15:50–16:10 

A New Variable Pairing Method and Its Application in Non-square Multivariable 


Ren, Li-hong 




Variable pairing is important for the selection of decentralized control 

structure. A new method is proposed basing on the open-loop step response, 

according to ISE. This method is first used in the variable pairing 

of square multivariable systems , then is extended to non-square 

multivariable systems. Non-square multivariable systems are squared 

down by this method. Typical cases including square and non-square 

systems are employed to show that both steady-state and transient system 

properties are considered in the proposed method and this method 

is useful for the reasonable variable pairing . 

◮ FrB05-2 16:10–16:30 

Synchronization for Lur’e Type Complex Dynamical Networks with 

Time-Varying Delay Based on Linear Feedback Controller, pp.1389– 

1394 

Duan, Wenyong 

Cai, Chenxiao 

Zou, Yun 




This paper discusses the synchronization problem for a class of Lur’e 

type complex dynamical networks with time-varying delay. Based on a 

Lyapunov-Krasovskii functional, some new delay-dependent synchronization 

criteria are derived in the form of linear matrix inequalities by 

employing a delay decomposition and free matrix method. And a strategy 

for synchronization is presented based on a linear feedback controller. 

Some numerical examples are presented to illustrate the effectiveness 

and advantage of the proposed synchronization criteria. More 

less conservative is shown with comparison to the existing results. 

◮ FrB05-3 16:30–16:50 

Distributed Estimation For Discrete Time Systems With Time-Varying 

Observation Delay, pp.1881–1886 

Wang, Wei 

Han, Chunyan 

Feng, Shulin 




In this paper, we will consider the problems where observation delay 

is time-varying and design distributed estimators by combining a consensus 

strategy. The optimal local estimator for every node with timevarying 

observation delay is first designed, which plays an important 

role in the distributed estimator design. Then the optimal distributed 

estimator for any choice of the consensus gain is proposed by adding a 

modification on the optimal local estimator. The suboptimal estimator is 

proposed by approximating the Riccati equations of optimal distributed 

estimator. Finally, a sufficient condition for asymptotical stability of the 

suboptimal distributed estimator is given. 

◮ FrB05-4 16:50–17:10 

Global Stabilization for a Class of Integrator Chain with Bounded Control, 

pp.2245–2250 

Fang, Haijun 

MKS Instrument 

In this paper, a clase of integrator chain systems with bounded control 

is considered. A nested control law is proposed so that the closed-loop 

system is globally stabilized with the bounded control. Effectiveness of 

the proposed control law will be shown through simulation. 

◮ FrB05-5 17:10–17:30 

Estimation of the domain of attraction for asymmetric saturated linear 

systems via Polyhedral Lyapunov Functions, pp.1253–1258 

Zhao, Guanglei 




This paper considers the problem of estimating the domain of attraction(DA) 

for continuous-time single-input asymmetric saturated linear 

systems. The stability analysis is implemented based on the polyhedral 

Lyapunov function rather than on the classical quadratic Lyapunov function. 

According to the saturation property, the saturated linear systems 

are regarded as switched linear systems, and a new sufficient condition 

for a polytope to be an estimation of the DA is derived. Moreover, 

a novel algorithm is proposed to compute the estimation of the DA. The 

effectiveness of the developed methods is illustrated with numerical examples 

◮ FrB05-6 17:30–17:50 

Output feedback stabilization of polytopic-type uncertain discrete systems 

with interval-like time-varying state and input delays, pp.2119– 

2124 

Ge, Xiaohua 

Jiang, Xiefu 


Central Queensland Univ. 



The output feedback stabilization of polytopic-type uncertain discrete 

104



systems with interval-like time-varying state and input delays is studied. 

Based on a new bounding inequality technique, combining a parameterdependent 

Lyapunov functional, a stability criterion is firstly presented 

in terms of a set of simple convex feasibility tests. Then, the output feedback 

stabilization conditions are formulated in the form of non-convex 

matrix inequalities, of which a feasible solution can be obtained by solving 

an LMI-based minimization problem. The newly proposed inequality 

lies in the partitioning idea of the varying interval and shows its more 

tightness over some existing bounding techniques. No free weighting 

matrix is involved. Two illustrative examples are finally given to verify 

the advantage and effectiveness of the proposed method. 

FrB06 15:50–17:50 Room 302 


Chair: Yang, Hua 

Co-Chair: Chen, Xi 



◮ FrB06-1 15:50–16:10 

Recursive Identification for Wiener-Hammerstein Systems Using Instrumental 

Variable, pp.3043–3048 

Chen, Xi 

Fang, Hai-Tao 



An identification method is discussed that deals with the Wiener- 

Hammerstein systems of general nonlinearity. By introducing a suitable 

instrumental variable a new algorithm is presented to recursively 

estimate the linear subsystems using stochastic approximation algorithm. 

The kernel nonparametric method is used to estimate the nonlinear 

function. The consistent analysis of the method is given under 

mild condition. A simulation example is provided justifying the proposed 

method. 

◮ FrB06-2 16:10–16:30 

Roll and Pitch Model Identification for Miniature Unmanned Helicopter 

Based on Subspace Method, pp.3059–3063 

Bai, Meng 

Li, Minhua 



The dynamic model of a miniature unmanned helicopter is needed to 

develop for autonomous helicopter flight. A subspace identification 

method for the roll and pitch coupling model is proposed. The roll and 

pitch coupling model of a hovering miniature unmanned helicopter is 

deduced to obtain an identification model structure for use in the subspace 

method. The roll and pitch model is identified based input and 

output data using the subspace method. Simulation results demonstrate 

that the identified model can reflect the roll and pitch dynamic 

effectively with higher system identification precision. 

◮ FrB06-3 16:30–16:50 

Blind Identification of Multi-Rate Sampled Plants, pp.3220–3225 

Yu, Chengpu 

Zhang, Cishen 

Xie, Lihua 


Swinburne Univ. of Tech. 


This paper presents a blind identification algorithm for single-input 

single-output (SISO) sampled plants using an oversampling technique 

with each input symbol lasting for several sampling periods. First, a 

state-space equation of the multi-rate sampled plant is given and its 

single-input multioutput (SIMO) autoregressive moving average (AR- 

MA) model is formulated. A new blind identification algorithm for the 

SIMO ARMA model is then presented, which exploits the dynamical autoregression 

information of the model contained in the autocorrelation 

matrices of the system outputs but does not require the block Toeplitz 

structure of the channel convolution matrix used by classical subspace 

methods. A method for recovering the transfer function of the SISO system 

from its associated SIMO transfer functions is further given based 

on the polyphase interpretation of multi-rate systems. Finally, the effectiveness 

of the proposed algorithm is demonstrated by simulation 

results. 

◮ FrB06-4 16:50–17:10 

Data-driven Subspace Approach to MIMO Minimum Variance Control 

Performance Assessment, pp.3157–3161 

Yang, Hua 

Li, Shaoyuan 



A new data-driven approach is proposed for the estimation of the Minimum 

Variance Control (MVC) benchmark, which eliminates the need 

of estimating the interactor-matrix or extracting the model/Markov parameter 

matrices. Using the parity space, the proposed subspace approach 

gives equivalent estimation of the MVC performance bounds in 

multivariable feedback control system. The basic procedure is to identify 

a parity space of the system residual, instead of the process model, 

directly based on closed-loop data. Therefore, the MVC performance 

indices are estimated to make control performance assessment. The 

equivalence of the proposed approach to the conventional interactormatrix 

based approaches for the estimation of the MVC-benchmark is 

proved and illustrated through simulations. 

◮ FrB06-5 17:10–17:30 

Adaptive Generalized Function Lag Projective Synchronization and Parameter 

Identification of a Class of Hyperchaotic Systems with Fully 

Uncertain Parameters and Disturbance, pp.3265–3269 

Chai, Xiuli 

Wu, Xiangjun 

Guo, Junyan 

Henan Univ. 

Henan Univ. 

Inst. of Image Processing & Pattern Recognition 

Generalized function projective lag synchronization(GFPLS) is characterized 

by the output of the drive system proportionally lagging behind 

the output of the response system and ratio of the two systems is desired 

function scaling matrix. In this paper, GFPLS between different 

chaotic systems with uncertain parameters, i.e. GFPLS between Chen 

and Lorenz chaotic system is studied by applying an adaptive control 

method. Based on Lyapunov stability theory, the adaptive controllers 

and corresponding parameter update rules are constructed to make 

the states of two diverse chaotic systems asymptotically synchronize 

up to the desired scaling matrix and to estimate the uncertain parameters. 

The numerical simulations are provided to show the effective and 

robustness of the results. 

◮ FrB06-6 17:30–17:50 

Yaw Dynamic Model Identification for Miniature Unmanned Helicopter, 

pp.3162–3166 

Li, Minhua 

Bai, Meng 



Yaw dynamic model of a miniature unmanned helicopter is needed to 

develop for heading control. A yaw dynamic model is deduced based on 

miniature unmanned helicopter characteristics in hover. Different from 

a large helicopter, the yaw damping system of a miniature helicopter 

is realized through the negative feedback of helicopter heading rate, 

which is provided by an angular rate gyro. Akaike Information Criterion 

is used to solve the problem of determining model order. Based on 

flight experimental data, a least square method is adopted to estimate 

the unknown parameters in the yaw dynamic model. And the identified 

model is verified by comparing the model output data with the collected 

flight experiment data. 

FrB07 15:50–18:10 Room 303 

Robotics (II) 

Chair: Xian, Bin 

Co-Chair: ILYAS, MUHAMMAD 

Tianjin Univ. 

Beihang Univ. 

◮ FrB07-1 15:50–16:10 

Modeling and Variable Structure Control of a Vehicle Flexible Manipulator, 

pp.3657–3662 

Xu, Yongjun 

Qiao, Yanfeng 


Liu, Keping 

Li, Yuanchun 

Jilin Univ. 





Jilin Univ. 

In this paper, the mathematical modeling and the application of a new 

trajectory tracking control technique for hydraulic-driven rigid-flexible 

105


manipulator are concerned. To get a closer dynamic behavior of the real 

system, both the flexible manipulator linkage and the actuator dynamics 

are considered. The exact dynamic model of flexible manipulator is derived 

using Lagrange principle and assumed modes method. The partial 

decoupled dynamic equation is derived using nonlinear decoupling 

feedback control method. The whole dynamic model is established by a 

driven Jacobin matrix, which represents the coupling between hydraulic 

servo system and mechanical system. A variable structure controller 

with inverse dynamics is designed for trajectory tracking. To weaken 

the chattering of control signal, saturation function is used to instead of 

sign function. The experimental results investigate the effectiveness of 

the proposed approaches. 

◮ FrB07-2 16:10–16:30 

Data Acquisition and Processing of the Onboard Sensor System of a 

Quadrotor Unmanned Aerial Vehicle, pp.3705–3710 

Guo, Fang 

Xian, Bin 

Li, Haotao 



tianjin Univ. 

Tianjin Univ. 

Tianjin Univ. 

automation 

Tianjin Univ. 

In recent years, quadrotor helicopter has become a hotspot of the research 

about unmanned aerial vehicles (UAVs). The data acquisition of 

the vehicle’s attitude and position in real time is important to achieve 

autonomous flight control for quadrotor UAVs. In this paper, an ARM 

embedded system is employed as the main controller, and an attitude 

heading reference system (AHRS) is utilized as the main onboard sensor. 

By using C++ programming under Linux operation system, the 

data acquisition and processing of the AHRS is implemented, and then 

the attitude and position data of the quadrotor UAV are obtained in real 

time to provide reliable flight state information for the autonomous 

flight control. The on-board flight data is sent to ground control system 

and stored via a communication module based on the UDP network 

protocol. 

◮ FrB07-3 16:30–16:50 

Research on the Ground Control System of Quadrotor Unmanned Aerial 



Xian, Bin 

Yang, Yungao 

Guo, Fang 


Tianjin Univ. 

Tianjin Univ. 

Tianjin Univ. 

tianjin Univ. 

automation 

The quadrotor unmanned aerial vehicle (UAV) has been a focus for 

unmanned aerial vehicles. Ground control system (GCS) is an important 

part of the quadrotor UAV flight control system, and it can promote 

the flight performance and safety of quadrotor UAVs. In this paper, a 

quadrotor UAV GCS is designed and implemented, and the GCS realizes 

the following functions, including receiving and storing of the flight 

data, the real-time localization utilizing the customization of electronic 

maps like MapX and Google Earth, the visual display of flight states via 

virtual instruments and the waypoint planning. Outdoor experiments 

have been done to verify the GCS’s performance. It can be concluded 

that the proposed GCS in this paper can work stably, and it can guarantee 

the integrity of the flight data, the accuracy of the instruments 

display and map localization. 

◮ FrB07-4 16:50–17:10 

SINS Initial Alignment Using Wavelet De-noising Method for Aircraft 

Navigation, pp.3921–3926 

ILYAS, MUHAMMAD 

YANG, Yunchun 

Ren, Zhang 

Beihang Univ. 

NavTech. 

Beihang Univ. 

The error in the initial attitude determination is one of the most crucial 

sources of error for accurate inertial navigation. Estimation of the initial 

attitude angles accurately is the guarantee of precise determination 

of the position, velocity and attitude of a moving platform. For military 

aircrafts, the high precision and short time of initial alignment are 

key requirements. The required inertial signals (accelerometer specific 

force and gyro rates) are buried in high frequency measurement noise. 

When the aircraft engine is power on, the high frequency noise is introduced 

due to engine vibration and other disturbances. Kalman filter 

have been extensively used for initial fine alignment of Strapdown Inertial 

Navigation Systems (SINS). In this paper, Multi-resolution wavelet 

de-noising (MRWD) method is proposed to eliminate the high frequency 

noise from inertial sensor measurements before using for initial fine 

alignment. The proposed method is tested using real data sets collected 

when vehicle engine was power on. It has been observed that 

the accuracy of initial alignment can be improved through the proposed 

method. 

◮ FrB07-5 17:10–17:30 

Orientation Coordination of Multiple Robots based on a Nonlinear Consensus 



Sun, Hexu 

Chen, Haiyong 

Liu, Ning 




hebei Univ. of Tech. 

In this paper we design a control strategy for orientation coordination 

of multiple mobile robots system based on a nonlinear consensus algorithm. 

By using the nonlinear terms defined to indicate the orientation 

difference among neighbors, each robot control its own motion 

direction, and then all robots’orientations converge to the average of 

their initial values. A scheme of communication topology design is proposed. 

It is proved that the coordinated control algorithm distributed 

on that kind of topology can make multiple single integrators with input 

limits reach average consensus. Finally, the comparison of the numerical 

simulation results under conditions of two different topologies shows 

the effectiveness of the control algorithm. 

◮ FrB07-6 17:30–17:50 

Octree-based Spherical hierarchical model for Collision detection, 

pp.3870–3875 

Ouyang, Fan 

Zhang, Tie 



This paper has analysed the most commonly used three bounding representation 

for robot links at the beginning. after that, the advantages of 

hierarchical structure and Octree data structure are presented. Moreover, 

this paper has proposed an octree-based Spherical representation 

which has both of the advantages of octree structure and spherical 

bounding representation. Two main parts of the proposed representation 

model are described in details which include Octree-based cube 

model and three-layer cuboid model. In addition, principles of collision 

detection and procedures of establishing the model are also given. finally, 

an example of collision detection in two-robot system is given 

using the proposed models. The result indicates the proposed model is 

effective for the collision detection of two-robot system. 

◮ FrB07-7 17:50–18:10 

Evaluation of Task Realizability for Robot Systems in Representation 

Space, pp.3816–3821 

Wu, Ban 

Su, Jianbo 



This paper investigates task realizability for robot systems with a representation 

space-based technique. It is a general technique which can 

be applied to different robot systems with various tasks. In this paper, 

initial results on evaluation of motion planning tasks are presented 

to show the effectiveness of the proposed technique. Path planning 

tasks of a two-link revolute manipulator in presence of obstacles are 

exemplified. Optimal strategy for a realizable task is solved out, while 

root causes for unrealizable tasks are diagnosed and strategies are designed 

to fix unrealizable tasks. Simulation results demonstrate that the 

proposed technique helps in determining whether a task is feasible and 

fixing infeasible tasks. 

106



FrB08 15:50–17:50 Room 310 

Invited Session: Data-driven Control System Design and Analysis 


Co-Chair: Li, Kang 



◮ FrB08-1 15:50–16:10 

Model-Based Predictive Control for Distributed Parameter Systems 

Based on Local Modeling Approach, pp.1287–1292 


Zhang, Yang 

Shi, Hongbo 


Shanghai Urban & Rural Construction & 

Transportation Committee 


In this paper, a model-based predictive control strategy based on local 

modeling approach is proposed for distributed parameter system. 

As the partial differential equation (PDE) descriptions of the systems 

are unknown, the local modeling approach is used to estimate the dynamics 

of the system based on the input-output data. Based on finite 

local models, each local controller output can obtain through minimizing 

the local optimization objective. The global controlled outputs can 

be solved by linear programming where the deviations of the global s- 

patial temporal outputs from their spatial set points over the prediction 

horizon are considered as the optimal objective. The accuracy and efficiency 

of the proposed methodologies are tested in the cross-flow heat 

exchanger. 

◮ FrB08-2 16:10–16:30 

Nonlinear Dynamic Process Monitoring Based on DLLE-SVDD, 

pp.3131–3136 

Ma, Yuxin 


Shi, Hongbo 




A novel process monitoring method for dynamic nonlinear industrial 

processes is proposed by combining dynamic Locally Linear Embedding 

with Support Vector Data Description. Firstly, the data matrix is 

augmented taking correlation of the samples into consideration. Then, 

LLE manifold learning algorithm is performed for nonlinear dimensionality 

reduction and feature extraction. The mapping matrix from data 

space to feature space was calculated by using local linear regression 

which guarantees the real-time property. Next, in order to avoid the influence 

of noise and disturbance on the traditional statistics, the fault 

detection model is obtained based on SVDD in the feature space, in 

which a corresponding monitoring index and its control limit are determined. 

Finally, the feasibility and efficiency of the proposed method are 

shown through two simulation examples. 

◮ FrB08-3 16:30–16:50 

Fuzzy Clustering Based Spatiotemporal Fuzzy Logic Controller Design, 

pp.3167–3172 

Zhang, Xianxia 

LI, Jiajia 

Jiang, Ye 

Su, Baili 

Qi, Chenkun 

Zou, Tao 







Three-dimensional fuzzy logic controller (3-D FLC) is a novel FLC developed 

for spatially-distributed parameter systems. In this study, we 

concentrated on the data-driven based 3-D FLC design. Firstly, an initial 

rule-base of 3-D FLC is learned by fuzzy c-means algorithm from 

spatial-temporal data set. Then, the rule-base is reduced by using 

distance-based similarity measure to check similar fuzzy sets and similar 

rules. Finally, the parameters are refined by a gradient-descent 

approach. A catalytic packed-bed reactor is taken as an application to 

demonstrate the effectiveness of the proposed 3-D FLC design method. 

◮ FrB08-4 16:50–17:10 

A Regression Approach to LS-SVM and Sparse Realization based on 

Fast Subset Selection, pp.612–617 

Zhang, Jingjing 


Li, Kang 


The Least Squares Support Vector Machine (LS-SVM) is a modified 

SVM with a ridge regression cost function and equality constraints. It 

has been successfully applied in many classification problems. But, the 

common issue for LS-SVM is that it lacks sparseness, which is a serious 

drawback in its applications. To tackle this problem, a fast approach 

is proposed in this paper for developing sparse LS-SVM. First, a new 

regression solution is proposed for the LS-SVM which optimizes the 

same objective function for the conventional solution. Based on this, 

a new subset selection method is then adopted to realize the sparse 

approximation. Simulation results on different benchmark datasets i.e. 

Checkerboard, two Gaussian datasets, show that the proposed solution 

can achieve better objective value than conventional LS-SVM, and 

the proposed approach can achieve a more sparse LS-SVM than the 

conventional LS-SVM while provide comparable predictive classification 

accuracy. Additionally, the computational complexity is significantly 

decreased. 

◮ FrB08-5 17:10–17:30 

Input Selection for Dynamic RBF Models in Process Monitoring, 

pp.3037–3042 

LIU, Xueqin 

Li, Kang 

Li, Shaoyuan 

Fei, Minrui 





This paper investigates the monitoring of continuous processes using 

dynamic nonlinear principal component analysis (NPCA). Previously, it 

was shown that integrating the RBF networks with principal curves significantly 

had increased the sensitivity of fault detection for nonlinear 

processes. Despite this, the previous method may not function well for 

processes which exhibit strong dynamic characteristics. An effective 

method of capturing dynamic behaviour is to consider a time-lagged 

data extension. However, the augmented data matrix may lead to the 

inclusion of a large number of variables in the RBF network input, and 

hence increase the computational load and network complexity. To prevent 

this, an input selection scheme, based on the nonlinear dynamic 

relationship underlying the process variables, is introduced. This selects 

the most important and relevant time-lagged variables before constructing 

the RBF network model. Consequently, a modified dynamic 

NPCA approach is now proposed. The advantages of this improvement 

are demonstrated using a benchmark simulation example from the literature. 

FrB09 15:50–17:50 Room 311A 

Invited Session: Nonlinear and Networked Systems 

Chair: Wang, Yuan 

Co-Chair: Xie, Lihua 



◮ FrB09-1 15:50–16:10 

Persistent awareness coverage for networked mobile sensors with 

awareness loss, pp.4291–4296 

Song, Cheng Univ. of Sci. & Tech. of China & City Univ. of Hong 

Kong Joint Advanced Research Center 

Feng, Gang 

WANG, Yong 



In this paper persistent awareness coverage problem for mobile sensor 

networks with awareness loss is addressed, where the goal is to cover 

the mission domain periodically and guarantee full awareness coverage 

of a finite set of points of interest. A closed path for mobile sensors 

is designed so that the persistent awareness coverage task can be accomplished. 

Then, it is proved that the persistent awareness coverage 

task can be accomplished for a given network of mobile sensors if and 

only if there exists a solution to a set of linear inequalities. 

◮ FrB09-2 16:10–16:30 

Control of Discrete-Time Periodic Linear Systems with Input Saturation 

via Multi-Step Periodic Invariant Set, pp.1372–1377 

Zhou, Bin 

Li, Dewei 



107


Lin, Zongli 


This paper studies local control of discrete-time periodic linear systems 

subject to input saturation by using the multi-step periodic invariant set 

approach. Multi-step periodic invariant set refers to a set from which all 

trajectories will enter a periodic invariant set after finite steps, remain 

there forever, and eventually converge to the origin as time approaches 

infinity. A couple of problems including the (robust) estimation of 

domain of attraction, (robust) local stabilization, and disturbance rejection 

are considered. Compared with the conventional periodic invariant 

set approach which has been used in the literature for local stability 

and stabilization of discrete-time periodic linear systems subject to input 

saturation, this new invariant set approach is capable of significantly 

reducing the conservatism by introducing more auxiliary variables in the 

set invariance conditions. Moreover, the new approach allows to design 

(robust) stabilizing periodic controller whose period is the same as the 

open-loop system, which is different from the existing periodic enhancement 

approach by which the period of the controller is multiple times of 

the period of the open-loop system. Several numerical examples are 

worked out to show the effectiveness of the proposed approach. 

◮ FrB09-3 16:30–16:50 

Simultaneous Stabilization and Synchronization of Non-Identical Linear 

Multi-Agent Systems with Switching Topology, pp.976–981 

Hu, Guoqiang 

Xie, Lihua 



In this paper, we study a simultaneous stabilization and synchronization 

(SSS) problem for one class of linear multi-agent systems with nonidentical 

agent dynamics and switching information-exchange topology. 

We show that a single control loop can be designed for each agent to 

enable some internal agent states to be stabilized while some other external 

states to be synchronized. We design a distributed control law 

based on local measurements and information exchanged from neighboring 

agents to enable SSS. The sufficient conditions to achieve SSS 

are obtained by properly designing a SSS reference system, followed 

by specific approaches of designing the control gain matrices. An example 

and simulation results are provided to demonstrate the effectiveness 

of the proposed method. 

◮ FrB09-4 16:50–17:10 

Remarks on Integral-ISS for Systems with Delays, pp.2227–2232 

Tiwari, Shanaz 

Wang, Yuan 

Jiang, Zhongping 



Polytechnic Univ. 

The notion of integral input-to-state stability (integral-ISS) is now recognized 

as a central concept in nonlinear systems analysis. In this work, 

we explore the Razumikhin approach to the integral-ISS property for 

systems with delays. The main idea is to treat the delayed state variable 

as a disturbance input. The results in this work extend the previous 

results on the Razumikhin method in the contexts of asymptotic stability 

and ISS to the context of integral-ISS. 

◮ FrB09-5 17:10–17:30 

Optimal Control of Finite-valued Networks, pp.2274–2279 


Zhao, Yin 

Liu, Jiangbo 



Bradley Univ. 

Control of finite-valued networks, including Boolean networks, is currently 

a hot topic. In this paper the optimization control of the networks 

with present value performance criterion is discussed. The problem is 

formulated as a finite strategy game between human and machine. It 

is firstly proved that the optimal strategy can be found in the set of periodic 

strategies, which makes the problem finitely computable, though 

the computational complexity of exhaustion might be a severe problem. 

Then an efficient numerical method is developed to solve the problem. 

Some interesting examples are presented to demonstrate the efficiency 

of our results. 

◮ FrB09-6 17:30–17:50 

Robust Flight Control System Design for an Indoor Miniature Coaxial 

Helicopter, pp.2918–2924 

Wang, Biao 

Wang, Fei 

Chen, Ben M. 






A complete flight control scheme with detailed design methodology is 

proposed for an indoor miniature coaxial helicopter with fixed collective 

pitch. To avoid system complexity and the problem of minimum 

phase control, the helicopter dynamic model is decomposed into two 

cascaded subsystems: an inner one for attitude and heading control 

and the outer one for trajectory control. H-infinity control technique and 

robust and perfect tracking method are used to respectively design the 

inner- and outer-loop controllers. By using the so-called asymptotic 

time-scale and eigenstructure assignment approach, the design process 

becomes very systematic and effective. The performance of the 

autonomous flight control system has been successfully validated in 

actual flight tests. 

FrB10 15:50–17:50 Room 311B 

Invited Session: Nonsmooth Control of Nonlinear Systems 

Chair: Li, Shihua 

Co-Chair: Wang, Jiankui 


Tianjin Univ. 

◮ FrB10-1 15:50–16:10 

Finite-time observer for a class of time-varying nonlinear systems, 

pp.2647–2652 

Du, Haibo 


Yang, Shizhong 

Li, Shihua 





The problem of global finite-time convergent observer design for a class 

of nonlinear systems with time-varying coefficients is investigated in this 

paper. To solve this problem, a new time-varying finite-time convergent 

observer is first designed for the nominal system without nonlinearities. 

Using a recursive argument, an explicitly homogeneous Lyapunov 

function is constructed to prove the finite-time convergence of the error 

dynamic system. Then, using the homogeneous domination approach, 

we scale the finite-time observer with an appropriate choice of gain to 

obtain a global finite-time convergent observer for the nonlinear system. 

◮ FrB10-2 16:10–16:30 

Non-Smooth Robust Nonlinear Control for Robotic Manipulators, 

pp.1447–1452 

Zhao, Dongya 

Xie, Lihua 

Zhu, Quan-Min 



Univ. of the West of England 

A new non-smooth robust nonlinear control approach is proposed for 

robotic manipulators. Compared with the existing robust nonlinear control 

for robots, the new developed can achieve higher control precision 

and faster convergence speed. The stability property is analyzed in 

terms of Lyapunov redesign method. An illustrative example is presented 

to validate the effectiveness of the proposed approach. 

◮ FrB10-3 16:30–16:50 

Finite-time Robust Stabilization of Dynamic Feedback Nonholonomic 

Mobile Robots Based on Visual Servoing with Input Saturation, 

pp.3686–3691 

Chen, Hua 

Wang, Chaoli 

Zhang, Dongkai 

Yang, Fang 

Hohai Univ., Changzhou Campus 




In this paper, the finite-time robust stabilization problem is addressed 

in the presence of uncalibrated visual parameters for a class of dynamic 

feedback nonholonomic robots based on visual servoing with input 

saturation. The objective is to design a nonsmooth and bounded state 

feedback law such that the robots system is both Lyapunov stable and 

finite-time convergent within any given settling time. A new saturated 

switching controller is proposed directly based on the original system, 

which can effectively avoid the problem of singularity caused by using 

108



state or input transformation. Finally, the simulation results show the 

effectiveness of the proposed control design approach. 

◮ FrB10-4 16:50–17:10 

Stabilizing the Attitude of a Flexible Spacecraft with Fast Convergence, 

pp.1676–1681 

Ding, Shihong 


Jiangsu Univ. 


To achieve the high accuracy attitude stabilization, by utilizing finitetime 

control technique, a non-smooth attitude stabilizing control strategy 

for flexible spacecrafts is investigated in this paper. Based on a 

backstepping-like control scheme, the non-smooth attitude controller 

can be constructed step by step. The rigorous mathematical stability 

analysis of the overall closed-loop system is made by means of the 

cascaded systems theory. Simulation results show that not only can the 

attitude be stabilized precisely, but also the elastic vibration of flexible 

appendages can be suppressed effectively. 

◮ FrB10-5 17:10–17:30 

Finite-Time Consensus Problem for Multiple Non-holonomic Mobile A- 

gents, pp.1739–1744 

Wang, Jiankui 

Qiu, Zhihui 


Yang, Weichao 

Tianjin Univ. 

Tianjin Univ. 

Tianjin Univ. 

Tianjin Univ. 

In this paper, the problem of finite time consensus is discussed for 

multiple non-holonomic mobile agents. The objective is to design a 

distributed finite time control law such that the controlled multiple nonholonomic 

mobile agents can reach consensus within any given finite 

settling time. We propose a novel switching control strategy with the 

help of time-rescalling technique and graph theory. The numerical simulations 

are presented to show the effectiveness of the method. 

◮ FrB10-6 17:30–17:50 

Flux Estimation of Induction Motors Using High-order Terminal Sliding- 

Mode Observer, pp.1860–1863 

Feng, Yong 

Zhou, Minghao 

Shi, Hongyu 

Yu, Xinghuo 




RMIT Univ. 

This paper presents a rotor flux estimation method for induction motors 

based on high-order terminal sliding-mode observer. A terminal sliding 

mode manifold is designed for the observer and a control strategy is applied 

to stabilize the observer. A high-order sliding-mode mechanism is 

utilized in the observer controller to generate a smooth control signal, 

which can be directly used for the estimation of the rotor flux. The estimate 

of the rotor flux can be used for implementing the field orientation 

control of an induction motor. The effect of the equivalent low-pass filter 

in the high-order sliding-mode mechanism can be regulated according 

to the performance requirements. 

FrB11 15:50–17:50 Room 311C 


(II) 





◮ FrB11-1 15:50–16:10 

Three-dimensional Deployment Optimization of Sensor Network Based 

on An Improved Particle Swarm Optimization Algorithm, pp.4395–4400 

Lian, Xiaoyan 

Zhang, Juan 

Chen, Chen 

Deng, Fang 





Compared with the traditional two-dimensional (2D) deployment form, 

three-dimensional (3D) deployment of sensor network has greater research 

significance and practical potential to satisfy the detecting needs 

of targets with complex properties. In this paper, a method for 3D deployment 

optimization of sensor network based on an improved Particle 

Swarm Optimization (PSO) algorithm is proposed. Many factors 

such as coverage scale, detection probability and resource utilization 

are synthetically considered to optimize the sensor network’s overall 

detection performance. To evaluate the network’s performance, four 

indexes are presented and the 3D deployment space is divided into 

different height levels. Accordingly, the mathematical model is formulated 

by weighting the performance indexes and height levels due to 

their importance degrees. In order to solve the optimization problem, 

an algorithm called WCPSO is carried out, which has a dynamic inertia 

weight and adaptable acceleration constants. Verified by the simulation 

results, the presented 3D deployment optimization method effectively 

improves the sensor network’s detection performance. The method 

in this paper can provide guidance and technical reference in future 

application of relevant research. 

◮ FrB11-2 16:10–16:30 

Optimization of a 3-PRS parallel manipulator based on interval analysis, 

pp.2452–2456 

Zhang, Xu 

Fang, Hao 



This paper addresses the problem of mechanism optimization of the 

3-PRS parallel manipulator, a mechanism consisting of a moving platform 

and a base platform connected by three serial PRS chains. In 

order to optimize the geometrical parameters, first the inverse kinematic 

of the 3-PRS parallel manipulator is analyzed and the requirement of 

specific workspace of 3-DOF independent motions is defined by satisfying 

the constraints of the actuator length. Then due to existence of 

the unexpected parasitic motions, the parasitic motions are expressed 

as the functions of the independent motions so that the evaluation of 

the actuator length only depends on the desired independent motions. 

Therefore an algorithm based on interval analysis is designed to 

optimize the design parameters. Interval-based optimization can provide 

almost all the solutions satisfying the requirement of the specific 

workspace. A numerical example of the optimization is presented and 

the comparison of two groups of design parameters is given to validate 

the effectiveness of the proposed interval-based optimization algorithm. 

◮ FrB11-3 16:30–16:50 

A Differential Evolution Algorithm with Two Speed-up Methods for NF- 

SSP with SDSTs and RDs, pp.490–495 

Qian, Bin 

Du, Puze 

Hu, Rong 

Che, Guolin 




kmust 

This paper presents a differential evolution algorithm with two speedup 

methods (DE TSM) for solving the no-wait flow-shop scheduling 

problem (NFSSP) with sequence-dependent setup times (SDSTs) and 

release dates (RDs). The criterion is to minimize the total completion 

time. To balance the exploration and exploitation abilities of our 

DE TSM, DE-based global search is utilized to find the promising regions 

or solutions over the solution space, and a fast local search according 

to two speed-up methods is designed to fast exploit the neighborhoods 

of these regions. Simulation results based on a set of random 

instances and comparisons with several effective meta-heuristics 

demonstrate the superiority of DE FNES in terms of searching quality 

and efficiency. 

◮ FrB11-4 16:50–17:10 

Implementation of Control Algorithm for Three-Dimensional Pursuer 

Convoy, pp.2005–2010 

Feng, Shulin 

Wang, Wei 





In this paper, we consider the problem of modeling and controlling a 

convoy in the three-dimensional space. The guidance laws applied for 

convoy are the velocity pursuit and the deviated pursuit, which steer the 

pursuer using the rate of line-of-sight (LOS) between successive pursuers. 

On the basis of the differential equations for the range, the pitch 

angle of LOS and the yaw angle of LOS between successive pursuers, 

109


the guidance laws mentioned above are applied to derive decentralized 

control laws for pursuer convoy. The results of simulation indicate the 

feasibility and effectiveness of the proposed algorithm. 

◮ FrB11-5 17:10–17:30 

Design of Networked Air Defense Fire Control Simulation System 

based on BOM, pp.3214–3219 

Chen, Shi-jun 

Zhang, Juan 

Chen, Chen 




Networked air defense fire control simulation system featured with complicated 

interactions, varied system structure etc, and is usually faced 

with some difficulties in its development, such as the low capability of 

model reuse, the lack of regulations for model development and inflexible 

modification of federation. To overcome the difficulties, a framework 

in hierarchy of networked air defense fire control simulation system is 

constructed according to BOM development standard, and the component 

method for designing the basic model, compound model and 

federate is proposed, which effectively improved the efficiency of system 

development and the capability of model reuse. This method is not 

only applicable to the design of networked air defense fire control system, 

but also to the design of other simulation system with polytropic 

structure. 

◮ FrB11-6 17:30–17:50 

An ant colony algorithm for permutation flow shop problem, pp.596–600 

Shang, Ke 

State Key Laboratory for Manufacturing Sys. 

Engineering, Xi’an Jiaotong Univ. 

Feng, Zuren 

Ke, Liangjun 



In this paper, a new ant colony optimization algorithm, called finite 

grade ant colony optimization, is proposed to solve permutation flow 

shop problem, its main characteristic is that the updated quantities of 

pheromone trails are independent of objective function values, and the 

heuristic information provide by Moccellin is adopted. The developed algorithm 

has been applied to the benchmark problems given by Taillard, 

Comparison results demonstrate that the performance of the proposed 

algorithm is promising. 

Poster Session PFrA 

July 6, 13:30-14:50 



Co-Chair: Huo, Wei Beijing Univ. of Aeronautics & Astronautics 

◁ PFrA-01 

Research on Indoor Location Technology Based on RSSI and Back 

Propagation Neural Network , pp.80–83 


Shi, Xiaowei 



Indoor wireless location Based on RSSI (received signal strength indicator) 

firstly need to determine the parameters A and N of the RSSI-d 

relation model. In the conventional method, the parameters A and N is 

subjectively designated based on experience and positioning environment, 

this lead to inaccurate, low precision, and weak universal. Based 

on a large number of RSSI-d experimental data, a totally new method 

using BP neural network to fit RSSI-d relationship curve is proposed. 

Input RSSI value into the BP input layer, we can get distance value at 

the output layer, then the Taylor Series expansion algorithm is used to 

determine the coordinates of the blind node. Finally, compared with the 

traditional positioning algorithms ,the experiment result shows that the 

new algorithm improves the positioning accuracy and universality. 

◁ PFrA-02 

AUTO DRAVCNC PROCESSING DATA INQUIRY SYSTEM, pp.196– 

198 

Liu, Hongjun 

Men, Xiyao 



The final product is often organized in the way of assembly in process of 

mechanical design. The assembly body generally contains many parts, 

which structure is affected by several main parameters, and some 

parameters among those parts should in keeping with each other. The 

paper presents a method to do it by the UG OPEN API redevelop interface. 

By providing parameters extraction dialog, the main parameters 

of the UG assembly model are extracted from model. Then the parameters 

can be changed in PDM . Through the traverse the way for the 

assembly and all parts of the body automatic update drive, avoiding 

one of the documents by change travel, leakage change, and wrong 

which result into products design problem. After synchronous update, 

each drawing is outputted in the way of CGM file. 

◁ PFrA-03 

Further Understanding of the Meaning of Fuzzy Multi-stage Conditional 

Statements, pp.251–254 

Yu, Fusheng 

Wang, Xiao 



When building fuzzy inference engine, a group of fuzzy rules are given 

in advance, which are usually called fuzzy multi-stage conditional statements. 

These fuzzy multi-stage conditional statements can be divided 

into two categories. One type of fuzzy multi-stage conditional statements 

is linked by “if-else”; while the other is not. In literature, researchers 

usually did not distinguish the forms of them. In our opinion, 

these two kinds of fuzzy multi-stage conditional statements have different 

meanings, and this will influence the true value domain of them. In 

this paper, we will probe into the meaning of these two types of fuzzy 

multi-stage conditional statements, and discuss how to build their true 

value domains. 

◁ PFrA-04 

Architecture and Cooperation Mechanism of MAS for Information Integration 

Emergency-Service Oriented, pp.341–345 

Yu, Haixin 

Chen, Jie 

Zhang, Juan 




Emergency system denoted in this paper aims to harness natural disasters 

via investigating the situation of disasters and making contingency 

plans. Hence, integrating information of disasters from a complex distributed 

network is the foundation of the emergency system. This paper 

focuses on how to integrate information rapidly though multi-agent 

system technology. Based on this, we proposes a hierarchical architecture 

which organizes immense amount of information efficiently and 

the cooperation mechanism based on task auction. Furthermore, we 

build feature space to measure the information resource’s capability of 

supplying information, and evaluate the architecture and cooperation 

mechanism through its application in integrating information of a geological 

disaster. 

◁ PFrA-05 

Migration & Competition-based Particle Swarm Optimization for Parameter 

Estimation, pp.590–595 

Ren, Ziwu 

Soochow Univ. 

Enlightened by some knowledge of ecology and swarm competition, an 

improved multigrouped particle swarm optimization based on migration 

and competition, namely PSOMC, is proposed for parameters estimation 

of non-linear systems. The PSOMC is not concerned with the evolution 

of a single population, but instead is concerned with the evolution 

of multiple parallel swarms; moreover it incorporates some concepts, 

such as reintroduction, swarm competition, adjustment of swarm size, 

migration of particles between the swarm, and recycling, to enhance the 

global exploration ability and the local exploitation capability. Numerical 

simulations of two benchmark functions are used to test the performance 

of PSOMC. Furthermore, simulation on three different kinds 

of models is given to illustrate the effectiveness and efficiency of the 

proposed parameters estimation scheme. 

◁ PFrA-06 

Research on the Machine Tool Selection of RMS, pp.623–627 

Zhou, Fengxu 

Li, Aiping 

Xu, Liyun 

Tongji Univ. 

tongji Univ. 

tongji Univ. 

110



Xie, Nan 

Tongji Univ. 

According to production tasks, making manufacturing resources reconstruction 

via machine tool selection is one of the key issues to achieve 

manufacturing system reorganization. At the time when machine tool 

is selected, qualitative factors and quantitative factors must be considered. 

In order to solve the problem well, the evaluation index system 

with a hierarchical structure was presented. A multi-objective optimization 

model was established, and then the model was analyzed by using 

hierarchical weighting methods. Finally, a practical application was provided 

to illustrate the scientific validity of the model and the effectiveness 

of the methodology. 

◁ PFrA-07 

A Hybrid Method for Short-term Load Forecasting in Power System, 

pp.696–699 

Zhu, Xianghe 

Qi, Huan 

Huang, Xuncheng 

Sun, Suqin 

Huazhong Univ.of Sci. & Tech.Wuchang Branch 




In order to improve the accuracy of power load forecasting, this paper 

proposes a hybrid model based on Ensemble Empirical Mode Decomposition 

(EEMD), least square-support vector machine (SVM) and BP 

nature network as a short-term load forecasting model. At first, the actual 

power load series is decomposed into different new series based 

on EEMD. Then the right parameters and kernel functions are chosen 

to build different LS-SVM model respectively, to forecast each intrinsic 

mode functions, due to the change regulation of each of all resulted 

intrinsic mode functions. Finally, we use the BP network to reconstruct 

the forecasted signals of the components and obtain the ultimate forecasting 

results. Simulation results show that the proposed forecasting 

method possesses accuracy. 

◁ PFrA-08 

Group Decision-Making Based Case Retrieval and Its Application, 

pp.773–778 


YAN, Aijun 

Zhao, Hui 

Wang, Pu 





The distribution of the case feature attribute weights directly affects the 

case retrieval result. Aim at improving the retrieval precision, a retrieval 

method is proposed based on group decision-making for optimizing 

the case feature attributes weights. Firstly, multiple groups of initial 

weights are obtained by genetic algorithm. Then, the multiple sets of 

retrieval results produced by these weights are optimized through group 

decision-making method, and the weights can be dynamic adjusted 

through the deviations between individual decision results and group 

result. The simulation results indicate that the proposed approach can 

fully excavate the potential knowledge of attribute weights and thus result 

in higher retrieval accuracy in a case-based reasoning system. The 

PID adjusting comparison experiment of typical two order delay system 

verifies the effectiveness of the new method. 

◁ PFrA-09 

Attribute Reduction Method Using Water-Filling Principle for Case- 

Based Reasoning, pp.779–782 

Zhao, Hui 

YAN, Aijun 


Wang, Pu 





As the large number of feature attributes in Case-based reasoning system 

(CBR) brings a huge information redundancy which reduces the 

retrieval efficiency, a novel reduction method based on Water-Filling is 

proposed to remove those unnecessary attributes. In the method, the 

importance of each attribute could be calculated by utilizing the ratio 

of the standard deviation and the mean value of each attribute data as 

evaluation parameter, and the impotance result of each attribute is then 

used to guide the reduction process. The experiments on glass identification 

showed that the new method could get a better retrieval accuracy 

as well as a greater efficiency compared with the methods which do not 

conduct the reduction process. 

◁ PFrA-10 

Application of Self-organizing Feature Map Neural Network Based on 

Data Clustering, pp.797–802 

Hu, Xiang 

Yang, Yun 

Zhang, Lihong 

Xiang, Tao 

Hong, Chengqiu 

Zheng, Xiaotong 



Tinghua Univ. 




Outlier detection is of much importance in preprocessing of data collected 

from complex industry system, for the data has strong nonlinearity 

and poor stability, involving much noise. Outlier detection based 

on clustering, rejects abnormal data points which have significant difference 

from others according to the definition of similarity. Self-organizing 

Feature Map (SOM) Neural Network algorithm has the self-study and 

adaptive functions of neural networks, so as to be a hot research in 

clustering analysis recently. This paper first introduces Self-organizing 

Feature Map algorithm based on artificial neural network, and then improves 

the algorithm by using weighted Euclidean distance, finally uses 

the software of MATLAB to analyze some actual data of electrical power. 

The result shows that SOM algorithm achieves a very good effect in 

clustering, and the MATLAB toolbox shows favorable visual effects. 

◁ PFrA-11 

Flow Rate Control and Resource Allocation Policy with Security Requirements 

in OFDMA Networks, pp.1020–1025 

Zhu, Xingzheng 

Yue, Jianting 

Yang, Bo 

Guan, Xinping 





OFDMA-based network performs well in maximizing the overall 

throughput, and meanwhile, problems with security requirements are 

focused to satisfy the increasing needs of confidential data transmissions. 

According to information theory, however, secure transmission 

undesirably antagonizes larger flow rate, thus a tradeoff is expected to 

balance these two matters. In this paper, we consider a downlink situation 

in OFDMA network, where all users request secure transmission 

from the base station yet the resources including power and subcarriers 

are limited such that an appropriate policy on resource allocation can 

play an instrumental role. Dynamic queueing and Lyapunov optimization 

are two highlights in this process. We utilize dynamic queueing 

so that merely instantaneous state of the network is enough, which is 

advantageous for easier implementations. On the other hand, by the 

means of Lyapunov optimization, we also prove our algorithm can obtain 

a performance of flow rate admission extremely close to the optimal. 

Applying this policy can reduce computational complexity significantly 

but still perform close to optimality. 

◁ PFrA-12 

Augmented Dimension Algorithm Based on Sequential Detection for 

Maneuvering Target Tracking, pp.1323–1327 

Pan, Baogui 

Peng, Dongliang 

Shao, Genfu 




In order to solve the problem that target tracking algorithm based on 

single model has poor tracking performance when the target occurs 

high maneuver and that IMM algorithm has low accuracy in tracking a 

constant velocity target, an augmented dimension algorithm based on 

sequential detection for maneuvering target tracking is proposed. First, 

the KF-UKF joint filtering is proposed. The Kalman filter based on the 

CV model is used to estimate the state of a constant velocity target. 

When the target maneuver is detected, the dimension of the CV model 

is augmented, and the unscented Kalman filter is used to estimate 

the state. Second, a fading memory sequential detection algorithm is 

111


proposed to detect the maneuver. Once the maneuver is detected, 

the augmented state vector and covariance matrix is compensated so 

that the modified model can match the actual motion mode. Simulation 

results show that this algorithm improves the accuracy of tracking 

by selecting the matching filter depending on the different mode of the 

target as well as modify the tracking state in real time. 

◁ PFrA-13 

Consensus of multi-agent systems with time delay based on nonlinear 

algorithm, pp.1628–1633 

Liang, You-ming 

Liu, Chenglin 

Liu, Fei 

Inst. of Automation, Jiangnan Univ. 


Jiangnan Univ., China 

Based on nonlinear coupled algorithm, consensus problem of first-order 

multi-agent systems with time-varying input delay is investigated. Firstly, 

by utilizing Lyapunov theorem and LaSalle invariance principle, sufficient 

consensus condition for the multi-agent systems is obtained when 

the directed interconnection topology is strongly connected. Secondly, 

the above conclusion is extended to the case of weakly connected digraph 

by applying algebraic graph theory and matrix theory. Lastly, 

computer simulation examples are presented to demonstrate the effectiveness 

of the obtained results. 

◁ PFrA-14 

Structured Singular Value Synthesis Based Low Power Level Steam 

Generator Water Level Controller Design , pp.1661–1665 


Liu, Yuyan 

Hao, Zulong 




A Uncertainty kinetic model of feed water value position to steam generator 

water level plant is built. The derived model can express uncertainty 

in quantitative manner, including parameter perturbation and unmodeled 

dynamic due to operation condition change of steam generator 

and feed water pump. Then a robust controller is designed using the 

derived model based on structured singular value synthesis method. 

Robust stability and robust performance of the water control system can 

be guarantee under parameter perturbation and unmodeled dynamic if 

suitable performance weight functions are chosen. Simulation result 

shows that good control performances are obtained under all combinations 

of steam generator operation condition and feed water pump 

operation condition. 

◁ PFrA-15 

The study of a new Call Admission Control method Based on selfsimilar 

traffic, pp.15–19 

Hu, Yanpu 

Yang, WenFu 

Wang, Yong 

Dong, Ying 







With the rapid development of the network, more and more new kinds 

of network traffic emerge in the network. These new traffic exhibit 

the properties of self-similarity and long-range dependence. Connection 

Admission Control (CAC) is one of the important mechanisms for 

guarantee the QoS while improving the utilization rate high-efficiently. 

At first, self-similar traffic service model based on CAC scheme in this 

paper are studied. And then the formula for effective bandwidth of admitted 

aggregated traffic sources is got. A CAC algorithm of self-similar 

traffic based on fairness is proposed. The equality mechanism is introduced 

in the admitting deterministic process. Simulation has analyzed 

the factors of effective bandwidth and has proved the validity of the algorithm. 

◁ PFrA-16 

Parameter optimization of PID controller based on Complex System 

Genetic Algorithm in Electro-hydraulic Servo Control System, pp.30– 

35 

Yu, Jue 


Zhuang, Jian 

Yu, Dehong 



In this paper, a novel method for finding a set of appropriative 

proportional-integral-derivative (PID) controller parameters in an 

electro-hydraulic servo control system is presented. In this method, 

a new genetic algorithm called complex system genetic algorithm (C- 

SGA) is designed, which integrates complex system theory into genetic 

algorithm and redesigns GA’s elements such as selecting operator, 

crossover operator and mutation operator. And correspondingly, a performance 

criterion including the information of overshoot, rise time, settling 

time and steady-state error is also proposed as our fitness function. 

Then the application of CSGA to get optimal PID parameters is 

described in detail. At last, by the actual compare experiments of using 

our proposed method and traditional way in an electro-hydraulic servo 

control system respectively, it demonstrates that our method can find 

optimal PID parameters effectively and rapidly. 

◁ PFrA-17 

The Direct Adaptive Fuzzy Robust Control for Satellite Attitude Control, 

pp.36–41 

Guan, Ping 

Zhang, Wei 

Liu, Xiao He 

Xue, Li 





In this paper, the direct adaptive fuzzy robust control is applied to the 

attitude stabilization control of the satellite, and the detailed design procedure 

of the fuzzy control system is presented. The controller directly 

acting on the controlled system doesn’t require the modeling of the 

uncertain nonlinear system. The direct adaptive fuzzy controller is designed 

for the satellite, and the robust control term is added to the system 

in order to resist the external disturbances. The adaptive law of the 

rule parameter is derived. Simulation results show that precise attitude 

control is accomplished in spite of the uncertainty in the system. 

◁ PFrA-18 

The Adaptive Fuzzy Sliding mode control of Hypersonic Vehicle, pp.51– 

56 

Guan, Ping 

Xue, Li 

Liu, Xiao He 

Wang, Sai 




Beijing informationSci.&Tech. Univ. 

In this paper, the adaptive fuzzy sliding mode control is applied to the 

hypersonic vehicle. The detailed design procedure of the fuzzy sliding 

mode control system is presented. The adaptive fuzzy control is utilized 

to approach the uncertain functions of the hypersonic vehicle and 

the adaptive law is derived. According to the tracking errors, weighted 

parameters of the fuzzy system are adjusted on-line to enable the 

system to approximate the uncertain nonlinear functions effectively.In 

addition, a robust controller is employed to enhance the performance 

of the whole system. Simulations results show the proposed controller 

is robust in the present of the parametric uncertainty and have better 

dynamic and static performances. 

◁ PFrA-19 

Adaptive fuzzy and H∞control of robotic manipulators with uncertainties, 

pp.74–79 

Liu, Yuliang 

Yang, Li 

Duan, Hailong 



,Tianjin Univ. of Tech. & Education 

In this paper, a new adaptive fuzzy and H∞control method of robotic 

manipulators with uncertainties is proposed. The control method combines 

the fuzzy adaptive control with the H∞control theory for trajectory 

tracking of robot manipulators. While establishing the model of the 

system, the influence of both the modeling errors and the external disturbances 

can be attenuated to a prescribed level. In addition, it is not 

necessary for the controller proposed to transform the robotic dynamics 

into nonlinear systems, and the controller thus obtained does not contain 

the inverse of matrices and can do without singularities. Based on 

112



Lyapunov method, the adaptive learning law is given and H∞tracking 

performance is illustrated. The simulation studies on a 2-DOF robot 

verify the effectiveness of the proposed method. 

◁ PFrA-20 

Minimum Time Dynamic Optimization Using Double-layer Optimization 


Guo, Xuan 

Du, Wenli 

Qi, Rongbin 

Qian, Feng 





Abstract―A double-layer optimization algorithm (DLOA) was proposed 

to solve the minimum time dynamic optimization problem. The first step 

of DLOA was to discrete time region and control region. The inner optimization 

is to construct optimal control problem with free final states. 

Differential evolution algorithm is used to find the optimal solution in 

given terminal time, then the optimization results was compared with 

the threshold set. In the outer, DLOA calculated the time range of next 

iteration according to the inner calculation. When applied to typical 

minimum time dynamic optimization problem, DLOA demonstrated a 

competitive optimal searching ability and more accurate optimization 

results. DLOA could solve the optimization problem with local optimum 

and applied to models without gradient information. 

◁ PFrA-21 

Design of Low-cost acquisition system for multi-channel Analog Signal 

for PLC and its Application in Temperature Control System, pp.93–98 

Li, Ruonan 

Du, Xiuxia 

Li, Pingkang 




Based on the concept of low cost automation, a kind of multi-channel 

analog acquisition program based on traditional analog circuits and digital 

I/O ports of PLC is presented in this paper, including hardware circuit, 

program design and principles of signal acquisition. Compared 

with the expensive PLC analog module, the system is designed with 

features such as low cost, easy to implement. This paper makes use 

of PID module of PLC and self-tuning technology, applying this analog 

acquisition program to the temperature control system, PWM signal is 

generated to control solid state relays to accurately control temperature, 

at the same time the data exchange between computer and PLC 

has been completed, the interface is friendly, the experimental results 

show the feasibility and practicality of the program. 

◁ PFrA-22 

Study on the Optimal Passive Creeping Control of the Snake-like Robot, 

pp.99–103 


Wu, Chengdong 

Li, Bin 





Sci. 

Abstract - Reducing the energy dissipation can improve the movement 

efficiency of a snake-like robot. Avoiding side slipping is the main way 

to reduce the energy dissipation. A creeping control method for the 

snake-like robot, named optimal passive creeping, is proposed. This 

method makes the robot avoid slipping by inputting the proper torque 

which is adjusted by its body shape. The purpose of the control method 

is to insure that the snake-like robot’s velocity is the fastest before 

side slipping. In simulation, the angles, torques, energy states of the 

snake-like robot are revealed, and whether the snake-like robot has 

slipped is analyzed by accumulating the energy dissipation. The simulations 

demonstrate the validity of the optimal passive creeping control 

method. 

◁ PFrA-23 

Takagi-Sugeno Fuzzy PD Controller for a 3-DOF Stabilized Platform, 

pp.108–112 

Leghmizi, Said 


This paper presents a fuzzy PD control system for stabilizing platform of 

a ship carried antenna with three degree of freedom (3-DOF). The system 

under consideration is a system with strong interactions between 

three channels. By using the concept of decentralized control, a control 

structure is developed that is composed of three control loops, each of 

which is associated with a single-variable fuzzy PD controller. Takagi- 

Sugeno (TS) fuzzy control algorithm is used to implement the fuzzy 

controller. Simulation tests were established using Simulink of Matlab. 

The obtained results have demonstrated the feasibility and effectiveness 

of the proposed approach. Simulation results are represented in 

this paper. 

◁ PFrA-24 

Repetitive Adaptive Control Based on Sliding Filter Observer in Servo 

Turntable, pp.116–121 

Gan, Ming Gang 

Ma, Hui-xia 


Bejing Inst. of Tech. 

A repetitive adaptive compensation scheme based on observer is proposed 

to improve low speed position tracking precision for the nonlinear 

dynamic friction and periodic force ripples in a DC motor turntable system. 

Based on LuGre friction model, the adaptive component is used 

to compensate the dynamic friction, while the repetitive component is 

used to restrain force ripple. At the same time, dual sliding filter is 

adopted to approximate the parameter estimation error of the observed 

state so that the parameters update rate can be driven by tracking error 

signal as well as estimating error signal to improve the position tracking 

performance. The system asymptotic stability is guaranteed by Lyapunov 

function. Simulation results verify contrastively the effectiveness 

and superiority of the repetitive adaptive compensation scheme. 

◁ PFrA-25 

Iterative Learning Control Algorithm with Self-adaptive Steps, pp.122– 

127 

WANG, Yan 

Beihang Univ. 

Most Iterative Learning Control (ILC) algorithms being used currently 

still follow the essence of the original idea of Arimoto, in which the 

control error with its derivative and integral are composed to deduce 

the iterative-learning-rate. Although these ILCs gain a wide range of 

successful applications in various control fields, they are still with intrinsic 

shortcomings of conventional ILC such as huge computation, big 

storage capacity demanded for algorithm data, and the control law is 

sensitive to control errors. To solve these problems and enhance ILC 

performances, a novel ILC with self-adaptive steps is proposed as a 

new approach. In this approach, the control step for each iteration is 

set according to the error sum of the last time iteration, and the step 

value will decrease as the iteration goes which could obtain the merits 

of quick convergence speed at early stage and high control precision at 

late stage of control process; while the sign of the step is decided by the 

instant sampling value of the control error. The stability of the algorithm 

is analyzed using newly designed adaptive steps. Both simulation and 

experimental results demonstrate that the self-adaptive steps method 

proposed in this paper could reduce iterative calculation and storage 

size of the algorithm, it also avoids error amplification in error differentiating. 

Moreover, the method is with easy-to- set steps within a wide 

range. 

◁ PFrA-26 

Real-Time Simulation of Hydraulic Turbine Governing System Based 

on ARM, pp.170–173 

GUO, Jiang 

Wu, Nian 

Gu, Kaikai 

Bai, Weimin 

Zeng, Bing 

Liu, Yajin 

Wuhan Univ. 

Wuhan Univ. 

Wuhan Univ. 

Wuhan Univ. 

Wuhan Univ. 

Wuhan Univ. 

Abstract―Simulation of hydraulic turbine governing system plays an 

important role in teaching, testing and experimental study. This paper 

presents the overall structure of the ARM-based real-time simulation 

system on the basis of a comprehensive analysis of the characteristics 

113


of hydroelectric process. For the different object to build different fineness 

of model, this paper uses multi-scale modeling approach and rational 

scheduling mechanism to realize optimal operation of simulation, 

as well as designing the real-time enhanced program on a LINUX system. 

By comparison with the off-line system simulation of SIMULINK, 

the results show that the system is fully capable to meet the requirement 

of hydraulic turbine governing system on real-time, reflecting the 

advantages of real-time continuous simulation. 

◁ PFrA-27 

Periodicity Analysis of Uncertain Neural Networks With Multiple Time- 

Varying Delays, pp.174–179 

Lou, Xuyang 

Ye, Qian 

Feng, Wei 

Cui, Baotong 





The problem of global robust periodicity is studied for a class of neural 

networks with norm-bounded parameter uncertainties and multiple 

time-varying delays. Some linear matrix inequality (LMI) representations 

of delay-dependent periodicity criteria guaranteeing the existence, 

uniqueness and global asymptotic stability of periodic solution for all admissible 

parametric uncertainties are presented. The proposed method 

is based on the S-procedure and an extended integral inequality which 

can be deduced from the well known Leibniz-Newton formula and the 

Moon’s inequality. The results extend some models reported in the 

literature and improve conservativeness of those in the case that the 

derivative of the time-varying delay is assumed to be less than one. 

◁ PFrA-28 

Solar Sail Trajectory Optimization Based on Improved Imperialist Competitive 


WANG, Yong 

Zhu, Min 

WEI, Yiheng 

Zhang, Yang 




Shanghai Inst. of Satellite Engineering 

In this paper we discuss the minimum-time orbit transfers of solar sail 

spacecraft for Mercury sample return missions using solar radiation 

pressure. In order to find the minimum-time trajectory transfers, the 

optimal pitch angle of the solar sail has to be determined. We transform 

the optimal control problem to parameterized nonlinear programming 

problem by using the direct shooting method. Then, the improved imperialist 

competitive algorithm is used to optimize the pitch angle and the 

transfer time of the solar sail. Thus, we can obtain the optimal transfer 

trajectory according to the sail’s orbital dynamics. Furthermore, 

transfers from the Earth orbit to Mercury and from the Mercury orbit to 

Earth of the solar sail is simulated, the results show that it meets the 

orbit terminal constraints well and the results in this paper are better 

than the existing results. 

◁ PFrA-29 

Indirect Adaptive Fuzzy Sliding Mode Control for Uncertain Multi-link 


Mu, Xiaojiang 

Ge, Li 

shenzhen Inst. of information Tech. 


An indirect adaptive fuzzy sliding mode controller is presented for trajectory 

tracking control of uncertain multi-link robots. This approach 

combines adaptive fuzzy control and global fast sliding mode control, 

and adopts a global fast sliding mode manifold which can drive system 

to reach balance point in finite time. Three fuzzy systems are designed 

to learn the uncertain robot parameters and external disturbances. So 

the control system can automatically track the robot parameters and 

disturbances, and reduces chattering of the controller. Prediction estimation 

for robot parameters and disturbances is not needed too. The 

control speed is quickened by simplifying the robot structure and decreasing 

learning parameters of the uncertain robot. Moreover, the system 

stability is proved by Lyapunov principle. Simulation results verify 

the validity of the control scheme. 

◁ PFrA-30 

Fuzzy Control for Hyperchaotic Systems via Nonuniform Sampling Approach, 

pp.214–219 

Yang, Dedong 

Wang, Jinhuan 

Sun, Hexu 

Yang, Peng 




Heibei Univ. of Tech. 

In this paper, the stabilization problem for the hyperchaotic Chen system 

is investigated via nonuniform sampling approach. First, the hyperchaotic 

Chen system is represented by the Takagi–Sugeno (T–S) 

fuzzy model via the sector nonlinearity approach. Second, utilizing the 

descriptor model transformation and the input delay approach, an e- 

quivalent continuous-time generalized form is obtained and sufficient 

conditions for the existence of state feedback controller are derived in 

terms of linear matrix inequities (LMIs), which guarantee the asymptotic 

stability for the controlled closed-loop system. Lastly, simulation results 

are given to show the validity of the present scheme. 

◁ PFrA-31 

Identification and Prediction of Nonlinear Multi-parameter Based on 

Least Squares Support Vector Machine, pp.224–228 

Hou, Yuan-bin 

Li, Ning 



The circulating fluidized bed boiler is key equipment in intrinsically safe 

coal gangue power generation, after analyzing of the typical fault and 

hidden fault of main components of boiler system, as well as the treating 

methods, directed towards the nonlinear characteristics of the oxygen 

content of flue gas of the induced draft fan has many factors influence, 

a method based on least squares support vector machine (LS-SVM) 

used in flue gas oxygen content model recognition is proposed. the 

measured crucial parameter of influence the stable operation of the 

boiler are used to identification and prediction, including the oxygen 

content of the flue gas, coal gangue flow and material return pressure 

imitation, as the results show that this method has higher precision ( 

the error is less than 6‰); Compared with general SVM, this method 

reduces the complexity of the calculation 

◁ PFrA-32 

Complex Dynamical Behaviors Analysis of a Voltage-controlled Memristive 


Sun, Junwei 

Shen, Yi 


Huazhong Univ. of Sci. & Tech., China 

The contribution is concerned on structural properties and general features 

of the new ideal circuit element, a memristor. The previous memristive 

system is only focused on the voltage versus current curves with 

increasing frequency of the alternating voltage source. The paper extends 

the notion of the voltage versus current curves to the resistance 

versus current curves and the voltage versus resistance curves, and 

those which are influenced by the initial conditions, frequency, voltage, 

a and b, and input excitation. The paper has emulated by MATLAB, 

more comprehensive and systematic analyzes and obtains a lot of the 

new characteristics of memristive system and important information. 

◁ PFrA-33 

Design and Simulation for Dual-Stage Actuator Systems with Multi-rate 

and Friction Compensation Controller, pp.245–250 

Wei, Dongfang 

Yang, Jun 





This paper studies the structure and model of a dual-stage actuator (D- 

SA) control system. A multi-rate controller with friction compensation is 

designed to reduce the computational load, which will greatly improve 

the system’s performance. The dual-stage actuator(DSA)control system 

consists of a linear motor and a piezoelectric actuator. By combining 

the system with properly designed control method, the two actuators 

are complementary to each other, so that the system can achieve 

fast response and high precision to meet the requirements in industry. 

The multi-rate control law can decrease the computational load with a 

114



slower update rate about the primary actuator,but the performance of 

the whole system is not descending. A Simulink platform for dual-stage 

actuator (DSA) control system is developed in this brief, the platform 

is applied to test the influence of the ratio m on whole system performance. 

Experimental results sufficiently demonstrate the usefulness of 

the dual-stage actuator (DSA) control system , as well as the effectiveness 

of the mentioned algorithm. 

◁ PFrA-34 

Research on Commutation fluctuation Self-Adaptive Control Suppression 

Strategy for Brushless DC Motor, pp.265–269 

Wang, Weihua 

Huang, Haibo 

Hubei Univ. of Automotive Tech. 

Hubei Automobile Industries Inst. 

In order to restrain the adverse influence of speed mutation and torque 

ripple when brushless dc motor runs at the commutation moment, at 

the same time, for the sake of avoiding the fact that traditional PI control 

tragedy highly depends on the precise mathematical model of the 

controlled system, this paper puts forward a kind of control tragedy of 

domain self-regulating fuzzy control. At first, the basic operating principle 

of brushless dc motor and the commutation ripple are analyzed, 

then the process of domain self-regulating fuzzy control is detailedly 

deduced, and the control tragedy is applied in the control system of 

brushless dc motor. The experiment result shows that relative to the 

traditional PI control tragedy, the domain self-regulating fuzzy controller 

has better system reliability and robustness, and gains better suppression 

effect of commutation ripple. 

◁ PFrA-35 

The Fuzzy Human-Simulated Intelligent Control for Hot-Rolling Strip 

Width, pp.270–274 

Tian, Jianyan 

Zhang, Guanyu 



Abstract: The hot-rolling strip process is a typical complex industrial 

processes, and the effect of the traditional control strategy is not perfect. 

Human-simulated intelligent controller, which does not rely on the accurate 

mathematical model and also has a good control effect for complex 

industrial system with delay or random disturbance, simulates the brain 

of experts. This paper has designed a fuzzy human-simulated intelligent 

controller whose parameters are corrected by fuzzy logic used to 

the hot-rolling strip width control. The simulation results by MATLAB 

show that the effect of the fuzzy human-simulated intelligent control 

based on fuzzy control parameters calibration is better than that of PID 

control, which provides a new way to solve the control problem of the 

complex industrial systems. 

◁ PFrA-36 

On-Ramp Local Control with Neural Network Method, pp.286–289 

Wang, Hao 

Xu, Jinxue 



Highway system is a strongly nonlinear system. Owing to the fact that 

neural network has good nonlinear approximation properties and antijamming 

capability, the neural network and PID control algorithm are introduced 

to the freeway on-ramp control, by adjusting the on-ramp rate 

to maintain the desired traffic density on the main highway. The stability 

of the highway system will be enhanced owing to the fact that RBF algorithm 

can overcome the disadvantage of conventional BP algorithm 

and classical ALINEA control strategy, and the anti-perturbation ability 

will also become stronger. Simulation results have shown that combining 

the neural network and PID control technology can relieve traffic 

congestion of the highway mainline. 

◁ PFrA-37 

A Wide Range of Course-changing Control Algorithm for Marine Vessel, 

pp.295–299 

Jia, Baozhu 

ZHANG, Gui-chen 



This paper develop a named fuzzy switched multi-model algorithm for 

marine vessel. The rudder angle is used to divide the course changing 

process into multiple local operating regimes. The local controller 

is designed in local operating regime by using parallel distributed compensate 

method. The proposed algorithm can improve the global control 

performance in course-changing within wide range. The Lyapunov 

stability theorem is employed to derive the stability conditions of closed 

loop system. Simulation results show that the proposed algorithm in 

this paper provides a satisfactory result. 

◁ PFrA-38 

Rotary Kiln Combustion Working Condition Recognition Based on 

Flame Image Texture Features and LVQ Neural Network, pp.305–309 


Ren, Xiudong 



According to the pulverized coal combustion flame image texture features 

of the rotary-kiln oxide pellets sintering process, a combustion 

working condition recognition method based on learning vector quantization 

(LVQ) neural network is introduced. Firstly, the numerical flame 

image was analyzed to extract texture features, such as energy, entropy 

and inertia, based on grey-level co-occurrence matrix (GLCM) to provide 

qualitative information on the changes in the visual appearance of 

the flame. Then kernel principal component analysis (KPCA) method 

is adopted to deduct the input vector with high dimensionality so as to 

reduce the LVQ target dimension and network scale greatly. Finally, 

LVQ neural network is trained and recognized by using the normalized 

texture feature datum. Test results show that the proposed KPCA-LVQ 

classifier has an excellent performance on training speed and correct 

recognition ratio and meets the requirement for the real-time combustion 

working conditions recognition. 

◁ PFrA-39 

Autonomous Navigation Research for Mobile Robot, pp.331–335 

Cai, Jian Xian 

Yu, Ruihong 

Cheng, Lina 




To solve the navigation problem of mobile robots in unknown environment, 

we develop a navigation scheme based on the bionic strategy 

which simulates operant conditioning mechanism. In this scheme, the 

tendency Cell is designed by use of information entropy which represents 

the tendency degree for state. The improved Q learning algorithm 

used as learning core to direct the learning direction. The Boltzmann 

machine is used to process annealing calculation, which can randomly 

selected navigation action. The selected strategy of action will tend to 

optimal with the learning process. Simulation analyses are carried out 

in mobile robot; results show that the proposed method is effective. 

◁ PFrA-40 

A New Stability Condition of Neural Networks with Time-Varying Delay, 

pp.336–340 

Chen, Yun 




This paper discusses stability of neural networks (NNs) with timevarying 

delay. Delay-fractioning Lyapunov-Krasovskii functional (LKF) 

method and convex analysis are applied to establish a new stability condition. 

Two possible cases for the delay are taken into account when the 

delay interval is equivalently divided into two subintervals. The maximal 

allowable delay that ensures global asymptotical stability of the neural 

network under consideration can be computed by solving a set of linear 

matrix inequalities (LMIs). The advantage of the method is illustrated 

by numerical examples. 

◁ PFrA-41 

The Study of Intelligent Space Environment Application and ManagementBased 

on Wireless Networkt, pp.424–428 

Duan, Ping 


In Intelligent Space, the paper was successfully constructed using Zig- 

Bee wireless sensor networks intelligent systems, and the corresponding 

software and hardware design. The system uses star topology network.The 

information is collected on each node (such as temperature, 

humidity, light intensity, etc) integration.he central node accords corresponding 

information to take automatically intelligent control of the en- 

115


vironment movement.It realized the human living environment.It is low 

power, low cost, easy to operate, easy to install under the Intelligent 

Space. 

◁ PFrA-42 

The Research on Eruption Peak Mutation model of Lake and Reservoir 

Alga Bloom, pp.2949–2952 

Zhao, Xiaoping 

Wang, Xiaoyi 

beijing Tech. & bussiness Univ. 

Beijing Tech. & business Univ. 

Lake and reservoir alga bloom’s eruption is resulted by multiple factors, 

and its formation mechanism is rather complicated. A simulation 

of this eruption has been conducted in sunshine-room laboratory, then 

analysis the primary factor influencing the alga growth by rough set theory, 

through mutation theory to determine the critical factors of eruption. 

On this basis, the peak mutation model featured by potential function 

of chlorophyll-a is constructed so that the characteristic of alga bloom 

is able to described. Finally, the simulation is proved that the model is 

effective and feasibility. 

◁ PFrA-43 

Active fault-tolerant control for satellite system via learning unknown 

input observer, pp.2965–2967 

Jia, Qingxian 

Guan, Yu 


Jiang, Yu 

Shen, Yi 






This paper presents an active fault-tolerant control( FTC) architecture 

for satellite attitude control system(ACS) by combining iterative learning 

ideology and unknown input observer(UIO). As an integral part of active 

FTC system(FTCS), a learning UIO(LUIO) is proposed to achieve attitude 

angular velocities estimation and robust reconstruction of adaptive 

compensation law simultaneously based on the disturbance decoupling 

principle of UIO and 𝐻∞control ideology. Finally, simulation 

results on closed-loop ACS of satellite example demonstrate the effectiveness 

of the proposed LUIO-based approach. 

◁ PFrA-44 

The Method of Multi-sources Fault Diagnosis in Gas Turbine & Compressor 

Unit Based on SDG and Bayes Theory, pp.2973–2976 

SONG, Yong-jie 

XU, Bao-chang 

China Univ. of Petroleum (Beijing) 

China Univ. of Petroleum(Bejing) 

With the development of Natural Gas Pipeline in China, gas turbine & 

compressor unit has been widely used, so the fault diagnosis of its e- 

quipment is important particularly. In this paper, the method based on 

SDG (Signed Directed Graph) and Bayes theory is applied to fault diagnosis 

of the equipment. According to SDG model and Bayes theory, 

this method finds the consistent path and gets the optimizing model of 

the diagnosis. Then the optimal combination is calculated by implicit 

enumeration method. Finally, this method is applied to the lubrication 

system of gas turbine & compressor unit. The results show that this 

method can complete the multi-sources fault diagnosis quantitatively 

and improve the diagnosis resolution effectively. 

◁ PFrA-45 

Research on Banknote Image Orientation Based on Least Square, 

pp.2983–2987 

Zhang, Ying 


Banknote sorting is an important operation in the bank. The banknote 

image orientation plays an key role in banknote recognition. In this 

paper, the theory of least square is researched deeply and is used in 

banknote orientation. Its properties and application circumstances are 

summed up by experiments. 

◁ PFrA-46 

Dynamic Characteristics for Evaporator in Organic Rankine Cycle, 

pp.2994–2998 

Hou, Guolian 

Li, Yanbin 



Zhang, Jianhua 

Zhou, Yeli 

North China Electric Power Univ., Beijing 


Organic Rankine Cycle (ORC) is suitable for recovering energy from 

low-grade heat sources. A moving boundary model is introduced to 

describe the transient phenomena of evaporator, which is an important 

component of ORC. Based on the partial-differential equations expressing 

the conservation principles of mass and energy, coupled with flue 

gas and tube wall energy equations , a set of ordinary-differential equations 

can be obtained by integrating separately over the three regions: 

unsaturated liquid , liquid-vapor mixture, and the superheat vapor. The 

state space equation of evaporator can be derived by linearizing the 

obtained equations at the operating point. Finally, the simulation results 

are presented to show the feasibility of the proposed method of 

modeling. 

◁ PFrA-47 

An improved fuzzy identification method based on Sigmoid data transfer 

function, pp.2999–3003 

Liu, Fucai 

Wang, Shu’en 

Dou, Jinmei 

Yanshan Univ. 

Yanshan Univ. 

Yanshan Univ. 

Unlike the traditional approaches that utilize original data patterns to 

construct the fuzzy model, an approach exploiting both data transformation 

techniques and heuristic method is proposed to simplify the 

modeling procedures. For the transferred data, firstly, the initial value 

of fuzzy if-then rules with nonfuzzy singletons (i.e., real numbers) 

in the consequent parts is generated by the heuristic method. Then, 

fine-tuning is done by gradient descent learning algorithm. The proposed 

method has better approximation accuracy and faster convergence 

speed. Simulation result demonstrates the superiority of the 

proposed model to the conventional methodologies. 

◁ PFrA-48 

Nonlinear System Modeling and Fault Detection Method Using Set 

Membership Estimation and T-S Fuzzy Model, pp.3031–3036 

Chai, Wei 


A modeling method is proposed and applied in fault detection for nonlinear 

dynamical systems with unknown but bounded noises. Since the 

Takagi-Sugeno (T-S) fuzzy model is a universal approximator, it is used 

to model the nonlinear dynamical system when the system runs without 

a fault. After some input and output data of the system are obtained, 

the input space is partitioned using a fuzzy clustering algorithm. Assuming 

that the system noise and approximation error are unknown but 

bounded, the consequence parameters of the T-S fuzzy model of the 

system are determined by means of a linear-in-parameter set membership 

estimation algorithm. An interval containing the actual output of 

the system running without a fault can be easily predicted based on the 

result of the estimation. If the measured output is out of the predicted 

interval, it can be determined that a fault has occurred. Simulation 

results show the effectiveness of the proposed method. 

◁ PFrA-49 

Sample Selection and Training of Self-Organizing Map Neural Network 

in Multiple Models Approximation, pp.3053–3058 

Gao, Dayuan 

Zhu, Hai 

Liu, Xijing 

Wang, Chao 

Navy submarine Acad. 


Navy Submarine Acad. 


The self-organizing map (SOM) neural network has been used widely 

in multiple models approximation (MMA). However, the clustering property 

of SOM may not be fit for MMA. This paper introduces the idea of 

active learning into the training of SOM, especially for MMA. The neural 

network selects actively the training samples according to the approximation 

error of local models. As a result, the distribution of the neural 

nodes is changed so that the performance of MMA is improved. The 

process of this training method and the performance improvement are 

illustrated by a simulation example. 

◁ PFrA-50 

116



A Rough T-S Fuzzy Model, pp.3072–3076 

Wang, Li 

Zhou, Xianzhong 

Shen, Jie 


Nanjing Univ. 


A rough T-S fuzzy model that uses rough set to design the structure 

of T-S fuzzy model is proposed. Fuzzy c-means clustering is used to 

transform the continuous attributes to the discretized ones and partition 

the input space. Heuristic attribute reduction algorithm based on attribute 

significance deals with the discretized decision table to remove 

redundant condition attributes. Concise decision rules are extracted according 

to the threshold of degree of support, confidence and coverage. 

The rules of T-S fuzzy model are got according to the extracted decision 

rules. Antecedent parameters of T-S fuzzy model are determined 

according to fuzzy partition result, and consequent parameters are i- 

dentified by least square method. Fuzzy rules of the proposed model 

have clear physical meaning and simplified structure. Moreover, a s- 

tudy algorithm is no longer needed to optimize the parameters of fuzzy 

model. Finally, the validity of the proposed model is verified by water 

treatment modeling experiment. 

◁ PFrA-51 

Dynamic Fault Tree Analysis based Fault Diagnosis System of Power 

Transformer, pp.3077–3081 

GUO, Jiang 

Shi, Lei 

Zhang, Kefei 

Gu, Kaikai 

Bai, Weimin 

Zeng, Bing 

Liu, Yajin 

Wuhan Univ. 

Wuhan Univ. 

Wuhan Univ. 

Wuhan Univ. 

Wuhan Univ. 

Wuhan Univ. 

Wuhan Univ. 

The process of transformer fault diagnosis and DFTA are first presented 

in this paper and then we apply DFTA to the field of transformer faults 

diagnosis. By establishing the fault tree of transformer, a practical, 

easily-extended, interactive and self-learning enabled fault diagnosis 

system based on DFTA for transformer is designed and implemented. 

With the implementation and application of the DFTA diagnosis system, 

it’s easy to get a reasonable result from the computer with the help of 

experts. The practical results demonstrated that the system can highly 

improve the accuracy of transformer fault diagnosis and effectively improve 

the reliability and safety transformer which brings much economic 

benefits in return. 

◁ PFrA-52 

Aging and Life Management Methods of Pressurizer Based on PDCA 

Cycle in Nuclear Power Station, pp.3082–3086 

GUO, Jiang 

Bai, Weimin 

Feng, Zhihui 

Gu, Kaikai 

Zeng, Bing 

Liu, Yajin 

Wuhan Univ. 

Wuhan Univ. 

Wuhan Univ. 

Wuhan Univ. 

Wuhan Univ. 

Wuhan Univ. 

Abstract―the Pressurizer is important equipment in PWR nuclear power 

station. There is only one Pressurizer, which adjust and keep pressure 

stable, overall the One Loop System. It is very important for safe 

operation of nuclear power station to keep integrity of Pressurizer. To 

carry out the work of aging and life management of Pressurizer more 

effectively and ensure the Pressurizer run safe and stably, this paper 

does research on Aging and Life Management Methods of Pressurizer. 

On the basis of PDCA Cycle, this paper realize the management of 

leading principles to aging management, operation control, checking, 

monitoring, evaluation and program of lifetime maintenance. 

◁ PFrA-53 

Frequency-domain Parameter Identification of Nonlinear Generator 

Excitation System Based on Improved Particle Filtering Algorithm, 

pp.3087–3090 

Liu, Ruilan 

Nanjing Univ. of Post &Telecomomunication 

Liu, Wei 

Guangxi Power Grid Electric Power Research Inst. 

A frequency-domain parameter identification method based on improved 

particle filtering algorithm is proposed to identify parameters of 

generator excitation system. The process model and measurement 

model of the nonlinear excitation system parameter identification are 

proposed, which can correct the position of each particle based on the 

residual and the error between the particle and the local optimal point 

in the iteration. The identification example based on real measured 

data from a power plant shows that the proposed method is simple to 

be implemented and can get better identification results especially for 

nonlinear excitation system. 

◁ PFrA-54 

Nonlinear torsional vibration dynamics of rolling mill’s drive system 

under spindle angle parametric excitation, pp.3091–3095 

Shi, Peiming 

Li, Jizhao 

Zhao, Dongwei 

Liu, Bin 

Han, Dongying 

Yanshan Univ. 

Yanshan Univ. 

Yanshan Univ. 

Yanshan Univ. 

Yanshan Univ. 

Considering the effect caused by the spindle angle and friction force 

of roll gap on the main drive system of rolling mill, the nonlinear torsional 

vibration dynamical equation of rolling mill’s drive system is 

established, which contains parametrical stiffness and nonlinear friction 

damping. The amplitude-frequency characteristic equation and bifurcation 

response equation are obtained by solving the dynamical equation 

using the multi-scale method. The example analysis was carried out 

on the 1780 rolling mill of Chengde Steel Group. It is shown that the 

increase of motor’s disturbance torque will aggravate the vibration of 

rolling mill’s drive system; the motor’s disturbance will enlarge when 

the angle is too large or too small. Then the reasonable control range 

of spindle angle is determined. It is best to keep spindle angle between 

degree of 2 and 5. 

◁ PFrA-55 

Fuzzy Identification of the Steam Multivariable Temperature System 

Based on Improved GK Clustering Algorithm, pp.3096–3101 

Li, Ruonan 

Du, Xiuxia 

Li, Pingkang 




Boiler steam temperature system shows non-linear and time-varying, 

so the accurate modeling of steam temperature system is particularly 

important. A kind of method of fuzzy identification based on improved 

GK clustering algorithm ( -sectional set fuzzy weighted GK clustering) is 

proposed in connection with the traditional Fuzzy clustering algorithm’ 

s defects such as low precision and slow search speed. By analyzing 

the correlation of input and output as weighted coefficient of fuzzy 

clustering algorithm, it is employed to cluster the input data of sample 

space. A more appropriate division of the input data is achieved, at the 

same time the sectional set fuzzy GK clustering is proposed to identify 

the model structure off line to improve searching rate, the method 

confirms the premise parameter by improved fuzzy partitions clustering 

algorithm and the consequence parameters is decided by LS algorithm. 

In this paper, the simulation of the temperature control TITO system of 

the boiler can illustrate that the method is accurate and effective. 

◁ PFrA-56 

Pitch-controlled Wind Turbine Synchronized Cutting-in Control and 

Modeling-Simulation, pp.3113–3117 

Xiao, Yunqi 

Lv, Yuegang 



As in cutting-in control process of pitch-controlled doubly-fed wind power 

generation system, the synchronized control strategy of DFIG can 

only regulate the stator voltages before cut in, the performance of pitch 

control for maintaining wind turbine rotating speed is studied, in order to 

prevent turbine over-speed. For accomplishing the whole cutting-in process 

simulation, a method based on modeling generator respectively 

and time-sharing simulation is also adopted. The simulating results val- 

117


idate that both the control strategies and Modeling methods discussed 

in this paper are efficient. 

◁ PFrA-57 

Turbine Machine Fault Diagnosis Using Modified Redundant Second 

Generation Wavelet Packet Transform, pp.3126–3130 

Li, Ning 

Zhou, Rui 

Shanghai Second Polytechnic Univ. 


Faulty features extraction is an essential problem in the field of largescale 

electromechanical equipment faulty diagnosis. Classical vibration 

faulty features extraction is based on spectral analysis method, while 

the wavelet transform provides a novel tool to solve this problem. In 

this paper, the problem of frequency band derangement inhering in redundant 

second generation wavelet packet transform (RSGWPT) was 

explained and the causes were pointed out. Then a modified redundant 

second generation wavelet packet transform which can make the 

order of decomposed subband signals to be consistent with the linear 

partition order of frequency band is proposed. The modified RSGW- 

PT discards the split and merge operations in the decomposition and 

reconstruction stages and directly use the constructed operators to accomplish 

prediction and update steps. Thus the signal length at each 

level is the same with the original signal, accordingly more information 

of the time domain features can be preserved, and at the same 

time the aliasing of RSGWPT can be inhibited effectively. This method 

was applied to analyze the simulated signals and the practical turbine 

machine vibration faulty signals. Testing results show that the proposed 

improved RSGWPT method is quite effective in extracting the faulty features 

from the vibration signal, so it can be effectively applied to the fault 

diagnosis of turbine machine. 

◁ PFrA-58 

Study on Time Registration method for Photoelectric TheodoliteData 

Fusion, pp.3137–3139 

YANG, Hong Tao 

GAO, Hui-bin 


Changchun Inst. of Optics, Fine Mechanics & 


In range measurement, theodolite and radar constitute a real-time 

tracking system at different sites to track the same target in the air and 

get useful information exactly and timely.As the optical theodolite and 

radar have different sampling frequency and measurement system, the 

data is sent to the fusion center is asynchronous.This paper proposed 

a time registration method based on multi-sensor data using Wavelet 

neural network algorithm,which not only better solved the basic problems 

of theodolite fusion tracking but also improve the efficiency of data 

fusion.Simulation experiment and comparison with other time registration 

method have shown the advantage of this method. 

◁ PFrA-59 

Parameter Identifiability of Quantized Linear Systems, pp.3140–3145 

Shen, Ying 

Zhang, Hui 



The parameter identifiability of quantized linear systems with Gauss- 

Markov parameters was discussed from information theoretic point of 

view. The presented definition of parameter identifiability was reviewed 

and extended to quantized systems by considering the intrinsic property 

of the system. Then the parameter identifiability of linear systems with 

quantized outputs was analyzed and the criterion of parameter identifiability 

was proposed based on the measure of mutual information. 

Furthermore, the convergence property of the quantized parameter i- 

dentifiability Gramian was analyzed. 

◁ PFrA-60 

Nonlinear Process Fault Diagnosis based on Slow Feature Analysis, 

pp.3152–3156 

Deng, Xiaogang 

Tian, Xue-Min 

Hu, Xiangyang 



Hekou Production Factory of Shengli Oilfield 

Invariant features of temporally varying signals are very useful for process 

monitoring. A novel nonlinear process fault diagnosis method is 

proposed in this paper based on slow feature analysis (SFA) which is 

a new invariant learning method. In the proposed method, input-output 

transformation functions are optimized to extract the nonlinear slowly 

varying components as invariant features. Based on feature variables, 

two monitoring statistics are constructed for fault detection and their 

confidence limits are computed by kernel density estimation. Simulation 

using continuous stirred tank reactor (CSTR) system shows that the 

proposed method outperforms the traditional PCA and KPCA method. 

◁ PFrA-61 

Fault Diagnosis of Hydraulic Variable Pitch for Wind Turbine Based on 

Qualitative and Quantitative Analysis, pp.3181–3185 

Han, Xiaojuan 

Zhang, Hao 

Chen, Yueyan 

Zhang, Xilin 







Qualitative analysis and quantitative analysis are combined to carry on 

hydraulic variable pitch system fault diagnosis of wind turbine. Fault tree 

model of hydraulic system is established by the analysis of hydraulic 

system fault symptoms set. Petri net model of hydraulic system fault 

can be obtained by fault tree using the matrix operations of Petri net to 

achieve the conversion from qualitative to quantitative which can make 

up the shortcoming of fault tree model inclining to qualitative analysis 

when the basic event is difficult to determine its occurrence probability. 

The validity of the model is verified by simulation example. 

◁ PFrA-62 

Modeling and Control Simulation for Force Couple Leveling System of 

Hydraulic Press, pp.3186–3190 

Du, Chunyan 


Jia, Chao 

Tianjin Univ. 


tianjin Univ. of Tech. 

This paper studies force couple leveling system of heavy hydraulic 

press. A multi-input and multi-output nonlinearmodel is established on 

the basis of analyzing hydraulic structure and block kinematics. A cascade 

control structure is designed for the leveling system. In the outer 

loop, each corner follows the average displacement other two corners 

adjacent to it using PI method, and the desired pressure of inner loop 

is given. In the inner loop the cylinder pressure is controlled using parameter 

variable PD method. Modeling and control simulation in Matlab 

show that the designed control method has a good synchronization effect 

. 

◁ PFrA-63 

Research on the Application and Compensation for Startup Process of 

FOG Based on RBF Neural Network, pp.3195–3199 

SHEN, Jun 

MIAO, Ling-juan 

GUO, ZIWEI 


Beijing Inst. of Tech., 


As the core components of Fiber Optic Gyroscope (FOG) are sensitive 

to temperature, there is a certain temperature drift error in the working 

process of FOG. In particular, during the period from supplying power 

to achieving the nominal precision, the temperature drift of FOG is 

much higher. In this paper, for reducing the drift in the startup process 

of FOG and shortening the time of FOG startup, a scheme based on 

Radial Basis Function (RBF) neural networks is designed to compensate 

the drift in the startup process of FOG. The RBF neural network 

use the two inputs and single output scheme that use the temperature 

of FOG and the temperature change rate as the inputs and use the drift 

of FOG as the output. In the room temperature, the RBF neural network 

is used to compensate for the startup process of FOG, and the results 

show that the method can effectively reduce the drift and startup time 

of the FOG. This method is used in a certain type of FOG North Finder 

and can greatly reduce the North Finder preparation time and improve 

the north-seeking accuracy. 

◁ PFrA-64 

Short-Term Wind Speed Prediction Model of LS-SVM Based on Genet- 

118



ic Algorithm, pp.3200–3204 

Han, Xiaojuan 

Chen, Fang 

Cao, Hui 

Li, Xiangjun 

Zhang, Xilin 



China Space Tech. Acad. 



According to nonlinear feature of various factors related to wind speed, 

the method of least squares support vector machine (LS-SVM) for 

short-term wind speed prediction was put forward in this paper. The 

influence of parameters selection of LS-SVM on prediction accuracy 

was analyzed. The genetic algorithm was adopted to realize parameters 

optimization of LS-SVM and establish short-term wind speed prediction 

model of LS-SVM based on Genetic Algorithm. It was verified 

that the method proposed in this paper can quickly and effectively carry 

on short-term wind speed prediction by simulation example. 

◁ PFrA-65 

Fault Diagnosis Based on Genetic Algorithm for Optimization of EBF 

Neural Network , pp.3205–3207 

Wang, Ya-hui 

Huo, Yifeng 



Ellipsoidal basis function(EBF) can make the partition and limitary of 

input space. Compared with the Guassian function of radial basis function(RBF) 

neural network, the EBF can make the partition of input s- 

pace more specific, which has the higher capability of pattern recognition. 

However, the neural network has a common problem of training 

the weight and threshold. The evolution of genetic algorithm(GA) can 

maximumly optimize the training time of neural network. In this paper, 

a new method based on GA-EBF neural network was proposed. The 

simulation experiment shows that the proposed method has a higher 

rate of fault diagnosis than that of RBF neural network. 

◁ PFrA-66 

Nonsingular Terminal Neural Network Sliding Mode Control for Multilink 

Robots Based on Backstepping, pp.20–23 

Xu, Chuanzhong 

Wang, Yongchu 



A new method of nonsingular terminal neural network sliding control 

based on backstepping for tracking control of multi-link robot manipulators 

is introduced in this paper. The proposed scheme combines the 

advantages of the adaptive control, neural network and sliding mode 

control strategies without precise system model information. It has 

on-line learning ability to deal with the parametric uncertainty and disturbances 

by adjusting the control parameters. A neural network sliding 

mode controller is designed via the Lyapunov stability theory in order 

to guarantee the high quality of the controlled system. The simulation 

results show that this method is feasible and effective. 

◁ PFrA-67 

A Vehicle License Plate Location and Correction Method Based On the 

Characteristics of License Plate, pp.42–46 

Fang, Jun 


With the development of the city, more and more license plate recognition 

systems are applied to the intelligent transportation management. 

License plate recognition is divided into three steps: license plate location, 

character segmentation and character recognition, therefore, the 

positioning of the license plate is an essential step in the process of 

license plate recognition. Meanwhile, in the character segmentation 

process, the tilt angle plate influences the exact character segmentation. 

For different tilt angles of the plate, this paper presents a license 

plate location based on the feature information of the license plate, and 

a tilt angle correction method, Combined with the Hough Transform. A 

large number of experiments show that the method is better to solve 

the above problems, and adapt to a variety of license plate location and 

correction. The method has a strong environmental adaptability and 

robustness. 

◁ PFrA-68 

Routing Algorithm Based on Swarm Intelligence, pp.47–50 

Lv, Yong 

Zhu, Yaodong 

JiaXing Univ. 

JiaXing Univ. 

Swarm intelligence inspired by the social behavior of ants boasts a 

number of attractive features, including adaptation, robustness, decentralized 

and self-organizing nature, which are well suited for routing in 

modern communication networks. This paper describes a new adaptive 

dynamic routing algorithm for packet-switched communications networks 

based on simple biological “ants”that explore the network and 

learn good routes, using a novel variation of reinforcement learning. 

Simulation results confirm that the algorithm is shown to significantly 

improve the network’s relaxation and its response to perturbations. 

◁ PFrA-69 

Fuzzy control strategy based on the Particle Swarm Optimization Algorithms, 

pp.57–60 

Han, Shaoze 

WuHan Digital Engineering Inst. 

The designer’s experience determines the parameters setting of Fuzzy 

controller, a new fuzzy control method based on the modified PSO algorithms 

is proposed in this paper. Besides, with this method, the application 

of the fuzzy controller in heat-setting machine is studied by 

simulation and good results have been obtained. A comparison with 

conventional PID control approaches widely used in the plant is performed. 

◁ PFrA-70 

Research on Fleet Networked Air Defense Sensor Cooperative Control, 

pp.113–115 

Zhuan, Yifu 


Chen, Rong 

Navy 91550 unit. Dalian 


502unit,NAEI 

department of command engineering 502unit,NAEI 

After the research of fleet networked sensor cooperative control frame, 

and analyze the flow course, a new method of fleet networked sensor 

cooperative target assignment calculation and cooperative control was 

provided by agent cooperative idea. The model needs to be studied 

deeply by next step so that to supply useful suggestions for army e- 

quipments development. 

◁ PFrA-71 

Adaptive Visual Servo Control of UAV Ground-Target-Autonomous- 

Tracking System , pp.133–137 

Chen, LongSheng 

NanChang HangKong Univ. 

A novel adaptive servo control method is proposed for UAV 

GTATS(Ground-Target-Autonomous-Tracking System), which consists 

of basic control law for UAV and visual tracking controller for GTATS. 

The adaptive servo control method only depends on target information 

in the image plane and kalman filtering technology. Based on this proposed 

method, a dynamic motion target can be tracked without target’s 

3D velocity. Synchronously, in order to estimate the optimal system s- 

tate and target image velocity which is used later by the visual tracking 

controller, a self-tuning Kalman filter is adopted to estimate interesting 

parameters on-line in real-time. Further, Because the visual tracking 

controller is working entirely in image space, the dynamic characteristics 

of the image signal are analyzed and a kinematics model is developed 

for the target in the image plane by the geometrical relations 

among the UAV, the target and the camera. Finally, The performance of 

the controller is demonstrated by both theoretical stability analysis and 

simulation results. 

◁ PFrA-72 

Simulation Research on maximum power point tracking based on perturbation 

and observation, pp.150–153 



The maximum output power of photovoltaic(PV) depends on the ambient 

temperature and light conditions. Maximum power point tracking 

always enable PV arrays produce maximum output power. Perturbation 

and observation (P&O) is an important and efficient method among 

maximum power point tracking because it has many advantages such 

119


as simple algorithm, high reliability and fast tracking. P&O method is 

used to track the output power of PV system with the variation of solar 

intensity. The simulation results show that P&O can accurately track 

the maximum power point 

◁ PFrA-73 

Non-fragile Guaranteed-cost H1 Control for a Class of Nonlinear 

Sampled-data System, pp.154–158 

Wang, Shi-gang 


This paper considers the non-fragile costguaranteed control problem for 

uncertain nonlinear sampled-data system and controller gain perturbations. 

Firstly, the continuous control plant of sampled-data system is 

transformed into a discrete system model with nonlinear.Then, the Lyapunov 

stability theory and the linear matrix inequality (LMI) approach 

are applied to design a non-fragile cost-guaranteed controller, which results 

in the closed-loop system being asymptotically stable and the system’s 

performance index being less than a given value. At the same 

time, the existence condition and the design approach of a non-fragile 

cost-guaranteed controller are presented. Finally, simulation examples 

are employed to verify the validity of the proposed control algorithm. 

◁ PFrA-74 

Adaptive Proportional Guidance Law for Reentry Vehicles with Impact 

Angle and Terminal Velocity Constraints, pp.159–163 

Xie, Daocheng 

Wang, Zhongwei 

national Univ. of defense Tech. 


Adaptive proportional guidance law is studied during the reentry phase 

of vehicle, considering terminal velocity and impact angle constraints. 

3-D adaptive proportional guidance law satisfying impact angle constraint 

is derived, guidance equations are expressed in longitudinal and 

lateral plane respectively, needed guidance command of angle of attack 

and sideslip angle is generated. Guidance command of angle of attack 

and sideslip angle is appended when considering reentry velocity. 

Synthesized guidance command is the sum of needed guidance command 

and appended guidance command. Effect of attitude control for 

vehicle is compared using optimal guidance and adaptive proportional 

guidance law, the attitude of vehicle is stable and the vehicle is guided 

to target point using adaptive proportional guidance laws. Trendline of 

landing error, reentry velocity and terminal angle varying with target velocity 

are analyzed, adaptive proportional guidance law performs much 

better than optimal guidance, simulation results indicate that adaptive 

proportional guidance law is robust to maneuvering target. 

◁ PFrA-75 

Adaptive Fuzzy Path Following Control for a Nonholonomic Mobile 


Shi, Wuxi 

Tianjin Polytechnic Univ. 

This paper addresses an adaptive fuzzy path following control scheme 

of a mobile robot with uncertainty of its center of mass. A fuzzy system 

is used to approximate the uncertainty function of the controller, 

the parameters in fuzzy system are adjusted by the tracking error. The 

approximation error can be efficiently counteracted by employing robust 

compensator. The proposed design scheme guarantees that all 

signals in the closed-loop system are bounded, and the tracking error 

converge to the origin. A simulation example is used to demonstrate 

the effectiveness of the proposed scheme. 

◁ PFrA-76 

Study on a Sliding Mode Variable Structure Vector Control of Induction 

Motor Drives, pp.209–213 

Liu, Huan 

Cui, Han 



Compared with the conventional control strategy of IM. FOC can offer 

the excellent performance as DC motors. However, the control performance 

of the resulting linear system depends critically on very accurate 

coordinate transformations and flux angle estimation. In this paper ,an 

indirect field-oriented induction motor drive with a sliding-mode controller 

is presented .The design includes rotor speed estimation from 

measured stator terminal voltages and currents .The estimated speed 

is used as feedback in an indirect vector control system achieving the 

speed control without the use of shaft mouted transducers. Stability 

analysis based on Lyapunov theory is also presented, to guarantee the 

closed loop stability. The simulation experimental waveforms and results 

are given. 

◁ PFrA-77 

The Application of PowerGREP in Corpus Processing for Foreign Language 

Teaching, pp.220–223 

Liu, Huan 

Cui, Han 



Corpus annotation is an important but difficult part in corpus linguistic 

research. In addition to speech tagging, bulk or automatic generation 

of other types of labels, including the labeling of emantics, syntax, discourse 

and pragmatics, are difficult to achieve. This paper describes 

the the application of PowerGREP in corpus processing for foreign language 

teaching, focusing on the three main functions including retrieval, 

editing and replacement, and collection as well. In addition, taking the 

data of BNC as an example, the paper showed how to apply Power- 

GREP in automatic or semi-automatic corpus processing. 

◁ PFrA-78 

Multi-objective Optimization of Airport Gate Assignment , pp.260–264 

Liu, Changyou 

Liang, Yutao 


China Aviation Univ. of China 

It is often focus on not only the efficiency improvement, but also the 

safety enhancement in the practical operations of busy airport. A model 

of multi-objective airport gate assignment problem with the safety 

constrains of the taxi-in and push-out conflict avoidance is proposed in 

the paper and an optimizing solution is given by ant colony algorithm. 

The illustrative examples with the realistic flight data show the validity 

of our approach for offering both the safety and the efficiency to busy 

airport operations. 

◁ PFrA-79 

Indirect Position Detection of SRM Based on Genetic Algorithm, 

pp.275–279 

Xiao, Li 

Sun, Hexu 


Hebei Univ. of Technolog 

Due indirect position detection of SRM based on traditional BP neural 

network have shortcomings of long training time, slow convergence and 

easy to fall into local minimum, this paper presents a method of indirect 

position detection based on BP neural network optimized by genetic 

algorithm. The method uses the global optimization ability of genetic algorithm(GA) 

to correct weights and thresholds of BP network, then uses 

the trained BP network to achieve the non-linear mapping between 

the current, flux and rotor position of motor. Simulation results demonstrate 

that the genetic algorithm has a significant effect to improve performance 

of BP neural network, and improves the detection accuracy, 

then achieve indirect position detection of switched reluctance motor. 

◁ PFrA-80 

Dynamic Path Optimization Method Based on Ant Colony Algorithm and 

Group Decision-making, pp.300–304 

Huang, Yan Guo 


The paper built an urban road network model through analysis of urban 

traffic flow characteristics. The minimizing total travel time of vehicle 

in the road network was taken as control target, and the dynamic path 

model was built. The ant colony algorithm was used to find out the 

optimum path from start point to destination by collecting the real-time 

traffic information of the road network. Then using the theory of group 

decision making, the dynamic path optimization method was put forward. 

In this method, the two parameters of distance between adjacent 

intersections and section traffic flow saturation which have influence on 

the control target was considered, and they were combined with ant 

colony algorithm, and the optimal path was gotten through the group 

decision making for different results of the algorithm, and the realization 

of the optimization method was given. The dynamic path optimization 

process of regional network was described by programming with Matlab 

120



through a simulation example. The results show that the new method 

in this paper had better control effect compared with other methods 

◁ PFrA-81 

The Design of Video Image’s Storage Converter Based on FPGA, 

pp.346–349 

Liu, Qingqian 

Wang, Bo 

Zheng, Zhihui 




In the processing of video image data, the data need to be stored and 

transferred to other functional units in or out of the system in real time. 

Especially in a complex system, it needs a special platform to store and 

transfer video image data in real time. This article presents a design of 

video image’s storage converter based on FPGA. This storage converter 

can convert the video image data format. Its storage density is 

very high. It can communicate with personal computer, functional u- 

nits in the system and functional units in other systems. The storage 

converter can also ensure the real time of video image data. 

◁ PFrA-82 

A Dynamic Algorithm for Community Detection in Social Networks, 

pp.350–354 

Kong, Bing 

Chen, Hongmei 

Liu, WeiYi 

Zhou, Lihua 

Yunnan Univ. 

Yunnan Univ. 

Yunnan Univ. 

Yunnan Univ. 

Social networks can be modeled by graphs with nodes and edges, and 

communities are sub graphs within networks. This paper proposes a 

new dynamic algorithm based on the modularity given by Newman and 

Girvan (NG modularity for short ). Further more, this paper applies 

the proposed algorithm to real network data. The experimental results 

show that our algorithm can dynamically detect communities in a network, 

and the communities detected with the algorithm fits better with 

the real communities. 

◁ PFrA-83 

Offensive and defensive strategy of web crawler, pp.355–358 

Jiang, Yuanshu 

Guo, Liyong 



Crawling strategies of web crawler affect not only the quality of search 

engine, but also the working status of web server. Many web servers 

restrict the access of unknown crawler or the crawler with excessive 

visiting frequency. This paper analyzes these restrictions and proposes 

a strategy of proxy-based, login by simulating verification code automatically; 

give some guidance on the design of web crawler. 

◁ PFrA-84 

Estimate Method to Solve the Time Lag of Sintering Forecast, pp.359– 

361 

Zhang, Tao 


Sintering end control systems generally lag, if the destination ahead 

of sintering, the sintering time is too long, sintering heat, so less than 

the blast furnace sinter production of each component content. If the 

backward end of sintering, sintering not fully carried out, part of the s- 

intering machine in the ring on a cold, cold cooling ring less than the 

required temperature, resulting in sintering temperature as the feed belt 

to blast furnace burning, leading to the back process can not proceed. 

Using estimate method to implement burn-through point forecast can 

be a good solution to this problem. 

◁ PFrA-85 

Genetic Algorithm with Three Dimensional Chromosome for Large S- 

cale Scheduling Problems, pp.362–367 



Yin, Hong Li 




In so many combinatorial optimization problems, Job shop scheduling 

problems have earned a reputation for being difficult to solve. Genetic 

algorithm has demonstrated considerable success in providing efficient 

solutions to many non-polynomial-hard optimization problems. 

In the field of job shop scheduling, genetic algorithm has been intensively 

researched, and there are nine kinds of methods were proposed 

to encoding chromosome to represent a solution. In this paper, we 

proposed a novel genetic chromosome encoding approach, in this encoding 

method, the operation of crossover and mutation was done in 

three-dimensional coded space. Some big benchmark problems were 

tried with the proposed three-dimensional encoding genetic algorithm 

for validation and the results are encouraging. 

◁ PFrA-86 

Application of Ontology Intelligence Search Engine based on Ant 

Colony Algorithm, pp.374–378 

Zhang, Ruihua 

Yang, Tianqi 

Jinan Univ. 

Jinan Univ. 

As the search efficiency is not high and it’s difficult to find user’s interested 

information, this paper proposes the combination of ant colony 

algorithm and ontology to create search engine. By the clear formal 

specification of body shared concept, the search engine has intelligent 

search features, and then it can use ontology to provide users’ 

interested search results. In the process of ontology’s usage, in order 

to make different ontology interoperate, the paper introduces similarity 

computing, and then improves it. Based on the characteristics of 

ant colony algorithm, it introduces multi-pheromone to change search 

engine strategy, so it can improve search engine efficiency. While the 

multi-pheromones apply to different servers, with the increase of search 

times, it can make the finding of best path more effective. 

◁ PFrA-87 

Wireless order system Based on WinCE, pp.379–383 

Xiao, Jianan 

Wang, Nana 



This article has introduced a meal-ordering system based on ARM9, 

WINCE, wireless technology, which is both cost-effective and convenient. 

It can conquer the shortcomings now exist in the current system. 

The system we designed has a user-friendly colorful interface and is 

universal. Customers no longer need a servant while ordering and can 

communicate directly with the counter. Restaurant will be able to enhance 

its overall image and core competitiveness in the catering industry. 

◁ PFrA-88 

The Hot Topics Evaluation and Key Issues in Online News about Emergency 

Events, pp.407–412 

Chen, Liping 

Du, Junping 

Shimohara, Katsunori 

Song, Maoqiang 




Doshisha Univ. 


This paper concentrates on the evaluation system of hot topics in the 

online news about the emergency events. First, the overall technology 

framework of the system was established and description was formulated 

on the key issues need to be resolved prior to the evaluation of 

the hot topics. Focusing on the vector representation of the news documents 

and clustering algorithm of the news topics, a modified TF-IDF 

text representation model was formulated and an improved selection 

method of initial topic clustering center was established. The evaluation 

model of the hot topics was then established to evaluate the heat 

value of the clustered topics based on extracting websites feature parameters, 

such as the time properties of the news reports, reporting 

properties, and user attention. Finally, the paper takes news of 2011 

Japan Earthquake as data source for algorism and model evaluation. 

The results showed that the identification and evaluation system of hot 

topics in online news about the emergency events was valid, and the 

evaluation result of hot topics by the systems agreed with the expected 

results. It lays a foundation for the subsequent studying about the 

tracking and evolution of the hot topics. 

◁ PFrA-89 

121


Intelligent Test Paper Generation System Based on Slicing Processing, 

pp.506–511 

Ma, Fengning 

Dong, Yao 

Shi, Jin 

Zhang, Ying 

Tianjin Univ. 



Tianjin Univ. 

nowadays, paperless test is very popular in the universities. The classical 

test paper generating methods have not satisfied the increasing 

test requirement. So, an intelligent test paper generating algorithm 

based on the slicing processing is proposed, which classifies the indexes, 

such as the type and amount of questions, the knowledge point, 

difficulty and discrimination of the test paper, with the value type and 

flexible feature. The actual application indicates that the method has a 

balance between the success ratio and efficiency, and provides the theory 

evidence for the efficient intelligent test paper generation system. 

◁ PFrA-90 

An improved Transfer Learning Algorithm for Document categorization 

Based on data sets reconstruct, pp.575–578 

Sun, Wei 

Qian, Xu 

China Univ. of Mining & Tech.(Beijing) 



(Beijing) 

Traditional machine learning and data mining algorithms usually assume 

that the training and test data have the same feature space and 

data distribution, but in the real application this assumption is often d- 

ifficult to establish, and always lead the existing model to outdate. As 

a new learning mechanism, transfer learning can solve this problem effectively, 

in this paper, we will propose an improved transfer learning 

algorithm for document categorization based on data sets reconstruct, 

we also describe the main idea and the step of the algorithm, then use 

experiment to test the algorithm and compare it with other algorithms, 

the result of experiment proves the algorithm we proposed in this paper 

is better than the others in some extent. 

◁ PFrA-91 

A New Ant Colony Optimization with Global Exploring Capability and 

Rapid Convergence, pp.579–583 

Deng, Xiangyang 

Yu, Wenlong 

Zhang, Limin 




Ant colony optimization (ACO) is a meta-heuristic algorithm, and is 

widely applied in combinatorial optimization. To enhance the ACO’s 

global exploiting capability and convergence, a new pheromone update 

strategy is presented, which results in a gradually transition of the ant 

colony’s diversity, and an improved ACO algorithm called ACO+ is 

proposed. For a solution to the traveling salesman problem (TSP), a s- 

tatistical model of traversed ants of sub-routes is introduced to rank the 

sub-routes, and an adaptive pheromone trails update mechanism is implemented, 

which integrates with the iteration-best pheromone update 

strategy. The algorithm can effectively combine the global exploring capability 

and convergence rate. Experiments show that the ACO+ has a 

good performance and robustness. 

◁ PFrA-92 

Study on Control Strategy for Vehicle Braking Force on Low Adhesive 

Cornering Road, pp.618–622 

Song, Dandan 

Yang, Tao 



Along with reducing the centrifugal force based on decreased speed using 

vehicle braking, the maximum lateral force of the wheel could bear 

diminishing gradually. In this paper, the deficiency of vehicle’s anti-lock 

braking system on cornering road is analyzed, a control strategy of vehicle 

braking on low adhesive cornering road is presented, vehicle’s 

ABS and the biggest lateral force of the wheel could bearing when vehicle 

occur side slip are comprehensive considered, and the maximum 

braking force applied to vehicle can be determined according to relatively 

small value of both. Simulation shows the validity of the proposed 

control method. 

◁ PFrA-93 

A Novel Prototype Architecture for Equipment Tele-control and Simulation, 

pp.633–637 



Yin, Hong Li 




Due to the working hazardous or other conditions, such as tele-medical, 

tele-embodiment, operations should be executed with a fully remote 

control and monitoring. So, tele-control and reality simulation are crucial 

in these environments. However, there exists a lack of an effective 

system architecture that integrates remote condition monitoring and 

control of automated equipment; that give much consideration about 

data transfer time delay via TCP/IP data package. This paper presented 

a novel prototype architecture for tele-control and reality simulation, 

which can guarantee the non-distortion-transfer of control information 

and reduce the action time difference between local simulated virtual 

equipment and remote real equipment, couple the remote control and 

virtual reality together. In order to demonstrate and validate the effectiveness 

of the novel architecture, a 3 DOF Fischertechnik industry 

robot remote operation and monitoring system have been developed. 

Experimental results are encouraging and demonstrate a promising 

application in any other relevant environment. 

◁ PFrA-94 

Keep the Geometries: Image Segmentation by K-MSVC with Random 

Region Grouping and Propagation, pp.672–679 

LIN, Yining 

Wei, Wei 

DAI, Yuanming 




We propose new techniques to address low-level image segmentation 

problem under clustering theory. The goal of this paper is to provide 

a compromised solution between methods that produce two different 

kinds of segmentation results: one generates coherent regions but 

views disjoint regions as totally different objects, and the others do not 

consider the spatial relationship at all. For our approach, spatial geometries 

are partially preserved and disjoint regions are also allowed 

to be grouped into a single cluster. The approach is built on the feature 

space clustering algorithm called K-MSVC, but constrained by the 

graph to maintain the capability of partially preserving the spatial coherence. 

A new type of graph called Random Grouping Graph (RGG) is 

introduced then, to overcome the high computational cost on the gridgraph 

based image representation. It’s fast to construct, greatly reduce 

the graph size and can speedup other graph-based segmentation 

algorithms. Though with less vertices, the segmentation on RGG 

works better than on downsampled version of the image. Nontrivial experimental 

results on the Berkeley Segmentation Dataset demonstrate 

that our method outperforms the existing algorithms and yields more 

satisfactory results. 

◁ PFrA-95 

Routing in Wireless Sensor Networks Using Swarm Intelligence, 

pp.680–684 

Lv, Yong 

JiaXing Univ. 

Wireless Sensor Networks consisting of nodes with limited power are 

deployed to collect and distribute useful information from the field to 

the other sensor nodes. Energy consumption is a key issue in the sensor’s 

communications since many use battery power, which is limited. 

The sensors also have limited memory and functionality to support 

communications. Ant Colony Optimization, a swarm intelligence based 

optimization technique, is widely used in network routing. This paper 

describes a new routing approach for Wireless Sensor Networks consisting 

of stable nodes based an Ant Colony Optimization algorithm that 

explore the network and learn good routes, using a novel variation of 

reinforcement learning. Simulation results show that proposed algorithm 

provides promising solutions allowing node designers to efficiently 

operate routing tasks. 

122



Chair: Lin, Zongli 

Co-Chair: Hou, Zhongsheng 

Poster Session PFrB 

July 6, 15:00-16:20 



◁ PFrB-01 

Reliable H ∞ filtering in low-frequency domain for networked control 

systems subject to packet loss and quantization, pp.1670–1675 

Wang, Heng 

Long, Yue 

Ye, Dan 




This paper studies the problem of reliable H ∞ filtering for networked 

control systems (NCSs) subject to packet loss and quantization in lowfrequency 

domain. By considering the influences of quantization, packet 

losses and possible sensor stuck failures, the NCSs are modeled as 

discrete-time switched systems with constant delays. Subsequently, an 

analysis condition to capture low-frequency specifications is presented. 

With the aid of the derived condition, a procedure of reliable filter synthesis 

is given in terms of linear matrix inequalities (LMIs). Finally, an 

example is given to illustrate the effectiveness of the proposed method. 

◁ PFrB-02 

An Included Angle Gridding Algorithm for Multimodel Decomposition of 

Hammerstein Systems with Input Multiplicity, pp.1745–1749 

Du, Jingjing 

Zhang, Xinliang 

Song, Chunyue 




This paper proposes an included angle gridding algorithm for Hammerstein 

systems with input multiplicity for which the common nonlinearity 

inversion control method can not be used. Different from the common 

gridding method, the included angle gridding algorithm takes the nonlinearity 

of a Hammerstein system into account so that the efficiency 

of the consequent multimodel decomposition can be improved largely. 

Based on the decomposition result, a Multi-PI controller is designed. 

Simulations demonstrate the effectiveness of the proposed gridding algorithm. 

◁ PFrB-03 

Nonlinear Iterated Mappings based Model and Tracking Control of 

Boost Converter, pp.1838–1843 

Bin, Yang 

Dong, Haolong 

Feng, Nenglian 


BJUT 

BJUT 

On account of the inductance current continuous conduction mode 

boost converter, a approximate discrete time nonlinear model is proposed 

in this paper on the basis of the nonlinear iterated mappings 

method. Then, an exact feedback linearization method over the discrete 

time nonlinear system is introduced, in order to deduce a partial 

linearized form. Utilizing this form, a control algorithm is derived according 

to the pole placement. The simulation results indicate that the 

performance of the proposed control algorithm presents a fast dynamic 

response, an accurate tracking feature and a strong robustness towards 

the disturbance from the input voltage. 

◁ PFrB-04 

Improved results on asymptotic stability criteria for neutral neural network, 

pp.1893–1898 

Wu, Xueli 

Li, Yang 

Hou, Lifeng 

Wang, Yuehua 





The stability of constant delay neutral neural network dynamic system 

is studied in this paper. A new Lyapunov functional method is proposed. 

The method can solve the problem which stability of constant 

delay neutral neural network dynamic system. 

◁ PFrB-05 

Improved results on exponential stability criteria for neutral neural network, 

pp.1899–1903 

Zhao, Zhe 


Zhen, Ran 

Wei, Shaoqing 

Wang, Yuehua 




Dynamical behavior of neutral neural networks with distributed delays 

is studied by employing suitable Lyapunov functional, delay-dependent 

criteria to ensure local and global asymptotic stability of the equilibrium 

of the neural networks. Our results are applied to classical neutral 

neural networks with time delay and some novel asymptotic stability 

criteria are also derived. The obtained conditions are shown to be less 

conservative and restrictive than those reported in the known literature. 

◁ PFrB-06 

Parallel Rail Transit System Based on ACP Approach, pp.2048–2053 

Dong, Xisong 

XIONG, Gang 

Dong, Fan 

Lv, Yisheng 

Sun, Xubin 



CAISA 



This paper presents a novel parallel system for Rail Transit based on 

ACP approach (Artificial systems, Computational experiments, Parallel 

execution), which is proposed to address issues on safety, efficiency 

and reliability of their operation and optimization. Firstly, the construction 

of dynamic, overall and real Artificial Rail Transit Systems and 

their demonstration are provided. Then, based on these systems, the 

platform, content and analysis of computational experiments and comprehensive 

evaluation system are researched. Finally, parallel control 

and management of the actual systems via parallel execution can be 

achieved. By Parallel Rail Transit Systems, a set of recommendations 

and strategies of railway can be formed, which would improve the 

overall functionality of the comprehensive transportation system. 

◁ PFrB-07 

Working Process Simulation of Roll Forming Machine Based on Virtual 

Reality Technology, pp.2072–2075 

Wang, Dong 

Cao, Yuchao 

Zhang, Tuo 

Qin, Shoutong 





Taking the virtual reality development software-Open Inventor as platform, 

the three-dimensional models are created by using Solidworks 

software. Based on the great engine mechanism of Open Inventor development 

platform, the gear’s meshing transmission and the Chain 

wheel’s rotation are realized by SoRotor node. The simulation is realized 

by programming on the VC++ development platform programming, 

and the complex working process of roll forming machine is simulated 

and displayed by programming, which give a better virtual demonstration. 

The simulation lays the foundation for the future working process 

simulation. 

◁ PFrB-08 

An Agricultural Irrigation System Based on GIS and RFID, pp.2076– 

2080 

Wang, Hui 

Li, Juan 

Zhao, You-gang 

Jiang, Zhongmin 





To solve the serious problem of water waste for agricultural irrigation 

in our country, an agricultural irrigating system is developed by combining 

GIS with RFID which charges according to water flow. The system 

implements the quantitative charge and automatic accounting by 

AT89S52 micro-controller, RF card and MF RC500 as its core components; 

and implements the visualized management which can display 

the irrigation condition in specific area and quantitative information of 

user’s water consumption by utilizing the GIS and SQL DB. This system 

helps to overcome the problem of single performance and incompatibility 

of devices, as well as the unavailability of sharing data etc. The 

system has simple operation, friendly interface, and high performanceprice 

ratio, which can be widely applied in irrigation areas and irrigation 

123


reservoirs, and has a wide application prospect. 

◁ PFrB-09 

Output Feedback Organically-Structured Control of Expanded Power 

System based on Parameter Optimization, pp.2085–2090 

Li, Xiaohua 

Xu, Yanying 



A new optimized organically-structured control method combined with 

the dynamic output feedback and genetic algorithm is proposed for automatic 

generation control (AGC) design of interconnected power system 

with the extension structure. Genetic algorithm is used to optimize 

the related parameters emerged in the process to solve the controller 

based on the dynamic output feedback control of the system with the 

extension structure. The blindness to choose the parameters is avoided. 

The fitness function evaluating synthetically the time domain response 

performance is chosen, and the constraint matrix is optimized in 

the design. The simulation research is done against a kind of interconnected 

power system model. Consider a new area subsystem is add to 

original system on line, the automatic generation control law of the new 

area is designed by using the proposed method. The simulation results 

show that the control performances optimized by genetic algorithm are 

better than the before, and the effectiveness of this method is proved. 

◁ PFrB-10 

A Unified Control Design for A Class of Discrete-Time Markov Jump 

Systems: A Game Approach, pp.2130–2135 

Hou, Ting 

Zhang, Weihai 

Ma, Hongji 




This study is devoted to investigating the inherent relationship among 

the H 2 , H ∞ , and H 2 /H ∞ control design for discrete-time Markov 

jump systems with multiplicative noise. From a new perspective of 

nonzero-sum Nash game, we will show that the Nash equilibrium solution 

may present a unified treatment approach for these three types 

of controller design by taking adequate parameters in the quadratic performance 

indices. A numerical example is also supplied to illustrate the 

proposed results. 

◁ PFrB-11 

Based on (w,z) parameter attitude stability control of axisymmetric 3D 

pendulum, pp.2160–2164 

Lv, Wenjun 

GE, Xinsheng 



This paper studies attitude control problem of axisymmetric 3D pendulum 

based on (w,z). 3D rigid pendulum (that is, put three-dimensional 

rotation) composed by a frictionless rigid body of fixed-point supported. 

When axisymmetric case, the symmetry axis is the inertia axis for the 

rigid body , the 3D rigid pendulum is axisymmetric 3D pendulum. According 

to its center of mass and friction-free fulcrum fixed relative position, 

axisymmetric 3D pendulum can be divided into two cases: one 

is the center of mass below the pivot, that is hanging posture; the other 

is the center of mass above the pivot, that is the inverted posture. As 

the Euler angle existed singularity problems, a new attitude described 

method was introducted, that is (w,z) parameter description. And argued 

the attitude kinematics equation of axisymmetric 3D pendulum, 

given the direction cosine matrix by the (w,z) parameters described in . 

At the same time the use of (w,z) parameters, design a new control law 

makes the axisymmetric 3D rigid body placed in its inverted position 

asymptotically stable . Simulation results also verify the control method 

effect for axisymmetric 3D pendulum . 

◁ PFrB-12 

Bus Rapid transit (BRT) Parallel System Based on ACP Approach, 

pp.2485–2490 

Dong, Xisong 

XIONG, Gang 

Dong, Fan 

Zhu, Fenghua 



CAISA 


Bus Rapid Transit (BRT) is an effective way to improve urban traffic status. 

But, because of its complexity, it is difficult in its operation management 

and scheduling. In this article, based on ACP approach, Parallel 

BRT System is constructed, which can detect real-time passenger flow 

at stations, traffic flow at stations and intersections and queuing length 

of vehicles on the road; to provide short-term passenger and traffic saturation 

prediction in order to timely arrange transportation management 

and relieve congestion; to assess, improve and optimize the emergency 

management of holidays, events, accidents and other emergencies; 

to improve the quality of real-time scheduling functions based on the 

measurement results detected from videos, and so on. This system 

has been applied in Guangzhou Zhongshan Avenue BRT, which was 

applied for BRT’s monitoring, warning, forecasting, emergency management, 

real-time scheduling and other needs, to guarantee BRT’s 

smooth, safety, efficiency and reliability. 

◁ PFrB-13 

Analysis and Optimum Design of Loop Filter in GNSS Receiver, 

pp.2491–2496 

Jiang, Yi 

Zheng, Kai 



The design of loop filter is very important in GNSS receivers. According 

to design methodology and filter coefficient calculation method, the performance 

of four filter design methods are analyzed and discussed for 

frequency response, loop bandwidth and noise equivalent bandwidth. 

The range of BNT in different methods and deviation of ideal noise e- 

quivalent bandwidth are investigated by simulation. According to the 

simulation results, the optimal loop filter design in GNSS receiver is 

given. The conclusions can be used for GNSS receiver loop design. 

◁ PFrB-14 

Optimization Target Resetting Distributed Model Predictive Control for 

Accelerated Cooling Process , pp.2764–2769 

Zheng, Yi 

Li, Shaoyuan 

Wang, Xiaobo 

GE-Global Research (Shanghai) 


BAOSHAN IRON & STEEL CO.,LTD. Research 

Inst. 

In accelerated cooling (ACC) processes–large scale systems, since the 

starting temperature varies along plate, the cooling curves of platepoints 

are different to each other for obtaining the uniform characteristic 

of the whole plate. To satisfy the complicated control targets–the various 

cooling curves, and real-time control requirement, an novel method 

called Optimization Target Resetting Distributed Model Predictive Control 

(OTR-DMPC) is proposed. In this method, the whole system is 

divided into many subsystems, and each subsystem is controlled by a 

local Model Predictive Control (MPC) to decrease the computation cost. 

And neighborhood optimization is used to coordinate subsystem-based 

MPC to guarantee the global performance. Meanwhile, the optimization 

target of each subsystem-based MPC is recalculated in each control 

period according to the current starting temperature. The experimental 

results show the efficiency of the proposed method. 

◁ PFrB-15 

A New Solution to Weapon-Target Assignment Problem, pp.384–387 

Wang, Rui 

Xi’an Hi-Tech. Inst. 

Wang, Zhengyuan 11 

Liu, Guoqing 

The Second Artillery Engineering College 

Liu, Lingxia 11 

Wang, Guohua 11 

Zhang, Xinyu 11 

Weapon-target assignment problem (WTA) is NP. A new solution is proposed 

to WTA according to its feature. Firstly, an initial solution created 

by heuristic rules is regarded as the current best approximation, then 

multi-point adjustment is adopted to search better solution in the neighbour 

of the current best approximation. The current best approximation 

will be updated by the better solution searched. So it is improved by 

repeated iteration step by step and better approximation is achieved at 

last. Experimented results show that the the heuristic solution to WTA 

proposed here can get high quality solution and it is valid. 

124



◁ PFrB-16 

Study on the Signal Control Problem with Pedestrians Non-complying, 

pp.388–391 

LIU, QIN 


To solve the signal control problems created by the mutual interference 

between motor traffic and pedestrians in cities, the signal control model 

with pedestrians non-complying is proposed based on the traditional 

signal optimization model. A particle swarm algorithm is proposed to 

solve the signal control problem. Based on an intersection of Tianhe 

district in Guangzhou City, the model is calculated and simulated 

through programming. As it shows, the proposed method achieved 

substantially better performance than did traditional approaches without 

considering pedestrians non-complying. 

◁ PFrB-17 

Blind Signal Detection Directly Using Functional Networks , pp.402–406 

RUAN, Xiu-kai 

Wenzhou Univ. 

Functional Network(FN) has been used in many field successfully, 

but has no blind equalization or detection method using FN been expressed. 

The original idea of blind signal detection directly algorithm 

using the framework of multi-input multi-output Functional Networks 

(MIMO-FN) is given out. The method of designing the network structure 

and the role of network state are shown, etc. Then, the advantages and 

disadvantages of the proposed algorithm is analyzed. 

◁ PFrB-18 

Automated Simulation of Flapper and Foil System, pp.413–417 

Hu, Ying 

kunming Univ. of Sci. & Tech. 

Numerical approach using the large-eddy simulation based on the compressible 

hydrodynamic equations is developed and employed in simulating 

on turbulent flow around a system of two flappers and a stationary 

foil. Similar conclusions concerning the mean flow profiles and turbulent 

intensity can be found in the simulation of flow around the flapper 

and foil system. Extensive studies are conducted on the effect of the 

unsteady outer flow, which is produced by the two upstream flappers, 

on the boundary layer of the stationary foil. As the unsteadiness in the 

outer flow is small, the response of the boundary of the stationary foil 

is mainly the first harmonic of the flappers’oscillation. An interesting 

phenomenon of the tangential velocity phase shifting in the stationary 

foil’s boundary layer is observed. 

◁ PFrB-19 

A Novel Swarm Intelligence Optimization Inspired by Evolution Process 

of A Bacterial Colony, pp.450–453 

Li, Ming 

Southwest Forestry Univ. 

Traditional swarm intelligence algorithms lack of evolution ability and 

are easy to fall into premature convergence. Therefore, a new kind of 

swarm intelligence algorithm, called bacterial colony optimization (B- 

CO) algorithm, was proposed in this paper. The solution space of the 

problem was considered as a certain culture medium. A single bacterium 

or a few bacteria were placed randomly in the space. The BCO 

algorithm was designed through simulating the evolution process of the 

bacterial colony. The BCO itself has a certain evolutionary mechanism 

and could be terminated naturally, which had given a new termination 

criterion for swarm intelligence algorithms. A series of simulation experiments 

on three test functions were used to verify the effectiveness 

of the BCO algorithm. The simulation results showed that the BCO 

algorithm can converge to the global optimization solution. 

◁ PFrB-20 

Application of Neural network Model to Guangxi Ensemble Precipitation 


Nong, Mengsong 

Nanjing Univ. of information Sci. & Tech. 

Using the method of artificial neural networks and principal component 

analysis (PCA) to study on a variety of numerical forecast products 

for the same precipitation forecast. The results showed that the fitting 

accuracy of the principal component analysis artificial neural network 

ensemble model is better than each sub-product and the experimental 

results of the independent samples also shows its better prediction 

accuracy and stability. The model is a good prospects for business 

applications. 

◁ PFrB-21 

Hybrid Particle Swarm Algorithm with Application to Distributed Generation 


Wu, Haitao 

Huang, Fuzhen 


Shang Univ. of Electrical Power 

In this paper a hybrid intelligent algorithm combining particle swarm algorithm 

with natural selection mechanism is proposed and applied to 

the distributed generation planning. Based on the need of load and 

cost, a model for distributed generation planning is developed, in which 

the constraint condition is introduced by the penalty function. Experimental 

results show the feasibility and efficiency of the hybrid particle 

swarm algorithm and the performances of the proposed method and 

the basic particle swarm algorithm are compared in the example. 

◁ PFrB-22 

Evolutionary game analysis on opportunistic behavior of purchasing alliance 

with Con t ract mechanism, pp.468–473 

Xiong, Weiqing 

NingBo Univ. 

This paper analyzes the evolutionary process of purchasing alliance 

members with Contract mechanism .Contract mechanism is divided into 

complete contract mechanism and incomplete contract mechanism. 

On the basis of fewer preventive costs and meeting certain relations 

between cost and income, complete contract mechanism is able to restrain 

the opportunistic behavior of purchasing alliance members in the 

following two circumstances: the one is larger compensation coefficient; 

the other is modest compensation coefficient and good purchasing alliance 

environment. Incomplete contract mechanism is able to restrain 

the opportunistic behavior of purchasing alliance members when meeting 

fewer preventive cost, moderate compensatory coefficient and good 

purchasing alliance environment. 

◁ PFrB-23 

A Control Method of Substrate Feeding about Lysine Fermentation, 

pp.479–483 


Wu, Weirong 

Wang, Bo 



JinagSu Univ. 

A fuzzy neural network inverse model is established in order to solve 

the optimal and the maximum output rate in lysine substrate feeding 

fermentation process control through research the structural and parameters. 

The model is more robust, more adjusts the membership 

function automatically and more dynamics in the rule optimal control 

than the traditional rule-based fuzzy control. And it is trained by the optimal 

production data in the actual process of lysine substrate feeding. 

The output of the substrate feeding inverse model is the real-time input 

of system. Experimental results show that lysine productivity improved 

significantly and achieve real-time online control by the method in lysine 

substrate feeding process control. 

◁ PFrB-24 

Defect Recognition of Cold Rolled Plate Shape Based on RBF-BP Neural 

Network, pp.496–500 

Li, Xiaohua 

Zhang, Junjie 



By means of the analysis for the defect pattern of plate shape, a shape 

defect recognition method for cold rolled strips is proposed based on 

RBF-BP neural network in this paper. The memberships relative to 

six basic patterns of common plate shape defects are identified. This 

method syncretizes the advantages of RBF and BP neural network. 

There are very fast approaching speed and high precision of network 

recognition. The simulation of the proposed method is done, and 

the simulation results are compared with the results of the recognition 

method by using BP neural network. The results show that the 

recognition method proposed in this paper gives better effect than the 

one making use of single network. And it is more suitable for real-time 

125


shape control. 

◁ PFrB-25 

Research on Intelligent Control Methods of Iron Ore Rotary Kiln Pellets 

Sintering Process, pp.501–505 


Ren, Xiudong 



The iron ore rotary kiln pellets sintering production system is a typical 

complex nonlinear multivariable process with strongly coupling and 

large time delays. The up-to-the-minute research results of integrated 

modeling and optimized operation of rotary kiln pellets sintering production 

process based on intelligent control methods. It mainly includes the 

soft-sensor modeling, working state recognition and intelligent control 

strategy. In the end the existent problems and future research directions 

in the rotary kiln control field is concluded. 

◁ PFrB-26 

Non-Stationary Type-2 Fuzzy Sets, pp.512–517 

Zhao, Liang 


In this paper, we introduce non-stationary type-2 fuzzy set (NST2FS), 

which is the important extension of its type-1 counterpart. NST2FS has 

distinct characteristics with uncertain membership function and alteration 

over time. According to different mathematical formalizations of 

its instantaneous T2FS (IT2FS), NST2FS can be divided into two categories, 

i.e. homogeneous and heterogeneous counterparts. The paper 

concentrates on homogeneous continuous NST2FS (HCNST2FS), 

of which each IT2FS is continuous. A plane representation theorem 

is the cogent tool to deal with these basic set operators (intersection, 

union and complement) and approximate fuzzy reasoning. Some s- 

elected important properties are also proved in the process. On this 

basis, homogeneous continuous nonstationary type-2 fuzzy logic system 

(HCNST2FLS) is discussed in the following section, which focuses 

on the elaboration of its calculation procedure. One numerical case, 

that is, blurred ’or’logic operator, is carried out in order to evaluate 

the superior performance of our proposing system. 

◁ PFrB-27 

A Discrete Artificial Bee Colony Algorithm for the Blocking Flow Shop 


Deng, Guanlong 

Cui, Zhe 

Gu, Xingsheng 



East China Univ. of Sci. & Tech., China 

This paper presents a discrete bee colony algorithm for solving the 

blocking flow shop scheduling problem with makespan criterion. Based 

on the idea of iterated greedy algorithm, some new schemes for employed 

bee, onlooker bee and scout bee are designed. The performance 

of the proposed algorithm is tested on the well-known benchmark 

set of Taillard, and the computational results validate the effectiveness 

of the discrete bee colony algorithm in comparison with two 

recently proposed state-of-the-art algorithms. In addition, some new 

best known solutions for the benchmark set are provided for the considered 

problem. 

◁ PFrB-28 

Application of Fuzzy Clustering Model in Groundwater Runoff Zones 

Classification and Indicator Correlation Analysis, pp.528–531 

DOAN, THANH NGHI 

Yu, Xuefeng 

Zhang, Zhongyuan 




Abstract - There are many factors influencing the groundwater runoff 

strength(GRS) classification. Some factors have positive correlation, 

the others have negative correlation. In order to classify GRS exactly,it 

is necessery to find the correlation between the indicators and the 

GRS. In this paper ,a new method of correlation analysis is proposed 

based on the fuzzy clustering model(FCM). With real cases, we discuss 

the application of FCM and obtain satisfactory results 

◁ PFrB-29 

A Method of UUV Path Planning with Biased Extension in Ocean Flows, 

pp.532–537 

Yan, Zheping 

Zhao, Yufei 





The problem of path planning for UUV in time-varying ocean flows is 

considered. A method of path planning with biased extension is proposed 

to guarantee that the path is feasible and optimal. Considering 

the anisotropy of ocean flows and the motion constraint of UUV, a simplified 

space is obtained after the sub-nodes are expanded in special 

orientation. An integrated cost function is proposed to fulfill the requirement 

of different missions, including the energy expenditure, travel time, 

and obstacle avoidance. In the end, A* based algorithm is applied for 

path search. Simulation results illustrate the proposed approach is simple 

and fast, and the path posed is feasible for trail. 

◁ PFrB-30 

Research of PSO Algorithm with Variable Constraints in Process System, 

pp.544–548 

Ding, Qiang 

Chen, Hong 

Jiang, Aipeng 

Wang, Chun-lin 

Lin, Weiwei 


Tourism College of Zhejiang 


Hang Zhou Dian Zi Ke Ji Da Xue 


To solve chemical problems with variable and nonrigid constraints, a 

method based on particle swarm optimization (PSO) algorithm was p- 

resented. By mathematical analysis and transform, the variable constraints 

were regard as an item to be optimized. Then the item multiplied 

by penalty and combined with the primary objective function. So 

the primary problem was transferred to the multi-objective function, and 

can be solved by multi-objective PSO algorithm. With problems solved 

by multi-objective PSO and analysis of the solutions related with variable 

constraints, reasonable solution and optimal scheme can be obtained. 

The proposed method was used to optimize a chemical design 

problem and a parameter estimation problem. The results demonstrate 

that the proposed method is effective. 

◁ PFrB-31 

A New Approach to Solve the Mission Assignment Problem for Cooperative 

UCAVs Using Immune Particle Swarm Optimizations, pp.549–554 

WANG, GUODONG 

Deng, Zhidong 

Shenyang aircraft design & research Inst. 

Tsinghua Universtiy, China 

This paper first builds a mathematical model for the mission assignment 

problem (MAP) of cooperative multiple uninhabited combat aerial 

vehicles (UCAVs). To address challenges posed by the specific MAP 

problem, we propose a new immune particle swarm optimization (NIP- 

SO) approach through incorporating immunity memory, diversity clone, 

and immune selection in artificial immune algorithm into standard PSO. 

The simulation results achieved on a typical scenario show that our 

NIPSO approach for multiple UCAVs is capable of substantially speeding 

up convergence and has stronger ability to find the global optimum 

than that of classical PSO. The MAP solution for cooperative UCAVs is 

significantly improved. 

◁ PFrB-32 

a Novel Model for Selecting Parameters of SVM with RBF Kernel, 

pp.566–569 

YAN, Zhi-gang 


Based on the viewpoint of similarity measurement, researched the influences 

of the error penalty parameter C and the RBF kernel parameter 

σon support vector machine’s generalization ability. As the result, the 

parameter C adjust the similarities between the sample categories and 

σadjust the similarities among the samples, C and σmutually restrict 

and balance each other in a certain range, the shape of the optimal 

parameter range like a fan, the more reasonable parameters’value locate 

at the center of the fan, where the values of C and σare smaller. 

A novel method for selecting parameters was presented, firstly, roughly 

grid searched the reasonable parameter range with a big step size, 

then selected the optimized parameters in the delineated area through 

126



bilinear-grid search method finely. Experiment results show that the improved 

method has a better performance both at accuracy and speed, 

moreover, which can avoid excessive values and enhance the stability. 

◁ PFrB-33 

Fundamental Issues of Networked Decoupling Control Systems, 

pp.827–830 


Bai, Yan 


P.R.China 


The concept of networked decoupling control system (NDCS for short) 

is proposed and its fundamental issues are investigated, including its 

configurations, network-induced delays, and data packet dropout. With 

the proposed concepts of node-device connectivity matrix and network 

transmission matrix, three typical configurations of NDCSs are also p- 

resented and the locations of networks in the system are clearly pointed 

out. The proposed approach can provide a reference for characterizing 

an NDCS with other much more complicated configurations. 

◁ PFrB-34 

Networked Real-time Controller Based on PC/104, pp.831–834 

An, Bao-ran 


The networked real-time controller designed in this paper is based on 

the hardware platform of PC/104 CPU and DAQ, and the operating system 

of real-time Linux kernel, and it is developed with the server of algorithm 

receiving, data monitoring and the networked real-time application, 

it successfully realizes the real-time control and online monitoring. 

The final experimental verification shows that the real-time capability of 

the controller can reach the level of microseconds, which can satisfy 

the general requirements of industrial control. 

◁ PFrB-35 

Co-design-based H∞Control for Wireless Networked Control Systems, 

pp.835–839 

LI, Jinna 

Liu, Dan 

Li, Minghui 

Yu, Haibin 


Sci. 

Shenyang Univ. of Chemical Tethnology 



Sci., China 

This paper investigates H∞control for the networked control systems by 

the approach of jointly controlling network systems and control systems. 

Based on the available power and rate models, the argument systems 

of networked control systems combined with the plant are constructed. 

Sufficient conditions for asymptotical stability with H∞norm bound 

is obtained in terms of Lyapunov theory and linear matrix inequality 

(LMI) technique, which can guarantee the actual signal-to-interference 

ratio (SNR) tracking the desired SNR, stability of the control systems, as 

well as optimal capability of resisting disturbance. Finally, a numerical 

example is given to illustrate the effectiveness of the proposed method. 

◁ PFrB-36 

Distributed Output Regulation of Switching Multi-agent Systems Subjectto 

Input Saturation, pp.840–845 

Wang, Xiaoli 

Ni, Wei 

Yang, Jie 




In this paper, we consider the distributed output regulation problem 

of linear multi-agent systems subject to input saturation with switching 

topology. It is natural to take the semiglobal frame for distributed output 

regulation with input saturation which allows us to use distributed linear 

feedback controller. The basic problem is to design distributed feedback 

controller for the considered multi-agent systems subject to input 

saturation in order to achieve asymptotically tracking and disturbance 

rejection. A systematic distributed linear design approach based on the 

solvability condition is proposed for the semiglobal distributed output 

regulation with switching interconnection topology. 

◁ PFrB-37 

Kernel Based Nonlinear Regression for Internet Round Trip Time-delay 


Yang, Yanhua Shenyang Inst. of Automation, Chinese Acad. of 

Sci. 

Time delay degrades the performances of Internet-based control systems 

or teleoperation system, and even causes instability of closedloop 

systems. If the Round Trip Time-delay (RTT) is acquired exactly 

previously, it is helpful to improve the performance of Internet-based 

control systems. Therefore, analysis or prediction for Internet time 

delay has become a hot research problem. This paper proposes the 

long-range nonlinear autocorrelation of RTT based on the real data of 

Internet delay measurements. Then, according to the characters, we 

present a sparse matrix based kernel regression (SMKR) scheme to 

predict RTT. Finally, simulation results show that the forecasting precision 

using this method is higher than that using sparse multivariate 

linear regressive (SMLR) method, which demonstrates the validity of 

the proposed approach. 

◁ PFrB-38 

XCP Bandwidth Compensation Algorithm Based on Sliding Mode Control 

in Time Vary Network, pp.857–862 

YIn, Fengjie 

liaoning Univ. 

Network propagation delay and system parameters are always changing, 

to this case, the paper applied sliding mode control theory to set 

reasonable output bandwidth in dynamic network for XCP, and thus to 

compensate the uncertainty existing in the feedback factor. The source 

adjusted the sending rates according to it so that the network output 

could have high throughput and a smaller queue length. The algorithm 

has strong robustness caused by the network jitter due to the uncertainty 

of network model and the time change of the network parameters, as 

well as the time delay of the system state. Simulation results show 

that the algorithm can improve the bandwidth utilization effectively in 

dynamic networks, and compare with PII-XCP algorithm, this algorithm 

can adapt to the dynamic nature of the network and time delay. 

◁ PFrB-39 

H-infinity State Estimation for Networked Systems with Markov Interval 

Delay, pp.881–885 

Zhang, Yong 

Liu, Zhenxing 

Zhou, Lei 


WuHan Univ. of Sci. & Tech. 

Nantong Univ. 

In this paper, the H-infinity state estimation problem for networked systems 

with Markov interval delay characteristic is investigated. A Markov 

chain is introduced to describe the delayed networked systems, considering 

the difficulty of obtaining the ideal knowledge on all transition 

probabilities, and a Markovian jump systems model with partially unknown 

transition probabilities is established. Based on the obtained 

new model, by utilizing observer as state estimation generator, the addressed 

state estimation problem is converted into H-infinity attenuation 

problem. Then, with the help of stochastic Lyapunov-Krasovskii 

functional approach, the sufficient condition for the desired modedependent 

observer is constructed in terms of linear matrix inequalities, 

which depend on not only delay interval but also partially known 

transition probabilities. The effectiveness of the proposed method is 

demonstrated by simulation examples. 

◁ PFrB-40 

Simulation Research of Networked Control System Based on Ethernet 

and Matlab, pp.898–902 

Peng, Daogang 

Lin, Jiajun 

Zhang, Hao 

LI, Hui 





Networked control system, as a hotspot in the research fields of control 

theory and control engineering applications, is widely concerned by researchers 

at home and abroad. The expansibility, maintainability and 

reliability of industrial process control system can be greatly improved 

with the application of Ethernet technology. In this paper, the network 

127


controller and the controlled object are set in the different computers in 

the net and a simulation platform of networked control system based 

on Ethernet is designed, which uses the mixed programming of Visual 

C++ and Matlab. This kind programming adopts TCP/IP protocol and 

uses Socket to encapsulate into DLL so as to realize the transmission 

of network data. And then build simulation models in the Matlab environment 

on two computers at the same time, whose communication are 

worked by calling packaged Socket function. Through the simulations 

of typical controlled object in the process of industrial production, the 

feasibility and effectiveness of the control scheme has been proved. 

◁ PFrB-41 

The Study of Wireless Sensor Network Energy, pp.433–437 

Duan, Ping 


Based on the ZigBee Wireless Sensor Network technology with its low 

cost, low power consumption and low complexity, which has great concerned 

application prospect. In view of its node energy limited, replace 

the battery isn’t convenient and so on, this paper with an actual application, 

from the physical layer of hardware structure and network protocol 

in two aspects of the power consumption. Put forward by use of the low 

power consumption and has a variety of work mode based on the chip, 

the physical layer protocol reduces power consumption , could guarantee 

the algorithm of the quality and reduce the system of communication 

power consumption. Among them, reduce power consumption 

algorithm including: dynamic power management, the determination of 

sleeping time. 

◁ PFrB-42 

Research and Application of Whole-process Virtual Simulation for Tunnel 

Shield System, pp.3208–3213 

Han, Yanling 

No.999, Huchenghuan Road,College of 

information, Shanghai Ocean Univ., Pudong New 

Area, Shanghai 

Cao, Shouqi 

Shanghai Ocean Univ. 

Aiming at the complexity, dynamics and special safeness of the construction 

process for tunnel shield, we applied the computer dynamic 

simulation technology into the whole-process simulation realization for 

shield construction, constructed 3D virtual simulation platform based on 

OpenInventor technology and gave the overall framework of the system; 

in the paper, the soil dynamic simulation model in the process 

of tunneling operation was researched and ground settlement computation 

model was constructed, and the relative arithmetic was given. 

The whole process of shield construction was emulated dynamically 

and the situation of the soil change and the ground surface settlement 

were analyzed and feedback in time, and the actual construction process 

was controlled and guided availably by coordinating with configuration 

software. At last, we took the new construction project of Xianxia 

West Road in Shanghai Hongqiao comprehensive communication hub 

as application background and verified by test running, the result indicated 

that the simulation system satisfied the construction request on 

the scene of tunnel. It explained the feasibility of simulation technology 

and simulation model, and provided aided decision-making means for 

risk controlling of actual shield construction process. 

◁ PFrB-43 

Using fuzzy theory and information entropy to detect leakage for 

pipelines, pp.3232–3235 

Chen, Zhigang 


When detect leakage for pipelines in complicated conditions it is difficult 

to determine reasons between pipeline fault symptoms and fault causes. 

In order to obtain the complicated subordinate relationships and 

to improve the accuracy of pipeline leakage fault diagnosis and other 

operations, a fault diagnosis method based on the fuzzy mathematics 

theory and the advantages of information entropy quantitative diagnostic 

method is proposed. In this study, fuzzy mathematics theory and 

some typical information entropy were introduced and how to establish 

fuzzy diagnosis matrix and extract characteristic information entropy is 

also analyzed in leakage detection for pipelines. Some imitation and 

field examples were given. The experiments showed that the mapping 

relationships between the pipeline fault symptoms and the fault causes 

was most consistent with the actual situation. 

◁ PFrB-44 

Model Identification for Closed-loop Multivariable Processes Based on 

Min-max Critical Frequency Search, pp.3260–3264 

Luo, Yunhui 

Liu, Hongbo 

Cai, Wen-Jian 

Jia, Lei 





This paper presents an improved method of model parameters identification 

in frequency-domain for closed-loop multivariable processes. 

Based on reference input and process output data during the closedloop 

sequence step tests, the process frequency-responses are estimated 

with signal frequency analysis. Using a min-max critical frequency 

search algorithm, only a least possible number of frequency points 

are obtained for model fitting. Then the first order plus delay time transfer 

functions are determined by implementing the linear least-square 

method. Compared with existing methods, the proposed identification 

technique has the advantage of less computation burden and is easy 

for industrial applications. Simulation results show the simplicity and 

effectiveness of the proposed method. 

◁ PFrB-45 

An Adaptive Observer for Actuator and Sensor Fault Diagnosis in Linear 

Time-Varying Systems, pp.3281–3285 

GAO, Fei 

JIANG, Guangwen 

ZHANG, Zebang 

SONG, Jingyu 

Sys. Engineer Research Institude 

CSSC 

CSSC 

CSSC 

Fault detection and isolation is important and effective to improve the 

safety and reliability of engineering processes and systems. However, 

for systems with redundant sensors, it is important to detect faulty sensors 

as early as possible to reduce their effect on the performance of 

the control systems. In this paper, an observer for linear time-varying 

systems with both sensor and actuator faults is derived by extending 

the one that considers only actuator faults. Guidelines for selection the 

design parameters of the observer are discussed. The global exponential 

convergence is established for noise-free systems, whilst for noisy 

systems, the estimation errors are shown to be bounded and converged 

in the mean to zero if the noises are bounded and have zero means. 

The proposed technique is applied to detect both actuator and sensor 

faults in a satellite navigation system. 

◁ PFrB-46 

The Bi-Ramp Type Demand and Price Discount Inventory Model for 

Deteriorating Items, pp.3298–3304 

Zhou, Youjun 

LiuZhou Teachers College 

The paper consider a inventory model for deteriorating items with being 

out of stock at the beginning, in which the supplies providing price 

discount and price-dependent and time-varying demand and so forth. 

The existence of the optimal solution is proved and an algorithm of the 

optimal ordering policy is given. Finally, we carry out the numerical 

experiments. 

◁ PFrB-47 

A Data-Driven Based Adaptive Fault Diagnosis Scheme for Nonlinear 

Stochastic Distribution Systems via 2-step Neural Networks and Descriptor 

Model, pp.3311–3315 

Zhang, Yumin 

Liu, Yunlong 

Guo, Lei 

Beihang Univ 

Beihang Univ. 

Beihang Univ. 

A data-driven based adaptive sensor fault diagnosis (FD) and compensation 

scheme for stochastic distribution control (SDC) systems is studied 

in this paper, where an augmented descriptor model is employed. 

Unlike traditional SDC systems, the driven information is the output 

probability density function (OPDF), which is a kind of image mapping 

information to the true output values. A mixed 2-step adaptive neural 

128



network (NN) framework is studied, where the static NN is to describe 

the OPDF while the dynamic NN is to identify nonlinearity, uncertainty 

of system and to refine the OPDF model based on data of the input 

and statistic information of the output. To identify the sensor fault, an 

augmented descriptor system is employed, where the augmented state 

includes the plant state and the sensor fault. As a result, an adaptive 

strategy is given for nonlinear parameter estimation and sensor fault 

identification simultaneously. A sensor compensation rule is given to 

restore the plant by adding it to output feedback controller. The simulation 

examples are given to verify the effectiveness of the presented 

algorithm. 

◁ PFrB-48 

Study on Periodic Feeding Control for a Semi-batch Polymerization Process, 

pp.3316–3321 

Zhao, Rongchang 

Cao, Liulin 

Wang, Jing 


BUCT 

Beijing Univ. of Chemical Tech., China 

The relationships between period operation and Molecular Weight Distribution 

(MWD) of polymeric products were investigated firstly aiming 

at a semi-batch free-radical homo-polymerization process. Then, using 

the duty-cycle of the periodic feeding as a control variable, a novel 

method of controlling the quality of polymer was proposed. The objective 

function of maximum possible monomer conversion (XM) with a 

specific value of polydispersity (PDI) of MWD was chosen as the control 

index. Particle Swarm Optimization (PSO) was utilized to solve the 

optimization problem. Simulation results indicated that under periodic 

operation of reactant feeding, the wider variation range for PDI of MWD 

can be obtained, and the PDI in a semi-batch polymerization reactor 

can reach the required goal in terms of adjusting the duty-cycle of periodic 

feeding. 

◁ PFrB-49 

Key Technology of Network Monitoring and Early Warning System for 

Wharf Mooring, pp.3336–3339 

Qiu, Zhanzhi 

Sun, Lei 

Liu, Yongchao 




Aiming at the needs of domestic large-scale open-wharf ship mooring, 

design solution and other issues about network monitoring and early 

warning system for wharf mooring are studied. Network monitoring 

solutions, system architecture, monitoring data collection, transmission 

methods and distributed data storage technology are described. The 

system can monitor and predict mooring control through DB technology, 

web technology and software engineering technology so that ensure 

the security of wharf mooring and efficient solutions. 

◁ PFrB-50 

Power Reliable Analysis of Coalmine Emergency Monitoring System in 

Catastrophic Environment, pp.3345–3350 

Ma, Fengying 


The emergency refuge system is significant for coalmine safety in 

catastrophic Environment. The emergency monitoring is the important 

part of the emergency system, which detects lots of parameters of the 

system. In order to assess the reliability of the underground substation 

power in the coalmine emergency monitoring system, the accelerated 

life tests under constant stress were presented based on the exponential 

distribution. Through a comparative analysis of lots of factors, the 

temperature was chosen as the constant accelerated stress parameter. 

With regard to the data statistical analysis, the type-I censoring 

sample method was put forward. The mathematical model of the coal 

mine monitoring power supply was established and the average life expectancy 

curve was obtained under different temperatures through the 

analysis of experimental data. The results demonstrated that the mathematical 

model and the average life expectancy curve were fit for the 

actual very well. It is concluded that the reliability study of the substation 

power provides an important foundation for the coalmine emergency 

monitoring system. 

◁ PFrB-51 

Distributed Storage and Prediction Method for Mooring Monitoring System, 

pp.3351–3354 

Sun, Lei 

Qiu, Zhanzhi 



Distributed storage and prediction method of monitoring data for mooring 

monitoring system is researched. Aiming at the real time requirement 

of monitoring and prediction for mooring monitoring system, 

used Hadoop distributed file system to store generous monitoring data, 

and used Map/Reduce framework to predict monitoring data, which 

solve the problem of mass data storage in limited memory and timeconsuming 

dynamic prediction. Simulation analysis shows that the distributed 

data storage and prediction method can solve the problem of 

massive data storage, and enhance the prediction efficiency of the system, 

so that mooring monitoring system achieve the design requirements 

of the system. 

◁ PFrB-52 

Energy consumption monitoring of the steam pipe network based on 

affinity propagation clustering, pp.3364–3368 

You, Xiazhu 

Du, Wenli 

Zhao, Liang 

Qian, Feng 





Abstract - The steam system is an important part of chemical utility 

system, but there are widespread phenomenon about lack of testing 

information, energy consumption configuration depend on given experience 

and wasting energy. So this paper puts forward a method about 

the steam pipe network system’s status identification of different energy 

consumption based on the steam pipe network’s characteristics 

of complex structure, much steam equipment, lack of testing information 

and difficult to build accurate mathematical model. The method 

based on affinity propagation clustering that can solve big set of data’ 

s clustering problem quickly and effective. As it is hard to find preference 

parameters and damping factor, this paper uses PSO to find the 

most optimal parameters in order to achieve the best clustering effect. 

This method is applied test both in classic data set and the steam pipe 

network of ethylene plant’s status identification, the results show the 

effectiveness of this method. 

◁ PFrB-53 

Individual Pitch Control of Large-scale Wind Turbine Based on Load 

Calculation, pp.3384–3388 

Gao, Feng 


Wind shear and tower shadow makes wind speed in the rotor rotating 

plane changing differently, so it increase the load difference of every 

blade seriously. This paper model 1.5MW wind turbine for individual 

pitch control, and analyze the main reason of periodic load fluctuation. 

Then individual pitch control based on load calculation was researched 

combining the actual of the load is difficult to measure. Pitch angle 

was distributed by weight coefficient to realize individual pitch control. 

The weight coefficient was computed according to the computed axial 

load based on blade element theory, at the same time the blade elements 

were divided by weight coefficient to ensure the real-time request 

for control algorithm. Simulations indicate that the proposed individual 

pitch control method not only meet the basic power control request but 

also reduce fluctuation of load, so it can solve the problem of fatigue 

load in process of development to large-scale. 

◁ PFrB-54 

Adaptive decoupling control systems based on SVM for large supercritical 

CFB boilers combustion system, pp.3401–3406 

Liu, Han 


An αth-order inversed decoupling control method based on least 

square support vector machines (LS-SVM) is presented to resolve the 

difficulties of inverse modeling with the traditional inverse control methods 

in this paper. The nonlinear offline inverse model of plant is built by 

LS-SVM, which is cascaded before the original system to decouple a 

129


complex multivariable input and output system into several independent 

single input single output pseudo-linear sub-systems. In order to make 

it have better robustness, with single neuron adaptive PID as subsystem 

additional linear controller to constitute a closed-loop system. The 

characteristics of combustion system of circulating fluidized bed boilers 

also are analyzed and control system based on presented methods 

is presented. The simulation results demonstrated that the presented 

method could achieve accurate decoupling control with robustness. 

◁ PFrB-55 

Research on Control Method Combined with Load Coordinate for Dry 

Desulfurization of Slurry Fluidized Bed Boiler, pp.3407–3411 

Jiang, Aipeng 

Ding, Qiang 

Lin, Weiwei 




It is the most effective resource use practices for slurry and slime to be 

used as fuel for Fluidized bed boiler. The dry desulfurization of sludge 

Fluidized bed boiler is a large time delay system, and load disturbance 

of this system changes frequently. In order to achieve stable control 

of SO2 emission, and meet environmental requirements, a fuzzy control 

technology combined with the optimal feed-forward was designed. 

Combined with field experience, fuzzy controller was designed by fuzzy 

control technology, and then the integral process was added to achieve 

non-error track. Based on the objective to minimize disturbance impact, 

and in order to coordinate the desulfurization control and steam 

load control, a nonlinear programming problem for solving the optimal 

feed-forward parameters was established, from which the most excellent 

feed-forward form can be obtained. Results of 440T/H fluidized 

bed boiler show that the proposed method has satisfactory control effect. 

SO2 concentration can fully meet environmental emissions requirements, 

and its fluctuation is relatively small. 

◁ PFrB-56 

Research on combustion control and heat efficiency’s online computing 

of slime fluidized bed boiler, pp.3412–3416 

Jiang, Aipeng 

Lin, Weiwei 

Ding, Qiang 




With the requirements for promoting the energy saving and environmental 

protection, it is the main trend for fluidized bed boiler to realize 

combustion control and online computing of heat efficiency. In order to 

achieve combustion automatic control of fluidized bed boilers, and improve 

self-control rates and on-line monitoring of boilers’ heat efficiency. 

In this study, the combustion control scheme was firstly designed for a 

75 tons fluidized bed boiler. the automatic contorl for feed water, superheated 

steam temperature, superheated steam pressure and oxygen 

were realized in the DCS system. Then the overall automatic control 

of combustion was achieved with advanced control technology. Next, 

based on the presented work, on line calculation of heat efficency 

was realzied, witch which various parts of heat losses and total heat 

efficiency can be real-time monitored to guide the optimal operation of 

the boiler.It is very important for energy saving and power management 

requirements. 

◁ PFrB-57 

Simulation Experiment Platform for Optimal Control of the Raw Slurry 

Blending Process in the Alumina Production, pp.3443–3447 

Guo, Wanli School of Electrical Engineering, Liaoning Univ. of 

Tech. 

Bai, Rui 


Raw slurry blending process is the important process in the alumina 

production. This process has the complicated characteristics such as 

the fluctuation of chemical content of the raw materials, variation of 

the working station, and uncertainty. It is difficult to use the conventional 

control strategy to realize the optimization control for the raw s- 

lurry blending processes. Lots of experiments and simulations for the 

advanced and complicated optimal control must be implemented before 

they are utilized in the industrial process. In order to provide a 

appropriate research platform for the optimal control of the raw slurry 

blending process, a hardware-in-the-loop simulation platform is proposed 

with the view of industry. The proposed hardware-in-the-loop 

simulation platform is different to the conventional simulation software, 

which is composed of the optimal model computer, virtual equipment of 

the actuator and sensor, Rockwell PLC. All the signals in this platform 

are the standard industry signals. The proposed platform can be use 

as an effective platform for the research of the modeling, control and 

optimization of the raw slurry blending process. 

◁ PFrB-58 

Shielding Properties Analysis of underground Methane Sensor based 

on Finite Element Simulation and Electromagnetic Measurement, 

pp.3448–3453 

Ma, Fengying 

Ma, Fengying 



In order to clear up false alarms, issued by underground methane 

sensors, caused by Electromagnetic Interference (EMI), a novel field 

way was brought forward in which the simulation and radiated emission 

measurement were adopted to accomplish the analysis of shielding 

property about methane sensor. The data was acquired through 

the R&S spectrum analyzer FSP7. With analyzing the influence of cable 

aperture on enclosure shielding properties through simulation and 

measurement, the optimization of explosion-proof enclosure was finished 

according to the distribution of radiated interference characteristics 

and simulation of the trumpet structure shield performance. As 

a result, with the optimization of explosion-proof enclosure, the false 

alarms of methane sensor significantly reduced; however, there were 

still occasionally false alarms when nearby electrical equipments were 

switched on and off. Therefore the complex EMI filter was put forward 

at power ports and the EMI was controlled within the allowable range. 

The running results demonstrated that the problem of false alarms had 

been resolved successfully. It is concluded that the improved shielding 

and filtering are highly significant in enhancing the Electromagnetic 

Compatibility (EMC) of the methane sensor. 

◁ PFrB-59 

Social Learning with Bounded Confidence, pp.3485–3490 

Liu, Qipeng 




Motivated by the homophily principle in social networks, this paper investigates 

a social learning model with bounded confidence, in which 

two individuals are neighbors only if the difference of their beliefs is not 

larger than a constant called bound of confidence. Each individual updates 

her belief through Bayesian inference based on her private signal 

plus consensus algorithm based on the beliefs of her neighbors. We 

find that the whole group can learning the true state only if the bound of 

confidence is larger than a positive threshold, which implies that people 

should try to communicate with others whose beliefs are quite different 

with themselves, in addition to those similar to themselves. Furthermore, 

we introduce a neighborhood-preserved strategy to guarantee 

that once two individuals are neighbors they will be neighbors forever. 

We show that social learning in the revised model can be realized with 

much smaller threshold, and therefore, provide an effective mechanism 

for social learning. 

◁ PFrB-60 

The Network Structure of Optimal Synchronizability for Bounded Regions 

Case, pp.3497–3502 

Wang, Lifu 

Liu, Yunjing 

Wu, Zhaoxia 

Kong, Zhi 

Wang, Xingang 


North-east Univ. at Qinhuangdao 




To study the network sturcture of optimal synchronizability with given 

node and edge number, we use simple graph to analyze the topology 

structure of the optimal synchronizability network. Then, the optimal 

network characteristic of N node N-1 edges, N edges, or N+1 edges 

130



are founded. Espectively, An approach to construct the optimal synchronizability 

network with N nodes and N+1 edges is presented for 

given node and edge number. And, it is found that the class optimal 

synchronizability network structure with three chains. Every chain size 

is as same as possible, that is, maximum and minimum chain the difference 

between the number of nodes is less than or equal to 1. Moreover, 

the difference between maximum degree and minimum degree is less 

than or equal to 1 in the network with N+1 edges. Then, an example is 

given to illustration the process of construction optimal network with N 

node and N+1 edges. At last, we discuss on the optimal network with 

N node and N+2 edges. 

◁ PFrB-61 

Impact of Evacuee Behavior on Evacuation Clearance Time, pp.3520– 

3525 

Lv, Yisheng 

Zhu, Fenghua 

XIONG, Gang 

Yao, Qingming 

Chen, Songhang 

Ye, Peijun 







Evacuation is an effective strategy to mitigate damage of man-made or 

natural disasters. Evacuation clearance time is one of the key indicators 

in evacuation planning and management. Evacuees’destination 

choice and route choice behavior are two crucial factors used to estimate 

evacuation clearance time. However, these two factors are viewed 

as constants in previous research, and the quantification of the impact 

of these two factors is lacking. In this paper, the authors report impact 

of variation of evacuees’destination choice and route choice behavior 

on evacuation clearance time. The impact analysis is done based on a 

case study by using an artificial transportation system platform called 

TransWorld. And the best values of evacuees’destination choice and 

route choice behavior are given, respectively. The computational experimental 

results illustrate that if evacuation managers adopt reasonable 

strategies to guide evacuees’destination choice and route choice, 

it can significantly reduce evacuation clearance time. The simulation 

methodology, computational results and discussion can be used for future 

emergency evacuation planning. This study also provides potentials 

of new emergency evacuation management and control strategies 

from the perspective of evacuee behavior. 

◁ PFrB-62 

Sensitivity Analysis in Equilibrium Property of Photosynthetic Carbon 

Metabolism, pp.3549–3554 

Xia, Chao 

Guo, Jin 

Zhao, Yanlong 

Lin, Zhiwei 





Equilibrium property of the photosynthetic carbon metabolic network 

plays an important role on how to improve photosynthesis. In this paper, 

with respect to the independent subsystem of the photosynthetic carbon 

metabolic network, the one-at-a-time sensitivity measure is used 

to analyze sensitivity in whether or not it has multiple equilibria. Finally, 

we can get the maximum field of the two most sensitive parameters 

under the condition that the subsystem has only one equilibrium. 

◁ PFrB-63 

Mobile Robot Active Observation and Mapping Based on Factored 

Method, pp.3577–3582 

Yuan, Jing 

Huang, Yalou 

Sun, Fengchi 

Huang, Shuzi 

Chen, Huan 

Nankai Univ. 

Nankai Univ. 

Nankai 

Nankai Univ. 

Nankai Univ. 

This paper investigates the active observation and mapping of the mobile 

robot in SLAM problem. Firstly, based on factored solution to the 

simultaneous localization and mapping (FastSLAM), we apply the approximately 

optimal particle filter in the sense of statistics, as well as 

the unscented Kalman filter (UKF) to estimate the configuration of the 

robot and the position of the landmarks respectively. Then, by choosing 

the accuracy of SLAM and the environmental information as the optimization 

function, we convert the active observation and mapping into 

a problem of the optimal control for the mobile robot. By solving this 

optimal control, the robot can use the active control inputs to explore 

the environment and observe the landmarks adaptively and effectively. 

Finally, simulation results are presented to show the effectiveness of 

our approach. 

◁ PFrB-64 

A Method of Map Building for Robots in Unknown Indoor Environments, 

pp.3642–3647 

Liu, Shuhua 

Northeast Normal Univ. 

A new method of map building is presented for mobile robots in unknown 

indoor environments. It combined Internal Spiral Coverage (IS- 

C) algorithm, A* algorithm and wildfire algorithm to build the map in 

unknown indoor environments. The rasterization of sensor detection 

zone can improve the accuracy of map building which is affected by the 

error of the sensor data. Once an obstacle is explored, the robot will 

immediately go around it to identify. Simulation results show that the 

proposed method of map building is very effective in different indoor 

environments. 

◁ PFrB-65 

Research on Robot Motion Control Based on Local Weighted kNN-TD 

Reinforcement Learning, pp.3648–3651 

Han, Fei 


Univ. 

Jin, Lu 

Yang, Yuequan 

Cao, Zhiqiang 



Univ. 




Learning is an important capability for an individual robot, which provides 

an effective way for understanding, planning, and decisionmaking 

in a complex environment. For robot motion control, a local 

weighted k-nearest neighbors states selection method based on environment 

information and task information is presented. Based on this 

method, TD reinforcement learning algorithm is combined to reduce the 

misclassified probability of kNN-TD method, which is finally verified by 

the simulations. 

◁ PFrB-66 

An Algorithm for Sub-optimal Attribute Reduction in Decision Table 

Based on Neighborhood Rough Set Model, pp.685–690 

Liu, Zunren 

Wu, Gengfeng 

Yu, Zhongqing 

Shanghai Univ.; Qingdao Univ. 


qingdao Univ. 

In this paper, some concepts of upper approximation and lower approximation 

and so on are defined concisely and strictly on neighborhood 

rough set model.According to the fruit fly optimization algorithm’s idea, 

an new algorithm (NBH SFR) to get a sub-optimal attribute reduction 

on neighborhood decision table is proposed.The validity and feasibility 

of the algorithm are demonstrated by the results of experiments on four 

UCI Machine Learning database.A detailed analysis of delta operator 

to influence on the results is given. And the delta operator formula to 

obtain a sub-optimal reduction is proposed.Moreover,the experiments 

also show that it is impossible to solve multi-dimensional big dataset 

based on kernel-based heuristic algorithm ideas. 

◁ PFrB-67 

Traffic Signal Timing and Phase Optimization for Intersection Based on 

IPSO, pp.711–714 

JIN, Chengjun 


Urban area traffic signal control is a complicated issue. Based on the 

characteristics of vehicle delay, this paper proposes a method for signal 

timing and phase optimization. Particle swarm optimization (PSO) 

131


is adopted to optimize the multiphase traffic signal timing and phase 

sequence at the intersection. The optimization objective is to minimize 

the average vehicle delay of every cycle at the intersection. The simulation 

results demonstrate that the proposed method can efficiently 

reduce the average vehicle delay at the intersection and improve the 

traffic capacity of the intersection. In addition, the algorithm is fast and 

stable. 

◁ PFrB-68 

RNA secondary structure prediction algorithm based on combinatorial 

optimization algorithm and SVMs method, pp.715–719 

He, Jingyuan 


A new RNA secondary structure prediction algorithm that can predict 

pseudoknots is proposed,it combines the stem-loop combinatorial 

optimization algorithm and SVMs(Support Vector Machines,SVMs) 

method.The algorithm firstly finds out the optimal stem-loop structure 

and suboptimum structures based on dynamic neighbor topology particle 

swarm optimization algorithm,and then puts these loops into SVMs.The 

output from SVMs can decide whether there exist a pseudoknot.The 

experimental results demonstrate the superiority of our algorithm 

over the other methods in terms of solution quality and convergence 

rates. 

◁ PFrB-69 

Stigmergy Agent and Swarm Intelligence based Multi-agent System, 

pp.720–724 

Duan, Junhua 

Zhu, Yi-an 

Huang, Shujuan 




Social insect colonies can accomplish complex daily life tasks including 

foraging, nesting, preventing enemy and other things in spite of the 

simplicity of their individuals, and all the procedures are executed without 

any central controller. As a decentralized system, insect colonies 

exhibit flexibility and robustness, two features that would be desirable 

in an artificial system. Agent and multi-agent are good executor candidates 

for stigmergy and swarm intelligence. Stigmergy Agent structure 

is presented inspired from animal society, and stigmergy architecture 

is extended to extend stigmergy adaptability in more extensive application 

systems. Improved bi-partite graph is presented to organization 

structure of multi-agent system. Stigmergy Agent and swarm intelligence 

based multi-agent system will have promising prospects in many 

practical applications. 

◁ PFrB-70 

Research on the Fuzziness of Rough Set Model based on Compatible 

Relation over Double Universes, pp.783–787 

Zhan, Zhenyu 

Liu, Wenqi 

Shi, Mengting 




According to the relation between two elements in different universes, a 

fuzzy compatible relation is established as a generalization of rough set 

model over two universes. Firstly, new fuzzy approximation operators 

are presented basing on a fuzzy set with parameter which is defined 

in this article. Meanwhile, several important properties and theorems 

are proved as well. Next, by using the calculations of elements in the 

fuzzy matrix, a more effective method to obtain the approximate sets is 

proposed. Finally, an example is applied to illustrate the significance of 

the model. 

◁ PFrB-71 

A Concept Architecture Model with Autonomous Coordination for Flying 

Formation, pp.793–796 

Liu, Zilin 

Logistic Engineering Univ. 

Flying formations are an attractive prospect for improved science return 

as they provide a natural capability for temporal, spatial and signal separation 

with further engineering and economic advantages. As satellite 

numbers increase, the traditional ‘‘remote-control’’approach begins 

to break down. It is therefore essential to push control into space; to 

make spacecraft more autonomous. This article analyses some available 

typical autonomous coordination paradigms and then a hybrid autonomous 

coordination hierarchy for formations is presented, which is 

designed to minimize the traffic of intersatellite links and aid scalability 

to larger numbers (hundreds) of small satellites for tasks allocation. 

◁ PFrB-72 

Stability Analysis of Local Swarms in an Environment with a Quadratic 

Profile, pp.809–814 

Wang, Liangshun 

Fang, Huajing 



This paper first constructs an model of local swarms with a quadratic 

profile. Then study the stability properties of the collective behavior of 

the swarm and provide conditions for collective convergence to more 

favorable regions of the profile. It is shown that the individuals of the 

swarms will aggregate and eventually enter into a bounded hyperball 

around the swarm center or objective position. Aimed at removing the 

local trap, this paper gives the bound of parameter . 

◁ PFrB-73 

Web Design and Implementation for Remote Control, pp.920–924 

Chen, Wanmi 

Chen, Yucheng 



With the development of website, a great many changes took place and 

in many fields, people combine our traditional marketing with network 

marketing. There are many tools for the website design, and Apache, 

PHP (Hypertext Preprocessor) and MySQL has been the most widely 

used web development tool for its free and open source. Differences 

between PHP, JSP and ASP.NET have been discussed in this dissertation. 

The system which will be designed use the website to control 

the remote system. Thus, the environmental and demand analysis in 

the design, the development model based on PHP, architecture of field 

remote system, the development process based on Apache, PHP and 

MySQL, details data communication methods and the implement process 

of the website are also discussed. 

◁ PFrB-74 

Intervening a group of mobile agents in three dimension, pp.970–975 

Li, Xuejing 


How we intervene the multi-agent systems (MAS) without changing the 

local interactions such that the system exhibits the expected collective 

behavior is an interesting and important topic. In this paper, we will 

investigate the intervention of a class of MAS, where all agents move 

in three dimensional Euclidean space. To guide all agents to move 

with the same expected direction, we put some information agents (also 

called leaders) into the system. Different from [11, ]the heading of each 

agent is determined by two coupled angle sequences, which makes the 

theoretical analysis quite hard. By estimating some characteristics concerning 

the initial states and applying the mathematical induction, we 

will provide a quantitative result on the proportion of leaders needed to 

guarantee the expected consensus. 

◁ PFrB-75 

Guaranteed Cost Control for Uncertain Networked Switched Fuzzy 

Time-Delay Systems , pp.982–986 

Liu, Yi 

Yao, Lu 

Sun, Li-Ying 

Tianjin Inst. of Urban Construction 

Neusoft Group (Dalian) Co., Ltd 


According to switching technique, the problem of guaranteed cost control 

for uncertain networked switched fuzzy time-delay systems based 

on Takagi-Sugeno (T-S) fuzzy model is investigated. Considering the 

influence of time-delay under single-packet transmission condition, a 

sufficient condition for the existence of the robust guaranteed cost control 

law is presented based on single Lyapunov function and multiple 

Lyapunov functions method, and the switching law is also designed to 

implement the asymptotic stability of fuzzy switched systems. Finally 

an illustrative example is used to demonstrate the effectiveness of the 

proposed approach. 

◁ PFrB-76 

132



Research and Implementation of Security System in Intelligent Residential 

District, pp.987–990 

Zhang, Guofeng 

Gao, Yan 

Xie, Changwu 



BIT 

An implementation scheme for security system in intelligent residential 

district is presented in this paper. The security system is composed of 

home security subsystem, access control system and residential security 

control network. Hardware and software architecture of the home 

subsystem and design of the control network are introduced in detail. 

Problems of wired networks are solved due to the application of ZigBee 

technology in home subsystem. An interactive management system 

is designed for centralized management of residential district security, 

and it makes the security system more efficient and people-oriented. 

◁ PFrB-77 

Networked Control Systems Based on New Smith Predictor and Internal 

Model Control, pp.1014–1019 

Du, Feng 

Li, Jinyu 

Ren, Jia 

Zhang, Yu 

Zhou, Hui 

Guo, Cheng 

Hainan Univ. 

Hainan Univ. 

Hainan Univ. 

Hainan Univ. 

Hainan Univ. 

Hainan Univ. 

Some analyses and researches are done about networked control systems 

(NCS) those are based on the new Smith predictor and internal 

model control (IMC) from the system structures in this paper. The research 

and simulation results show that the choice of strategy of the 

new Smith predictor is more flexible than internal model control, but the 

adjusting parameter of the internal model control is relatively less than 

the new Smith predictor. Both of them are suitable for the NCS with 

random, time-variant or uncertain network delays which are larger than 

one or even tens sampling periods, also for a certain changing plant 

model (parameters). 

◁ PFrB-78 

Stability and Stabilization of Singular Systems: Strict LMI Sufficient 


Zhang, XueFeng 


This paper discusses strict linear matrix inequalities(LMIs) sufficient 

condition which can be removed of the equality constraint for linear 

continuous singular systems. The criteria of stability and stabilization 

of continuous singular system are presented. The conditions are expressed 

in terms of strict positive definite LMIs which can reduce the 

equality constraint and semi-positive definite LMIs and are much more 

tractable and reliable in numerical computation than existing conditions. 

The conditions make use of only the system parameter matrices 

(E, A, B) directly by a restricted equivalent transform without introducing 

other adjective parameters. A counterexample is given to illustrate 

the criteria are only a sufficient condition but not are a necessary condition. 

The idea presented can help to find necessary and sufficient 

strict LMI criteria without equality constraint conditions of stability and 

stabilization of continuous singular system. 

◁ PFrB-79 

New Delay-dependent Absolute Stability for Uncertain Lur’e System 

with Interval Delay, pp.1062–1066 

Li, Peiran 

Bao, Zhejing 

Yan, Wenjun 



CEE, Zhejiang Univ. 

This paper deals with the absolute stability analysis for Lur’e system 

with interval time-delay and norm bounded parameter uncertainties. By 

constructing the Lyapunov-Krasovskii functional, which is utilized the 

full lower and upper bound information of time-delay, some new delaydependent 

robust stability criteria are obtained in terms of linear matrix 

inequalities(LMIs) with convex combination technique. The resulting 

criterion has advantages over some previous ones in that it involves 

less decision variables than free weighting matrix method and doesn’t 

introduce the additional dynamics which leads to conservative results. 

The numerical examples of maximum allowable delay bound problem illustrate 

that the obtained absolute stability criteria are less conservative 

than previous ones. 

◁ PFrB-80 

Decentralized Finite-Time Stabilization of Large-Scale Interconnected 


FU, Qin 


The concept of decentralized finite-time stabilization for large-scale interconnected 

nonlinear systems is introduced by the definition of finitetime 

stability for linear systems.The design of decentralized state feedback 

controllers and decentralized dynamic output feedback controllers 

is given for a class of large-scale uncertain interconnected nonlinear 

systems.A sufficient condition is provided by using linear matrix inequality. 

When the feedback control laws are applied to the systems,the 

closed-loop systems are finite-time stable. A simulation example shows 

the feasibility and effectiveness of the conclusion. 

◁ PFrB-81 

The Modeling and Simulation on Sliding Mode Control Applied in the 

Double Inverted Pendulum System, pp.1089–1091 

Li, Zhongjuan 

Zhang, Xinzheng 

Chen, Guohai 

Guo, Yuguang 





This paper first describes the research background of inverted pendulum,then 

derives the mathematical model of planar double inverted 

pendulum system by use of Lagrange modeling method.After partial 

linearization,we can arrive at the pendulum’s equation of state s- 

pace,which is a controllable,observable,but absolute unstable system 

after analysing.Using the pole―placement method,a variable 

structure controller was designed for the stabilization and robust control 

of the double inverted pendulum.In order to reduce the chattering 

of the controller,a new algorithm for the sliding mode control was proposed. 

Simulation results verify the validity and feasibility of the proposed 

approach. 

◁ PFrB-82 

Adaptive Hydraulic Active Suspension Control for Traversing Rough 

Terrain, pp.1130–1134 

Shi, Mina 

Jilin Univ. 

This paper presents the adaptive hydraulic active suspension applied 

to the six-wheel off-road vehicle. Both four-link articu-lated mechanical 

structure and power transported hydraulic system are constructed. 

Nonlinear mathematical model is estab-lished for the nonlinear uncertain 

hydraulic system. The advantages of self adaptive suspensions 

and active suspensions are combined and introduced into the suspension 

system. The vehicle can reposition its center of mass by adjusting 

joint angles to enhance tipover stability during rough terrain traverse. 

A double loop auto disturbances rejection control (ADRC) method is 

employed and pressure control system is designed with displacement 

as random value responding to changes in load. The ex-perimental 

results show that the adaptive hydraulic active suspension has greatly 

improved the vehicle adaptability and stabil-ity in rough terrain. 

◁ PFrB-83 

Further Results On Delay-dependent Stability and Robust Stability of 

Linear System with Interval Time-varying Delay, pp.1166–1171 

Wang, Jian-an School of Electronics Information Engineering, 


Yin, Zhi-gang 

Jiujiang Univ. 

This paper is concerned with the delay-dependent stability and robust 

stability of linear uncertain system with interval time-varying delay. By 

splitting the delay-interval into two segments of equal length, some new 

types of Lyapunov functional are constructed for each segment. Based 

on convex combination technique, several improved delay-dependent 

stability criteria are derived in terms of linear matrix inequality. The 

proposed method involves neither model transformation nor any free 

133


weighting matrix, so it can reduce the complexity in computational demand. 

Numerical examples are given to demonstrate the effectiveness 


◁ PFrB-84 

Construction on Traction Converter System of EMU, pp.1188–1191 

Sun, Yannan 

Li, Changxian 

Li, Xiumei 




The traction converter system of EMU is constructed based on Matlab/Simulink. 

The system consists of a transformer, a rectifier, an inverter 

and an induction motor. The rectifier is controlled by the transient 

current control theory with SPWM technology. In the inverter the indirect 

vector control theory with current hysteretic band PWM strategy is 

adopted. Based on the system the traction control and constant speed 

control are tested. The simulation results present that the system is 

with good dynamic and steady state performance and fulfill the running 

demand of EMU. 

◁ PFrB-85 

Stability and stabilization for quadratic systems with state saturation 

nonlinearities, pp.1212–1217 

Fu, Chen 

Xu, Shengyuan 



This paper develops stability and stabilization theorems for a class of 

quadratic systems with state saturation nonlinearities. Based on the 

introduction of a row diagonally dominant matrix with negative diagonal 

elements and a particular representation for the quadratic terms, 

sufficient conditions for stability and stabilization of quadratic systems 

with state saturation nonlinearities are derived in terms of a “quasi” 

-linear matrix inequality (LMI) form. Iterative LMI algorithms then are 

presented for checking global asymptotic stability and stabilization of 

the system. Numerical examples are provided to demonstrate the effectiveness 

of the proposed approach. 

◁ PFrB-86 

Design and Simulation of Large Civil Aircraft’s Thrust Management 

System, pp.1224–1229 

Zheng, Yi 

Wang, Xiaoyan 

Yang, Sen 

Wang, Xinmin 





In order to optimize the flight thrust performance, this article brings forward 

the concept of thrust management system (TMS). The TMS is 

composed by thrust management computer, auto throttle (A/T), engine 

control system and cockpit control and display system. This article analysis 

the four systems in detail, then designs the thrust control system 

under the landing mode. Simulation results show the designed system 

accord with flight quality guideline well. This research bases a 

foundation for further research of the thrust management, which has 

significant meaning for increasing the flight security management level, 

saving fuel and decreasing flight cost. 

◁ PFrB-87 

Optimize the PID parameters based on improvment of immune algorithm, 

pp.1259–1262 

Lu, Jinyun 

Pan, Haipeng 



According to the disadvantages of general PID controller parameters 

optimization methods, this paper presents an improved immune algorithm. 

Main characteristics of this algorithm is to generate an initial 

population using homogenization techniques, self-adapt crossover operator 

and mutation operator. Result of simulation shows that the new 

method is superior to Z-N optimization algorithm. This method can 

overcome the premature phenomenon effectively and has good convergence 

performance and optimizing capacity. 

◁ PFrB-88 

Delay-dependent Robust Stability for Markovian Jump Singular Systems 

with Disturbances, pp.1340–1343 

Cui, Wenxia 

Donghua Univ. 

This paper is concerned with robust stability problem for Markovian 

jump singular time-delay systems with disturbances. In terms of strict 

linear matrix inequality(LMI), a new necessary and sufficient condition 

is proposed for the unforced Markovian jump singular system to be s- 

tochastically admissible. Based on this, the sufficient conditions are obtained 

for the existence of performance, which guarantees the singular 

Markovian jump systems with disturbances to be regular, impulse-free 

and stochastically stable. One numerical example is given to demonstrate 

the applicability of the proposed method. 

◁ PFrB-89 

A Robust Adaptive Control for Trajectory Tracking of Underactuated 

Surface Ships, pp.1395–1399 

Liu, Yang 

GUO, Chen 



We proposed a robust adaptive control method for trajectory tracking of 

underactuated surface ship with parameters uncertainties and disturbances. 

Based on the diffeomorphism transformation, the new tracking 

variables are given. In kinematics loop, we design the reference yaw 

angle and the reference speed. The nonlinear adaptive controller is 

designed in dynamics loop to asymptotically approach to the desired 

variables. The stability of the closed-loop system is analyzed by using 

Lyapunov theory. Simulation results illustrate the effectiveness of the 

proposed control method. 

◁ PFrB-90 

Static Output Feedback Pole Assignment based on the Regional Attractors, 

pp.1400–1405 

Yang, Lingling 

Zhang, Yun 

Chen, Zhenfeng 

ZENG, Qi-jie 





Regional pole assignment is studied for a class of linear systems via 

static output feedback. The shortage of the optimization problem 

subject to a set of bilinear matrix inequalities(BMIs) is analyzed. Then 

the algorithm of static output feedback pole assignment based on the 

regional attractors is proposed. A set of attractors in the satisfactory 

region is determined firstly. After that, the optimization direction of 

the output feedback matrix can be obtained via the one-dimensional 

searching method of variable polling. And a feasible solution can be 

attained by iterative computation. Computational results are presented 

demonstrating the effectiveness of the provided algorithm. 

◁ PFrB-91 

Consistency of Signal Coordinate Control Models under Different Traffic 


Lu, Kai 

Liu, Jiuming 

Wang, Shiming 

Liu, Shijie 

Xu, Jian-min 






Considering the different time that platoon reaches downstream intersection, 

this paper proposes corresponding stop and delay coordinated 

control models for under-saturated and over-saturated traffic conditions. 

With the cumulative arrival-departure diagrams, uniform stop and 

delay models for under-saturated and over-saturated traffic conditions 

are proved to be consistent and accurate by formula derivation. It provides 

a basis on deducing the stop and delay general formulae under 

signal coordinate control mode theoretically. 

◁ PFrB-92 

Based on the heating furnace temperature Fuzzy-PID control method 

research, pp.1553–1557 

Cui, Guimei 

Xiao, Peng 



134



Li, Yani 


This paper mainly represents the Fuzzy-PID control in gas heating furnace 

gas supply control system. The program make fuzzy control and 

the conventional PID control together, using coordinate factor online to 

determine the flow of gas, the scheme with fuzzy control and the conventional 

PID control advantages to suppress interference and noise 

and improve quality control system is effective, especially the control 

method is simple and the required control short calculation time, can 

satisfy the requirement of real-time control. The system has been put 

into operation . It has the dynamic response good and stability higher 

characteristic have high practical value and value of popularization. 

◁ PFrB-93 

Application on Non-destructive Inspection of Terahertz Wave, pp.1570– 

1573 

Yan, Fang 

Univ. Of Sci. & Tech. Of The Inner Mongol 

With the progress of the terahertz (THz) radiation source, detectors and 

system technology, Non-Destructive testing (NDT) based on the THz 

are possible. Because terahertz radiation is Transparent in the most 

of dielectric,the NDT based on the THz has great potential on inspecting 

the hidden defects of conductive materials. This paper reviews the 

application of terahertz radiation in NDT. The basic principle and characteristics 

of terahertz NDT are summarized, and the latest advancements 

in applied and theoretical researches about terahertz NDT are 

presented. 

◁ PFrB-94 

Inverse Kinematics Solution of Deep Space Probes Mast and Windsurfing, 

pp.1580–1584 

Song, Pei 

Ju, Hehua 

Li, Hui 




This paper describes an approach of inverse kinematic control for solar 

wings, data transmission antenna. Based on the structure characteristic 

of detector, we can describe the best point direction of solar wings 

and data transmission antenna effectively. They finished the judgment 

of requirements for different working modes and calculation of sustained 

time and energy combining with ephemeris calculations and environment 

preferment system, finished the description of performance 

cost for the heuristic optimization of detector mission planning. 

◁ PFrB-95 

Research on PID Control Technique for Chaotic Ship Steering based on 

Dynamic Chaos Particle Swarm Optimization Algorithm, pp.1639–1643 

Huang, Qian 

Dalian Naval Acad. 

For the difficulty of tuning the PID controller parameter to control a complex 

chaotic system, in order to overcome the shortage of basic particle 

swarm optimization, this paper improves the inertia weight and introduces 

the uncontrolled chaotic system into the PSO algorithm, proposes 

the improved dynamic chaos particle swarm optimization algorithm. 

This paper applies the algorithm to the chaos PID controller parameter 

optimization and takes a simulation study on the PID control method 

for the chaotic ship steering control, the results show that the controller 

parameter optimized by the proposed method can quickly stabilize the 

chaotic system to fixed point, and the control effect is obvious. 


Co-Chair: Zhao, Qianchuan 

Poster Session PFrC 

July 6, 16:30-17:50 



◁ PFrC-01 

Particle-swarm optimization algorithm for model predictive control of MI- 

MO with constraints, pp.2576–2581 

Wang, Shubin 




The constraints of output variables, input variables and intermediate 

variables exist widely in chemical process control. The inconsistency in 

different constraints may make constrained model predictive controller 

have no feasible solutions, which will bring harmful effect to practical 

production. To ensure the implementation of model predictive control, 

using its global optimization performance and constraint handling 

mechanism, a new particle-swarm optimization algorithm with the function 

of constraint handling, was proposed in this article. Taking into 

account the form of constraints and the constraints characteristics of 

MIMO (multi-input multi-output) predictive control system, this thesis, 

based on convex polyhedron geometry, discuss the feasibility of constrained 

model predictive control. Combined with duality theorem, the 

output constraints of system are transformed into constraints of input. 

After that, the constraints form which meets the requirements of control 

algorithm is obtained. Finally, particle swarm optimization algorithm is 

used to conduct the optimization of predictive control system. The simulation 

results of MIMO model with constraints showed the advantages 

and effectiveness of this algorithm. 

◁ PFrC-02 

Dynamic Optimization of Polymer Flooding for High-salinity Reservoir 

Based on Maximum Principle, pp.2601–2606 

Lei, Yang 

Li, Shurong 

Zhang, Xiaodong 

Guo, Lanlei 




Sinopec Shengli Oilfield Company 

Polymer flooding for high-salinity reservoir is one of the most important 

technologies for enhanced oil recovery (EOR). In this paper, an optimal 

control problem (OCP) of a distributed parameter system (DPS) is 

established, in which the functional of performance index is profit maximum 

and the governing equations are the fluid equations in porous 

media. The control variables are chosen as the polymer concentrations. 

The constraint conditions include boundary constraints and other 

inequality constraints. To cope with this OCP of DPS, the necessary 

conditions for optimality are obtained through application of Pontryagin’ 

s maximum principle. A gradient method is proposed for the computation 

of optimal injection strategies. The numerical results of an example 

for high-salinity reservoir illustrate the effectiveness of the proposed 

method. 

◁ PFrC-03 

Numerical Solution to Optimal Control of Switched Systems with Statedependent 

Switchings, pp.2788–2793 

Ji, Qin 

Song, Chunyue 



Given the fixed order of subsystems, the numerical solution to the optimal 

control of switched systems with state-dependent switchings and 

control constraints is explored. For the optimal control problem, when 

solving numerical solution by control vector parameterization based algorithm, 

a singular Jacobian problem is encountered in the process of 

solving ordinary differential equations (ODEs) with multi-point boundary 

value. In order to avoid the problem, an improved numerical algorithm 

is proposed in the paper, and the numerical solution can be obtained 

through the algorithm. Moreover, to find a global optimal solution, the 

filled function method is first adopted in such optimization problem. Numerical 

examples testify to the effectiveness of the proposed approach. 

◁ PFrC-04 

Fuzzy Guaranteed Cost Control for a Class of Nonlinear Systems via 

Observer-based Output Feedback, pp.2582–2587 

He, Guannan 

Ji, Jing 



In this paper, we consider the problem of guaranteed cost control for a 

class of nonlinear systems. Firstly, a Takagi-Sugeno (T-S) fuzzy model 

is employed to approximate the nonlinear dynamic system subject 

to external disturbance and measurement noise. Next, based on the 

fuzzy model, the fuzzy observer-based controller is developed with the 

guaranteed cost performance. In order to minimize the cost function, 

sufficient condition for the existence of model reference tracking output 

feedback controller is derived in terms of linear matrix inequalities 

(LMIs), which can be solved using the convex optimization techniques. 

135


Finally, a numerical example is given to illustrate the effectiveness of 


◁ PFrC-05 

Intersection Analysis of Input and Output Constraints in Model Predictive 

Control and On-line Adjustment of Soft Constraints, pp.2543–2549 

ZHOU, Xiaolong 

Wang, Shubin 





The constraints of input variables and output variables commonly exit 

in the actual industrial production process. Due to the interference 

and different constraints between conflicting, the constraint conditions 

can not be all satisfied, appearing to look for less feasible solutions 

and global optimal solution and then bringing negative effects on the 

actual production. Based on Polyhedral pole, the constrained model 

predictive control feasibility and the soft constraints adjustment algorithm 

when infeasibility are discussed in this paper. The method in this 

article considers the feasibility analysis and the reasonable soft constraints 

adjustment before the rolling optimization in each step, which 

makes the whole control process meet the requirements of constraint 

conditions without changing the basic structure of MPC. Through the 

simulation results of the constrained CSTR system, the validity and feasibility 

of the algorithm are verified. 

◁ PFrC-06 

New Active Queue Management Scheme Based on Statistical Analysis, 

pp.2562–2565 

Xu, Qin 

Sun, Jinsheng 



Most Active Queue Management (AQM) schemes try to stabilize the 

instantaneous queue length through mapping the congestion measurement 

into packet drop probability. In this paper we proposed a new 

AQM scheme called Exact Dropping Probability (EDP) without visible 

congestion measurement and mapping. It’s developed through statistical 

analysis for a dumbbell topology network. The simulation results 

demonstrate that EDP can control the instantaneous queue length converging 

to the desired value under various scenarios. We compare the 

performance of EDP with other existing AQM schemes like PI, REM 

and RaQ, and show that EDP has the fastest reactivity and the smallest 

oscillation. 

◁ PFrC-07 

Non-fragile Robust Design for a Control System of Air Fuel Ratio, 

pp.2607–2610 

Heng, Qinghai 

Lu, Jing 

Lu, Yang 




Non-fragile robust control problem of an air fuel ratio is studied. Under 

the premise of guarantee certain performances (overshoot and the 

settling time of the system, etc.), it is proposed that the μ-synthesis 

non-fragile robust performance design for the control system of the air 

fuel ratio is essentially to tolerate the greatest uncertainty. It is pointed 

out that this problem is really a non-fragile robust performance problem, 

the problem with both non-fragile robust stability and performance requirements 

(bandwidth and other performances). It is also suggested 

that the design of the non-fragile robust performance can be formulated 

as a μ-synthesis problem, if the uncertainty weighting function and the 

performance weighting function to system bandwidth and disturbance 

attenuation are revised appropriately. The μ-synthesis non-fragile robust 

PI controllers are solved. Results of the design look as if they were 

normal PI controllers, but their control quality, robustness and adaptability 

are better than the latter, and their structures are simple, definite, 

useful etc. The results show that the proposed method is efficient and 

feasible. 

◁ PFrC-08 

MLD-based Predictive Control of Energy Management for Hybrid Electric 

Bus, pp.2806–2811 

Kou, Zhitao 


Song, Chunyue 

Pan, Zheng 



In this paper, the energy management strategy of HEB (hybrid electric 

bus) is designed based on MLD (Mixed Logical Dynamical) model. After 

establishing simplified models of vehicle’s fuel consumption and 

battery’s SOC(state of charge) change, the dynamics of the HEB are 

analyzed in each operating modes and associated MLD models are formulated. 

Then the problem of solving the energy management strategy 

is turned into a MILP (mixed integer linear programming) problem and 

real-time HEB energy management strategy is gotten by solving the 

MILP problem. Meanwhile, a receding-horizon strategy is employed to 

resist random disturbances and make up model mismatches. The simulations 

show that the presented approach improved fuel economy and 

reduced emissions. 

◁ PFrC-09 

Application Research on Data-based Third-order Linear Extended S- 

tate Observer Filter for Hot Strip Width, pp.2903–2907 

Wang, Lijun 

Li, Qing 

Li, Jiangyun 

Tong, Chaonan 

Yin, Yixin 

Dong, Jie 







A practical data-based third-order linear extended state observer (ESO) 

filter is proposed based on state estimated by ESO. The filter is simple 

to use with only one parameter to tune. Then its transfer function, parameters 

tuning method and discrete simulation realization are given 

respectively. The proposed ESO filter is applied to the tracking of sine 

signal and square signal, the filter of signal with white Gaussian noise, 

and the filter of the width data for hot strip rolling mill in real time with 

measurement noise. Simulation results show the proposed ESO filter 

is practical without signal model, noise model and statistical characteristic 

of the measurement noise. Only according to present moment of 

the measurement data, it could meet the requirement with appropriate 

parameter. 

◁ PFrC-10 

Multi-Agent Failure Prediction based on Data Assimilation Theory, 

pp.3146–3151 

Huang, Xun 

YAN, Junwei 

Liu, Min 

Tongji Univ. 

Univ. of TongJi 

Tongji Univ. 

In the context of preventive maintenance being valued, focusing on the 

defect of closure and low problem solving ability presented by single failure 

prediction system, combined with meteorological data assimilation 

theory and multi-agent technology, failure prediction of steel continuous 

casting equipment was researched. A distributed failure prediction 

system based on ensemble Kalman filter (EnKF) and multi-agent technology 

was developed, which overcomes the inelasticity of conventional 

prediction method used in a nonlinear environment. A prediction model 

with higher precision and higher efficiency was built, whose feasibility 

and effectiveness were verified by an actual case. 

◁ PFrC-11 

Fault Diagnosis Observer via Hybrid Fuzzy and Importance Sampling 

Schemes , pp.3254–3259 


Wang, Hong 



this paper introduces a new direction of research to estimate states 

as well as detect and diagnose (Gaussian and non Gaussian) faults. 

Therefore, a new observer (FO) has been introduced and designed via 

a new filter for each output of plant. The new filter FSISF based on 

Fuzzy and Sequential Important Sampling algorithms to estimate and 

predicates the. Furthermore, the observer estimates the unknown s- 

tates of the plant according to the diagnosed fault, previous predicate 

weight and the residual of the plant. As results, a nonlinear Dc motor 

model considered as a benchmark to test the new observer (FO), 

136



where, the good results of the simulation results have shown that the 

proposed observer is a robust observer against the colored, white noise 

and non Gaussian noise and fault. 

◁ PFrC-12 

Study and Design of Digital Simulation System for Pumped Storage 

Hydropower Units, pp.3068–3071 

Chen, Qijuan 

Wuhan Univ. 

According to the characteristic of the pumped storage hydropower units, 

a simulation system based on theory and method of intelligent control 

maintenance management system (ICMMS) is presented, in which all 

the operation and maintenance simulation are coming to be realized. 

On the modeling ways, the concept of all condition modeling for the 

pumped storage hydropower units is put forward, so as to get identical 

operation and maintenance environment between prototype units and 

virtual units. 

◁ PFrC-13 

Amplitude Envelope Analysis for Feature Extraction of Direct-Driven 

Wind Turbine Bearing Failure, pp.3173–3176 

Xin, Weidong 

Liu, Yibing 

He, Ying 

Su, Boxian 





Made measurements on direct-drive wind turbines to get vibration signals 

of main shaft, then the vibration characteristics of normal and fault 

units were analyzed and studied. On the basis of a comparative analysis 

of vibration signals characteristics between normal ant fault units, 

which were on time domain, frequency domain and the cepstrum domain, 

narrowband envelope analysis have been made. The difference 

of envelope spectrum for vibration signals between normal and fault u- 

nits is revealed. The results show that, narrowband envelope analysis 

can identify bearing fault state effectively. 

◁ PFrC-14 

Application of Over-complete ICA in Separating Turbine Vibration 

Sources, pp.3177–3180 

An, Hongwen 

Liu, Yibing 

Yan, Keguo 

Wang, Yu 

Yang, Huan 






The over-complete ICA problem is always meet in the application of engineering. 

That is to say, the number of unknown sources is more than 

the number of observed signals. At this time basic ICA model is not 

suitable. This text utilizes the component of priori knowledge as additional 

input signal (addition virtual channel), to increase the number of 

the input signals. And it can solve the engineering application problem 

of over-complete ICA. This method is tested through a group of actual 

turbine vibration signals. The similarity coefficient is introduced to verify 

the effect of source separation. 

◁ PFrC-15 

The PSO-LSSVM Model for Predicting the Failure Depth of Coal Seam 

Floor, pp.570–574 

YAN, Zhi-gang 


Analyzed the samples of the failure depth of coal seam floor collected 

in mining fields, studied the main influence factors being associated 

with the failure depth. In order to avoid overfitting problem of artificial 

neural network (ANN), a new least squares support vector machines 

(LS-SVM) model is presented to forecast the nonlinear failure depth of 

coal seam floor under the influence of mining based on particle swarm 

optimization(PSO) method. PSO is used to choose the parameters of 

LS-SVM, which can avoid the man-made blindness and enhance the 

efficiency, even improve the generalization performance. The experimental 

results show the method is feasible and precise, with reliable 

theoretical foundation and good practical performance. 

◁ PFrC-16 

Singularity Avoidance in Single Gimbal CMG Using the Theory of Potential 

Functions, pp.1103–1108 

Chakravorty, Jhelum 

Indian Inst. of Tech. Bombay 

The control moment gyro (CMG) actuators used in attitude control of s- 

pacecraft suffer from the singularity problem where they fail to generate 

the output torque in some particular direction called the singular direction. 

In connection to the avoidance of the singularity, the notion of the 

potential function is discussed and shown to behave as an avoidance 

potential. Using the theory of the superquadratic isopotential contours 

around the singular point, a suitable algorithm is developed to avoid 

a small region enclosing the singular point. The isopotential contour, 

where the avoidance potential exceeds some pre-fixed value can be 

avoided. The stability of the CMG system using the potential function 

is discussed. Comparative studies using PID and PD controllers are 

done. 

◁ PFrC-17 

Hypersonic Vehicle control based on Integral Sliding Mode Method, 

pp.1762–1766 

Liu, Helong 

Zong, Qun 

Tian, Bailing 

Wang, Jie 

Tianjin Univ. 

School of Electrical Engineering & 

Automation,Tianjin Univ. 

Tianjin Univ. 

Tianjin Univ. 

Considering the impact of parametric uncertainties on Hypersonic Vehicle 

steady tracking control, a control approach based on integral sliding 

mode was proposed. First of all, linearized model of the Hypersonic Vehicle’s 

longitudinal dynamics with uncertainties was obtained based 

on small disturbance linearization method with chosen trim point. Then, 

an integral sliding mode surface that guaranteed the global robustness 

of the control system was selected based on the uncertainty model. 

Next, sliding mode control law was designed and stability was analyzed 

based on the Lyapunov theory. At last, a simulation platform was built 

and results showed the effectiveness of the control approach through 

Monte-Carlo simulation. 

◁ PFrC-18 

Integrated Optimal Algorithm for Flexible Job Shop Scheduling with 

Due-date Window , pp.638–642 

Song, Juan 

Ningxia Univ. 

The objective of Flexible job shop scheduling problem (FJSP) with duedate 

window is to minimize the total E/T (earliness and tardiness) penalties 

for NP-hard problem. In this paper, an integrated optimal genetic 

algorithm is developed to optimize flexible scheduling objective of E/T 

with due-date window. In addition, mathematical model is established 

with respect to minimize E/T penalties, subjected to a set of practical 

restrained conditions. To testify optimization feasibility, the due-date 

window is sorted into common and discrete due-date window in the 

experiments. The proposed approach has been simulated on several 

benchmarks. Simulation results show that the hybrid algorithm proposed 

has satisfactory performance. 

◁ PFrC-19 

Job-shop Scheduling Optimization Design Based on An Improved GA, 

pp.654–659 

Zhao, Zixiang 


Bing, Zhigang 

Tianjin Univ. 

Tianjin Univ. 


The job-shop scheduling problem (JSP) is a hard combinatorial optimization 

problem. In this paper, the mixed programming of MATLAB 

and VC is considered to solve the JSP. Firstly, MATLAB is used to implement 

an improved genetic algorithm (GA), and then the program is 

packaged into a dynamic link library (DLL). Secondly, VC is used to 

realize the input/output interface and call the GA DLL to schedule and 

store the scheduling results into the ORACLE database. Finally, the 

simulation results show that the improved GA has good optimization 

performance in solving JSP and the mixed programming design is effective. 

137


◁ PFrC-20 

Medium and Long-term Electric Load Forecasting based on Chaos 

SVM, pp.660–663 

Wang, Deji 


Because traditional prediction algorithm can not accurately forecast 

long-term electricity load, chaos SVM prediction algorithm was introduced 

and some of its characteristics were discussed􀋊 The 

kernel function was chosen under the guidance of the geometric information. 

The experiment shows that the algorithm is more accurate and 

effective than the others. 

◁ PFrC-21 

The Motion Planning in the Automatic Generation of Mobile Phone 3D 

Animation, pp.725–731 

Qiu, Zhenzhen 


Full life cycle automatic generation of computer animation is a new animation 

technology which combines artificial intelligence with multimedia 

technology.In this system,we introduce this technology into 3G mobile 

phone. The target is that animation information of mobile phone message 

is extracted and 3D animation is generated automatically under 

computer aided and sent to the receiver of message. Motion planning is 

one of the core modules in mobile phone 3D animation auto-generation 

system. In this thesis ,we build action’s ontology using semantic web 

technology. Firstly, motion planning module uses topic and action 

atoms extracted from message to reasoning the corresponding action. 

Secondly, it processes the capture data of action using motion editing 

technology according to the animation scenes selected in plot planning. 

Finally, it adds action to the specific character model and enriches the 

effect of animation. The paper mainly describes the overall design, 

detailed design of motion planning and the specific editing process of 

action. 

◁ PFrC-22 

Plot planning of 3D animation based to Semantic Web in Chinese text, 

pp.732–738 

Nie, Junlian 


Plot planning is a very important part in the mobile phone 3D animation 

generation system , according to the abstract information, which is 

extracted from a Chinese text, it plans the plot of a specific action or 

animated scene. In this paper we present a practice of applying the 

Semantic Web technologies in the domain of Chinese text, build an ontology 

and a rule base to support the plot planning of the mobile phone 

3D animation generation system, which enable the computer to understand 

the meaning of the semantic knowledge. 

◁ PFrC-23 

An Improved Adaptive Harmony Search Algorithm, pp.743–747 

Kong, Zhi 

Wang, Lifu 

Wu, Zhaoxia 

Qi, Shiqing 

Zou, Dexuan 




Northestern Univ. at Qinhuangdao 

Jiangsu Normal Univ. 

This paper is concerned with the global adaptive harmony search 

(GAHS) algorithms for solving optimization problems. GAHS employs 

global information to the adaptive harmony search algorithm. The proposed 

GAHS algorithm is tested numerically and contrasted with improved 

harmony search (AHS) algorithm, and particle swarm optimization 

(PSO). Our simulation results reveal that GAHS is superior to AHS 

and PSO in terms of robustness and efficiency. 

◁ PFrC-24 

Parameter Acquirement Methods for Rule-based Model of Virtual Plant 

Based on Optimal Algorithms, pp.748–752 

Ding, Weilong 

Xu, Lifeng 

Hu, Chen 

Zhang, Yuping 

Zhejiang Universy of Tech. 

hangzhoiu 

hangzhou 

China National Rice Research Inst. 

Rule-based model is an effective technique to dynamically simulate the 

morphological development of a plant. It is thus used widely in the 

field of plant modeling and visualization. Before a virtual plant model 

with high quality performance is established, it is a key step to provide 

suitable parameters for the rule-based model. There are several disadvantages 

in the traditional/manual ways to design the model, e.g. with 

low efficiency. Therefore, how to obtain appropriate parameters for the 

rule-based model has attracted many researchers devoting themselves 

to this area. In the past twenty years, Genetic Algorithm and Gene Expression 

Programming have been used to optimize the production rules 

of Do L-system and Parametric Do L-system. Due to the complexity of 

the structure of a plant, researches’attentions are mostly paid in the 

narrow area of simple plant morphology restrictively. In this study, the 

parameter-acquired methods for rule-based model, which is based on 

Genetic Algorithm and Gene Expression Programming, are summarized. 

And the relative techniques and the possible development in the 

future are discussed as well 

◁ PFrC-25 

A Study on Obstacle Avoidance for Mobile Robot Based on Fuzzy Logic 

Control and Adaptive Rotation, pp.753–757 

Wen, Sufang 

Wang, Lin 



This paper presents a fuzzy logic control algorithm for the obstacle 

avoidance of mobile robot in unknown environment. A reasonable and 

applicable control response table is constructed by using fuzzy inference. 

Meanwhile, in dealing with the deadlock problem, the algorithm 

offers an adaptive rotation angle based on the detection distance. Simulation 

results in different kinds of environments prove the effectiveness 

and feasibility of the algorithm. 

◁ PFrC-26 

Application and research of Multi-label Naïve Bayes Classifier , pp.764– 

768 

Qin, Feng 

Tang, Xianjuan 

Cheng, Zekai 




Multi-label learning and application is a new hot issue in machine learning 

and data mining recently. In multi-label learning, the training set 

is composed of instances each associated with a set of labels, and 

the task is to predict the label sets of unseen instances through analyzing 

training instances with known label sets. In this paper, authors 

research on classifying multi-label data based on Naïve Bayes Classifier(NBC), 

which is extended to multi-label learning. Training and testing 

procedures are adapted to the characteristics and assessment criteria 

of multi-label learning problem. The adapted NBC is realized through 

programming on MBNC experimental platform and applied to the nature 

scene classification, the results show that it is effective. 

◁ PFrC-27 

Research on Personality Behavior in Intelligent Virtual Environment, 

pp.769–772 

Shi, Lin 

Li, Zhigang 

Zhang, Jiajie 


Tang Shan College 

Hebei Polytechnic Univ. 

Against the special requirements of realistic in the intelligent virtual 

environment, this paper takes full account of the impact of personality 

on behavior, and proposes a ”plan-TO-action” converter based on 

Gaussian probability distribution function and the FSM (Finite State Machine). 

The converter‘s work is to evaluate the parameters such as 

time and place in the planning process, combined with the IVA’s personality, 

to produce the final action. The converter is applied in prototype 

system for smart home. 

◁ PFrC-28 

Algorithm Research for Gene-sequence-based 3D Model Reconfiguration 

, pp.788–792 

NING, Shu-Rong 

Yuan, Shengyun 

Lin, Rui 


Univ. of Sci. &Tech. Beijing 

univsrsity of Sci. & Tech. beijing 

138



Zhu, Yuan 


How to improve the efficiency of animation production and reduce the 

difficulties of animation development have become core issues in research 

on animation technology. By the analysis of existing animation 

design technology, three main processes of animation design have 

been improved: model design, preservation and rendering. Instead 

of individual modeling, the reconfigurable model design algorithm has 

been used. In order to preserve efficiently, the common characteristic 

of a class of things have been picked up to model in class level, then the 

model should be divided into several modules. Abstraction on the basis 

of gene sequences makes new model can be generated by dynamic 

adjustment. Reconfigurable model design algorithm based on gene 

sequence can improve the efficiency of model design, simplify the variable 

quantity of data information, and reduce the space consumption of 

storage. 

◁ PFrC-29 

Design Methods of General Fuzzy Systems as Function Approximators, 

pp.815–819 

Wang, Xiuhong 

Yang, Zhenguang 

Gao, Qian 

Wei, Xinjiang 

Ludong Univ. 

Ludong Univ. 

Ludong Univ. 

Ludong Univ. 

In this paper, design methods of general fuzzy systems as function approximators 

are given respectively by the following three different conditions 

to be approximated: 1. The formulas of the functions are known; 

2. The formulas of the functions are unknown, but for any input of the 

functions, we can get the corresponding output; 3. We only know limit 

pairs of input-output data of the functions. Finally, an example is given 

to show how to design a general fuzzy system to approximate a given 

function by a required accuracy. 

◁ PFrC-30 

Fault Diagnosis Method of the Locomotive Brake Based on Wavelet 


DING, Jian-bo 


Proposed a locomotive brake fault diagnostic expert system approach, 

by using wavelet multiscale edge detection principle to get fault symptom 

extraction, and combined with the knowledge representation 

method of weighted fuzzy production rule, in order to make the failure 

symptoms and findings fuzzy and weight conclusions of fuzzy fault 

symptoms, weight based, fuzzy rule inference algorithm by further diagnostic 

reasoning, fault diagonosis of locomotive brake to solve the 

uncertainty of the precess. A practical example indicates that the diagnosis 

system can improve the real-time and veracity of the diagnosis 

for air brake of diesel locomotive effectively. 

◁ PFrC-31 

Intelligent Search Algorithm for Modern Tobacco Agriculture, pp.823– 

826 

Wang, Deji 


In this paper, semantic cosine similarity based o n tobacco agriculture 

ontology is introduced into SVM to cluster the web agriculture information. 

Experiments proof that it is more efficient and powerful. The 

final comparison between search engine based on the algorithm and 

general search engines, also proof that the algorithm is effective and 

accurate. 

◁ PFrC-32 

Distributed Control for Synchronization of Networked Lagrange Systems 

via Estimated State Feedback, pp.903–908 

Chen, Gang 

Lin, Qing 



This paper investigates the synchronization control problem for a group 

of networked Lagrange systems without relative velocity measurements. 

Distributed observers are proposed to estimate the neighbors’ 

velocities. Based on the estimated states, the distributed controllers 

are designed to achieve the synchronization of the networked nonlinear 

systems. In contrast to other approaches, the development in this paper 

is suitable for the general directed communication topology, which 

is only required to have a spanning tree. Simulation examples are included 

to demonstrate the effectiveness of the proposed algorithms. 

◁ PFrC-33 

Configurations and Modelling of Networked Feedforward-Feedback 

Control Systems, pp.909–913 

Huang, Congzhi North China Electric Power Univ.,Beijing, 

P.R.China 

Bai, Yan 


The concept of networked feedforward-feedback control system is proposed 

based on practical industrial process control, and its configurations 

as well as the development of its uniform model are investigated in 

detail. Feedforward-feedback control system, wherein the control loops 

are closed via real-time networks, is called networked feedforwardfeedback 

control system(NFCS for short). Three different configurations 

of NFCSs are proposed based on industrial process control and 

the locations of networks are explicitly pointed out, respectively. The u- 

niform closed-loop system models for the three different configurations 

of NFCSs are developed via the augmented vector method, taking into 

account the network-induced delays and data packet dropout simultaneously. 

◁ PFrC-34 

MIMO network control systems with asynchronous sampling based on 

dividing the time slice, pp.925–930 

Fan, Weihua 

Chen, Xiaodu 

Xie, Ronghua 

Chen, Qingwei 





The modeling and control of a class of MIMO networked control systems 

are studied in this paper. First, for the problem of packet collisions 

caused by the synchronous sampling of the sensor nodes in multi-loops 

network control systems, the strategy of asynchronous sampling based 

on dividing the time slice is proposed. By analyzing the data transmission 

characteristics of networked control system with network-induced 

delay at the case of asynchronous sampling strategy, a discrete mathematical 

model of multi-loops networked control system is established. 

With the Lyapunov function and the Linear Matrix inequalities, the sufficient 

condition for asymptotic stability and output feedback control law 

of the system are proposed. Finally, a numerical example and simulation 

example based on True-time toolbox are given to verify the feasibility 

and effectiveness of the method. 

◁ PFrC-35 

Constrained Kalman Filtering With Observation Losses, pp.937–941 

Luo, Zhen 

Fang, Huajing 



In this paper, we consider networked constrained Kalman filtering with 

observation losses. The observation losses of communication network 

is modeled as an i.i.d. Bernoulli process. Based on physical consideration, 

at each time step through projecting the unconstrained Kalman filter 

solution onto the state constraint surface, the constrained estimation 

can be derived, which significantly improves the prediction accuracy of 

the filter. We study the statistical convergence properties of the error 

covariance matrix, showing the existence of a critical value for the arrival 

rate of the observation, beyond which a transition to an unbounded 

state error covariance occurs. Simulations are provided to demonstrate 

the effectiveness of the theoretical results. 

◁ PFrC-36 

Information consensus for multi-agent systems via nonlinear protocols, 

pp.942–945 

Zhai, Junyong 


Xu, Shouhuai 



The Univ. of Texas at San Antonio 

In multi-agent system, the communication topology of agent network 

plays a very important role in its consensus problem. In this paper, we 

139


consider a new consensus nonlinear protocol for networks of dynamic 

agents that allow the agents to agree in a distributed and cooperative 

fashion. We consider the network with external disturbance. We use 

a Lyapunov approach to prove that the asymptotical consensus can be 

reached. Finally, we perform a simulation study to verify the efficiency 

of the proposed scheme. 

◁ PFrC-37 

Studied on Anti-interference Based on Dynamic Surface Control, 

pp.996–998 

Gao, Qian 

He, Naibao 



The longitudinal control of NSV (Nearspace Vehicle)under uncertainty 

is studied. An approach of backstepping control based on adaptive 

dynamic surface is proposed. RBFNN is used to approximate the unknown 

compound disturbance and the robust item is introduced into the 

virtual controller to cancel the influence of compound disturbance and 

improve the robustness and adaptation of the system. Simulation results 

show that the proposed strategy possesses good robustness and 

briefness. 

◁ PFrC-38 

Estimation of CAN Bus Reliability Based on Real-time Performance, 

pp.999–1004 

Chen, Xi 

Jin, Wen 

Zhang, Huiqun 

Lv, Weijie 

Tianjin Univ. 

Tianjin Univ. 

Tianjin Univ. 

Tianjin Univ. 

With the rapid development of CAN Bus, many scholars and engineers 

focus on how to evaluate the reliability of CAN Bus. It is widely accepted 

that the calculation of the probability of successful transmission 

is an effective method to analyze both the real-time performance and 

message correctness. However it was proved that the probability of 

successful transmission is not applicable to calculating the reliability of 

timely delivery because of the statistical correlation. And hence artificial 

neural network was induced to simulation to calculate the reliability. 

The method was based on the analysis of international and national 

standard and protocol of CAN Bus. The reliability was approximated 

by the artificial neural network and the simulation was adopted to both 

calculation of the reliability in short period in harsh electromagnetic interference 

environment and validation of artificial neural network. The 

method reduced consumption of both data and time when the simulation 

was only utilized for the calculation of reliability over long time. 

◁ PFrC-39 

Synchronization for a class of Complex Dynamical Networks, pp.1010– 

1013 

Li, Changbin 

He, Yi 

Wu, Aiguo 

Tianjin Univ. 

Tianjin Univ. 

Tianjin Univ. 

This paper is concerned with the global synchronization problem for a 

class of complex dynamic networks. We first give a sufficient condition 

for the synchronization of complex networks with any connection. Then 

in the case that the network connection is directed and has a spanning 

tree, the global synchronization problem is further investigated. These 

results are easy to be used for the synchronization of various complex 

dynamical networks 

◁ PFrC-40 

Networked H infty Filtering for T-S Fuzzy Systems Based on Event- 

Triggered Scheme, pp.1037–1042 

Wang, Qi 

Jia, Xinchun 

Chi, Xiaobo 

He, Dezhi 

Ma, Weiwei 

Shanxi Univ. 

Shanxi Univ. 

Shanxi Univ. 

Shanxi Univ. 

Shanxi Univ. 

This paper studies H∞filtering problem for T-S fuzzy systems in network 

environment. Considering the limitations of network communication 

bandwidth, a new discrete event-triggered communication scheme 

is introduced to avoid the transmission of unnecessary information. 

Whether or not the measured output is sent depends on the sampled 

output of the recent transmission and the error between the current 

sampled output and the sampled output of the recent transmission. 

By Lyapunov-Krasovskii functional approach, a sufficient condition for 

the existence of the delay-dependent networked H∞filter is established 

in terms of linear matrix inequalities for filtering error systems. The 

proposed event-triggered communication scheme not only reduces the 

use of communication bandwidth, but also ensures the desired system 

performance. Moreover, a co-design method of the filter and eventtriggered 

communication scheme is provided. Finally, a numerical example 

is given to show the effectiveness of the proposed approach. 

◁ PFrC-41 

Quantized Feedback Stabilization of a class of Networked Control Systems 

with Delay and Packet Dropout, pp.1043–1047 

Ren, Jun 

Zhou, Chuan 

Guo, Yu 

Chen, Qingwei 

Nanjing Univ. of Sci. & Tchnology 




In this paper, the problem of quantized feedback stabilization for a class 

of networked control systems with time delay and packet dropout is investigated. 

First, a primitive quantizer is applied to the communication 

channel and the lower bounds of bit rate is given to guarantee convergence 

of quantization error. Second, dynamic quantization error is 

represented by quantization range via coordinate transformation, then 

stabilization problem of closed-loop networked control systems is studied 

and a state feedback controller design method is given to guarantee 

the closed-loop system mean-square stable through Lyapunov stability 

theory and linear matrix inequality techniques. 

◁ PFrC-42 

Short-term Load Forecasting Based on Least Square Support Vector 

Machine Combined with Fuzzy Control, pp.1048–1051 

Gao, Rong 

ludong daxue 

A short-term load forecasting method based on least square support 

vector machine (LS-SVM) combined with fuzzy control was proposed. 

The peak load and valley load was forecasted by LS-SVM model which 

was built by analysis of load data and meteorological data. Then the 

peak load and valley load was tuned by fuzzy rules which has been 

built by forecasting error data . One day and one week ahead load has 

been got by combing peak load and valley load with similar day load 

change coefficient. The load data and meteorological data of Shan 

Dong electrical company of 2008 was utilized to test the forecasting 

model. The simulation result shows the proposed method can improve 

the predicting accuracy. 

◁ PFrC-43 

Development on Gas Leak Detection and Location System Based on 

ZigBee, pp.429–432 

Duan, Ping 

Ding, Chengjun 



The Gas Leak Monitoring and Warning and Location System is based 

on ZigBee Wireless Sensor Networks (WSN), it is used on production 

safety for the Petrochemical Industry. Gas-sensitive sensors and Zig- 

Bee WSN are used in this system. ZigBee is reliable, low-cost, lowpower 

and low-maintenance wireless network, it is very suitable for the 

real-time data detective of the potential risk areas. The computer analyses 

the data from the monitoring site of the WSN and displays on the 

window. In this way, the system can give an early warning of a leak 

accident. Also, the system can collect the data of the leak accident and 

locate the leakage point. These functions can not achieve so perfectly 

with the former leak detection systems. 

◁ PFrC-44 

A Hierarchical Iterative Closest Point Algorithm for Simultaneous Localization 

and Mapping of Mobile Robot, pp.3652–3656 

Zhang, Qizhi 


140



Zhou, Yali 


Simultaneous localization and mapping (SLAM) problem of a mobile 

robot is studied in this paper. An improved particle filters approach is 

adopted to reduce the number of particles. A laser range finder is utilized 

to measure the distance of obstructs, and the accurate proposal 

distribution are obtained by scan match method, which is realized by 

a hierarchical iterative closest point (ICP) algorithm. A roughly global 

optimal estimation of robot pose is first obtained by directly searching in 

the discrete space of pose, and then the estimation of robot pose is refined 

by gradient descend method. So an accurate estimation of robot 

pose can be obtained by the hierarchical scan match approach. Experimental 

tests are carried out with our real mobile robot in an indoor environment. 

Experimental results show that the consistent map can be obtained 

by the proposed scan match approach. The efficiency of the proposed 

scan match approach is also validated by the RoboCup@Home 

competition. 

◁ PFrC-45 

A Solution of Inverse Kinematics for 7-DOF Manipulators and Its Application, 

pp.3711–3717 

Huang, Qiulan 

Wu, Jun 

Xiong, Rong 




This paper provides a solution of inverse kinematics for 7-DOF manipulators, 

which is derived by homogeneous transformation matrix described 

in equivalent angle-axis representation. The end-effector’s 

posture calculated by this approach has excellent precision. The manipulator 

can avoid collision and singularity while meeting the parameters 

of the joints through the optimization of the redundancy. Moreover, 

in order to increase the comfort level of the arm, we improve the 

method based on Jacobian pseudo-inverse to calculate the angular velocity. 

The time it takes is so short that it can be applied to on-line 

application. We applied this method to a manipulator of a ping-pong 

robot and proved its validity. 

◁ PFrC-46 

Acquisition of the Horse Movement Trajectory and Its Reproduction in 

the 6-DOF Parellel Robot Horse, pp.3718–3723 

Xiao, Jinzhuang 

Li, Pengfei 

Wang, Hongrui 

Wang, Liling 

Hebei Univ. 

Hebei Univ. 

Hebei Univ. 

Hebei Univ. 

The robot horse sports is a modern fitness development direction. By 

analyzing the 6-DOF parallel robot horse platform mechanical structure 

and motion characteristics, its model of spatial movement was established. 

ADIS16355 was used as the sensor to collect acceleration data 

and angular velocity data, which was stored in SD card by the serial 

interface of AVR microcontroller. Matlab was used to read the data and 

establish the mathematical formula of spatial movement to obtain the 

displacement data and angle data, consequently the periodic motion 

data of horse was extracted. The data was made into a loop and transplanted 

into robot horse to reproduce the movement trajectory of the 

real horse. 

◁ PFrC-47 

A self-localization method based on omnidirectional vision and MTi for 

soccer robots, pp.3731–3736 

Xiong, Dan 

Lu, Huimin 

Zheng, Zhiqiang 




Self-Localization is the basis to realize mobile robot’s autonomous 

ability such as motion planning, control decision and cooperation. Omnidirectional 

vision is one of the most important sensors for RoboCup 

Middle Size League soccer robots, and Motion Trackers instrument 

(MTi) is a kind of inertial sensors which can measure the attitude 

of the robot in real-time and accurately. In this paper we propose a 

self-localization method which is based on omnidirectional vision and 

MTi. By combining with matching optimization localization, global localization 

and localization tracking can be realized quickly for our soccer 

robots.The experimental results show that global localization can 

be realized effectively while highly accurate localization is achieved in 

real-time. 

◁ PFrC-48 

Modeling and Simulation of Omni-directional Lower Limbs Rehabilitation 

Training Robot, pp.3737–3740 

Jiang, Ying 

Bai, Baodong 

Wang, Shuoyu 



Kochi Univ. of Tech. 

An omni-directional lower limbs rehabilitation training robot is designed 

to improve patient’s locomotion, who suffers from impairment in walking 

ability after neurology injuries. By analyzing the kinematic and dynamic 

characteristics of the robot, its motion control model is provided. 

A multi-body dynamic model of a full robot is established by using 

ADAMS software. In addition, the co-simulations under Matlab and 

ADAMS are carried out by defining I/O interface. The results show that 

the established model of robot is correct. 

◁ PFrC-49 

A Decentralized Adaptive Controller Design for Lower Extremity Rehabilitation 

Robot, pp.3753–3757 

Mi, Wenjun 

Wu, Zhizheng 

Qian, Jinwu 




Rehabilitation is the major therapy in stroke and spinal cord injured individuals. 

Robot-aided treadmill training has been applied for several 

years to assist, enhance and evaluate neurological and orthopedic rehabilitation. 

This paper presents a compliant patient cooperative control 

approach for Lower Extremity Rehabilitation Robot (LERR) with automatic 

gait adaption. Firstly, based on nonlinear inverbility decoupling 

theory the system is decoupled into some independent second-order 

integral systems. Then based on the decoupled sub-systems, the admittance 

and adaptive control methods are further investigated for the 

gait trajectory planning and tracking. Finally, the performance of the 

proposed controller is verified in the MATLAB-Adams co-simulation environment. 

◁ PFrC-50 

A Fuzzy-Model-Based Gravity Center Adjustment and Inclination Control 

for Stair-climbing wheelchair, pp.3764–3769 

Wang, Dongxiao 

Gao, Xueshan 

DUAN, Xingguang 

Zhang, Weimin 

HUANG, Qiang 

Liu, Yun-Hui 

Beijing Inst. of Tech. (BIT) 






Balance control is a critical step to ensure tip-over stability for stairclimbing 

wheelchair. This paper presents a novel fuzzy method for adjusting 

the gravity center and controlling inclination of a stair-climbing 

wheelchair. The proposed fuzzy controller is designed based the kinematic 

relationship between the position of the gravity centre and the 

obliquity of the wheelchair in obstacle environments. It optimally compensates 

for changing of the gravity centre by adjusting obliquity of the 

wheelchair using a pair of actuators. To validate the proposed method, 

we have carried out simulations and experiments. The results demonstrate 

that the proposed method can efficiently compensate for changing 

of the gravity center to prevent the overturn. 

◁ PFrC-51 

Autonomic Mission Planning for Lunar Rovers in Complex Environment, 

pp.3782–3787 

Xu, Hongxia 


Autonomic mission-planning is important to lunar rover because it can 

improve the reliability and efficiency of science exploration. The complex 

environment of rovers makes the planned results are affected by 

many factors. A practical mission sequence could not be made without 

considering these comprehensive factors. Aimed at the practical need 

141


of mission-planning for lunar rovers, a planning domain modeling technology 

has been put forward, taking account of lunar terrain traversabilities, 

earth communication opportunities, solar energy requirements, 

temperature limits and mechanical interventions. When rovers traverse 

on the moon, a dynamic searching algorithm called SD*Lite is developed. 

All way stations along the path from start station to ending station 

and working mode sequence at each way station can be gained based 

on the searching algorithm. 

◁ PFrC-52 

A Cognizing Topology Map Building in A Wide-Range Indoor Structural 

Environment, pp.3794–3799 

Wu, Hao 

Tian, Guohui 

Zhou, Fengyu 



Univ. of Shandong 

When the exploration scope of the robot is expanded to large regions, 

the problem becomes more prominent. In this paper, artificial labels 

are used to provide semantic concepts and relations of the surrounding, 

which can solve the complexity and limitations of semantic recognition 

and scene understanding only with robot’s vision. Imitating s- 

patial cognizing mechanism of human, the robot constantly receives 

the information of artificial labels at cognitive-guide points in a wide 

range of structured environment to achieve the perception of the environment 

and robot navigation. Immune network algorithm is used to 

form the environmental awareness mechanism with ”distributed representation”. 

Color recognition and SIFT feature matching algorithm are 

fused to achieve the memory and cognition of scenario tag. Then the 

cognition-guide-action based cognizing topology map is built. Along 

with the continuously abundant map, the robot need not rely on the artificial 

label forever, and it can plan path and navigate freely. Experiment 

results show that the artificial label designed in this paper can improve 

the cognitive ability of the robot, navigate the robot in the case of indoor 

structural environment, and build the cognizing topology map favorably. 

◁ PFrC-53 

Design and Analysis of A Light Wall-Climbing Robot, pp.3805–3809 

Ke, Zibo 

Zhou, Yulan 

Yu, Zhiqiang 

Yantai Univ. 

Yantai Univ. 

Univ. of Yantai 

The paper is to design a light wall-climbing robot,using vacuum adsorption 

and quadruped robot as method, analyzing the principles of 

the pneumatic control and making specific design. Then, a mechanism 

design to simulate the movement of leg is presented in this paper 

,researching the effects of the parameters, and choosing appropriate 

parameters to design leg mechanism. In conclusion, simulation and 

analysis are made based on Pro/E. 

◁ PFrC-54 

A Novel Semi-Autonomous Throwbot for Reconnaissance Application, 

pp.3822–3827 

Li, Yue 


In this paper, a miniature reconnaissance robot with the functions of 

anti-impact and semi-autonomous motion is presented. The throwbot 

could be thrown or shot in the building by the soldier for real-time detection 

in anti-terrorist scene. A prototype has been developed to illustrate 

the concept. The finite element analysis and thrown experiments 

confirm the anti-impact ability of the mechanism. Besides working in 

telecontrol mode, the robot could explore regional area autonomously, 

which simplifies the teleoperator’s operation. The experiments show 

that the behaviour-based intelligent control strategy is fit for the robot’ 

s application at last. 

◁ PFrC-55 

The Design of a New Upper Limb Rehabilitation Robot System Based 

on Multi-source Data Fusion, pp.3840–3845 

Meng, Fancheng 


The aim of the paper is to develop a upper limb rehabilitation robot 

system (DPRs) capable of making both patient assessment and robotaided 

assessment. Using the rehabilitation robot system, patients can 

repeatedly train in high efficiency and can replicate many of the human 

training. In the paper ,First a diamond rehabilitation robot(DR)was desigend 

and its model was also given .Then an evaluation unit to improve 

patient assessment based on multi-source data fusion tecnology was 

proposed, the evaluation unit mainly was used to yeild a qualititative 

value based on the quantitative analytical models ,and then according 

to the qualititative value of the performance evaluation and active index, 

a resistance/assistance control strategy was designed to realize 

rehabilitation training more effectively. Finally the rehabilitation robot’ 

s control system was simulated and analyzed.Experiments showed that 

the DPRs purposed is helpful for the disabled patients and it can coordinate 

and train the patient’s upper limb comfortingly and safely,and 

also has dynamic performance and high flexibility. 

◁ PFrC-56 

Determining the Feasible Set of Motion States in Target Tracking Using 

Extended Set-Membership Filter, pp.3876–3880 

Qiu, Quan Beijing Research Center of Intelligent Equipment 


Zheng, Wengang 

Han, Jianda 

Beijing Research Center of Intelligent Equipment 



This paper presents a new object recognition strategy for target tracking 

using extended set-membership filter (ESMF). Before the target recognition 

algorithms are applied on the sensory information, ESMF is employed 

to determine the demarcation of a guaranteed searching area in 

which the true position of the target can be certainly found. Then the 

target recognition algorithms will work in a smaller region rather than 

searching in the whole range of the sensory information. The new s- 

trategy can improve the speed and accuracy of the target recognition 

algorithms. Experiments are conducted to prove the feasibility of the 

strategy. 

◁ PFrC-57 

Research on Dive Plane Trajectory Tracking Control Method of AUV 

Under Current Disturbance, pp.3887–3891 

Yan, Zheping 

Deng, Chao 

Zhou, Jiajia 

Zhao, Yufei 





In order to realize the dive plane trajectory tracking control for AUV under 

the current disturbance, state-dependent riccati equation algrithom 

is presented for trajectory tracking. When AUV far away from trajectory 

point, driving pitch angle same as line-of-sight angle, when AUV nearby 

the trajectory point, make the velocity vector of AUV same as the trajectory 

tangent vector. In order to realize the rudder saturated constraints 

problem, introducing the hyperbolic tangent S type function, equivalent 

substitution rudder Angle variables. Through the pseudo-linearization 

of dynamic equations, and calculation of Algebraic Riccati equation, 

the nonlinear feedback control law is obtained. Avoid the errors caused 

by the traditional processing of linearization. The method is simple in 

structure, convenient design. The simulation results show that the proposed 

controllers can effectively overcome current disturbance, realize 

longitudinal plane trajectory tracking control of AUV. 

◁ PFrC-58 

Damping Control of Rehabilitation Robot for Upper Limbs, pp.3898– 

3902 

Li, Qingling 

Song, Yu 



Active motions of upper limb is an absolutely necessary and effective 

means for paralysis patients recovery. This paper describes a force 

based active rehabilitation method supplied by an wearable exoskeletal 

rehabilitation robot. In order to predict motion intention of patients accurately, 

static model of the robot is built. Random Sample Consensus is 

introduced to calibrate static parameters. On this basis, terminal force 

control method based impedance control theory is study for the robot 

motions. Experimental results show that the proposed active damping 

142



rehabilitation strategy can be applied to patients after stoke at different 

recovery stage. 

◁ PFrC-59 

Human-machine Interfaces based on Electromyography and Kinectapplied 

to Teleoperation of a Mobile Humanoid Robot, pp.3903–3908 

Wang, Baocheng 


Xie, Qing 



the Ruijin Hospital, Shanghai Jiao Tong Univ. 

Human-robot control interfaces have received increased attention during 

the past decades.With the introduction of robots in everyday life. 

In this paper, a novel Human- Machine Interface (HMI) is developed, 

which contains two components: one is based on the surface electromyography 

(sEMG), which is from muscles of the human upper limb 

and the other is based on the Microsoft Kinect sensor. Two major features 

of such interface are their relatively simple data acquisition and 

processing systems, which need just a few hardware and software resources. 

The proposed interface allows the user to control in real time 

an mobile humanoid robot arm in 3-D space, using upper limb motion 

estimation based only on sEMG recordings and the Kinect input. The 

efficiency of the method is assessed through real-time experiments, including 

random arm motions in the 3-D space with variable hand speed 

profiles. 

◁ PFrC-60 

The development of an Exoskeleton Robot forCo-Manipulation of Human 

Upper Limb Movement, pp.3909–3914 

Ye, WenJun 

School of Naval Architecture, Ocean & Civil 

Engineering 


Xie, Qing 


Ruijin Hospital Luwan branch 

In this paper, an exoskeketon robotic device is developed mainly for 

support and rehabilitation, as well as improvement of human capabilities. 

In order to enhance their ability to finely interact with a human 

limb, it is necessary to develop a control interface for exoskeletons, 

which would guarantee comfort and safety, as well as efficiency and robustness. 

The developed exoskeleton robot utilizes electromyographic 

(EMG) signals from muscles of the upper limb to estimate human arm 

motion, and robust motion control is proposed to deal with the dynamics 

of robot and human upper limb and fulfill the co-manipulation of 

human-robot. The extensive experiments are conducted to validate the 

effectiveness of system. 

◁ PFrC-61 

Distributed Control for Mobile Robots Using PSD Sensors, pp.3943– 

3947 

Ge, Bin 

Zou, Xuan 

Yasuda, Gen Ichi 

Dalian Univ. 

Dalian Univ. 

Nagasaki Inst. of Applied Sci. 

This paper presents an experimental study of environment recognition 

and movement control of an autonomous two-wheeled mobile robot using 

infrared range finding sensors. Multiple PSD sensors are arranged 

to detect obstacles and walls in the surroundings of the robot, and environment 

features are estimated using a set of range sensors data. The 

experimental mobile robot uses incremental encoders mounted on the 

wheels to locate itself in a small area. Using these sensors, some intelligent 

behaviors, such as wall following and obstacle rounding, have 

been implemented. Upon these behavior modules, the behavior control 

system is constructed for sensory navigation without any environment 

map and global self-localization. The experimental results show that 

the mobile robot can carry out a navigation task, even when the robot 

knows little about the environment. 

◁ PFrC-62 

Research and Design of Expert System for Urban Transit Train Signal 

System, pp.3983–3986 

Zou, Hui 


This paper designs an expert system which can apply for analyzing on 

urban transit train signal system blocking. The system can be used for 

blocking and analyzing in various blocking signal systems. In the end, 

a urban rail transit has been designed by the system, and the results 

verify that the system is effective 

◁ PFrC-63 

Distributed containment control of networked fractional-order systemswith 

multiple leaders, pp.3987–3992 

Liu, Xueliang 

Xu, Bugong 

SCUT 


This paper is concerned with a containment problem of networked 

fractional-order system with multiple leaders under a fixed directed interaction 

graph. Based on the neighbor rule, a distributed protocol is 

proposed in delayed communication channels. By employing the algebraic 

graph theory, matrix theory, Nyquist stability criterion and frequency 

domain method, it is analytically proved that the whole follower 

agents will flock to the convex hull which is formed by the leaders. 

Furthermore, a tight upper bound on the communication time-delay 

that can be tolerated in the dynamic network is obtained. As a special 

case, the interconnection topology under the undirected case is 

also discussed. Finally, some numerical examples with simulations are 

presented to demonstrate the effectiveness and correctness of the theoretical 

result. 

◁ PFrC-64 

Multiple UAVs Collision Avoidance Trajectory Coordination Using Distributed 

Receding Horizon Optimization , pp.3999–4003 

Peng, Hui 


To the problem of multiple UAVs collision avoidance trajectory coordination 

in the same aerial space, a distributed receding horizon optimization 

model was established. Then, a state prediction based trajectory 

coordination algorithm was presented. Several simulation results show 

that our method can bring a within-network collision avoidance capability 

for multiple UAVs, and multiple UAVs can coordinate with each other 

effectively on condition that there are communication link between two 

conflict UAVs. 

◁ PFrC-65 

Research on the Spatial Diffusion of Industrial Clusters based on WH 

Migration Model, pp.4016–4019 

Li, Munan 

South China Univ. ot Tech. 

Recently, the diffusion behavior of industrial clusters has become one of 

the vital important phenomenons of the clusters upgrade or regulation. 

In this paper, we referred one of the classic immigration model based 

on the theory of phase-transition, which was brought by Weidlich and 

Haag; and adjusted the WH immigration model to explain the diffusion 

of the clusters. Finally, we got a novel immigration model of enterprise 

agents whose conclusions’are consistent with the empirical analysis 

of the ceramic cluster in Foshan city. 

◁ PFrC-66 

Design and Research of Time-Varying Delay Teleoperation, pp.1666– 

1669 

Cui, Yan 

Ju, He-Hua 

Cao, Ping 

Dong, Guocheng 



INNER MONGOLIA MENGDA NEW ENERGY 

CHEMICAL INDUSTRY BASE DEVELOPMENT 

CO.,LTD 


this paper designs a new program for mobile robot teleoperation system 

based on time-varying delay. Then, based on the stability and traceability, 

external-gain method and two-way symmetrical PID feedback is 

introduced. Moreover, the stability of system is analyzed and verified 

by Passive Theory and Lyapunov Theorem. At last, experimental results 

show that the program can ensure the stability and improve the 

traceability effectively. 

◁ PFrC-67 

Simulation Study on Low Voltage Operation of DFIG During Grid Three 

Symmetric Short Circuit Fault, pp.1936–1941 

143


Wang, Ping 

Liang, Fujun 

Qi, Shengbiao 

Tianjin Univ. 

Tianjin Univ. 

Tianjin Univ. 

When grid occurs short circuit fault and voltage drops, because of coupling 

relationships between stator and rotor windings of doubly-fed generator, 

over-current is generated in stator and rotor windings. At the 

same time, power that grid-side converter absorbs from the grid is out of 

balance from the power that rotor-side converter outputs, which makes 

redundant energy accumulated in DC-link and leads to over-voltage 

and under-voltage produced. Over-current may damage rotor-side converter 

and makes wind turbine detach from grids, and DC-link capacitance 

will be break down for the over-voltage. Therefore, it is necessary 

to adopt protection circuit to realize the low voltage ride through of D- 

FIG system .In this paper, based on the established simulation model 

of DFIG, combination protection circuit composed of rotor-side Crowbar 

and DC-link protection circuit and it’s control strategy are researched 

during voltage drops for three symmetric short circuit fault of DFIG system, 

and simulated through Matlab/simulink software. 

◁ PFrC-68 

Finite-Time Stabilization for a Class of Distributed Parameter Systems, 

pp.1948–1952 

FU, Qin 


The concept of finite-time stability and stabilization for distributed parameter 

systems is introduced by the definition of finite-time stability for 

linear systems.The design of state feedback controllers and dynamic 

output feedback controllers is given for a class of distributed parameter 

systems.A sufficient condition is provided by using linear matrix inequality(LMI). 

When the feedback control laws are applied to the systems,the 

closed-loop systems are finite-time stable. 

◁ PFrC-69 

On initial alignment of large azimuth misalignment for SINS on the static 

base in Krein space, pp.1964–1968 

FENG, JIN 

YU, FEI 

ZHANG, PENG-Yu 

Zou, Meikui 





The error equation for the azimuth misalignment angle for SINS on the 

static base is nonlinear, and it’s an urgent problem to align it when the 

angle is large. This paper proposes a method for the initial alignment 

of large azimuth misalignment based on Krein space linear estimation. 

The sum quadratic constraint (SQC) for nonlinear system is obtained 

by using Lipschitz condition of nonlinearity. Thus proposed Krein space 

approach is used to tackle the estimation problem for a class of nonlinear 

cases. Using kalman filterof krein space robust properties, we 

establish a nonlinear measurement error equation in Krein space, and 

give a recursive formula for the measurement error estimation. The 

initial alignment of large azimuth misalignment is simulated Finally, a 

numerical example is given to demonstrate the effectiveness of the 

proposed approach.By the Kalman filter method in Krein space which 

shows that the result generated by the Kalman frlter method in Krein 

space has effective and superior. 

◁ PFrC-70 

Research on QFT Controller Design for LOS Stabilization System of 

Opto-electronic Load for UAV, pp.1982–1986 

Xu, Dexin 


HE, Kunpeng 


Wei, Yanhui 


Zhao, Jiangpeng Troops 92854 

In order to decrease affection of parameters uncertainty of line-of-sight 

stabilization system of opto-electronic load for UAV, quantitative feedback 

theory design method is adopted on the servo controller of line-ofsight 

stabilization system of opto-electronic load for UAV. The simulation 

result indicates that the performance of QFT controller is better than 

the traditional controller. QFT method can deal with the special problem 

effectively since parameters uncertainty probably decrease control 

performance, it has strong robustness and can solve the controller design 

problem with parameters uncertainty of line-of-sight stabilization 

system of opto-electronic load for UAV. 

◁ PFrC-71 

Chaos Phenomenon in the DC-DC Switching Converters, pp.2039– 

2043 

Zhang, Yumei 

Qin, Hong 

Qu, Yanhua 

Wu, Jianhua 





DC-DC switching converter is an important part of power electronics, 

belonging to strong nonlinear time-varying systems. In the course 

of their traditional research, which rarely involves the study of nonlinear 

phenomena. However, During switching converter is working state, 

it can produce a transient pulse containing high frequency harmonics 

that can emit electromagnetic interference, electromagnetic interference 

can cause pollution to the network and direct impact to other electrical 

appliances to work by conduction, radiation, all these phenomena 

indicates that the existence of chaos. Because chaos is a state as a 

result of instability, the uncertainty will lead the operation of the system 

to phenomena like Random, which affect the control performance of 

converter, even lead to not working, the study of chaos in the switching 

converter and its control methods which have important guiding significance 

in the correct designing and debugging switching converter will 

help us to avoid or eliminate this phenomenon in the design, so that the 

converter works in a stable cycle. 

◁ PFrC-72 

Adaptive Integral-Type Sliding Mode Control for a Class of Uncertain 


Sun, Li-Ying 


An integral-type sliding mode control method for a class of uncertain 

nonlinear systems in parameter feedback form is developed. The design 

procedure can be divided into two steps. Firstly, design a nonlinear 

integral-type sliding manifold for the uncertain system such that not only 

the sliding motion can occur on that manifold, but also the system’ 

s motion along the manifold is asymptotically stable. The integral-type 

sliding surface is constructed by adding an integral term into the linear 

sliding surface. The additional integral in the proposed sliding surface 

provides one more degree of freedom; Secondly, based on the Lyapunov 

stability theorem, design a control law, such that the system’s 

states are driven towards the manifold and stay there for all future time, 

regardless of uncertainties. 

◁ PFrC-73 

Exponential Synchronization of a Class of Chaotic Neural Network, 

pp.2194–2197 

Qiao, Zongmin 

Zhang, Pei 

Hefei Normal Univ. 

anhui Univ. 

Synchronization of chaotic system has become an important area of 

study. A chaotic system is a nonlinear deterministic system that possesses 

complex and unpredictable behaviour. Based on nonlinear state 

coupling, this paper derived a delay independent sufficient exponential 

synchronization condition based on the Lyapunov stability method and 

the Halanay inequality lemma. The proposed condition is less conservative 

than previously established ones in the literature. 

◁ PFrC-74 

Adaptive Observer Design for a Class of MIMO Nonlinear Systems, 

pp.2198–2203 

ZHAO, Lili 

Li, Ping 

LI, Xiuliang 




For a class of uniformly observable nonlinear multi-input-multi-output 

(MIMO) systems with unknown parameters in both state and output e- 

quations, an adaptive observer is designed in a constructive manner 

based on the techniques of high gain observer and adaptive estimation. 

The high gain adaptive observer for joint state and unknown pa- 

144



rameter estimation is conceptually simple. The new algorithm makes 

use of a time varying gain matrix for unknown parameter estimation, 

which simplifies the initialization and parameter tuning. In order to establish 

the global exponential convergence of the adaptive observer, 

a persistent excitation condition is required. Consequently, the global 

exponential convergence for simultaneous estimation of states and unknown 

parameters is formally established following a simple procedure. 

A numerical example is presented to illustrate the performance of this 

adaptive observer. 

◁ PFrC-75 

A hybrid genetic algorithm/fuzzy dynamic programming approach to 

two-machine flowshop problems, pp.2399–2402 

Zhang, Hong 

Li, Jun 

Zhang, Desheng 




Multistage flowshop problems are considered to be fuzzy optimization 

problems, whose objective is to minimize total completion time of the 

two-machine flowshop problem with fuzzy processing times and fuzzy 

makespan. A solution procedure consisting of a genetic algorithm and 

fuzzy dynamic programming is proposed to obtain a near-optimal solution 

for the fuzzy model. The main advantage of this approach lies 

in the GA’s capability to find the global optimum or quasi-optimums 

and the FDP’s high performance to get a local optimum. Finally, an 

illustrative example is given to evaluate performance and to clarify the 

effectiveness of the proposed solution procedure. 

◁ PFrC-76 

Parameters Optimization and Dynamic Characteristic Analysis of Maglev 

Spindle Control System, pp.2403–2406 

Deng, Liang 

Song, Fangzhen 

SONG, Bo 




On the basis of the maglev spindle structure design, the PID control 

system is designed and its mathematical model is established. The 

influence of the PID control parameters on the response performance 

of the system is studied by using the MATLAB SISO. The global optimization 

design of the PID control system is achieved by using SRO 

(Simulink Response Optimization) module, and the system response 

performance of the maglev spindle achieves the best. It provides a 

more efficient and fast method for the parameter selection of maglev 

spindle control system. 

◁ PFrC-77 

Simulation Design and Application of Attack-Defense System in the Sea 

Based on HLA, pp.2417–2420 


Chen, Jie 

Yan, Jiangang 


502unit,NAEI 


301unit,NAEI 

Institution department of command engineering 

502unit,NAEI 

Considering the attack-defense characters in the sea under the information 

circumstance, the formation air defense simulation system 

is designed in the framework of HLA based on Multi-Agent System. 

To simulation subjects of the whole system, the conception of 

semi-autonomous/ autonomous, aggregate/platform level Agent is introduced, 

which improves the fidelity, convenients for hereditability in 

the application, minifies the cost of design of large-scale distributed 

simulation system. 

◁ PFrC-78 

Study of An Improved Algorithm for Arterial Traffic Coordinated Control 

Considering The Effect of Turning-Traffic Flow, pp.2421–2425 

Liu, Shijie 

LIU, Xiao-He 

Liu, Jiuming 

Xie, Qiuyan 


First Aviation College of the Air Force in Xinyang, 

Henan Province 



Xu, Jian-min 


An improved algorithm for arterial traffic coordinated control was described, 

The algorithm used optimization of the maximum bandwidth 

for the upper target􀋈and used the optimization of the minimum 

delay of the turning traffic flow for the lower target, to get the best 

phase difference. A mixed integer linear programming was built based 

on MAXBAND method. And evaluation result using Vissim simulation 

shows improvements in average delay and stops with the improved algorithm 

for arterial traffic coordinated control. 

◁ PFrC-79 

Hybrid Load Forecasting Method Based on Fuzzy Support Vector Machine 

and Linear Extrapolation, pp.2431–2435 

Jiang, Xin 

Liu, Xiaohua 

Gao, Rong 

Ludong Univ. 

Ludong Univ. 

ludong Univ. 

For the load affected by many factors and near the far smaller feature, a 

hybrid load forecasting method based on fuzzy support vector machine 

and linear extrapolation is proposed. The similar day is selected by the 

integrated effects of meteorology and time, and the fuzzy membership 

of the corresponding training sample is obtained by normalized similarity. 

Using the fuzzy support vector machine to predict the maximum and 

minimum loads of the forecasting day, then the load is combined with 

the load curve trend obtained by the linear extrapolation based on the 

similar day. The simulation results show that the proposed method can 

improve the predicting accuracy. 

◁ PFrC-80 

Study on On-ramp Control Strategy of Urban Freeway Based on Fuzzy 


Wang, Shiming 

Xu, Jianmin 

Luo, Qiang 




The on-ramp control strategies are classified after expounding mechanism 

of ramp control strategies of urban freeway. The applicability 

is summarized respectively based on advantages and disadvantages 

of each strategy. A new on-ramp control method of urban freeway is 

proposed by using the fuzzy control algorithm and the support vector 

machine algorithm, which can satisfy with demands which ramp control 

strategies should change with real-time changing of traffic condition. At 

last, some ramp control strategies are given by using control algorithm 

in this paper, based on the survey data of a main road in Guangdong 

province. The new control strategies and previous control strategies 

are applied to ramp control respectively, and new control strategies can 

satisfy dynamic changes of traffic condition by analyzing survey data, 

and have a better effect on the on-ramp control than old control strategy. 

◁ PFrC-81 

The Solution of The Fuzzy Volterra Integral Equations of The Second 

Kind, pp.2556–2561 

Gong, Huarong 

Yantai Engineering Tech. College 

Fuzzy mathematical analysis and integral characteristics and composite 

trapezoidal formulae were used to study the solution of the Fuzzy 

volterra integral equations of the second kind. First, -cut method of 

Fuzzy set was used to transform the fuzzy integral equations into the interval 

number equations, then the calculation rules of the interval numbers 

and the integral characteristics were used to transform the interval 

number equations into the interval number equations, then the calculation 

rules of the interval numbers and the integral characteristics were 

used to transform the interval number equations into the deterministic 

integral equations, at last, the analytical methods or composite trapezoidal 

formulae were utilized to solve the deterministic integral equations 

mentioned above, the given examples show the availability and 

the effectiveness of the methods introduced here. 

◁ PFrC-82 

Intelligence Fusion Based Control Strategy of Purifying System for 

Drinking Source Water, pp.2592–2596 

Wang, Jing 

Chongqing College of Electronic Engineering 

145


Bi, Jianchao 


To solve the fuzzy control rule being difficult to change with the status 

variation in water temperature and mixed solidification and so on, the 

paper presented a sort of fusion control strategy based on human intelligence 

simulated. It studied the purified process such as mixed solidification 

and so on, analyzed the impact factors of mixed solidification, 

summarized the cybernetics characteristic, and constructed the engineering 

control algorithm based on human intelligence simulated. And 

it took one order model with large lag and inertia as an example to make 

simulation respectively by the presented algorithm and PID, compared 

the response curve in control quality, anti-jamming, and robustness of 

parameter change, it indicated the former being quicker in response 

time, and higher in control precision. The simulation results show it is 

strong in adaptation and robustness to the controller constructed by the 

presented control strategy. 

◁ PFrC-83 

Sliding Mode Control based on Particle Filter for Position Tracking System, 

pp.2597–2600 

DAI, Wen-zhan 

HE, Li Ming 

Yang, Aiping 




Motor servo control system is of nonlinear, multivariable, strong coupling. 

A scheme of sliding mode control is proposed based on particle 

filter for position tracking system. Simulation results show that system’s 

tracking performance is much better than Kalman filter, and it can significantly 

improve the system dynamic performance and robustness. 

◁ PFrC-84 

The aircraft flutter model parametric identification based on frequency 

domain global optimization algorithm, pp.2611–2617 

Yao, Jie 


With regard to the aircraft flutter flight test stochastic models coexisting 

input and output observation noise, this paper deduces the simplified 

form of the maximum likelihood cost function about the stochastic model 

by virtue of the frequency domain maximum likelihood estimation 

principle. Then a global optimization iterative convolution smoothing i- 

dentification method is derived to significantly reduce the possibility of 

convergence to a local minimum and weakly dependent of the starting 

values’choice by using the global optimization theory. The identification 

method modifies the iterative method with a stochastic perturbation 

term and guarantees the algorithm converge to a global minimum. The 

simulation with real flight test data shows the efficiency of the algorithm. 

◁ PFrC-85 

PID Controller Parameters Optimization for the Main Steam Temperature 

System Based on Immune Algorithm-Particle Swarm Optimization, 

pp.2618–2623 

Yuan, GuiLi 

Zhu, Lei 



In this paper, PID control model was set up for the main steam temperature 

control system of modern large-scale power plant, combined 

the immune algorithm and particle swarm optimization, and improved 

by adding adaptive crossover and adaptive mutation. It is optimized 

the main steam temperature control system which in typical load conditions 

with the improved algorithm. By comparison with the traditional 

genetic algorithm shows, this algorithm has certain advantages on convergence 

and robustness 

◁ PFrC-86 

Research on sampling period of discrete variable structure controller 

Based on the Two-wheeled self-balanced Vehicle, pp.2653–2656 


Liu, Xin-wen 

Li, Jing 

Li, Da-yu 

SU, Yongzhen 



Zhengzhou Lighting Management Bureau 


air defense Acad. 

Two-wheeled self-balanced becomes a typical device to test various 

control method because of its unique characteristics. The sliding mode 

of VSC (Variable Structure Control) has strong robustness, so it is 

applied to the two-wheeled self-balancing vehicle would be a great advantage 

,but there is few research on it. To realize the computer control 

based on the discrete variable structure controller, we have to research 

the sampling period. In this paper, we give a method to find the longest 

sampling period of VSC based on two-wheeled self-balanced Vehicle. 

◁ PFrC-87 

One Nearspace Hypersonic Aircraft Dynamic Surface Adative Backstepping 

Control Design Based on Nonlinear Neumatic/ Thrust Vectoring 

Composite Control, pp.2669–2673 

Dai, Shaowu department of automatic control engineering 

301unit,NAEI 

Chen, Jie 

Song, Chao 


301unit,NAEI 

School of Automation Sci. & Electrical Engineering, 

Beijing Univ. of Aeronautics & Astronautics, 

One nearspace hypersonic aircraft model based on pneumatic/ thrust 

vectoring composite control was built, compound controler optimization 

scheme was adopted to cooperate pneumatic/thrust vectoring compound 

controler equivalent dynamic characteristic to reference model 

during dynamic assign.In the course of adaptive backstepping design, 

the dissertation employes the dynamic surface control strategy to eliminate 

the explosion of terms by introducing a series of first order filters to 

obtain the differentiation of the virtual control inputs. Lyapunov stability 

theorem guarantees the error uniformly bound. Both theory analysis 

and 6 DOf simulation verification show the simpleness and arithmetic 

availability. 

◁ PFrC-88 

The Control Theory of Permanent Magnet Synchronous Motor Based 

on Anti-windup Control, pp.2801–2805 

Ma, Jingcong 

Zheng, Yang 

Dalian Maritime Univ., China 


This article reviews and compares several aspects of permanent magnet 

synchronous motor speed control method of the anti-windup. In 

generally, motor control aspects of the state equation is modeled as 

linear systems in real-time control systems, at the same time the system 

will generate much higher than normal current and voltage values.Therefore, 

PI control is likely to have links to over-amplification of 

the control value, This phenomenon is called over-saturation of the motor, 

The existence of this phenomenon will cause the system to nonnormal 

operation, causing system instability. So we will see in the article, 

several anti-saturation control algorithms, they will be in permanent 

magnet synchronous motor sensorless vector control algorithm and 

control algorithm for anti-saturation control and the speed controller in 

its use, to reduce saturated part of the motor speed, and the comparison 

of each other. 

◁ PFrC-89 

Two-Degree-of-Freedom Control Scheme for Cascade Control Systems, 

pp.2812–2816 

Zhang, Jinggang 

Zhao, Zhicheng 


taiyuan Univ. of Sci. & Tech. 

A new two-degree-of-freedom control structure is proposed for cascade 

control systems. Analytical method of controller design is derived for 

the general process model. Its dominant merits are that the primary 

deficiency of tuning trouble occurring in a conventional cascade control 

system is essentially overcome. With this structure, the set-point tracking 

and disturbance rejection property of primary and secondary loop 

are no longer coupled. Meanwhile, the approximation for the closedloop 

transfer function of the secondary loop is no longer necessary 

in the design of primary loop controller, so the system control performance 

is improved. The simulation results demonstrate that the proposed 

method is superior to the existing methods. 

◁ PFrC-90 

Research on the controller of Two-wheeled self-balanced Vehicle 

146



Based on the sensitivity analysis, pp.2833–2837 


Li, Yanzhao 

Liu, Xin-wen 

Xie, FangMing 

Wang, Qiang 


China Nuclear Power Engineering 

Co.,Ltd.,Zhengzhou Branch 




The two-wheeled self-balancing vehicle is a typical complex system 

with multi-variable, nonlinear and strong coupling, parameter uncertainty 

and so on. Therefore, the two-wheeled vehicle is an ideal platform for 

testing and learning various control methods. And system sensitivity is 

a theory for studying the effect with uncertainty parameters to system 

performance. Use the theory, this paper optimizes the important parameter 

of the vehicle and analyses the LQR controller .Simulation and 

experimental results show that the controller based on sensitivity analysis 

is robust. 

◁ PFrC-91 

Modified Backstepping Control for Time-Delay System of SVC , 

pp.2861–2864 

Zhang, Rui 

Sun, Li-Ying 

Zhang, Bo-Qi 




A new modified backstepping approach was suggested for time-delay 

system of static var compensator (SVC). The transient stability problem 

is studied for a single machine-infinite bus system. In the recursive 

design procedure of the feedback control law, the class-k functions 

are introduced. The method preserves useful nonlinearities, improves 

the transient response of closed-loop system, and effectively 

resolves the time-delay problem of response process of static var compensator. 

Simulation results of MATLAB expatiate in further verification, 

that the actual sample simulated shows the effectiveness of the 

proposed method. 

◁ PFrC-92 

Recursive identification of parameters in the minimum variance control, 

pp.2870–2877 

Yao, Jie 


This paper focus on the parameter recursive identification problems in 

minimum variance control system from the perspective of identification. 

Consider the unknown parameter vector of the ARMAX model in the 

minimum variance closed loop control, we propose multi-innovation recursive 

least-squares identification method and separable iterative recursive 

least-squares identification method to identify and estimate the 

unknown parameters vector in the ARMAX model on line. When excited 

by the white noise, the two identification methods will give the unbiased 

estimation about the unknown parameter vector. When excited by the 

color noise, only the separable iterative recursive least-squares identification 

method can give the unbiased estimation 

◁ PFrC-93 

The Predictive Control for Nonlinear systems Based on Dynamic Approximate 

Hammerstein model, pp.2887–2890 

Hu, Zhiqiang 


The generalized predictive control algorithm, it’ s parameter adaptive 

recursive predicting is presented for nonlinear discrete-time systems 

with heavy delays based on dynamic approximate Hammerstein 

model nonlinearization increment minimized recursive predicting model 

method. generalized predictive control is employed for nonlinear systems 

with heavy delays. the simulation result for several typical nonlinear 

systems are given to demonstrate the correctness and effectiveness 

of approach proposed. 

◁ PFrC-94 

Fault Diagnosis of Underwater Vehicle with Neural Network, pp.2931– 

2934 

WANG, Jianguo 


To aim at the character that the uncertainties of the complex system of 

underwater vehicle (UV) bring to model the system difficult, a wavelet 

neural network (WNN) is proposed to construct the motion model of 

UV. The adjustment of the scale factor and shift factor of wavelet and 

weights of WNN is studied. The WNN has the ability not only to approach 

the whole figure of a function but also to catch detail changes of 

the function, which makes the approaching effect preferably. Residuals 

are achieved by comparing the output of WNN with the sensor output. 

Fault detection rules are distilled from the residuals to execute thruster 

fault diagnosis. The feasibility of the method presented is validated by 

simulation experiment and sea trial results. 

◁ PFrC-95 

Design of FDI system for the nonlinear Control System, pp.2935–2938 

Zhou, Jing 

Zhu, Xun 

Jianghan Univ. 

jianghan Univ. 

Contrary to the parameter-depended uncertainty system, the order of 

the linear matrix inequalities is reduced, then the RPRO fault detection 

and fault isolated filters are constructed. Then a RPRO fault isolation 

filter is designed for occurrence of both actuator fault and sensor fault 

in the aerocraft’s closed-loop control system, and fault diagnosis system 

is structured based on the fault isolation filters. Through the output 

of the fault diagnosis system, we can alarm the fault timely and the 

advantages of this approach are highlighted. 

147


Saturday, July 7, 2012 

PL-4 8:30-9:30 Room 305 



National University of Singapore 

◮ PL-4 8:30-9:30 


Huang, Jie 


In this talk, we will describe the cooperative output regulation problem 

for multi-agent systems. The formulation of this problem generalizes 

the leader-following consensus problem in the sense that the problem 

simultaneously addresses asymptotic tracking and disturbance rejection, 

accounts for large model uncertainty, and accommodates a general 

leader system. Like the full information output regulation problem, 

this problem can be handled by the feedforward control approach and 

the internal model approach. These two approaches can also be used 

to handle other control problems of multi-agent systems such as formation, 

rendezvous, flocking. We will also briefly overview some recent 

results on this problem, and point out some possible future research 

topics on this problem. 

PL-5 9:50-11:50 Room 305 


Chair: Tarn, Tzyh-Jong 

Washington University, USA 

◮ PL-5 9:50-11:50 


Chua, Leon O. 


Although the memristor was first postulated axiomatically in 1971, and 

although a proof of principle had been published via a bulky circuit using 

operational amplifiers, it was the hp publication of a nano memristor in 

the 1 May 2008 issue of Nature that had triggered worldwide attention, 

with the number of publications on memristor applications exploding at 

an exponential rate. This 2-hour lecture presents the circuit and system 

theoretic concept of the memristor and shows why it had generated so 

much unprecedented interest in both academia and industry. Among 

other things, it will illustrate why memristors will replace flash memories 

and DRAMs in the near future, and why brains are made of memristors. 

It will also show how action potentials (spikes) from the Hodgkin-Huxley 

axon model are generated by the sub-critical Hopf bifurcation nonlinear 

dynamics of the sodium and potassium memristor Hodgkin-Huxley neuron 

model operating near the edge of chaos. 

SaA01 13:30–15:30 Room 203A 

Networked Control 

Chair: Wang, Wei 

Co-Chair: Zhai, Feng 



◮ SaA01-1 13:30–13:50 

The Optimization Method for a Kind of Uncertain Networked Systems 

Based on the Stripping Principle and Optimal Control, pp.964–969 

Wang, Wei 


In this paper, based on using the stripping principle and optimal control, 

we consider the method of optimization for a kind of uncertain networked 

systems which are composed of some heteromorphic subsystems. 

The method can be summarized as follows. Facing the complex 

structure of networked control system and the unavoidable uncertainties 

and/or disturbances, we use the integral feedback with a variable 

gain for each of the subsystems to remove or eliminate the interconnected 

parts, uncertainties and disturbances. So we translate the control 

problem of networked systems into that of decentralized control. 

And then we use optimal control for the remainder parts of the subsystems 

respectively. Then the fastness of the networked systems can be 

improved. The theoretical analysis and simulation results are provided 

to indicate the effectiveness of the proposed approach. 

◮ SaA01-2 13:50–14:10 

Uplink Dead Zone Analysis for Co-Channel Two-Tier Femtocell Network, 

pp.991–995 

Han, Qiaoni 

Guan, Xinping 

Ma, Kai 

Liu, Zhixin 

Yanshan Univ. 

Yanshan Univ. 

Yanshan Univ. 

Inst. of Electrical Engineering,Yanshan Univ. 

This paper is concerned with the dead zone problem of uplink communication 

in two-tier femtocell network. In the uplink communication, 

the interference generated by a macrocell user at the edge of the 

macrocell base station (MBS) coverage area may cause the signal-tointerference-plus-noise 

ratio (SINR) received at the nearby femtocell 

base station (FBS) below the requirement, and the region in which the 

nearby FBS can not meet its SINR requirement is called a Femtocell 

Dead Zone (FDZ). In order to guarantee the quality-of-service (QoS) 

of macrocell users and femtocell users, the transmission power of femtocell 

users should be controlled properly. In this work, the uplink interference 

scenario that exists between a femtocell and a macrocell is 

modeled and discussed in detail, and then, the fundamental limit of the 

achievable SINR at MBS and FBS is shown. Numerical results present 

a straightforward way to establish the FDZ and show the impact of the 

uplink interference from macrocell user on the QoS of the nearby femtocell 

user. 

◮ SaA01-3 14:10–14:30 

Security Protection Technology of Intelligent Electricity Selling Terminal, 

pp.1005–1009 

Zhai, Feng 

Zhao, Bing 

Cen, Wei 




In order to solve the problem of power user’s difficulty for powerpurchase, 

intelligent electricity selling terminal is widely used in power 

selling system. Data’s secure transmission and storage is the bases for 

intelligent electricity selling terminal to implement its function, ensuring 

the security of data has become the key technologies to be resolved for 

intelligent electricity selling terminal. Based on the SIM1 data encryption 

technology, PKI digital certification authentication technology and 

SSL communication technology, this paper has proposed the realization 

mode of the data’s safety protection technology between intelligent 

electricity selling terminal and power selling master station system. 

◮ SaA01-4 14:30–14:50 

Improved delay-dependent stability criteria for networked control systems, 

pp.892–897 

Song, Yan 




This paper is concerned with robust H∞control of uncertain networked 

control systems with interval time-varying delay and packet dropout. 

For less conservative transformation, we propose a new integral inequality 

by dividing time interval into two subintervals and defining an 

appropriate scalar instead of introducing free weighting matrix. Based 

on this integral inequality, a new delay-dependent stability criteria for 

the systems are obtained by solving a convex domain. Moreover, we 

may obtain the feasible solutions by solving a minimization problem in 

terms of linear matrix inequalities. Finally numerical examples are used 

to illustrate the effectiveness and validity of the proposed methods. 

◮ SaA01-5 14:50–15:10 

Stability Analysis and Control of Bifurcation in a TCP Fluid Flow Model 

of Wireless Networks, pp.1026–1030 

Liu, Feng 

Wang, Hua O. 


China Univ. of GeoSci. 

Boston Univ. 


In this paper the bifurcation behavior of a TCP fluid flow model for Internet 

congestion control in Wireless Networks is investigated. These 

bifurcation behaviors may cause heavy oscillation of average queue 

length and induce network instability. An impulsive control method was 

proposed for controlling bifurcations in the Internet congestion control 

148


Book of Abstracts: Saturday Sessions 

system. Simulation results show that the nonlinear behavior of the system 

can be controlled by this method. 

◮ SaA01-6 15:10–15:30 

Stabilization of Wireless Networked Control Systems with Packet Loss 

and Impulse Disturbance, pp.1031–1036 

QU, Feng-Lin 




Yuan, Fushun 

Anyang Normal Univ. 

Zhan, Xisheng Huazhong Univ. of Sci. & Tech./Hubei Normal Univ. 

This paper considers the stabilization problem for the wireless networked 

control system(WNCS) with impulse disturbance in discretetime 

domain. The packet loss is modeled according to an independent 

and identically distributed(i.i.d) Bernoulli process. And the control input 

becomes zero when the data are lost. The necessary and sufficient 

conditions on the existence of stabilizing controllers are established. 

An iterative linear matrix inequality approach is employed to calculate 

the state-feedback gains. A numerical example is provided to illustrate 


SaA02 13:30–15:30 Room 203B 

Robust Control (I) 

Chair: Wang, Yijing School of Electrical Engineering & Automation 

Co-Chair: Sun, Weiwei 


◮ SaA02-1 13:30–13:50 

Robust H∞control of linear systems with intervalnon-differentiable 

time-varying delays, pp.1507–1512 

Emharuethai, Chanikan 




In this paper, robust H∞control for a class of linear systems with timevarying 

delay is studied. The time delay is a continuous function belonging 

to a given interval, which means that the lower and upper bounds 

for the timevarying delay are available, but the delay function is not necessarily 

differentiable. Based on Lyapunov-Krasovskii theory combined 

with Leibniz-Newton’s formula, new delay-dependent sufficient conditions 

for the exponential stabilization and a prescribed H∞performance 

level of the closed-loop system for all admissible uncertainties, are established 

in terms of LMIs. Numerical examples are given to illustrate 


◮ SaA02-2 13:50–14:10 

Robust stability for uncertain switched systems with interval timevarying 

delay, pp.1305–1310 

La-inchua, Teerapong 




This paper addresses the design of switching rule for robust stability of 

a class of uncertain switched system with delay. The system to be considered 

is autonomous and the state delay is time-varying. Using Lyapunov 

functional approach, restriction on the derivative of time-delay 

function is not required to design switching rule for the robust stability 

of switched systems with time-varying delays. The delay-dependent 

stability conditions are presented in terms of the solution of LMIs which 

can be solved by various available algorithms. 

◮ SaA02-3 14:10–14:30 

Robust Stability Analysis of Hamiltonian Systems with Time-Varying 

Delays and Uncertainties, pp.1832–1837 

Sun, Weiwei 

Wang, Pan 



This paper investigates the robust stability for time-delay Hamiltonian 

systems with polytypic uncertainties. Both delay-independent 

and delay-dependent criteria are established based on the dissipative 

structural properties of the Hamiltonian systems and the Lyapunov- 

Krasovskii (L-K) functional approach. All the proposed stability conditions 

are expressed in terms of Linear Matrix Inequality (LMI). Numerical 

examples demonstrate the validity of the new results proposed 

in this paper. 

◮ SaA02-4 14:30–14:50 

Robust Finite-Time Stochastic Stability Analysis and Control Synthesis 

of Uncertain Discrete-Time Markovian Jump Linear Systems, pp.1925– 

1929 

Zuo, Zhiqiang 

Li, Hongchao 

Wang, Yijing 

Liu, Yi 

Tianjin Univ. 

Tianjin Univ. 


Tianjin Univ. 

In this paper, the problems of finite-time stochastic stability analysis 

and controller synthesis of uncertain discrete-time Markovian jump linear 

systems are investigated. The uncertainties considered are that 

partial elements of the of transition probabilities are not available. By 

introducing the concept of finite-time stochastic stability for Markovian 

jump systems, a sufficient condition is proposed to guarantee that the 

state of the system does not exceed a certain bound in mean square 

sense during a fixed time interval. It is shown that the system which 

is not mean square stable may be finite-time stochastic stable and vice 

versa. For the controller synthesis case, mode-dependent state feedback 

controller can be developed based on the above stability analysis 

result. Some numerical examples are presented to illustrate the effectiveness 

of the proposed methods. 

◮ SaA02-5 14:50–15:10 

Robust Exponential Delay-Dependent Stability Criteria for Neutral Systems 

with Constant delay, pp.1698–1703 

Warakorn, Sudsanguan 

Rojsiraphisal, Thaned 

Burapha Univ. 

Faculty of Sci., Chiang Mai Univ. 

In this paper, exponential stability criteria of linear neutral systems with/ 

without uncertainties are investigated. By applying a change of variable, 

the Leibniz-Newton formula, integral inequalities and Lyapunov- 

Krasovskii functionals without adding free matrices, improved exponential 

stability-delay dependent criteria of the systems are obtained in the 

form of linear matrix inequality (LMI). At the end, three numerical examples 

are given to indicate that the results presented in this research are 

effective and better than some criteria of previous works. 

◮ SaA02-6 15:10–15:30 

Robust H ∞ Fuzzy Control for Nonlinear Discrete-Time Systems by 

Nonquadratic Lyapunov Function Approach, pp.1710–1715 

Horng, Wen-Ren 

Fang, Chun-Hsiung 

Kaohsiung Univ of Applied Sci. 

he serves as the President of National 

Kaohsiung Univ. of Applied Sci. 

In this paper, a new relaxed condition is proposed to deal with H-inf 

control for nonlinear discrete time systems that are represented by T- 

S fuzzy model. The main results are derived based on nonquadratic 

Lyapunov function and employed non-PDC controller. The new relaxed 

design conditions are expressed in terms of linear matrix inequalities, 

which can be efficiently solved by software. Finally, illustrative examples 

are given to show the performance of our approach. 

SaA03 13:30–15:30 Room 203C 

Adaptive Control and Learning Control 

Chair: Guo, Jing 

Co-Chair: Wei, Airong 



◮ SaA03-1 13:30–13:50 

Adaptive Control for Clutch Engagement on Starting up of Vehicle, 

pp.1230–1234 

Dong, Yuehong 

Jiao, Xiaohong 

Chen, Dongzhi 

Yanshan Univ. 

Yanshan Univ. 

Yanshan Univ. 

Control problem is investigated for dry clutch engagement process during 

a standing start in AMT vehicles. Based on the control-oriented simplified 

driveline dynamic model under consideration of parametric uncertainties 

of the driveline due to the mechanical wear and tear, the e- 

lastic deformation of shaft and so on, an adaptive tracking control strategy 

is examined paying particular attention to the engagement comfort 

while not deteriorating the slipping time by using the crankshaft speed 

and the clutch disk speed as state variables and the engine torque and 

the clutch torque as control variables. The resulting analytically derived 

adaptive controller guarantees the fast smooth engagement of 

149


the clutch and the engine stall avoidance. Simulation results show the 

good performance obtained with the proposed controller. 

◮ SaA03-2 13:50–14:10 

Adaptive dynamic friction compensation control of electrohydraulic servo 

system, pp.1682–1687 

Guo, Jing 

Yang, Jizhi 



Low-speed performance and tracking accuracy of simulator’s electrohydraulic 

servo system are effected by its nonlinear friction torque. 

Adaptive dynamic friction compensation control strategy with nonlinear 

observers of electrohydraulic servo system is proposed by building dynamic 

equations and LuGre friction model. The strategy can modify 

dynamic friction parameters constantly of servo system to compensate 

friction and improve low-speed performance by on-line adaptive rate. 

The effectiveness and superiority of the strategy are verified by comparing 

with feed-forward friction compensation control method. 

◮ SaA03-3 14:10–14:30 

Adaptive Simultaneous Stabilization of Two Port-Controlled Hamiltonian 

Systems Subject to Actuator Saturation, pp.1767–1772 

Wei, Airong 

Wang, Yuzhen 

Hu, Xiaoming 




This paper investigates the adaptive parallel simultaneous stabilization 

(APSS) of two multi-input nonlinear Port-Controlled Hamiltonian (PCH) 

systems subject to actuator saturation, and proposes a number of results 

on the design of the APSS controllers. Using both the dissipative 

Hamiltonian structural and saturated actuator properties, the two systems 

are combined to generate an augmented PCH system subject to 

actuator saturation, with which some results on the control designs are 

then obtained. Study of an illustrative example with simulations shows 

that the APSS controller obtained in this paper is effective. 

◮ SaA03-4 14:30–14:50 

The adaptive fuzzy PID control study of active vibration isolation system, 

pp.1120–1123 

Lin, Yan 

Liu, Wending 



With the development of space technology, the adverse effects to the 

spacecraft payload caused by the inevitable vibration from electromechanical 

system become increasingly prominent. To effectively reduce 

low frequency vibration, this paper presents a design scheme which 

takes voice coil motor servo system as active actuator of vibration isolation 

platform, and for the actual vibration isolation system, it is not 

possible to establish a precise mathematical model, so the combination 

of adaptive fuzzy control and PID control are applied to the active 

vibration isolation system based on voice coil motor. Simulation results 

show that the self-adaptive fuzzy PID control system has a good 

stability and anti-jamming capabilities, could isolate external vibration 

interference in a very wide frequency range, provides new and reasonable 

control ideas for the complex conditions and high index of control 

systems. 

◮ SaA03-5 14:50–15:10 

Iterative learning control for linear switched systems with arbitrary 

switched rules, pp.1182–1187 

Bu, Xuhui 

Yu, Fashan 





In this paper, the problem of iterative learning control for a class of linear 

discrete-time switched systems with arbitrary switched rules is considered. 

It is assume that the considered switched systems are operated 

during a finite time interval repetitively, and then the iterative learning 

control scheme can be introduced. It is also shown that under some 

given conditions, the D-type iterative learning control law can guarantee 

the asymptotic convergence of the output error between the desired 

output and the actual output for the entire time interval through the iterative 

learning process. An example is given to illustrate the effectiveness 


◮ SaA03-6 15:10–15:30 

Iterative Learning Control Based on Modified Steepest Descent Control 

For OutputTracking of Nonlinear Non-minimum PhaseSystems, 

pp.1361–1366 

Naiborhu, Janson 

Institut Teknologi Bandung 

Iterative learning control (ILC) refers to a class of self-tuning controllers 

where the system performance of a specified task is gradually improved 

or perfected based on the previous performance of identical tasks. In 

this paper, based on the modified steepest descent control we proposed 

the iterative learning control algorithm for nonlinear nonminimum 

phase system. By applying the modified steepest descent control we 

have the extended system with relative degree greater one than original 

systems. By extending result of Gosh, cs [1], the convergence of 

algorithm is guaranteed. 

SaA04 13:30–15:30 Room 203D 

Systems Engineering and Engineering Optimization 

Chair: Jia, Qing-Shan 

Co-Chair: Bai, Danyu 



◮ SaA04-1 13:30–13:50 

New Approximation Algorithms for Two-machine Flow Shop Total Completion 

Time Problem, pp.2388–2392 

Bai, Danyu 


This paper considers the two-machine flow shop scheduling problem to 

minimize the sum of completion times. We design two heuristic algorithms, 

Triangle Shortest Pro-cessing Time first (T-SPT) and Dynamic 

Triangle Shortest Processing Time first (DT-SPT), for F2——ΣCj and 

F2—rj—ΣCj respectively. Moreover, for the further evaluation of the 

heuristics numerically, two new lower bounds with performance guarantee 

are provided for F2——ΣCj and F2—rj—ΣCj, respectively. At 

the end of the paper, a series of simulation experiments are conducted 

to show the effectiveness of the new heuristics. 

◮ SaA04-2 13:50–14:10 

Optimal Scheduling of Storage Devices for Building Energy Saving, 

pp.2393–2398 

Xu, Zhanbo 

Jia, Qing-Shan 


Wu, Jiang 

Wang, Dai 

Chen, Siyun 







An intelligent building energy system which has power grid, autonomous 

generators, renewable energy resources, storage devices, 

and controllable loads has been proposed. In this paper, we consider 

an allocation and scheduling problem of electrical and thermal storage 

devices together with other devices in building energy system to 

minimize the overall cost while satisfying the occupants demand. The 

above problem is formulated as mixed integer programming problem. 

The CPLEX solver is used to solve the proposed problem. By the proposed 

method, we can fix the types of storage devices and allocate the 

capacity of the fixed storage devices with demand profiles and their investment 

cost. The analysis of storage devices is demonstrated using 

numerical cases. 

◮ SaA04-3 14:10–14:30 

Optimal DTC Strategy of PMSM in Electric Vehicle, pp.2447–2451 

Zhai, Li 


The optimal strategies for torque ripple restraint of direct torque control(DTC) 

strategy of Permanent magnet synchronous motor(PMSM) in 

low-speed of electric vehicle are studied in the paper. The subdivision 

of voltage vector control strategy and seven-stage hysteresis comparator 

control strategy are proposed to restrain torque ripple. The optimal 

DTC strategies models for PMSM are established and simulated in the 

MATLAB/Simulink. The simulation results are gained to verify effectiveness 

of the optimal strategies 

150



◮ SaA04-4 14:30–14:50 

An evacuation algorithm for large buildings, pp.2497–2502 


Liu, Huan 

Fudan Univ. 

Fudan Univ. 

Evacuation in an emergency is always an important issue for large 

buildings. The purpose of evacuation is to evacuate people in the shortest 

possible time from a dangerous area to a safe place. In order to 

complete the evacuation mission effectively, an effective algorithm is 

needed. The proposed evacuation algorithm in this paper made a reasonable 

model for the building and then starting from a single evacuee, 

using recursive methods to get an optimal evacuation plan. This algorithm 

overcomes the limitation of fast flow control algorithm which can’ 

t be applied in a multi-SN situation. Besides, because of the low complexity 

of the algorithm, it can be used in real-time environments. 

◮ SaA04-5 14:50–15:10 

A Gradient Information Based Real Time Pricing Mechanism for Microgrid 

in Energy Intensive Enterprise, pp.2473–2478 

Wang, Zhaojie 

Gao, Feng 

Zhai, Qiaozhu 

Wu, Jiang 


Zhang, Hao 

Liu, Kun 

Pan, Li 

Dong, Mingyu 

xi’an jiaotong Univ. 









In energy intensive enterprises (EIE) with self generation power plant, 

real time electricity cost is affected by dynamic change of its power 

consumption and generation. Centralized power demand control is 

usually hard to be used in EIE due to complex constraints of production 

process. A rational price signal which is able to lead end users to reduce 

power during peak hours or shift load from peak hours to off-peak 

hours is valuable. A dynamic pricing mechanism in microgrid for EIE 

is researched in this paper, aiming at reducing total electricity cost of 

EIE. By typical case study, some reference ideas about dynamic pricing 

problem for EIE microgrid are presented. 

◮ SaA04-6 15:10–15:30 

Online Strategy for Scheduling A Hydroelectric Station, pp.2479–2484 

Ru, Hai 

Gao, Feng 


Zheng, Fei Feng 





The scheduling problem of reservoir hydroelectric station during the 

flood season caused major concern in academia and industry many 

years. Due to the uncertainty of flood, the schedule strategy should 

guarantee the enough flood prevention capacity of hydroelectric, meanwhile, 

a certain water head should also be kept in order to guarantee 

the power generation efficiency. Therefore, it’s a challenging to design 

the strategy of optimal scheduling for hydroelectric in flood season. 

This paper in response to this objective, online algorithm will be 

applied here to analysis optimal strategy, which manages the reservoir 

scheduling in terms of online strategy and competitive analysis. Then 

the evidence from Ankang reservoir are collected to test the competitive 

ratio of online strategy. The result and model of the study in this 

paper have guiding significance and reference value to decision makers 

facing the similar situation. 

SaA05 13:30–15:30 Room 203E 

Control Engineering (I) 

Chair: Hua, Changchun 

Co-Chair: Wang, Fei 

yanshan Univ. 

Jilin Univ. 

◮ SaA05-1 13:30–13:50 

PD Control for Teleoperation System with Delayed and Quantized Communication 

Channel, pp.2318–2323 

Yang, Xian 


Inst. of Electrical Engineering, Yanshan Univ. 

yanshan Univ. 

Yan, Jing 

Guan, Xinping 

Yanshan Univ. 


This paper is concerned with the problem of teleoperation systems over 

networks. As is well known, quantization and time delay are unavoidable 

in computer network communications. Compared with previous 

work, we consider both of them when analyzing such systems. The 

controller used in this paper is proportional-derivative(PD) controller, 

but the output signals of master and slave systems are quantized before 

being communicated. New criteria are presented to show that the 

controller can stabilize the master-slave system under variable time delay 

and quantization. Finally, simulations are given to demonstrate the 

effectiveness of our results. 

◮ SaA05-2 13:50–14:10 

LQR Control for a Self-balancing Unicycle robot, pp.1424–1429 

Gong, Daoxiong 

Pan, Qi 

Zuo, Guoyu 

Deng, Wenbo 





A self-balancing unicycle robot, which has a wheel for balancing and 

movement in the longitudinal plane (pitch angle) as well as a flywheel 

for balancing in the lateral plane (roll angle), is studied in this paper. 

The non-linear dynamic equations of the unicycle robot on a slope are 

analyzed using the Lagrangian dynamic formulation, then a linear model 

of the robot is derived at the equilibrium point, and 3 linear quadratic 

regulators (LQR) are designed to control the robot on slopes with the 

angle of inclination varying from -11°to 11°. Simulation results validated 

that the unicycle robot can achieve good performance both on 

level plane and on slope. 

◮ SaA05-3 14:10–14:30 

Modeling and Control for an In-plane Morphing Wing, pp.1430–1435 

Shi, Rongqi 

Song, Jianmei 



System modeling and controller design for an in-plane morphing wing, 

which is proposed by NextGen Aeronautics, Inc., are investigated in the 

present paper. The model of the morphing wing is developed based on 

Lagrange method. The elastic forces of the skin and the aerodynamic 

load acting on the leading edge are both taken into account. The generalized 

forces are obtained by virtual work principle. Since the morphing 

wing is a strongly coupled, over-actuated and nonlinear system with 

multi-input and multi-output, the control system design includes a control 

allocator, a dynamic inversion controller and two PID controllers. 

The control allocator is designed base on pseudo inverse method; the 

dynamic inversion controller is applied to make the original system decoupled 

into two independent linear systems by proper nonlinear feedback 

transformation; two classical PID controllers are adopted for the 

linearlized systems. The correctness of the model is verified by ADAMS 

simulation and the effectiveness of the control system is tested by Matlab. 

The results indicate that, integrating Lagrange equation, pseudo 

inverse control allocation, dynamic inversion control and classical PID 

method, is an effective way to solve problems of modeling and control 

for morphing wings. 

◮ SaA05-4 14:30–14:50 

Dynamics Modeling and Real-time Fault-tolerant Control of A Rotor 

Aerial Robot, pp.1144–1149 

Hou, Xuyang 




The present micro aerial vehicle (MAV) is lack of maneuverability and 

security when operates in the restricted environment. To solve such 

problem, a rotor powered disk-type autonomous aircraft–Micro Aerial 

Robot(MAR) was designed, which adopts built-in blades and redundant 

actuators structure to enhance vehicle’s safety and robustness 

,and adopts the inner and outer two rotor systems and disk-type airframe 

structure, which help the MAR has better aerodynamic characters 

and maneuverability compared to the common MAV. The six degrees 

of freedom (6 DOF) dynamic model was built using the Newton 

151


- Euler method. A real-time fault-tolerant adaptive control method was 

designed to achieve real-time and accurate tracking control of the aircraft 

when the parameters associated with the input variables suffer 

uncertain disturbance. Simulation of the simplified mode of the aircraft 

were carried out in the matlab/SIMULINK environment, which verified 

that the aircraft had good maneuverability and robustness which is necessary 

when it works in the restricted environment. 

◮ SaA05-5 14:50–15:10 

Stability Control of Vehicle with Tire Blowout Using Differential Flatness 

Based MPC Method, pp.2066–2071 

Guo, Hongyan 

Wang, Fei 

Chen, Hong 

Guo, Dapeng 

Jilin Univ. 

Jilin Univ. 

Jilin Univ. 

FAW - Volkswagen Automotive CO., LTD 

In order to reduce the fatal damage of tire blowout during vehicle operation, 

a differential flatness based model predictive control (MPC) 

method is proposed. A 3 degrees of freedom (DOF) vehicle dynamics 

model with tire blowout is established, and then the flat outputs are 

given. The flatness property of the vehicle dynamics is discussed accordingly, 

and the MPC for the vehicle with a tire blowout stability control 

is formulated, where the lateral acceleration is considered as safety 

constraint. Simulations of vehicle with a tire blowout driving on the d- 

ifferent road are carried out to verify the effectiveness of the proposed 

method. 

◮ SaA05-6 15:10–15:30 

High Stabilization Control of Sensorless Brushless DC Motor On High 

speed, pp.2022–2026 

Lv, Haotun 

Wu, Zhong 

Beihang Univ. 


In the absence of position sensor, the brushless DC motor controller 

often used back electromotive force (EMF) zero crossing detection 

method to obtain the rotor position and speed during operation. Back 

EMF detection circuit noise and interference introduced by the PWM 

signal will affect the Back EMF zero-crossing detection signal, resulting 

in fluctuations and drift, using conventional MT solution method to extract 

rotor speed information can not effectively filter out interfering signals, 

and achieve high stability speed control. To improve speed stability 

control performance, accurate information to extract the rotor speed 

is needed as the feedback of controller. This paper designed a secondorder 

state observer based on the speed of detection, compared with 

the conventional speed demodulation algorithm can effectively filter the 

back EMF zero-crossing signal interference, and accurately extract the 

rotor speed information. Experimental results show that the speed detection 

using this method, can achieve high stability control of motor 

speed. 

SaA06 13:30–15:30 Room 302 

Fault Diagnosis 

Chair: Zhang, Ke 

Co-Chair: Yao, Lina 



◮ SaA06-1 13:30–13:50 

On Design of Fault Detection Filter for Nonlinear Markovian Jump Systems 

with Sensor Saturations, pp.2953–2958 

Dong, Hongli 

Wang, Zidong 

Liang, Jinling 

Gao, Huijun 





This paper addresses the fault detection problem for discrete-time 

Markovian jump systems with incomplete knowledge of transition probabilities, 

randomly varying nonlinearities and sensor saturations. An 

optimized fault detection filter is designed such that 1) the fault detection 

dynamics is stochastically stable; 2) the effect from the exogenous 

disturbance on the residual is attenuated with respect to a minimized 

H-infinity-norm; and 3) the sensitivity of the residual to the fault is enhanced 

by means of a maximized H-infinity-norm. The characterization 

of the gains of the desired fault detection filters is derived in terms of 

the solution to a convex optimization problem. Finally, a simulation example 

is employed to show the effectiveness of the scheme proposed 

in this paper. 

◮ SaA06-2 13:50–14:10 

Reduced-Order Fault Estimation Observer Design for Discrete-Time 


Zhang, Ke 

Jiang, Bin 

Shi, Peng 

Shumsky, Alexey 



Univ. of Glamorgan 

Far Eastern Federal Univ. 

This paper addresses the fault estimation problem for discrete-time dynamic 

systems. First, using a specific system decomposition, we propose 

a new reduced-order fault estimation observer (RFEO) with multiconstrained 

idea. A detailed discussion of the RFEO is given. Then, 

with the help of the slack-variable technique, fault estimation performances 

are further enhanced to reduce the conservatism generated 

by the direct design method. Simulation results of a discrete-time aircraft 

application are presented to illustrate our contributions. 

◮ SaA06-3 14:10–14:30 

Design and Implementation of Fault Diagnosis Expert System for Missile 

Circuit, pp.2977–2982 

Qiu, Sihai 


Liu, Zhongxin 

Nankai Univ. 

Nankai Univ. 

NanKai Univ. 

To solve the fault diagnosis problem of relay control circuits widely used 

in missiles, this paper designs a rule-based fault diagnosis expert system. 

This system includes five modules: knowledge acquisition module, 

problem solving module, reasoning explaining module, interface 

module and management and control module. To solve the problem 

of knowledge acquisition known as the bottleneck of expert system, 

knowledge acquisition module includes a sub system which acquires 

rules automatically from circuit description files. As a result, knowledge 

resource has been enriched and the disadvantage of over dependency 

to expert experience, which is common in expert system, has been 

reduced. According to the experiment result with a relay control circuit, 

this system has the ability of fast and accurate reasoning and preliminary 

knowledge automatic acquiring. Finally, the prospect of application 

of this system has been briefly analyzed. 

◮ SaA06-4 14:30–14:50 

Fault diagnosis for non-Gaussian time-delayed stochastic distribution 

control system, pp.2988–2993 

Yao, Lina 


Stochastic distribution control is a new branch of stochastic system control. 

The two main features of this system are that the system output 

is the probability density function of system variables and the input is 

time vector in the conventional meaning. The control goal of such s- 

tochastic distribution systems is to choose an input to make the output 

probability density function track a given probability density function. 

Time delay is widespread in our lives, and lots of kinetic equations for 

practical processes contain the time delay element. The effectiveness 

of the fault detection and diagnosis will be reduced when time delay is 

not considered. In this paper, the rational square-root B-spline is used 

to approach the output probability density function. A fault diagnosis 

method based on the nonlinear adaptive observer is used to diagnose 

the fault. Finally computer simulations are given to demonstrate the 

effectiveness of the proposed algorithm. 

◮ SaA06-5 14:50–15:10 

Data-based Fault-tolerant Control of the Semiconductor Manufacturing 

Process based on K-Nearest Neighbor Nonparametric Regression, 

pp.3008–3012 

Luo, Ming 

Zheng, Ying 

Liu, Shujie 




Run-to-run (R2R) control is the most commonly method in semiconductor 

manufacturing process. Generally, it is based on mathematical 

152



model, but for the complexity of the practical manufacturing process, it 

is difficult to set up the mechanical process model. This paper presents 

a data-based fault tolerant approach. Taking the disturbance and the 

fault into account, it adopts a large amount of historical data to predict 

the output of the single-product and multi-products semiconductor 

manufacturing process by the K-nearest neighbor (K-NN) nonparametric 

regression method. Then fault detection is achieved and a alarm 

is given, furthermore the traditional exponent weight moving average 

(EWMA) controller is improved to achieve fault-tolerant control. The 

results of simulation show that the approach is effective. 

◮ SaA06-6 15:10–15:30 

Fault Tolerant Control for a Robust Nonlinear Robotics via Optimal 

H∞Controller, pp.3248–3253 


Wang, Hong 



This paper presents a new optimal Fault tolerant control FTC, which 

includes anew optimal theorem to design a controller and new reconfiguration 

algorithm to reconfigure the controller law. The achieved FTC 

technique has been applied for Double Two Joints Inverted TJIRA robots 

arms which raise a plate where the weight of it distributed evenly on 

the two TJIRA. The study has been carried out different conditions in 

terms of an additive fault and a white noise as disturbance, where the 

angles of the two arms position have been considered. As results, Matlab 

simulation of a TJIRA is undertaken while CVX optimizing software 

is used to obtain the optimal parameters and to verify the performance 

of the proposed theorem. The simulation results are obtained to show 

the high performance of the proposed FTC technique which reflects on 

the speed of the arms motion to overcome on the effects of the fault 

and noise. 

SaA07 13:30–15:30 Room 303 

Advanced Control Algorithms and Applications (I) 

Chair: Heng, Qinghai 

Co-Chair: WANG, Xin 



◮ SaA07-1 13:30–13:50 

Multiple Models Direct Adaptive Controller Using Dimension-By- 

Dimension Technology, pp.2630–2635 

WANG, Xin 

ZHENG, Yihui 

Li, Lixue 

Yang, Hui 




East China Jiaotong Univ. 

For a non-minimum phase system, a multiple models direct adaptive 

controller using Dimension-by-Dimension technology is presented. The 

multiple models are constituted with controller directly, which lower the 

calculation and avoid the ill-condition matrix solution. The multiple models 

are composed of multiple fixed controllers, one free-running adaptive 

controller and one re-initialized adaptive controller. The fixed controllers 

are derived utilizing system prior information directly and guaranteed 

to cover the whole region in which the parameter changes. To 

solve the problems such as too many models, long computing time and 

so on, Dimension-By-Dimension technology is proposed. It adopts onedimension 

optimization method in series instead of multiple-dimension 

optimization method in parallel to reduce the number of the controllers 

greatly. At last the global convergence is obtained. In the simulation 

example, when compared with the convenient multiple models adaptive 

controller, if the same number of the fixed models is adopted, system 

transient response are improved effectively. 

◮ SaA07-2 13:50–14:10 

Robust Control of Hydro Turbine Speed Governor, pp.2680–2684 

Heng, Qinghai 

Lu, Jing 

Lu, Yang 




This paper presents the design of the μrobust performance of a hydro 

Turbine speed governor. It can be used to solve the μrobustness problems, 

which can’t be done by the conventional H∞robust performance 

designs. High frequency oscillation and large amplitude of output and 

control variable of the control system are exhibited by a conventional 

H∞robust performance design method. It is pointed out that the design 

of the robust performance can be formulated as a optimization 

μ-synthesis method, if the uncertainty weighting function and the performance 

weighting function are revised appropriately. The selections 

of the weighting functions are studied and the optimization μ-synthesis 

robust PI controllers are solved. The simulation results show that the 

proposed method is valid. The realization of the controllers doesn’t involve 

extra cost to the control equipment in existence. 

◮ SaA07-3 14:10–14:30 

H ∞ Loop Shaping Based Low Order Structured Robust Controller Design, 

pp.2710–2715 


Liu, Yuyan 



Standard H ∞ loop shaping design has two drawbacks. The 

first is that it is difficult to select appropriate loop shaping 

weights, and the second is that the resulting controller is very complex. 

To resolve these two drawbacks, a low order structured robust 

controller design scheme is proposed. The scheme is based on fixed 

structure weight optimization and static H ∞ loop shaping controller 

synthesis. Giving control points of shaped plant singular value bounds 

and structure of controller, then controller with desired performance 

specifications and maximal robust stability margin can be derived 

via linear matrix inequality based iteration optimization. Third order 

robust controller for steam generator water level plant is designed 

using this method, and comparisons are made among the proposed 

method and other two H∞loop shaping design schemes under different 

control condition, including step change of level reference, step change 

of steam flow rate and change of operation point. Simulation results 

show that the control system has good control performance, and its 

robust stability is better than the ones designed by other two schemes. 

◮ SaA07-4 14:30–14:50 

Model-free Subspace-based Control Scheme for Grid-Connected Voltage 

Source Inverters, pp.2674–2679 

Chen, Jianmin 

Yang, Fuwen 





In this paper, both model-free subspace-based LQG control and H- 

inf control schemes are used to investigate the tracking control performance 

and robustness of grid-connected voltage source inverters 

(VSIs). The proposed approaches do not require any identification or 

other modeling process. The controller can be directly derived from the 

experimental inputs/outputs measurements. Both the subspace-based 

LQG controller and H-inf controller are recursively designed. Simulation 

results show subspace-based LQG controller has a better tracking control 

performance than subspace-based H-inf controller, whereas, H-inf 

controller has stronger robustness than LQG controller. 

◮ SaA07-5 14:50–15:10 

Research on Global Motion Estimation Based on Adaptive SIFT Algorithm, 

pp.2758–2763 


An, Jiancheng 

Cao, Luguang 




Based on time redundancy in video image sequences an adaptive SIFT 

(Scale-invariant feature transform) algorithm is proposed . According 

to the latest three models’outputs in global motion estimation, the 

algorithm predicts overlapping regions between reference and current 

frames by using Lagrange parabolic interpolation, and then extracts 

feature points in the smaller region instead of the whole image. In this 

way, it can eliminate a large number of information redundancies to increase 

the processing speed of each frame, improve the effectiveness 

of feature points and reduce the mismatch. Experimental results show 

that the improved algorithm has the features of strong adaptive ability, 

rapidity and high matching accuracy, and it can be applied to the 

real-time positioning. 

153


◮ SaA07-6 15:10–15:30 

An Exemplar Test Problem on Parameter Convergence Analysis of 

Temporal Difference Algorithms, pp.2925–2930 

Brown, Martin 

Tutsoy, Onder 



Reinforcement learning techniques have been developed to solve difficult 

learning control problems having small amount of a priori knowledge 

about the system dynamics. In this paper, a simple unstable 

exemplar test problem is proposed to investigate issues in parametric 

convergence of the value function. A specific closed-form solution 

for the value function is determined which has a polynomial form. It is 

proved that the temporal difference error introduces a null space associated 

with the finite horizon basis function during the control trajectory. 

The learning problem can be only non-singular if the termination is handled 

correctly, and a number of possible solutions are introduced. This 

result was only revealed because of the derived closed form solution 

for the value function. 

SaA08 13:30–15:30 Room 310 

Award: Theory (I) 


Co-Chair: Zhang, Huanshui 



◮ SaA08-1 13:30–13:50 

A New Optimal Control Method for Discrete-Time Nonlinear Systems 

with Approximation Error, pp.185–190 

Wei, Qinglai 

Liu, Derong 

Inst. of Automation 

CASIA 

In this paper, a new optimal control method is proposed for discretetime 

nonlinear systems based on iterative adaptive dynamic programming 

(ADP) algorithm. The method can obtain an iterative control law, 

and make the iterative performance index function converge to optimal. 

In each iteration, if the iterative control law and iterative performance 

index function are not accurately obtained, then the convergence conditions 

of the iterative ADP algorithm are used. According to the convergence 

conditions, the iterative performance index functions can converge 

to a small neighborhood of the optimal performance index function. 

Finally, a simulation example is given to illustrate the performance 


◮ SaA08-2 13:50–14:10 

Emergence of Flocks with Local Interactions, pp.3515–3519 

Chen, Ge 

Liu, Zhi-Xin 



In a recent paper, Cucker and Smale proposed a multi-agent model 

to study the flocking behavior, where they assume that all agents can 

interact with all other agents. Though this model attracted much attention 

of researchers, the global interactions used in that paper changed 

the nature of multi-agent systems. In this paper we will investigate a 

new flocking model, in which the global interactions between agents 

are replaced by local interaction functions. We will provide necessary 

conditions and sufficient conditions for consensus. 

◮ SaA08-3 14:10–14:30 

Cooperative Robust Output Regulation of Linear Uncertain Multi-Agent 


Su, Youfeng 

Huang, Jie 



The cooperative output regulation of a linear multi-agent system has 

been studied recently for the case where the system uncertain parameters 

vary in a sufficiently small neighborhood of their nominal value. 

This problem is handled by an internal model design which converts 

the problem into a simultaneously eigenvalue placement problem of an 

augmented multi-agent system. In this paper, we further consider the 

cooperative robust output regulation problem for a class of minimum 

phase linear multi-agent systems in the sense that the controller allows 

the system uncertain parameters to vary in an arbitrarily prescribed 

compact subset. For this purpose, we introduce a new type of internal 

model that allows the cooperative robust output regulation problem 

of the given plant to be converted into a robust stabilization problem of 

an augmented multi-agent system. We then solve our problem by combining 

a simultaneous high gain state feedback control technique and 

a distributed high gain observer technique. A special case of our result 

leads to the solution of the leader-following robust consensus problem 

for a large class of uncertain multi-agent systems. 

◮ SaA08-4 14:30–14:50 

Stochastic Approximation Based PCA and Its Application to Identification 

of EIV Systems, pp.3276–3280 

Zhao, Wen-Xiao 

Chen, Han-Fu 



The stochastic approximation based principal component analysis (S- 

APCA) algorithm is introduced to recursively estimate the eigenvectors 

and the corresponding eigenvalues of a symmetric matrix A based on 

observations A k = A+ε k with ε k → 0 as k → ∞. The estimates are 

strongly consistent. The SAPCA algorithm is then applied to identifying 

the matrix coefficients of the multivariate errors-in-variables (EIV) systems, 

and the estimates are also strongly consistent. The performance 

of SAPCA algorithm is testified by a simulation example. 

◮ SaA08-5 14:50–15:10 

Reaching Optimal Consensus for Multi-agent Systems Based on Approximate 

Projection, pp.2794–2800 

Lou, Youcheng Acad. of Mathematics & Sys. Sci., Chinese Acad. 


Shi, Guodong 

Johansson, Karl Henrik 

Hong, Yiguang 

KTH Royal Inst. of Tech. 



In this paper, we propose an approximately projected consensus algorithm 

for a multi-agent system to cooperatively compute the intersection 

of several convex sets, each of which is known only to a particular n- 

ode. Instead of assuming the exact convex projection, we allow each 

node to just compute an approximate projection. The communication 

graph is directed and time-varying, and nodes can only exchange information 

via averaging among local view. We present sufficient and/or 

necessary conditions for the considered algorithm on how much projection 

accuracy is required to ensure a global consensus within the 

intersection set, under the assumption that the communication graph 

is uniformly jointly strongly connected. A numerical example indicates 

that the approximately projected consensus algorithm achieves better 

performance than the exact projected consensus algorithm. The results 

add the understanding of the fundamentals of distributed convex 

intersection computation. 

◮ SaA08-6 15:10–15:30 

Quantum Multi-Channel Decoupling, pp.2286–2290 

Liu, Pei-Lan 

Li, Jr-Shin 





We study multiple-input multiple-output (MIMO) quantum systems and 

explore quantum decoupling techniques that can be used to facilitate 

the design of quantum networks. We provide a quantum state feedback 

control law to change the structure of a quantum system in order 

to decouple the multiple channels. We show that an MIMO quantum 

system can be decoupled as an aggregate of n separated single-input 

single-output (SISO) quantum systems from an input-output point of 

view. We also discuss the conditions under which quantum decoupling 

can be achieved. We illustrate our method with several qubit and atomic 

systems. 

SaA09 13:30–15:30 Room 311A 

Award: Application (I) 


Co-Chair: Chen, Hong 


Jilin Univ. 

◮ SaA09-1 13:30–13:50 

Bilateral Teleoperation of Force/Motion for aRobotic Manipulator with 

Communication RandomDelays, pp.3915–3920 

Kang, Yu 


154



Li, Zhijun 

Zhai, Dihua 

Cao, Xiaoqing 


Uiversity of Sci. & Tech. of China 

Shanghai Jiao Tong Univ 

In this paper, bilateral teleoperation control is proposed for holonomic 

constrained robotic manipulators with stochastic time-varying delays in 

communication channels. A unified control model is introduced for representing 

well-known holonomic systems with an environmental constraint. 

By integrating Markov jump systems, the motion/force tracking 

control strategy is developed. Furthermore, a feasible solution for the 

derived linear matrix inequality guarantees the attenuation of unsymmetrical 

stochastic delay in an propobalty sense. Finally, the proposed 

control are validated by extensive simulation and experimental studies. 

◮ SaA09-2 13:50–14:10 

Mathematical Model Building and Optimization Control of Horizontal 

Continuous Heat Treatment Furnace, pp.2412–2416 

Hu, Lingyan 

Wang, Xingcheng 

College of Information Engineering,Dalian Univ. 

Dalian Maritime Unversity 

According to the large continuous annealing furnace widely used to produce 

ductile iron pipes in metallurgy area, heat transfer mathematical 

model was built in the paper. Based on the pipe setting temperature 

curve and boundary conditions, temperature distribution in the furnace 

is deduced by solving heat transfer differential equation. The aim is 

to optimize the control temperature curve. In order to make the temperature 

uniform in the furnace, the pulse combustion control program 

is developed. The actual application shows that the product quality is 

enhanced in a large scale with the optimization curve. The maneuverability 

is good. The whole system meets the process requirements 

perfectly. It also proves the truth and practicability of the model algorithm 

◮ SaA09-3 14:10–14:30 

Occluded Object Grasping Based on Robot Stereo Vision, pp.3698– 

3704 

Lin, Chuan 

Chen, Yen-Lun 

Hao, Weidong 

Wu, Xinyu 






To solve the problem of occlusion for robot grasping, a novel method is 

proposed, by which the robot can grasp an occluded target object accurately. 

In this paper, the principles of binocular vision, camera calibration, 

stereo rectification and stereo matching are described. By utilizing 

the stereo-vision algorithm, robots can obtain the three-dimensional information 

of objects. For accurate grasping, the depth information of 

the object is optimized, where the principles of connected consistency 

and threshold setting have been applied in the depth image. The ORB 

algorithm is applied to find the occlusion dynamically. The approach for 

grasping the occluded object is proved to be effective by experiments. 

◮ SaA09-4 14:30–14:50 

Intelligent Switching Control for Cement Raw Meal Calcination Process, 

pp.280–285 

Qiao, Jinghui 




In raw meal calcination process, since boundary conditions of raw meal 

change frequently, the decomposition rate of raw meal (RMDR) cannot 

guarantee the desirable ranges. Therefore, C5 feeding tube was 

blocked and the load of rotary kiln will increase. To solve above problem, 

an intelligent switching control method is proposed to control the 

calciner temperature into their setpoints. This method for raw meal calcination 

process consists of four modules, namely a easy calcination 

controller, a difficult calcination controller, a abnormal condition controller, 

and a switching mechanism. The proposed approach can select 

right controller according to the change of the working conditions and 

has been successfully applied to the raw meal calcination process of 

Jiuganghongda Cement Plant in China and its efficiency has been validated 

by the practical application results. 

◮ SaA09-5 14:50–15:10 

Design of Entry Trajectory Tracking Law for a Hypersonic Vehicle via 

Inversion Control, pp.1092–1097 

Pu, Zhiqiang 

Tan, Xiangmin 

Fan, Guoliang 






A nominal altitude-velocity longitudinal entry trajectory is planned and 

tracked for a Generic Hypersonic Vehicle (GHV) in this paper. The entry 

corridor is presented which is defined by the dynamic pressure, normal 

acceleration, heating constraints, and the so-called Quasi-Equilibrium 

Glide Condition (QEGC). The flyability of the vehicle along the nominal 

trajectory is carefully analyzed for further validation of the selected 

nominal trajectory. The control scheme mainly consists of two loops: a 

guidance loop and a posture loop, of which the latter is separated into 

the slow and fast loops with the time-scale separation theory. Inversion 

control is employed in these three loops, and an integration feedback 

approach is especially added into the inversion controller to eliminate 

the tracking error. Simulations demonstrate that the nominal trajectory 

is designed appropriately and tracked well. 

◮ SaA09-6 15:10–15:30 

Finite-Horizon Neural Optimal Tracking Control for a Class of Nonlinear 

Systems with Unknown Dynamics, pp.138–143 

Wang, Ding 

Liu, Derong 

Li, Hongliang 


CASIA 

chinese Acad. of Sci. 

A neural-network-based finite-horizon optimal tracking control scheme 

for a class of unknown nonlinear discrete-time systems is proposed. 

First, the tracking control problem is converted into designing a regulator 

for the tracking error dynamics under the framework of finite-horizon 

optimal control theory. Then, with convergence analysis in terms of cost 

function and control law, the iterative adaptive dynamic programming 

algorithm is introduced to obtain the finite horizon optimal controller to 

make the cost function close to its optimal value within an ”-error bound. 

Furthermore, in order to implement the algorithm via dual heuristic dynamic 

programming technique, three neural networks are employed to 

approximate the error dynamics, the cost function, and the control law, 

respectively. In addition, a numerical example is given to demonstrate 

the validity of the present approach. 

SaA10 13:30–15:30 Room 311B 

Invited Session: Guidance and Control of Flight Vehicles: Theory and 

Application 

Chair: He, Fenghua 

Co-Chair: Liu, Fuchun 



◮ SaA10-1 13:30–13:50 

Square Division Method for Attitude Dynamic Analysis of Modular S- 

pace Station, pp.1350–1355 

Guo, Jian 

Guo, Yang 

Zhang, Peng 

Yao, Yu 

Zhao, Hui 

Yang, Baoqing 







This paper presents a numerical method to calculate the aerodynamic 

force and torque for the complex spacecraft considering components 

blocking. The Square Division Method (SDM) is applied to 

analyze and therefore evaluate the space station’s structure. The 

efficiency and accuracy of the SDM are investigated in the simulation 

section primarily. Then major structures of the space station are 

analyzed and evaluated by testing their performance in the Local- 

Horizontal-Local-Vertical (LVLH), Inertial-Oriented-Attitude (IOA) and 

Torque-Equilibrium-Attitude (TEA) modes. The long-term momentum 

accumulation is chosen as the criterion to evaluate appropriate flight 

modes for each structure. Based on the simulation results, appropriate 

operation modes are decided for each structure. 

155


◮ SaA10-2 13:50–14:10 

Consensus of Second-Order Multi-Agent Systems with Disturbance 

Generated by Nonlinear Exosystem, pp.1574–1579 

Zhang, Xuxi 




In this paper, the consensus of second-order multi-agent systems with 

exogenous disturbances generated by nonlinear exosystem is investigated. 

Firstly, a dynamic gain technique based disturbance observer 

is presented to estimate the exogenous disturbance generated by 

nonlinear exosystem. Secondly, based on the presented disturbance 

observer, consensus protocol is further proposed. A rigorous consensus 

analysis is performed. Finally, an example is provided to show the 

effectiveness of the proposed results. 

◮ SaA10-3 14:10–14:30 

Optimal Sweep-based Persistent Surveillance Using Multiple Unmanned 

Aerial Vehicles with Level of Interest, pp.2441–2446 

Yao, Yu 

Zhang, Peng 

Liu, Hugh 

He, Fenghua 



Univ. of Toronto 


This paper addresses the problem of sweep-based persistent surveillance 

(SBPS) using multiple Unmanned Aerial Vehicles (UAVs). Due 

to the variation of priority distributed in the area under surveillance, a 

novel concept of level-of-interest (LoI) is introduced to represent the 

relative priorities of the target areas. Inspired by an “age”- related 

algorithm in literature, we develop a novel recursive scheme with the 

LoI consideration. The algorithms on both single UAV case and multiple 

UAVs case are developed to decide the optimal target-choice based 

on the age-based performance index. The potential benefits of the proposed 

algorithms are demonstrated by simulations in both single UAV 

cases and multiple UAVs cases. 

◮ SaA10-4 14:30–14:50 

Robust Analysis of Different Guidance Laws of Terminal Guidance System 

under Model Uncertainties, pp.1716–1721 

Wang, Xingdan 

Yao, Yu 

Yang, Baoqing 

Guo, Jian 





The purpose of this paper is to analyze the robustness of the given guidance 

laws by quantify the influence of model and target maneuver uncertainties 

on system performance valued by miss distance. The Pure 

Proportional Navigation (PPN), True Proportional Navigation (TPN) and 

Generalized True Proportional Navigation (GTPN) guidance laws of terminal 

guidance system are considered in this paper. The analyzing 

performance is tackled by a differential linear matrix inequalities (DL- 

MI) approach and this approach is demonstrated on a simple concrete 

example. The solution of DLMI is also proposed in this article which 

idea is to reduce it to an algebraic linear matrix inequality (LMI) by discretization. 

A numerical example is given to analyze the robustness 

of terminal guidance system (TGS) with different guidance laws in the 

end. 

◮ SaA10-5 14:50–15:10 

Application of Extended Kalman Filter to Unmanned Helicopter Navigation, 

pp.2291–2295 

Liu, Fuchun 

Zhang, Qian 


South Chian Univ. of Tech 

The attitude control of unmanned helicopter needs accurate acceleration, 

velocity and attitude information. The error induced by using low 

cost and low precision inertial component which widely used in the s- 

trapdown inertial navigation system cannot be neglected. In order to 

reduce the effect of the error, the navigation equation of unmanned helicopter 

is established by using quaternion method and the filtering problem 

of the GPS/INS navigation system is investigated based on the extended 

kalman filter theory. The last numerical simulation demonstrates 

the effectiveness of the proposed design, and the design satisfies the 

engineering accuracy. 

SaA11 13:30–15:30 Room 311C 

Invited Session: Complex Networks and Multi-Agent Systems: Some 

Recent Advances 

Chair: Lu, Jinhu 

Co-Chair: Yu, Xinghuo 


RMIT Univ. 

◮ SaA11-1 13:30–13:50 

Neuronal Network Control for Robust Entrainment to Natural Oscillations 

- a Case Study, pp.4995–5000 

Chen, Zhiyong 

Zhang, Hai-Tao 


Huazhong Univ. Sci. Eng 

Biological control mechanism has motivated central pattern generator 

(CPG) circuits as the basic architecture of nonlinear control laws to 

achieve coordinated oscillations of engineered systems. In this paper, 

the coordinated oscillations of a multi-link pendulum on slope (MPOS) 

system are investigated. In an MPOS model, the gravity of the links 

and their interaction (friction) with the slope excites and damps the 

oscillating motion, respectively. With the sensory feedback from the 

links’angular states, the entrainment to a class of natural oscillations 

is achieved by a CPG based controller. The entrainment is robust with 

respect to a certain environmental and body variations. 

◮ SaA11-2 13:50–14:10 

On the Lyapunov Exponent of Consensus Algorithm, pp.931–936 

Chen, Yao 

Lu, Jinhu 

Dong, Hairong 

Yu, Xinghuo 

Inst. of Sys. Sci. 



RMIT Univ. 

Nowadays, consensus of multi-agent systems (MAS) is an intriguing 

topic which receives increasing attention from various disciplines recently. 

Traditionally, the analysis of consensusability for a given MAS 

with switching topology can be ttributed to some techniques such as 

eigenvalue analysis, convergence of Markov chain, Lyapunov candidate 

construction, and so on. However, all these techniques are based 

on some restrictive conditions and hardly be generalized to the case 

of general switching topology. This paper aims at providing an inner 

explanation that the general consensus problem of MAS with switching 

topology can be transformed into the numerical calculation of the Lyapunov 

exponent for a given set of matrices. Consequently, the consensus 

of MAS with general switching topology can be resolved by using 

the existing numerical algorithm of Lyapunov exponent. 

◮ SaA11-3 14:10–14:30 

Consensus decision-making of animal groups in motion, pp.952–957 

Liu, Zhi-Xin 


In this paper, we will investigate the effectiveness of leaders in animal 

groups by a discrete-time multi-agent system. The model is composed 

of two subgroups of agents: informed agents(called leaders) and uninformed 

agents(called followers). All agents move in the plane with 

a constant speed, but with different heading update laws. For the followers, 

the heading is updated according to the average direction of 

neighbors, while the heading of leaders is taken as the balance between 

the local interaction of neighbors and the preferred direction. For 

such a system, we will provide a necessary condition and a sufficient 

condition for the consensus decision-making. 

◮ SaA11-4 14:30–14:50 

Pinning Synchronization Criterion for Impulsive Dynamical Networks, 

pp.1976–1981 

Yu, Wenwu 


This paper focuses on the problem of global synchronization for impulsive 

coupled dynamical networks via pinning control. Controlling 

impulsive synchronization of the network by changing the differences 

between some chosen nodes and the reference signal step by step is 

the main idea discussed in this paper, regardless of whether there is a 

synchronized or desynchronized impulses for the dynamical networks. 

In the previous literature, results are devoted to investigating criterion 

for synchronization with impulsive control on all the nodes in dynamical 

156



networks. Thus some questions arises: What is the relation between 

strength of impulsive signal and the impulsive interval? How do the 

chosen pinning nodes affect the dynamics of the network? In this paper, 

a synchronization criterion is derived for impulsive dynamical networks 

for pinning control, and clear relations between the strength of 

impulsive signal, the impulsive interval, and pinning nodes are provided. 

Numerical examples are given at last to show the effectiveness of 

results. 

◮ SaA11-5 14:50–15:10 

Modelling Complex Software Systems via Weighted Networks, 

pp.3533–3537 

WANG, Beiyang 

Lu, Jinhu 

Wuhan Univ. 


It is well known that many large-scale software systems show the typical 

characteristics of complex networks. However, some recent research 

results indicate us that some essential features of general software 

networks topology are different from the corresponding functions 

of real-world software systems. To bridge the above gap, this paper 

aims at developing a weighted network model to model the practical 

large-scale software systems from the partial dependency of software 

systems. Based on the proposed model, we further investigate the statistical 

characteristics of several representative software systems. Furthermore, 

we compare the traditional general software network model 

and the proposed weighted software network model based on two software 

systems. 

SaB01 15:50–17:50 Room 203A 

Optimal Control and Optimization 

Chair: Chen, Michael Z. Q. 

Co-Chair: Han, Chunyan 



◮ SaB01-1 15:50–16:10 

Multi-objective optimization for a conventional suspension structure, 

pp.1235–1240 

Hu, Yinlong 




This paper investigates the multi-objective optimization of ride comfort, 

suspension deflection and tyre grip performance measures for a conventional 

suspension structure by deriving the analytical solutions for 

a quarter-car model. The optimization results are compared with two 

other configurations, one having the same complexity in construction 

but employing an inerter while the other being the simplest suspension 

network with one damper and one spring only. The motivation is to 

investigate the possibility and situations where the inerter can be replaced 

by some cheaper element such as the spring. The results show 

that for a low static stiffness and in the situations that ride comfort is 

less important than suspension deflection and tyre grip (such as race 

cars), the considered structure would be a reasonable alternative for 

the one employing an inerter. 

◮ SaB01-2 16:10–16:30 

Optimal control of quantum systems under different manipulation conditions, 

pp.2091–2096 

Wei, Hua 

Zhou, Wei 

Dai, Yi 

Zou, Fengxing 

Zhang, Ming 

NUDT 

NUDT 

NUDT 

NUDT 

NUDT 

In this paper, we explicitly explore how to manipulate single qubits by 

one-rotation controls under different manipulation conditions. It is revealed 

that one can construct control Hamiltonian and adjust the controls 

to manipulate quantum states. Furthermore, we comprehensively 

discuss how to optimize control magnitude in terms of a new kind of 

weighted time-energy performance. A comparison has been made a- 

mong the optimal performances under different manipulation conditions. 

Three concrete examples indicate the feasibility and efficiency of this 

approach on optimal control of two-level quantum systems. 

◮ SaB01-3 16:30–16:50 

Optimal Filtering on Continuous-time systems with Markovian Communication 

Delays and packet dropouts, pp.2027–2032 

Han, Chunyan 

Wang, Wei 





This paper is concerned with the optimal filter problems for networked 

systems with random transmission delays, while the delay process is 

modeled as a multi-state Markov chain which incorporates the data 

losses naturally. By defining an indicator function of the random delay, 

the optimal filter problems are transformed into the ones of the standard 

Markov jumping parameter measurement system. We first present an 

optimal Kalman filter, which is with time-varying, path-dependent filter 

gains, and the number of the paths grows exponentially in time delay. 

Thus an alternative optimal Markov jump linear filter is presented, in 

which the filter gains just depend on the present value of the Markov 

chain, and as a result, the obtained filter is again a Markov jump linear 

system. It can be further shown that the proposed Markov jump linear 

filter converges to a constant-gain filter under appropriate assumptions. 

◮ SaB01-4 16:50–17:10 

FFSM Trajectory Optimization via Multiphase Gauss Pseudospectral 

Method, pp.1615–1620 


Li, Shi 



Based on the multiphase Gauss pseudospectral method, this paper 

presents a new optimal control method to solve the nonholonomic trajectory 

planning of the free-floating space manipulator (FFSM) system. 

The general case of path planning problem with constraints is formulated 

as a standard multiphase Bolza problem. Accuracy is improved by 

increasing degree of the polynomial approximation in particular mesh 

intervals based on the relative errors of states. Numerical simulation results 

demonstrate that the proposed method is effective and available 

for solving the FFSM trajectory optimization problem. 

◮ SaB01-5 17:10–17:30 

A novel Varible Gain Unscented kalman filter and its application in the 

Integrated Navigation system, pp.1160–1165 

Zhang, Limin 


Zhang, Xinghui 

college of information technicail Sci. 

Nankai Univ. 

tianjin Univ. of Tech. & education 

The unscented kalman filter is a widely used nonlinear filter in nonlinear 

system. But because of inaccuracies of system modeling and other 

reasons, when the disturbance or observation anomaly appears, UK- 

F filtering algorithm does not have the ability of tracking the mutation 

state of system, so the system is likely to become unstable. Iin this paper, 

the reasons for cause these problems of UKF are analysed firstly, 

and then,some improvements are made to it. Then, this paper gives a 

detailed introduction of unscented transformt, according the theory of 

strong tracking filter, puts forward a new kind of variable gain unscented 

kalman filter. At last,this paper does some simulation experiment to 

compare variable gain UKF filter with standard UKF. The results show 

that the variable gain UKF has the ability of tracking the mutation state 

of system when the disturbance or observation anomaly appears,and 

variable gain UKF really makes the system more robust and stable. 

◮ SaB01-6 17:30–17:50 

Comparison of Nonlinear Filtering Approach in Tightly-coupled GP- 

S/INS Navigation System, pp.1176–1181 

Nie, Qi 

Beijing Aerospace Automatic Control Inst. 

This paper proposes the fusion of GPS measurements and inertial sensor 

data from gyroscopes and accelerometers in tightly-coupled GP- 

S/INS navigation systems. Usually, an extended Kalman fiter (EKF) is 

applied for this task. However, as system dynamic model as well as the 

pseudorange and pseudorange rate measurement models are nonlinear, 

the EKF is sub-optimal choice from theoretical point of view, as it 

approximates the propagation of mean an covariance of Gaussian random 

vectors through these nonlinear models by a linear transformation, 

which is accurate to first-order only. The sigma-point Kalman filter (SP- 

157


KF) family of algorithms use a carefully selected set of sample points 

to more accurately map the probability distribution than linearization of 

the standard EKF, leading to faster convergence from inaccurate initial 

conditions in position and attitude estimation problems, which achieves 

an accurate approximation to at least second-order. Therefore, the performance 

of EKF and SPKF applied to tightly-coupled GPS/INS integration 

is compared in numerical simulations. It is found that the SPKF 

approach offers better performances over standard EKF. 

SaB02 15:50–17:50 Room 203B 

Robust Control (II) 

Chair: Brdys, Mietek 

Co-Chair: Zhu, Jiandong 



◮ SaB02-1 15:50–16:10 

Robust Adaptive Neural Network Control for Strict-Feedback Nonlinear 

Systems with Uncertainties, pp.1328–1333 

SUN, Gang 

Wang, Dan 

Peng, Zhouhua 

Lan, Weiyao 

Wang, Hao 

Wang, Ning 




Xiamen Univeristy 



In this paper, we present a robust adaptive neural network control design 

approach for strict-feedback nonlinear systems with uncertainties. 

In the controller design process, all unknown terms at intermediate 

steps are passed down and approximated by a single neural network 

at the last step. By this way, the structure of the designed controller is 

much simpler, and the control law and the adaptive law can be given directly. 

The result of stability analysis shows that the proposed scheme 

can guarantee the uniform ultimate boundedness of the closed-loop 

system signals, and the control performance can be guaranteed by an 

appropriate choice of the control parameters. The effectiveness of the 

proposed approach is demonstrated by simulation results. 

◮ SaB02-2 16:10–16:30 

Design Robust Gain-Scheduling Multi-Objective Controller based on Iterative 

LMI, pp.1558–1563 

Li, Wen Qiang 

Cao, Wenjing 

SU, Tao 

LI, Lian 

Naval Aeronautical Engineering Inst. 



Control Enginering 

The problem of robust gain-scheduling multi-objective was studied. 

Multi-channels was adopted to design robust gain-scheduling multiobjective 

controller for LPV systems with polytopic structure, the slack 

variables whose number can adjust was added for decoupling the systems 

matrix and Lyapunov functions, so the different Lyapunov functions 

can selected at different vertex. This method can reduce the conservatism 

of traditional method which use single Lyapunov function at 

every channel. The slack variables bring the bilinear matrix inequalities 

in the controller synthesized and a simple but available method to 

decide the initial value of the iterative LMI was proposed. At last, the robust 

gain-scheduling multi-objective controller was designed for active 

suspensions using proposed method, the multi-objective include pole 

placement and mixed H 2 /H ∞ . From simulation result, it can be seen 

that the performance was increased 30 percent, and the method was 

proved efficiency from simulation and theory. 

◮ SaB02-3 16:30–16:50 

Optimised Robust Placement of Hard Quality Sensors for Robust Monitoring 

of Quality in Drinking Water Distribution Systems, pp.1109–1114 

Langowski, Rafal 

Brdys, Mietek 

Qi, Ruiyun 




A problem of optimised robust placement of the hard quality sensors 

in Drinking Water Distribution Systems under several water demand 

scenarios for robust quality monitoring is formulated. Numerical algorithms 

to solve the problem are derived. The optimality is meant as 

achieving at the same time a desired trade off between the sensor capital 

and maintenance costs and resulting robust estimation accuracy of 

the monitoring algorithm for several water demand scenarios insuring 

robustness with respect to the demand uncertainty. The robust estimation 

algorithm recently developed by the authors is applied as a soft 

quality in design of the sensor placement algorithms. The method and 

algorithm are validated by application to Chojnice DWDS case study. 

◮ SaB02-4 16:50–17:10 

Cooling Control of Aluminum Plate with a Peltier Device Thermal 

Process by Using a Robust Right Coprime Factorization Approach, 

pp.1115–1119 

WANG, Dong-yun 

Zhang, Lei 



In this paper, an aluminum plate with a Peltier devices are used as a 

model. For the Peltier actuated thermal process is a nonlinear control 

affine system and the real system is usually contains fault owing 

to various factors, it is difficult to achieve excellent control consequent. 

In this paper, robust right coprime factorization is applied to analyse 

the thermal process. Then the derivation of the controller is also given 

for achieve robust stability and tracking performance. Finally, simulation 

and experimental results are presented to support the theoretical 

results. 

◮ SaB02-5 17:10–17:30 

Sliding Mode Control for Robust Consensus of Linear Multi-agent Systems, 

pp.1378–1382 

Zhao, Ni 

Zhu, Jiandong 



This paper investigates the robust consensus problem for general highdimensional 

linear multi-agent systems with uncertainties. A distributed 

protocol based on sliding mode control is proposed to realize the consensus 

under matched uncertainties. For diminishing the chattering 

phenomenon, a second-order sliding mode protocol is designed. Finally, 

numerical simulations are given to verify the effectiveness of the 

proposed protocols. 

◮ SaB02-6 17:30–17:50 

Robust Altitude Control for a Small Helicopter by Considering the 

Ground Effect Compensation, pp.1796–1800 


In this paper, a nonlinear robust control technique is proposed to control 

the altitude of a small helicopter for hover as well as vertically takeoff/landing 

near ground surface in the presence of strong horizontal 

wind gusts. A heave motion model of small helicopter is considered to 

derive the proposed controller for the purposes of capturing dynamic 

variations of thrust due to the horizontal wind gusts and ground effect. 

A recursive (backstepping) design procedure is used to design the 

robust controller for vertical dynamics based on Lyapunov approach. 

Simulation results demonstrate that the proposed robust backstepping 

controller is capable of controlling the altitude for hover flight of a small 

helicopter near ground surface in the presence of strong horizontal 

wind gusts. 

SaB03 15:50–17:50 Room 203C 


Chair: Tong, Guofeng 

Co-Chair: YIN, Ziqiang 


Inst. of Oceanographic Instrumention 

Shandong Acad. of Sci. 

◮ SaB03-1 15:50–16:10 

Research on Structured Light 3D Vision in the Remanufacturing System 

based on Robotic Arc Welding, pp.4527–4531 

YIN, Ziqiang Inst. of Oceanographic Instrumention Shandong 


Three-dimensional sensing technologies based on computer vision 

make it possible to rapid establish the three-dimensional model of the 

worn parts for remanufacturing system. This research design a novel 

structured light three-dimensional vision sensor for the remanufacturing 

system based on robotic arc welding. The sensor is mainly constituted 

of two devices: the MTC-368CB CCD camera and the ML-645 struc- 

158



tured light projector. The sensor system can be calibrated by a simple t- 

wo step calibration method. The three-dimensional sensing experiment 

and analysis are conducted to verify the feasibility and the practicality of 

the sensor system by taking a semi-cylinder, a mouse and a calibration 

block as remanufacturing object. The experiment results indicate that 

the maximum sensing error is less than 1 mm, which is acceptable for 

remanufacturing system based on robotic arc welding. 

◮ SaB03-2 16:10–16:30 

An Omni-directional vSLAM based on Spherical Camera Model and 3D 

Modeling, pp.4551–4556 

Tong, Guofeng 

Wu, Zizhang 

Weng, Ninglong 

Hou, Wenbo 



ISE 

NEU 

This paper presents an efficient Omni-directional Visual Simultaneous 

Localization and Mapping (vSLAM) algorithm based on spherical camera 

model and 3D modeling. In the paper, the robot has the ability 

of Omni-directional vision, which makes the algorithm more adaptive 

in an unknown environment. To get spherical panoramic images, we 

choose the panoramic image acquisition and mosaic equipment (divergent 

camera cluster). The improved SURF on spherical image, is 

adopted for feature extraction and matching. According to the theory of 

multiple view geometry of the spherical camera model, the 3D modeling 

is conducted for the surrounding environment. By using the feature 

points with high robustness, the location and pose of the robot can be 

estimated. In the process of system updating, the particle filter combined 

with Kalman filter is used for it can perform well in a complex environment. 

The results of numerical simulations and experiments have 

been included in this paper to verify the performance of the proposed 

approach. 

◮ SaB03-3 16:30–16:50 

Tracking the Rotating Targets in Aerial Videos , pp.4574–4578 

Dong, Qiang 

Liu, Aidong 


Huazhong Research Inst. of Electro-Optical Tech. 

In aerial videos, there are two main situations where the appearance 

of a target would vary. One is occlusion, and the other is rotation of 

the target. The former could be solved by track forecast which may use 

Kalman filter, Particle filter or some other similar methods. Aimed at the 

latter situation, incremental visual tracking (IVT) has been brought up. 

However, in aerial videos the rotation of targets (vehicles mainly) mostly 

occurs in the imaging planes, where IVT cannot make the bounding box 

rotating the same rotation angle of targets. Accordingly, we present a 

tracking system with specially designed methods that solves the issue. 

And it could track the rotating target continuously and give out the rotation 

angle in aerial videos. With a rotatable bounding box to indicate the 

target, the observers would read the information from the screen more 

easily and the system would extract the target status more accurately. 

The system is developed employing Correlation analysis and Kalman 

filter as well. Experimental results are presented on several 

aerial video sequences captured by the authors. 

◮ SaB03-4 16:50–17:10 

UAV Image Denoising Using Adaptive Dual-Tree Discrete Wavelet 

Packets Based on Estimate the Distributing of the Noise , pp.4649– 

4654 

Liu, Fang 


Unmanned Aerial Vehicles (UAV)digital images are often badly degraded 

by noise during dynamic acquisition and transmission process. 

Denoising is very important and difficult for UAV-vision Guided, because 

natural scene image is complicated and having lots of the edges and 

texture details. The image denoising algorithm based on adaptive dualtree 

discrete wavelet packets(ADDWP) which combine the dual-tree 

discrete wavelet transform(DDWT) and the wavelet packets is proposed 

in this paper. In ADDWP, DDWT subbands are further decomposed into 

wavelet packets with anisotropic decomposition, so that the resulting 

wavelets have elongated support regions and more orientations than 

DDWT wavelets. To determine the decompoisition structure, we using 

the signal-to-noise ratio to estimate the distributing of the denoising 

in order to search the more denoising subbands to decomposition 

it again. So we can get adaptive decompoisition structure of wavelet 

packets. The new algorithm has significantly lower computational complexity 

than a previously developed optimal basis selection algorithm. 

For denoising the ADDWP coefficients, a statistical model is used to 

exploit the relation of the coefficients in order to distinguish the noise 

and the signal. The proposed denoising scheme gives better performance 

than several state-of-the-art DDWT-based schemes for images 

with rich directional features. The visual quality of images denoised by 

the proposed scheme is also superior. 

◮ SaB03-5 17:10–17:30 

A Perception-motivated Image Interpolation Algorithm , pp.4754–4759 

Zi, Lingling 

Du, Junping 

Liang, Meiyu 







Image interpolation, or to obtain a high-resolution image from a corresponding 

low-resolution image, is still a hard question. In order to 

better solve this question, we demonstrate a partitioned image interpolation 

model and propose a perception-motivated image interpolation 

algorithm according to human eye visual mechanism (PMIA). The P- 

MIA main implementation includes two processes. Firstly the original 

image is divided into the attention region, the transition region and the 

general region and then different interpolation mode can be used based 

on different regions. Conducted experiments have shown that our algorithm 

can spend less time to produce the most satisfactory image. From 

the perspective of engineering application, our method can reduce the 

communication bandwidth and time consuming. 

◮ SaB03-6 17:30–17:50 

The Study on Infrared Image Mosaic Application Using Immune Memory 

Clonal Selection Algorithm, pp.4831–4836 

Dong, Lin 

Fu, Dongmei 

Yu, Xiao 

Yang, Tao 





Infrared imaging technology is widely used in industrial and military 

fields. When a large target need to be photographed but one picture 

could not accommodate; then several different areas of this target must 

be photographed firstly, afterwards these split areas need to make image 

mosaic. To do this, this paper proposes an image mosaic algorithm 

which is called immune memory clonal selection algorithm. The algorithm 

determines the matched positions of infrared images, after finding 

the feature points of infrared images by using Susan algorithms. Simulations 

of the proposed algorithm show that the method is effective. 

The algorithm is applicable not only in infrared image mosaic, but also 

in visible image mosaic with complex background. 

SaB04 15:50–17:50 Room 203D 

Industrial Automation and On-line Monitoring 

Chair: Li, Pingkang 

Co-Chair: Wang, Zaiying 



◮ SaB04-1 15:50–16:10 

A Dynamic Relative Gain Array Based on Model Predictive Control, 

pp.3340–3344 

JIANG, Huirong 




Loop pairing is a major concern in the stage of decentralized control 

system design of complex industrial processes. Without regard to the 

effects of the dynamic interaction,RGA was transformed to other impoved 

dynamic relative gain array. A new approach to defining a new dynamic 

RGA (s-DRGA) is presented based on multivariable state feedback 

predictive control(SFPC). The approach assumes the availability 

both of steady state and a dynamic process model. The new DRGA 

159


is defined based on the dynamic variable relation matrix and steady s- 

tate array. Several cases are studied and the comparisons with existing 

methods indicate that the proposed method is a useful tool to give the 

best pairing scheme. 

◮ SaB04-2 16:10–16:30 

Study on the Temperature Dual Control of CSTR with Coil Cooling and 

Jacket Cooling and Application, pp.3359–3363 

Wang, Zaiying 


Abstract―The CSTR is one of important equipments in chemical industry, 

and CSTR is above 90% of reactors used in main chemosynthesis 

material(plastic, synthetic rubber, synthetic fibre)production. Temperature 

dual control system solution is proposed based on analysis of 

CSTR structure with coil cooling and jacket cooling, and of insufficiency 

for temperature single loop control. The dual control system takes 

advantage of both coil cooling and jacket cooling so that both dynamic 

and static state performances of the control system get more satisfactory. 

Through theory analysis and semi-physical simulation, the advantage 

of the CSTR temperature dual control system is verified in control 

precision and dynamic response, and energy-saving and consumption 

reducing. 

◮ SaB04-3 16:30–16:50 

Saturated Output Feedback High-Gain Control for the Water-level of U- 

Tube Steam Generators, pp.3378–3383 

Dong, Zhe 


U-tube steam generator (UTSG) is one of the most crucial facilities 

in a pressurized-water nuclear reactor (PWR). Water level control of 

a UTSG is of major importance in order to secure the sufficient cooling 

inventory for the reactor core and, at the same time, to prevent the damage 

of turbine blades. Due to high nonlinearity and nonminimum phase 

characteristics of the UTSG dynamics and the fact that the feedwater 

flowrate is bounded, it is very necessary to establish an effective saturated 

nonlinear water-level controller. In this paper, a saturated output 

feedback high-gain control (SOFHC) for nonlinear SISO systems is p- 

resented. Furthermore, the SOFHC is applied to control the water-level 

for the UTSG of a nuclear heating reactor (NHR). Numerical simulation 

results show the high performance of the SOFHC and the influence of 

the feedback gain and maximal feedwater flowrate to the control performance. 

◮ SaB04-4 16:50–17:10 

Incremental PCA based online model updating for multivariate process 

monitoring, pp.3422–3427 

Hou, Ranran 

Wang, Huangang 

Xiao, Yingchao 

Xu, Wenli 





Principle Component Analysis (PCA) has been used widely for process 

monitoring in industry systems. But the data drifting problem, which 

commonly exists in the actual process, disables the monitoring model, 

and subsequently makes the monitoring system come out with plenty of 

false alarm. Therefore the efficiency of PCA based process monitoring 

is degraded in practical use. This paper presents an incremental PCA 

based online model updating method for multivariate process monitoring. 

The proposed method is based on the characteristic that industry 

processes preserve the correlation between variables under normal 

production conditions, which enables the method update the direction 

of loading vectors as well as the mean value and the standard deviation 

of the model automatically. Our method has low computational 

complexity, limited storage demand and robust to normal data drifting. 

Finally, the performance of the proposed algorithm is compared with 

conventional PCA and EWMA-PCA methods on a benchmark dataset 

of semiconductor etch process, through which our method is proved to 

be efficient. 

◮ SaB04-5 17:10–17:30 

An Approach to Rainfall Simulator Automation and Performance Evaluation, 

pp.3428–3433 

Cai, Jun 

Li, Pingkang 

Wang, Peng 



Beijing jiaotong Univ. 

A computer-PLC (Programmable Logic Controller) control solution was 

proposed for the artificial trough rainfall simulator. The rainfall process 

with continuous changing intensities was simulated automatically with 

the control strategy. Frequency-tunable pulse signal of specified duty 

ratio was generated precisely using pulse module of PLC to drive nozzles 

oscillating at required frequency. Thus, an arbitrary rainfall intensity 

within the output range of the simulator was achieved. Control configuration 

for trough rainfall simulator was built using Forcecontrol manmachine 

software. Parameters settings and rainfall intensities tracking 

during the rainfall simulation were realized. Simultaneously, a rainfall 

simulator performance evaluation procedure was performed based on 

a single nozzle rainfall test with six selected working states. A criterion 

of dynamic rainfall uniformity was proposed to estimate the performance 

of a rainfall simulator during the entire rainfall simulation. A 

Locally weighted scatterplot smoothing (LOESS) and weighted averaging 

approach was adopted in the evaluation procedure. 

◮ SaB04-6 17:30–17:50 

A Vison-based Grasping Strategy for the Mineral Sorting, pp.3454– 

3459 

Zhang, Yuren 

Qiao, Hong 

Su, Jianhua 

Huang, Kaiqi 


Inst. of automation, Chinese Acad. of Sci. 




Nagoya Univ. 

In industry application, e.g, mineral sorting, various mineral should be 

picked from the conveyer. The problem can be regarded as grasping 

prior unknown objects. In this paper, we aim to present a method to 

grasp the objects with a three-finger gripper guided by vision. Based 

on our previous works on attractive region, the way to locate a stable 

grasp is illustrated in the rotation space of the object. More specifically, 

the down-hill direction of the attractive region is used to guide the 

motion of the camera. And then, a grasp planar is proposed to generate 

hypothetic grasps from the contour of object. The vision and the 

grasping detection are coupled to reconstruct only few points for contacting, 

such that, the method can be conducted effectively. At last, the 

efficiency of the method is illustrated by a simulation. 

SaB05 15:50–17:50 Room 203E 

Control Engineering (II) 

Chair: Xiao, MingQing 

Co-Chair: Zhang, Weidong 



◮ SaB05-1 15:50–16:10 

Energy-Balancing-Based Control Design for Power Systems, pp.2364– 

2369 

Sun, Yajie 


Wang, Hong 




This paper focused on the development of the Hamiltonian theory and 

building Hamiltonian model, especially power system. To obtain better 

control result of Hamiltonian system, adaptive control and energybalancing-based 

control are considered. Combined those two methods 

with Hamiltonian control system, by using simulation, the performing 

result can be achieved. 

◮ SaB05-2 16:10–16:30 

Local Observer for Axial Flow Aeroengine Compressors, pp.2233–2238 

Gao, Xuejun 

Huang, Tingwen 

Liu, Jun 



Texas A& M Univ. 



Instability phenomena occurred in aeroengines are certainly very undesirable. 

The unstable flow not only causes a drastic reduction in the 

performance of the compressor but also can damage to engine’s components 

during operations. Thus it is necessary and critical to employ 

160



various feedback control mechanism to stabilize the system. Current all 

proposed feedback controllers in literature require the full information 

of the disturbance at the duct entrance, which is an infinite-dimensional 

quantity and thus is impossible for a direct on-line measurement in reality. 

In this paper, we construct a local state observer which can deliver the 

full information of the disturbance by only sensing on an arbitrarily s- 

mall area at the duct entrance. The approach relies on the semigroup 

theory as well as operator spectrum analysis. Numerical simulations 

are provided to illustrate the effective of the proposed observer by 

demonstrating different types of flow situations for the aeroengine compressors. 

◮ SaB05-3 16:30–16:50 

Missile Controlled by Aero-Fin and Divert Thrusters Using H2 Decoupling 

Analytical Design, pp.1383–1388 

Xue, Mantian 

Zhang, Weidong 

Jia, Xiaohong 

Jia, Jie 

McMaster Univ. 


Luoyang Photoelectric Tech. 

Nanchang Hangkong Univ. 

Reaction jet thruster provides extra moment in order to change the trajectory 

of the missile rapidly. However, it will induce interactions between 

the airflow and the thruster’s jet. In this paper, a simple H2 

decoupling analytical design under internal model control structure is 

adapted to a novel designed model of divert controlled missile. Using 

this model, controller can handle the system uncertainty and the 

coupling problem induced by reaction jet thruster. Simulation results 

showed the effectiveness of this design method. 

◮ SaB05-4 16:50–17:10 

Constrained Model Predictive Control for Backing-up Tractor-Trailer 

System, pp.2165–2170 

Bin, Yang 


In this paper, Lyapunov theory is used firstly to analyze the motion characteristics 

of the tractor-trailer system. Then, a novel mixed logical 

dynamics (MLD) model is proposed to describe the kinematic of the 

tractor-trailer system. For the resulting MLD model, an extended cost 

function with quadratic form is addressed, in which the steering angle 

of the tractor, as well as two errors combining the position and the orientation 

of the trailer are considered. To minimize the tracking error 

of the trailer, and smooth the steering angle behavior of the tractor, 

an equivalent linear quadratic tracking problem is presented. In view 

of this problem, an explicit model predictive control (MPC) controller 

is designed, by employing the multi-parametric mixed-integer quadratic 

programming (mp-MIQP) technique. Finally, the controller performance 

is evaluated through simulation, and achieves the asymptotical stability, 

accurate tracking feature and the constraint fulfillment properties, even 

in the case of the non-smooth tracking reference path. 

◮ SaB05-5 17:10–17:30 

Adaptive position tracking control for Bilateral teleoperation with constant 

time delay, pp.2324–2328 


Yang, Yana 

Liu, Xiaoping 

yanshan Univ. 

Inst. of Electrical Engineering 

Carleton Univ., Canada 

This paper addresses the position tracking problem for the teleoperation 

systems. With the use of neural network approximating unknown 

nonlinear functions, a neural network based sliding mode adaptive controller 

is designed to realize the synchronization of the master site and 

the slave site. By choosing Lyapunov Krasovskii functions, we prove 

that the motion tracking error converges to zero asymptotically. Finally, 

the simulations are performed to show the effectiveness of the proposed 

method. 

◮ SaB05-6 17:30–17:50 

MLP-based Nonlinear Modelling for Energy Saving in Forming Section 

of Paper Machines, pp.2358–2363 

Ding, Jinliang 




Afshar, Puya 

Wang, Hong 



Due to the increasing cost of energy and the demand of reducing the 

environmental footprints, energy saving is becoming an important subject 

in the industry operation. To realize the energy consumption optimization 

of papermaking, the energy model should be established while 

the product quality and process model also need to be constructed, 

which are taken as the constraints for optimization. This paper describes 

the identification of a forming section of paper machines with 

Multilayer Perception (MLP) Neural Networks. The process model, 

product quality model and energy consumption model are established 

for the energy saving in papermaking. The real industrial step tests are 

performed and the data are used to model training and validation. The 

models are validated by means of mean-squared error (MSE), fit measure 

and Akaike’s Final Prediction Error (FPE). The results show the 

effectiveness of the established models, which are suitable for the next 

work of energy optimization. 

SaB06 15:50–17:50 Room 302 

Sesor Networks 

Chair: Chen, Cailian 

Co-Chair: Liu, Zhigang 



◮ SaB06-1 15:50–16:10 

Critical Transmission Range for Connectivity in Aeronautical Ad-hoc 


Yan, Jianshu 

Song, Ge 

Lee, Hua 

Hua, Cunqing 

Chen, Cailian 

Guan, Xinping 

SJTU 

Univ. of Michigan-Shanghai Jiao Tong Univ. Joint 

Inst. 

SJTU 

SJTU 



Due to the tremendous increases in demand for performance based 

navigation and in-flight broadband Internet access in aeronautical industry, 

Aeronautical Ad-hoc Networks (AANETs) have been deemed as 

a viable solution to provide aircraft-to-aircraft communications without 

the aid of ground stations and satellite system. In this paper, we attempt 

to address the fundamental concerns arising in the AANETs, namely, 

under what conditions the AANETs are connected such that end-toend 

communications between aircrafts are feasible. We consider the 

aircrafts on a single flight path and introduce a 2-dimensional AANET 

model based on the specifications of International Civil Aviation Organization 

(ICAO). Based on this model, the Necessary Transmission 

Range (NTR) and Sufficient Transmission Range (STR) are derived as 

a function of the aircraft density, flight path length and airspace separation, 

which represent the critical conditions under which the AANET 

is disconnected or connected respectively. The analytical results are 

validated through simulation studies. 

◮ SaB06-2 16:10–16:30 

An analysis of energy balanced deployment strategy for strip-based 

wireless sensor networks, pp.4472–4477 

Wang, Zhengjie 

Zhao, Xiaoguang 

Qian, Xu 


Inst. of Automation,the Chinese Acad. of Sci. 



(Beijing) 

Stripe-based wireless sensor networks can be deployed in many scenarios, 

such as roads, bridges, tunnels and metros. The deployment 

of this type of networks must be considered in detailed because the 

network is characterized by the long distance and narrow width. The 

paper gives an analysis of balanced energy consumption deployment 

of this network. The discussion is presented according to two type of 

width of network. The main factors which affect the network lifetime are 

analyzed in detailed and the simulation is presented. The simulation 

result shows that the short width of networks has little effect on network 

lifetime. Also, the maximum network lifetime can not be reached when 

161


the nodes have short communication radius. The network lifetime can 

be affected by the long width of networks. The network lifetime can be 

maximized when the nodes have long communication range. 

◮ SaB06-3 16:30–16:50 

Auction Based Task Assignment for Pursuit-Evasion Game in Wireless 


Du, Rong 

Chen, Cailian 

Yang, Bo 

Guan, Xinping 





In this paper, we address pursuit-evasion games with the auxiliary of 

wireless sensor network. Wireless sensor network can provide global 

information, such as the positions of evaders, for pursuers to improve 

the performance. However, some ready strategies cannot be used directly 

because of the positioning errors of wireless sensor network. By 

analyzing the performance of Lion Strategy, A Lion-Mirror-Tackle Strategy 

is proposed for single evader model to assign the roles of pursuers 

and reduce the time and energy consumptions of capture. In the multipursuer 

multi-evaders model, evaders are assigned for several groups 

of pursuers and a decentralized and realtime assignment strategy 

based on auction process is proposed, to provide fast response to the 

motion of evaders. The simulation shows the efficiency of our algorithms. 

◮ SaB06-4 16:50–17:10 

Distortion Analysis for Delay Tolerant Data Collection for High-speed 

Wireless Sensor and Actor Networks, pp.4452–4457 

Liu, Yaxiong 

Chen, Cailian 

Yu, Hangchen 

Guan, Xinping 





In this paper, we propose to leverage high-speed rail as mobile actor to 

assist structural health monitoring (SHM) data collection and delivery 

for wireless sensor and actor networks (WSANs). Due to high density 

in the network topology, sensor observations have spatial and temporal 

correlation. With the spatiotemporal correlation, we first provide a twophase 

data collection scheme. In the first phase, the WSANs transmit 

the data to a sink node and then aggregate the data using the spatial 

correlation. In the second phase, when the high-speed train passing by 

the tunnel or the bridge, the sink node transmits the data to the train and 

then aggregate the data with consideration of the temporal correlation. 

Also because of the high speed mobility of the train, the communication 

link between the sink and the train may be fail, so we need to estimate 

the lost packet. We then estimate spatiotemporal distortion to denote 

the event collection’s reliability/fidelity. Through extensive simulation, 

we discuss several key elements that will affect the reliability. 

◮ SaB06-5 17:10–17:30 

Interacting Multiple Sensor Unscented Kalman Filter, pp.4409–4413 

Liu, Zhigang 

Wang, Jinkuan 



Due to the log-normal model of the received signal strength(RSS), the 

range measurements have variance proportional to their actual range, 

and so this results in degradation of the tracking performance with the 

range increasing. To deal with this problem, we consider the collaborative 

tracking procedure in a cluster as a Markov jump nonlinear system, 

and the design the interacting multiple sensor unscented Kalman filter(IMSUKF) 

algorithm via multiple measurement models in a cluster, 

which is different with the interacting multiple model(IMM) algorithm. 

This approach consists of three parts: one-step unscented Kalman filter 

sensor, probability update, and estimate fusion. Finally, simulation 

results show the effectiveness of the proposed method. 

◮ SaB06-6 17:30–17:50 

A measurements fusion filter for the multi-sensor system with correlated 

noises, pp.4458–4462 

Li, Shengwei 

Feng, Xiaoliang 

Henan Univ. 

Hohai Univ. 

Lu, Yazhou 

Henan Univ. 

Considering the sensor’s working environmental impact,the correlation 

of the system noise must be considered when fusing the measurements. 

The measurement fusion filtering method of a linear system with 

the correlation of measurement noises and the fusion time input noise 

and the correlation of measurement noises is researched in this paper. 

A centralized measurement fusion filtering method is present firstly, and 

then, a novel fusion measurement with low dimension is given based 

on the weighted least-square estimation theory. Utilizing a derived new 

measurement equation, a novel weighted measurement fusion filtering 

method is proposed in this paper. The final simulation illustrates the e- 

quivalent relation of the proposed measurement fusion filtering method 

and the optimal centralized measurement fusion method. 

SaB07 15:50–17:50 Room 303 

Advanced Control Algorithms and Applications (II) 

Chair: Sam, Yahaya 

Co-Chair: Guo, Wei 



◮ SaB07-1 15:50–16:10 

A Novel Model Algorithmic Controller with Fractional Order PID Structure, 

pp.2517–2522 

Guo, Wei 

Song, Ying 

Zhou, Li 

Deng, Ling 





In this paper, a novel MAC controller with fractional order PID structure 

(called FOPID-MAC) is proposed, which combined with the advantages 

of both fractional order PID and MAC. By introducing steady-state error 

weighted items into predictive control performance index in a broad 

sense, the predictive control algorithm is rebuilt according to fractional 

order PID control. The integral and derivative orders of FOPID-MAC 

controller improve the design flexibility of MAC. Moreover, the influence 

of the integral and derivative orders on system performance is analyzed 

in time domain. The results of simulation with two illustrative examples 

(including EPA) show validity and good performances of the proposed 

FOPID-MAC controller, which grounds well for engineering application. 

◮ SaB07-2 16:10–16:30 

Path Planning based Quadtree Representation for Mobile Robot Using 

Hybrid-Simulated Annealing and Ant Colony optimization Algorithm, 

pp.2537–2542 

Zhang, Qi 

Ma, Jiachen 

Liu, Qiang 

Harbin Inst. of Tech. Harbin 



In this paper, a new path planning approach combining framedquadtree 

representation with hybrid-simulated annealing (SA) and ant 

colony optimization (ACO) algorithm called SAACO is presented to improve 

the efficiency of path planning. The utilization of framed-quadtree 

representation is for improving the decomposed efficiency of the environment 

and maintaining the representation capability of maps. Simulated 

annealing and ant colony optimization were applied for robot path 

planning problem respectively and there have been plenty of accomplishments 

in recent year. Lots forms of SA depend on random starting 

points and how to efficiently offer better initial estimates of solution sets 

automatically is still a research hot point. We use ACO to supply a good 

initial solution for SA runs. According to the theoretical analysis and 

results obtained from simulation experiment, the presented SAACO algorithm 

can solve successfully the mobile robot path planning problem, 

which leads robot to seek the specific destination in the free-collision 

path and increases the speed of the robot navigation. Some excellent 

properties of this method have also been proved that is robustness, 

self-adaptation. 

◮ SaB07-3 16:30–16:50 

Point-to-Point Trajectory Tracking with Two-Degree-of-Freedom Robust 

Control for a Non-minimum Phase Electro-hydraulic System, pp.2661– 

2668 

Ghazali, Rozaimi 

Universiti Tun Hussein Onn Malaysia 

162



Sam, Yahaya 


Electro-hydraulic actuator (EHA) system inherently suffers from uncertainties, 

nonlinearities and time-varying in its model parameters which 

makes the modeling and controller designs are more complicated. The 

main objective of this paper is to perform a robust control design using 

discrete-time sliding mode control (DSMC) with two-degree-of-freedom 

(2-DOF) control strategy. The proposed controller consists of feedback 

and feedforward combination which capable to reduce phase lag and 

steady state error during the trajectory tracking of EHA system. The 

feedforward controller is developed by implementing the zero phase error 

tracking control (ZPETC) technique which the main difficulty arises 

from the nonminimum phase system with no stable inverse. A pointto-point 

trajectory is used in the experimental works to evaluate the 

performance of the DSMC. Experimental results reveal that the DMSC 

with 2-DOF control structure is highly robust and capable to deal with 

the uncertainties and disturbances occur during the position tracking 

control for different point of trajectories. It is also shows that the proposed 

controller can achieve better tracking performance as compared 

to conventional LQR and PID controller. 

◮ SaB07-4 16:50–17:10 

Multi-grade resin quality adaptive estimation for gas-phase polyethylene 

process, pp.2838–2843 

Zhao, Zhong 

Hu, Chuan 

Liu, Yang 




A major difficulty affecting the control of product quality in industrial 

polymerization reactors is the lack of suitable on-line polymer property 

measurements. In this article, a parameter updating law is deduced 

based on the predictive model of industrial polyethylene process resin 

quality. According to the off-line lab analytical data, an asymptotic tracking 

state observer design method is proposed to update the estimation 

of resin quality and predictive model parameter. The application results 

with the proposed method to an industrial plant verified the feasibility 

and effectiveness. With the proposed method, polymer properties of 

industrial polyethylene process can be on-line estimated and make it 

possible for achieving the advanced on-line product quality control. 

◮ SaB07-5 17:10–17:30 

A RLS Run-to-Run Control Approach for Semiconductor Manufacturing 

Process, pp.2642–2646 

Liu, Shujie 

Zheng, Ying 

Luo, Ming 

Wang, Yanwei 





Exponentially weighted moving average (EWMA) is a commonly used 

model-based algorithm for semiconductor manufacturing process Runto-Run 

(R2R) control. However, it’ s very difficult to set up the 

mathematical modeling for the actual semiconductor manufacturing 

process. Therefore, data-based methods such as Recursive Least 

Squares(RLS) have received wide attention nowadays. This paper proposes 

a variable forgetting factor RLS R2R control approach for semiconductor 

manufacturing process with and without drift disturbance. 

The variable forgetting factor resolves the drift disturbance well, and 

this method is much superior to generic RLS algorithm in convergence 

speed and tracking effect. It has both a strong ability to track parameters, 

and a small convergence estimate error. Simulation results prove 

the feasibility and accuracy of the algorithm. 

◮ SaB07-6 17:30–17:50 

Active Control of Periodic Impulsive Noise in a Non-minimum Phase 

System Using Repetitive Control Algorithm, pp.2770–2775 

Zhou, Yali 

Yin, Yixin 

Zhang, Qizhi 




In this paper, active control of periodic impulsive noise is studied. The 

stability of an active noise control (ANC) system using repetitive control 

(RC) algorithm is first analysed. Based on the stability condition, 

a novel non-causal stable inversion approach combined with an optimal 

criterion is used to design the RC controller for an ANC system 

with a non-minimum phase secondary path. A non-causal transversal 

finite impulse response (FIR) filter is used as the RC learning filter to 

compensate the phase lag of the plant. Computer simulations have 

been carried out to validate the effectiveness of the proposed algorithm. 

The plant model used in the computer simulations is obtained from 

a practical ANC system in our laboratory. Simulation results show that 

the proposed scheme can significantly reduce periodic impulsive noise 

and the convergence rate is fast for a non-minimum phase plant. 

SaB08 15:50–18:30 Room 310 

Award: Theory (II) & Invited Session: Control, Informatics, and Systems 

Biology 

Chair: Luh, Peter B. 

Co-Chair: Yang, Ruoting 



◮ SaB08-1 15:50–16:10 

Robust Altitude Control of an Unmanned Autonomous Helicopter Using 

Backstepping , pp.1650–1654 


In this paper, a nonlinear robust control technique is proposed to control 

heave motion for hover as well as vertically take–off/landing of an unmanned 

autonomous helicopter in the presence of external wind gusts. 

A heave motion model of a small helicopter is considered to derive the 

proposed controller for the purposes of capturing dynamic variations 

of thrust due to the external disturbances. A recursive (backstepping) 

design procedure is used to design the robust controller for vertical dynamics 

based on Lyapunov approach. To show the effectiveness of the 

proposed control method and its ability to cope with the external uncertainties 

in the vertical dynamics, results are compared with a classical 

PD controller. Comparative studies demonstrate that the proposed 

robust backstepping control method greatly enhance the performance 

over the classical PD controller and it is applied to RUAV hovering condition 

as well as vertical take-off/landing. 

◮ SaB08-2 16:10–16:30 

Function Perturbation Impact on the Topological Structure of Boolean 


Li, Haitao 

Wang, Yuzhen 

Liu, Zhenbin 

Shandong U 



This paper investigates the function perturbation impact on the topological 

structure of Boolean networks by using the semi-tensor product 

method, and presents a set of new results. First, a new necessary 

and sufficient condition is presented to guarantee that an attractor is invariant 

after function perturbations. Second, a necessary and sufficient 

condition is established to analyze how do the attractors of Boolean networks 

change with the Boolean function perturbations. Finally, as applications, 

the intervention problem of a WNT5A Boolean network and 

the function perturbation identification problem of a D. melanogaster 

segmentation polarity gene network are investigated, respectively. The 

study of the practical examples shows that the new results obtained in 

this paper are very effective in the function perturbations impact analysis 

of Boolean networks. 

◮ SaB08-3 16:30–16:50 

Epidemic Spreading on Complex Networks with Weighted Adaptive S- 


Zhou, Yinzuo 

Zhou, Jie 





We introduce a weighted adaptive network model to investigate the epidemic 

dynamics based on a susceptible-infective-susceptible (SIS) pattern, 

where the weight of a link describes the contact strength between 

two connected individuals. In the model, a susceptible node is able to 

transfer the weight of the link connecting an infected neighbor to a link 

connecting one of its susceptible neighbors. It is found that this weight 

adaption process could strongly aggravate the destructiveness of an 

163


epidemic and leads to a new population relation. Moreover, the effectiveness 

of a simple epidemic control strategy on a weighted adaptive 

network is examined. The results show that the weight adaption process 

may reduce the strategy efficiency. Analysis are presented and 

the results support our numerical simulations. 

◮ SaB08-4 16:50–17:10 

Identification of overlapping communities in protein interaction networks 

using multi-scale local information expansion, pp.5071–5076 

Li, Huijia 

Liu, Zhi-Ping 

Chen, Luonan 

Zhang, Xiang-Sun 





Most existing clustering approaches require the complete graph information, 

which is often impractical for large-scale protein-protein interaction 

networks. We proposed a novel algorithm which does not embrace 

the universal approach but instead tries to focus on local ties and model 

multi-scales of biological interactions in these networks. It identifies 

functional leaders and modules around these leaders using local information. 

It naturally supports overlapping information by associating 

each node with a membership vector that describes its involvement of 

each community. In addition to uncover overlapping communities, we 

can describe different multi-scale partitions allowing to tune the characteristic 

size of biologically meaningful modules. The high efficiency and 

accuracy of the proposed algorithm make it feasible to be used for accurately 

detecting community structure in real biomolecular networks. 

◮ SaB08-5 17:10–17:30 

Colored Petri Nets to Model Gene Mutation Classification, pp.5077– 

5082 

Yang, Jinliang 

Gao, Rui 







The genetic code is the triplet code based on the three-letter codons. 

Choosing a feasible model for processing these codons is a useful 

method to study genetic processes in molecular biology. As an effective 

model of discrete event dynamic systems (DEDS), Colored Petri Net 

(CPN) has been used for modeling several biological systems. According 

to the genetic code table, CPN is employed to model the process 

of genetic information transmission. In this paper, we propose a CPN 

model to classify the type of gene mutations via contrasting the bases 

of DNA strands and the codons of amino acids along the polypeptide 

chain. This model is helpful in determining whether a certain gene mutation 

will cause the changes of the structures and functions of protein 

molecules. The effectiveness and accuracy of the presented model are 

illustrated by the examples in this paper. 

◮ SaB08-6 17:30–17:50 

Core Module Network Construction for Breast Cancer Metastasis, 

pp.5083–5089 

Yang, Ruoting 

Daigle, Bernie 

Petzold, Linda 






For prognostic and diagnostic purposes, it is crucial to be able to separate 

the group of ”driver” genes and their first-degree neighbours, 

(i.e. ”core module”) from the general ”disease module”. To facilitate 

this task, we developed a novel computational framework COMBINER: 

COre Module Biomarker Identification with Network ExploRation. We 

applied COMBINER to three benchmark breast cancer datasets for i- 

dentifying prognostic biomarkers. We generated a list of ”driver genes” 

by finding the common core modules between two sets of COMBINER 

markers identified with different module inference protocols. Overlaying 

the markers on the map of ”the hallmarks of cancer” and constructing 

a weighted regulatory network with sensitivity analysis, we validated 

29 driver genes. Our results show the COMBINER framework to be 

a promising approach for identifying and characterizing core modules 

and driver genes of many complex diseases. 

◮ SaB08-7 17:50–18:10 

Closed-Loop Blood Glucose Control Using Dual Subcutaneous Infusion 

of Insulin and Glucagon Based on Switching PID Controller, pp.5023– 

5029 

Gao, Xiaoteng 

Wang, Youqing 



Glucose management is an important clinical task for diabetic patients, 

and intensive insulin therapy is widely considered a promising way for 

the glucose management. However, the intensive insulin therapy has 

one potential risk: hypoglycemia, but there is no antagonist to compensate 

hypoglycaemia in the intensive insulin therapy. Dual infusion of 

insulin and glucagon can overcome this shortcoming. In this paper, a 

switching control algorithm was proposed to design and optimize the 

insulin and glucagon infusion rates simultaneously, and this algorithm 

has been implemented in a virtual type 1 diabetic subject. The in silico 

results demonstrate that the proposed algorithm can reduce hypoglycaemia 

significantly. 

◮ SaB08-8 18:10–18:30 

Study on Some Modeling Problems in the process of Gene Expression 

with Finite State Machine, pp.5066–5070 

Gao, Rui 


Finite state machine (FSM) theory has great potentialities in understanding 

key concepts and analyzing molecular biological systems, e- 

specially in the process of gene expression. Based on the previous 

research work, this paper extends the study on the control problems in 

metabolism and gene mutation with FSM. The goal is to interpret how 

to apply control technologies to process of metabolism, and how to e- 

liminate the effects to secondary structures of protein caused by gene 

mutation. We hope the proposed model-based analysis will provide an 

exploration of new interdisciplinary theories intersected by information 

science, control theory, and the Molecular Biology. 

SaB09 15:50–17:50 Room 311A 

Award: Application (II) 


Co-Chair: Yang, Chunhua 


Central South Univ., China 

◮ SaB09-1 15:50–16:10 

Error Modeling and Analysis in Dynamic Wafer Handling, pp.3977– 

3982 

Cheng, Hongtai 

Chen, Heping 

Mooring, Ben 

Stern, Harold 



Lam Research Corporation 


Wafer handling robots are used to transfer wafers in semiconductor 

manufacturing. Typically a pick-measure-place method is used to transfer 

wafers accurately between stations. The measurement step is performed 

using an aligner, which is time-consuming. To increase the 

wafer transfer efficiency, it is desirable to speed up the measurement 

process or place it in parallel with other operations. Hence optic sensors 

are installed at each station to estimate the wafer eccentricity onthe-fly. 

The estimate process is mainly consist of two stages: sensor 

calibration and wafer eccentricity estimation. Theoretical analysis and 

numerical optimization methods are used to accomplish these tasks. 

Based on the wafer handling robot kinematics model, robot kinematics 

error, sensor calibration error and eccentricity identification error are 

analyzed in this paper. The effect of data sampling methods are also 

discussed. The proposed methods are validated using a wafer handling 

robot system. Experiment results demonstrate that the error analysis 

methods can greatly reduce the wafer eccentricity estimation error 

on-the-fly. Hence the developed methods can be used to improve the 

wafer handling accuracy and reduce the wafer handling cycle time in 

semiconductor manufacturing. 

◮ SaB09-2 16:10–16:30 

Planning Expected-time Optimal Paths for Target Search by Robot, 

pp.3881–3886 

164



Zhang, Botao 

Liu, Shirong 



Chen, Zhiyong 

Huang, Jie 



In this paper, a path planning approach for finding an optimal path is 

proposed to reduce the expected-time in target search by robot. This 

approach employs a heuristic algorithm to generate a basic path and 

minimize the expectedtime. Considering different direction may lead to 

different expected-time in a same loop, a direction choosing method is 

presented to improve the performance of this heuristic algorithm. Then, 

based on the improved algorithm, a two-level path planning approach is 

investigated. At the top level, the improved heuristic algorithm is used to 

generate a sequence of observation points. At the lower level, the Artificial 

Potential Field (APF) is employed to plan paths among observation 

points. Simulations and experiments demonstrated that this approach 

can reduce the expected time for target search. 

◮ SaB09-3 16:30–16:50 

Optimal Operation Strategies for Batch Distillation by Using A Fast 

Adaptive Simulated Annealing Algorithm, pp.2426–2430 

Wang, Lin 

Pu, Zhonghao 

Wen, Sufang 




Batch distillation processes are widely used in the chemical industry. 

In this work, the optimal operation strategies for such processes are 

studied by using a fast adaptive simulated annealing (FASA) algorithm. 

Simulated annealing algorithm is stochastic in nature, and it converges 

towards a global optimum. However, its computational load is usually 

much too heavy. In this study, a FASA algorithm was presented with 

fast and adaptive moves in the searched neighborhood range to decrease 

the computation load. According to the characteristics of batch 

distillation process, a FASA-based parallelized optimization computation 

approach was proposed and then it was applied to a model of a 

batch distillation plant. The optimal operation strategies with respect to 

minimal production time and maximal profit were studied. The results 

show the effectiveness of the method. 

◮ SaB09-4 16:50–17:10 

PID Control of Glucose Concentration in Subjects with Type 1 Diabetes 

based on a Simplified Model: An In Silico Trial, pp.5051–5055 

Li, Peng 

Yu, Lei 

Guo, Liquan 

Dong, Jixiang 

Hu, Ji 

Fang, Qiang 

SIBET 

Suzhou Inst. of Biomedical Engineering & 

Tech.,CAS 

SIBET 



SIBET 

An artificial pancreas system (APS) mimics the function of a real pancreas 

through monitoring a diabetic’s blood glucose and administering 

the right dose of insulin via an automatic control loop. It is hailed as a 

promising cure of diabetes, though this technology is still years away 

from commercial use due to a few technological bottlenecks. The simulation 

model of insulin-glucose metabolism of type 1 diabetes mellitus 

(T1DM) is an essential part of APS. In order to simplify the parameter 

identification task so that the model can be implemented electronically 

with ease, this paper presents a simplified model based on Routh 

approximation model reduction method. The results show that the approximation 

error between the simplified model and the original model 

is so small that can be neglected. Based on the simplified model, a 

PID controller is designed to maintain normoglycemia (90mg/dl) in subjects 

with T1DM. The in silico simulation results show that the glucose 

concentration is controlled well, the risk of hyperglycemia and hypoglycemia 

is reduced a lot. This suggests that the simplified model describes 

the insulin-glucose metabolism process accurately, and the PID 

control algorithm is well-suitable to guide the further development of an 

APS. 

◮ SaB09-5 17:10–17:30 

Parameter Convergence Analysis in Adaptive Disturbance Rejection 

Problem of Rigid Spacecraft, pp.1418–1423 

The asymptotic rejection of rigid spacecraft systems under multi-tone s- 

inusoidal disturbances with unknown frequencies was studied recently. 

It was shown that the problem can be solved via an adaptive control approach. 

However, the convergence issue of the estimated frequencies 

to the unknown frequencies has not been investigated. In this paper, 

we further give some sufficient conditions for guaranteeing the convergence 

of the estimated frequencies to the unknown frequencies. 

◮ SaB09-6 17:30–17:50 

H2 Performance Limitation of a Class of Nonlinear Non-minimum 

Phase Systems, pp.1317–1322 

Lu, Di 

Fan, Guoliang 





In this paper, we study H2 performance limitation of the nonlinear nonminimum 

phase systems with a ’new’structure that the linear combination 

of all the states of the systems serves as the input of the dynamics 

where the unstable zero-dynamics is. It is shown that the best 

attainable tracking performance of the above system is equal as the 

minimum energy of stabilizing the ’extended’dynamics which is relevant 

to all the system’s states except for the state whose differential 

equation contains the system’s control input explicitly. In the end, we 

use two examples to illustrate our theorem. The first one is a simple 

comparison between two systems with different structures and the second 

one is a more practical object - the aircraft’s longitudinal motion. 

For the latter one, we derive the H2 performance limitation of the nonlinear 

aircraft’s longitudinal motion. 

SaB10 15:50–17:50 Room 311B 

Invited Session: Modeling and Control: Challenges from Automotive 

Industry 


Co-Chair: OHATA, AKIRA 

Sophia Univ. 


◮ SaB10-1 15:50–16:20 

Benchmark Problem for Nonlinear Identification of Automotive Engine, 

pp.3305–3310 

OHATA, AKIRA 


As automotive engines have been becoming complex due to the pressures 

from CO2 emission reduction, safety and drivability, the automotive 

industry has encountered the issue that experiments are exponentially 

increasing. Model-based calibration was introduced to the steady 

state calibration area and the automotive industry succeeded to reduce 

the experiments by half in the case of typical gasoline engine. According 

to the success, the automotive industry intends to expand the technology 

to the transient calibration area that is highly connected with 

nonlinear identification. In this benchmark problem, the challengers 

are asked to develop identification methods including design of experiments. 

Their results will be evaluated by the accuracies and the data 

sizes used in the identification. 

◮ SaB10-2 16:20–16:50 

JSAE-SICE Benchmark problem II: Fuel Consumption Optimization of 

Commuter Vehicle Using Hybrid Powertrain, pp.606–611 

Yasui, Yuji 

Honda R&D CO., Ltd 

The Technical Committee on Vehicle Control and Modeling was established 

by JSAE (Society of Automotive Engineers of Japan) and SICE 

(The Society of Instrument and Control Engineers) in order to promote 

collaboration between automotive industry and academics. The committee 

provides “Benchmark problem 2: Fuel Consumption Optimization 

of Commuter Vehicle”for academics. This is a research to design 

energy management control to minimize fuel consumption of a hybrid 

electric vehicle. The vehicle is used under commuting condition with 

traffic jam. This paper describes a simulation environment, a vehicle 

model and driving condition of the benchmark problem. Furthermore, 

the energy management control using onboard optimization algorithm 

is introduced as a sample of research of benchmark problem in this 

165


paper. 

Chen, Qijun 

Tongji Univ. 

◮ SaB10-3 16:50–17:20 

Integrated Vehicle Dynamics Control of an In-Wheel-Motor Vehicle, 

pp.2335–2339 

Hirano, Yutaka 


For future mobility intended to decrease energy consumption by applying 

smaller and lighter body and lower RRC (Road Resistance Coefficient) 

tires, there is a possibility that maneuverability and stability will 

become worse than current conventional vehicles. To solve this problem, 

a benchmark problem is settled by ‘Committee of vehicle control 

and modeling research’of JSAE aiming to utilize the knowledge of 

system control academia for actual industry problems. The problem is 

to develop an integrated control of independent driving / braking torque, 

steering angle and camber angle of each wheel of an in-wheel-motor 

vehicle for both improvement in vehicle maneuverability and stability index 

and also minimizing energy consumption. In this paper, definition of 

the problem, vehicle and test conditions and a model for the simulation 

are described. 

◮ SaB10-4 17:20–17:50 

SICE Benchmark Problem of Engine Control and a Challenging Results, 

pp.2340–2345 

Shen, Tielong 

Zhang, Jiangyan 

Sophia Univ. 

Sophia Univ. 

As an internal combustion engines, automotive gasoline engines involve 

nonlinearities and stochastic characteristics that are usually difficult 

to handle in modeling and control. To promote the applications 

of advanced control theory in the engine systems, The SICE Technical 

Committee on Advanced Control of Automotive Powertrains proposed a 

benchmark problem that targets the starting speed control of the gasoline 

engine, since during the starting phase, the thermal property of the 

engine changes dramatically and causes the difficulty in control system 

design. This paper will give an detailed explanation of the benchmark 

problem statement first, and then introduce a challenging result proposed 

by the authors that shows the effectiveness of nonlinear modelbased, 

coordinative control between the multi-loops such as ignitions, 

injections of each cylinders and throttle operating. Experimental validations 

will be also demonstrated conducted on a commercial care used 

V6 engines. 

SaB11 15:50–17:50 Room 311C 

Invited Session: Control of High-Performance Engineering Systems 

Chair: Pang, Chee Khiang 


◮ SaB11-1 15:50–16:10 

Neural-Network Based AUV Path Planning in Estuary Environments, 

pp.3724–3730 

Li, Shuai 

Guo, Yi 



For the path planning problem of autonomous underwater vehicles (AU- 

Vs) in 3-dimensional (3-D) estuary environments, traditional methods 

may encounter problems due to their high computational complexity. In 

this paper, we proposed a dynamic neural network to solve the AUV 

path planning problem. In the neural network, neurons get input from 

the environment, locally interact with the neighbors and update neural 

activities in real time. The AUV path is then generated according to 

the neural activity landscapes. Stability, computational complexity of 

the neural network, and optimality of the generated path are analyzed. 

AUV path planning in 3-D complex environments without currents, with 

constant currents, and with variable currents are studied through simulations, 

which demonstrate the effectiveness of this approach. 

◮ SaB11-2 16:10–16:30 

Leader-follower and communication based formation control of multirobots 

, pp.229–232 

Wang, Zhuping 

Mao, Ying 

Chen, Guangmou 

Tongji Univ. 

Tongji Univ. 

Tongji Univ. 

In this paper, leader-follower strategy and communication technology 

are combined together to carry out the formation control of multiple 

nonholonomic mobile robots. The controller is proposed based on 

a kinematic model using Cartesian coordinates for the followers and 

a classical control algorithm for the leader is used. In order to improve 

the adaptability to the environment, communication based on a 

kind of cross-platform technology Qt is proposed to realize the selfpositioning 

and mutual communication among robots, in which multithreading 

technology is also adopted. Simulation results prove the effectiveness 

of the proposed plan. 

◮ SaB11-3 16:30–16:50 

Tracking Control for Nonholonomic Mobile Robots with Visual Servoing 

Feedback , pp.3864–3869 

Wang, Baolei 

Wang, Chaoli 



This paper discusses a tracking control problem of nonholonomic mobile 

robots with visual servoing feedback. By comparing corresponding 

target points of an object from two different camera images, geometric 

relationships are exploited to derive a transformation that relates the actual 

position and orientation of the mobile robot to a reference position 

and orientation. This transformation is used to synthesize a rotation and 

translation error system from the current position and orientation to the 

fixed reference position and orientation. Lyapunov-based techniques 

are used to construct a robust controller to stabilize the error system 

with a constant, unmeasurable depth parameter. The contribution of 

this paper is that Lyapunov techniques are used to construct a robust 

controller that makes mobile robot position and orientation converge to 

the desired configuration despite the lack of an object model and the 

lack of depth information. Simulation results are provided to illustrate 

the performance of the controller 

◮ SaB11-4 16:50–17:10 

Consensus Output Regulation Without State Estimation, pp.2306–2311 



This paper deals with consensus output regulation of network connected 

multi-agent systems. Every agent or subsystem has identical dynamics, 

and it is required to follow a desired trajectory which is generated 

by a linear dynamic system, known as the exosystem. Only some 

subsystems have access to the desired trajectory, and other subsystems 

will have to reply on the exchange of information through the network. 

In this paper, a consensus control design is proposed to ensure 

that the outputs of all the subsystems converge to the same desired 

output trajectory by exploiting the internal model design strategy. The 

proposed control design only uses the relative outputs of the subsystems, 

and does not require the estimation of subsystem state variables. 

◮ SaB11-5 17:10–17:30 

design of decoders based on memristors, pp.5116–5121 

Zhou, Jing 


A Memristor is a nonlinear resistor with memory, and is considered as 

the fourth type of basic circuit element except resistor, capacitor and inductor. 

With special circuitry characteristics, memristor can be used for 

both information storage and logic operations. In traditional Von Neumann 

computer architecture, address decoder is an important component 

connecting processor and memory. This paper aims at designing 

a decoder based on memristors. We first analyze and model the characteristics 

of memristor with HSPICE, and then design decoders based 

on NOR and AND operation respectively with memristors and compare 

their performance. The experimental results and analysis show that our 

design is right in function and has good scalability. 

◮ SaB11-6 17:30–17:50 

The Charging and Discharging Characteristics of Memcapacitor Storage 

with Applications, pp.5139–5143 

He, Pengfei 

Wang, Lidan 



166



Duan, Shukai 


Memcapacitor is similar with the memristor, it is the passive electronic 

component with memory, reserving data even power is off, so it has 

wide promising applications in different areas. This paper addresses a 

memcapacitor model, discusses its properties with data reading, data 

storing, charging and discharging, designs the circuits about data reading 

and storing, a series of simulation results verify the effectiveness 

of this circuits, apply in the memcapacitor to the single-phase bridge 

rectifier circuit, The output wave of memcapacitor-based single-phase 

bridge rectifier circuit is better than the ecumenical capacitor. 

Chair: Zhao, Qianchuan 

Poster Session PSaA 

July 7, 13:30-14:50 


◁ PSaA-01 

Research of Dynamic Texture Mapping Technology realized by Multisoftware 

collaboratively, pp.3191–3194 

Wang, Dong 

Zhang, Tuo 

Cao, Yuchao 

Li, Dalei 





At present, the virtual prototype technology has been widely used in the 

world. VRML and Open Inventor are especially outstanding in the field 

of virtual reality technology and 3-D graphics development technology, 

but between them there are still some incompatibility problems in front 

of dynamic texture mapping. This paper introduces a kind of method 

about solving the incompatibility problems to realize work process simulation, 

using many kinds of software, based on the virtual prototype 

technology. 

◁ PSaA-02 

Prediction of Flooding Velocity in Packed Tower Using Least Squares 

Support Vector Machine, pp.3226–3231 

Li, Changli 

Liu, Yi 

Yang, Jie 

Gao, Zengliang 



Zhejiang Univ. of Technolog 


The flooding velocity is an important but difficult to accurately predict 

parameter for the packed column design. With the appearance of new 

packing shapes, traditional empirical models are insufficient to satisfy 

the requirement of engineering applications. In this paper, a novel approach 

using least squares-support vector machine (LS-SVM) is proposed 

to predict the flooding velocity in the randomly dumped packed 

towers. To evaluate the performance of the LS-SVM model applied to 

predict the flooding velocity, it is compared with the traditional empirical 

models and the neural network models. It is found that the LS-SVM 

model can provide the best performance of all, with an average absolute 

relative error less than 8 %. The results demonstrate that LS-SVM 

offers an alternative approach to model and predict the flooding velocity 

in the randomly dumped packed towers. 

◁ PSaA-03 

Development and Research on Multi-National Banknote Sorting System, 

pp.3331–3335 

Zhang, Ying 


Banknote sorter is a kind of machine for paper currency, which is 

applied to sorting, separating, discriminating, recording, statisticsing, 

seeking out deformity and choosing cash for ATM. It is a high-tech financial 

processing device integrated with optics, mechanics, electronics 

and software. This paper introduces the structures, features and 

functions of multination banknote sorting system and emphasizes on 

the design methods on image scanning, image processing and image 

recognizing. The design processes include the introduction of mechanical 

structure, the design of system hardware, the sampling method of 

multispectral image, the arithmetics of image processing and recognizing, 

and the construction of banknote tracing system. 

◁ PSaA-04 

Application of PLC and Touch-screen in The Power Window Control 

System, pp.3355–3358 

Li, Binglin 

Tao, Quan 



In order to meet the requirements of intelligent window in intelligent 

building, based on PLC high reliability, advanced control technology 

and touch screen’s friendly interface and easy and flexible configuration 

programming, design of the power window control system of 

intelligent machines combined with the PLC and touch screen. Satisfactory 

results have been obtained through simulation experiments and 

commissioning. The working principle, hardware construction, software 

program and function characteristics of the designed intelligent window 

control system were expounded in detail. The design of fire associated 

control functions, the force power ban when program run overtime, skylight 

communication abnormalities alarm were discussed. The design 

of the intelligent windows control system has run smoothly, safety and 

high reliability and content request to a modern intelligent building. 

◁ PSaA-05 

Optimization of Power Plant DCS Control Based on Data Communication, 

pp.3434–3437 

Fu, Junjie 

Zhao, Zhigang 

Long, Junfeng 

Cai, Jun 

Li, Pingkang 






A DCS (distributed control system) optimization approach introducing 

advanced algorithm realized in MATLAB into the control configuration 

was proposed. UDP and Modbus/TCP protocol communication program 

was written to establish two way data communication between the 

EDPF-NT plus DCS and MATLAB. Two timer objects in MATLAB were 

used to ensure real-time transmission of the data. Independence and 

reliability was guaranteed by using communication interface offered by 

DCS. An applying example of drum level fuzzy-PID control was given 

to prove the feasibility of this approach. 

◁ PSaA-06 

QP-based SMP Scheme for Robots with Pseudoinverse Method Compared 

and Singularities Discussed, pp.3583–3588 

Zhang, Yunong 

Xiao, Lin 



To achieve the self-motion planning (SMP) of redundant robot manipulators, 

this paper investigates an SMP scheme by considering joint 

physical limits. The scheme is then reformulated and unified as a 

quadratic program (QP) subject to equality and bound constraints. For 

comparison purposes, the classical pseudoinverse-type method is p- 

resented. Simulation results based on a three-link planar manipulator, 

PUMA560 and PA10 manipulators further substantiate the efficacy and 

superiority of the SMP scheme. Besides, the effect of design parameters 

λ and γ is investigated, and the singularities of the self-motion for 

redundant manipulators are discussed. 

◁ PSaA-07 

Research of the Robot for the Elderly Based on Star Positioning, 

pp.3639–3641 

Tian, Liguo 

Li, Meng 

Chen, Zhiliang 

Duan, Haillong 





With acceleration of the population aging process, the people pay more 

and more attention on the development of the robot for the elderly. On 

the basis of technical research and development of service robot positioning 

technology in recent years, the paper designed the system of 

the robot for the elderly based on star positioning. The system used 

ARM9 processor S3C2440 as the core, equipped with IPC and star 

positioning module and various sensor modules, could complete some 

assistant works for the old people’s living, such as stable walking, environmental 

monitoring, grasping objects and so on. After tested, the 

system works well and achieved the design requirements. 

167


◁ PSaA-08 

Lateral Stabilization of a Single Wheel Robot Applying Electromagnetic 

Force, pp.3675–3680 


Zhu, Xiaoqing 

Li, Yalei 

Wei, Ruoyan 





The lateral stabilization of single wheel robot is a challenge for researchers. 

We proposed a new mechanism to solve the problem by 

applying electromagnetic force. The mechanism was described briefly, 

and after dynamic analyses of the robot, its state space equation was 

obtained. The prototype was built in ADAMS, and integrated with MAT- 

LAB a PID controller and a LQR controller were performed respectively. 

A simple physical experiment was carried out on inverted pendulum applying 

Lorentz force. Simulation and physical experiment results show 

that the proposed mechanism is feasible and more energy efficient. 

◁ PSaA-09 

Modeling and Control for UW-Car in Rough Terrain, pp.3747–3752 

Mao, Lifei 

Huang, Jian 

Ding, Feng 


Matsuno, Takayuki 




Nagoya Univ. 

Toyama Prefectural Univ. 

There has been an increasing interest in a kind of underactuated mechanical 

systems, mobile wheeled inverted pendulum (MWIP) models, 

which are widely used in the field of autonomous robotics and intelligent 

vehicles. A novel transportation system, UW-Car, which is composed of 

an MWIP system and a movable seat is proposed. The reduced-order 

dynamic model of a UW-Car system running in a rough terrain is obtained 

by applying Lagrangian motion equations. A sliding mode control 

approach is proposed based on the dynamic model. Comparing with 

the linear quadratic regulator (LQR) approach, the new method guarantees 

that a UW-Car system can run in a rough terrain while keeping 

the body upright and the seat on some fixed position. The theoretical 

results are finally demonstrated through numerical simulations. 

◁ PSaA-10 

A Novel Pursuit Strategy for Fast Evader in Indoor Pursuit-Evasion 

Games, pp.3834–3839 

Fan, Xinyi 

Zhu, Shanying 

Du, Rong 

Chen, Cailian 

Guan, Xinping 






This paper is concerned with the pursuit problem of indoor pursuitevasion 

games (PEGs) with multiple pursuers and one fast evader. The 

system is implemented in multiple mobile robots with the connection 

over wireless communication. A model of the pursuit problem of fast 

evader in indoor PEGs is presented to consider the case of unknown 

position of evader. To solve this PEG, we introduce the concept of dirty 

set to represent all possible locations of the evader. Based on an intuitively 

defined cost function, we then propose the ISCD algorithm to 

guarantee the shrink of the dirty set until the evader is successfully 

captured. Experiment results show that ISCD algorithm has guaranteed 

convergence property and the capture occurs even with a small 

number of pursuers after a few steps. 

◁ PSaA-11 

Research and Development of Comprehensive Monitoring and Management 

Platform for Substation, pp.3948–3953 

Wang, Hua 

Yan, Gangfeng 

1.Zhejiang Univ. 2.Zhejiang Univ. of Sci. & Tech. 

college of electrical engineering 

Substation is an important part of power system and its operation s- 

tatus exerts great effect on the safety of power system. Comprehensive 

monitoring management platform for substation (CMMP) is a digital 

monitoring platform which has uniform interfaces; flexible expansibility 

and hierarchical network control ability, and it is custom-designed 

for safe operation of substation. CMMP consists of direct current realtime 

monitoring subsystem, intrusion detection and hierarchical video 

monitoring subsystem, temperature and humidity and fire monitoring 

subsystem based on WSNs. CMMP adopt composite structure of C/S 

and B/S which would be convenient for computer and other mobile end 

to query monitoring information. Owing to the perfect private network 

of power system, CMMP can monitor both local and cross-domain substations. 

In addition, CMMP can classify and manage all kinds of alarm 

information from substation and realize distribution monitoring centralized 

management. 

◁ PSaA-12 

Design on Logistics Management Automation System for Waterway Integrated 

Port, pp.3954–3957 

Ao, Chaohua 

Wu, Qian 

Yang, Xiaoyi 




Aimed at the puzzle of being lower in management efficiency of waterway 

integrated port resulted from being lack of informatization tool, 

the paper designed a logistics management automation system. In the 

paper, it made the requirement analysis, constructed the system architecture 

by means of hybrid pattern with C/S plus B/S, designed the 

related function module and user interface, and through programming it 

realized an actual logistics management automation system based on 

.Net Framework. The running result of a certain waterway integrated 

port in inland river waterway demonstrated that it improved logistics efficiency 

outstandingly. The better running effect validates its feasible in 

design. 

◁ PSaA-13 

Study on the Continuous Direction Control for the Snake-like Robot, 

pp.1276–1281 


Wu, Chengdong 

Li, Bin 





Sci. 

Direction control is an important part of the study on the snakelike 

robot. A direction control algorithm which is based on the passive 

creeping, named continuous direction control, is proposed. This 

method controls the movement direction of the robot’s head by the 

exponential function of the reference angle amplitude, and makes the 

body follow the head by adjusting the torque amplitude of the body. The 

fixed point movements and the fixed direction movements of the snakelike 

robot are simulated. In simulations, the angles, energy states are 

revealed, and the errors are analyzed by comparing the moving direction 

with the expected direction. The simulations demonstrate the validity 

and veracity of the continuous direction control method. 

◁ PSaA-14 

A Game Theoretical Bandwidth Allocation Mechanism for Cloud 

Robotics, pp.3828–3833 

Wang, Lujia 




Cloud robotics is driving interest in both academia and industry, since 

it would allow robots to off-load compute intensive tasks, combine with 

multiple robots and even download new skills. For resource sharing a- 

mong users in cloud robotics, bandwidth allocation is the fundamental 

and dominant task in these networks. However, poses many technical 

challenges that are still outstanding, since incast congestion happens in 

high-bandwidth and low-latency networks, when multiple synchronized 

users send data to a same receiver in parallel [1]. In this paper, we introduce 

a resource allocation framework for cloud robotics, and propose a 

game-theoretic problem formulation and linear pricing scheme of bandwidth 

allocation, we also implement a congestion control algorithm by 

using optimal parameters derived from the game-theoretic algorithm. 

Simulation results demonstrate that the proposed mechanism achieves 

better performance of bandwidth allocation in cloud robotics scenarios. 

168



◁ PSaA-15 

Multivariable Generalized Predictive Control Based on Receding Feedback 

Correction in Binary Distillation Process, pp.1098–1102 

Li, Chaochun 

Tao, Lili 

Cheng, Hui 

Qi, Rongbin 

Qian, Feng 






An improved generalized predictive control (GPC) algorithm has been 

proposed in this paper. In order to cut off computation burden and get 

good performance of disturbance rejection, receding feedback correction 

mechanism is used to reject the disturbance instead of the traditional 

online identification mechanism. Furthermore, two factors are 

presented to tune for good dynamic performance. Simulation has been 

carried out with a binary distillation process model, named wood& berry 

model, which is a typical multivariable process. The simulation result 

proves the efficiency of proposed algorithm. 

◁ PSaA-16 

Nonlinear PID Synchro Control on Broaching Machine with Dual Cylinder, 

pp.1124–1129 

Ni, Jing 

Shao, Bin 

Chen, Guojin 




In view of the shortcomings on synchro-motion of slide carriage in 

hydraulic broaching machine driven by single cylinder, an electrohydraulic 

servo system with dual cylinder is designed and its nonlinear 

model was presented. Then the theory of taylor expansion to second 

order of output vector, optimal control and nonlinear disturbance 

observer designing are introduced, and a nonlinear PID synchro controller 

for electro-hydraulic servo system with dual cylinder is provided 

on the basis of tracking and synchronization index. The simulation and 

factual application in hydraulic broaching machine show that the proposed 

controller has better tracking and synchronization performance 

than conventional PID controller. The nonlinear PID synchro-controller 

is effectively for electro-hydraulic servo system with dual cylinder in hydraulic 

broaching machine. 

◁ PSaA-17 

Controllability and Observability of Matrix Riccati Type Differential Systems, 

pp.1135–1138 

Xu, Dengguo 

Chuxiong Normal Univ. 

This paper is concerned with the controllability and observability for a 

class of matrix Riccati type differential systems. The solution of such 

matrix systems is obtained via using variation of parameters. Meanwhile, 

the sufficient and necessary conditions for state controllability 

and state observability of such systems are established. 

◁ PSaA-18 

Robust Non-fragile Control for Non-linear Singular Discrete Systems 

with Time-delay, pp.1139–1143 

Fu, Xingjian 

Liu, Xiao He 

Hou, Ming 

Li, Yingchun 




North China Inst. of Astronautic Engineering 

This paper mainly studies the problem of designing a non-fragile robust 

controller for a class of singular discrete non-linear systems with delayed 

state and uncertainties. Based on Lyapunov stability theory and 

linear matrix inequality (LMI), LMI-based sufficient condition for the singular 

discrete nonlinear systems to have robust performance is derived. 

The condition enables us to determine robust stability of time-delay uncertain 

singular systems easily by solving the LMI. Furthermore, the 

non-fragile controller is designed to guarantee that the closed loop system 

is regular, causal and stable for a class of singular discrete nonlinear 

systems. This new non-fragile control approaches can yield less 

conservative result than the simultaneous stability approaches. At last, 

a numerical example is included to illustrate the efficiency o f the proposed 

approaches in this paper. 

◁ PSaA-19 

The Analysis and Design of Diphasic Pacemaker Pulse System Based 

on Microcontroller, pp.1192–1195 

Xu, Longjian 

Zhang, Houwu 

Yao, Kaixue 

Guizhou Univ. 

Guizhou Univ. 

Guizhou Univ. 

With the purpose of replacing traditional defibrillation which can bring a 

lot of shortcomings, the research of new treatment modalities for AF has 

become an inevitable choice. Designed a diphasic pacemaker pulse 

stimulation, Adopt low energy terminate treatment to patients. The system 

has digital FM, Am and other auxiliary functions to meet the needs 

of different conditions. Through the LCD and displayed the related parameters 

directly to users, making it more convenient and user-friendly. 

with some innovative and practical value. 

◁ PSaA-20 

Design And Implementation of a Constant Selling Oil Automatic System 

Based on Fuzzy PID Algorithm, pp.1207–1211 

WANG, LI 

ZHAO, ZHENG 



In this paper, we propose a novel strategy to design a reliable and 

practical constant selling oil automatic system ,through the use of S7- 

226PLC, flow meter, inverter and other hardware devices. A kind of 

fuzzy PID control algorithm was researched to guarantee the system 

accuracy. The fuzzy PID controller can control the inverter output frequency 

by changing the voltage of the inverter and self-tuning PID parameters 

(Kp, Ki, Kd), and thus it controls the pump speed to achieve 

the demanded accuracy of the constant selling oil. Finally ,the paper 

adopted C#.NET technology to design the management software. The 

software can accomplish system functions such as communication with 

PLC,realization remote control, data management and others. Experimental 

results and field running shows that the system achieves the 

accuracy,security and energy saving demands of the constant selling 

oil automatic system, also obtains a satisfactory control effect. 

◁ PSaA-21 

Research on the Brittleness Problem of Power System with Wind 

Turbine Generator System Based on the Cellular Automata Theory, 

pp.1218–1223 

Guo, Jian 

Yang, Yong 



The wind turbine generator system (WTGS) is connected to the grid 

from one point of power system. When the faults of power grid occur, 

the behavior of WTGS has an important influence on motivating the 

brittleness of power system. The model of power system with WTGS 

is built up by using cellular automata theory. Taking IEEE standard-6 

node power system for example, based on the presented algorithm and 

steps, the brittleness problem of power system with WTGS is studied. 

With the number and low voltage ride through (LVRT) of WTGS being 

connected to grid considered, the analysis and conclusion are also given. 

The presented method can provide help for choosing the types of 

WTGS to the grid in reality. 

◁ PSaA-22 

Bifurcation and stability study in coupling nonlinear rotating machinery’ 

s systemunder the influence of time-varying stiffness, pp.1263–1268 

Liu, Shuang 

Li, Yanshu 

He, Minjia 

Wang, Bohua 

Liu, Bin 

Yanshan Univ. 

Yanshan Univ. 

Yanshan Univ. 

Yanshan Univ. 

Yanshan Univ. 

Abstract - Considering unstable oscillation in the process of rotating 

machinery caused by changing in load lubrication and gear meshing 

stiffness in shaft system, the coupled nonlinear dynamic equation of 

rotating mechanical drive system is established with time-varying stiffness 

and nonlinear friction force based on Lagrange theory. The average 

equation of system is solved with the aid of method of multiple 

scales. Besides, according to Hopf bifurcation theory the stability of 

169


system is analyzed, the necessary and sufficient condition of Hopf bifurcation 

and periodic motion’s stability are given, and the influence of 

supercritical and subcritical bifurcation on torsional vibration of rotating 

mechanical drive system is analyzed under the condition of parametric 

resonance and internal resonance. Last, the numerical simulation 

verifies the results. A significant contribution of this study is to ensure 

smooth running of rotating machinery system, and provide a theoretical 

basis for the future design of mechanical component. 

◁ PSaA-23 

Performance Bound of Parallel Cascade Control System Based on 

Minimum Variance and Generalized Minimum Variance Benchmarking, 

pp.1334–1339 

Guo, Jian Wen 

Du, Wenli 

Qian, Feng 




Abstract - The procedure is reviewed based on minimum variance (MV) 

and Diophantine decomposition for the parallel cascade control system. 

A novel method is derived based on the previous methods in this paper. 

The optimal controllers can be obtained directly by using the decomposition 

technique of Diophantine equations other than the closed loop 

operating data. Besides, the theoretical performance bound can be 

computed simultaneously. The generalized minimum variance (GMV) 

Benchmarking for parallel cascade control system is also put forward 

. The results of the proposed methods are demonstrated through a 

simulation example and compared with the previous results. 

◁ PSaA-24 

Robust Formation Control of a class of Multi-agent Systems by Output 

Regulation Approach, pp.1412–1417 

Sun, Weijie 

Qiao, Yupeng 

Suo, Xudong 

south china Univ. of Tech. 



In this paper, we study the robust formation control problem of a class of 

multi-agent systems based on the generalized internal model principle. 

We show that this problem finally boils down to a robust stabilization 

problem of a nonlinear system, the solution of which can be solved 

by using the existing robust stabilization technique. The efficiency of 

the proposed design method is verified by one typical formation control 

problem of multi-agent systems. 

◁ PSaA-25 

A fractional-order multi-scroll hyperchaotic Chua system and its synchronization, 

pp.1436–1441 

Xi, Huiling 

Guangzhou Univ. of Tech.;North Univ. of China 

In this paper, a four-dimensional fractional-order multi-scroll hyperchaotic 

Chua system is proposed. By utilizing the fractional calculus 

theory and computer simulations, it is found that multi-scroll hyperchaotic 

attractor exists in this fractional-order Chua system with order 

less than 4. Furthermore, synchronization between two fractional-order 

multi-scroll hyperchaotic Chua systems is achieved by a suitable linear 

controller applied to the response system and the corresponding 

numerical simulations are performed. 

◁ PSaA-26 

UKF based Robust Attitude Control for Helicopter, pp.1442–1446 

Song, Qi Shenyang Inst. of Automation, Chinese Acad. of 

Sci. 

Han, Jianda 

JIANG, Zhe 



In order to handle the model uncertainty and the external disturbance, 

a robust attitude control method for helicopter robots is proposed in this 

paper. Unscented Kalman Filter (UKF) and backstepping technique 

are adopted in the attitude control design. Model-based backstepping 

control is presented to keep the desired helicopter attitude. UKF is 

employed for online estimation of both motion states and model errors 

of the helicopter. Such estimation results are further incorporated into 

the controller of helicopter. The backstepping control enhanced by real 

time model errors feedback can achieve a robust tracking performance 

even with the occurrence of external disturbance. Simulations conducted 

on the helicopter illustrate the effectiveness of the proposed attitude 

control. 

◁ PSaA-27 

Global asymptotic stability of uncertain nonlinear system with input constraint, 

pp.1483–1488 


Sun, Changyin 

Wang, Li 






In this paper, the problem of global asymptotic stability of a class of 

uncertain nonlinear system with input constraint is considered by using 

the development of the adding a power integrator technique. And in 

order to analyze the effect of the input constraint, the auxiliary design 

system is used. In addition, if the system satisfies some conditions, the 

global finite time stability of the nonlinear system can be proved. Finally, 

simulations are listed to illustrate the effectiveness of the proposed 

result. 

◁ PSaA-28 

Multiperiodicity and Attractivity Analysis for a Class of High-order 

Cohen-Grossberg Neural Networks, pp.1489–1494 

Sheng, Li 

Gao, Ming 



In this paper, the multiperiodicity of a class of high-order Cohen- 

Grossberg neural networks (HOCGNNs) with special activation functions 

is discussed by using analysis approach and decomposition of 

state space. The activation functions of this class of neural networks 

consist of nondecreasing functions with saturation, standard activation 

functions of cellular neural networks, etc. It is shown that the n-neuron 

HOCGNNs can have 2 n locally exponentially attractive periodic orbits 

located in saturation regions. In addition, a condition is derived for 

ascertaining the periodic orbit to be locally exponentially attractive and 

to be located in any designated region. Finally, an example is given to 

show the effectiveness of the obtained results. 

◁ PSaA-29 

Delay-dependent Stability for Uncertain Stochastic Neural Networks 

with Distributed Delays, pp.1495–1500 

Gao, Ming 

Sheng, Li 



This paper deals with the problem of delay-dependent robust stability 

for a class of uncertain stochastic recurrent neural networks (USRNNs) 

with discrete and distributed delays. In such systems, both parameter 

uncertainties and stochastic perturbations are taken into account. The 

parameter uncertainties are norm-bounded and the stochastic perturbations 

are in the form of a Brownian motion. Based on the Lyapunov 

stability theory and the linear matrix inequality (LMI) technique, some 

delay-dependent stability criteria are derived, which guarantee the global 

robust asymptotic stability in the mean square for the USRNNs. Two 

simulation examples are provided to illustrate the effectiveness of the 

proposed criteria. 

◁ PSaA-30 

Cutting-in Control with Smooth Switching Strategy for Doubly-fed Wind 

Power Generator, pp.1513–1517 

Xiao, Yunqi 

Lv, Yuegang 



Synchronized cutting-in control strategy and power control strategy after 

gird-connection of doubly-fed wind power generator is analyzed in 

detail. Since two different generator control systems are employed between 

cutting-in moment, a smooth switching strategy is presented, too. 

Simulating results are shown that the synchronized cutting-in control s- 

trategy adopted can meet grid connecting requirement, and switching 

strategy proposed can accomplish smooth switching control process of 

rotor exacting voltages with stator currents steadily raising. This research 

has practical application value. 

170



◁ PSaA-31 

Fuzzy Energy-to-Peak Control for Nonlinear Markovian Jump Systems, 

pp.1518–1523 

Shen, Hao 

Wang, Jing 


Zhang, Handong 





This paper deals with the problem of the energy-to-peak control for 

nonlinear Markovian jump systems via Takagi-Sugeno fuzzy model approach. 

Our purpose is to design retarded feedback controller such 

that the resulting closed-loop system is stochastically stable with a prescribed 

energy-to-peak performance. By using a novel augmented Lyapunov 

functional method and the LMI relaxation technique, a sufficient 

condition is proposed for the solvability of the energy-to-peak control 

problem. Finally, a numerical example is given to demonstrate the effectiveness 


◁ PSaA-32 

Cascade Temperature Control for Bench-Scale Batch Reactor –An Application 

of Predictive Functional Control Technique, pp.1564–1569 

Song, Yiming 

Northwest Univ. 

An advanced control concept, namely Predictive Functional Control 

(PFC) technique, is applied for temperature control of a bench-scale 

batch reactor. First principles models for individual process unit are 

developed. Based on achieved models, significant process variables, 

which are difficult or impossible to measure online, are estimated from 

easily measured variables, and cascade PFC control strategy has been 

projected and implemented in Programmable Logic Controller (PLC). 

The dynamic of individual process subunits is explicit taken into consideration. 

Moreover, model uncertainty and various process disturbances 

are compensated by modification of internal model. The experimental 

results present an excellent capability of tracking the set point. 

◁ PSaA-33 

Asymptotic Rejection of General Periodic Disturbances with Time- 

Varying Gains, pp.1591–1596 

Chen, Pengnian 

Qin, Huashu 



Adaptive control for a class of nonlinear systems with periodic disturbances 

is considered. The period of the periodic disturbances is known. 

The gains of the periodic disturbances depend on states of the system 

and time and do not satisfy global Lipschitz condition. A adaptive control 

approach without use of saturation control is proposed, which can 

guarantee that the tracking error converges to zero. An example is presented 

to illustrate the utility of the approach. 

◁ PSaA-34 

Inverse optimal design of a class of stochastic nonlinear systems with 

uncontrollable linearization, pp.1597–1602 

WANG, Qiangde 

WEI, Chunling 



The global asymptotic stochastic stability and inverse optimal control 

problem are developed for a class of stochastic nonlinear systems with 

lower triangular form. The systems considered are not feedback linearizable 

and the Jacobian linearization is uncontrollable. By the use 

of adding a power integrator, a feedback domination design approach 

is presented and a smooth controller is constructed to guarantee the 

global asymptotic stability in probability and the inverse optimality. The 

simulation result shows the effectiveness of the control schemes. 

◁ PSaA-35 

Stability of a multi-state repairable system with two repair distributions, 

pp.1603–1608 

Zheng, Fu 

Bohai Univ. 

In this paper, the well-posed-ness and stability of the multi-state system 

with two repair distributions were discussed by applying the c 0 semigroups 

theory of function analysis. As application of the above results, 

a new method of computing the stationary availability of the repairable 

system was given. Moreover, Numerical results were provided to investigate 

the effects of various system parameters on the steady-state 

availability of the system. 

◁ PSaA-36 

Nonlinear Sliding Mode Formation Control for Underactuated Surface 

Vessels, pp.1655–1660 

Meng, Wei 

GUO, Chen 

Liu, Yang 

dalian maritime Univ. 



A sliding mode control law of formation control for multiple underactuated 

surface vessels is proposed. The three degrees of freedom dynamics 

model is considered. Since the sway axis is not directly actuated, 

the vessels are underactuated. The formation model is obtained based 

on the leader-following approach. The controller is designed by using 

Lyapunov’s direct method and sliding mode control technique. In the 

dynamics design, the first-order surface in terms of the surge motion 

tracking errors and the second-order surface in terms of sway motion 

tracking errors are introduced. The stability analysis of the sliding mode 

control law is taken based on Lyapunov theory. Numerical simulations 

are provided to validate the effectiveness of the proposed formation 

controller for underactuated surface vessels. 

◁ PSaA-37 

Robust H-infinity Control for Switched Singular Systems withLinear 

Fractional Uncertainties: a Strict LMI Approach, pp.1704–1709 

Lin, Jinxing 

Gao, Zhifeng 

Gai, Shaoyan 




In this paper, for a class of switched linear singular systems with linear 

fractional uncertainty, the robust H-infinity control problem is investigated 

by using a strict linear matrix inequality (LMI) appoach. First of all, 

for the unforced system, a sufficient condition is given for generalized 

robust stabilization with prescribed disturbance attenuation level only 

by switching. Then, the robust H-infinity control synthesis via switched 

state feedback is solved by LMI approach. The obtained results generalize 

and improve the previous works on switched singular systems 

and switched state-space systems. 

◁ PSaA-38 

Optimal Control for Markov Jump Systems with Constrained Control Input, 

pp.2142–2147 

Wu, Yanrui 

Wu, Youli 

Fang, Yangwang 

Hu, Shiguo 

shaan xi Univ. of Sci. & Tech. 

Air Force Engineering Univ. Engineering College 

Air Force engineering Univ. 

air force engineering Univ. 

Based on stochastic maximum principle, the optimal control algorithm 

is proposed for Markov jump system with complete information, further 

more, the method is extended to Markov jump system with incomplete 

information. The optimal controller design method is studied with statistical 

linearization method for Markov jump system with constrained 

control inputs. And simulation examples are given to test and verify the 

function of optimal controller designed in this paper. 

◁ PSaA-39 

Fractional-order QFT Controllers for unstable plants based on Automatic 

Loop Shaping, pp.2148–2153 

Meng, Li 


This paper presents the design of a robust fractional order controller for 

unstable plants with only one unstable pole based on quantitative feedback 

theory (QFT) using Particle Swarm Optimization (PSO). In this 

work, a fractionalorder compensator, with a flexible controller structure, 

is introduced into the QFT design to give a better approximation of optimum 

open loop in Nichols. The fractional order controller is designed 

for not only minimum phase system but also uncertain non-minimum 

phase and unstable plants in QFT. It has been demonstrated that the 

fractional order controller can provide smaller high frequency gain than 

the integer order controller due to its extra tunable parameters. 

171


◁ PSaA-40 

TC on PVTOL, pp.2154–2159 

Zhang, Yunfan 

Li, Donghai 

Lao, Dazhong 




The control of the planar vertical takeoff and landing (PVTOL) aircraft 

is a hot research problem. This paper introduces a two-order cascade 

Tornambe’s controller(TC) to PVTOL model after change one time, 

and then compares with other TC control methods. The simulation 

shows that the method of this paper also has a good performance on 

tracking, disturb rejection and performance robust. Because it has a 

simple construction and is easy to adjust parameters, so the method of 

this paper is a control method to select. 

◁ PSaA-41 

Co-design of static quantized scheduling strategy and state feedback 

control for networked control systems, pp.2182–2187 

Du, Mingli 

Zhou, Chuan 

Wu, Yifei 

Chen, Qingwei 





A co-design scheme of static period quantized scheduling and the state 

feedback controller for a class of networked control system with limited 

bandwidth and fixed time delay using mid-tread uniform quantizers is 

proposed in this paper. Through allocating bandwidth, the quantized 

scheduling strategy and feedback controller design are considered simultaneously. 

The quantized measurement signals are dealt with by 

utilizing the sector bound method, in which the quantization error is 

treated as sector-bounded uncertainty, and the NCS is modeled as a 

discrete-time switching system with parameter uncertainties. Based on 

the Lyapunov function and period switching system theory, a design 

procedure for feedback controller under this strategy is given to maintain 

the asymptotic stability of closed-loop system. 

◁ PSaA-42 

Support Vector Machine Generalized Predictive Control for Turboshaft 

Engine, pp.2296–2301 

Xiao, Lingfei 

Zhu, Yue 



Based on support vector machine theory, a generalized predictive controller 

is designed for turboshaft engine. Using dynamic response data, 

through support vector machine regression, the turboshaft engine nonlinear 

model is established. By linearizing the nonlinear model at each 

sampling cycle, the prediction model of turboshaft engine is constructed. 

According to generalized predictive control algorithm, the optimal 

control signals are obtained. Simulation results show that the control 

system can follow the change of collective pitch very well, the speed 

of power turbine can back to given value fast and possess small overshoot. 

◁ PSaA-43 

Stock Turning Point Recognition using Multiple Model Algorithm with 

Multiple Types of Features, pp.4020–4025 

Qin, Xiaoyu 

Peng, Qinke 

Xi’an jiaotong Univ. 


Stock turning point has been playing a significant role in the stock investment 

as buying or selling stocks around it can make a good return. 

The existing methods recognize the turning points according to different 

types of features but the effect is not so satisfactory. In this paper, we 

firstly build multiple types of features what reflect many aspects of characteristics 

of stock and utilize mutual information to partition them into 

several subsets. Then we use SVM to train the turning point recognition 

model on every feature subset. Finally, we combine the multiple models 

into an ensemble and apply Particle Swarm Optimization (PSO) to 

optimize the combination coefficients. Experimental results show that 

our method is more effective. 

◁ PSaA-44 

Improvement and application of the Delphi method, pp.4026–4029 

Ji, Wen 

Wang, Jianhui 

Fang, Xiaoke 

Gu, Shusheng 




Northeastern Univ., China 

In view of extreme points of the Delphi method, this paper introduces 

the penalty factor to improve it and overcome the influence of subjective 

factors. Then, it applies the improved Delphi method to determine 

weight factor of stroke rehabilitation evaluation indicator in the context 

of stroke rehabilitation evaluation system. Simulation results show the 

effectiveness of the improved method. 

◁ PSaA-45 

A Study on the System Structure of Multi-UCAV Cooperation Mission 


Ye, Wen 

Naval Aeronautical & Astronautical Univ 

Mission planning for multi-UCAV cooperation is one of the keys to 

take the advantages of multiple UCAVs combating cooperatively and 

to maintain harmonies between the mission complexities and UCAV a- 

bilities. A distributed and concentrated composite architecture of multi- 

UCAV cooperation mission planning system was constructed. Based 

on the analysis of the cooperative UCAVs mission hierarchy, a hierarchical 

and iterative mission planning frame was introduced. The logic 

architecture of multi-UCAV cooperation mission planning system was 

put forward, and two key sub-problems, that is mission assignment and 

path planning, were analyzed. 

◁ PSaA-46 

Design and Development of the Encoding Software in the Identification 

System of the Hydropower Plant, pp.4035–4039 

GUO, Jiang 

Feng, Zhihui 

Gu, Kaikai 

Bai, Weimin 

Zeng, Bing 

Liu, Yajin 

Wuhan Univ. 

Wuhan Univ. 

Wuhan Univ. 

Wuhan Univ. 

Wuhan Univ. 

Wuhan Univ. 

Abstract―With the development of power industry, power plants have 

been expanding. So in the planning and design, construction and operation 

stages, there is a wealth of information produced. However, the 

information management in all power plants are all complex and not 

standardized, which make the management’s work very inconvenient, 

and that include all system, equipments and etc. In order to achieve 

the standardization and intelligent management, hydropower industry is 

placing more importance on the ERP implementation,while the core 

foundation data of the ERP system are from the device code. On the 

basis of a comprehensive study of domestic and foreign industrial applications, 

building the encoding software in the identification system of 

hydropower plant has been studied and corresponding design and instances 

of system are given. This paper studies achieves the management 

of the power plant information code and laid a stable foundation 

for the implementation of the ERP project. 

◁ PSaA-47 

Life Cycle Management Support System of Nuclear Power Plant Based 

on Economic Analysis, pp.4040–4044 

GUO, Jiang 

Gu, Kaikai 

Feng, Zhihui 

Zeng, Bing 

Bai, Weimin 

Liu, Yajin 

Wuhan Univ. 

Wuhan Univ. 

Wuhan Univ. 

Wuhan Univ. 

Wuhan Univ. 

Wuhan Univ. 

Nuclear power around the world has shifted to expand the lifetime of 

nuclear power plant as long as possible on the condition of safety and 

reliability instead of constructing new ones. In order to achieve the 

goal of life extension, the nuclear power plant all attach great importance 

to the life cycle management of critical equipment. LEA is a core 

of the life cycle management of critical equipment and an analysis s- 

trategy to improve the usability of critical equipment and achieve the 

maximization of life value and long period of revenue through optimiz- 

172



ing decision-making process of critical equipment. In this paper, how to 

calculate the economic indicators involved in LEA is given. In ensuring 

the premise of reliability and safety, the model of economic analysis is 

proposed to the purpose of optimal cost. Finally, based on the model of 

economic analysis, building nuclear power plant equipment for life cycle 

management support system has been studied and the corresponding 

design and instances of system are given. 

◁ PSaA-48 

Design and Development of J2EE-Based Identification System for Power 

Plants, pp.4045–4049 

Wu, Zhifang 

GUO, Jiang 

Sun, Yu 

Gu, Kaikai 

Zeng, Bing 

Bai, Weimin 


Wuhan Univ. 

Wuhan Univ. 

Wuhan Univ. 

Wuhan Univ. 

Wuhan Univ. 

Abstract―In recent years, with the rapid development of network and 

extensive adoption of J2EE platform, multi-layer Web structure based 

on B/S grows up gradually. Aiming at the intelligent and standardized 

management of power plant, this paper gives a detailed introduction of 

the structure and function of the identification system for power plants, 

as well as designing a database with great flexibility and extensibility 

and a system based on J2EE design. This system has improved 

the encoding specification, correctness and efficiency greatly. That is 

to say, the system, having been put into practice in a power plant in 

Guangxi, saves time and labor to a great extent. 

◁ PSaA-49 

Improved LS-SVM Based Classifier Design and Its Application, 

pp.4050–4054 

Wang, Peng 

YAN, Aijun 



Abstract―For least squares support vector machine (LS-SVM) classifier 

to the loss of sparseness and generalization, a pruning modeling 

method is proposed based on Quadratic Renyi entropy. The kernel principal 

component is adopted for data pre-processing, and the training set 

is divided randomly. Then the concept of quadratic Renyi entropy is introduced 

as the basis of training and pruning in LS-SVM classifier. UCI 

typical datasets of classification are used for testing the performance of 

this new model. Experimental results show that the new algorithm takes 

full account the location of the Lagrange multiplier, thus the sparseness 

and generalization ability of the classifier can be improved. 

◁ PSaA-50 

Multi-criteria Decision Making Approach Based on Interval Value Vague 

Sets, pp.4061–4064 

Shi, Yu 

Hu, Xiaodong 

Wang, Weiping 

Shandong Jiao Tong Univ. 

Air Force Command College(AFCC) 

Univ. of International Relations 

Based on real-value vague sets risk preference score function, this 

paper presents an approach for determining scoring value of interval 

vague and applies it to multi-criteria decision making problems. The 

approach completely embodies risk preference of decision maker as 

well as the effect of various uncertain information to decision making. 

Finally, an example is given to illustrate the effectiveness of the approach. 

◁ PSaA-51 

ACP based 3D Emergency Drills System for petrochemical plants, 

pp.4065–4070 

Jing, Sifeng 


XIONG, Gang 

LIU, Xiwei 

Shang, Xiuqin 

Wang, Weixing 







To further enhance emergency management skills of an organisation’s 

emergency response personnel, emergency response training,especially 

3D emergency drill, is currently becoming more and more 

important in the petrochemical sector. So, a novel 3D emergency drills 

system is designed and developed based on ACP approach, which can 

be used for mocking emergency response plan drills and evaluating the 

plan. A case study reveals that the performance of the system is good 

and the system can meet the needs of emergency response training 

and optimizing emergency response plan in petrochemical plants. 

◁ PSaA-52 

Fault Recovery Based Mission Scheduling of AUV for Oceanographic 

Survey, pp.4071–4076 

Yan, Zheping 

Zhao, Yufei 

Chen, Tao 

Jiang, Ling 





This paper presents a mission control approach and an on-board fault 

recovery to enable an autonomous underwater vehicle (AUV) to carry 

out the oceanographic survey autonomously. The challenges of AUV’ 

s operability in clutter environment have led the researchers to develop 

intelligent control architectures composed of distributed, independent 

and asynchronous behaviors, so the multi-agent system is applied. 

A fault recovery architecture is designed to call the decision algorithm 

and supervise the implementation of the autonomous mission based on 

petri net formalism, which can respond to the changes in the environment 

and internal issues. The feasibility and the algorithm effectiveness 

of the architecture and mission control algorithm are checked by lake 

experiment in nominal and degraded situations. 

◁ PSaA-53 

Descending rolling horizon procedure for equipment replacement problem, 

pp.4082–4087 

Wang, Bing 

Liu, Dongpo 

Liu, Jing 


Beijing Sciample Tech. Co.,Ltd 


To handle the computational complexity of large-scale optimization 

problem, a special rolling horizon decomposition procedure is developed 

to address equipment replacement problems with long decisionhorizons 

and multiple alternatives in this paper. The global objective 

is to minimize the total equipment replacement expense. The 

rolling horizon procedure decomposes the whole decision-horizon into 

serial rolling sub-decision-horizons where equipment replacement 

sub-problems are established based on local objectives involving partial 

global objective. The rolling horizon procedure can easily handle 

the computational complexity of a large-scale equipment replacement 

problem. However, the rolling segmentation and merely local optimization 

are not able to necessarily lead to the global optimal solution because 

local objectives are not completely consistent with the global one. 

A terminal penalty is added into the local objective function of each subproblem 

to lessen the disadvantageous impact of horizon decomposition. 

The terminal penalty is the maximum evaluation of replacement 

expense increment for the latter sub-decision-horizons due to merely 

considering local optimization in the current sub-problem without any 

consideration of the global objective. The theoretical analysis proves 

that the rolling replacement procedure with terminal penalty can make 

the global objective function values descending as serial sub-problems 

are solved and their partial solutions are merged into the global solution 

step by step. An extensive experiment was conducted to test the 

effectiveness of descending rolling replacement procedure. The computational 

results also demonstrate that this procedure is better than 

traditional rolling horizon procedure for equipment replacement problem 

while only moderate computational efforts are needed. 

◁ PSaA-54 

Distributed Generation System Design Based on Energy Storage Converter 

Technology, pp.4088–4092 

Han, Xiaojuan 

Cui, Xiwang 

Zhang, Xilin 




173


Yan, Tao 

Du, Zhanzhan 





Because of energy shortage, environment pollution is serious day by 

day. Various countries are trying to find a kind of energy use with little 

environmental pollution and high utilization rate. Distributed generation 

overcomes many weaknesses of traditional centralized power supply 

with the advantages of less environmental pollution, high energy utilization 

rate, reliability and flexibility etc, pay attention to all over the world 

more and more. Converter technology is the difficulty to widely use distributed 

generation. In this paper, energy storage system structure and 

control strategy for distributed generation are studied through theoretical 

analysis, simulation and experiment platform and design distributed 

generation system based on energy storage converter technology. 

It can be seen that energy storage converter can satisfy the relevant 

technical requirements applied in distributed generation system. The 

effectiveness of the method proposed in this paper is verified by the 

practical tests. 

◁ PSaA-55 

Data-driven artificial system of parallel emergency management for 

petrochemical Plant, pp.4103–4107 

Shang, Xiuqin 

XIONG, Gang 


LIU, Xiwei 





A data-driven system of parallel emergency management is designed 

to manage production safety emergencies caused by natural or humaninduced 

disasters in the petrochemical plant, combining with the parallel 

management theory based on ACP (Artificial Systems, Computational 

Experiment, and Parallel Execution) approach. Data is acquired 

by use of techniques including video monitoring and detection, which 

is the premise of building Artificial System. Based on mass data of the 

key state variables, Artificial System is designed by using fuzzy expert 

system and other intelligent modeling algorithms. Finally, the parallel e- 

mergency solution is provided for emergency management in one case 

of ethylene plant, and it can make a great improvement to the emergency 

management of the plant. 

◁ PSaA-56 

A PSO Algorithm Based on Group History Experience, pp.4108–4112 

Yan, Zheping 

Li, Benyin 

Deng, Chao 




Particle swarm optimization groups adjust the search strategy to obtain 

evolution by fully sharing information. Rational utilize of the group information 

also determine the efficiency and performance of particle swarm 

algorithm. The group historical experience particle swarm optimization 

(GHEPSO) is proposed, particles are not influenced only by the group 

optimal position of the current iterative time and by their historical optimal 

position, but also by the group optimal position of previous iterative 

time at the same time. This algorithm more fully use the group experience 

information than basic PSO algorithm. The performance of the 

algorithm is analysed through several typical test functions, camparing 

this algorithm with basic particle group algorithm. The result shows that 

GHEPSO is better to solve the problem of multi-modal function than the 

basic PSO. And the optimized effect will be more improved if GHEPSO, 

MPSO and TVAC can be combined together. 

◁ PSaA-57 

A Novel Two-subpopulation Particle Swarm Optimization, pp.4113– 

4117 

Yan, Zheping 

Deng, Chao 

Zhou, Jiajia 

Chi, Dongnan 





The performance of the particle swarm is mainly influenced by individual 

particles experience and group experience in the period of evolution 

for particle swarm optimization. To make full use of the two factors and 

effectively improve the particle swarm optimization performance, Introduced 

a novel Two-subpopulation Particle Swarm Optimization, The 

proportion of individual experience and group experiences is different 

in each subpopulation swarm. If the proportion of individual experience 

greater than the group experience, the particle swarm search 

space abroad, whereas, the proportion of group experience greater 

than individual experience, the particle swarm search the local area fully. 

The proposed Two-subpopulation particle swarm optimization combines 

both advantages, make the search more fully and not easily into 

the local minimum points. Finally simulations were carried out and the 

results showed that the proposed Two-subpopulation particle swarm 

optimization, obviously better than the basic particle swarm algorithm 

in search precision and stability. 

◁ PSaA-58 

Intelligence Decision Supporting Algorithms of Production Planning 

based on Hopfield network , pp.4122–4125 

SU, Jinlong 

Tongji Univ. 

This paper is one of the series of papers about the research of kinds of 

Neural Networks’application on the auto-decision of production planning. 

This one lays emphasis upon the design of the optimal production 

planning intelligent algorithms to keep the manufacture financing stability 

in product cycle, and consider what kind of algorithms are useful 

and effective. The author mainly concern and research into the optimal 

production planning algorithms designed by Fuzz Hopfield Networks, 

such as problems of manufacture financing by optimal product schedule. 

The analysis and experiment results are elaborated in turn, which 

are all prove its effectiveness and feasibility. 

◁ PSaA-59 

Project Development Management System of Financial Equipment Enterprises 

Based on PDM , pp.4135–4140 

Cui, Wenhua 

Liu, Xiaobing 





Based on the project flowchart management information system model 

in the process of enterprise products development, a UML model of 

the project management information system based on the PDM technology 

for the financial equipment enterprises is proposed. The design 

and debarment of the proposed project management information 

system are finished completely. The UML modeling method and .NET 

technologies are adopted to exploit the data accessing model and the 

project management information model. The system plays emphasis on 

the product project flowchart management and document management, 

whose major function modules include of project view management, 

test view management, document view management and organization 

view management. 

◁ PSaA-60 

Steady-State Identification with Gross Errors for Industrial Process U- 

nits, pp.4151–4154 

Tao, Lili 

Li, Chaochun 

Kong, Xiangdong 

Qian, Feng 





Identification of steady state is an important task for satisfactory control 

of many processes. Due to the disadvantages of the traditional 

steady-state identification (SSI) methods, the adaptive polynomial filtering 

(APF) method was used for SSI in this paper. Furthermore, 

the presence of gross errors can corrupt the steady-state identification 

method, giving undesirable results. The APF steady-state identification 

with the new 3δformula method was modified for gross errors detection 

by using the quartile method based on first order differential in this 

paper. This method was applied to the simulated data and data from a 

crude oil distillation unit. Simulation results and comparisons with the 

traditional methods confirmed the validity of the proposed method. 

◁ PSaA-61 

174



Parameters Estimation of BLDC Motor for Energy Storage System, 

pp.4190–4194 

Wu, Junfeng 

Changchun Inst. of Optics,Fine Mechanies & 

Physics 

This paper presents a statistical method to estimate the parameters of 

the brushless dc (BLDC) motor of a flywheel system for energy storage. 

The principle of the estimation is based on least square estimation under 

a reasonable constraint. Method is suitable to avoid using complicated 

test apparatus. Torque constant, static friction coefficient, vicious 

friction coefficient and inertia moment can be estimated. Furthermore, 

copper loss, iron loss, mechanical loss and other loss can be computed 

from these estimation and measurement data. By the proposed 

method, it is demonstrated experimentally that accuracy estimation can 

be achieved. 

◁ PSaA-62 

The Design and Implementation of Metro Carriage LED Controller Software 

Based on Ethernet, pp.4201–4205 

Liu, Quanli 

Bian, Mingkun 

Wang, Wei 




Currently, most LED controllers for the metro carriage display are driven 

directly by I/0 of MCU and the display interface is very simple, therefore 

it’s difficult to meet the multiform requirements of video terminal. 

In order to meet this requirement, this paper designed the software 

system of new type metro display LED controller based on Ethernet, 

analyzed its function and achieved an implementation. This new type 

metro display LED controller takes Linux and Qt system as a developing 

platform and communicated with TCMS in process data and message 

data through the Ethernet. This new type metro display LED controller 

has been applied to a driverless metro and the result shows that its 

response speed is fast and its display effect tends to be diversiform. 

◁ PSaA-63 

Conducted EMI Measurement and its Suppression Measure of Elevator 

System, pp.4226–4229 

Wen, Xiao-Qin 


With widely use of high –speed power switching devices, EMI problems 

of Elevator system become more serious. It discussed the interference 

noise of PWM inverters, and the main noise of converter is 

conducted interference and Radiation interference. Then the measurement 

method of conducted EMI is proposed and use it with real elevator 

system. Lastly the suppression of common mode EMI in PWM inverter 

is revealed, and give some suggests for engineering praceice. 

◁ PSaA-64 

Modeling of Belt Conveyor System based on Least Square Support 

Vector Machine, pp.2944–2948 

Chen, Wei 

Li, Xin 

Hefei Univ. of Tech. 

hfut 

The paper presents nonlinear modeling study of belt conveyor system 

using the least square support vector machine (LS-SVM). Belt conveyor 

is a nonlinear, severe disturbance and time-varying system. So far, 

most of the existing models are based on mechanism laws, which are 

very useful for belt conveyor design. However, they are too complicated 

to be applied to control system design. To facilitate a valid control 

strategy design, this paper tries to avoid the internal complexities and 

presents a black-box model of the belt conveyor based on LS-SVM. 

The simulation tests reveal that it is feasible to establish the model using 

LS-SVM. Compared with the models based on the NARMA model 

using least square method, the simulation results are shown that the 

model based on LS-SVM algorithm is superior to nonlinear regression 

analysis in data fitting. 

◁ PSaA-65 

Polynomial process algebra, pp.3004–3007 

Liu, Bai 


In this paper we present a polynomial process algebra (PPA) like basic 

process algebra which can be used to model both polynomial behavior 

of parallel systems. It provides a nature framework for the concurrent 

composition systems, and can deal with the nondeterministic behavior. 

This process algebra is obtained by the polynomial transition systems 

which we defined. In this paper we concentrate on giving the syntax 

and semantic, and meanwhile defining the bisimulation equivalence. In 

the last we give an example to illustrate it. 

◁ PSaA-66 

Fuzzy Systems-Based Adaptive Fault-Tolerant Dynamic Surface Control 

for a class of High-order Nonlinear Systems with Actuator Fault, 

pp.3013–3018 

Shen, Qikun 

Jiang, Bin 





The problem of adaptive fault-tolerant dynamic surface control (DSC) 

for a class of high-order nonlinear uncertain systems with actuator fault 

is discussed, and a novel design scheme of adaptive fuzzy controller is 

proposed in this paper. First, the approach removes the condition that 

the upper or low boundary of fault is known. Moreover, the problem of 

explosion of complexity in traditional backstepping design is overcome 

by introducing the first order filter, and the possible controller singularity 

in feedback linearization is avoided without projection algorithm. 

In addition, the approach removes the assumption that the control coefficients 

are known exactly or unknown but lower bounded by known 

positive constants. By theoretical analysis, the closed-loop systems is 

shown to be semi-globally uniformly ultimately bounded, with tracking 

errors converging to a small neighborhood of origin by appropriately 

choosing design constants. Finally, simulation results demonstrate the 

effectiveness of the proposed method. 

◁ PSaA-67 

A Green Wave Band Model Considering Variable Queue Clearance 

Time, pp.3025–3030 

Wang, Shi Ming 

Xu, Jian-min 

Yang, GuiGen 

Chen, ChangJia 



Public Security Office of Guangdong Province 


The variable queue clearance time model is derived by anngalyzing 

the relation between the adjacent upstream intersection’s departure 

traffic flow pattern and the offset time. To take the variable queue clearance 

time into consideration, the proposed revised MAXBAND model 

combines the variable queue clearance time model and traditional 

MAXBAND model. After the process of variant substitute, the revised 

MAXBAND model can still be solved by classical MILP solution method. 

An example with two intersections is given, and the results of the proposed 

model and simulation indicate that, the revised model is better 

than traditional model in arterial control since the consideration of variable 

queue clearance time. In the example arterial, the revised model 

increases the effective green-wave bandwidth by 31.6%, and decreases 

the average arterial delay by 12.6%. 

◁ PSaA-68 

Key Technology Research on Fault Detection and Diagnosis for Brokenwing 

Small Unmanned Aerial Vehicle, pp.3049–3052 

Huang, Junjie 


An accident with any component failure of the small unmanned aerial 

vehicle (SUAV) will happen in the operation process. At present the 

sensor and actuator faults are mostly studied and the damaged body 

considers very few. The research status of the air dynamics and fault 

detection and diagnosis (FDD) are firstly introduced in the paper. The 

methods of the pneumatic parameters and properties on the brokenwing 

SUAV and the key technologies of FDD are analyzed. At last the 

experiment of the broken-wing SUAV is established by using the FDD 

subsystems. A specific plan and research methods is provided through 

the paper. Meanwhile it provides the theory and research foundation 

for reconstruction control system. 

◁ PSaA-69 

175


Electric load forecasting based on improved LS-SVM algorithm, 

pp.3064–3067 

Yan, Gang 

Tang, Gao-hui 

Xiong, Ji-ming 




An Improved least squares support vector machine (LS-SVM) algorithm 

is proposed for 24 points electric load forecasting. First of all, facing 

with the problem how to choose the optimal LS-SVM algorithm parameters, 

an improved LS-SVM algorithm based on chaos optimization is 

put forward to obtain the optimal LS-SVM algorithm parameters and 

corresponding model parameters. Then, a method of 24 points electric 

load forecasting based on the improved LS-SVM algorithm is presented, 

which makes 24 points forecasting models respectively. Compared 

with the RBF neural network method, the prediction accuracy of the 

proposed method is better than that of neural network method, so the 

validity and the superiority of the proposed method are proved. 

◁ PSaA-70 

Identification of dynamic parameters and friction coefficients for a 

heavy-duty hydraulic manipulator, pp.3102–3106 

Li, Bo 

Yan, Jun 

Guo, Gang 

Wang, Haito 

Zhang, Meijun 









This paper investigates the dynamic parameter identification of a single 

heavy-duty hydraulic manipulator, as well as the estimation of coefficients 

of nonlinear friction model for the hydraulic cylinder. The conventional 

recursive least square method and generalized least square 

method are adopted respectively. Comparison results show that the 

prediction error of the generalized least square method is reduced by 

41% to the conventional recursive least square method; it is more robust 

to the colored system noise. 

◁ PSaA-71 

Classifying imbalanced dataset based on minority detection, pp.3236– 

3241 

Liu, Tong 


Classifying imbalanced dataset has recently become an important 

problem in many industrial and financial applications. In this paper, 

a novel method for combing minority detection algorithm and solving 

optimization problem to classify imbalanced data set was presented. 

We empirically evaluate the proposed approach using a number of UCI 

data sets, and experiment results show that our novel method is superior 

to the state-of-the-art methods in the literature and scales well to 

large, high dimensional databases 

◁ PSaA-72 

PID Fault Tolerant Control System Design with Multi-performance indices 

Constraints, pp.3286–3291 

Feng, Zhimin 

Zhang, Gang 

Ningbo Univ. 

ningbo Univ. 

The paper discussed the problem of PID fault tolerant control system 

design for linear systems with state and static output feedback. Based 

on linear matrix inequality approach, the consistency theory on circular 

pole index, steady variance index and H-infinity constraints is set 

up, and the ranges of consistent indices are analyzed in detail. Furthermore, 

the effective controller design method for systems with constrains 

of consistent indices is provided. A simulation example shows the efficiency 

of the design method. 

◁ PSaA-73 

Fault Diagnosis of Induction Motor Rotor Based on BP Neural Network 

and D-S Evidence Theory, pp.3292–3297 

Zhang, Lieping 

GuiLin Univ. of Tech. 

WANG, Shoufeng 

Guilin Univ. of Tech. 

Directing to the shortage of single method of BP neural network or D- 

S evidence theory in rotor fault diagnosis, a fault diagnostic method 

for induction motor rotor was proposed, which was based on BP neural 

network and D-S evidence theory. The BP neural network method 

was applied to the fault diagnosis firstly, and then, the partial diagnostic 

results of BP neural network were taken as the basic probability assignment, 

finally, the D-S evidence theory was applied to fuse different 

results from all the neural networks and got the finally diagnostic results. 

The experiment simulation results of fault diagnostic example show 

that the method is available for the induction motor rotor fault diagnosis 

and has better classified diagnosis ability than single fault diagnostic 

method. 

◁ PSaA-74 

Generalized Canalizing Mapping and Its Applications, pp.2376–2381 

Qi, Hongsheng 


Canalizing mapping of k-valued and mix-valued logical functions are 

proposed, which are the generalized canalizing functions/mappings of 

Boolean functions to general logical functions. The verification and 

some basic properties are investigated and the related formulas are 

obtained. Then its two applications are investigated:(1) Stabilization 

of logical dynamic networks: Based on canalizing mapping, certain design 

technique for stabilizers is proposed. (2) Disturbance decoupling of 

logical dynamic networks: Canalizing mapping provides an easy way to 

construct a control, which solves the disturbance decoupling problem. 

◁ PSaA-75 

Design and Realization of S7-200 as Modbus-RTU Master Station, 

pp.3322–3325 

Si, Wenhui 

Wei, Jianping 

Shandong Jiaotong Univ. 


The paper introduces a design method that the S7-200 PLC uses as a 

modbus RTU master station. According to the free-port communication 

protocol of Port 0, the part functions of modbus RTU master station are 

realized by program. The details of the program will be discussed and 

explained carefully in the paper. 

◁ PSaA-76 

Experimental Validation of a Trajectory Planning Method with Continuous 

Acceleration Implemented on a DSP-Based Motion Controller, 

pp.3326–3330 

Qiang, Yanhui 


A trajectory planning method in joint space which provides a continuity 

of position, velocity and acceleration just with simple numerical simulations 

was already presented by us before. This paper, therefore, 

presents techniques in detail for implementation of it. To guarantee 

high performance, a novel DSP-based multiaxis motion controller is developed 

for executing the trajectory planning method on-line. Hardware 

design and software design of the motion controller is described. Finally, 

the proposed trajectory planning method is tested based on the 

proposed motion controller for an arc welding robot. Experimental results 

and performances evaluation are also presented in this paper. 

◁ PSaA-77 

Cascade PID control of desuperheating water system of supercritical 

power generation unit once-through boiler, pp.3369–3371 

Du, Ronghua 

Zhang, Pengtao 

Wang, Hongbin 

Guo, Ruijun 





The stability control problem of desuperheating water system of supercritical 

power generation unit once-through boiler is studied. The 

cascade PID control strategy is adopted and applied to desuperheating 

water control system. Because the valve opening of the desuperheating 

water regulator can be adjusted in real time with the unit load changing, 

the controlled system can maintain the main steam temperature in a 

stable working condition, ensures the security and stability of unit oper- 

176



ating, and improves the disturbance rejection capability of closed-loop 

control system. 

◁ PSaA-78 

AC electric power steering system modeling with Feed-forward fuzzy 

control algorithm, pp.3372–3377 

Hu, Jianfeng 

Duan, Jianming 



In this paper, using the permanent magnet synchronous motor (PMSM) 

as the motor of electric power steering system; establish mathematical 

models of the system, the completion of the EPS system architecture 

design. Adopt the composite method of Feedforward and the fuzzy PI 

control track on the target current,it’s improve the speed and accuracy 

response of system, reducing the tracking delay. Combine with 

steering system model, two freedom degrees vehicle model designing 

the power curve of control algorithm and simulating the overall system. 

The results of simulation show that the design of the curved power algorithm 

achieves to a good target of power control, and the system is 

stability. 

◁ PSaA-79 

Adaptive Digitally Controlled Flyback Converter Based on Current and 

Phase Margin Estimation, pp.3389–3394 

Yuan, Youchen 

Shao, Zhiwei 



This paper introduces design and implementation of a digital controller 

for an experimental low-power flyback converter in a solar-powered system 

with power management. Multiple operating modes are used to 

maintain high efficiency over wide range of input voltage and load. A 

current, crossover frequency and phase margin estimation technique to 

perform load-dependent mode switching and control system stability is 

proposed and tested. An adaptive tuning digital PID regulator design 

example is described, with emphasis on practical limitations imposed 

by the LM3S1138 controller and the program of arithmetic due to sampling 

and processing. The MIMO loop adaptively adjusts the regulator 

parameters to minimize the error between the desired and estimated 

crossover frequency and phase margin. Small-signal models are derived, 

and the MIMO control loop is designed to achieve stability and 

performance over a wide range of operating conditions. Experimental 

results of demonstrating system functionality are presented for a flyback 

converter. 

◁ PSaA-80 

Harmonic Compensation Rectifier with Smart Control Technology, 

pp.3395–3400 

Yuan, Youchen 


A method of current harmonic suppression and voltage waveform improvement 

in power supply system is introduced. The key technology 

is to control the rectifier’s input current, which includes reversed components 

of harmonic current, and part of them will be eliminated, while 

the voltage will be drifted toward the sinusoidal waveform. Direct digital 

control technique is employed in a switching power supply to control the 

input current and the output voltage simultaneously based on weighted 

duty cycle process. In the smart switching power supply appliance, 

the reference corresponding to the output voltage is a constant value 

in general, but to control input voltage, the reference voltage is in sinusoidal 

waveform. Both of the input and output voltage is regulated by 

the weighted duty cycle as well as the current adjustment. The input 

current is changing immediately with the weighted duty cycle, but the 

output current is determined by the average of the weighted duty cycle 

in every half of the power period. The smart control rectifier can also 

be utilized to many other power electronic applications. 

◁ PSaA-81 

A Vision-based Fault Diagnosis System for Heliostat in A Central Receiver 

Solar Power Plant, pp.3417–3421 

SONG, YANG 

Huang, Wenjun 



Zhu, Xuemei 

Nanjing normal Univ. 

This paper presents an automatic heliostats fault detection and diagnosis 

system using machine vision techniques and common CCD devices 

for a solar power plant. The heliostats of a solar power plant reflect solar 

radiation onto a receiver placed at the top of a tower in order to 

provide a desired energy flux distribution correlated with the coolant 

flow through the receiver, usually in an open loop control configuration. 

Each heliostat maintains reflection of the moving sun onto the receiver. 

A long time running will make the mechanical components which 

control the heliostat to modify the azimuth angle and pitch angle break 

down, so the heliostats cannot reflect sunlight to the receiver or even 

stop working. In a large power plant, there may be hundreds to hundreds 

of thousands of heliostats which will increase the complexity of 

manually recognizing and detecting which heliostat is fault or brokendown. 

Each heliostat can be equipped with sensors or some other 

equipment to detect whether fault occurs, but it will greatly increase the 

cost. So a novel method for fault diagnosis, which is based on image 

processing and machine vision, is presented in this paper. Experiments 

have shown promising results. 

◁ PSaA-82 

Sensorless Estimation and Simulation of PMSM Based on Highfrequency 

Signal Injection, pp.3438–3442 

Wu, Maogang 

Chen, Xing 


HangZhou DianZi Univ. 

The sensorless estimation principle of rotor speed and position for 

PMSM by high-frequency signal injection is given. The characteristic 

and filtering method of high-frequency current signal, rotor position 

signal extraction based on victor transformation and heterodyning processing 

method are introduced in detail. The simulation models are 

made, simulation results are obtained. The efficiency is verified by simulation 

results, problems need be resolved such as sample frequency 

of high-frequency current, filter design and its delay, interference with 

high-frequency signal from other signals, real-time compensation of rotor 

position, decoupling control high-frequency signal system and base 

frequency system, estimation response, and so on. 

◁ PSaA-83 

Adaptive Synchronization of Uncertain and Delayed Chaotic Systems 

with Its Circuit Realization, pp.3465–3470 


Che, Weiwei 

Wang, Dan 




The asymptotic synchronization problem of a class of uncertain and 

delayed chaotic systems is addressed with adaptive compensation designs 

in this paper. General uncertainties on systems and time-delays 

in the coupling network are eliminated by the adaptive control scheme, 

as well as its circuit realization are proposed. Then, an approach that is 

based on application of Lyapunov stability theory for the synchronization 

error system with adaptive parameters is introduced to prove the 

asymptotic synchronization result of the overall chaotic system. Finally, 

the proposed adaptive schemes are physically implemented by circuits 

and tested by simulation on a Chua’s circuit network. 

◁ PSaA-84 

Effect of Complex Network Structure on Synchronizability, pp.3481– 

3484 

Wang, Dan 

Li, Yanping 





we introduce a evolving model that characterizes the weighted scalefree 

networks with high clustering coefficient by adjusting a parameter. 

The average clustering coefficient exhibits power-law decay as a 

function of degree of node. Triad Formation can distinctly enhance the 

clustering coefficient of networks. The effect of the evolution mechanism 

on synchronizability is analyzed. The simulation results show 

that synchronizability of both the type I network and the type II network 

decrease as the average clustering coefficient increases in weighted 

177


scale-free networks. 

◁ PSaA-85 

The Site Selection of Distribution Center Based on Linear Programming 

Transportation Method, pp.3538–3542 

Liu, Xiaohui 

Beihua Univ. 

The distribution center has a tight with the supply point and demand 

point which are significant in logistics system. A better site selection of 

distribution center has an important impact on the improvement of logistics 

economy benefit. This paper analyzes the common site selection 

methods of distribution center, such as Kuehn-Hamburger Model, Cluster 

Algorithm, Analytic Hierarchy Process, and Genetic Algorithm. It uses 

linear programming transportation method to select site through the 

research and comparison of site selection method of distribution center. 

The paper introduces the site selection steps of linear programming 

transportation method. Based on the construction of logistics network 

schema of linear programming transportation method, it takes into account 

the factors such as relation between supply and demand, freight 

rates, transportation distance and the expense of the distribution center. 

It analyzes and solves by using linear programming transportation 

method and ultimately assume the proposal of site selection of distribution 

center. This proposal has a widespread value of popularization 

and practice. 

◁ PSaA-86 

Soft Tissue Deformation ANSYS Simulation of Robot-Assisted Percutaneous 

Surgery, pp.3561–3566 

Zhao, Chengtao 

Zeng, Qingjun 

Liu, Haixia 




In order to reduce deflection of the stabbing needle and increase the 

accuracy of the robot-assisted needle insertion into soft tissue, A physical 

model of stabbing needle is built with elastic beam. We put forward 

a algorithm to analyze force added on stabbing needle and got 

the deflection and angle of element node. The theoretical calculation 

and experimental simulation shows the validation and consistency of 

the algorithm. The dynamic process of the soft tissue deformation during 

needle insertion is decomposed into a set of discrete quasi-static 

stages. We got every note’s displacement and force distribution of 

the soft tissue by simulating needle insertion into two and three dimensional 

tissue, which provide significant reference with predicting soft 

tissue deformation and planning before surgery. 

◁ PSaA-87 

Adaptive Neural Network Control of an Aerial Work Platform’s arm, 

pp.3567–3570 

Jia, Pengxiao 


An aerial work platform (AWP) is a type of off highway vehicle with a 

long beam to provide temporary access to inaccessible areas.The motivation 

of the research is to increase its productivity, safety and reduce 

the manipulation complexity during the operation process. In this paper, 

a simplified two-link model of AWP’s arm is given. The control 

scheme based on neural network modeling technology is employed to 

steer the AWP’s arm to track the desired trajectories asymptotically, 

which requires neither the evaluation of inverse dynamical model nor 

the time-consuming training process. The simulation results validate 

the effectiveness of the proposed approach. 

◁ PSaA-88 

PSO-based Parameters Opatimization of Multi-Robots Formation Control 

in Unknown Enviroment , pp.3571–3576 

Liu, Qiang 

Ma, Jiachen 



This paper proposed a PSO-based algorithm for parameters optimization 

of multi-robot formation navigation in unknown environment. 

In order to achieve formation navigation in unknown environment, 

each robot in formation adopts motor schema-based reactive control 

architecture which has four primitive behaviors called move to goal, 

keep formation, avoid static obstacle and avoid robot behaviors.The 

behavior output to direct the movement of robot is made by the combination 

of four primitive behaviors. Particle Swarm Optimization algorithm 

as an unsupervised learning method for a reactive control architecture 

greatly reduces the effort required to configure reactive control 

parameters of multi-robot formation system. The validity of this method 

is verified through computer simulations in different types of in environments 

by robot simulation software MissionLab. 

◁ PSaA-89 

A Novel Inverse Dynamics Control Strategy with Different Phases for 

the Quadruped Robot, pp.3601–3606 

Li, Bin 

Shao, Xuesong 

Wang, Wei 

Guo, Yajuan 







Aiming to reduce the computation and implement compliant control, 

this paper proposes a novel inverse dynamics control strategy based 

on the floating-base rigid body system. The control strategy assumes 

that each leg of the quadruped robot organizes itself into an independent 

autonomous system, a serial robot. Based on this assumption, 

the kinematics and the dynamics models of the quadruped robot have 

been created. The dynamical model supposes two different models according 

to the leg’s state. In the stance phase the serial robot affixes 

its base frame to the shank and iterates the rigid body dynamics algorithms 

from the knee joint to the body. When the serial robot is in the 

swing phase, the dynamics algorithm is propagated from the hip joint to 

the shank, whose computing direction is just the reverse against the direction 

of which the serial robot is in the stance phase. The quadruped 

system doesn’t need the fixed base to the system and avoids calculating 

the virtual joints of 6-DOF. Therefore, the algorithm proposed in this 

paper makes real-time computation of the quadruped robot dynamics 

possible. In order to evaluate the efficiency of the inverse dynamical 

control strategy, experiments are accomplished based on a practical 

quadruped robot. The experiments, which were done on a rubber mat 

and on asphalt, demonstrated that the quadruped robot is able to walk 

adaptively. 

◁ PSaA-90 

Formation Control for Multiple Robots in Uncertain Environments, 

pp.3607–3610 

Li, Li 

Yang, Li 

Li, Bing 




A formation control approach for multiple robots in uncertain environments 

is proposed, which adopts the formation parameter matrix to establish 

the relative relationship among robots, decomposes the globallevel 

formation control problem into the-off-axis point of the follower 

tracking the-off-axis point on the virtual robot which has the same orientation 

with the lead robot and maintains a desired relative distance and 

desired observation angle with respect to its lead robot, and designs 

the tracking control law. Simulation results are given to demonstrate 

the feasibility and effectiveness of the approach. 

◁ PSaA-91 

Research On Teleoperation for DFFSR Without Time Delay Based on 

Virtual Reality, pp.3611–3616 

Li, Huazhong 


Virtual modeling and simulation technology about teleoperation without 

time delay for Dual-arm Free Flying Space Robots (DFFSR) have been 

mainly researched. First, present research situation on teleoperation 

time delay problem has been described. Then, solution to time delay 

based on Virtual Reality (VR) Preview technology has been presented. 

Finally, teleoperation system for DFFSR without time delay based on 

VR has been developed and simulation experiment has been carried 

on. The simulation results show that VR technology has taken very 

much important play in effectively overcoming problem of teleoperation 

time delay for DFFSR. 

178



◁ PSaA-92 

Research and Design of Multi-agent Model Structure for Embedded 

Robot Dog , pp.3629–3633 

Min, Huasong 

Huang, Lei 

Gan, Xun 

Wuhan Univ. of Sci. & Tech.,Hubei province 



This paper proposes an embedded intelligent robot model based on 

Multi-agent, and implement it on a robot dog platform with embedded, 

modular components. In order to make the software structure flexible 

and does not depend on specific hardware, we design and divide robot 

into few agents by the idea, which is “thinking and learning, perception 

and interaction, action and control”. What’s more, the model takes 

account of both intelligence and response capability for emergency by 

deliberative module and reflex module. The mechanism of Case-based 

Reasoning is applied to realize the deliberation of robot dog. In the 

simulation and actual integration test, the robot dog is able to walk, 

avoid obstacles, perceive and process external signals such as voice 

input and infrared. The results proved that preliminary success was 

achieved. 

◁ PSaA-93 

Improved S-Plane Control for Underwater Vehicles, pp.3634–3638 

WANG, Jianguo 


Research on control for underwater vehicle is undertaken. S-plane control 

is verified to be effective in control of underwater vehicles. But there 

are problems in steady precision and parameter adjustments. In order 

to obtain higher steady precision, intelligent integral is brought in, 

and expert S-plane control is presented to tune the parameters on-line 

based on expert control and S-plane control according to practical experience 

and control knowledge. To prevent control output jumping, 

fuzzy neural network is adopted to fit the production rules in knowledge 

base. Experiments are conducted on the simulation platform, and the 

results show that expert S-plane controller performs well in current environment, 

and has better robustness than S-plane controller. 

◁ PSaA-94 

Position and Singularity Analysis of 4-SPS/PS Parallel Manipulator, 

pp.3663–3668 

Zhou, Jiehua 

PENG, Xiafu 

xiamen Univ. 

Xiamen Univ. 

The parallel manipulators with n¡6 spherical joint-prismatic jointspherical 

joint (SPS) type active legs and one passive constrained leg 

have attracted more attention and been used in many practical applications. 

This kind of parallel manipulator has a larger capability of load 

bearing and a simple structure of active leg. The paper used 4-SPS/PS 

four degree-of-freedom (DOF) parallel manipulator for the simulation of 

ship’s heave and swing motion. Mobility property of the 4-SPS/PS 

parallel manipulator is analyzed by screw theory. The inverse position 

kinematics model is established and the analytical solution is obtained. 

It uses extra sensor and Sylvester resultant elimination to derive analytical 

solutions of forward position kinematics including two real solutions 

and two imaginary solutions, and uses vector cross product to derive 

the Jacobian matrix of 4-SPS/PS parallel manipulator. Furthermore, it 

introduces three performance indicators to analyze the singularity. In 

order to obtain accurate analysis results of singularity, it calculates the 

minimum singular value, condition number and manipulability degree of 

Jacobian matrix for different combination of pose, respectively. 

Chair: Hou, Zhongsheng 

Poster Session PSaB 

July 7, 15:00-16:20 


◁ PSaB-01 

Design of Intelligent Control System for Electric Vehicle Road Train, 

pp.3958–3961 

Cheng, Liangliang 

Xu, Yong 


Guilin Univ. of Electronic Sci. & Tech. 

Abstract―The modern automobile industry is a double-edged sword, 

with human society to the high prosperity, at the same time it’s built a 

peremptory energy crisis, traffic jams fronts. Electric vehicle research, 

let crisis do see the dawn vehicle technology development. And the 

electric vehicle road train research is still blank in the domestic, research 

and development EV road train main functional modules using 

CAN bus communication independently. The train operates of 3-10 vehicles 

connection grouping, the number of drivers can be reduced by 

more than 3 times, the road resources consume for nothing by up to 

about forty percent, transportation using efficiency of resource utilization 

keeps great potential. 

◁ PSaB-02 

Magnetic Field Analysis of Permanent Magnet Array for Planar Motor 

Based on Equivalent Magnetic Charge Method, pp.3966–3970 

Huang, Rui 

Feng, Jian 



Planar motor is an important research area in complex automation system. 

In this paper, a simple and efficient topological structure of Halbach 

magnet array for planar motor is proposed. The Equivalent Magnetic 

Charge (EMC) method is used to analyze the proposed array. The 

calculation results of EMC method, compared with the results of Finite 

Element Method (FEM) using ANSOFT, indicate that the EMC method 

is reasonable and accurate. So the EMC method in this paper is applicable 

to simplify 3D model of complex automation system in order to 

research the optimal decoupling method and control strategy. 

◁ PSaB-03 

SIFT-feature-based Accuracy Measurement Method for Microoperation 

Stage , pp.4184–4189 

Zhang, Ailong 

Inst. of Robotics & Automatic Information 

Sys.,Nankai Univ. 

Zhao, Xin 

Sun, Mingzhu 

Lu, Guizhang 

Nankai Univ. 

Nankai Univ. 

Nankai Univ. 

Measuring accuracy of micro-operation stage is quite important due to 

its significant role played in helping manipulator perform well in location. 

The paper uses microscopic image to mark the position of microoperation 

stage, converts the displacement of stage to the offset of 

images and then proposes a SIFT-feature-based method of accuracy 

measurement. Firstly, the paper gives solutions to the key techniques 

involved in the measurement, including SIFT-feature-based offset measurement 

of images, calibration of microscopic image and stage’s 

return to reposition measurement point. Then the paper makes exact 

definition of location accuracy and reposition accuracy according to 

the motion demand of micro-operation stage and designs the measurement 

experiment. Furthermore, experiment is accomplished on MR601 

stage which verifies the validity of the accuracy measurement method. 

Meanwhile the result shows that the deviation of 200μm location in Y 

direction of MR601 stage can be generally within 3μm and the standard 

deviation of reposition accuracy can be 0.527μm. It can be concluded 

the reposition accuracy of MR601 stage in Y direction is good. 

◁ PSaB-04 

Design and Implementation of a Synchronized Three-phase Electricity 

Acquisition Module, pp.4215–4219 

Fan, Yuling 

Zhang, Lijun 

Zhuan, Xiangtao 

Wuhan Univ. 

Wuhan Univ. 

Wuhan Univ. 

This paper focuses on using a TI’s DSP to design an AC electricity acquisition 

module, which can accomplish the function of distributed synchronous 

AC electrical parameters sampling. The hardware diagram 

of the design used a DSP and other auxiliary devices to set up an AC 

electricity acquisition module with the ability of high frequency distributed 

simultaneous electrical parameters sampling. Specifically, a GPS 

was mounted on the module to synchronize sampling between different 

modules and a SD card was employed to store the collected data. 

Regarding the software design, a PC control/monitor program was developed. 

The electricity acquisition terminal is capable of 200KHz simultaneous 

sampling when it works without SD card while it is verified 

179


that the highest sample frequency is 10KHz when it works with a SD 

card. 

◁ PSaB-05 

Three-Dimensional Stage Self-Calibration: A General Theory Framework, 

pp.4220–4225 

Zhu, Yu 

Hu, Chuxiong 

Hu, Jinchun 




In this paper, a general self-calibration theory framework is presented 

for three-dimensional precision stages to determine the stage error. 

Four measurement views of a cubic artifact on the three-dimensional 

precision stage are utilized to provide the symmetry, transitivity and 

redundance. The first-order components of the stage error, i.e., the 

nonorthogonality and the scale difference, are determined through the 

first three measurement views. And the residual components of the 

stage error are then figured out by a least-square based calculation 

law, which completes the stage error determination. Additionally, the 

parasitic errors related to the artifact and the misalignment errors of all 

measurement views can all be identified through algebraic manipulations. 

Computer simulation is finally carried out and the results validate 

that the proposed algorithm can accurately realize the stage error. The 

proposed scheme actually provides a framework as a principle to solve 

the three-dimensional self-calibration problem for practical engineers. 

◁ PSaB-06 

Coherent integration weak target detection algorithm based on short 

time sliding window, pp.4264–4266 

Guo, Yunfei 

Zhang, Yunlong 

Xue, Anke 




For the problem of weak target detection under lower signal noise ratio 

in radar detection system, a key technique is to enhance target echo 

energy by long time coherent integration. During the integration period, 

the target may migrate across radar range cells with integration time 

increasing. Direct coherent integration will lead to echo energy spread 

in range cells and deteriorate integration effect. A coherent integration 

algorithm based on short time sliding window is proposed in this paper. 

It can correct range migration before integration. Radar signal is formed 

into a first in first out queue. Then according to constraint condition, the 

queens are continually renewed by the sliding window. The queens are 

coherent integrated at last. Simulation result shows that the proposed 

algorithm can correct range migration efficiently and improve coherent 

integration capability. 

◁ PSaB-07 

Frequency Estimation of Multisection Signals with Same Frequency and 

Length based on Spectrum Correlation, pp.4283–4286 

Shen, Yanlin 

Tu, Yaqing 

XIAO, WEI 

Su, Dan 





Aiming at multisection sinusoidal signals with the same frequency and 

length, a frequency estimation algorithm of multisection signals with 

the same frequency and length based on spectrum correlation is proposed. 

It can improve the frequency estimation precision of short-time 

sinusoidal signal under the condition of low SNR. Firstly, generate a 

spectrum zoom sequence and calculate the spectra of corresponding 

frequencies; Secondly, design a phase-difference compensating factor 

matrix to overcome phase discontinuity among different sections of signals, 

and generate a search-frequency array to modify unknown parameters 

in the phase-difference compensating factor matrix; Thirdly, adjust 

the spectrum zoom sequence corresponding to accumulation spectrum 

and calculate the accumulation spectrum; Finally, calculate spectrum 

correlation sequence with corrected power spectra and accumulation 

spectrum, and search the maxima of spectrum correlation sequence 

and then its corresponding frequency is the frequency estimation value. 

Simulated experiment results demonstrate that the proposed algorithm 

has better frequency estimation precision and noise immunity than 

existing methods, and significant theoretical and practical value. 

◁ PSaB-08 

A Dynamometer Design and Analysis for Measurement the Cutting 

Forces on Turning Based on Optical Fiber Bragg Grating Sensor, 

pp.4287–4290 

Liu, Mingyao 

Tao, Xiaoliang 



Abstract - The cutting forces generated in metal cutting have a direct 

influence on tool wear or failure, quality of machined surface and accuracy 

of the work piece. In this paper, a turning dynamometer that 

can measure static and dynamic cutting forces has been designed and 

constructed, we put Fiber Bragg Grating sensor into cutting force. In 

order to reduce interference of the test data, we designed an octagonal 

ring as sensitive element. We used strain gauges and Fiber Bragg 

Grating sensor to detect surface strain of the elastic rings and get different 

sensor’s data. Contrast the data to optimization the fiber grating 

mounted places. Finally, experiments with different sensors to show 

that the Fiber Bragg Grating sensor could be used reliably to measure 

cutting forces. 

◁ PSaB-09 

On Binary Switching Finite Networks, pp.4347–4349 

Yu, Tao 

Zhou, Xingxing 

Xu, Changqing 



Suzhou Univ. of Sci. & Tech.; Zhejiang A&F Univ. 

We call a finite graph G = (V, E) a binary network if the state set of 

its nodes has only two elements,say, 0 and 1, representing respectively 

’OFF’ and ’ON’ state. A switch at node v switches both the state of v 

and the state of each of its neighbors. It is shown in [1,5] that given 

any intial state of a network of order n > 3, we can always reach at a 

consistant status, i.e., either all the nodes are ON or all are OFF. In this 

paper we consider a more general problem: Given a subset S ⊂ V , 

can we reach to a state such that the state of each node within S is 1(or 

0) while the states of nodes outside S is another? We present some 

sufficient conditions for some specific S that satisifes this condition. 

◁ PSaB-10 

A new estimate method for linear constrained systems, pp.4463–4466 

Wen, Chuanbo 

Cai, Yunze 

Xu, Xiaoming 

Shanghai Dianji Univ. 



This paper deals with the problem of optimal filtering for linear discrete 

constrained dynamic systems. The constraint matrix and constraint 

vector in this system are allowed to vary not only in the value but also 

in the dimension. Firstly, the original constrained state is transformed 

into a new reduced state model without constraint. Then, the prediction 

of the reduced state is given by using the least square method. Finally, 

the optimal estimate of original state is produced by the update process 

similar to the Kalman filter. A numerical example is presented to 

demonstrate the effectiveness of the new method. 

◁ PSaB-11 

Subscriber Location in CDMA Wireless Networks, pp.4390–4394 

Ali, Zahid 

Memon, Qurban 

KFUPM 

UAE Univ. 

Location estimation in Wireless networks has become an important feature 

for improvement in public safety service. Its potential applications 

include location sensitive billing, asset tracking, fraud protection, mobile 

yellow pages, fleet management, etc. Several location techniques 

using terrestrial wireless network elements and radio signals have been 

proposed over the years, but multipath propagation, multiple access interference 

(MAI), and non-line-of-sight propagation have impeded the 

accuracy in mobile station (MS) positioning. Traditional location algorithms 

have derived location estimates assuming single user environment. 

However this assumption is not correct as measurement bias is 

introduced due to MAI. An interference cancellation based delay locked 

loop (DLL) for MS delay estimation is proposed in this paper, which is 

180



subsequently used for radio location. A system model is presented and 

simulations are carried out that show that proposed method provides 

MS position location quite accurately even in the presence of MAI. 

◁ PSaB-12 

A Soft Sensor Method Based on Integrated PCA, pp.4258–4263 

Shao, Weiming 

Tian, Xue-Min 

China Univ. of Petrolieum 


Feature extraction methods such as Kernel Principal Component Analysis 

(KPCA) and Principal Component Analysis (PCA), are often used 

for soft sensor modeling in industrial process with high dimensional data. 

A kind of soft sensor method based on Integrated Principal Component 

Analysis (Integrated PCA) is proposed for some weakness of 

KPCA and that of PCA. This approach combines nonlinear information 

extracted by KPCA with linear information extracted by PCA and it can 

not only reduce the dimensionality of input data, but also make full use 

of linear and nonlinear information. Partial Least Squares (PLS) is used 

to obtain the final soft sensor model and Particle Swarm Optimization 

(PSO) is applied to get the optimal parameters of Integrated PCA and 

those of KPCA. Finally, the proposed method is applied to build soft 

sensor models of diesel oil boiling point and other industrial objects 

and is proved to have better ability of generalization by being compared 

with other feature extraction methods. 

◁ PSaB-13 

Selection of Effective Singular Values based on least Squares Error 

Spectrum, pp.4429–4434 

GAO, Xue-jin 

Cheng, Li 

LIU, Guang-sheng 




In order to solve the selection of effective singular values,first the characteristic 

of the singular values of both ideal signals and noise signals 

was analyzed, a determination method of least squares error was 

proposed. Then the impact on signal de-noising by analyzing different 

number of singular values, indicated that the number of effective 

singular values were accurate and reliable by using the determination 

method of least squares error. Finally, the method had been successfully 

applied to the sensor signal,obtained the Feature curve with smaller 

error and more accurate results in the determination of glucose concentration 

. The results show that: the extracted signals have higher 

SNR and better integrity. 

◁ PSaB-14 

Will scene information help realistic action recognition, pp.4532–4535 

Chen, Xian-gan 

Liu, Juan 

Liu, Haihua 

Wuhan Univ. 

WuHan Univ. 

South-Central Univ. for Nationalities 

The question of scene information whether can help realistic action 

recognition has been investigated in this paper. The salience region 

of each frame in video was acquired by using Itti-Koch algorithm. The 

information outside the salience region represented scene information. 

Two action recognition methods were tested on the YouTube action 

dataset. One method got rid of partial scene information, while the 

other contained scene information. The obtained impressive results 

showed that scene information can help realistic action recognition. 

◁ PSaB-15 

Position Control of an Electric Clutch Actuator, pp.2776–2781 

Wang, Bin State Key Laboratory of Automotive Simulation & 


Gao, Bingzhao 

Chen, Hong 

Zhang, Zhenwei 

Yue, Hanqi 

Jilin Univ. 

Jilin Univ. 

Jilin Univ. 

State Key Laboratory of Automotive Simulation & 


A nonlinear-feedforward-feedback controller is designed for an electric 

clutch actuator, which adopts ball screw as the speed reduction mechanism. 

Ball screw has high transmission efficiency. However, it does 

not self-lock. Moreover, the nonlinear friction force, load force, and the 

nonlinear characteristics of the motor drive circuit increase the difficulty 

of the position control of the actuator. The designed controller contains 

two parts: the feedforward controller designed by differential flatness 

which can deal with the systems nonlinearities, and the feedback 

controller designed by PID method which guarantees system stability. 

Simulation result shows that, compared with pure PID controller, the 

designed controller provides better performance especially in the case 

of small displacement adjusting. 

◁ PSaB-16 

Stability Analysis of a Class of Hybrid Systems, pp.3971–3976 

He, Fenghua 

Yao, Yu 

Zhang, Shuqi 

Zhang, Peng 



HIT 


In this paper, the stability analysis problem of a class of hybrid system 

which involves multiple subsystems and these subsystems interconnect 

and interact with each other is considered. A stability analysis 

approach is proposed and proved in which a Lyapunov function candidate 

can be constructed in terms of that of subsystems. A numerical 

example is given to show the effectiveness of the proposed method. 

◁ PSaB-17 

Application Research of Neural Networks in Fruit and Vegetable Harvesting 

Robot, pp.1790–1795 

Wu, Weirong 




Neural networks was used in the camera calibration of binocular stereo 

vision , segmentation and recognition of fruit or vegetable images , fruit 

or vegetable classification , orientation error amendment of fruit or vegetable 

, obstacle avoidance of robot manipulator , sensor data fusion 

about fruit and vegetable harvesting robot. The research results in all 

the aspects mentioned above of fruit and vegetable harvesting robot 

with neural networks were introduced in this article, and some the highlighted 

issues were finally discussed. It is expected that this research 

may be used as a step stone for further study in the area. 

◁ PSaB-18 

Guaranteed Cost Control for Polynomial Fuzzy time-delay Systems by 

Sum-of-Squares Approach, pp.1806–1811 

Li, Weihong 

Wang, Weiqun 



Guaranteed cost control of polynomial fuzzy time-delay systems is investigated 

via a sum of squares (SOS) approach in this paper. Based 

on polynomial Lyapunov-Kraovskii functional, guaranteed cost controller 

and robust guaranteed cost controller, which minimize the upper 

bound of the given performance function, are designed. The uncertainty 

considered is norm-bounded type. Both of the design conditions 

presented are in terms of SOS. Two examples are provided to show the 

utility of the theoretical results obtained. 

◁ PSaB-19 

Fault Detection and Isolation for Switched Linear Systems: A Switched 

Lyapunov Function Approach, pp.1812–1817 

Zhao, Xingang Shenyang Inst. of Automation, Chinese Acad. of 

Sci. 

Li, Jian 

Ye, Dan 



This paper is concerned with the problem of fault detection and isolation 

(FID) for discrete-time switched systems under an arbitrary switching 

signal. A family of fault detection and isolation filters is designed. In 

virtue of the frequency of the faults in a given range, the finite-frequency 

H − performance for the switched system is firstly defined. Based on a 

switched Lyapunov functional approach, a procedure of FID filters synthesis 

is established by Linear matrix inequalities (LMIs), and the filter 

gains are characterized in term of the solution of a convex optimization 

problem. A numerical example is used to demonstrate the effectiveness 

of the proposed design method. 

181


◁ PSaB-20 

The Stability Analysis of Nonlinear Time Variant System and the Application 

Based on Mathematical Simulation, pp.1844–1847 

Zhang, Shuzheng 

Zhou, Fengqi 

Jiang, DeSheng 

Lu, Hongli 

Northwestern Polytechnical Univ. Xian 

northwestern polytechnical Univ. 



By researching the stability analysis methods of the complicated nonlinear 

time-variant system, some new analysis approaches are presented 

in this paper, which include the parameters fixing in Simulink and the 

system containing nonlinear links linearization in Matlab. In addition, 

the feasibility of using the analysis method of nonlinear time-variant 

system and the decoupling method for anti-aero missile control system 

with channel couple are discussed. The simulation results show that 

the stability analysis for a missile is provided in Simulink, which satisfy 

the requirement of analysis of anti-aero missile control performance. 

◁ PSaB-21 

Longitudinal Control of Intelligent Vehicle Based on Hybrid Automata 

Model, pp.1848–1853 

Ge, Yanrong 


Zhang, Guoxiang 




Based on the analysis of longitudinal control scenarios, a longitudinal 

control model of the intelligent vehicle in virtue of hybrid automata is 

built. Driving situations are divided into cruise control, speed following, 

inter-vehicle distance adjustment modes. Three corresponding control 

strategies are proposed depending on the driving modes. Then 

an algorithm of the longitudinal controller is designed. Not only is the 

following control of the target vehicle in the same lane achieved, but 

also, when the target vehicle changed, the following control of the new 

target vehicle is achieved through the control strategies of inter-vehicle 

distance adjustment. Simulation results show that the designed strategy 

can achieve a variety of scenarios in both high-speed driving and 

low-speed stop-and-go situations. 

◁ PSaB-22 

Dynamic Graph Hybrid System: a Modeling Method for Complex Networks 

with Application to Urban Traffic, pp.1864–1869 


He, Zhonghe 

Shi, Jianjun 

Han, Xingguang 





In this paper, we present a modeling framework for complex networks, 

which is called Dynamic Graph Hybrid System (DGHS for short). First, 

we give a descriptive definition of DGHS. Furthermore, as a typical motivational 

example of DGHS, we apply it to model urban traffic signal 

control (UTSC) network, and the UTSC network model we propose is 

in fact a development of the well-known cell transmission model (CTM) 

in traffic flow theory. In addition, the problem of combination of vertices 

of UTSC network is considered. 

◁ PSaB-23 

Distributed Collaborative Processing Based on Task Allocation for Wireless 

Sensor and Actuator Networks, pp.1887–1892 

Mo, Lei 

Xu, Bugong 



In this paper, we discuss the issues and challenges involved in collaborative 

sensing and actuation algorithms for environment control in 

wireless sensor and actuator networks. The collaborative processing of 

sensor and actuator nodes brings significant advantages over WSNs, 

including improved accuracy and timely actions upon the sensed phenomena. 

Our approach focus on tailor sensor nodes readings and adjust 

actuator nodes actions that will incur the minimum energy cost to 

the system subject to the constraints that users’ preferences regarding 

the state of the system are approximately satisfied. First, we formulate 

the mathematical models of WSANs system. Then, a mechanism 

is proposed to optimally assign tasks to actuator nodes and control 

their actions in a coordinated way to accomplish the tasks based on the 

characteristics of the events. Finally, we develop a distributed control 

scheme which enables actuator nodes to make control decisions locally. 

Simulations demonstrate the effectiveness of our proposed methods. 

◁ PSaB-24 

A New Control Method and Stability Analysis of Linear System with 

Variable Time-delay, pp.1904–1909 

Sheng, Jie 

Li, Shuang 

Ji, Haibo 

Unversity of Sci. & Tech. of China 

USTC 


This paper proposes an effective control method aimed at the linear 

system with time-varying delay. We address the problem by means of 

switching among a finite family of candidate controllers. Our control 

method is based on semi-discretization method and hysteresis-based 

switching algorithms, where the former is introduced to design the candidate 

controller under one fixed time delay and the latter acts as the 

principal law of the switching. We prove the stability of our switching 

delay system and conclude that three factors, the size of the candidate 

controller set, the value of the switching coefficient, the changing rate 

of the delay, affect the system’s stability, which can also be demonstrated 

by several groups of experiments. Simulation results show the 

correctness of our design. 

◁ PSaB-25 

Globally Exponential Synchronization of 4D Four-wing Hyperchaotic 


Liu, Xuezhen 


The globally exponential synchronization (GES) was proved based on 

the Lyapunov theorem for globally exponential stability. To demonstrate 

the theoretical predictions, the GES of 4D four-wing hyperchaotic systems 

was studied. Therefore, two nonlinear control laws were designed 

to obtain GES of the four-wing hyperchaotic system. The effectiveness 

of the control laws is demonstrated by using of both numerical and theoretical 

analysis. 

◁ PSaB-26 

Active disturbance rejection control for the yaw tracking for unmanned 

helicopter, pp.1915–1919 

JIANG, Zhe 


The yaw dynamics of helicopter involve input nonlinearity, time-varying 

parameters and the couplings between main and tail rotor. With respect 

to such a complicated dynamics, the normal control is difficult 

to realize good tracking performance while maintaining stability and robustness 

simultaneously. In this paper, an active disturbance rejection 

control (ADRC) method is proposed. we design an extended state observer 

(ESO) to estimate the impact from parametric uncertainties and 

disturbances, and realize online compensations. Time-varying parameters 

and other uncertainties in the helicopter dynamics can be rejected 

by active disturbance rejection control to achieve small tracking error. 

The simulations results with respect to the dynamics identified from a 

real helicopter-on-arm testbed are presented. The simulation results 

are further demonstrated the improvements of the proposed algorithm 

under the time-varying parameters and other uncertainties. 

◁ PSaB-27 

ADRC Controller Used in Dynamic Positioning System of a Rescue 

Ship, pp.1942–1947 

Lei, Zhengling 

GUO, Chen 

Liu, Yang 




Dynamic positioning system of marine vehicles has been undergoing a 

rapid development and application. In this paper a maneuvering mathematical 

model and a pure sway motion model with three degrees of 

freedom are established, an ADRC controller for the dynamic positioning 

system of a rescue ship is designed. The results of simulation indicate 

that the control performance and robustness of ADRC is significantly 

better than conventional PID controller, the outputs of rudder 

angle and pitch of CPP are smoother and gentler improved , the steady 

182



state accuracy of control output is better, the adaptability of parameters 

is extensive. Taking pure sway motion of the rescue ship for example, 

rapidity and effectiveness of a proposed multi-mode controller are verified. 

◁ PSaB-28 

Indirect Adaptive Fuzzy Controller Design for Vertical Direct-driven Servo 

System, pp.1953–1957 

Xu, Xing 

Cai, Tao 

Wang, GuangHui 

Xu, Zhishu 





In order to deal with the uncertainties and nonlinearities within a permanent 

magnet line synchronous motor (PMLSM) vertical servo system, 

such as friction, thrust ripple and external disturbance, an indirect 

adaptive fuzzy (IAF) controller designed. The IAF controller comprises 

a supervisory controller and an equivalent controller. The supervisory 

controller is proved to guarantee the tracking error asymptotically stable 

without any requirement on the optimal approximation error (OAE). 

Simulation illustrate that the IAF controller can make the servo system 

track the reference signal accurately and has good robustness. 

◁ PSaB-29 

Passivity-Preserving Model Reduction for Descriptor Systems with H ∞ 

Norm Error Bounds, pp.1958–1963 

Wang, Jing - 

This paper considers the passivity-preserving model reduction problem 

for positive real descriptor systems. The model reduction principle is 

presented first, then a sufficient and necessary conditions is provided 

to check whether the high-order descriptor systems can be reduced 

with preservation of passivity. A model reduction algorithm is provided. 

Moreover, the presented method provides an H ∞ norm bound on the 

approximation error. 

◁ PSaB-30 

Simulation Testing Method of V/STOL Flight Control Strategy, pp.1969– 

1975 

Tian, Ye 

He, Yang 

Li, Xinyuan 

Zhu, Jihong 

Tsinghua 

Tsinghua 

Tsinghua 


Recently the V/STOL has become the developing direction of future 

fighter while the simulation testing method is an important way to s- 

elect and optimize V/STOL flight control strategies. According to the 

characteristics of the V/STOL flight, this paper proposes three kinds 

of control strategies and two kinds of control response-types, designs 

a simulation testing method, thereby, analyzes the control strategies 

and response-types based on the simulation results. With the standardized 

target detection and guidelines, our simulation testing method 

provides standard experiments for normal tests of V/STOL flight control 

strategy. The contrast of V/STOL flight control strategies can be 

implemented by this method. The simulation results show that the new 

integrated control strategy, compared with others, provides higher isolation 

of operating channel, more stability of flight and smoothness of 

modes switching. Under the low-speed circumstance, the Translational 

Rate Command widely used in control strategies have the advantages 

of flexibility, stability and facility for pilot to achieved accurate positioncontrol. 

◁ PSaB-31 

The Robust Adaptive Control of Arc Furnace System Based on Backstepping 

Method, pp.1987–1992 

Liu, Xiao-he 

Gao, Nan 


School of Automation, Beijing Information Sci. & 

Tech. Univ. 

Three-phase arc furnace system as a kind of typical nonlinear, threephase 

strong coupling system which has complex and uncertain disturbance, 

traditional control method such as PID is hard to obtain the good 

control effect. Robust control is just brought out to deal with system uncertainty, 

whose research emphasis is to discuss the resistance of the 

system’s certain characteristics or index to some disturbance. This article 

in view of the electric arc furnace system’s nonlinear mathematical 

model, combining with the feedback linearization output tracking control, 

designed index robust adaptive control which based on Backstepping 

method. The simulation results show that: the design of the L 2 

index robust adaptive controller has better tracking performance and robustness 

than PID controller. It proves the correctness of the designed 

algorithm from the hardware point of view. Anti-jamming performance 

also meets the system control requirements. 

◁ PSaB-32 

Energy-based Robust Nonlinear Control of Multiple Static Var Compensators 

in Power System, pp.1993–1998 

Ding, Qingqing 

LIU, Yanhong 

Li, Jianyong 




Using the energy-based Hamiltonian function method, this paper investigates 

the robust nonlinear control of static var compensators (SVCs) 

in power systems with nonlinear loads. First, the uncertain nonlinear d- 

ifferential algebraic equation model is constructed for the power system. 

Then, the dissipative Hamiltonian realization of the system is completed 

by the means of variable transformation and pre-feedback control. 

Finally, based on the obtained dissipative Hamiltonian realization, a robust 

nonlinear controller is put forward. The proposed controller can 

effectively utilizes the internal structure and the energy balance property 

of the power system. Simulation results verify the effectiveness of 

the control scheme. 

◁ PSaB-33 

Improved Robust H∞Filtering for Polytopic Delta Operator Systems, 

pp.2011–2016 

Zhang, Ying 

Zhang, Rui 



The problem of robust H-infinity filtering for polytopic Delta operator systems 

is investigated. The main purpose is to obtain a stable and proper 

linear filter such that the filtering error system remains robustly stable 

with a prescribed H-infinity attenuation level. Based on the bounded 

real lemma, a more efficient evaluation of robust H-infinity performance 

can be obtained by a matrix inequality condition which contains additional 

free parameters. When applying this new matrix inequality condition 

to be the robust filter design, these parameters provide extra degrees 

of freedom in optimizing the guaranteed H-infinity performance 

and lead to a less-conservative design. A numerical example is included 

to illustrate the effectiveness of the proposed approach. 

◁ PSaB-34 

Robust Nonlinear Model Predictive Control Algorithm Based on Reduced 

Precision Solution Criteria, pp.2033–2038 

Wan, Jiaona 

Zhang, Tiejun 

Wang, Kexin 

Fang, Xueyi 

SHAO, Zhijiang 

Research Inst. of Highway ministry of Transport 

Research Inst. of Highway, Ministry of Transport 




This paper discusses the robustness of nonlinear model predictive control 

(NMPC) based on sub-optimal solution obtained under reduced precision 

solution (RPS) criteria. NMPC needs to solve the optimal control 

problem (OCP) quickly and the input is injected to the controlled 

plant in time. Traditional convergence criteria in optimization algorithms 

usually cost excessive long computation time with little improvement 

of solution, which results in degradation of control performance eventually. 

RPS criteria are new convergence criteria for deciding whether 

the current iterate is good enough and whether the optimization procedure 

should be terminated. It can terminate the optimization process 

timely. This work pays special attention to robustness of the closedloop 

system controlled by NMPC with RPS criteria when model plant 

mismatch exists. Simulations demonstrate that the proposed algorithm 

owns good robustness and stability. 

183


◁ PSaB-35 

Control based on variable coefficient of a nonlinear Broer-Kaup system, 

pp.2044–2047 

Ma, Yulan Beijing Tech. & Business Univ., Beijing 100048 

Xiong, Lingchun 

beijing Tech. & business Univ. 

Wang, Wei 


A new variable separation in the (G ′ /G)-expansion algorithm is introduced 

to obtain the solutions for a nonlinear Broer-Kaup system with 

variable coefficient. Control based on the variable coefficient is studied 

for the system. Different variable coefficient functions greatly influence 

the traveling way, velocity and amplitude of the solutions. Visualized 

effects are plotted. 

◁ PSaB-36 

Average Dwell-Time Approach to Stabilization of Networked Systemswith 

Actuator Saturation, pp.2097–2102 

Zhou, Rujuan 

Zhang, Xiaomei 

Nantong Univ. 

Nantong Univ. 

The output feedback control problem is studied for networked discretetime 

systems with actuator saturation and packet losses. The proposed 

output feedback controller is both saturation-dependent and packetloss-dependent. 

The resulting closed-loop system is expressed as a 

switched discrete-time system. A sufficient condition for the local and 

asymptotic stability of the closed-loop system is developed by using 

an average dwell-time approach and a saturation-dependent Lyapunov 

function. The output feedback control law is then proposed in terms of 

linear matrix inequalities. 

◁ PSaB-37 

Development of intelligent control systems on disaster prevention and 

energy saving of underground space, pp.2103–2108 

He, Yi 

Li, Changbin 

Wu, Aiguo 

Tianjin Univ. 

Tianjin Univ. 

Tianjin Univ. 

This paper analyzes the features of disaster prevention and energysaving 

of underground space, and introduces the control systems and 

its subsystems. The 

necessity of intelligent control system is analyzed including its benefits. 

Then its development status and trends are reviewed including: 

intelligent 

detection systems of disaster information, intelligent traffic monitoring, 

intelligent ventilation control system, intelligent lighting control systems, 

intelligent fire alarm control systems, and intelligent drainage control 

systems. Then the future developments are discussed. 

◁ PSaB-38 

Research on Parameter Design of Tank Stabilizers of Ship, pp.2109– 

2112 



Jin, Hongzhang 

Zhao, Yufei 

Zhang, Xun 






Modern robust control theory was brought in the parameters design of 

tank stabilizers for ship, which can solve the nonlinear and model uncertainty 

exist in ship-tank-stabilizer system. An objective function for 

parameters optimization of tank stabilizers was established, which is 

suitable for tanks of different structure and system composed of multiple 

tanks. The simulation result shows that the stabilizers designed 

with this method can provide more effective roll reduction under nonlinear 

condition. And the double-tank system had more effective roll 

reduction in extended frequencies. 

◁ PSaB-39 

Research on Trajectory Scheduling and Control Method of UUV for Terrain 

Survey Mission, pp.2113–2118 

Chen, Tao 

Yan, Zheping 





Zhao, Yufei 


According to trajectory tracking problem in terrain survey mission, trajectory 

scheduling and control method is researched for unmanned underwater 

vehicle (UUV). Cross track error and line of sight combined 

guide is used for trajectory indirect control. In order to ensure precision, 

track and heading error are both used to switch guide method. 

Meanwhile, inserting medi-task scheduling strategy is used to improve 

the survey effect. Compared with ordinary strategy, it can improve stability 

while task switching and reduce overshoot and oscillation. At last, 

the results of lake trial are present, which show that combined guide 

method is effective. By compare with two different scheduling trials, the 

results indicate that medi-task scheduling strategy can improve control 

character while task switching, and increase control precision, which is 

advantageous for terrain survey. 

◁ PSaB-40 

Fractional order QFT Controller for Nonlinear Systems, pp.2136–2141 

Meng, Li 


This paper presents the design of a robust fractional order controller for 

the nonlinear RC circuit based on quantitative feedback theory (QFT). 

In this work, a fractional-order compensator, with a flexible controller 

structure, is introduced into the QFT design to give a better approximation 

of optimum open loop in Nichols. It has been demonstrated that 

the fractional order controller can provide smaller high frequency gain 

than the integer order controller due to its extra tunable parameters. 

◁ PSaB-41 

Data-based Predictive Control for Networked Control Systems, 

pp.2302–2305 

Wang, Yan 

Ji, Zhicheng 


Southern Yangtze Univ. 

As the network is introduced into the system, it will increase complexity 

of system dynamics and plant modeling, data-driven control theory 

is proposed to design the controller independent of the model in this 

paper. Construct the output data matrix and the input data matrix using 

input / output current data and historical data, establish a linear 

relationship between the two, thus get the predictive controller gains. 

Simulation results show that the proposed controller is effective. 

◁ PSaB-42 

Cross-Layer Energy Efficiency Design in Wireless Sensor Networks, 

pp.2312–2317 

Tang, Xiufang 

Wang, Yan 

Institution Electrical Automation 


Based on the analysis of the reliability and energy-consumption in 

WSNs, In this paper, we propose a cross layer-based MAC protocol 

abbreviated as CoDyMAC(Cross-layer and Dynamic balance energyconsumption 

MAC). CoDyMAC realizes multi-hop data transmission in 

a listen / sleep cycle by utilizing the interactions among MAC, Routing 

and Physical layers, and meanwhile introduces the selection mechanism 

of communication node and take the redundancy under control 

after weighing the energy-consumption and the reliability of data transmission. 

Thus it dynamically balances the energy consumption and 

prolongs the network life. Through the performance analysis and the 

simulation study, CoDyMAC shows its advantages such as shorter latency, 

lower energy-consumption, and high reliability to SMAC, ASMAC 

and ELMAC. 

◁ PSaB-43 

A New Method of Evolving Hardware Design Based on IIC Bus and 

AT24C02, pp.104–107 

Li, Kangshun 

Chen, Yan 

Liu, Hezhuan 


South China Agricultural Univ. 


A new method of evolving hardware design based on I2C bus and 

AT24C02 is presented in this paper. This method has expanded the 

application of I2C-bus, it is implemented on A FPGA/ SOPC by using 

I2C-bus and AT24C02 to simulated I2C-bus transmission timing, it 

184



change the method of evolving hardware design by using I2C-bus and 

AT24C02 but not using evolutionary algorithm, overcome the disadvantage 

of long time of the traditional method, and achieve a fast, high 

efficiency and higher accuracy I2C interface design techniques. The 

experiments show that the circuits evolved by using this new method 

based on I2C-bus and AT24C02 communication interface can save 

transmission time, increase the bus utilization, and has achieved good 

results 

◁ PSaB-44 

Collaborative Monitoring of Underground Gas disaster Based on Fuzzy 

Information Fusion, pp.4230–4234 

Ma, Fengying 

Ma, Fengying 



In order for gas monitoring system to increase precision and real-time 

capacity, a gas disaster collaborative monitoring system was presented. 

The system could perform intelligent measurement of gas disaster 

and coal dust disaster with the expert systems. In order to solve 

the problem of coal-dust explosion limit reducing once the gas gathering 

occur, the explosion parameters were detected by multi-sensor 

and the information of dust, gas concentration and temperature from 

sensors were carried on fuzzy processing to be fused based on fuzzy 

information and fusion theory. The system can finish fusion computing 

and decision-making of various system parameters to realize the early 

prediction for mine explosion successfully. The experimental results 

indicate that the discerning accuracy and reliability of mine explosion 

detection system is greatly increased based on fuzzy information and 

data fusion. It is concluded that the system is of great significance to 

coal mine production safety. 

◁ PSaB-45 

The Prototype IEEE 1451.4 applied in the IOT, pp.4241–4244 

LI, ZHI 

Qin, Chang-ming 

Zhang, Huo 


the Guilin Univ. of electronic & Tech. 


As the key technology applied in the internet of the things (IOT), the 

transducer technology plays a driving effort on the development of the 

IOT. IEEE 1451.4 defines a mixed-mode interface(MMI) for the smart 

transducers and the formats of the Transducer Electronic Data Sheet 

(TEDS),which adds the play-plug function to the analog transducer. 

With the prototype applied in IOT, it will save much time used to recognize 

and describe the transducer and will supply with more useful 

information, such as calibration and orientation parameters. It will bring 

much convenience to the management of the transducer code in IOT. 

This paper briefly introduces the IEEE 1451.4, gives an instance of applying 

the IEEE1451.4 prototype in IOT to measure the real-time temperature. 

◁ PSaB-46 

A Cooperative Framework for Target Tracking in Wireless Sensor Networks 

, pp.4249–4254 

Li, Xun 

Wang, Jianwen 



A Wireless sensor network (WSN) can be deployed in advance for 

tracking a moving target. The sensor nodes can be arranged at some 

expected positions, the computing complexity for locating the target 

hereby will be mitigated. In this paper, we propose a cooperative framework 

for multiple sensor nodes to track the moving target. A lightweight 

distributed method to locate the target is presented in the framework. 

We analyze the impacts of the time synchronization error and distance 

measurement error on the target track errors. Based on the analysis, 

the parameters involved in this method are discussed. With the suitable 

parameters, the method can assure the accuracy of the track while simplifying 

the calculation of the localization. 

◁ PSaB-47 

Distributed Luenberger Observers for Linear Systems, pp.4267–4271 

Ni, Wei 

Wang, Xiaoli 



Yang, Jie 

Chun, Xiong 



The distributed Luenberger observers for linear systems under switching 

topology is considered. These observers are arranged in a communication 

graph configuration, and perform estimation tasks by distributed 

local data fusion in the sense that each observer receives measurements 

from local sensors and exchanges information with its neighbors. 

The objective is convergence of each observer state to that of the given 

linear system. Leader-following consensus algorithms is applied to 

the distributed observers design. The communication graph allows to 

be time-varying. A modified averaging approach is utilized to aid the 

convergence analysis under the jointly connected graph topology. An 

illustrated example is presented to validate the result. 

◁ PSaB-48 

Distributed Extended Kalman Filter based on Consensus Filter for Wireless 


Long, Hui 

Qu, Zhihua 

Fan, Xiaoping 






Distributed state estimate is one of the most fundamental problems for 

wireless sensor network. This paper addresses a type of distributed extended 

kalman filter that is extended from linear distributed kalman filter. 

Central extended kalman filter is an effective tool for nonlinear state filter 

of multisensor network. In this paper central extended kalman filter 

is decomposed into n micro extended kalman filters with inputs that are 

provided by consensus filters. When system process model and observation 

model are nonlinear, it is proved that distributed extended kalman 

filter can provide an identical state estimate of system state. Two target 

tracking examples are employed for simulation demonstration. All 

sensor nodes are able to take a nonlinear observation to moving target, 

dynamical cluster that is composed of several sensor nodes execute 

observation and error covariance matrix consensus filter. Each sensor 

in cluster obtain system estimate through distributed extended kalman 

filter. Simulation results show the proposed algorithm is effective for 

nonlinear distributed state estimate. 

◁ PSaB-49 

Improved CPHD Filtering With Unknown Clutter Rate, pp.4326–4331 

Zheng, Xuetao 

Song, Liping 

Xidian Univ. 

Xidian Univ. 

To accommodate the model mismatch in clutter rate, a cardinality probability 

hypothesis density (CPHD) filter with unknown clutter rate has 

been proposed by Mahler. It has proved to be a promising algorithm for 

multi-target tracking in complex environment. However, in Mahler’s algorithm, 

the calculation of the number of clutters without observations 

is determined by the hybrid cardinality distribution and hybrid probability 

of misses, it will cause the confusion between undetected targets 

and clutters. To solve this problem, an improved CPHD filter is proposed 

which increases an estimation of the number of targets based 

on the measurement likelihood in the process of update and then modifies 

the hybrid cardinality distribution by treating the confused targets 

as detected ones more reasonably. Simulation results show that the 

improved CPHD filter is superior to the traditional method in both the 

estimates of clutter number and target state. 

◁ PSaB-50 

Reduced-rank Space-Time Adaptive Processing to Radar Measure Data, 

pp.4332–4336 

Wen, Xiao-Qin 


This paper firstly introduces the correlation dimension nonhomogeneity 

detection, to select the secondary range cell and estimate 

the correlation matrix. Then respectively discusses reduced-rank STAP 

based on direct form process (DFP) and generalized sidelobe canceller 

(GSC). Those approaches all take advantage of the low rank nature of 

clutter and jamming observations, and the reduced-dimension transformation 

applied to the data are necessarily data dependent. Lastly uses 

185


the Mountain Top measure data to validate these reduced-rand STAP 

technique. Theory analysis and simulation results all show that those 

schemes can make the residual power least, and reduce computational 

burden. 

◁ PSaB-51 

Large Current Measurements Using a Fibre Optics Current Sensor, 

pp.4337–4340 

Wang, Li 


An optical ac-current sensor of vast range and high accuracy is designed 

based on magnetostriction effect and fiber-loop cavity ring-down 

technique. The model of the sensor quantified the coupling mechanisms 

among the current, magnetic field, strain and ring-down time of 

the fiber loop is proposed. The simulation result according to the model 

shows that both the sensitivity and the measurement range dependent 

on mechanical prestress and the sensor can measure current over 

50kA with an accuracy of 0.2%. This result can provide the theoretical 

foundation for development of new current sensor with high-sensitivity 

◁ PSaB-52 

An Optimal Parking Space Search Model Based on Fuzzy Multiple Attribute 

Decision Making, pp.4350–4355 

Yu, Shouyuan Shenzhen Inst.s of Advanced Tech.,Chinese Acad. 


LI, Baopu 

Zhang, Qi 



Shenzhen Inst.s of Advanced Tech. 


This paper proposes a new method based on multiple attribute decision 

making to search for the optimal parking space in a parking lot. 

The advantages and limits of different kinds of property weights are investigated 

in this paper. By improving the limits of subjective weight and 

combining subjective weight with objective weight, we get an integrated 

weight. Using this hybrid weight, we introduce how to apply multiple 

attribute decision making to search for the optimal parking space. Experiments 

show that this method has a satisfying performance and can 

find the real-time optimal parking space. The proposed method can be 

applied to city parking guidance and information system. 

◁ PSaB-53 

A New Method to Eliminate Negative Frequency Interference Based on 

Wavelet Transformation and Grey Correlation Theory, pp.4356–4361 

Mao, Yuwen 

Tu, Yaqing 

Yang, Huiyue 



Univ. of logistical enginering 

As for the common low-frequency roll vibration signals in the fields of 

mechanical vibration and fault diagnosis, the negative frequency interference 

is an important factor to influence the precision of spectrum 

analysis and correction. In order to eliminate the negative frequency 

interference, a new method for the analysis and correction of extremely 

low frequency signal based on wavelet transformation and grey correlation 

theory is presented. First, the original signal is constructed as a s- 

tandard reference sequence. Second, the original signal is transformed 

via one-dimensional wavelet transformation to calculate the grey correction 

degree between transformed signal components and the standard 

reference sequence, by which the grey correction sequence can 

be obtained. The value of the grey correction degree of negative frequency 

interference components is small, so the negative frequency 

interference components can be picked out as ”useless ingredients”. At 

last, the remaining signal components are reconstructed to restore the 

original signal in which the negative frequency interference has been 

eliminated. Simulation experiments and verification examples have 

shown the effectiveness of the proposed method. 

◁ PSaB-54 

Analysis of Phase Difference Tracking Methods for Signal of Coriolis 

Mass Flowmeter, pp.4368–4373 

Yi, Peng 

Tu, Yaqing 

Xie, Min 




Shen, Ting’ao 


In order to achieve a high precision flow measurement and to improve 

its dynamic response speed of the mass flowrate, it is important to 

calculate the phase difference of the two sensor output signals accurately 

and to track its variation in time. The phase difference tracking 

methods in studying Coriolis mass flowmeter at home and abroad are 

described comprehensively, and seven main methods were summarized 

firstly, including the method based on digital phase-locked loop, 

the method based on quadrature demodulation, the method based on 

discrete Fourier transform, the method based on adaptive notch filtering, 

the method based on Hilbert transform and etc. Then the basic 

idea, the advantages and disadvantages of the main methods were 

analysed and compared. Finally, analysis and prospect in this field 

were discussed. 

◁ PSaB-55 

A multi-scale analysis approach to selecting signals related with combustion 

status, pp.4374–4377 

Hao, Zulong 


In order to improve the diagnostic accuracy of boiler combustion status, 

it is necessary to select more signals related with combustion status 

than flame signal and furnace pressure signal. However, it is inefficient 

only using mechanism analysis when selecting because of the complicated 

input-output mapping relation of boiler combustion. Based on 

multi-scale analysis, a new method selecting related signal is presented 

according to the fluctuation similarity of thermal signals. Firstly, components 

of different frequency ranges are obtained via multi-level wavelet 

decomposition and reconstruction. Then, correlation coefficients are 

calculated based on the assumption of fluctuation similarity between 

components in same frequency range, which can be used to describe 

the correlation degree of those components. By this method, the signals 

related with combustion status can be selected through calculating 

the correlation coefficients of their high frequency components. The 

calculation results using on-site data prove the validity of the proposed 

method. 

◁ PSaB-56 

An Actuator Real-time Placement Algorithm Based on Regular Hexagonal 

Grid for Wireless Sensor and Actuator Networks, pp.4378–4384 

LIU, Xinhua 


Network coverage is one of key issues for designing a practical Wireless 

Sensor and Actuator Network (WSANs). In view of real-time coverage 

and convergence of mobility coverage, an actuator real-time placement 

algorithm based on regular hexagonal grid for WSANs (ARP-RGH) is 

proposed. In this algorithm, firstly, a regular hexagonal grid plot algorithm 

is used to choose the idea location of actuators for maximizing the 

monitoring area coverage of the network from a theoretical perspective; 

and then, the actuators are spreaded in the form of uniform distribution 

by a moving algorithm based on virtual forces; at last, according to 

the ideal location calculated before, the optimal location of actuators is 

given by real-time coverage rounds algorithm. ARP-RHG algorithm implements 

the optimal coverage of actuators under real-time coverage 

constrain conditions, and makes the actuators avoid moving into the 

holes of sensor distribution. The results of the simulation show that the 

ARP-RHG algorithm has good coverage and convergence. 

◁ PSaB-57 

Improved Pattern Amendment Inversion Algorithm for Dust Fast Realtime 

Measurement, pp.4423–4428 

Ma, Fengying 


Due to the low reliability and the bad real-time performance for coal dust 

concentration measurement, an improved pattern amendment inversion 

algorithm was presented. The pattern classification was performed 

according to diffraction angular with dust information to meet various 

needs of coal mining. Simulation indicates the minimum recognition 

time is reduced to 0.05 times of that before. Thereupon, transitional 

patterns were supplemented and the precision increased markedly. 

But sometimes there was gross error. Therefore, the pattern amend- 

186



ment function was introduced and the eigenvectors of amendment patterns 

were worked out. A ranking method of the amendment patterns 

was proposed and the normalized eigenvectors of amendment patterns 

ranked were stored in advance. During measurement the optimal patterns 

were recognized in universe and amended in local area according 

to the principle of the minimum of variance sum. Then the dust content 

could be inversed with the total light ratio of signal to optimal pattern. 

Experiments proved the error of total dust and respiring dust declined 

from 6% to 2% and from 9% to 3%, respectively. It is concluded that 

the improved algorithm has enhanced the precision and real-time performance 

of dust sensor remarkably. 

◁ PSaB-58 

Fault-tolerant Data Aggregating using Median Filtering in Cluster-based 

Homogeneous WSN, pp.4441–4445 


Tan, Zongkai 

Fan, Xiaoping 

Long, Hui 

Li, Yongzhou 

Liu, Limin 





automatication institution 

Central South Univ., Changsha 

The nodes of Wireless Sensor Network (WSN) easily produce fault sensor 

readings because of the harsh environment and the energy restriction 

and so on. To enhance reliability and save energy, this paper proposes 

a median filter and weighted pseudo-median filter based aggregating 

method for in-cluster data of homogeneous WSN, which combines 

fault nodes self-checking and clustering data fusion together. In 

the aggregating scheme, each node receives the fusion result from the 

cluster head (CH) and estimates the confidence grade of its measuring 

result for next aggregation round, if a node is not a fault one, it transmits 

its confidence grade and filtered measuring value to the CH. Based on 

the proposed weighting rule of median filter, the CH sets the current 

weight of each node’s data, which depends on the confidence grade 

determined by node itself and the current number of nodes to aggregate 

data in the cluster. The CH computes and outputs the weighted 

pseudo-median of the received data as the fusion result. The simulation 

and analysis show that on the premise of minimum redundancy, 

the proposed method is valid under the circumstance of high fault node 

rate and the high fusion accuracy is obtained even in the case of a s- 

mall proportion of communication error or data packet dropout without 

communication detecting or confirmation. 

◁ PSaB-59 

A Street Lamp Clustered-control System Based on Wireless Sensor 

and Actuator Networks, pp.4484–4489 

LIU, Xinhua 


In order to resolve the problems of city street lamp control and management, 

a street lamp clustered-control system based on wireless sensor 

and network (WSANs) is proposed in this paper. In the system, the related 

information of vehicles running on the road will be detected by the 

sensors deploying along the both side of the road, and then the sensing 

data will be passed to the actuators being equipped on the lamps 

through multi-hop wireless communication. The actuators gather the 

information from the sensors and regulate the brightness of the lamp 

by the distributed and cooperated control algorithm. According the security 

rulers of driving a vehicle, a certain number of lamps in a security 

distance before and after the vehicle will be turned on. The street lamp 

clustered-control system is good at energy saving and validated by the 

experiment results. 

◁ PSaB-60 

Research on blind detection of image splicing based on run length matrix 

combined properties, pp.4545–4550 

Liu, Han 


Image splicing is a technique commonly used in image tampering. In 

order to achieve image splicing blind detection, a blind, passive, yet 

effective splicing detection method is proposed in this paper. In this 

method run length matrix is used to extract image feature and generate 

the identification model with combination of Neighborhood DCT Coefficient 

Co-occurrence Matrix Feature and Markov Feature. Support 

vector machines (SVM) also is selected as classifier for training and 

testing while genetic algorithm is used to optimize parameters based 

on evaluation criteria AUC. Experimental results show that there is high 

classification accuracy for obtained model by this method. 

◁ PSaB-61 

CRM System Design of Financial Equipment Enterprises Based on XM- 

L , pp.4141–4146 

Cui, Wenhua 

Liu, Xiaobing 





Based on the characteristics of the financial equipment manufacturing 

industry and the customer relationship management theory, a customer 

relationship management system based on the product maintainer is 

designed. Then the UML modeling method is adopted to design and 

implement a CRM system after analyzing the functions of the customer 

relationship management system. Focusing on the production services 

and the qualify tracing, the system accomplishes the main modules 

of customers resource management, stock management, product services 

and quality tracking management. Based on the technologies of 

XML, Web and .NET and wireless Wap, an XML-based .NET data access 

model is proposed to implement the information integration and 

data exchange between CRM system and other systems or heterogeneous 

databases. 

◁ PSaB-62 

An improved particle swarm optimization based on wolves’ activities 

circle, pp.4557–4562 

Wei, Bin 


Recently nature-inspired algorithms have attracted a lot of attentions. 

Particle swarm optimization (PSO) is one of the most successful natureinspired 

algorithms. However, studies showed that PSO has some 

drawbacks such as easy to fall into the local optimum and slow convergence 

rate in the later iterations. In this paper, inspired by the wolves’ 

activities circle we propose a novel PSO (named PSO WOLVES). The 

PSO WOLVES was tested on eight benchmark functions and compared 

with three modified PSO, and the results showed that our algorithm not 

only has better search ability but also has faster convergence speed. 

◁ PSaB-63 

A New Color Image Sharing Scheme with Natural Shadows , pp.4568– 

4573 

Chiu, Pei-Ling 

Lee, Kai-Hui 

Peng, K.-W. 

Cheng, S.-Y. 





Conventional visual secret sharing (VSS) schemes generate noiselike 

random pixels on shares to hide secret images. However, these 

schemes suffer from two problems, one related to security and one related 

to management. First, the noise-like shares arouse suspicion, 

which leads to security problems for participants who are involved in a 

VSS scheme. Second, participants cannot visually identify each share, 

especially if they hold more than one share simultaneously, which leads 

to the management problem. To address the management problem previous 

researchers developed extended visual cryptography schemes 

that add a meaningful cover image on each share. Generally, however, 

these approaches introduce a more serious pixel expansion problem 

than conventional VSS schemes. In addition, there are still many noiselike 

shares, which do not effectively reduce the security problems. This 

paper proposes a natural-image-based secret image sharing scheme 

(NSISS) that can share a color secret image over n − 1 arbitrary natural 

images and one noise-like share image. Instead of altering the 

contents of the natural images, the encryption process extracts feature 

images from each natural image. In such a way, the unaltered natural 

images are innocuous, thus greatly reducing the security problem. Experimental 

results indicate that the proposed approach is an excellent 

187


solution for solving the management and security problems. Moreover, 

the proposed approach avoids the pixel expansion 

WEI, Junming 

WANG, Zhiliang 

Australian National Univ. 


◁ PSaB-64 

Research and Application of Image Enhancement, pp.4596–4600 

Zhang, Ying 


According to the characteristics of gray image, histogram equalization, 

image smoothing, image sharpening and Wavelet transform for image 

processing are adopted, and the improved algorithm to make up some 

limitations and shortcomings are used in this paper. By analyzing the 

experimental results, the range of applications of the different enhancement 

algorithms are obtained. 

◁ PSaB-65 

A particle filter tracking algorithm based on adaptive feature fusion s- 


XU, Fen 

North-china Univ. of Tech. 

The design and implementation of a particle filter tracking algorithm 

based on adaptive feature fusion of color histogram and edge orientation 

histogram is introduced. Experimental results show that the feature 

fusion tracking algorithm is more robust, especially when the target is 

moving in a varying environment, compared to that of single feature 

tracking algorithms. The adoption of two features increased the computational 

complexity inevitably. To avoid degeneracy of tracking speed, 

integral edge orientation images are built up. The final algorithm, running 

on a Pentium IV computer, can track pedestrians walking at normal 

speed effectively. 

◁ PSaB-66 

Solving Integrative Matching Model of Inventory in Continuous Casting 

and Hot Rolling Processes by Improved Genetic Algorithm, pp.5133– 

5138 

Li, Haitao 


To solve the problem of matching slabs and coils against orders in the 

process of hot rolling production in steel industry, we introduce matrices 

in which the element indicates difference between slab material and s- 

teel required in order, and also matrices in which the element indicates 

difference between coil and steel required in order. A multi-objective 

0-1 programming model is established. And then, an improved genetic 

algorithm with sub-integer encoding method and heuristic repair strategy 

is proposed. Finally, effectiveness of the model and algorithm is 

verified by simulation based on actual production data. Simulation experiments 

show that the proposed method could gain more scientific 

and reasonable matching results. 

◁ PSaB-67 

Dynamic Model and Balancing Control for Two-Wheeled Self-Balancing 

Mobile Robot on the Slopes, pp.3681–3685 

Peng, Kui 


Zuo, Guoyu 




This paper studies the balance control of two-wheeled self-balancing 

robot on the slope. The dynamic model was first established using Lagrange 

method, which was proved correct by the simulation results of 

its zero-state response and zero-input response. A set of balance e- 

quations were then obtained from the kinetic model, and the Lyapunov 

method was carried out to estimate its stability and controllability. Finally, 

the robot’s linear model in the equilibrium position was obtained. 

The linear quadratic optimal regulator was designed to control its attitude 

and speed. The simulation results show that the two-wheeled 

self-balancing robot in the slope situations can keep balance without 

displacement. 

◁ PSaB-68 

A Novel Scenario of Task Planning for Multi-robot Collaboration System, 

pp.3770–3775 

Shi, Zhiguo 

Hu, Kaihang 

Tu, Jun 


Univ.of sci.&Tech.Beijing 


This paper presents a novel mission planning solution, which aims to 

further improve the efficiency of accomplishing task in multi-robot collaboration 

system. The novel mission planning solution includes task 

decomposition and task allocation; besides, it divides task into decomposable 

and non-decomposable. The decomposable task needs to 

meet certain conditions, and the necessary and sufficient conditions 

for decomposability of a task are able to apply parallel composition and 

natural projections. This paper mainly researches on the decomposable 

task and introduces four decomposability conditions. In addition, 

the task allocation algorithm is based on reputation mechanism, which 

is gained from the evaluation of the completion of historical tasks. Task 

attempts to be assigned to the robot with relatively higher reputation, 

which can greatly improve the success rate of its mandate implementation. 

Finally, through the simulation results it can draw the conclusion 

that the novel mission planning solution can be used in multi-robot collaboration 

system, and has good efficiency. 

◁ PSaB-69 

Residual Vibration Suppression Using Off-line Learning Input Shaping 

Method for a Flexible Joint Robot, pp.3858–3863 

Qiang, Yanhui 


This paper presents a two-impulse input shaping method using off-line 

learning method to suppress the residual vibration of a flexible joint 

robot which is considered as perform repetitive tasks. It has been 

proved that the two-impulse input shaping method has the ability to suppress 

time-varying or nonlinear residual vibration. However, the parameters 

of the input shaper are difficult to select. In this paper, a method 

based on the off-line learning is presented to determine the proper parameters. 

We found that the torque of the joint can reflect the residual 

vibration through analysis of relations between the residual vibration 

and torque of the flexible joint robot. Thus, the torque signal of the joint 

is used to measure the vibration magnitude and no additional sensors 

for vibration measurement are required. For the nonmeasurable of the 

phase of the residual vibration, only the vibration magnitude is used to 

update the parameters of the input shaper off-line until the minimum 

vibration is obtained. The initial parameters of the input shaper also 

are estimated in this paper. Simulations are conducted to suppress 

residual vibration of a flexible joint robot, thereby demonstrating the effectiveness 

of the off-learning learning input shaping method. 

◁ PSaB-70 

Airfoil design and aerodynamic force testing for flapping-wing Micro Air 


Xia, Wen 

Su, Gang 

Shengyang Ligong Univ. 

Shenyang Inst. of Automation Chinese Acad. of 

Sci. 

We designed a X-type wing of flapping-wing Micro Air Vehicles (MAV) 

and doing some aerodynamic force testing by the flapping-wing MAV 

aerodynamic force testing device which designed by ourselves. In order 

to study the relationship of aerodynamic force on the flapping-wing 

models, we do these tests of different flapping-wing models in different 

angles of attack and flapping frequency. Found that X-wings flapping 

wing MAV rely mainly on aerodynamic wings folded back when the compressed 

gas emitted and received; increase the X-wings flapping wing 

MAV flapping frequency can significantly increase aerodynamic wings; 

and X-flapping wing MAV Aerodynamic wing root shoot ratio and aspect 

ratio with the decrease, inversely proportional to the relationship. 

◁ PSaB-71 

Development of Flapping-wing Micro Air Vehicle in Asia, pp.3939–3942 

Zhang, Weiping 


The purpose of this paper is to provide a review of recent developments 

in Flapping-wing Micro Air Vehicles (FMAV) in Asia in past few 

years. The paper discusses recent Asian research in the development 

of FMAV and summarizes two major strands of research divided by 

insect-like FMAVs and bird-like FMAVs. Also the paper summarizes 

188



the developing status of FMAV and proposes the future research focus, 

development trends and application prospects. 

◁ PSaB-72 

Comparison of Several Communication Methods between Upper Computer 

and CompactRIO, pp.3962–3965 

Chen, Wanmi 

Su, JinXia 



Communication is one of the most important steps in constructing a 

robotic system. Such a system has been built based on NI LabVIEW 

and compactRIO. In this paper, several methods of communication between 

host computer and compactRIO were introduced. By comparison 

in the experiment, we found that each method has their unique 

benefits and drawbacks. The method of shared variables is the most 

convenient; the method of TCP/IP is the most reliable; the method of 

UDP has the highest performance. When a system is set up, the most 

suitable method of them should be selected according to the user’s 

requirement. 

◁ PSaB-73 

An Approach for Real-time Urban Traffic State Estimation by Fusing 

Multisource Traffic Data, pp.4077–4081 

Zhang, Ning 

Xu, Jianmin 

Lin, Peiqun 

Zhang, Minjie 





Data fusion is an important tool for estimating urban traffic state when 

various traffic data are available. In order to get more accurate and 

comprehensive traffic state, this paper proposes an improved reliability 

revaluated Dempster-Shafer fusion algorithm (RRDSF) and a framework 

of real-time traffic state estimation system for fusing multi-source 

data, tests on the accuracy by real-world traffic data. The framework of 

real-time traffic state estimation system proposed in this paper shows 

the feasibility of developing advanced data fusion system for real-time 

traffic state estimation. The results report in this paper demonstrate 

that the proposed model can fuse data from loop detectors and probe 

vehicles to more accurately obtain traffic state estimation than using 

either of them alone and encourage us to do further work. 

◁ PSaB-74 

Dynamic Game Model with Strategic Element on Cooperation and Confront, 

pp.4098–4102 

Dai, Yeming 

GAO, Hongwei 

WANG, Guirong 

qingdao Univ. 

Qingdao Univ. 

Qingdao Univ. 

The key characteristic of the game model in this paper is that each 

player, before making a choice, informs other players by declaring he 

will cooperate or play individually, and the decision to cooperate or not 

will be strategic element of player. The optimal trajectory of game in 

finite extensive form with the simple coalitional structure is constructed 

and the algorithm of value of game for this sort of game is given in this 

paper. 

◁ PSaB-75 

Dynamic Group Method Based on Health Management Applied to Mission 

Tasking for Multi UAVs, pp.4118–4121 

Hu, Wei 

Liu, Gui 

Shenyang aerospace Univ. 


Multi Agent system is one of an effective way for resolving task assignment 

problem for multi UAVs. The state of agent, especially the 

anomalous state, would badly reduce the efficiencies of agents. For 

the reason, this paper proposes a dynamic group method based on 

health management in multi agent system to improve the agent efficiency 

when it performs a mission. The method introduces a decision 

Agent also a member Agent dynamic group model and evaluate function 

based on health management, which is efficient to coordinate all 

the Agents in the system. Simulation results show the validity of the 

method. 

◁ PSaB-76 

The application of RSA digital signature algorithm in the Power Dispatching 

work ticket Transmission system, pp.4132–4134 

Kang, Chaohai 


Abstract―In order to solve the authenticity and integrity problem during 

the Transmission of Power Dispatching work tickets, It need encrypt 

of the Power Dispatching work tickets. This paper analyzes the main 

processes of RSA digital signature algorithm and gives the algorithm 

implementation based on the C# language, the algorithm is applied to 

an Electric Power Dispatching work ticket Transmission system of an oil 

extraction plant, the proven reliability of the algorithm to ensure that the 

electronic work tickets of security and non-repudiation. 

◁ PSaB-77 

Particle swarm optimization algorithm in the quantitative analysis of N- 

ear Infrared Spectroscopy, pp.4147–4150 

Ma, Bibo 


In this paper, particle swarm optimization (PSO) is used to establish 

the model of quantitative analysis of near infrared spectral for cereal’ 

protein. In this paper, 42 samples are selected for the study. Among 

them, 22 samples are used for modeling, and 20 samples are used for 

forecasting. The results show that the correlation coefficient of Modeling 

is 0.98, standard residual is 0.289; the correlation coefficient of 

forecasting is 0.96, standard residual is 0.397. For the method of PSO, 

whether it is used for modeling or forecasting, the results are very satisfactory. 

So, this method could be used in quantitative analysis of near 

infrared spectra. 

◁ PSaB-78 

The research of a programmable and high precision gas pressure generator, 

pp.4155–4159 

Li, Xuezhe 

North China Inst. of Sci. & Tech. 

High precision and programmable pressure generating device is needed 

in pressure system test, pressure meter calibration and other occasions, 

which is used to generate a variety of standard pressure. The 

traditional pressure generator using manual pressure has low accuracy, 

low efficiency, high price. In the paper, a high precision and full 

automatic gas pressure generator is proposed, which applies intelligent 

control theory to the pneumatic control system, solves the nonlinear 

and uncertainty. The paper also introduces the principle and implementation 

of system project. The system can implement PC SPC output 

through RS232 communication interface. Experiments show that the 

system has advantages of high precision, pressure change smoothly, 

small size and flexible operation etc. 

◁ PSaB-79 

Research on the Orientation Method of HMD Based on Image Processing, 

pp.4160–4162 

Wu, Weiling 

naval aviation engineer Inst. 

Aimed at the problems such as narrow detecting range, low sensitivity 

and heavy helmet weight during tracking the head position in HMD, 

a new method based on the video image processing is given, which 

can detect the bearing angel and pitching angel of HMD relative to the 

aircraft. 

◁ PSaB-80 

Development of Portable Plant Components Measurement Instrument 

Based on Near-infrared Spectroscopy, pp.4163–4167 

Ma, Hao 


In the paper a portable multi-function instrument was developed, which 

can quantitatively measure the content of plant components in accordance 

with different components of plant leaves have a different absorption 

on different NIR spectra. The instrument is mainly composed 

of an independent leaves clamp and a multi-function host. Based on 

the method of Dual-wavelength the integral type leaves clamp was designed 

in the paper, which was composed of dual-LED, spectra filter, 

lens, photoelectric receiving device and so on. The design simplified 

the optical structure, enhanced the stability of apparatus, and further 

189


improved the signal-to-noise ratio and precision. The host mainly contained 

four parts, including micro-control system, driving circuit of light 

source, photoelectric conversion and signal conditioning circuit and L- 

CD display circuit. In addition, in the structure of host an open data interface 

was reserved for very easily connect to the leaves clamp, which 

achieved real-time, quick and nondestructive measurement of different 

plant components (chlorophyll and moisture of plant leaves were basically 

studied in this paper. The characteristic wavelengths 680nm and 

940nm was used to measure chlorophyll, 880nm and 974nm was used 

to measure moisture). In the calibration experiment the instrument have 

a excellent results of the prediction. The correlation coeffieient between 

the prediction value and the true value of moisture content and chlorophyll 

content repectively is 0.86 and 0.92. The standard error of prediction 

repectively is 0.016 and 0.020. 

◁ PSaB-81 

High Precision Attitude Estimation Algorithm Using Three Star Trackers, 

pp.4168–4173 

Chen, Binglong 

Geng, Yunhai 

Yang, Xu 

Harbin Inst. of Technonlogy 



This paper presents an attitude estimation algorithm for the satellite 

with three star trackers. The attitude estimation accuracy can be improved 

with modified attitude matrix using measurements from other s- 

tar trackers. The attitude determination algorithm with two star trackers 

was reviewed firstly, and then a modified attitude estimation algorithm 

using three star trackers is proposed to enhance the precision of the 

attitude estimation. The extended Kalman filter is used to estimate the 

attitude of the nonlinear satellite system, and a federated configuration 

is adopted to process the estimated states and covariance matrices for 

high accuracy attitude estimation. Using the proposed algorithm, the 

satellite can perform missions continuously. A mathematical simulation 

is performed to verify the effectiveness of the adoption attitude estimation 

algorithm, using a designed PD control, five reaction wheels as 

the attitude control system and three star trackers mounted with a titled 

angle and a federated EKF as attitude determination system. 

◁ PSaB-82 

Application of Blind Sources Separation in Plant Leaves Classification, 

pp.4174–4179 

WU, Ying 

GUO, Tian-tai 

JIANG, Jie-wei 




This paper discussed the application of Blind Sources Separation (B- 

SS) in plant leaves classification. Firstly, collection of two different types 

of plant leaves was performed using the Nexus-870 Fourier transform 

infrared spectroscopy, and wavelet analysis was adopted to compress 

the immense sample data, thus accelerating the data processing 

speed .Then the BSS algorithm FastICA algorithm was used on the 

compressed spectral data to increase the difference between the different 

signals. Finally, BP neural network algorithm was used to achieve 

the classification of plant species. Experiments showed that processing 

data in near-infrared spectroscopy through BSS can not only improve 

the speed and accuracy of BP neural network, but also enhance its 

classification correctness, and the classification results with the proposed 

method was satisfactory. 

◁ PSaB-83 

Defect depth automation measurement based on image processing for 

TOFD parallel scanning, pp.4180–4183 

Shan, Mingguang 

Liu, Shengchun 



To get more precise defect depth in parallel scanning of ultrasonic timeof-flight 

diffraction (TOFD) technique, a method for automatically measuring 

defect depth using defect image data is proposed in this paper. 

The method extracts defect image data from B-scan image through a 

series of image processing to fit a parabolic curve. Defect depth will be 

easily obtained by combining the quadratic coefficient of fitted parabolic 

curve with that of formula based on the principle of ultrasonic TOFD 

parallel scanning. Experiment result shows that the defect depth measuring 

error is less than 0.5 mm with this method and demonstrates 

strong practicability, high degree of automation and high measurement 

precision of the proposed method, reducing ultrasonic testing workload 

of the operator. 

◁ PSaB-84 

The Discussion of Semi-structured Sample Modeling on Expanding 

2.5D Method, pp.4195–4200 

Yu, Shun 

Zhang, Tie 



It is important to build entity modeling which it exists in the outdoor 

environment with human activities in the nature increasingly. Outdoor 

entity can be divided into structured, semi-structured and unstructured. 

This paper uses expanding 2.5D method to model semi-structured entity 

which it is very representative. The semi-structured entity can be 

divided into simple standard sample, complex standard sample, simple 

deformed sample and complex deformed sample in accordance with 

half-dimensional information gradual deepening complexity. Analyze 

data structure, build sample modeling. After that, build entity modeling 

with some operation. 

◁ PSaB-85 

A Single Chip Multi-functional DDS Waveform Generator based on FP- 

GA with SOPC Design Flow, pp.4206–4210 

Ruan, Yue 

Tang, Ying 

Yao, Wen-ji 


Xu, Sen 






This work presents a highly integrated single chip multi-functional, 

multi-waveform signal generator which can generate various waveforms, 

with digital controller inside to adapt embedded and low power applications. 

The proposed system is composed by Nios II, DDS (Direct 

Digital Synthesis) and other peripherals. Nios II is a reconfigurable, 

programmable and optimizable soft-core embedded CPU. DDS is used 

to generate required waveforms. Together with modern EDA tools, the 

system HW/SW co-design and FPGA implementation is accomplished, 

using typical SOPC design flow. Utilizing characteristics of Nios II, the 

core and peripheral logical units that system need are put together and 

implanted into a single FPGA chip. The Avalon bus is used to connect 

peripheral modules (such as function switch buttons and 7-segment 

LED display units) to Nios II’s Avalon bus main port (instruction and 

data control port). The realized system is flexible to reduce, extend, 

with low power consumption, and has System on Programmable Chip 

(SOPC) function which means the system’s software and hardware is 

online programmable. 

◁ PSaB-86 

Pulse Monitoring System Based on Feedback Algorithm, pp.4211– 

4214 

An, Peng 

Zeng, Ming 

Ningbo Univ. of Tech. 


Pulse monitoring system was designed with integrated AD module in 

micro-controller chip and the feedback algorithm. HK-2000B-type sensor 

was used for collecting the pulse signal. Customize the design process, 

some components, the preparation of the LCD driver and display 

the menu; in addition, by using the results of the feedback signal sampling 

algorithm improves the measurement accuracy. After a long and 

diverse system of the test, the accuracy and stability while significantly 

reduce the measurement time, with high practical value. 

◁ PSaB-87 

The Smulation and Model of Twisted Lever-arm Effect in Transferalignment 

of Stradown Inertial Navigation, pp.4235–4240 

Pan, Shuang 

Navial Submarine Acad. 

Based on the theory of twisted lever-arm effect, this paper provided a 

new model for the lever-arm effect caused by the shipboard weapons’ 

190



transfer-alignment, not considering the dynamic twist. Deflection angle 

of the second-order Markov process model was established and set up 

in simulink. Composed with the non-twisted lever-arm effect, the result 

showed that the acceleration error caused by dynamic twist was large 

and need compensated.b 

◁ PSaB-88 

Monitoring System for Forest Fire Based on Wireless Sensor Network, 

pp.4245–4248 

Zhu, Yingli 

Jiangxi Sci. &Tech. Normal Univ., 

The forest is considered as a precious and indispensable nature resource, 

but forest fire which can destroy forest resource safety and 

threaten human-living environment is considered as one of the severest 

disasters. How to monitor and collect information of forest fire at 

any time, it is a difficult problem for Forest Fire Prevention Departments 

to urgently solve. With the development of sensor technology, MEM- 

S and wireless communications, wireless sensor network (WSN) has 

wide application in all kinds of fields. In order to prevent forest fire occurrence, 

this paper designs a monitoring system for forest fires based 

on wireless sensor network and GPRS network. The system gives the 

hardware design of wireless sensor nodes and software implementations, 

and chooses CC2531 to achieve the process of data acquisition 

and transmission, then sends the data through GPRS module to the 

remote monitoring center. By means of WSN and GPRS network, the 

system accomplishes data acquisition and long distance transmission. 

◁ PSaB-89 

Noisy Blind Source Separation Based on Adaptive Noise Removal, 

pp.4255–4257 

Tang, Hui 


A novel natural gradient algorithm with two-step pre-processing is proposed 

to solve the noise blind source separation problem. That is bias 

removal techniques followed by a de-noise processing based on least 

square. Then obtain an algorithm jointly estimating mixing-matrix and 

decreasing noise. In the condition of that sources are mutually independent 

and added-noise is independent with any source, computer 

simulations verify the algorithm is effective and perform better than traditional 

algorithm. 

◁ PSaB-90 

Research on Wireless Communication Based on GPRS in Greenhouse, 

pp.4272–4276 

Shi, Bing 

Changzhou Univ. 

Abstract: the wireless data exchange system based on GPRS used in 

monitoring vital environmental factors in greenhouse is designed, the 

principle and the blue print of the system are described and the terminal 

hardware and software are also presented in details. A conclusion 

is presented at last. 

◁ PSaB-91 

A robust and optimal search tactic using multiple looks, pp.4297–4304 

Nguyen, Bao 

Bourque, François-Alex 

Defence R&D Canada 

Department of National Defence 

We provide an optimal strategy for an autonomous vehicle to detect 

a target in a mine hunting operation or in a search & rescue mission. 

We assume that the target exhibits mirror symmetry. By mirror symmetry, 

we mean that the left hand side of a target is the mirror image of 

the right hand side of the same target, and the probability of detection 

is maximal when a target is viewed at zero degree while that probability 

is minimal when viewed at ninety degrees and is monotonous between 

the maximum and the minimum. The optimal strategy consists of 

choosing look angles to inspect a target such that the probability of detection 

is maximal. We use a novel combination of variational calculus, 

symmetry principles, number theory and algebra to determine analytically 

the optimal angles. The solutions will help the operators plan for 

an effective strategy in a generic search and detection operation. Such 

a strategy is robust as most targets of interest possess approximate 

mirror symmetry. 

◁ PSaB-92 

Design and Implementation on SOAP-Based Things Management Protocol 

for Internet of Things, pp.4305–4308 

Dai, Guiping 

Beijng Univ. of Tech. 

With development of integration requirements for things’ information 

based on different connection technology in Internet of Things, higher 

level connection-technology-independent protocols are needed to 

shield different connection technologies. Things management protocol 

is one of these protocols and can be used to exchange things’ information. 

Architecture of Internet of Things and necessity of things management 

protocol are discussed. A SOAP-based things management 

protocol is designed and implemented, including operations supported, 

interface definitions for these operations, implementation structure and 

processing for this protocol. 

◁ PSaB-93 

Study on A Real-time Optimal Multi-sensor Asynchronous Data Fusion 


Qi, Guoqing 


Oriented on asynchronous data in multi-sensor data fusion system, a 

real-time fusion architecture is put forward, which is in unbiased minimum 

variance sense. The proposed asynchronous fusion algorithm 

can solves the measurements sequentially that does not need to preset 

fusion periods for fusion center to synchronize asynchronous data 

transmitted from multi sensors. Simulation result has proven the efficiency 

of the algorithm, and it also illustrates that the algorithm can 

improve the estimation precision obviously when the target is maneuvering 

and there is system error in measurement. 

◁ PSaB-94 

The Research of Environmental Monitor System in Brewage House of 

Alcohol Factory Based on WSN, pp.4401–4404 

Zhang, Kan 

Wang, Weihong 

BeiHang Univ. 

Beihang Univ. 

The paper presents a wireless sensor network system, including 433 

MHz, ZigBee wireless network, data collection node and data concentrator 

node. This system is mainly to apply to monitoring winery’s 

environment, the principle of which is that data collection node uses 

433 MHz wireless network to send data , data concentrator node uses 

433 MHz wireless network to receive the data from data collection n- 

ode , then sends the data to the sever by ZigBee module through the 

processing of MCU. Meanwhile the personal can examine this data to 

monitor the change of winery’s environment. The practice proves the 

function of WSN system and guarantees the quality of liquor-making. 

Chair: Huo, Wei 

Poster Session PSaC 

July 7, 16:30-17:50 


◁ PSaC-01 

Recognition of Crude Drugs Based on SVM, pp.4688–4690 

Ming, Zhiyuan 

Yunnan Univ. of Nationalities 

Support Vector Machine (SVM) is a machine learning theory based 

on statistical learning algorithms, which are lots of unique advantages 

on solving the small sample, nonlinear and high dimensional pattern 

recognition. This article uses kernel function on support vector machine 

to identify propolis in Yunnan. Compared with traditional algorithms, it 

can solve the small sample, nonlinear and other issues. The experiments 

show the performance is good when using SVM kernel function 

on solving the herbs recognition. 

◁ PSaC-02 

Composite Materials Evaluation by the Wavelet Transform and Fuzzy 


Gao, Zhiqi 


This paper studies the evaluation of bonding quality for thin composite 

plate. The main methods are wavelet transform and fuzzy pattern 

recognition. At first, ultrasonic NDT (Non Destructive Testing) detection 

method is introduced and the characteristic of the echo signal is 

191


discussed. And then, the wavelet transform is used to process echo 

signals, the low-frequency energy and the high-frequency energy are 

extracted as characteristics of echo signal in time-frequency domain. 

At last, a fuzzy pattern recognition algorithm is designed to evaluate the 

bonding quality of the composite material, it is based on the maximum 

membership degree principle. Simulation proved that the algorithm can 

recognize different echo signals quantitatively and effectively. 

◁ PSaC-03 

A Fast Stereo Matching Algorithm Used in Target Recognition of Mobile 

Robot, pp.4771–4774 

Yu, Naigong 

Lin, Jia 

Huang, Can 






For the accuracy and real-time requirements of stereo matching when 

mobile robot recognizes the target, an improved winner-take-all (WTA) 

algorithm which is based on the parallel binocular system is proposed. 

First, extract the relatively big difference points between two images 

as feature points. Then, implement the stereo matching for the feature 

points using WTA algorithm and only implement a simple verification 

for nonfeature points. Nonfeature points’disparity values are 

the ones of neighboring pixel. Finally, obtain a dense disparity map. 

The extracted feature points focus on the disparity discontinuity regions 

and the matching accuracy of the algorithm is equivalent to other existing 

algorithms.But calculation speed of the algorithm is faster and its 

edge feature is better.So it is a stereo matching algorithm with accurate 

matching and good real-time. 

◁ PSaC-04 

Smelting Process Smoke Detection using Multivariate Image Analysis, 

pp.4865–4868 

Zhang, Hongwei 

Song, Zhihuan 



To solve the problems that available technology was difficult to detect 

smoke concentration in the smelting process, this paper presented 

investigations into the smoke detection using multivariate image analysis. 

Firstly, smoke image matrix was transformed to a new one with 

single row and high dimension columns. Then the transformed matrix 

was processed by principal components analysis (PCA) to extract principal 

components (PCs). Finally, smoke concentration was detected by 

the two extracted PCs and threshold values. The correlations between 

PCs and smoke concentration were also identified and analyzed. The 

results of industrial application show that the proposed method could 

detect the smoke concentration effectively. 

◁ PSaC-05 

Optical Flow Estimation with Parameterized Data Term and Warping, 

pp.4633–4637 

Xu, Jintao 

Feng, Zuren 

Lu, Na 




Horn/Schunck approach which has been widely used in variational optical 

flow estimation consists of a data term and a smoothness term. 

In this paper, a parameterized data term is proposed. The parameter 

can adjust the proportion of the two images’gradient for obtaining 

the Euler-Lagrange equations. This combination makes the optical flow 

more robust to noise and illumination changes. Firstly, the classic model 

is analyzed, especially the coefficients of the Euler-Lagrange equations. 

Then, a model with parameterized data term is proposed. This 

model has formulated the combination of image gradients theoretically 

and enabled more flexible combination. Finally, a multi-resolution technology 

has been used for solving the nonlinearity of optical flow. In this 

process, the warping step is also parameterized and the influence of 

the parameter is analyzed. The experiments demonstrate the benefit of 

our parameterized model to the classical one. 

◁ PSaC-06 

Hardware Design of The Wireless Automatic Meter Reading System 

Based on GPRS, pp.4536–4540 

Zhang, Ying 


With the rapid development of computer network and electronic information 

technology, automatic and intelligent electronic products play 

roles that can not replaced by people themselves. Reading technology 

is a new technology that is applied in remote automatic data collection, 

transmission and processing for water, electricity, gas supply and management 

system, etc.. Hardware of wireless communication system 

based on GPRS was designed for remote automatic meter reading in 

this paper. The module supports UDP communication protocols, and 

can transmit remote data by the methods of messaging and network 

communications. 

◁ PSaC-07 

ANALYSIS OF MULTI-BIOMETRIC ENCRYPTION AT FEA-TURE- 

LEVEL FUSION, pp.4563–4567 

Fu, Bo 


The basic concepts and methods on performance and effectiveness 

evaluations at the feature-level fusion model of multi-biometric encryption 

are concentrated on in this paper. From the cryptographic theory 

point of view, firstly, the formal definitions related to mul-ti-biometric 

cryptosystems are formulated. Under some extreme conditions, the security 

and privacy of mul-ti-biometric cryptosystems at the feature level 

are ana-lyzed and rigorously proved. Finally, a close relationship between 

security and privacy and the fundamental tradeoff between the 

accuracy and security are studied. 

◁ PSaC-08 

Classification Network of Gastric Cancer Construction based on Genetic 

Algorithms and Bayesian Network, pp.4676–4681 

He, Yiheng 

BeiJing Univ. of Tech. 

One of the most important link in improves diagnostic accuracy and 

disease cure rate is accurate classification of disease.The current gene 

chip’s development and widely applications making the diagnosis based 

on tumor gene expression profiling expectedto be on a fast and effective 

clinical diagnostic method. But the sample of gene is small and 

the expression data is multi-variable. In this article, we uses three data 

sets on gene expression profiles of gastric cancer for the construction 

of classification model, First, screened the gene which significantly 

changed in expression pattern, and use these genes as a set of the 

feature to reduce the number of variables, and then using genetic algorithms 

and bayesian network model to build the classifier, the build 

process uses these three gene expression data to learn classifier. Classification 

accuracy is calculated by leave-one cross-validation (LOOCV) 

and it reached 99.8%. Last we use the GO and pathway to analysis the 

classifier’s network structure. 

◁ PSaC-09 

Distributional Clustering Using Nonnegative Matrix Factorization, 

pp.4705–4711 

Zhu, Zhenfeng 

Ye, Yangdong 



In this paper, we propose an iterative distributional clustering algorithm 

based on non-negative matrix factorization (DCMF). When factorizing a 

data matrix A into CXM, an objective function is defined to impose the 

conditional distribution constraints on the base matrix C and the coefficient 

matrix M. It has been observed that, in many applications, the 

conditional distributions of instances are often employed to normalize 

the data dimensions. Taking these factors into account, we simplify the 

existent updating rules and obtain the iterative algorithm DCMF. This algorithm 

satisfies the constraints described above on condition that the 

instance matrix is preprocessed as a conditional distribution. DCMF 

is simple, effective, and only needs to initialize the coefficient matrix. 

As a result, the base matrix can be viewed as a centroid matrix and 

the coefficient matrix just records the membership of fuzzy clustering. 

Compared with several other factorization algorithms, the experimental 

results on text, gene, and image data demonstrate that DCMF achieves 

8.06% clustering accuracy improvement, 35.08% computational time 

192



reduction, and 61.30% hard clustering fuzziness decrease. 

◁ PSaC-10 

Error Concealment in JPEG2000 Coded Images, pp.4760–4765 

Bou Matar, Amjad 

Memon, Qurban 

UAE Univ. 

UAE Univ. 

In multimedia communication and data storage, compression of data is 

essential to speed up the transmission rate, minimize the use of channel 

bandwidth, and minimize storage space. The drawback of compression 

is that compressed data are more vulnerable to channel noise during 

transmission. In this work, error concealment methodology is proposed 

that has the ability of both error detection and concealment, and 

thus guarantees minimum use of channel bandwidth. The methodology 

requires transmission of edge information of the original image along 

with JPEG2000 compressed image. At the receiver, the edge information 

of received wavelet coefficients is computed and compared with the 

received edge information of the original image to determine the corrupted 

coefficients. Three methods of concealment, each including a 

filter, are investigated to handle the corrupted regions/coefficients. The 

simulation results conducted on test images for different values of bit error 

rate are presented to demonstrate the performance of the proposed 

approach. The objective quality measure such as peak-signal-to-noise 

ratio (PSNR), root-mean-square error (rms) and subjective quality measure 

are used to evaluate processed images. 

◁ PSaC-11 

An Alternative Clustering Algorithm based on IB Method, pp.4791–4796 

Lei, Yang 

Ye, Yangdong 

Lou, Zhengzheng 




Alternative clustering aims at exploring another reasonable clustering 

which is distinctively different from an existing one. This paper presents 

a novel alternative clustering algorithm based on the IB method, named 

Alt sIB. Our approach aims to ensure the clustering quality by maximizing 

the mutual information between clustering labels and data observation, 

whilst ensuring the clustering distinctiveness by minimizing the 

information sharing between the two clusterings. We employ a nonparametric 

MeanNN differential entropy estimator for the mutual information 

estimation and optimize the objective function iteratively in a 

sequential way. The experimental results indicate that the proposed Alt 

sIB algorithm could uncover the reasonable and different clusterings 

from the dataset efficiently. Compared to the existing NACI algorithm 

and minCEntropy algorithm, the Alt sIB’s performance is better. 

◁ PSaC-12 

Bifurcation Control Design for Simplified HH Neuron Model: A Physiological 

Approach, pp.4953–4956 

Li, Huiyan 

Wang, Jiang 

Jin, Qitao 

Deng, Bin 

Wei, Xile 

Che, Yan-Qiu 


Tianjin Univ. 

Tianjin Univ. 

tianjin Univ. 

Tianjin Univ. 


Neurons can encode identical stimulus input by generating nonidentical 

firing patterns of action potential sequence. The different firing 

patterns are resulting from different bifurcations from the dynamical 

viewpoint. However, the physiological basis of bifurcation control has 

not been clear. Here we design a wash-out filter bifurcation control from 

a physiological view. Then simplified HH model is selected to verify the 

proposed method. We finally derive the physiological basis of wash-out 

filter control is affecting the result of competition between currents with 

different dynamics in the sub-threshold potential. 

◁ PSaC-13 

Change excitability of Morris-Lecar Model via Physiological Bifurcation 


Li, Huiyan 

Wang, Jiang 

Jin, Qitao 


Tianjin Univ. 

Tianjin Univ. 

Deng, Bin 

Wei, Xile 

Che, Yan-Qiu 

tianjin Univ. 

Tianjin Univ. 


Neurons can encode identical stimulus input by generating nonidentical 

firing patterns of action potential sequence. The different firing 

patterns are resulting from different bifurcations from the dynamical 

viewpoint. Here we design a bifurcation control from a physiological 

view by employing the wash-out filter. Then Morris-Lecar model is selected 

to verify the proposed method. We finally derive the physiological 

basis of wash-out filter control is affecting the result of competition 

between currents with different dynamics in the sub-threshold potential. 

◁ PSaC-14 

Realization of Quantum Hadamard Gate Based on Lyapunov Method, 

pp.5096–5101 

Wen, Jie 

Cong, Shuang 

Zou, Xubo 




This paper studies the realization of the quantum Hadamard gate of 

spin with a single electron. First the canonical form of the unitary rotation 

gate which is feasibly applied in plane control fields is given. Because 

the Hadamard gate can’t be realized directly by applying the 

plane control fields, so the matrix operator of Hadamard gate is decomposed 

into two matrices which are feasible in canonical forms. These 

two canonical matrices correspond to the rotate-operation and reflectoperation 

of the vector coordinates on the Bloch sphere, respectively. 

Then the Hadamard gate can be realized in two steps by choosing appropriate 

control fields, in such a way the realization of Hadamard gate 

becomes to design external tailored control fields. Finally, the control 

fields are designed based on Lyapunov method. Numerical simulation 

experiments and results analysis are given. 

◁ PSaC-15 

Cooperative Game Theoretical Research for Aircraft Deicing Operation 

Scheduling, pp.2407–2411 

Xing, Zhiwei 

Lian, Guan 



Aims at the plan and scheduling of aircraft deicing resources in large 

airports, this paper analyzes the operation and problems of aircraft deicing, 

and the cooperative game-theoretic analysis method is adopted 

for the solution of deicing delay that based on the aircraft ground deicing 

process coalition modeling in this paper. Finally, the Repast software 

is used for constructing above multi-agent based the coalition model. 

The domestic departures data of Beijing Capital International Airport is 

used for simulation experiments. The results show that coalition game 

model is an effective incentive to airline cooperative behaviour. Validation, 

fairness and utility of the multi-agent based scheduling model are 

verified by simulation experiments. The conclusion is provided at last. 

◁ PSaC-16 

A Kind of Robust Controller for Uncertain Linear System LQ Tracking 

Problem, pp.2436–2440 

Yang, Yang 

GUO, Chen 

Shen, Zhipeng 

Du, Jialu 



dalian Maritime Univ. 


In this paper, the problem of linear quadratic tracking with infinite timeinvariant 

is discussed. The description of matching uncertain linear system 

is presented and the error equation of the system is established, 

which can be considered as the general error dynamic system (GEDS). 

Hence, the tracking problem is transformed into stabilization issue. A 

kind of robust linear quadratic tracking controller is designed by solving 

a Riccati inequation which contains the uncertain information with 

the LMI method. By Lyapnov function, it can be proven that the controller 

guarantee all signals in the closed loop system robust stable. 

In addition, a simulation example is provided, which illustrates that the 

proposed controller results in robust performances to the model perturbation. 

The effectiveness of the designed control law is verified. The 

193


work done in the paper also improves the controller design method for 

linear quadratic tracking problem. 

◁ PSaC-17 

H ∞ Model Reduction for Positive 2-D Discrete Systems in Roesser 

Model, pp.1733–1738 

Wang, Cuihong 

Shanxi Normal Univ. 

In this paper, we are concerned with H ∞ model reduction of 2-D discrete 

systems in Roesser model. For positive 2-D discrete systems, the 

aim is to construct a positive 2-D reduced-order system such that the 

error system satisfies a prescribed H ∞ norm bound constraint. Based 

on a system augmentation approach, a new sufficient condition is proposed 

to ensure that the error system is stable and satisfies a prescribed 

H ∞ norm. Then the existence condition of the reduced-order 

system matrixes are given and an corresponding iterative linear matrix 

inequality(LMI) algorithm is presented. Finally, a numerical example to 

illustrate the effectiveness of the proposed design procedures is presented. 

◁ PSaC-18 

Optimal Feedback Reentry Guidance of Hypersonic Vehicle Based on 

Improved Gauss Pseudospectral Method, pp.2457–2462 

Sun, Yong 




This paper presents an optimal feedback reentry guidance for the hypersonic 

vehicle. The proposed method implements a closed-loop 

feedback approach via fast computation of a series of open loop optimal 

control problems. At each sampling period, the optimal control 

problem is solved by the improved Gauss pseudospectral method (IGP- 

M), which is an efficient method to solve the general nonlinear optimal 

control problem. The IGPM approximates the state and control by the 

polynomials at the Legendre-Gauss (LG). The costate can be obtained 

and the optimality of the solution is also checked easily. The proposed 

method can deal with the state and control constraints explicitly. The 

various performance can be realized by considering different cost function. 

Simulation results demonstrate that the hypersonic vehicle tracks 

the reference trajectory well in the presence of the uncertainty of aerodynamic 

force. 

◁ PSaC-19 

The stability analysis of the machine drive system, pp.2463–2467 

Zhang, Huiduan 


In consideration of the bearing stiffness, the contact deformation between 

the ball screw and the worktable, and the contact deformation 

between the worktable and the guide, lateral, longitudinal and torsional 

vibration of the preload screw and vibration of the worktable are studied. 

Dynamic model of the ball screw drive system of machines is established 

using Lagrange equation. The model is analyzed by the mode 

superposition and Runge-Kutta method and the stability boundaries are 

plotted. Effects of system’s parameters on stability of the ball-screw 

system are discussed. The present work supplies a base for designing 

the drive system. 

◁ PSaC-20 

Studied on Anti-interference Control Based on the Disturbance Observer, 

pp.2503–2505 

He, Naibao 

Gao, Qian 



The adaptive law is designed to approximate the disturbance, and two 

controllers are designed for the slow-loop system under uncertainties 

and the fast-loop system in interference environment, respectively. In 

addition, a fast adaptive disturbance observer is employed by adding 

nonlinear exponential item into adaptive laws of parameters and approximating 

errors to make the tracking errors are converged to zero 

in limited time. Moreover, the control characters are systematically 

analyzed. Finally, the superiorities in rapidity and convergence are 

demonstrated by simulation analysis of the control of NSV at hypersonic 

speed. 

◁ PSaC-21 

Control Three-Phase Star-Connected Switch Three-Level Rectifier 

Considering Alternative Performance Indices, pp.2506–2511 

Zhang, Shaoru 

Hebei Normal Univ. 

With the development of power electronics, the power quality of AC 

power supply is increasingly paid attention to. In general, diode uncontrolled 

rectifier and thyristor phase controlled rectifier are adopted for 

AC/DC convertion, which inject plentiful harmonic and reactive power 

into AC mains, and result in serious pollution. So lots of reseaches have 

been focus on high input power factor and minimum harmonic injection 

into AC mains. A novel control method for three-phase star-connected 

switch three-level rectifier was proposed in order to meet harmonic current 

distoration limits set by IEEE-519. The desired source currents 

are output from a current compensator, the compensator gain and the 

phase delay at each phase for each order harmonic are determined by 

an optimal control algorithm, where the permissible levels of individual 

and total harmonic distortion, power factor, as well as active power 

consumption are taken into account. The desired source currents can 

drive the bidirectional switches. The simulation and experimental results 

show that this control strategy is effective in minimizing source current 

distortion and maximizing load power factor. The proposed converter is 

suitable for a wide power application. 

◁ PSaC-22 

Nonlinear MPC for Attitude System of Miniature satellite using Multiple 

MEMS Actuators, pp.2523–2528 

Jiang, Yu 


Jin, Jing 




This paper presents a configuration scheme of multiple MEMS reaction 

wheels for CubeSat. In this configuration three pairs of arrays of 

MEMS reaction wheels are installed symmetrically within the CubeSat’s 

surface, wherein each array is composed by 4* 4 co-rotating elements. 

Through counter-rotating of each pair of symmetrical arrays three zero 

momentum wheels are achieved. Assuming that control moment of 

MEMS reaction wheel have on-off form, two attitude control methods 

are designed. When change of angle is very small for attitude stabilization, 

constraints set of the disturbance and state is described as 

polytope, and constraint tightening method is used to design control 

law for linearized attitude equation. When performing large angle for 

attitude maneuver, an extended state observer is designed to estimate 

the disturbances. For predicting the attitude angle, the fliess expansion 

method of nonlinear attitude equation is proposed. The optimal control 

law is obtained by solve minimum of attitude predicting error finite times. 

Simulation results show the effectiveness of the proposed methods. 

◁ PSaC-23 

Structural Properties of Multi-Agent Linear Systems with Applications 

to Leader-Following Consensus, pp.2550–2555 

Ni, Wei 

Wang, Xiaoli 

Yang, Jie 

Zhao, Ping 





The controllability and observability of leaderfollowing multi-agent linear 

systems under switching topology are considered. As for the controllability 

problem, the admissible control input for each follower agent can 

only use relative and local information from its neighbors and the control 

objective is the convergence of each follower’s state to to that of 

the leader agent. As for the observability problem, the output of the 

multi-agent systems is all the information transmitted in the multi-agent 

network. These properties are used in the leader-following consensus 

problem under switching topology. 

◁ PSaC-24 

Prediction Model of Sintering Burden Based on Information Entropy and 

Chaos PSO Algorithm, pp.2566–2569 

Qin, Ling 

Wuhan Polytechnic Univ. 

Considering the characteristics of the nonlinear, complexity and relativ- 

194



ity of the sintering burden system, the prediction model of sintering burden 

is established by BP neural network. In addition, a new optimization 

method of the sintering experiment is proposed, based on information 

entropy and chaotic improved particle swarm algorithm. The initial particle 

colony is produced by information entropy to increase the variety 

of the initial colony. The strategy of dynamic nonlinear adjustment is 

used for the inertia weight in this paper according to the iteration times, 

so as to improve the algorithm’s searching capability. And the traversal 

characteristic of chaos optimization is introduced to overcome effectively 

the local convergence of standard particle swarm algorithm. The 

simulating results show that the improved particle swarm algorithm has 

faster converge, fewer iteration times and stronger global optimization 

ability. 

◁ PSaC-25 

Establishment and Optimization of Prediction Model for Recovery Rate 

of Alloying Elements, pp.2588–2591 

Fang, Xiaoke 

Yu, Liye 

Zhang, Wenle 

Wang, Jianhui 


State Key Laboratory of Hybrid Process Industry 

Automation Sys. & Equipment Tech. 



Steel quality depends on the alloying model precision. While the precision 

is mainly dependent on the recovery rate of alloying elements calculation, 

the prediction model for recovery rate of alloying elements was 

established based on the BP neural network. The simulation shows that 

using POS algorithm to optimize the model is still easy to fall into local 

minimum, so a simulated annealing (SA) thought was introduced to improve 

it. By the comparison we can see that SA-PSO algorithm can 

overcome above shortcomings. This algorithm strengthens the global 

convergence ability. It can optimize the model while ensuring high precision 

and improve the training convergence rate at the same time. The 

simulation results proved that this model is effective. 

◁ PSaC-26 

Robust adaptive control for a class of switched nonlinear systems with 

unmodeled dynamics, pp.2636–2641 

Zhu, Baicheng 


An, Yao 




An adaptive neural network control scheme is proposed for a class 

of nonlinear switched systems with unmodeled dynamics in purefeedback 

form. The design is based on the dynamic surface technique, 

the approximation capability of neural networks and the dwell-time approach. 

The design makes the approach of dynamic surface control 

be extended to the nonlinear switched system with unmodeled dynamics, 

and relaxes the extent of application of the approach of dynamic 

surface control. Compared with the existing literature, the proposed approach 

relaxes the requirements of the system. And the explosion of 

complexity in traditional backstepping design caused by repeated differentiations 

of virtual control is avoided. By theoretical analysis, the 

closed-loop control system is shown to be semi-globally uniformly ultimately 

bounded. 

◁ PSaC-27 

A New Wavelet Coefficients Correlation Denoising Method Applied in 

Fault Detection, pp.2657–2660 

Xiao, Qian 


In fault detection of power system, the detection for mutations signal 

is very important. The application of wavelet coefficients correlation 

denoising in signal detection for noisy fault problem is relatively 

widespread. However, after doing wavelet transform to the noisy signal, 

the wavelet coefficients of each scale will produce a small offset. 

This paper presents a wavelet coefficients correlation denoising method 

based on the cross-correlation function. Cross-correlation algorithm is 

used to calculate the offset between each scale coefficient and original 

noisy fault signal. Then do correlation analysis to the shift scale signal 

to get accurate mutation signal, so as to determine the location of 

faults. 

◁ PSaC-28 

Trajectory tracking control for mobile robot based on the fuzzy sliding 

mode, pp.2706–2709 

Xie, Mujun 

LI, Li-ting 






The trajectory tracking control problem of the uncertain mobile robot 

with nonholonomic constraints is analyzed. Sliding mode control is presented 

based on the kinematics models analysis. Switching function of 

sliding model control is designed according to back-stepping method. 

Trending law control is selected to improve the system dynamic performance. 

In order to solve the constant speed problem caused by 

conventional trending law control, fuzzy control is used to adjust trending 

speed in the real time. The simulation results demonstrate that the 

fuzzy sliding mode controller improves the rapidity of trajectory tracking, 

and reduces the tracking error and the chattering of the control output. 

◁ PSaC-29 

Path Following of Underactuated UUV Based on Backstepping, 

pp.2734–2739 

Yan, Zheping 

Chi, Dongnan 

Jia, Heming 

Zhou, Jiajia 





An algorithm of path following for Unmanned Underwater Vehicle (UU- 

V) based on backstepping is proposed in this paper. To design the path 

following controller by the backstepping technique, the algorithm compares 

desired path predefined and the path information collected from 

the sensors of vehicle and combines with Lyapunov theory analyzing 

the stability of the control system. The path following method for the 

vehicle with two inputs and three outputs in the horizon plane has been 

clarified. The sway coupled virtual control input is introduced. The aim 

for the controller designed is to drive the real location and orientation 

tend to the desired path information, to guide the whole velocity inclining 

to the tangent of desired path, and to guarantee the heading asymptotically 

converging to zero ultimately. Lake experiment data shows the 

validity of the method presented. 

◁ PSaC-30 

Feedback Linearization Robust Control of Arc Furnace Electrode Regulator 

System Based on dSPACE Simulation, pp.2740–2745 

Liu, Xiao-he 

Gao, Yuan 



The method of feedback linearization robust control based on dSPACE 

hardware-in-the-loop simulation for arc furnace electrode regulator system 

is discussed. With the linear feedback method of differential geometry 

dealing with non-linear part of electric arc furnace system, the 

robust controller was designed. Then the hardware-in-the-loop simulation 

system was built, and several simulations was done. Simulation 

results show that the feedback linearization robust control has shorter 

response time and smaller overshoot than the PID control. 

◁ PSaC-31 

Decentralized Controller Design Based On 3-Order Active-disturbancerejection-control, 

pp.2746–2751 

Tian, Lingling 

Li, Donghai 

Huang, Chun E 

Beihang Univ. 



In this paper, the design method of decentralized controllers using 3- 

order active-disturbance-rejection-control (3-ADRC) is presented. 3- 

ADRC can compensate the non-modeled dynamics and external disturbances 

of the system by using 3-order extend state observer, and 

decouple among loops. A set of parameters is obtained by optimum 

algorithm. By introducing a significant reduction to the control parameters, 

the decentralized 3-ADRC controller is easy to be tuned. The 

proposed method is applied to seven examples from literature. Simula- 

195


tion results show that the decentralized 3-ADRC is feasible and better 

than referenced methods in the control of complicated multi-loop systems. 

◁ PSaC-32 

Nonlinear robust control with high gain observer for governor of hydroturbine 

generator sets, pp.2752–2757 

Liu, Song 

Li, Donghai 

Huang, Chun E 

Guodian United Power Co. Ltd 



A nonlinear robust control strategy for governor of hydro-turbine generator 

sets is presented. Different from traditional nonlinear robust control, 

a high gain observer instead of coordinate transformation is used, 

so that the controller does not depend on precise model of the plant, 

and only need to measure the rotor speed. The family of controller 

parameters can be easily tuned with simple simulation experiments. 

The controller based on non-elastic water hammer mode has been successfully 

applied to elastic conditions. Simulation results show that the 

nonlinear controller can achieve not only good dynamic performance 

during three-phase short circuit faults and load disturbances, but also 

excellent robustness. 

◁ PSaC-33 

Receding horizon tracking control for nonlinear discrete-time systems, 

pp.2817–2821 

Wang, Hai-Hong 

Hu, Nai-Ping 



A novel real-time receding horizon tracking control (RHC) strategy is 

presented for nonlinear discrete-time systems with quadratic criteria for 

fast controller response. The control law is derived by using the receding 

horizon concept from the optimal tracking problem. First, the 

original problem is reduced to an equivalent optimal regulator problem 

for an off-line system by augmenting the state variables and then the 

optimal control input sequences are successfully derived by a so-called 

successive approximation approach (SAA) based on vector iteration. 

An optimal tracking law is obtained by a feasible iterative process. Only 

the compensate term need to be calculated online in order to reduce 

the computation costs. Simulations are conducted to demonstrate the 

feasibility and optimality of the control law obtained. 

◁ PSaC-34 

Hazard Detection and Avoidance for Planetary Landing Based on Lyapunov 

Control Method, pp.2822–2826 


CUI, Pingyuan 

Hu, Haijing 




Future planetary landers must be capable of detecting hazards in the 

landing zone and maneuvering to a new and safe site, for the requirements 

of the scientific task. This paper presents an autonomous hazard 

detection and avoidance method based on Lyapunov control method for 

planetary landing. The terrain of the landing zone is first reconstructed 

using the feature points of pictures at two different time, and the plane 

of the landing zone was determined by fitting the terrain elevation data. 

Then, hazards in the landing zone were identified according to the 

vitual plane. In order to reduce the potential threats by the hazards, 

an avoidance control law is designed using Lyapunov function method. 

The control law can guarantee the landers reach the safe site, simultaneously 

decrease the landing speed to zero. The results of numerical 

simulation show that the method is satisfactory for hazards detection 

and avoidance with assumed environments. 

◁ PSaC-35 

Energy-Shaping and Passivity-based Control of Three-Phase PWM 

Rectifiers, pp.2844–2848 

Yu, Haisheng 

Qingdao Univ. 

Abstract - Applying the state error Port-Controlled Hamiltonian (PCH) 

system and energy-shaping control principle, output voltage tracking 

control and unity power factor regulation of three-phase Pulse Width 

Modulation (PWM) rectifiers are presented in this paper. A desired state 

error PCH system structure is assigned to closed-loop control system 

for the three-phase PWM rectifiers. The desired Hamiltonian function is 

given based on the energy-shaping theory. The controller is designed 

through interconnection and damping assignment method. Moreover, 

a proportional integral (PI) regulation is used to eliminate the steadystate 

error of the output dc voltage. The simulation results show that 

the proposed control method has good output voltage tracking control 

and unity power factor regulation performances. 

◁ PSaC-36 

An AQM Scheme Based on Adaptive Weight Cascaded PID Controller, 

pp.2849–2854 

Du, Fei 

Sun, Jinsheng 



本文将基于速率和基于队列长度的主动队列管理算法相结合 , 设计了 

一种基于权值自适应串级 PID 控制器的主动队列管理算法。主环 PID 的 

输入为队列长度误差 , 输出为基于队列的控制量 ; 副环 PID 的输入为包 

到达速率和出口链路带宽之间的差值 , 输出为基于速率的控制量。根据 

被控量相对于参考值的偏移程度决定两个控制量在总的控制量中所占 

比例关系。同时为了使包到达速率与网络环境相匹配 , 提出并利用了虚 

拟出口链路带宽的概念 , 当队列长度大于期望值时虚拟出口链路带宽 

小于实际值 , 反之亦然。仿真实验表明在各种网络环境下 ,AWCPID 都 

能够快速将队列长度收敛至参考值 , 并且性能明显优于基于单神经 

元 PID 的 AQM 算法。 

◁ PSaC-37 

An AQM Algorithm Based on Variable Structure PID Controller, 

pp.2855–2860 

Du, Fei 

Sun, Jinsheng 



Internet 是一个时变、强非线性、大时滞的系统 , 对于这样的系统 , 经 

典的固定参数的主动队列管理算法已经不能满足性能要求 , 因此需要设 

计具有自适应性的算法。本文提出一种基于变结构 PID 控制器的主动队 

列管理算法 , 变结构 PID 控制器具有与经典 PID 类似的结构 , 其改进之 

处在于分别设计了比例、积分和微分系数关于误差的函数 , 使得当误差 

较大时加强比例作用 , 减小积分和微分的作用 , 以加快系统响应速度 ; 

当误差较小时 , 减小比例作用 , 加强积分和微分作用 , 以增强系统稳定 

性 , 并改善稳态性能。相对于 PID 算法 ,VSPID 算法的复杂度仅略微增 

加 , 而仿真结果显示其效果明显优于前者。 

◁ PSaC-38 

Compressor Active Surge Controller Design based on Uncertainty and 

Disturbance Estimator, pp.2908–2912 

Xiao, Lingfei 

Zhu, Yue 



A nonlinear controller design method is proposed for active surge control 

of compression system. Close-coupled valve (CCV) is used to modify 

the characteristic of the compressor, and allows for stable operation 

beyond the original surge line. The controller is constructed on the uncertainty 

and disturbance estimator (UDE) approach. The algorithm 

provides excellent stabilization and disturbance rejection performance. 

Simulations are given to show the effectiveness of the method. 

◁ PSaC-39 

Data-based Dynamic Characteristic Modeling and Tracking Control for 

High-speed Train, pp.2913–2917 

Gao, Shigen 

Qi, Shuhu 

Dong, Hairong 

Ning, Bin 

Li, Li 

BJTU 




Beijing MTR 

This paper introduces a novel dynamic characteristic modeling for highspeed 

train (HST) speed-position tracking control under time-varying, 

unpredictable and unknown operational environments. This method involves 

the construction of a dynamic characteristic model and design of 

a golden-section adaptive controller, which is a data-based model-free 

controller design approach and requires no precise mathematical description 

of the plant. Based on the above methodology, speed-position 

tracking control and energy-saving operation problems of HST and are 

196



studied. The effectiveness and preciseness of the proposed model and 

corresponding controllers are verified via numerical simulations, with 

high energy efficiency concurrently. 

◁ PSaC-40 

M-Nearest Neighbor Selection for Two-Phase Test Sample Representation 

in Face Recognition, pp.4661–4666 

Ma, Xin Jun 

Wu, Ning 

Liang, TianCai 




The Two-Phase Test Sample Representation (TPTSR) has been proposed 

as a powerful algorithm for face recognition. In TPTSR processing, 

a classification task is divided into two steps. The first phase determines 

M nearest neighbors to the testing sample from the training set 

by a linear representation criterion, and the second phase classifies the 

testing sample into the class with the representative linear combination 

by the selected nearest neighbors in the first phase. However, the computational 

load for this method is relatively demanding, especially for a 

large training set and big number of classes. This paper studies alternative 

nearest neighbor selection criterions for the first phase of TPTSR, 

such as the Euclidean distance and City-block distance. Experimental 

results and theoretical analysis show that computational load can be 

significantly reduced by these relatively more straightforward criterions 

while maintaining a comparable classification performance with the 

original TPTSR method. 

◁ PSaC-41 

Affine Motion Segmentation from Feature Point Trajectories using Rank 

Minimization, pp.4667–4670 

YANG, Min 


In this paper, we examine the problem of segmenting tracked feature 

point trajectories of multiple moving objects in an image sequence. Using 

the affine camera model, this motion segmentation problem can be 

cast as the problem of segmenting samples drawn from a union of linear 

subspaces. We pose this problem as a rank minimization problem, 

where the goal is to decompose the corrupted data matrix as the sum of 

a low-rank dictionary plus a matrix of noise. Given a set of data vectors, 

low rank representation seeks the lowest rank representation among all 

the linear combination of the bases in a dictionary. For noisy data, this 

non-convex problem can be solved very efficiently in the inexact Augmented 

Lagrange Multiplier method. Our algorithm amounts to an SVD 

of the data matrix and a shrinkage-thresholding of its singular values. 

We have experimented on real image sequence, where we show good 

segmentation result, comparable to the state-of-the-art in literature. 

◁ PSaC-42 

A Simple String Matching Method for Shape Recognition, pp.4696– 

4700 

Wu, Wen-Yen 

I-Shou Univ. 

Shape recognition is an important problem in many applications. A simple 

string matching approach for shape recognition is proposed in this 

paper. The shapes are coded as their dominant points. The compactness 

of polygons formed by the centroid and three consecutive dominant 

points is used as the feature for recognition. The experimental 

results showed that the proposed method has better recognition rates 

and more consistent performance than that of using the conventional 

features. Further, the proposed method doesn’t need to set parameters, 

so that it is robust in the shape recognition. 

◁ PSaC-43 

Pose Detection of Partly Covered Target in the Micro-Vision System, 

pp.4721–4725 

Su, Jin 

Huang, Xinhan 

Wang, Min 


Huazhong Univ. 0f Sci. & Tech. 


Abstract - In this paper, we focus on the need for location and pose 

detection of partly covered target. In micro-vision system, the accuracy 

of location and pose detection directly decides the effect of latter 

micro-operation. Former methods can’t locate the target which 

was partly covered. According to the current problem in micro-vision 

system, this paper proposes a method of target location and pose detection 

in micro-vision system which based on Hough transform and 

template matching. For the aspect ratio and area of target is certain, 

by detecting the shape feature and using template matching, we detect 

the location of target which was partly covered. Firstly, use Canny algorithm 

and morphology method to obtain the edge of objects. Then, 

use the improved Hough transform algorithm to extract line segments 

features and locate the candidate target region according to template 

matching. Finally, recognize and locate the target by detecting whether 

the two segments are connected to be a signal region. After recognizing 

target, the slant angle of the target is detected based on Hough 

transform. This method can exactly locate the target and get the angle. 

The experimental results show the proposed method is better than the 

former ones under complex background and partly covered targets. 

◁ PSaC-44 

Air-Ground Vehicle Detection using Local Feature Learning and Saliency 

Region Detection, pp.4726–4731 

Xu, Qinghan 

JIN, Lizuo 

Fei, Shumin 

Jie, Feiran 




Sci. & Tech. on Electro-optic Control Laboratory 

Moving vehicle detection is very important for urban traffic surveillance 

and situational awareness on the battlefield. Algorithms with cascade 

structure like Adaboost are booming in the recent decade, and successful 

in real-time application. But most of them use a sliding window 

protocol on multi-scale images, this involve heavy computing, so only 

simple feature (e.g. Harr wavelet) is suitable. 

In this paper, a biologically inspired method is proposed. We learn 

patch-based features for vehicle detection by unsupervised learning, 

and then employ a visual saliency step after feature extraction. Instead 

of sliding window, a candidate region is sent to classifier only if its features 

are “salient” on whole image. As the number of candidate regions 

decreases dramatically, it allow us to utilize complex feature to increase 

description ability. Experimental result indicates less computational expense 

and good performance. 

◁ PSaC-45 

An Indoor Quadrotor Locating and Object-Following Algorithm using 

Monocular Vision, pp.4747–4753 

Chen, Xiaolong 

Tang, Qiang 

Che, Jun 




Using quadrotor as an indoor robot asks for accurate locating and control 

methods. To solve this problem, a composite algorithm combining 

MIMU(Micro Inertial Measurement Unit) and monocular vision is used. 

The algorithm uses the measurement of MIMU as the source of position 

updating, while monocular vision algorithm provides the detection 

result of the feature of reference lines on the ground. The location of 

the reference lines detected is then compared with the result of MIMU, 

and the combination of these two results brings more accurate locating 

result for quadrotor. Compared to using MIMU, the error can decrease 

from ±50cm to ±10cm when the height of quadrotor is 1m. An object 

following platform is used for validating the composite algorithm, 

and system identification method is used for modeling the AR.Drone. 

Trajectory following algorithm and object following method are also developed. 

The scene of an AR.Drone searching at the navigation area 

and following the UGV is presented at last. 

◁ PSaC-46 

Facial Expression Recognition in Video Sequences, pp.4766–4770 

Wan, Chuan 

Tian, Yantao 

Liu, Shuaishi 

Jilin Univ. 

Jilin Univ. 

Jilin Univ. 

This paper describes a method for recognition of continuous facial expression 

change in video sequences. ASM automatically localizes the 

facial feature points in the first frame and then tracks the feature points 

197


through the video frames. After that the step is the selection of the 20 

optimal key facial points, those which change the most with changes in 

expression. Since the distance of geometric features, a set of displacement 

vectors, is of a high dimensions, it is mapped into a low dimensional 

space, called feature space, by applying PCA expansion. Then 

estimation of input image is achieved by projecting it on to the feature 

space. After build the feature space, we trained SVM classification and 

tested it for result. 

◁ PSaC-47 

Sequence Detection of Planetary Surface Craters From DEM Data, 

pp.4775–4779 

Yu, Zhengshi 


CUI, Pingyuan 




The research on identification and recognition of impact craters on planetary 

surface is focused on how to detect them from background. A novel 

sequence algorithm is proposed to crater detection that utilizes DEM 

data instead of images. By investigating the features of ideal craters, 

several constraints can be developed to extract candidate crater edges 

from other topographies. Based on the fact that the shape of most 

craters is approximate to an ellipse, the Least Median Square Ellipse 

Fitting Method can be used to exclude pseudo-edges, and to reserve 

the real edges which contain the feature of the crater. The location, 

orientation and other physical parameters of the crater can be determined 

by fitting real edges to an ellipse based on Robust Least Square 

Method. Mathematical simulations are performed with the moon DEM 

data. The results show that the topography-based crater detection algorithm 

offers an effective method for identification and characterization 

of ellipse-like impact craters, and the accuracy is high enough. 

◁ PSaC-48 

A video tracking method based on Niche Particle Swarm Algorithm- 


Li, Xin 

Chen, Wenjie 

Shang, Zengguang 



The Chinese people’s liberation army 

In order to improve the stability and robustness in video tracking based 

on particle filter. We proposed Niche Particle Swarm Algorithm-Particle 

Filter (NPSA-PF) which applies Niche Particle Swarm Algorithm to the 

re-sampling stage in particle filter. The ability of Niche Particle Swarm 

Algorithm which improves the particles’local search ability and weakens 

the information sharing between particles, effectively reduces the 

tracking particles number and improves tracking stability and robustness. 

We use it in video tracking and the performance is validated to 

be effective. 

◁ PSaC-49 

An Efficient Approach of 3D Ear Recognition, pp.4784–4790 

Wang, Kai 

Mu, Zhichun 

He, Zhijun 




China Nuclear Power Engineering Co.,Ltd 

The Iterative Closest Point(ICP) algorithm is usually used for 3D ear 

recognition in the literatures. However, the high computational cost 

of ICP limits the application of 3D ear biometrics. In this paper, we 

present an efficient approach based on local feature and ICP for 3D 

ear recognition. The local features are detected and represented with 

LSP(Local Surface Patch), and used to compute the initial transformation 

for matching with ICP. An elite preservation strategy is introduced 

to refine the candidate gallery ears that a modified ICP algorithm with 

kd-tree index, distance and uniqueness constraints is applied to. The 

proposed approach achieved a rank-1 recognition rate 98.55% on Collection 

J2 of UND biometrics datasets. Matching an ear with a gallery 

requires only 1.73 sec on average. 

◁ PSaC-50 

A new method on Solving Correlation Dimension of Chaotic Timeseries, 

pp.4820–4824 

Qiao, Meiying 

Ma, Xiaoping 

China Univ. Mining & Tech. 


Traditional G-P algorithm exist two drawbacks in solving the correlation 

dimension of chaotic time series. The one is the subjective existence to 

determine scaleless range, the other is calculation error is large when 

the amount of data is small. For two shortcomings, the fuzzy C-means 

clustering is introduced to the G-P algorithm to determine the no-scales 

range. Least-squares fitting method is used to find the saturation correlation 

dimension value in determining the scalelesss range. Using different 

amount of Loren and Rossler data, such as 500,1000,2000,5000 

and 10000, verify the improved algorithm in this paper,. Simulation results 

show that the error relatively small if the delay time is small when 

the amount of 500, 1000 and 2000. With the length of data increases, 

the cluster centre value of the slope relatively flat closer to their ideal 

value. The conclusions are applicable to Lorenz and Rossler data. 

◁ PSaC-51 

3D Ear Modeling Based on SFS, pp.4837–4841 

Liu, Cong 

Mu, Zhichun 

Wang, Kai 

Zeng, Hui 






Ear recognition, by using ear for identification recognition, is a kind of 

new biometric identification technology. Currently there are a lot of ear 

recognition methods based on 2D images, while 3D data can provide 

more information. 3D reconstruction methods based on multiple 2D images 

have a common difficult which is to extract corresponding feature 

points of different images. In this paper, SFS(Shape From Shading) 

was used for 3D modeling by only one grayscale image. Light direction 

was estimated by analyzing singular points, which refers to the greatest 

grayscale point, in 2D grayscale image. In addition, in order to achieve 

more accurate 3D modes, the abnormal high brightness of the cavity 

of auricular concha is processed. The matching accuracy of the model 

can reach to 84%. Therefore the experiments shown that the method 

proposed in this paper is simple and effective to modeling 3D ear. 

◁ PSaC-52 

A Target Detection Method in Dynamic Scene Based on Harris Algorithm 

with Sub-block Threshold, pp.4842–4847 

Lu, Jinghua 

LEI, Yinghui 

Chen, Jie 

Zhang, Juan 





In this paper, an improved Harris algorithm for the target detection in dynamic 

scene due to the camera motion is presented. First, a sub-block 

thresholding method is proposed to solve the problem of uneven distribution 

of corners detected by Harris algorithm. Then, the improved 

Harris algorithm is used to extract feature points, which are used to 

estimate the parameters of global motion with the random-max consistency 

algorithm and the least-square method. Compensated by the 

result parameters, the reference image together with the current image 

are used to detect the target with the frame difference method. Experiment 

results show that the algorithm can detect the moving target more 

accurately in dynamic scene. 

◁ PSaC-53 

Robust Visual Tracking with Classifier-like Appearance Model and Entropy 


Song, Yu 

Li, Qingling 

Yan, Deli 

Kang, Yifei 




beijing jiaotong Univ. 

The detection based visual tracker treats tracking as the object and its 

surround background online classification problem. There are two 

main difficult issues in this method: one is to specify exact labels for 

the online samples, the other is to avoid template drift that caused by 

wrong update of the classifier-like appearance model. To overcome 

198



the problems, a novel tracking algorithm based on online Multiple Instance 

Learning (MIL) and entropy particle filter is proposed. Main contributions 

of our work are: (1) we introduce MIL in particle filter visual 

tracking framework to reduce the online training error of the classifierlike 

appearance model; (2) the appearance model consists of an initial 

fixed MIL classifier and an online dynamic MIL classifier; (3) a particle 

set maximum negative entropy criterion is designed to online fuse 

the two classifiers. Experimental results verify the effectiveness of the 

proposed algorithm. 

◁ PSaC-54 

Abnormal Detection based on Gait Analysis, pp.4859–4864 

Wang, Chao 

Wu, Xinyu 

Li, NanNan 

Chen, Yen-Lun 

Inst. of Advanced Integration Tech. 


shenzhen Inst. of advanced Tech. of chinese Acad. 




Abnormal behavior detection has recently gained growing interest from 

computer vision researchers. In this paper, the gait-analysis-based abnormal 

detection is proposed for walking scenes, where gaits of people 

are analyzed in all kinds of situations and the gait data are utilized to 

construct the basic gait model. Walking people in the crowd are tracked 

and their activities silhouettes are abstracted and compared with the 

basic gait model. Some of those activities which are significantly difference 

with the basic gait models are defined as abnormal behavior, 

where the activities silhouettes and gait models are measured by chamfer 

distance. The experiments verify that our system could effectively 

detect several kinds of activities different with walking. 

◁ PSaC-55 

An Improved Kernelized Discriminative Canonical Correlation Analysis 

and Its Application to Gait Recognition, pp.4869–4874 

WANG, KEJUN 

YAN, TAO 



Based on the canonical correlation analysis (CCA) and its extended algorithms, 

an improved kernelized discriminative canonical correlation 

analysis (KDCCA) was proposed in this paper. Compared with the existing 

KDCCA, there were two improvements. Firstly, when the kernel 

method was added,by improving the optimization objective function, 

the correlation between the final canonical correlation characteristics 

of the non-corresponding elements were reduced and improved classification 

results. Secondly, a more general class relationship matrix 

without sorting the samples was used for adding the class information. 

Finally, the proposed method was applied to gait recognition to solve 

the multi-view and different states problem. Experimental results show 

that the proposed method performs satisfactory recognition results. 

◁ PSaC-56 

Head Detection Based on 21HT and Circle Existence Model, pp.4875– 

4880 

Zhao, Min 

Sun, Dihua 

Tang, Yi 

He, Hengpan 




ChongQing Univ. 

A novel method for head detection was proposed in video sequences 

captured with fixed vertical mono-camera, which integrated hough 

transformation, hair-color distribution model and circle existence model. 

Target area was firstly detected using fast gradient hough transformation 

(FGHT). In order to overcome head area mis-detection and incapability 

of locating head area introduced by FGHT, hair-color classification 

was used to filter the candidate targets through modeling hair-color 

distribution. Furthermore, based on non-parameter probability theory, 

the probability of circle existence model was established, which finalized 

the stages of head detection by locating the head. Compared with 

average circle detection algorithm, experimental results indicate that 

the proposed head detection algorithm can eliminate false targets and 

greatly increase accuracy. 

◁ PSaC-57 

A Three Dimension Reconstruction method on a kind of Micro and Thin 

Laser Seam, pp.4881–4886 

Wang, Liwei 

Chen, Haiyong 

Sun, Hexu 






The laser weld is becoming more and more popular in the steel industrial 

production. However, the varying illumination, reflection and splatter 

lead to the irregular seam shape, which deteriorates the seam quality. 

In order to evaluate the seam shape and its quality, a 3D reconstruction 

method about micro and thin laser seam is proposed. The line structured 

light stripe is projected on the laser seam to be measured by a 

projector, and deformation of the stripe is captured by a CCD camera 

with industrial microscope lens. An image processing method that can 

efficiently locate the deformation of the stripe in the image plane is p- 

resented. Also, a novel procedure to automatically define the region of 

interest in the image is proposed. And then a straight line and curve fit 

is used to reduce the harm of the various disturbances and accurately 

gain the centre line of stripe. Furthermore, the characteristic points of 

the seam are obtained by using the distance search method. Finally, 

the proposed reconstruction method is applied to laser seam specimen, 

the desired performances are gained, and the results are satisfying. 

◁ PSaC-58 

A Novel 3D Ear Reconstruction Method Using a Single Image, 

pp.4891–4896 

Li, Chen 

Mu, Zhichun 

Zhang, Feng 

Wang, Shuai 




The First Research Inst. of Ministry of Public 

Security of P.R.C 


To achieve denser 3D ear model from less controlled 2D image, we 

explore a 3D Ear Morphable Model (3DEMM) for 3D ear reconstruction 

using a single 2D ear image. Considering the unique structure of 

ear, we propose a Triangle Mesh Hierarchical Growth (TMHG) based 

dense corresponding method. The proposed method can overcome 

the shortcoming of optical flow based method and achieve pixel level 

dense correspondences based on physiological features of ear without 

choosing a reference ear. Novel 3D ear shape can be recovered from 

a single ear image based on the proposed 3D ear morphable model. 

Extensive experimental results have shown that our proposed method 

can obtain denser 3D ear model with lower cost and higher efficiency 

than existing methods. 

◁ PSaC-59 

Spherical Terrain Matching for SLAM in Planet Exploration, pp.4907– 

4911 

Pan, Haining 

CUI, Pingyuan 

Wang, Huan 




This paper describes a scan matching algorithm for motion estimation 

near a planet surface using scanning laser scanner data in spherical 

coordinate. It is directly based on range finding data and followed by 

point to point terrain map alignment in the laser scanner’s spherical 

coordinate system. Laser scan matching of current and reference s- 

cans are enhanced by weighted terrain and distortion compensation. 

It is also accelerated by predicted vision window using inner dynamic 

model and SLAM results. The algorithm is tested using data acquired 

within virtual OpenGL environment and proved to be efficient for scan 

matching with terrain distortion. 

◁ PSaC-60 

Good Resolutions for Hough Transform, pp.4916–4920 

Tu, Chunling 

Van Wyk, Barend 



199


Du, Shengzhi 


This paper demonstrates the relationship between detection errors and 

resolutions (ρ- and θ- directions) when the Hough Transform (HT) is 

employed to detect straight segments in images. The inflexion of the 

error-resolution curve was uncovered. To comprehensively study the 

location of the inflexion, the effects of several factors are considered, 

such as the positions (ρ and θ), widths and lengths of straight segments, 

noise level, and the ratio of resolutions. An error surface according 

to ρ- and θ- resolutions is obtained in the paper to guide seeking of 

the best resolutions. The area containing “good” resolution settings is 

uncovered and modelled. 

◁ PSaC-61 

Integrating Method Improving HT-Butterfly Based Segment Detection, 

pp.4921–4925 

Du, Shengzhi 

Tu, Chunling 



This paper addresses an integrating method to be used in segments 

detection based on Hough Transform (HT) butterflies. The proposed 

method is employed to detect the edges of butterflies in higher accuracy 

than the commonly used existing method, and hence improves the 

performances (the accuracy and robustness) of segment detection. 

◁ PSaC-62 

A new method of texture synthesis based on gradient structure information 

searches along a spiral path, pp.4931–4935 

Tan, Yongqian 

Pu, Yuanyuan 

Qian, Wenhua 

Xu, Dan 

Yunnan Univ. 

Yunnan Univ. 

Yunnan Univ. 

Yunnan Univ. 

Most of the existing texture synthesis algorithms were proposed in the 

pursuit of the better synthesis quality or quick synthesis speed, but they 

could not satisfy requirements at the same time. In this paper, a new 

texture synthesis algorithm is presented. It adopts the color and the 

gradient as the similarity measurement of between the two matching 

patch, which improves their similarity extent. Moreover, a spiral-path 

searching method is used to search the best texture matching patches 

and the synthesis time can be decreased dramatically. The experiment 

results show that not only the synthesis speed is rapider t in great 

degree, but also the synthesis quality is improved. 

◁ PSaC-63 

Mechanical Model of Wheat and Vibration Analysis, pp.4965–4968 

Xu, Qicheng 

Sun, Changchun 

Sun, Yazhen 


Shenyang Jianzhu Univ. 


By using multi-freedom systems vibration theory, the mechanical model 

of wheat lodging resistance is established, the wheat lodging moment 

formula is deduced under the wind load. Considering the soil characteristics 

and the wheat root distribution, the wheat moment of lodging 

resistance is deduced, The moment the size of these two can predict 

whether wheat is lodging,Vibration mode analysis explain the reasons 

for the differences of the different varieties of wheat lodging resistance. 

Wheat numerical examples verify the rationality and practicality of the 

model. 

◁ PSaC-64 

IMPROVED DEMPSTER AND SHAFER THEORY TO FUSE REGION 

AND EDGE BASED LEVEL SET FOR ENDOCARDIAL CONTOUR DE- 

TECTION, pp.5013–5018 

Gu, Jason 

Dalhousie Univ. 

Data fusion is an important tool for improving the performance of a detection 

system when more than one classifier is available. The reasoning 

logic of Dempster-Shafer evidence theory for fusion is similar to that 

of humans. This paper discusses application of a data fusion method 

which is based on improvements to the Dempster-Shafer theory, to e- 

chocardiographic images in order to increase the detection accuracy of 

the endocardial contours. In this paper, edge and region based level 

sets are implemented. The Improved Dempster-Shafer evidence fusion 

algorithm is applied to combine the detected contours resulting in 

promising results as shown by computational experiments. 

◁ PSaC-65 

Noise Reduction of sEMG in SVD Based on Neural Network, pp.5035– 

5039 

Zhang, Li 

Li, Yang 

Xu, Zhuojun 

Tian, Yantao 

Jilin Univ. 

Jilin Univ. NangLing, school of communication 

engineering 

Jilin Univ. 

Jilin Univ. 

According to the characteristic that the surface electromyogram signal 

(sEMG) is very weak and influenced by noise vulnerability, this paper 

proposes a new method that uses the unsupervised Kohonen neural 

network weights optimized to determine the order of the reconstruction 

matrix during the process of the noise reduction in singular value 

decomposition (SVD) effectively. First, let the sEMG collected through 

the Butterworth band-stop filter to remove 50Hz power line interference. 

Then use the SVD to deal with the signal filtered. To make use of the 

characteristic of the noise platform is gently and centralized of the singular 

value spectrum of the signal with noise. Through the projection on 

longitudinal axis in spectrum, we apply the Kohonen network optimized 

to confirm the boundaries of the noise platform, and then to determine 

the effective order of the reconstruction matrix. Simulation results show 

that this method achieve the noise reduction of sEMG preferably. 

◁ PSaC-66 

A New Method for Perturbation Experimental Design in Gene Regulatory 

Network Identification, pp.5090–5095 

Wang, Xin Acad. of Mathematics & Sys. Sci., Chinese Acad. 


Inferring gene regulatory networks by high-throughput data is a fundenmental 

problem in systems biology. The interactions between genes, 

proteins and other small molecules are typically described by gene regulatory 

networks, which are nonlinear and sparce. We linearize the 

nonlinear system of the segmentation polarity network of Drosophila 

melanogaster and infer the interaction between genes in the network 

by perturbation experimental data. The genes expression level are 

measured by microarray experiments. we calculate the parameters’ 

changes forced by inputs of the experiment, and give a new method for 

experimental design in which the inputs facilitate precise estimation of 

the parameters. All the data in calculation is simulated in silico. 

◁ PSaC-67 

A wireless data acquisition system designed for greenhouse based on 

ZigBee, pp.4405–4408 

Li, Peng 

Wu, Yunjie 

Beihang Univ. 

Beihang Univ. 

This paper studies the design of a wireless data acquisition system applied 

in greenhouse by building a wireless sensor network based on 

ZigBee. The system includes acquisiton terminals, routing nodes, an 

ethernet gateway and a server. The terminals are solar powered with 

no external power supply, which can be flexibly deployed. Through multiple 

sensors connected to it, the system can collect the environmental 

information, including both the temperature and humidity of the air and 

the soil, the concentration of the carbon dioxide and the intensity of the 

illumination etc., and then transmits the collected data to the server for 

further query and analysis, which provides the reliable data support for 

the cropping. Currently, the system has been successfully assembled 

and operates properly in the agricultural field of Beijing, Hebei, Shandong, 

Inner Mongolia and many other provinces and cities in China. 

◁ PSaC-68 

An Improved Median Filter Algorithm Based On Light Sensor, pp.4414– 

4417 

Yu, Jianjun 

Wang, Guanwei 




200



Yang, Qiong 


Based on the sensitive character of light sensor, the paper applied the 

median filtering algorithm which was used for two-dimensional image 

process on the filtering of one-dimensional sensor signal and improved 

the algorithm with adding a determine step about heavy interference 

signal. The research showed that the miss-rate and the false-rate of 

improved median filtering algorithm are 0 and the deviate-rate decent 

to 0.2% from 5.1% in classic median filtering algorithm. Therefore, 

the improved median filtering algorithm can not only contain the advantages 

of classic median filtering algorithm, but also filter the heavy 

interference signal efficiently. The algorithm was better in eliminating 

the noise and keeping the real signal. 

◁ PSaC-69 

The Anti-interference analysis and design for Mode S reply Communication 

of Integrated TCAS, pp.4467–4471 

Huang, Yi 

Shi, ZhongKe 



Abstract―In order to effectively improve the reliability of Mode S reply 

communication of integrated TCAS, the anti-interference analysis and 

design for the communication circuit and receiving process are provided 

in this paper. The algorithms of preamble detection, DF-authentication, 

confidence detection of bit are realized by state machines and counters 

which were written with VHDL to improve the fault-tolerance. Antiinterference 

analysis and design for filter, modulator, demodulator, amplifier 

and detector is also provided to improve the reliability of communication. 

Finally, the anti-interference design is tested by wireless 

communication experiment. The result indicates that the algorithms 

which realized with FPGA can detect and decode Model S reply signal 

accurately with a certain fault-tolerant ability; the rise/fall time of pulse 

is less then 50ns, the jitter amplitude of signal is less than 3dB which 

agrees with RTCA standards for Model S reply communication. 

◁ PSaC-70 

Remarks on Outliers in Time Series of Stock Prices Based on Density, 

pp.4523–4526 

ZHAO, Qingjiang 

Kunming Univ. 

How to detect the outlier of stock prices effectively has become an intense 

concern of scholars in many fields. In this paper, the thoughts 

of Statistical theory and local density are introduced for researching the 

outliers in stock time series. A method detecting the outlier of stock time 

series based on density is proposed. Study on the outlier of time series 

in stock market is dealt with in two stages. In the first stage, the time 

series is partitioned by means of the sliding window with fixed length. 

In the second stage, the density-based method is introduced to detect 

the outliers with the local outlier factor. Empirical study, based on the 

daily closing price of SSE Composite Index, proved that the method is 

feasible and effective. 

◁ PSaC-71 

Support Vector Machine Based Aircraft Ground Icing Type Classification 

Forecast, pp.4541–4544 

Xing, Zhiwei 

Zhang, Hui 



Aircraft icing can seriously affect the safety of aircraft, and different 

types of aircraft icing have an effect on the aircraft safety to various 

extents. A SVM (Support Vector Machine) model for aircraft icing type 

prediction is presented to classify aircraft icing types. The input variables 

of icing type are analyzed, and then based on the analysis, the 

appropriate forecast methods are chosen and a SVM model for aircraft 

icing type classification is established. The SVM-based classification 

model is employed to identify aircraft ground icing type and compare 

with the classification model based on BP neural network. The experimental 

results show that the model based on the SVM method supplies 

high forecast accuracy, strong generalization ability with small samples, 

and has broad application prospect. 

◁ PSaC-72 

Fusion Algorithm of Infrared and Visible Images Based on Local Energy 

Using NSCT, pp.4579–4582 

DAI, Wen-zhan 

TAN, Libo 

Yang, Aiping 


Inst. of Automatic Control, Zhejiang Sci-Tech Univ. 


According to the characteristics of the infrared and visible image, an 

image fusion algorithm of infrared and visible images based on local 

average energy using NSCT is proposed. After the two original images 

-registered are decomposed by using NSCT, the low frequency 

sub-band coefficients and band-pass directional sub-band coefficients 

are given by fusion rules based on local average energy, and then the 

fused image is obtained by inverse NSCT .The simulation results show 

that the algorithm proposed in this paper is best among wavelet-based, 

Curvelet-based and NSCT-based method. 

◁ PSaC-73 

Fingerprint Orientation Reconstruction from Minutiae Points, pp.4583– 

4587 

Chu, Zhuang 

Yuan, Guosen 

Zhang, Xiyu 

Han, Lin 


College 


College 

CNNC CHINA NUCLEAR POWER ENGINEERING 

Co 

Xin Xiang Medical Univ. 

Fingerprint orientation reconstruction from minutiae points is an important 

procedure in fingerprint reconstruction. In this paper, a novel 

fingerprint orientation reconstruction method is proposed. Firstly, the 

initial orientation field is estimated through interpolation strategy. The 

orientation of central block is predicted by the nearest minutia in eight 

different sectors. Secondly, a 2-D Fourier series expansion method for 

orientation field is proposed. Using the coefficients of expansion function, 

orientation field can be presented and reconstructed effectively. 

The noise can also be eliminated evidently through this method. Furthermore, 

singular points which are always needed for other orientation 

model are not required by 2-D Fourier series expansion method. The 

experimental results comparing our method with other methods prove 

that our proposed method is very effective in representing and reconstructing 

the fingerprint orientation field. 

◁ PSaC-74 

Image fusion via wavelet transform based on local contrast, pp.4588– 

4591 

Zhang, Daosong 

Pan, Haipeng 



In this article, images obtained by multi-sensor use multi-resolution 

analysis of image fusion methods based on wavelet transform. Two 

source images obtained high frequency and low frequency components 

after wavelet transform. Using fusion rules with criteria based on local 

variance [7] to obtain low-frequency component. using an adaptive algorithm 

with local contrast of images to obtain the high-frequency component; 

at last, using inverse wavelet transform to rebuild a fusion image 

with useful information from source image. Results show that compared 

with traditional algorithm and general algorithm, fusion image 

obtained by the proposed algorithm increases Peak-to-peak Signal-to- 

Noise Ratio by 12.11% and 8%, reduces root mean square error by 

69.65% and 59.80%, increases correlation coefficient by 0.95% and 

0.52%. 

◁ PSaC-75 

An Algorithm for Human Eye Location in Complex Background Based 

on Image Processing, pp.4592–4595 

Wu, Minjie 

Mu, Ping’an 

Dai, Shuguang 

Zhang, Cai-Yan 


College of Optical & Electronics Engineering 



In order to reduce the impact of image background and illumination in 

face locating, this dissertation has put forward a new algorithm to locate 

human eyes, applying YCbCr model to extract human face region, and 

201


then locating eyes correctly according to geometry and pixel features 

of human eyes. Experimental results show that this algorithm can be 

applicable in images with different backgrounds and Non-uniform illumination 

environment. It has proved that it is real-time and accurate. 

◁ PSaC-76 

Adaptive Fuzzy Apporach to Background Modeling using PSO and 

KLMS, pp.4601–4607 

Li, Zilong 


This paper presents a new adaptive fuzzy approach for background estimation 

in video sequences of complex scene from the function estimation 

point of view. A Takagi-Sugeno-Kang (TSK) type fuzzy system is 

used as the function approximator in the study. The proposed approach 

uses a hybrid learning method combines both the particle swarm optimization 

(PSO) and the Kernel Least Mean Square (KLMS) to train the 

fuzzy approximator. In order to estimate background, we first interpret 

foreground samples as outliers relative to the background ones and so 

propose an Outlier Separator (OS). Then, the obtained results of OS 

algorithm are employed in the fuzzy approximator in order to train and 

estimate background in each pixel. Experimental results show the high 

accuracy and effectiveness of the proposed method in background estimation 

and foreground detection for various scenes. 

◁ PSaC-77 

Optimal Motion Control for IBVS of Robot, pp.4608–4611 

Gao, Cheng 

Univ. 

In conventional image-based visual servoing (IBVS), the robot endeffector 

(camera) motion is controlled directly according to image error, 

there isn’t direct control over the Cartesian velocities of the robot endeffector. 

As a result, the robot trajectories can be seemingly roundabout 

in Cartesian space. This paper presents a new control scheme, that is 

IBVS of rotation separated. As there is not interferences of translation 

and rotation of image, the performance of this approach is more advantage 

in the setting times and the motion trajectories of Cartesian space 

than classical IBVS, and can executes visual servoing task that conventional 

IBVS can’t accomplished. We illustrate new control scheme 

with two representative simulation result. 

◁ PSaC-78 

Machine-Vision Based Preceding Vehicle Detection Algorithm: A Review, 

pp.4617–4622 

Zhou, Jun-jing 

Duan, JianMin 

Yu, Hongxiao 




Onboard vehicle detection system is of great importance to reduce vehicle 

collision accident and increase the driving safety on road. It aims 

at detecting vehicles appearing around the ego vehicle using vehiclemounted 

camera, so as to alert the driver about driving environments 

and possible collision with other vehicles. In this paper, we analyze the 

detail difficulties lying in the problem and review most of the literatures. 

A typical vehicle detection algorithm includes two steps: hypothesis 

generation and hypothesis verification. After a vehicle is detected, it’s 

tracked. This paper introduces the principle of typical methods of detection 

and tracking and analyzes their respective pros and cons. Finally, 

we propose some research directions in the future. 

◁ PSaC-79 

Traffic Sign recognition Using Dual Tree-Complex Wavelet Transform 

and 2D Independent Component Analysis, pp.4623–4627 

Gu, Mingqin 

Cai, Zi-xing 



A novel traffic sign recognition algorithm is presented in the paper. This 

algorithm integrates the Dual-Tree Complex Wavelet Transform(DT- 

CWT) representation of traffic sign images and 2D Independent Component 

Analysis(2DICA) method. First traffic sign color-image is preprocessed 

with gray scaling, and normalizing to 6464 size. 

Then four levels DT-CWT images are used to represent gray image of 

traffic sign, so the image features could be obtained. Second, 2DICA 

and nearest neighbor classifier are used to recognize the traffic signs. 

The whole recognition algorithm is implemented for classification of 50 

categories of traffic signs and accuracy reach 97%. It also compares 

the presented algorithm with well-established image representation like 

template, Gabor, and feature selection techniques such as PCA, LPP, 

2DPCA at same time. Experimental results indicate that the proposed 

algorithm was robust, effective, and accurate to recognize traffic signs. 

◁ PSaC-80 

Feature Detection and Matching for Traffic Sign Images, pp.4628–4632 

Li, Lei-Min 

LI, Li 

Tong, Ru-qiang 

Li, Pei-xi 

Southwest Univ. of Sci. & Tech. 


School of Information Engineering , Southwest 



It is important to detect and recognize the traffic sign for mobile robot 

localization and navigation. In this paper, an algorithm frame of feature 

detection and matching has been developed which includes shape 

detection, Harris corner detection, SIFT feature matching and robust 

estimation method. Firstly, the color threshold segmentation algorithm 

in RGB color space is adopted to get the candidate region of traffic signs 

and the region growing method is applied to remove the noise in this 

image. Secondly, the shape features on the edge image are detected 

using template matching. Thirdly, Harris corner features are calculated 

and sorted, then the SIFT feature descriptors are computed on the 

extraction corner points. Finally, according to the minimum Euclidean 

distance the matching characteristic vectors are obtained between t- 

wo images􀋈then random sampling algorithm with robust estimation 

is used to reduce mismatch. Experiment result shows that this 

algorithm is efficient. 

◁ PSaC-81 

Machine Vision Based Localization of Intelligent Vehicle, pp.4638–4643 

Wang, Fei 

Duan, Jianmin 

ZHENG, Banggui 




Aiming at implementing the lane recognition and determining the host 

vehicle’s parameters of position and direction accurately according to 

lane line parameters, realize the lane departure warning, one kind of 

practical algorithm for lane recognition and the host vehicle localization 

has been proposed. In the algorithm, lane parameters are obtained using 

Hough Transformation. Combined the inner and outer parameters 

of CCD and lane parameters, the parameters such as the host vehicle’s 

location parameter, the direction variable and the lane width are 

obtained using the coordinate transformation. The experiments indicated 

that this algorithm has the good adaptive ability and anti-jamming 

ability when roadway structure and illumination condition changes. It 

can satisfy many kinds of initiative safety system’s requests in a certain 

extent. 

◁ PSaC-82 

Divide and Conquer Strategy for Spectral Clustering, pp.4644–4648 

Jia, Zhixian 

Xinjiang Univ. of Finance & Economics 

The spectral clustering algorithm’s space complexity is O(n-squared), 

while time complexity is O(n-cubed). When dealing with large amounts 

of data, the memory will overflow and run-time is too long. For the general 

problem of spectral clustering, if the clustering data of sub-problem 

between the original problem has the same probability distribution, it 

can be applied to divide and conquer strategy for the problem of spectral 

clustering, by the spectral clustering results of sub-problems to get 

the spectral clustering results of original problem. To spectral clustering 

image segmentation as a research object, we will discuss the divide 

and conquer strategy for spectral clustering in this paper. Experiments 

show that the application of divide and conquer method for spectral 

clustering image segmentation, we can get a perfect performance in 

image segmentation. 

◁ PSaC-83 

Improved Algorithm for the k-means Clustering, pp.4717–4720 

ZHANG, SHENG 

Shandong Jiaotong Univerisity 

202



Wang, Shouqiang 

ShanDong JiaoTong Univ. 

This paper investigates the standard k-means clustering and gives an 

improved algorithm based on selecting the initial centers and overcoming 

the local minimal values. Experiments show that the new algorithm 

is more effective and can get a better result than the standard k-means 

clustering. 

◁ PSaC-84 

The Motion Control of Mobile Self-Balancing Robot Base on Vision- 

Teleoperation, pp.4737–4740 


Li, Jun 

Xu, Feng 

Yu, Naigong 





Aiming at control of remote teleoperation for mobile robot using at 

search and rescue, especially for application in a complex environment, 

this paper presents a vision-based mobile robot remote control system. 

After the realization of the physical simulation, transplantation of this 

system to the mobile self-balancing robot control physical entities also 

success, giving a callback-style remote control instruction set system 

at the meantime. Forming a self-balancing robot balance control with 

a combination of motion control, vision systems and coordination of effective 

mechanical control system. 

◁ PSaC-85 

A Complex Number Algorithm of Image-Hiding, pp.4815–4819 

Wang, Hongxin 

Harbin Commerce Univ. 

Abstract - Under the premise of comprehensive studying with different 

methods of image-hiding, we propose a method of new imagehiding, 

complex number algorithm, which is lower distortion and higher 

security, by researching the differences between the blocks of coverimage 

and secret-image. This algorithm is simple and practicable. It 

has greatly improved the security of system, robustness and facticity of 

image-hiding. experiment result verified the advantage of the algorithm. 

◁ PSaC-86 

A Blind Watermarking Algorithm Based on Singular Value Decomposition 

and Quantification, pp.4887–4890 

Chen, Gang 

Ma, Huijie 

Chen, Ning 

Jianghan Univ.,Wuhan ,430056,China 



A blind watermarking algorithm based on singular value decomposition 

and quantization in Contourlet domain is presented to protect the copyright 

of digital images . Firstly, a host image is decomposed by using 

Contourlet transform, and the low-frequency subband is divided into 

blocks. Then, by applying SVD to each block, the binary watermark information 

is embbeded by quantizing each singular value vector of each 

block. According to the quantization principle, blind extraction could be 

realized. Finally, the experimental results are given to show that, this 

algorithm can not only ensure the invisibility of the watermarked image, 

but also resist effectively various attacks, such as the filtering, adding 

noise, JPEG compression, cropping and so on. 

◁ PSaC-87 

An Imaging Method for 360-Degree Panoramic Bird-Eye View, 

pp.4902–4906 

Yu, Chunxuan 

Fang, Xiang 

Tang, Shuangze 

Wu, Mengzhou 





An imaging method for 360-degree panoramic bird-eye view, which is 

applied to Panorama Parking Assist System, is presented. In order to 

remove the distortion of images collected, the camera is calibrated to 

obtain the parameters. Then, according to the relationship among coordinate 

systems, the conversion from the corrected image into bird-eye 

view is transformed by using the parameters, such as camera perspective, 

pitch angle and focal length, etc. Finally, image mosaic look-up 

table is established and the system can quickly generate the panoramic 

bird-eye view image by this table. Experimental results show that this 

method has a good imaging effect. 

◁ PSaC-88 

Transmission Rate Calculating and Analysis of Gear System Based On 

ECT, pp.4912–4915 

Li, Junjun 

SHI, Shiying 

Liu, Houyi 


Shan-dong Communication Univ. Shan-dong, JN, 

250023 


This paper analyses the structure and components of three gear system 

according to typical structure diagram, deduction the transmission 

rate calculating methods of electric control transmission gear system, 

so that can grasp the principal and the transmission rate calculation 

rapidly, it will provide the theory basis for designing and maintenance of 

ECT 

◁ PSaC-89 

A Microarray Image Gridding Method Based on Image Projection Difference 

Sequences Analysis and Local Extrema Searching, pp.4961– 

4964 

Liu, Jun 

Shenyang Ligong Univ. 

Microarray image gridding is one important step of microarray image 

analysis to determine 2D image coordinates of all array spots in the hybridized 

gene chip image. Accuracy of microarray image gridding will 

affect the reliability of gene-chip data extraction and even the final analysis 

results of gene-chip assays. Hower, in recent years, many new 

microarray image gridding methods presented have higher accuracy 

but have more computation complexity than those precedented ones. 

To promote gridding accuracy and decrease computation complexity simultaneously, 

we presented a novel and simple microarray image gridding 

method based on image projection sequences analysis and local 

extrema searching. Firstly we transformed 2D microarray image into 

vertical and horizontal 1D projection sequences, secondly utilized signal 

processing methods of low pass filtering and zero mean to filtered 

projection sequences, thirdly computed the first-order difference and 

second-order difference for the smoothed signals, and finally realized 

microarray image gridding according to the local extrema of difference 

sequences and span information of spots array on the microarray image. 

The subsequent gridding experiments showed that this method 

had met the requirements of computing accuracy and efficiency of microarray 

image gridding. 

◁ PSaC-90 

Optimal Control Strategy for an Multi-regional Epidemic Model, 

pp.5007–5012 

Sang, Zi 

Qiu, Zhipeng 

Kong, Qingkai 

Zou, Yun 

Nanjing Univ. of Sci. & Technoledge 

Nanjing Univiersity of Sci. & Tech. 



A multi-regional model is developed for the global spread of an emerging 

and re-emerging infectious disease. Intra-regional interventions and 

inter-regional interventions are incorporated into the model. Optimal 

control theory is applied to the formulation of a prevention and control 

strategy against diseases. The conditions for existence of an optimal 

control are derived by using Pontrayagin’s maximum principle. Numerical 

results show that optimal control strategy preforms effective. A 

briefly study on the impact of different levels of hygiene precautions on 

the formulation of an optimal control strategy is investigated. Simulation 

result further reveals that that intra-regional intervention and exit 

screening at the border of an region remains the best choice for an infectious 

disease, but entry screening should be given less priority or 

even abandoned in the entire optimal control program. 

◁ PSaC-91 

Design of a 5mW Capacitive Accelerometer based on MEMS and C- 

MOS Technology, pp.5102–5107 

Ruan, Yue 

Ji, Lujun 



203


Tang, Ying 

Yao, Wen-ji 


Xu, Sen 





This work presents an open-loop, fully differential capacitive MEMS 

accelerometer implemented in CMOS technology. The mathematical 

model of this open-loop system is derived, which considers non-ideal 

factors in circuits such as nonlinear distortion and noises. These nonideal 

factors have been discussed through system level simulation using 

MATLAB. Simulation results show that the system has good capacitive 

sensitivity and is robust to noises. The detailed design of interface 

circuit in the proposed MEMS accelerometer is presented, using sigmadelta 

(Σ-Δ) modulation. Finally, the chip-level physical layout of interface 

circuit is implemented and tested, using silicon-on-insulator (SOI) 

substrate with 1μm CMOS process. Results have shown the chip with 

area of 1.32mm2 and power consumption of about 5mW. The proposed 

MEMS accelerometer is designed for acceleration of less than 5G, and 

its resolution is 1.923mG. 

◁ PSaC-92 

Attitude And Altitude Instrument Based On DSP, pp.5108–5111 

Xu, Peng 

Li, Baokui 

Geng, Qingbo 

Fei, Qing 





Unmanned Aerial Vehicle can be referred to as UAV .This design implements 

a system that based on DSP to measure altitude, airspeed and 

attitude of UAV. The system uses DSP as the controller and operator, 

and uses two MS5534C and a URF03-TTL232 to measure the altitude 

and airspeed of UAV. To measure attitude, we need three accelerometers 

and three gyroscopes. SCI serial port can be used to read data 

from the module. This design collects the data of these sensors by 

query method, and uses quaternion algorithm to calculate the attitude 

of UAV. 

◁ PSaC-93 

Study of Strong Tracking Augmented Unscented Kalman Filter in Integrated 

Navigation System., pp.5112–5115 

Xu, Dexin 

Wang, Lu 

Li, Guangchun 

Ma, Tao 





In order to solve the problem of inaccurate state estimation and divergent 

outputs of the filter of the low-cost integrated navigation system, a 

strong tracking augmented unscented kalman filter is proposed in this 

paper. This method extends the strong tracking filter principle into the 

augmented unscented kalman filter, which improves the strong tracking 

ability of the system states mutation. Using the state switching technology 

to reduce the dimension during the Sigma points’ sampling and 

this improves the real-time property of the filter. Applying this method 

into the low-cost integrated navigation system, the experiments results 

prove that this method can track the state mutation quickly and inhibit 

divergent outputs of the filter. 

◁ PSaC-94 

Research of Fault Diagnosis Based on Matching Pursuit and Biomimetic 


Wang, Xiaozhe 

Wang, Jinping 



Abstract –Consider of influences of noise in sampling signals comprehensively, 

a method of fault diagnosis which combines matching pursuit 

(MP) and biomimetic pattern recognition (BPR) is put forward in 

this paper. Firstly, the matching pursuit (MP) algorithm is used to select 

optimum wavelets in different SNR situations from the Laplace wavelet 

dictionary. Then the feature vector is extracted according to the operation 

result of waveform MP and super high-dimensional detection 

feature spaces of biomimetic pattern recognition (BPR) is constructed. 

After that, the real-time detected partial discharge (PD) signals are 

cut, and the feature for each discharge pulse is extracted respectively, 

which realized the multiple fault recognition revolutionarily. Simulations 

show that the robustness and accuracy of fault pattern recognition is 

improved. 

204


Book of Abstracts: Sunday Sessions 

Sunday, July 8, 2012 

PD-1 8:30-10:10 Room 305 


Chair: Feng, Gang 

City University of Hong Kong, China 

◮ PD-1 8:30-10:10 


Chen, Ben M. 

Fu, Li-Chen 

Hu, Xiaoming 

Jiang, Zhong-Ping 


National Taiwan Univ. 

Royal Institute of Technology 

Polytechnic Inst. of New York Univ. 

WCICA 2012 proudly presents the plenary panel session on Beyond 

Control. We are honored to be able to invite four prominent professors 

in the field of control to be the panelists. The objective of the plenary 

panel session is to provide an opportunity for researchers, especially 

young researchers, to interact with world renowned experts in control 

and seek their views on control and more on “beyond control”. In particular 

they are going to share with us their views on control and its 

interaction with other fields such as bio, communications, robotics, automation, 

and energy systems. They will also share with us current and 

possible future developments of the fields as well as how to develop a 

research career and how to publish high quality papers. 

PD-2 10:30-12:10 Room 305 




◮ PD-2 10:30-12:10 


Chai, Tianyou 


Lueth, Tim C. 

Luh, Peter B. 

Xu, Yangsheng 

Northeast Univ., China 

Tohoku Univ. Japan 

Technical Univ. of Munich, Germany 

Univ. of Connecticut, USA 


WCICA 2012 proudly presents the plenary panel session on Future 

Robotics and Automation. We are honored to have invited eight epic 

international leaders in the field of robotics and automation to be the 

panelists. The objective of the plenary panel session is to provide an 

opportunity for researchers, especially young researchers, to interact 

with world class leading authorities in robotics and automation and 

seek their views on future robotics and automation. In particular they 

are going to share with us their views on the topic and its active interaction 

with other fields such as bio, communications, control, and energy 

systems. They will also share with us how to develop a successful research 

career and how to publish high quality papers. 

SuA01 13:30–15:30 Room 203A 

Stability and Stabilization (I) 

Chair: Guo, Yuqian 

Co-Chair: Chen, Ning 



◮ SuA01-1 13:30–13:50 

Stability Analysis for Continuous-time Three-dimensional Systems with 

State Saturation, pp.1282–1286 

Chen, Dongyan 

Ding, Yanhui 

Shi, Yujing 



School of Applied Sci., Harbin Univ. of Sci. & Tech. 

This paper concerns sufficient conditions of globally asymptotical stability 

at origin for three-dimensional continuous-time linear systems with 

state saturation. Through the judgment of the existence of equilibrium 

points distinct from the origin and the existence of steady orbital 

periodic solutions in the limited area, sufficient conditions for threedimensional 

continuous-time linear systems with state saturation to be 

globally asymptotically stable are given. 

◮ SuA01-2 13:50–14:10 

Exponential stabilization of neutral-type neural networks withinterval 

non-differentiable and distributed time-varying delays, pp.1293–1298 

Weera, Wajaree 




In this paper, the problem of exponential stabilization of neutral-type 

neural networks with various activation functions and interval nondifferentiable 

and distributed timevarying delays is considered. The interval 

time-varying delay function is not necessary to be differentiable. 

By constructing a set of improved Lyapunov-Krasovskii functional combined 

with Leibniz-Newton’s formula, the proposed stability criteria 

are formulated in the form of a linear matrix inequalities. Numerical 

example illustrate the effectiveness of the results. 

◮ SuA01-3 14:10–14:30 

Finite-time stabilization for a class of nonlinear switched time-delay systems, 

pp.1826–1831 

Wang, Ruihua 

Zong, Guangdeng 

Hou, Linlin 




In this paper, the problem of finite time stabilization is dealt with for 

a class of switched time-delay systems with nonlinear uncertainties. 

Firstly, the nonlinear uncertainties are transformed into the linear timevarying 

forms via the differential mean value theorem under some 

assumptions. Secondly, by applying the average dwell time method 

and convexity principle, a finite-time stability condition for the unforced 

switched time-delay systems is established. Then, a state feedback 

controller is designed which renders the considered system finite-time 

stable. All the conditions are presented in terms of strict linear matrix 

inequalities(LMIs), which can be easily checked by using recently 

developed algorithms in solving LMIs. Finally, a numerical example is 

provided to demonstrate the effectiveness of the main results. 

◮ SuA01-4 14:30–14:50 

The stability analysis of neutral neural network systems with distributed 

delays, pp.1876–1880 

Wu, Xueli 

Li, Yang 

An, Hui 

Wang, Yuehua 





Dynamical behavior of neutral neural networks with distributed delays 

is studied by employing suitable Lyapunov functionals, delay-dependent 

criteria to ensure local and global asymptotic stability of the equilibrium 

of the neural networks. Our results are applied to classical neutral neural 

networks with time delay and some novel asymptotic stability criteria 

are also derived. The obtained conditions are shown to be less conservative 

and restrictive than those reported in the known literature. 

◮ SuA01-5 14:50–15:10 

Quadratic Stability of Reset Control Systems with Delays, pp.2268– 

2273 

Guo, Yuqian 

Xie, Lihua 



This paper investigates robust stability of reset control systems with 

both uncertainties and transmission delays. Firstly, a generalized 

Lyapunov-Krasovskii theorem is proven. Secondly, the technique of 

parameter-dependent full-rank right annihilator of matrices is used to 

deal with the uncertain reset time instants caused by output matrix uncertainties. 

Based on this, several necessary and sufficient conditions 

for dissipativeness of reset mappings are established. Finally, some 

delay-independent and a delay-dependent robust stability results are 

given in terms of linear matrix inequalities (LMIs) by using certain kind 

of Lyapunov-Krasovskii functionals. An illustrative example is also given 

to explain the proposed results. 

◮ SuA01-6 15:10–15:30 

Parametric Stabilization of Large-Scale Nonlinear Systems, pp.2125– 

2129 

Chen, Ning 

Shen, Xiaoyu 



205


Gui, Weihua 


Sun, Xue 


This paper investigates parametric stability of nonlinear systems with 

uncertain parameters based on decentralized control. The main concern 

is that the equilibrium of the closed-loop system drifts with the 

change of the parameters. First, the impact of the parameters on the 

equilibrium location is investigated. Then, a decentralized linear controller 

is designed to make the closed-loop system parametrically stable. 

The proposed method combines different optimization techniques 

to produce a robust control that accounts for uncertain parametric variations, 

and the corresponding equilibrium shifts. Finally, simulation results 

are given to show the effectiveness of the proposed method. 

SuA02 13:30–15:30 Room 203B 

Multi-Agent Systems (I) 

Chair: Cai, Yunze 

Co-Chair: Zhang, Zhenning 



◮ SuA02-1 13:30–13:50 

Multi-agent Competitive Control Systems, pp.2263–2267 

Zhang, Zhenning 




In this paper, we consider the multi-agent competitive control systems 

with varying adjacent topology. The dynamics and the performance 

of each agent of the multi-agent system depend on both itself and its 

neighborhood. The optimal solutions are the Nash equilibriums, which 

are determined by coupled Hamilton-Jacobi-Bellman equations. We 

mainly concern with continuous-time affine nonlinear systems and linear 

systems. Finally, a two-agent linear control system is used to illustrate 

our results, where the two agents are related through a periodic 

switching. 

◮ SuA02-2 13:50–14:10 

Flocking of Multi-Agents Based on Consensus Protocol and Pinning 


He, Chenlong 

Feng, Zuren 

Ren, Zhigang 




In this paper we propose three controllers for flocking of multi-agent system, 

which are basic linear controller, controller with actuator saturation 

and controller with time delay under the condition that the information 

exchanging topology of agents is connected and fixed. Each controller 

is composed of two components as consensus algorithms for making 

the entire agents converge to a common state and pinning control for 

guiding the agents to the desired moving direction. The stability of controllers 

is analyzed and the effectiveness of controllers is demonstrated 

by numerical simulation. 

◮ SuA02-3 14:10–14:30 

Distributed Event-triggered Tracking Control of Multi-Agent Systems 

with Active Leader, pp.1453–1458 

Zhang, Yanqiong Acad. of Mathematics & Sys. Sci., Chinese 


Hong, Yiguang 


Event-triggered control is an effective protocol to design distributed control 

of multi-agent systems with the limited resources. In this paper, we 

consider a multi-agent tracking problem for a linear active leader based 

on event-triggered control. The state of the leader keeps changing and 

may not be measured. To estimate the state such a leader individually, 

a neighbor-based local controller together with a neighbor-based 

state-estimation rule is given for each autonomous agent, and the communication 

of the agents under consideration is event-driven. Then we 

prove that, with a constructed common Lyapunov function for switching 

topology, each agent can track the active leader with unmeasurable 

states. 

◮ SuA02-4 14:30–14:50 

On Necessary and Sufficient Conditions of the Consensusabilityfor 

Second-order Discrete Multi-agent Systems, pp.1727–1732 

Zhu, Jiandong 


In this paper, necessary and sufficient conditions of the consensusability 

are obtained for second-order discrete multi-agent systems without 

assuming that all the eigenvalues of each agent’s dynamical equation 

lie on or outside the unit circle, which is a basic assumption in [1]. The 

obtained results show that, if each agent’s dynamical equation has a 

stable eigenvalue, the condition ∏︀ j |λu j 

(A)| < (1 + r)/(1 − r) obtained 

in [1] may not be necessary anymore, where λ u j 

(A) denotes 

each unstable eigenvalue of the coefficient matrix A of every agent and 

r the eigenratio of the graph. Consensus protocols are designed for 

some numerical examples that do not satisfy the above condition in [1]. 

Moreover, using the proposed method in this paper, all the possible 

control gains can be designed constructively. 

◮ SuA02-5 14:50–15:10 

Distributed Filtering Basing Consensus for the Local Strongly Coupled 


Cai, Yunze 

Wang, Hua O. 

Xu, Xiaoming 


Boston Univ. 


The interactions between subsystems are important for large-scale systems. 

We introduce a local strongly coupled system which coupled by 

random communication between subsystems. Due to the intermittent 

communication, it is difficult to apply the standard Kalman or robust filter 

to design procedures to such systems. In this paper, we addressed the 

distributed robust filter design method for this kind of system based on 

consensus idea. The main result is a sufficient condition which guarantees 

a suboptimal level of disagreement of estimates in a coupled 

network of estimators. The condition is formulated in terms of feasibility 

of biaffine matrix inequalities (BMIs). The generic algorithm is 

used to treat the bilinear relation between filter parameters and the interconnection 

gains. The proposed approach is applied to the problem 

of formation-based robust synchronization. The numerical simulations 

show the effectiveness of the proposed filtering method. 

◮ SuA02-6 15:10–15:30 

Decentralized Sweep Algorithm of Multi-agent Systems with Adaptive 

Workload Assignment, pp.4320–4325 

Zhai, Chao 

Hong, Yiguang 



This paper discusses decentralized sweep coverage of a given region 

with parametric uncertainties by multiple agents. With parametric uncertainty 

in the environment, we present an adaptive sweep coverage 

algorithm to guarantee the region coverage by sweeping and workload 

partition. The adaptive technique with persistence condition is employed 

to sweep an unbounded region, and the upper bound for the 

extra time spent due to the environmental uncertainty is estimated for 

the covered region. 

SuA03 13:30–15:30 Room 203C 

Image Processing 

Chair: Yang, Wankou 

Co-Chair: Yu, Xiao 



◮ SuA03-1 13:30–13:50 

Compression of MR-Thermometry Images Using Reduced-Order 

Karhunen-Lo‘eve Basis, pp.4655–4660 

Niu, Ran 

GE Global Research (Shanghai) 

In this paper, we develop an approach to achieve MR thermal image 

compression by exploiting the spatial correlations of image voxels during 

thermal treatment. A set of reduced-order basis was identified using 

Karhunen-Lo‘eve (KL) decomposition of MR thermal images. Each 

image can be compressed with a relative small number of identified 

KL basis functions. The proposed approach can be used in real-time 

compression of imaging data in order to minimize a potentially massive 

amount of information that must be exchanged between the MR 

scanner and the treatment controller, and reduce computer storage requirements 

during thermal treatment. Simulation and MR thermometry 

experimental results demonstrate that the proposed image compres- 

206



sion method is also effective in suppressing high spatial-frequency MR 

measurement noises. 

◮ SuA03-2 13:50–14:10 

Local Linear Regression Classifier for Image Recognition, pp.4732– 

4736 

Yang, Wankou 

Sun, Changyin 

Ricanek, Karl 

XIA, Jianwei 



UNC Wilmingtong 

Liaocheng Univ. 

In the past several decades, much work has been done to design classifiers. 

Inspired by the locality idea of manifold learning, a local linear 

regression classifier (LLR classifier) is given in this paper. The proposed 

classifier consists of three steps. The first step is to search k 

nearest neighbors of a test sample from each special class, respectively. 

The second step is to reconstruct the test sample based on the 

k nearest neighbors from each special class, respectively. The third 

step is to classify the test sample according to the minimum reconstruct 

error. The proposed local linear regression classifier is evaluated 

on the CENPAMI handwritten number database, the ORL face image 

database and the ORL face image database. The experimental results 

demonstrate that an LLR classifier is effective in classification, leading 

to promising image recognition performance. 

◮ SuA03-3 14:10–14:30 

Key Frames-Based Video Super-Resolution Using Adaptive Overlapped 

Block Motion Compensation, pp.4712–4716 

Ge, Jing 

Zhang, Boyang 

Liu, Ju 




A video super resolution algorithm is presented, which is based key 

frame and adaptive overlapped block motion compensation(AOBMC). 

First, the key frames are high resolution frames and are seen as references; 

non-key frames are low resolution frames and are up-sampled 

the same size as the key frames. Then, non-key frames are super 

resolved by adaptive overlapped block motion compensation using adjacent 

high resolution key frames. The experimental results indicate the 

improved performance of proposed super-resolution algorithm on both 

the subjective visual quality and PSNR. 

◮ SuA03-4 14:30–14:50 

Fast Monotonic Blind Deconvolution Algorithm for Constrained TV 

Based Image Restoration, pp.4682–4687 

Liu, Haiying 

Lu, W.-S. 

Fu, Yanan 

Cheng, Yu 

Yan, Tingfang 

Li, Teng 



Univ. of victoria 






A new fast monotonic blind deconvolution algorithmic method is investigated 

based on the constrained variational minimization framework under 

the periodic boundary conditions. The contributions of our methodology 

are that the blur operator identification and image restoration can 

be simultaneously optimized even under high noise level as compared 

to previous methods. Specifically, the monotone fast iterative shrinkage/thresholding 

algorithm (MFISTA) combined with the fast gradient 

projection (FGP) algorithm, is extended to deal with our new proposed 

algorithm and guarantee the monotonic convergence rate. In addition, 

the deblurring subproblem is enhanced by incorporating a bisection 

technique to effectively identify a near optimal value for the regularization 

parameter of the TV-Frobenius objective function quickly and 

accurately. Initial experimental results for gray satellite and color wireless 

capsule endoscopy (WCE) images demonstrate the considerable 

performance of the proposed algorithm. 

◮ SuA03-5 14:50–15:10 

Application of Artificial Immune Algorithm in Image Segmentation 

Based on Immune Field, pp.4691–4695 

Yu, Xiao 

Fu, Dongmei 

Yang, Tao 




Image segmentation is one of the classic problems in image processing 

and computer vision field. Existed algorithms do not always reach a satisfactory 

purpose in fuzzy image segmentation. This paper is inspired 

by new development of medical immunology and proposes an artificial 

immune algorithm based on immune field. First, the article gives the 

concept of innate immune field, adaptive field and the immune field by 

learning from the operating mechanism between innate immune system 

and adaptive immune system. Second, the paper builds an artificial 

immune network of combination between innate immune and adaptive 

immune to divide the antigen feature space. This novel artificial immune 

algorithm is used for segmenting of object, background and thermal d- 

iffusion region in sheltered infrared image. Experimental results show 

that the method we proposed can solve the problem of incomplete target 

and edge distortion, and have a comparatively satisfying result with 

comparison to some segmentation methods, such as immune template, 

Prewitt operator, Sobel operator and negative selection. 

◮ SuA03-6 15:10–15:30 

A Mixed Edge Based Text Detection Method by Applying Image Complexity 


Li, Minhua 

Bai, Meng 



To detect text from an image with a different background, an adaptive 

text detection method based on image complexity analysis is proposed. 

Before text detection, this approach adopts an image complexity analysis 

step to classify image complexity into three categories: low complexity, 

middle complexity and high complexity. Then images with different 

complexity adopt different methods to extract image edges. The proposed 

text detection method takes a coarse to fine detection strategy 

which combines the edge-based method, connected component based 

method and the texture based method into a framework. Experimental 

results demonstrate the performance of the proposed method. 

SuA04 13:30–15:30 Room 203D 

Intelligent Managenment and Decision Making 

Chair: Wang, Ya-hui Beijing Univ. of Civil Engineering & Architecture 

Co-Chair: Dong, Xisong Inst. of Automation, Chinese Acad. of Sci., 

◮ SuA04-1 13:30–13:50 

Aided Decision-Making System of Public Transport Management for 

Guangzhou Asian Games, pp.3993–3998 

Dong, Xisong 

XIONG, Gang 

Dong, Fan 

Zhu, Fenghua 

LIU, Sheng 



CAISA 



For spectators and public transport demand during the 16th Asian 

Games and the first Asian Para Games held in Guangzhou in 2010, Aided 

Decision-Making System of Public Transportation Management for 

Guangzhou Asian Games had been developed to support public transport 

management decision. It can help public transport managers to enhance 

the level of public transport management from experience-based 

formulation and manual implementation to scientific computation-based 

formulation and automatic implementation by intelligent systems, to 

guarantee the traffic demand effectively during the games, and to improve 

the management of public transportation significantly. 

◮ SuA04-2 13:50–14:10 

Service Oriented Resource Configuration Estimation and Optimization 

in Cloud Computing–an Artificial Enterprise Method, pp.4004–4009 

LIU, Sheng 

Zhu, Fenghua 

Zhao, Hongxia 

YAO, Jian-shi 





At present service oriented resource configuration analysis and optimization 

research only takes computing resources into account. It fails 

207


to meet the requirement that enterprise run optimally in cloud manufacturing 

and SOE. This paper analyzes the relation between enterprise 

services and computing/manufacturing resources. Then the artificial 

enterprise model is built. Then various resource configuration 

schemes are loaded in it and the computational experiments are done 

to run these schemes. By comparing the experiment results the optimal 

scheme is obtained for the enterprise. Thus the demand of the 

enterprise is satisfied. 

◮ SuA04-3 14:10–14:30 

Convergence and Consensus Analysis of Multi-Choice Deffuant- 

Weisbuch Models, pp.4010–4015 

Zhang, Jiangbo 

Hong, Yiguang 

Acad. of Mathematic & Sys. Sci., C.A.S 


In this paper, we consider the convergence of generalized Deffuant- 

Weisbuch (DW) model. The generalized models result from the extension 

based on two multi-choice policies. The convergence for the 

multi-choice protocols are investigated with the help of stochastic analysis. 

The analysis is given to make all the opinion agents converge to 

their own opinion points for the first model. Moreover, the consensus 

can be achieved almost surely under mild assumptions for the second 

model. 

◮ SuA04-4 14:30–14:50 

An Efficient Surrogate Optimization Method for Solving Linear Mixed- 

Integer Problems with Cross-Coupling Constraints, pp.4055–4060 

Bragin, Mikhail 

Luh, Peter B. 

Yan, Joseph 

Univ. of Connecticut 


Southern California Edison 

Many optimization problems that frequently arise and have been extensively 

used in practice are modeled as linear mixed–integer programming 

problems. Among the many of such problems are transportation 

and assignment problems. In such problems, constraints that couple 

decision variables can be viewed as hyperplanes in their respective s- 

paces. These hyperplanes frequently intersect at different angles thus 

making the feasible set of the problems complex and leading to difficulties 

defining the convex hull when using the cutting planes method. 

The efficiency of branch-and-cut is therefore low, since the branching 

tree grows quickly due to the combinatorial nature of these problems. 

This paper overcomes these difficulties by using additional cuts that 

can better define the convex hull. Thus when the Lagrangian relaxation 

and surrogate optimization method is used, the computational burden 

of obtaining the multiplier updating directions is significantly reduced. 

In the surrogate optimization framework, the constraints of the original 

problem are relaxed by introducing the Lagrange multipliers. After a 

good dual solution is found, it is then improved to obtain a good feasible 

solution, while the dual value provides a lower bound on the feasible 

cost. Numerical examples indicate that the surrogate optimization can 

obtain feasible solutions when the standard branch–and–cut method 

cannot. Additional cuts help improve the feasible solutions and tighten 

the lower bound. 

◮ SuA04-5 14:50–15:10 

The Study on the Characteristics of Emergency Classification and 

Grading about the Electromechanical Equipment Integration Project, 

pp.4093–4097 

Wang, Ya-hui 

Hao, Xue-Jun 

Wang, Chaogang 

Wang, Nan 




Prudent machinery Co., LTD 

As an important part of a modern construction project, the electromechanical 

equipment integrating project needs to face internal and external 

environment changing and different continually happening emergency. 

It is more and more important to carry on emergency management 

to achieve the given objective of the project. This paper puts the 

project as the main body, based on the description and analysis of the 

emergency of the electromechanical equipment integrating project, proposes 

the electromechanical equipment project emergency management 

program, and establishes its completed system, which provides 

an important reference for the design of the emergency management 

system, and offers a new idea of project emergency management research. 

◮ SuA04-6 15:10–15:30 

Service Composition Execution Optimization based on State Transition 

Matrix For Cloud Computing, pp.4126–4131 

LIU, Sheng 

XIONG, Gang 

Zhao, Hongxia 

Dong, Xisong 

YAO, Jian-shi 






It is difficult to select a composite service with the lowest actual executing 

cost using the existing methods for QoS-aware service composition 

in cloud computing. By analyzing the dynamic execution process of 

composite service with state transition matrix, this paper proposes a 

new QoS aware optimal service composition method. In view of the 

effect of composite services reliability on the composite service performance, 

the method regards the cost averaged for one time of successful 

execution of a composite as its actual executing cost, and then selects 

the composite services with the aim of minimizing the composite 

service execution cost. The simulation result shows that the proposed 

method is superior to other methods in execution cost. 

SuA05 13:30–15:30 Room 203E 


Chair: Zhu, Xiaorui 

Co-Chair: Wang, Fan 


Shenzhen Inst.s of Advanced Tech., Chinese 


◮ SuA05-1 13:30–13:50 

Attractor Design and Prediction-based Adaption for a Robot Waltz 

Dancer in Physical Human-Robot Interaction, pp.3810–3815 

Wang, Hongbo 


Tohoku Univ. 

Tohoku Univ. 

Physical human-robot interaction between a human leader and a robot 

follower in waltz is studied in this paper. The dancers’ body dynamics 

in single-support phase are modeled as inverted pendulums. On the 

robot side, an ankle torque control method is proposed and applied. 

The control law forms a time-dependent vector field, which makes the 

nominal orbit of the robot to be an attractor. To physically interact with 

human, the human leader’s state is estimated from range image data 

by using an extended Kalman filter. Parameters of the robot’s orbit are 

then adjusted according to the leader’s estimated and predicted state. 

The proposed method is verified by simulation results. 

◮ SuA05-2 13:50–14:10 

Walking Control for Compass-like Biped Robot with Underactuated Ankle, 

pp.3852–3857 

Tang, Chong 

Yan, Gangfeng 

Lin, Zhiyun 




The paper aims at solving the walking control problem of a compasslike 

biped robot with underactuated ankle on the level ground or even 

uphill environment. The compasslike biped robot is equipped with a 

constraint mechanism to lock the hip angle when the swing leg retracts 

to a desired angle. For this system, an angular momentum based control 

is presented in order to make the biped robot walk on little downhill 

slope or even uphill. Existence conditions of limit cycle under angular 

momentum based control are presented. They can be used to determine 

whether there exists a gait on the slope or not. Furthermore, 

we apply the method of Poincare return map to analyze the stability 

property of the gait with angular momentum based control. Finally, an 

event-based control is adopted to make the walking gait of compasslike 

biped robot asymptotically stable. 

◮ SuA05-3 14:10–14:30 

A Real-Time Human Imitation System, pp.3692–3697 

208



Wang, Fan 

Tang, Cheng 

Ou, Yongsheng 

Xu, Yangsheng 





Chinese Acad. of Sci., Shenzhen Inst.s of 

Advanced Tech. 


Robot imitation is a useful tool in humanoid robot research, it provides 

a natural way of teaching a complex humanoid robot to accomplish 

human-like behaviors. However, it is never an easy task to build such 

a system robustly due to the high degree of freedom (DOF) in human 

motion and difficulty in humanoid control. In this paper, we present a 

system built up by the Microsoft Kinect box and the Aldebaran NAO Humanoid 

robot, which can mimic full body human motion in real time. By 

applying inverse kinematics through an optimization process, the human 

motion was split up into critical frames and represented by a list of 

robot joint angles. Then a second order continuous trajectory is formed 

to drive each joint toward the target angle, followed by balance control 

and selfcollision avoidance system. The tests show that the system is 

robust and flexible enough to imitate various human motions. 

◮ SuA05-4 14:30–14:50 

Automatic Violin Player, pp.3892–3897 

Huang, Hsing-Hisn 


The purpose of this research is to build an automatic violin player. The 

method is to study the violin playing skill, and to develop the automatic 

mechanism. This research is an extension work of the previous violin 

robot. The new robot consists of two mechanical systems. The first 

system is the bowing mechanism, which is able to control the bowing 

velocity and position. The second system is the violin control mechanism. 

This system can rotate the violin to a specific angle to coordinate 

with the string-changing movement of the bow. It also possesses a 

fingering device, which is able to press on several fixed positions in correspondence 

with the notes of the music. The automatic violin player 

utilizes a PC-based controller, and is able to play many famous songs. 

◮ SuA05-5 14:50–15:10 

Mechanical Design of a Slider-Crank Centered Robotic Dolphin, 

pp.3741–3746 

Wei, Changming 

Yu, Junzhi 



This paper addresses the mechatronic issues of a novel bio-inspired 

robotic dolphin capable of fast swimming. The robot mechanically consists 

of a dorsoventrally propulsion mechanism, a turning mechanism, 

and an up-and-down mechanism. Since the swimming robot is predominantly 

driven by a series of DC motors and servo motors, the possible 

alternative to improve the swimming speed is to maximize the motor 

efficiency. Based on this idea, a new slider-crank centered flapping 

mechanism with the continuous rotation of the DC motor is proposed to 

mimic the oscillations of the posterior body and the fluke. Finally, the 

preliminary robotic prototype is built to validate the effectiveness of the 

formed robotic design ideas. 

◮ SuA05-6 15:10–15:30 

Vision-based Unscented FastSLAM for Mobile Robot, pp.3758–3763 

Qiu, Chunxin 

Zhu, Xiaorui 

Zhao, Xiaobing 




This paper presents a vision-based Unscented FastSLAM (UFast- 

SLAM) algorithm combing the Rao-Blackwellized particle filter and Unscented 

Kalman filte(UKF). The landmarks are detected by a binocular 

vision to integrate localization and mapping. Since such binocular vision 

system generally inherits larger measurement errors, it is suitable 

to adopt Unscented FastSLAM to improve the performance of localization 

and mapping. Unscented FastSLAM takes advantage of UKF 

instead of the linear approximations of the nonlinear function where the 

effective number of particles is used as the criteria to reduce the particle 

degeneration. Simulations and experiments are carried out to demonstrate 

that the Unscented FastSLAM algorithm can achieve much better 

performance in the vision-based system than FastSLAM2.0 algorithm 

on the accuracy and robustness. 

SuA06 13:30–15:50 Room 302 

Pattern Recognition 

Chair: Zheng, Suiwu 

Co-Chair: Li, Kun 



◮ SuA06-1 13:30–13:50 

A Comparative Study of Endoscopic Polyp Detection by Textural Features, 

pp.4671–4675 

LI, Baopu 


Digestive tract cancer is a big threat to human and capsule endoscopy 

(CE) is a relatively new technology to detect the diseases in the small 

bowel. Since polyp is an important symptom of digestive cancer it is important 

to detect them by computerized methods. In this work, we comparatively 

investigate computer aided detection for polyps by machine 

learning based methods that are built upon color textural features. Four 

textural features, wavelet based features, color wavelet covariance, rotation 

invariant uniform local binary pattern and complete local binary 

pattern, are utilized to characterize the textural features in CE images, 

and performance of them are extensively studied in three different color 

spaces, that is, RGB, HSI and Lab color spaces. 

◮ SuA06-2 13:50–14:10 

Simplified Minimum Enclosing Ball based Fast Incremental Support 

Vector Machine (SVM) Algorithm for Person Detection and Tracking, 

pp.4936–4941 

Zheng, Suiwu 

Qiao, Hong 

Jia, Lihao 





Nagoya Univ. 

In order to meet the requirements of stable person detection and tracking 

techniques in dynamic visual system, we propose a simplified minimum 

enclosing ball based fast incremental support vector machine 

(SVM) algorithm for person detection and tracking. Based on the simplified 

minimum enclosing ball (MEB) method, we propose a simplified 

and fast incremental algorithm to compute the MEB. By utilizing the e- 

quivalence between MEB and the dual problem in SVM, we achieve the 

online and incremental adjustment of the SVM classifier coefficients. 

The proposed method do not need to solve the quadratic programming 

problem. It is fast for training. Moreover, it can achieve the online update 

of classifiers for object tracking with small sample size. Finally, the 

efficiency of the proposed incremental SVM is validated by detection 

experiments on dynamic pedestrians tracking system. 

◮ SuA06-3 14:10–14:30 

Fast Eye Localization Based on a New Haar-like Feature, pp.4825– 

4830 

Chen, Yefei 

Su, Jianbo 



This paper focuses on fast eye localization method. According to the 

priori proportional relationships of face features, we firstly set an appropriate 

candidate window from the face region detected. Secondly, 

histogram equalization is applied on the candidate region to eliminate 

illumination effects. This paper presents a new haar-like feature generating 

the confidence of the feature throughout the candidate region in 

order to locate eyeball accurately and rapidly. Our method is proved to 

be simple and robust against the disturbance caused by glasses, eyebrow 

and hair. The process of training and learning is not necessary 

because of the appropriate priori knowledge. Our experiment on three 

face databases shows that our method can be applied to real time eye 

position localization and even to pupil localization under most circumstances, 

achieving accurate results. 

◮ SuA06-4 14:30–14:50 

Detection of Pedestrian Crossing from Focus to Spread, pp.4897–4901 

Wang, Caifeng 

Liao, Fucheng 



209


Ma, Chao 

Capital Univ. of Economics & Business 

In order to single out pedestrian crossing from real-life scenarios, this 

paper does the priori and likelihood modeling in Bayesian framework 

based on defined block-based Markov random field. By obtaining its 

maximum a posteriori estimation of its central location and direction it 

focuses on the crossing based on coarse to fine technique and covariance 

matrix descriptor. Finally, it spreads around the central point and 

gets its scope by randomly generating some rectangles. Experimental 

results illustrates its role in real application. 

◮ SuA06-5 14:50–15:10 

Robot Aided Object Segmentation without Prior Knowledge, pp.4797– 

4802 

Li, Kun 


Chen, Xijun 




In robot perception system, distinguishing objects from complex environment 

is a difficult problem if without prior information. In this article, 

we study three cases that a robot may encounter in real-world application, 

no movable object, one object, or multiple objects, and then 

provide an object segmentation strategy through object manipulation 

for each condition. The result shows that this method can provide sufficient 

prior information for accurate objects segmentation from robot’s 

observation. Through this unsupervised algorithm, a robot can learn 

objects around reliably. 

◮ SuA06-6 15:10–15:30 

Adaptive Switching Anisotropic Diffusion Model for Universal Noise Removal, 

pp.4803–4808 

Wang, Wei 

Lu, Peizhong 

Fudan Univ. 

Fudan Univ. 

In this paper, a novel method is presented for universal noise removal 

from corrupted digital images based on Adaptive Switching Anisotropic 

Diffusion (ASAD) model. The originality of ASAD is utilizing Local 

Difference Factor (LDF) to identify impulse noise or Gaussian noise. 

Initially, LDF is computed from intensity values of pixels in a neighborhood 

using weighted statistics. Subsequently, directional weighted median 

(DWM) and anisotropic diffusion (AD) are adopted to filter noise 

respectively. In addition, we use LDF to control the diffusion process 

adaptively incorporating with local gradient. As LDF indicates the local 

statistical property of image pixels, image edges and details can be 

finely preserved while filtering out noise. Simulation results show that 

the restored images by our method have high peak signal-to-noise ratio 

and great image quality by efficiently removing salt-and-pepper noise, 

uniform impulse noise, Gaussian noise and mixed noise. 

◮ SuA06-7 15:30–15:50 

The Recognition of EEG With CSSD and SVM, pp.4741–4746 

Li, Mingai 

Lu, Chanchan 



With time-varying volatility and individual differences,EEG signals are 

difficult to analyse. The recognition performance of the traditional feature 

extraction is lowered due of the difficulty in tracking the dynamic 

changes of EEG. In this paper the Common Spatial Subspace Decomposition 

(CSSD) algorithm was improved(named Improved-CSSD), 

putting forward a kind feature extraction method which has the performance 

of adaptive ability.This method introducded control parameters,which 

added the training samples of the assistants to that of the target 

subject in some way .Finally, based on the data of the international BCI 

competition database, some simulation experiments were conducted 

by recognizing EEG signals by Improved-CSSD and SVM. Compared 

with the traditional CSSD, classification accuracy was increased about 

8.26% by Improved-CSSD. The result showed that the approach, proposed 

in this paper, had a good adaptability and a low time loss. 

SuA07 13:30–15:50 Room 303 

Advanced Control Algorithms and Applications (III) 

Chair: Zou, Yuanyuan 

Co-Chair: LIU, Jinkun 


Beihang Univ. 

◮ SuA07-1 13:30–13:50 

On-line Squaring of Non-square Hard Constraints of Input Variable by 

Coordinate Alternating in Model Predictive Control, pp.2529–2536 


Wang, Shubin 



Commonly there exit many square amplitude constraints in process 

control. These constraints are high and low limits of input variable, input 

variable variation and output variable. For the requirement of process or 

control, there may be a few non-square hard constraints of input variable 

in addition. These non-square constraints are composed of the 

amplitude limits of linear function of input variables, and they cannot 

be solved by MPC directly. To ensure the implementation of MPC, a 

method to transform non-square constraint into square constraint online 

by coordinate alternating is proposed. By using the previous step 

input value and the high limit of input variable variation, some appropriate 

treatments of these non-square constraints are made. Simulation 

results of two system control problems show the effectiveness of the 

proposed method. 

◮ SuA07-2 13:50–14:10 

Sliding Mode Control with Extended State Observer for the Boiler 

Steam Pressure of Fuel-steam Pressure System, pp.2570–2575 

CUI, Zhiqiang 

LIU, Jizhen 

LIU, Jinkun 

China Power Investment Corporation 


Beihang Univ. 

The boiler steam pressure is an important parameter reflecting the s- 

tate of boiler operation. Considering the disturbance and uncertainty of 

the fuel-steam pressure system, only using pressure signal, an extended 

state observer is designed, and sliding mode controller is designed 

based on disturbance and uncertainty compensation. From Lyapunov 

stability analysis, it is shown that the closed system stability can be 

guarantee. Simulation results are presented to validate the good system 

robustness and good tracking performance of the control system. 

◮ SuA07-3 14:10–14:30 

A predictive Energy Management Strategy for Hybrid Electric Bus 

Based on Greedy Algorithm, pp.2782–2787 

Pan, Zheng 

Song, Chunyue 



Bus runs on a fix route, which makes it possible to design a predictive 

Energy Management Strategy (EMS) for hybrid electric bus to achieve 

better overall efficiency. A new predictive EMS is proposed based on 

the prediction of the bus’s velocity profile. Firstly, the bus’s velocity 

profile is predicted via historic driving data and real-time driving data, 

which provide the future power demand. Then according to the prediction 

of the velocity profile, the torque spilt is optimized by greedy 

algorithm. The obtained EMS requires very little computational time 

and is suitable for real-time implement. Simulation shows that the fuel 

economy of the presented approach is better than the electric assist 

control strategy in the Advanced Vehicle Simulator (ADVISOR). 

◮ SuA07-4 14:30–14:50 

Predictive control design subject to multiple missing measurements, 

pp.2701–2705 

Zou, Yuanyuan 

Niu, Yugang 



This paper investigates the problem of predictive control for control 

systems with multiple missing measurements. An extended stochastic 

model is introduced to describe and compensate missing data. The 

state feedback control scheme is designed to minimize an upper bound 

on the expected value of an infinite horizon quadratic performance objective 

at each sampling instant. It is shown that the present scheme 

can guarantee the stochastic stability of the closed-loop system. 

◮ SuA07-5 14:50–15:10 

An approach of constraint boundaries tuning based on shadow price for 

two-layered predictive control, pp.2685–2690 

Zou, Tao 

Xiang, Weilong 



210



Ding, Baocang 

Li, Shaoyuan 



In this paper, cellular automata with boundaries are addressed by using 

the theories of semi-tensor product and Drazin inverse of matrices. 

For a cellular automaton with boundaries, a dynamical system model is 

constructed, then a necessary and sufficient condition for the reversibility 

is given, and a concept of generalized inverse cellular automaton that 

characterizes the local energy conservation is presented. Besides, a 

representation for the (generalized) inverse cellular automaton together 

with a unified algorithm to calculate it is given. Some examples are 

given to illustrate the algorithm. 

Shadow prices show the effect, which were caused by variations of constrained 

boundaries, to optimum value of objective function under the 

current optimal strategy. In this paper, a LP-MPC form of two-layered 

predictive control was described, and a constraint tuning strategy based 

on shadow price was proposed under this structure. The nature of disturbance 

can be evaluated according to the history data of process, 

then, combine with constraint conditions of the process, tuning boundaries 

will be obtained. The shadow prices for constrained boundaries of 

steady-state target calculation were counted based on solving a linear 

programming and its dual problem. Then, the constraint boundaries, 

which influence the objective optimum effectively, were handled selectively. 

The process will be pushed to allowable operation boundaries, 

to increase the economic benefit. Finally, in a practical process, a simulation 

example was conducted in order to verify the useful of shadow 

price to constraint tuning for two-layered predictive control. 

◮ SuA07-6 15:10–15:30 

Extended robust iterative learning control design for industrial batch 

processes with uncertain perturbations, pp.2728–2733 

Liu, Tao 

Shao, Cheng 


dalian Univ. of Tech. 

For industrial batch processes subject to uncertain perturbations from 

cycle to cycle, a robust iterative learning control (ILC) scheme is proposed 

in this paper to realize robust tracking of the set-point profile 

for system operation. An important merit is that only measured output 

errors of current and previous cycles are used to design a synthetic 

ILC controller consisting of dynamic output feedback plus feedforward 

control, for the convenience of implementation. By introducing a slack 

variable matrix to construct a less comprehensive two-dimensional (2D) 

difference Lyapunov function that guarantees monotonical state energy 

decrease in both the time and batchwise directions, sufficient conditions 

are established in terms of linear matrix inequality (LMI) constraints 

for holding robust stability of the closed-loop ILC system. By solving 

these LMI constraints, the ILC controller is explicitly formulated, together 

with an adjustable robust H infinity performance level. An illustrative 

example of injection molding is given to demonstrate the effectiveness 

and merits of the proposed ILC design. 

◮ SuA07-7 15:30–15:50 

Discrete-time Stochastic Iterative Learning Control: A Brief Survey, 

pp.2624–2629 

Shen, Dong 

XIONG, Gang 



This note gives a brief survey on discrete-time stochastic iterative learning 

control (SILC) from three aspects, namely, SILC for linear system, 

nonlinear system and system with other stochastic signal. Two major 

approaches, stochastic Kalman filtering approach and stochastic approximation 

approach, for SILC are proposed. Some open questions 

are also included. 

SuA08 13:30–15:30 Room 310 

Invited Session: Applications of Semi-tensor Product to Control 

Chair: Feng, Jun-e 

Co-Chair: Lv, Hongli 



◮ SuA08-1 13:30–13:50 

Generalized Reversibility of Cellular Automata with Boundaries, 

pp.418–423 

Zhang, Kuize 

Zhang, Lijun 

College of Automation, Harbin Engineering Univ. 


◮ SuA08-2 13:50–14:10 

Solving a Class of Fuzzy Relation Inequalities via Semi-tensor Product, 

pp.1465–1470 

Fan, Hongbiao 

Feng, Jun-e 

Zhang, Lequn 




The problem of solving a class of fuzzy relation inequalities (FRIs) is 

investigated. First, it is shown that if the FRI is solvable, there is a corresponding 

parameter solution set (briefly, PSS). Then the semi-tensor 

product of matrices is used to convert the logical inequality into its algebraic 

form via the vector expression of logical variables. Under this 

form all the PSS can be obtained. It is proved that all the solutions can 

be derived from their corresponding PSS. An example is presented to 

demonstrate the effectiveness of the algorithm provided in this paper. 

◮ SuA08-3 14:10–14:30 

Model-Input-State Matrix of Switched Boolean Control Networks and Its 

Applications, pp.1477–1482 

Zhang, Lequn 

Feng, Jun-e 



The model-input-state matrix of a Switched Boolean Control Network 

(SBCN) is introduced for the first time. This matrix contains all information 

of the model-input-state mapping. A necessary and sufficient 

condition for the controllability of SBCN is obtained. The corresponding 

control and switching law which drive a point to a given reachable point 

is designed. One sufficient condition for the observability of a SBCN is 

obtained. Under the assumption of controllability, one necessary and 

sufficient condition is derived for the observability. 

◮ SuA08-4 14:30–14:50 

Algebraic method to pseudo-Boolean function and its application in 

pseudo-Boolean optimization, pp.2468–2472 

Li, Zhiqiang 

Song, Jinli 

Xiao, Huimin 




In this paper, the optimization of pseudo-Boolean functions is considered. 

Boolean variables are expressed into their vector form. Using 

semi-tensor product, the pseudo-Boolean function is expressed as its 

normal form and algebraic form. Based on the normal form, we discuss 

the optimal approximation problem of pseudo-Boolean function. 

◮ SuA08-5 14:50–15:10 

Model Construction of Fuzzy Relation Matrices and Application in Intelligent 

Environmental Comfort Systems, pp.2239–2244 

Lv, Hongli 

Feng, Jun-e 





Using semi-tensor product (STP) of matrices, with observed datasets, 

this paper proposes a new and more general framework to construct a 

matrix-based fuzzy relation structure model for multi-input multi-output 

(MIMO) fuzzy control systems. The measured sampling data of inputs 

and outputs are assumed to be obtained from experiments. Instead of 

building the fuzzy logical rule sets of a fuzzy dynamical process, the algebraic 

form is constructed directly. The whole designing process can 

be realized via matrix expression and algebraic computing. Then this 

novel fuzzy controller is applied into a thermal comfort control system 

and works well with good controlled performance. The new technique 

proposes one general design method to obtain a fuzzy relation matrix 

expression of multiple variables fuzzy control systems. It is particularly 

suitable to design fuzzy controllers of non-decomposable multi-output 

systems, which is not solvable directly by the traditional decomposed 

control design methods. 

◮ SuA08-6 15:10–15:30 

Reachability and Controllability of BCNs Avoiding States Set, pp.2329– 

2334 

211


Li, Zhiqiang 

Song, Jinli 



In this paper, using semi-tensor product and the vector form of Boolean 

logical variables, the Boolean control network (BCN) is expressed as a 

bilinear discrete time system about state and control variables. Based 

on the algebraic form, the reachability and controllability of BCNs are 

discussed. Also, the necessary and sufficient conditions for reachability 

and controllability are given. At last, the control sequence that steers 

one state to another is constructed. The reachability and controllability 

discussed here are under certain constraint, where the trajectory of 

states are avoiding some undesirable states set. The definitions discussed 

in this paper have practical meaning. 

SuA09 13:30–15:30 Room 311A 

Invited Session: control problems for stochastic systems 

Chair: Zhang, Huanshui 

Co-Chair: Wang, Guangchen 



◮ SuA09-1 13:30–13:50 

Optimal Control for Stochastic Discrete-time Systems with Multiple 

Input-delays, pp.1529–1534 

Wang, Hongxia 


Wang, Xuan 

Harbin Insititute of Tech. 



The main purpose of the paper is to settle the stochastic linear quadratic 

optimal control problem for systems with multiple input-delays which 

is very intractable and remains to be solved. We introduce a different 

version of stochastic discrete-time maximum principle(SDMP) where it 

is shown that the auxiliary variable depends on the optimal system s- 

tate through a stochastic matrix and the expectation of the relationship 

matrix happens to be the solution to the standard generalized Riccati 

equation with the same dimension as the origin system. The relationship 

explores the key difference of stochastic LQ from the deterministic 

one. It enables us to obtain the kernel of the optimal cost function 

for stochastic control and further the analytical and explicit solution to 

the stochastic linear quadratic (LQ) control problem with multiple inputdelays. 

◮ SuA09-2 13:50–14:10 

Output feedback control for high-order stochastic nonlinear time-delay 

systems, pp.1541–1546 

Liu, Liang 

Xie, Xue-Jun 



This paper considers output feedback stabilization problem for a class 

of high-order stochastic nonlinear time-delay systems. By introducing 

the adding a power integrator technique in the stochastic case and 

a rescaling transformation, and choosing an appropriate Lyapunov- 

Krasoviskii functional, an output feedback controller is constructed to 

render the closed-loop system globally asymptotically stable in probability 

and the output can be regulated to the origin almost surely. 

◮ SuA09-3 14:10–14:30 

On detectability and observability of discrete-time stochastic Markov 

jump systems with state-dependent noise, pp.1644–1649 

Zhang, Weihai 

Tan, Cheng 



This paper mainly studies the notions of detectability and observability 

for discrete-time stochastic Markov jump systems with state-dependent 

noise. Two concepts called “W-detectability” and “W-observability” 

for such systems are introduced, which are shown to coincide with 

exact detectability and exact observability reported recently in literatures. 

Moreover, some criteria and interesting properties for both W- 

detectability and W-observability are obtained. 

◮ SuA09-4 14:30–14:50 

Partial information LQ optimal control of backward stochastic differential 

equations, pp.1694–1697 

Wang, Guangchen 

Wu, Zhen 



Xiong, Jie 

Univ. of Tennessee 

This paper is concerned with a class of linear-quadratic (LQ, for short) 

optimal control problems for backward stochastic differential equations 

(BSDEs, for short) with partial information. By virtue of stochastic 

filtering and the existence of forward-backward stochastic differential e- 

quations (FBSDEs, for short), the optimal solution is explicitly obtained. 

◮ SuA09-5 14:50–15:10 

Nonsmooth Adaptive Control Design for Uncertain Stochastic Nonlinear 


Zhang, Jian 

Liu, Yungang 



This paper investigates the problem of the global stabilization via state 

feedback and adaptive technique for a class of high-order stochastic 

nonlinear systems with more uncertainties/unknowns. First of all, two 

stochastic stability concepts are slightly extended to allow the systems 

with more than one solution. To solve the problem, a lot of substantial 

technical obstacles should be overcome since the presence of severe 

uncertainties/unknowns and stochastic noise. By introducing the appropriate 

control Lyapunov function and suitable adaptive updated law 

for an unknown design parameter, and by using the method of adding 

a power integrator, an adaptive continuous (nonsmooth) state feedback 

controller without overparameterization is successfully designed, which 

guarantees that the closed-loop states are bounded and the original 

system states eventually converge to zero, both with probability one. 

SuA10 13:30–15:30 Room 311B 

Invited Session: Intelligent information processing 

Chair: Wang, Biao 



Beihang Univ. 

◮ SuA10-1 13:30–13:50 

Similarity Matching Algorithm for Ontology-Based Similarity Matching 

Algorithm for Ontology-Based, pp.758–763 

Gao, Qian 


In recent years the extreme growth of digital documents brought to light 

the need for novel approaches and more efficient techniques to improve 

the precision and the recall of IR systems. In this paper I proposed a 

novel Similarity Matching Algorithm for Ontology-Based Semantic Information 

Retrieval Model to measure whether two ontologies are matching 

or not from the name, the attribute and the theme of the concepts. 

Simulation shows that for the same recall, the proposed algorithm can 

increase the precision and flexibility compared with the traditional semantic 

similarity matching methods. 

◮ SuA10-2 13:50–14:10 

Hybrid Artificial Bee Colony and Particle Swarm Optimization Approach 

to Protein Secondary Structure Prediction, pp.5040–5044 

LI, Mengwei 

Duan, Haibin 

Shi, Dalong 

Beihang Univ. 

Beihang Univ. 

Beihang Univ. 

Proteins are crucial in the biological process, and their structure determines 

whether they can function well or not. Since the theory presented 

by Anfinsen that proteins’space structure is entirely determined by the 

primary structure came out, it is possible for us to predict the structure 

of proteins through their primary structure without any experiment. In 

order to reach this target, the prediction problem can be formulated as 

an optimization problem that is set to find the lowest free energy conformation. 

In this work, a hybrid Artificial Bee Colony(ABC) with Particle 

Swarm Optimization(PSO) Algorithm is used to solve this problem. 

Considering that the two algorithms have complementary characteristics, 

we combine them together and find out a better optimization results 

through this new approach. Experimental results have demonstrated 

the feasibility and effectiveness of our proposed approach 

◮ SuA10-3 14:10–14:30 

Static H∞Loop-Shaping Control for Unmanned Helicopter, pp.2882– 

2886 

Tang, Jie 

Wei, Chen 

Beihang Univ. 


212



Yang, Fan 

Univ. of Beihang 

This paper presents a design procedure of static H∞loop-shaping control 

for linear time-invariant system. The H∞loop-shaping weights are 

selected by maximizing the robust stability margin of closed-loop system 

while ensuring that the resulting loop-shape lives in a pre-defined 

region. Existence conditions for a static output loop-shaping controller 

are given in terms of two linear matrix inequalities. A major advantage 

of the proposed method is its low computational complexity. Meanwhile, 

the controller has the simplest structure and the smallest complexity 

of realization. The efficacy of the proposed method is shown on the 

AF25B Unmanned Helicopter control design. 

◮ SuA10-4 14:30–14:50 

A New Approach for Long-term Person Tracking, pp.4926–4930 

Fu, Deqian 

Jhang, Seong Tai 

Linyi Unitersity 

The Univ. of Suwon 

Abstract –This paper investigates long-term visual person tracking using 

particle filter as the underlying framework and online boosting as the 

detection strategy. In the case of the being tracked person with abrupt 

motion, under occlusion or in low sample rate of video source, two main 

issues rise inevitably. One is the poor constraint of person motion model, 

and the other is the drastic variation of pose or incomplete appearance 

when the person reappears. We address the problems with an 

integrated framework of multiple observers, and online boosting algorithm 

with independent features and its static and dynamic combination 

aiming to balance the tradeoff of adaption and drift. 

◮ SuA10-5 14:50–15:10 

Research on the Strategy Method for the Final Translation Phase of 

Space Autonomous Rendezvous, pp.290–294 

Li, Nan 

Liu, Zhenghua 

Li, Huifeng 


Beihang Univ. 


Beihang Univ. 

Beihang Univ. 

In this paper, a Multi-mode switching guidance control strategy is proposed 

for the final translation phase space autonomous rendezvous 

and docking precision-guided control problem. Firstly, dynamic model 

for spacecraft rendezvous is established and analyzed under the target 

coordinates. Secondly,the final translation phase is divided into two 

parts, constant speed translation phase and inconstant speed translation 

phase. In the constant speed phase, PID controller is used for 

ensuring control precision. Considering the uncertainty and nonlinear 

characteristics of the system, a fuzzy controller is designed to realize 

the precise guidance for inconstant speed translation phase. The w- 

hole control process is based on the error threshold control method to 

reduce fuel consumption. The simulation results demonstrate the effectiveness 

of the proposed guidance control strategy. Finally, the method 

of converting the continuous control law into the relay-type control law is 

discussed. The relay-type control law is easier to realize in engineering 

application. 

◮ SuA10-6 15:10–15:30 

Design and Mathematical Modeling of a 4-Standard-Propeller (4SP) 

Quadrotor, pp.3270–3275 

Phang, Swee King 


Cai, Chenxiao Inst. of Automation, Nanjing Univ. of Sci. & Tech. 

Chen, Ben M. 




The recent development of small yet sophisticated sensors has led 

to the development of smaller unmanned aerial vehicles, especially 

in the form of quadrotor. When it is limited by the availability of the 

reverse propellers, the conventional quadrotor will no longer be realizable, 

and thus introducing the 4-standard-propeller (4SP) quadrotor 

design. This paper presents a comprehensive nonlinear modeling of a 

4SP quadrotor, and the guidelines to design it. The advantages of constructing 

such aircraft and its detailed working principle are first highlighted. 

A nonlinear mathematical model is then derived based on the 

first-principles approach. The model parameters are finally identified 

and verified through actual flight tests. 

SuA11 13:30–15:50 Room 311C 

Invited Session: Quantum Control and Quantum Information 

Chair: Xi, Zairong 

Co-Chair: Cong, Shuang 



◮ SuA11-1 13:30–13:50 

An Efficient Scheme for Multi-party Quantum State Sharing via GHZ 

Channels, pp.5122–5126 

Jiang, Min 

Dong, Daoyi 

Soochow Univ. 

Inst. of Sys. Sci., CAS 

We develop an innovative and efficient scheme to share an arbitrary 

multi-qubit state between agents via only one GHZ channel under control 

of agents in a network. Compared with existing ones in this literature, 

our scheme involves less qubits, only single-qubit measurements, 

Bell-basis measurement and CNOT gate operations and thus exhibits 

a higher overall efficiency. 

◮ SuA11-2 13:50–14:10 

Transition graph-based control of quantum eigenstates, pp.1750–1755 

Kuang, Sen 

Cong, Shuang 



Based on the analysis of the invariant set in quantum Lyapunov control 

under a performance index with degrees of freedom, the population 

control of eigenstates is studied. For isolated target eigenstates, this 

paper shows the existence of the degrees of freedom that guarantee 

exact convergence and obtains rigorous construction relation of those 

degrees of freedom. For non-isolated target eigenstates, we propose a 

path-planning-based method. The method considers transition paths of 

controlled systems, so can achieve intuitive and high-probability transition 

to desired target eigenstate accompanied with appropriate design 

of those degrees of freedom. 

◮ SuA11-3 14:10–14:30 

Implicit Lyapunov Control of Multi-Control Hamiltonian Systems Based 

on State Distance, pp.5127–5132 

Meng, Fangfang 

Cong, Shuang 

Kuang, Sen 




For closed quantum systems, if controlled systems are strongly regular 

and all other eigenstates are directly coupled to a target state, then 

such control systems can be asymptotically stabilized by means of the 

Lyapunov-based control. However, when the controlled systems are 

not strongly regular, or when there exists at least one eigenstate that is 

directly uncoupled to the target state, the situations will become complicated. 

This paper proposed a method based on an implicit Lyapunov 

function to overcome these two degenerate cases. Also, the proposed 

method is suitable for multi-control Hamiltonian systems. The convergence 

of closed-loop systems is analyzed by the LaSalle invariance 

principle. Finally, numerical simulation experiments on a 4-level system 

are done. The experiment results show the effectiveness of the implicit 

Lyapunov control method for degenerate cases and multi-control 

Hamiltonians. 

◮ SuA11-4 14:30–14:50 

quantum discord dynamics of two atoms under different quantum feedbacks, 

pp.1634–1638 

Wang, Hui 

Xi, Zairong 



The author studies the dynamics of two identical atoms resonantly coupled 

to a single-mode cavity under two different quantum feedback controls, 

i.e., the symmetric feedback control and the nonsymmetric feedback 

control. Correspondingly, the quantum discord of the system is 

induced to reach steady values under these two kinds of feedback controls. 

Furthermore, in some cases, the steady value of quantum discord 

of the system under symmetric feedback control is much larger than 

the steady value under nonsymmetric feedback control. The situation 

is just the opposite when the initial state is —eg¿. It is also found that 

the feedback is not good for getting a high discord when the initial state 

213


is in the form of Werner-like state (d=0). 

Botmart, Thongchai 

Srinakharinwirot Univ. 

◮ SuA11-5 14:50–15:10 

Quantum chaotic communication, pp.1854–1859 

Zhang, Jing 

Wu, Rebing 

Li, Chunwen 




Dept. Automation, Tsinghua Univ., 


The attack induced by the eavesdropper will lower the bit rate or even 

lead to the failure of quantum communication. To solve this problem, we 

introduce a so-called quantum chaotic communication method to mask 

and thus actively protect the quantum information by chaotic signal from 

being attacked. The encoding and decoding processes are realized by 

transmitting the communicated quantum signals through the auxiliary 

chaotic devices at both sides of the sender and receiver. The method is 

applied to two examples, in which continuous-variable coherent states 

and qubit states are successfully transmitted without being influenced 

by eavesdropping. 

◮ SuA11-6 15:10–15:30 

Optimal Control of Quantum Discord in a Common Environment, 

pp.1999–2004 

Song, Hongting 

Pan, Yu 

Cui, Wei 

Xi, Zairong 

Acad. of Mathematics & Sys. Sci. 




We get the dynamical evolution of quantum correlation for the general 

Werner state immersed in a common Ohmic bath. The optimal control 

can be derived using Pontryagin maximum principle. It is found that the 

control field obtained is helpful to suppress the environmental noises, in 

the sense of slowing down the decay of discord and prolonging its existing 

time. Our numerical work demonstrate that the controlled discord 

time is almost twice as long as the one without control. 

◮ SuA11-7 15:30–15:50 

Coherent Quantum Feedback Rejection of Non-Markovian Noises, 

pp.2209–2214 

Xue, ShiBei 

Wu, Rebing 

Zhang, Jing 




This paper explores the control of non-Markovian systems via coherent 

quantum feedback. In the spirit of classical control theory that is widely 

used in engineering, we acquire completely new insights in the closedloop 

design from the frequency domain point of view, which appears 

missing in quantum control theory. Based on the example of a bosonic 

system connected to a pair of coupled bosonic baths, a non-Markovian 

Langevin equation is established to model the closed-loop quantum dynamics. 

We find that, in contrast to the existing time-domain design 

methods, the frequency domain analysis is more natural on the memory 

kernel function, which can be reshaped by feedback to suppress the 

non-Markovian decoherence. For illustration, we consider the case that 

the coupling strength in the feedback loop is constant. It is found that 

the coherent feedback shifts the two components in the original noise 

spectral function in two opposite directions, thereby makes it possible 

to suppress the noise near the system’s working frequency. When the 

system to be controlled is Markovian, this simple scheme needs to be 

replaced by more careful design due to the flatness of the noise spectrum. 

As an example, the effectiveness of our scheme is demonstrated 

in photonic crystal systems. 

SuB01 15:50–17:50 Room 203A 

Stability and Stabilization (II) 

Chair: Zhou, Yingjiang 

Co-Chair: Dong, Zhe 



◮ SuB01-1 15:50–16:10 

Delay-dependent exponential stabilization for nonlinear systems with 

interval discrete and distributed time-varying delays via intermittent 

control, pp.1077–1082 

In this paper, the problem of exponential stabilization for a class of nonlinear 

systems with interval discrete and distributed time-varying delays 

is studied. The time delay is a continuous function belonging to 

a given interval. Based on the constructing of improved Lyapunov- 

Krasovskii functionals combined with Leibniz-Newton’s formula, new 

delay-dependent sufficient conditions for the exponential stabilization 

of the systems are first established in terms of LMIs without introducing 

any free-weighting matrices and independent on the derivatives of the 

interval time-varying and distributed delays. The controller design are 

proposed intermittent feedback control. Numerical examples are given 

to illustrate the effectiveness of our theoretical results. 

◮ SuB01-2 16:10–16:30 

Shifted-Ectropy Based Self-Stability Analysis Method for General Thermodynamic 

Systems and Its Application, pp.1406–1411 

Dong, Zhe 


Self-stability, which is the ability that system state can converge to an 

equilibrium point without any control input, is one of the most crucial 

features of every dynamic system. Self-stability analysis is the basis of 

designing a regulation strategy, and is also one of the key parts of the 

recently developed physical control theory. For electrical or mechanical 

systems, generalized Hamiltonian system theory provides a strong tool 

for not only self-stability analysis but also control law design. However, 

there is still no mature strategy for analyzing the self-stability of any 

thermodynamic systems. In this paper, after introducing the con-cepts 

of irreversibility function and shifted-ectropy for general thermodynamic 

systems, a new self-stability analysis approach based on regarding 

the shifted-ectropy as a Lyapunov function is established for general 

thermodynamic systems. Moreover, this newly built method is applied 

to analyzing the self-stability of the thermo-hydraulic loop of a modular 

high temperature gas-cooled reactor (MHTGR) 

◮ SuB01-3 16:30–16:50 

The Stability of Linear Discrete Time Delay Systems Over a Finite Time 

Interval: New Results, pp.1459–1464 


Engineering 



Popov, Dejan 




Engineering 

This paper gives sufficient conditions for the practical and finite time 

stability of a particular class of linear discrete time delay systems. Analyzing 

the finite time stability concept, these new delay-independent 

conditions are derived using an approach based on the Lyapunov-like 

functions. The practical and attractive practical stability for discrete time 

delay systems has been investigated. The above mentioned approach 

was supported by the classical Lyapunov technique to guarantee the 

attractivity properties of the system behavior. 

◮ SuB01-4 16:50–17:10 

Stability Analysis for A Class of Distributed ParameterSwitched Systems 

with Time-varying, pp.2017–2021 

Bao, Leping 

Fei, Shumin 

Zhai, Junyong 




In this paper we consider a class of switched systems governed by Partial 

Differential Equations on Banach space. We provide the results on 

stability of infinite dimensional distributed parameter switched systems 

with time-varying. Sufficient conditions for stability are derived via 

semigroup theory. 

◮ SuB01-5 17:10–17:30 

Global asymptotic stability of uncertain nonlinear system with state and 

input constraint, pp.2695–2700 


Sun, Changyin 

Wang, Li 




214





In this paper, the problem of global asymptotic stability of a class of 

uncertain nonlinear system with input constraint is considered. And by 

using the development of the adding a power integrator technique, an 

auxiliary design system is used to analysis the effect of the input constraint. 

In addition, physics constraints of virtual control laws are also 

considered when pi¿1. Finally, simulations are listed to illustrate the 

effectiveness of the proposed result. 

◮ SuB01-6 17:30–17:50 

On Non-Lyapunov Stability of Linear Discrete Time Delay Systems: 

LMIs Approach, pp.1535–1540 


Engineering 



Popov, Dejan 




Engineering 

This paper gives new contributions to the area of non-Lyapunov (finite 

time stability, technical stability, practical stability, final stability) for the 

particular class of linear discrete time delay systems. The idea of attractive 

practical stability is introduced for the first time. Moreover, based 

on the matrix inequalities and Lyapunov-like functions, some new sufficient 

conditions under which the linear discrete time delay system is 

finite time stable are given. Finally, an example is employed to verify 

the efficiency of the proposed Theorems as well as to show that results 

derived upon LMIs are less restrictive than those based on a classical 

approach. To the best knowledge of authors, such results have not 

been reported yet. 

SuB02 15:50–17:50 Room 203B 

Multi-Agent Systems (II) 

Chair: Thunberg, Johan 

Co-Chair: Wang, Hanlei 



◮ SuB02-1 15:50–16:10 

Passivity Based Synchronization of Multiple Robotic Agents with Uncertain 

Kinematics and Dynamics, pp.846–851 

Wang, Hanlei 


This paper studies the synchronization problem of networked robotic 

systems with uncertain kinematics and dynamics. Passivity of the 

robotic agents is established through adaptation to both the kinematic 

and dynamic uncertainties. This input-output passivation framework 

lays the foundation for the inter-coupling inclusion between the robotic 

agents, even in the case of non-uniform constant communication 

delays. An adaptive scheme is proposed to achieve controlled synchronization 

of the networked robotic systems on strongly connected 

graphs. With a weighted Lyapunov-Krasovskii function, we show that 

task-space synchronization errors of the networked robotic agents converge 

to zero. Simulation results are provided to demonstrate the performance 

of the proposed control schemes. 

◮ SuB02-2 16:10–16:30 

Exponential synchronization of complex dynamical systems with delay 

and derivative coupling via impulse, pp.875–880 

Zhu, Yakun 

Luo, Xiaoyuan 

Li, Shaobao 

Guan, Xinping 

Yanshan Univ. 

Yanshan Univ. 

Yanshan Univ. 

Yanshan Univ. 

In this paper, the problem of impulsive synchronization of a kind of generalized 

complex dynamical systems with delay and derivative coupling 

is investigated. Both the communication delay and derivative coupling, 

which provide more information about the complex dynamical systems, 

are considered. Based on a new Lyapunov function and impulsive 

stability technique that is used to play an important role for the synchronization 

problem in our research, some new delay-dependent synchronization 

criteria are proposed. Furthermore, one can find that the 

considered complex systems are suitable to investigate and simulate 

realistic complex systems such as networked multi-agent systems. And 

numerical simulations for two networked systems with six agents are 

shown to illustrate the effectiveness of our results. 

◮ SuB02-3 16:30–16:50 

Consensus Control of Networked Nonlinear Systems, pp.914–919 

Peng, Junmin 

Ye, Xudong 

Zhou, Wei 




In this paper, we proposed a consensus control for networked nonlinear 

systems with disturbance and uncertain parameter. The proposed algorithm 

is combined with adaptive compensation of unknown disturbance 

and output feedback control to achieve consensus, without knowing the 

disturbance’s amplitude frequency and phase, as long as the number 

of different frequencies is known. From the theorem presented in 

the paper, we can see the topology condition for achieving consensus 

is the same as networked linear systems. A second-order integrated 

example is used to illustrate the algorithm. The design procedure is 

divided into two step: in step I, we design a consensus control law for 

networked virtual agents by Lemma 1; in step II, we use output feedback 

control with complete compensation of unknown disturbance for 

adaptive tracking the virtual agents. 

◮ SuB02-4 16:50–17:10 


with Time-Varying Topologies, pp.946–951 

Song, Wenjun Acad. of Mathematics & Sys. Sci., Chinese Acad. 



Hong, Yiguang 

Hu, Xiaoming 




This paper addresses the attitude synchronization problem of multiple 

rigid body agents in SO(3) with directed and switching interconnection 

topologies. Using the axis-angle representation of the orientation, a 

distributed controller based on differences between the orientations of 

agents in a global frame is proposed. In the case of the balanced interconnection 

graph, the attitude synchronization is achieved with the 

wellknown mild assumption (that is, uniform joint strong connection). 

To demonstrate the effectiveness of the proposed control scheme, an 

illustrative example is provided. 

◮ SuB02-5 17:10–17:30 

Convergence Analysis for Quantized Consensus of Multi-agent Systems 


Li, Dequan Anhui Univ. of Sci. & Tech.; Shanghai Jiaotong 

Univ. 

Liu, Qipeng 




The available works about quantized average consensus require the 

update matrices being doubly stochastic, which amounts to confining 

agents to evolve on balanced digraphs with quantized information communication. 

By dropping the typical double stochasticity requirement 

for the update matrix, we revisit an existing average consensus protocol 

based on logarithmic quantization scheme and study the quantized 

consensus problem for general directed networks. An upper bound for 

the quantization precision parameter is derived to design the suitable 

logarithmic quantizer, then based on matrix transformation and Lyapunov 

technique, we provide testable condition under which the weighted 

average consensus can be achieved with the proposed quantized 

protocol. The proposed quantized protocol features little communication 

protocol overhead and suits well for directed digital networks. 

◮ SuB02-6 17:30–17:50 




Song, Wenjun 




215


Hu, Xiaoming 


In the present paper we consider the problem of attitude synchronization 

for a system of rigid body agents. We provide distributed kinematic 

control laws for two different synchronization problems. In the two problems 

the objective is the same, i.e., to synchronize the orientations of 

the agents, but what is assumed to be measurable by the agents differs. 

In problem 1 the agents measure their own orientations in a global reference 

frame, and obtain the orientations of their neighbors by means 

of communication. In problem 2 the agents only measure the relative 

orientations to their neighbors. By using the axis-angle representation 

of the orientation, we show that simple linear control laws solve both 

synchronization problems. Moreover we show that our proposed control 

laws work for directed and connected topologies on almost all SO(3) 

for problem 1 and on convex balls in SO(3) for problem 2. 

SuB03 15:50–17:50 Room 203C 

Signal Processing 

Chair: Jen, Fu-Hua 

Co-Chair: Zhou, Zhenwei 



◮ SuB03-1 15:50–16:10 

A method for parameters estimation of multiple sinusoids signal based 

on ANFs and SGA, pp.4277–4282 

Li, Ming 

Tu, Yaqing 

Su, Dan 




An iterative algorithm based on Adaptive notch filters (ANFs) and S- 

liding Goertzel algorithm (SGA) for the parameters , i.e. amplitudes, 

phases and frequencies, estimation of multiple sinusoids signal buried 

in noise especially in colored noise is proposed in this paper. Firstly, 

it uses ANFs to accurately estimate frequencies of sinusoids signal at 

every sample point. Secondly, the SGA computes Fourier coefficients 

for each sinusoid at the estimated frequencies. Thirdly, the parameters 

of multiple sinusoids are obtained. This approach is really different 

from other discrete spectrum correction methods that use DFT off-line 

to get the parameters estimation values for multiple sinusoids and the 

proposed visual method is on-line and provides a effectively, accurately 

and significant computational advantage. Extensive simulation tests 

have also been performed to verify the effectiveness of the ANFs and 

SGA based algorithm. 

◮ SuB03-2 16:10–16:30 

Application of an Adaptive Sequential Kalman Filter to SINS/GPS Navigation 

Data Fusion, pp.4309–4314 

Bai, Meng 

Li, Minhua 



For SINS/GPS integrated navigation system with unknown measurement 

noise covariance matrix, adopting the conventional Kalman filtering 

approach to estimate the navigation system errors will lead to a 

large state estimation error or even make the filter diverge. To solve this 

problem, an adaptive sequential Kalman filter is presented, in which 

the measurement noise covariance matrix is estimated on-line by an 

innovation-based adaptive estimation (IAE) method. Properly designed 

discontinuous feedback control law and serial measurement processing 

make the adaptive filter more suitable for real time implementation. 

Simulation results reveal that without an exact measurement noise covariance 

matrix, the adaptive sequential Kalman filtering approach can 

still estimate the errors of SINS/GPS integrated navigation system effectively. 

◮ SuB03-3 16:30–16:50 

Distributed Estimation for Time-Varying Target in Noisy Environment, 

pp.4341–4346 

Zhou, Zhenwei 

Fang, Hai-Tao 

Hong, Yiguang 




This paper studies the continuous-time distributed estimation problem 

for time-varying target under switching topologies and stochastic noises. 

There are three main features in this problem: only a portion of 

sensors have a access to the target; three kinds of stochastic noises 

arising in dynamic process, measurement and communication are considered; 

and the topological structure between sensors and target is 

switching. For this problem we propose a continuous-time distributed 

estimation algorithm. Under observability and connectivity, one upper 

and lower bound for the total mean square estimation error is established 

by using common Lyapunov method and Kalman-Bucy filtering 

theory, respectively. The numerical simulation also verifies the effictiveness 

of the proposed algorithm. 

◮ SuB03-4 16:50–17:10 

A Frequency Estimation Algorithm based on Spectrum Correlation of 

Multi-section Sinusoids with the Known Frequency-Ratio, pp.4385– 

4389 

XIAO, WEI 

Tu, Yaqing 

Su, Dan 

Shen, Yanlin 

Zhang, Lei 





Zhuozhou Comprehensive Storehouse 

Based on spectrum correlation of Multi-section Sinusoids with the 

Known Frequency-Ratio (hereinafter referred as MSKFR), a frequency 

estimation algorithm was proposed. This algorithm aims at improving 

frequency estimation of the short sinusoid at low Signal-to-Noise 

Ratio(SNR), and extending the applicable range of the multi-section 

signals fusion method. Firstly, an easy way to get MSKFR in application 

is introduced. Secondly, the frequency-ratio amend matrix is 

created to make spectrums of MSKFR almost as the same as spectrums 

of Multi-section Co- Sinusoids (hereinafter referred as MCS). 

Thirdly, through weighted-accumulating spectrums of MSKFR by the 

weighted factor, Optimization Weighted-Accumulation(OW-A) spectrum 

is gained. Fourthly, the correlation spectrum is constructed by correlation 

OW-A spectrum and the accumulation spectrum of MSKFR. Lastly, 

precise frequency estimation is obtained through spectral peak searching 

of the correlation spectrum. Simulation results demonstrate the superior 

performance of the proposed algorithm. 

◮ SuB03-5 17:10–17:30 

Covariance Intersection Fusion Wiener Signal Estimator for Timedelayed 

System, pp.4418–4422 

Gao, Yuan 

Deng, Zili 



It is often hard to settle the estimation problems for the signal systems 

with time delays. By modern time series analysis method, the systems 

with time delays can be transformed into those without time delays. By 

the measurement predictor and the white noise estimators, the local 

and the optimal information fusion Wiener signal estimators are presented. 

Applying the CI (Covariance Intersection) method, the CI fused 

Wiener signal estimators are derived, which avoids the calculation of 

the cross covariance matrx between local sensors. Their estimation 

accuracy is higher than those of the local Wiener estimators. A Monte- 

Carlo simulation result shows that the actual accuracy of the presented 

CI fusion Wiener smoother approximates to that of the corresponding 

optimal information fusion smoother, and based on the covariance ellipse, 

the geometric interpretation of the accuracy relation is shown. 

◮ SuB03-6 17:30–17:50 

Building an Autonomous Line Tracing Car with PID Algorithm, pp.4478– 

4483 

Jen, Fu-Hua 


This study describes about an autonomous line tracing car using PID 

algorithm. The line tracing car will run in a fixed route field. It can test 

the field at the first running and then take another run with speed as fast 

as possible. The PID algorithm is designed for this purpose. The algorithm 

corrects the position of the line tracing car on the track through 

feedback signal from infrared (IR) sensors. This can make a small car 

reach the speed at 157 cm per second. The integrated PID module 

allows tuning three PID gains to get better performance during the test 

run. The measurement & calculation modules store every passed sec- 

216



tion’s profile of route field. It includes the distance between marks and 

radius of curvature. After analysis, the main microprocessor will provide 

action details for every section. The line tracing car will accelerate on 

the straight line and will reduce velocity while changing the direction. 

SuB04 15:50–17:50 Room 203D 

Complex Systems 

Chair: Han, Jing 

Co-Chair: Xiang, Ji 



◮ SuB04-1 15:50–16:10 

Synchronized Output Regulation of Heterogeneous Networked Systems 

via Error Feedback, pp.3503–3508 

Xiang, Ji 

Li, Yanjun 

Wei, Wei 




A dynamic error-feedback controller is presented for the synchronized 

output regulation of heterogenous linear networked systems, where only 

the leader nodes have the information of the regulated errors while 

the remaining follower nodes have the information of the relative output 

errors with respect to their in-neighboring nodes. By using the 

small-gain theorem, the sufficient criterion is developed for more general 

networks to judge whether the synchronized output regulation can be 

achieved under the designed controller. This criterion can be regarded 

as to to impose an additional H ∞ constraint on the output regulation 

problem. The synthesis problem is addressed in terms of linear matrix 

inequality. The efficacy of the analytic results is illustrated by simulation 

examples. 

◮ SuB04-2 16:10–16:30 

Dynamic Configuration of Service based Processes in Cloud Computing 

using Linear Programming, pp.3509–3514 

Du, Yanhua 


To guarantee the successful execution of service based process, one 

important requirement is QoS-driven dynamic configuration of candidate 

services. The existing methods don’t simultaneously consider 

the temporal constraints from users, uncertain QoS information of candidate 

services and dynamic re-planning on run-time. In this paper, 

we propose a novel approach to dynamically configure the services for 

service based process under both uncertain QoS information and temporal 

constraints. Our approach can efficiently select the appropriate 

services for multiple service based process in practical business environment. 

Furthermore, if some temporal constraint is violated in execution, 

we provide solutions to re-plan them so that the violations can be 

addressed on run-time. The usability of our approach is validated on a 

practical example. 

◮ SuB04-3 16:30–16:50 

“Knowing More Is Less” in Combinatorial Games , pp.3526–3532 

Han, Jing 

Han, Huawei 

Wang, Xin 


Inst. of Sys. Sci., Acad. of Mathematics & Sys. 

Sci., Chinese Acad. of Sci. 



In Complex Adaptive Systems, agents co-adapt to each other through 

interaction. A typical example is game: players learn and adapt to the 

opponent through game playing. This paper studies the adaptive characteristic 

of co-adaptation through a combinatorial game “Five-in-arow”. 

The computer simulations show that a high-level player will win 

more if she knows the opponent’s next move, but a low-level player will 

lose more if she knows the opponent’s next move. To explore the reason 

of this phenomenon, an abstract theoretical model is built based on 

a full, k-ary game tree which also shows the above phenomenon. Some 

analysis and discussion are carried out based on this model. This result 

indicates that identification of the opponent only is not enough; she 

has to improve herself as well. 

◮ SuB04-4 16:50–17:10 

Convergence of Adaptive Linear Stochastic Differential Games: 

Nonzero-sum Case, pp.3543–3548 

Li, Yan 

Guo, Lei 



Complex systems with components or subsystems having game-like 

relationships are probably the most complex ones that we encounter 

everyday. Much progress has been made over the past half century 

on differential games which are used as a tool in modeling conflicts in 

the context of dynamic systems, however, almost all of the current literature 

assume that both the parameters and the structure of the game 

are known to the players. Since in many practical situations, the players 

may have unknown parameters, which motivate us to investigate 

a class of two-player zero-sum linear-quadratic stochastic differential 

games in [1] with unknown parameters. In this paper, we will further 

consider a class of two-player nonzero−sum linear quadratic s- 

tochastic differential games, with unknown parameters to both players. 

We will design adaptive strategies and prove that they will converge to 

the optimal ones under 

◮ SuB04-5 17:10–17:30 

A New Safety Certification Method for High-risk Flight Testing Subjects, 

pp.3555–3560 

Liu, Dongliang 

Xu, Haojun 

Zhou, Li 

Pei, Binbin 





Flight test subjects have high risk and difficult to evaluate quantificationally. 

A test pilot model based on distribution hypothesis verification 

is proposed to obtain the test pilot’s manipulation parameter. Delay 

time is selected as an example to illustrate the method. Aircraft model 

and hydraulic system model is established. Failure model of hydraulic 

system’s sensor and actuator malfunction, oil block and pump power 

were set up and simulated by Simulink and AMESim. To improve 

the precision of flight risk assessment, an improved extreme value risk 

evaluation model based on nonlinearly decreasing weight particle swarm 

optimization (NDW-PSO based EVT) is proposed. Dynamic simulation 

from component level to aircraft level was realized by a comprehensive 

virtual flight testing (VFT) framework based on AMESim, MAT- 

LAB/Simulink and Flightgear cross-connect. NO.43 risky subject about 

“hydraulic system malfunction”in Chinese GJB 626A-2006 was chosen 

as an example. Influence of hydraulic system failure to flight safety 

was analyzed and safety amelioration measures were proposed, which 

illustrate the former proposed method’s validity. 

◮ SuB04-6 17:30–17:50 

Design on Integrated Monitoring System for Main Waterway Station, 

pp.3471–3474 

Yuling, Pei 

Wu, Qian 

Yang, Xiaoyi 




Waterway management is an important tool to ensure safe smoothness 

of the waterway and enhance the traffic capacity. Aimed at the incompleteness 

in information resulted in safe fault triggered by limitation of 

command dispatch capability, and in waterway availability lowering etc, 

the paper designed and implemented a management system of main 

waterway station. Based on the integration of information fusion and 

adopted .NET frame, according to the hybrid architecture of B/S and 

C/S, it designed the related functional modules such as data acquisition, 

comprehensive management and command dispatch and so on, 

and realized the waterway managemant, command dispatch, dynamic 

monitoring of the vessel, vessel managemant, dispatch in maintenence 

and emergency rescue etc. The basic tests of the realized system show 

that it is reasonable in system design, and owns its feasibility and usability 

of engineering application. 

SuB05 15:50–17:50 Room 203E 

Biosystems 

Chair: Zhao, Chunhui 

Co-Chair: Li, Jr-Shin 



◮ SuB05-1 15:50–16:10 

217


Automatic Sleep Stage Classification Based on ECG and EEG Features 

for Day Time Short Nap Evaluation, pp.4974–4977 

Yu, Shanshan 

Chen, Xi 

Wang, Bei 

Wang, Xing-yu 





In this study, the Electrocardiogram (ECG) and Electroencephalogram 

(EEG) data recorded during day time short nap were analyzed. The ultimate 

purpose is to find out effective ECG features combined with usual 

EEG features for sleep stage determination during day time nap. Firstly, 

the ECG data was pre-processed in order to eliminate artifacts. After 

preprocessing, the second-order derivative of the ECG signal was calculated 

and clustered into two classes by K-means method. The peak 

positions of R wave were detected. Secondly, the Heart Rate Variability 

(HRV) was calculated according to the RR intervals (RRIs). Features 

of HRV of ECG were extracted in time-domain and frequency-domain. 

The redundant features were removed by the rough set method. Finally, 

the extracted features from the HRV of ECG were combined with the 

usual EEG features for sleep stage determination. The sleep stages including 

stage awake, stage 1 and stage 2 were distinguished by using 

Support Vector Machine (SVM). The obtained result indicated that the 

extracted ECG features improved the sleep stage classification accuracy. 

◮ SuB05-2 16:10–16:30 

Optimal Control in Molecular-level Gene Manipulation, pp.4978–4983 

Yu, Juanyi 

Li, Jr-Shin 



The sequential information stored in DNA determines the appearance 

and inheritance of different life forms and individuals. Precision control 

of DNA sequences at the molecular level is crucial to maintain the fidelity 

of genes and to ensure the accuracy of gene expression. In this 

paper, we propose state-space control models at the molecular level 

by converting character-based DNA sequences into state vectors and 

incorporating on/off controls for mutagens into DNA replication systems 

in different scales. Subsequently, we compute the optimal control 

sequence for minimizing the risk of applying mutagens and the offtrajectory 

penalty using dynamic programming algorithm. By the brute 

force method and simulation results, we conclude that the global optimum 

can always be achieved within a finite number of steps of deterministic 

DNA replication systems. The upper limit of steps to reach the 

global optimum depends on the length of the DNA sequence. 

◮ SuB05-3 16:30–16:50 

Multivariate Statistical Analysis Methods to Investigate Interindividual 

Glucose Dynamics for Subjects with Type 1 Diabetes Mellitus, pp.4989– 

4994 

Zhao, Chunhui 

Sun, Youxian 

Gao, Furong 



Hong Kong Univ. of Sci. & Tech. 

This paper investigates the interindividual variability of underlying glucose 

dynamics using multivariate statistical analysis methods for subjects 

with type 1 diabetes mellitus. Here two types of glucose dynamics 

are defined, the general dynamics and the output-relevant predictive 

dynamics. The concerned important issues are whether the underlying 

glucose dynamics change from subject to subject? Can a global 

(or universal) empirical model be developed from glucose data for a s- 

ingle subject and then used to explain the glucose dynamics for other 

subjects? These and related issues are investigated using multivariate 

statistical analysis methods based on clinical data for two groups 

of subjects. Together, these findings provide insights into more efficient 

development of data-driven models to understand and capture the glucose 

information in diabetes subjects. 

◮ SuB05-4 16:50–17:10 

Dynamic Feature Extraction of Epileptic EEG Using Recurrence Quantification 


Chen, Lanlan 


Zhang, Jian 

Zou, Junzhong 



Detecting the reliable transition point embedded in the electroencephalograms 

(EEGs) is a challenge in the field of epileptic research. 

In this research, a recurrence quantification analysis (RQA) is proposed 

to help medical doctors to reveal dynamical characteristics in EEGs of 

patients suffering from epilepsy. In contrast with traditional chaos methods, 

the merits of RQA method is that it can measure the complexity 

of a short and non-stationary signal without any assumptions such as 

linear, stationary and noiseless noise. In this study, EEGs with generalized 

epilepsy were collected in Epilepsy Center of Renji Hospital. The 

test results show that three RQA measurements, i.e. recurrence rate, 

determinism and entropy can track the complexity changes of brain 

electrical activity. RQA variables show a large fluctuation in pre-ictal 

stage, which reflects a transitional state leading to seizure activity. On 

the contrary, RQA variables fluctuate in relatively small bounds in ictal 

stage, which is due to organized and self-sustained rhythmic discharge. 

Therefore, RQA could be a promising approach in prediction and diagnosis 

for epileptic seizures. 

◮ SuB05-5 17:10–17:30 

Key-frame Selection in WCE Video Based on Shot Detection, pp.5030– 

5034 

Fu, Yanan 

Liu, Haiying 

Cheng, Yu 

Yan, Tingfang 

Li, Teng 








Wireless Capsule Endoscopy (WCE) is an imaging technology that enables 

close examination of the interior of the entire small intestine. A 

major problem associated with this new technology is that a large number 

of images need to be manually examined by clinicians. It is therefore 

useful to automatically reduce the number of frames that need direct 

interpretation by a clinician. In this paper a technique based on 

shot detection method is presented for automatic key-frame selection 

in WCE videos. The frames in the small intestine zone containing relevant 

features are extracted from the video sequence as the key-frames. 

Experimental results show that the proposed key-frame selection techniques 

significantly reduce the number of frames that need to 

be directly viewed by clinicians, and speed up the diagnosis procedures. 

◮ SuB05-6 17:30–17:50 

Patient Data Tracking in a Collaborative Healthcare, pp.5045–5050 

Memon, Qurban 

Khoja, Shakeel 

UAE Univ. 

IBA, Pakistan 

It has been found that patients do suffer from queuing at reception, 

pharmacy, appointments, and services departments of the hospitals. 

In this paper, patient tracking is presented such that it enables not only 

presence of the patient within each service area of the hospital but 

helps in retrieving relevant medical records of the patient from another 

hospital within a collaborative health care domain. The architectural 

challenges are investigated, and a framework for such a collaborative 

region is presented. The patient record database is developed, technologies 

are chosen and role based access for purpose of privacy is 

exemplified for a typical environment. The issues related to its deployment 

are also discussed. 

SuB06 16:10–17:50 Room 302 

Invited Session: Robot Sensing and Control 

Chair: Liu, Yun-Hui 


◮ SuB06-1 16:10–16:30 

Design of an Optimal Flight Control System with Integral Augmented 

Compensator for a Nonlinear UAV Helicopter, pp.3927–3932 

Tang, Yirui 

Li, Yangmin 



This paper presents the development of an optimal flight control system 

218



for a small-scale Unmanned Aerial Vehicle (UAV) helicopter. Complex 

and highly coupled dynamics of the helicopter naturally complicates the 

modeling process and the controller design. In this work, the comprehensive 

nonlinear model of the helicopter system is derived from the 

first-principles modeling and its parameters are verified with system i- 

dentification approaches. The derived nonlinear model is with modest 

level of complexity and the high-fidelity linearized model is adequate for 

flight control system design. Helicopter is a high-dimensional and inherently 

unstable system. It demands accurate and efficient control algorithms 

to stabilize the attitude of the helicopter. Fullstate feedback control 

is utilized in the controller design. However, onboard sensors can 

provide only partial states information for feedback. The unmeasured 

states are estimated by means of a reduced-order observer. Linear 

Quadratic Regulator (LQR) methodology and integral state augmentation 

are adopted in order to achieve the desired performance of the 

control system. The simulation results indicate the developed control 

system is competent and efficient enough to control the UAV helicopter. 

◮ SuB06-2 16:30–16:50 

A New Algorithm for Estimating 3D Structure and Robot Motion Using 

Visual Tracking and IMU/Compass, pp.4942–4947 

WANG, Kai 

Liu, Yun-Hui 



Monocular SLAM (Simultaneous Localization and Mapping) is popular 

in SLAM researches of the past few years. Filtering approaches and 

bundle adjustment style optimization are main popular strategies, with 

a lot of applications. This paper proposes a novel adaptive estimation 

based SLAM algorithm with application to a lake surface robot. Orientation 

and linear velocities of the robot, and accurate SURF feature 

tracking work as prerequisites of the algorithm. The algorithm is theoretically 

proved and experimentally validated in the paper. Key frames 

are selected in the SLAM process and stored with metric information 

of features points, to generate the environment map. Robot localization 

and sparse point based map could be estimated online at 50Hz with assistance 

of GPU. Moreover, dense point based map could be recovered 

offline for visualization. Finally, the corresponding simulations and experiments 

are carried out to validate performance of the new monocular 

visual SLAM algorithm. 

◮ SuB06-3 16:50–17:10 

Real-Time Bird Detection Based on Background Subtraction, pp.4507– 

4510 

Shakeri, Moein 

Zhang, Hong 



Detection of birds in air is an important problem across multiple applications 

including aviation safety, avian protection, and ecological science 

of migrant bird species. In this paper we describe a real-time detection 

system of birds in flight. Using a single fixed camera, our bird detection 

system is based on background subtraction and tracking through point 

correspondence. We make use of Zivkovic’s background subtraction 

approach which includes a non-parametric model and a Gaussian mixture 

model that is an extension of the standard method. We append a 

correspondence component based on point-tracking to the background 

subtraction algorithm to achieve reliable bird detection. Experiments 

were conducted to study the detection performance using objects of 

different size, color and velocity. The results show efficiency and accuracy 

of our system in the detection of fast motion objects such as 

birds. 

◮ SuB06-4 17:10–17:30 

Brain-Driven Micro-Biomanipulation with Sensing Feedback, pp.4517– 

4522 

Luo, Yudong 

Shen, Yantao 



This paper presents our development of an integrated brain-driven 

micro-biomanipulation system that can perform mind-controlled biomanipulation 

at micro scale. The system incorporates a non-invasive electroencephalogram 

(EEG) device with a high-precision automated micromanipulator 

through high speed network. The human manipulation 

mind measured by the EEG device can effectively drive the micromanipulator 

to perform the 2-D manipulation on bio-samples. During 

the manipulation, the trace of human manipulation mind is monitored 

by a custom-built high-precision position sensing detector (PS- 

D) interface unit. Extensive experimental results demonstrate the high 

performance of the developed network-enabled and brain-driven microbiomanipulation 

system. The research work can be further used to 

investigate manipulation behavior and neurobiofeedback mechanism 

of human brain for facilitating development of high-efficiency microbiomanipulation 

strategy of engineering approaches in micro/nano level. 

SuB07 16:10–17:50 Room 303 

Invited Session: Disturbance Rejection: Formulation, Methodology, and 

Applications 

Chair: Gao, Zhiqiang 


◮ SuB07-1 16:10–16:30 

An Energy Saving, Factory-Validated Disturbance Decoupling Control 

Design for Extrusion Processes, pp.2891–2896 

Zheng, Qing 

Gao, Zhiqiang 

Gannon Univ. 


This paper is focused on the design and factory testing of a disturbance 

decoupling control (DDC) approach for hose extrusion processes. 

A unique dynamic DDC strategy, based on the active disturbance 

rejection control (ADRC) framework, is designed and implemented in 

programmable logic control (PLC) code for temperature regulation in 

the volumetric flow of a polymer single-screw extruder. With the DDC 

method, it is shown that a largely unknown square multivariable system 

is readily decoupled by actively estimating and rejecting the effects of 

both the internal plant dynamics and external disturbances. The proposed 

DDC approach requires very little information on plant model 

and has the inherent disturbance rejection ability, and it proves to be a 

great fit for the highly nonlinear and multivariable extrusion processes. 

Recently, the DDC design strategy has been put under rigorous test 

at Parker Hannifin Parflex hose extrusion plant. Across multiple production 

lines for over eight months, the product performance capability 

index (Cpk) was improved by 30 percent and energy consumption is 

reduced over 50 percent. The production line data demonstrates that 

ADRC is a transformative control technology with great potentials in 

streamline factory operations, saving energy and improving quality, all 

at the same time. 

◮ SuB07-2 16:30–16:50 

On Model-free Accommodation of Actuator Nonlinearities, pp.2897– 

2902 

Zhao, Shen 

Zheng, Qinling 

Gao, Zhiqiang 




A unique method of dealing with various nonlinearities, particularly 

those associated with actuators, is proposed, implemented and tested 

in simulation and hardware. It is shown that the nonlinearities, even 

without mathematical models, can be effectively dealt with based on 

the concept of active disturbance rejection, by which the effect of the 

nonlinearities is estimated and cancelled in real time. Simulation and 

hardware tests give promising results. 

◮ SuB07-3 16:50–17:10 

Disturbance-Observer-Based Dynamic Inversion Tracking Control for a 

Hypersonic Vehicle, pp.2346–2351 

WANG, NA 

YANG, JIAN 

Guo, Lei 

Beihang Univ. 

Beihang Univ. 

Beihang Univ. 

Abstract―a dynamic inversion controller (DIC) is designed firstly to 

track control commands of 100-ft/s step velocity and 2000-ft step altitude 

for an air-breathing hypersonic vehicle in this paper. On the basis 

of first step, the influence of different disturbances and parametric 

uncertainties is analyzed on velocity response and altitude response, 

219


so the main disturbances are found. Then a non-linear disturbance 

observer (NDO) is presented to estimate and compensate the main 

disturbances. Finally, DIC based on NDO is designed, which is a compound 

of DIC and NDO. And simulation studies show that the controller 

based on NDO is robust and meets the performance requirements in 

presence of disturbances and uncertainties. 

◮ SuB07-4 17:10–17:30 

Tracking Trajectory of Heterogenous Multi-Agent Systems with Disturbance 

Observer Based Control, pp.2352–2357 

Yang, Hong-yong 

Guo, Lei 

Han, Chao 

Ludong Univ. 

Beihang Univ. 

BeiHang Univ. 

In this paper, the multi-agent systems are composed of two class 

different agents, one parts are supposed to moving on the double 

integrator dynamics, the other parts are driven by the single integrator 

dynamics. By applying the disturbance observer based control 

(DOBC), sufficient consensus criteria of the heterogenous 

multi-agent systems with exogenous disturbances are presented for 

with fixed topologies and switching topologies. A pinning control 

strategy is designed for a part of agents of the systems, and this 

pinning control can bring the states of multi-agent systems to an expected 

consensus track. Finally, the consensus of multi-agent systems 

with exogenous disturbances is verified by an example of four 

multi-agent systems. 

◮ SuB07-5 17:30–17:50 

Active Disturbance Rejection Control for the Gimbal Servo System of 

Magnetically Suspended Single Gimbal Control Momentum Gyroscope, 

pp.2865–2869 

Wei, Kongming 

Wu, Zhong 



To improve the accuracy of the output torque, maintain high precision 

attitude control and ensure the safe operation of the magnetically suspended 

bearing, the gimbal servo system of Magnetically Suspended 

Single Gimbal Control Momentum Gyroscope(MS-SGCMG) must have 

high control performance and reject the disturbance well. A novel Active 

Disturbance Rejection Control(ADRC) controller is designed to meet 

these requirements. The controller can cancel the disturbance by observing 

the output and relax the impulse brought by the sharp change 

of the gimbal command. A new method, based on observer, for resolving 

the resolver is introduced. The resolving process can be merged 

in the observing of the state of the servo system, which simplifies the 

controller. The experiment results show than the controller obtains high 

accuracy without overshoot and compensates the disturbance distinctly. 

SuB08 15:50–17:50 Room 310 

Biomedical Engineering 

Chair: Wang, Jiang 

Co-Chair: LI, Baopu 

Tianjin Univ. 


◮ SuB08-1 15:50–16:10 

Aeration control of activated sludge wastewater treatment process using 

optimal control, pp.4969–4973 

Wu, Jie 

East China Univ. of Sci. & technoledge 

In order to reduce energy consumption of aeration processes, an optimal 

control was studied. Simplified model of aeration system was built 

according to Activated Sludge Model No.1 (ASM1) presented by International 

Association on Water Quality (IAWQ), then the optimal control 

strategy based on reduced-order observer was brought forward for 

the aeration system. The proposed optimal control strategy was implemented 

on a benchmark wastewater treatment process-Benchmark 

Simulation Model 1(BSM1). Compared with PID control strategy recommended 

in benchmark, using the proposed control strategy, the effluent 

water quality and the time of effluent water quality exceeding 

the discharge standard of BSM1 are more stable, while more than 4% 

aeration energy is saved. 

◮ SuB08-2 16:10–16:30 

UKF-based state feedback control of abnormal neural oscillations in 

demyelination symptom, pp.4984–4988 

Jin, Qitao 

Wang, Jiang 

Li, Huiyan 

Deng, Bin 

Wei, Xile 

Che, Yan-Qiu 

Dong, Feng 

Tianjin Univ. 

Tianjin Univ. 


tianjin Univ. 

Tianjin Univ. 


Tianjin Univ. 

Fast axonal conduction of action potentials in mammals relies on myelin 

insulation. Demyelination can cause slowed, blocked, desynchronized, 

or paradoxically excessive spiking that underlies the symptoms observed 

in demyelination diseases. Feedback control via functional electrical 

stimulation (FES) seems to be a promising treatment modality in 

such diseases. However, there are challenges to implementing such 

method for neurons: high nonlinearity, biological tissue constrains and 

unobservable ion channel states. To address this problem, we propose 

an estimating and tracking control strategy for systems based on 

Kalman filter, in order to enhance the action potential propagation reliability 

of demyelinated neuron via FES. Our method could promote the 

design of new closed-loop electrical stimulation systems for patients 

suffering from different nerve system dysfunctions. 

◮ SuB08-3 16:30–16:50 

Research on digital pulse oximeter based on optical frequency converter, 

pp.5056–5060 

Ni, Pingqiang 

LI, Baopu 



This paper specifically describes the design of hardware and software 

of a digital pulse oximeter based on ARM core embedded system and 

light-frequency converter. In this study, a light-frequency converter instead 

of a photodiode was adopted to receive the light transmitting, 

which greatly simplified the design of hardware. Besides, traditional 

methods of calculate frequency of rectangle signal were improved and 

an algorithm with stable sampling rate and high resolution was proposed. 

Based on the filtering and detecting methods of the pulse signal, 

the baseline drift and the noise on high frequency were successfully removed 

by applying integer coefficient filter. A detecting algorithm based 

on difference and threshold was proposed, and the future points of the 

signal can be extracted effectively. The result shows that this oximeter 

can overcome usual noise, the software is very flexible and simple in 

structure. 

◮ SuB08-4 16:50–17:10 

Synchronization between Outputs of Neurons and Neuron Populations 

with Discrete Control Algorithm Basing on Least-square Method, 

pp.5001–5006 

Jia, Chenhui 

Wang, Jiang 

Deng, Bin 

Wei, Xile 

Che, Yan-Qiu 

Dong, Feng 

Tianjin Univ. 

Tianjin Univ. 

tianjin Univ. 

Tianjin Univ. 


Tianjin Univ. 

As a new method on curing mental diseases, Deep Brain Stimulation 

(DBS) gives great help to patients who do not respond to drug therapies. 

However, most of the DBS therapies used at present are using 

high-frequency signals as open-loop stimulating signals, whose mechanism 

is not sufficiently understood. In this paper, basing on the synchronization 

mechanism and the close-loop stability theory, we have 

designed a close-loop method to propose a potential therapy for curing 

mental diseases with deep brain stimulation. Through reconstruct 

the input-output dynamics with least square method, we can use a new 

regressive input-output model to describe the relationship between the 

input and output of the abnormal neuron population. Using the parameters 

estimated in the regressive model, we can design a set of DBS 

signals to make the output of abnormal neuron population accurately 

track the desired output signal. The method is robust and can be ap- 

220



plied even when the abnormal neuron population is disturbed by heavy 

noise. 

◮ SuB08-5 17:10–17:30 

Solving Graph Vertex Coloring Problem with Microfluidic DNA Computer, 

pp.5061–5065 

Niu, Ying 

Zhang, Xuncai 





The hugely storing information ability, parallel computing ability and 

lower computing energy cost make DNA computing to be a perfect 

computing paradigm. Nowadays it has been used to solve various 

computationally hard problems. In order to improve its reliability and 

simplify operations, microfluidic chips support an effective way to realize 

an automatable and universal DNA computer. In this paper we 

introduce microfluidic logic operators, simple fluidic switches and memory. 

Furthermore, the use of electronic fluidic control components in 

microfluidic systems will be demonstrated in such way as to perform 

dynamic operations and programming. Finally a proposal for an actual 

fluidic computer will be made which solves the graph vertex coloring 

problems. 

◮ SuB08-6 17:30–17:50 

Action potential initial mechanism control of a minimum model response 

to constant and sinusoidal stimulus, pp.4948–4952 

Li, Huiyan 

Wang, Jiang 

Jin, Qitao 

Deng, Bin 

Wei, Xile 

Che, Yan-Qiu 


Tianjin Univ. 

Tianjin Univ. 

tianjin Univ. 

Tianjin Univ. 


Neuron encodes the information inputs from the dendrites by generating 

different firing patterns. The different firing patterns result from 

different action potential initial dynamic mechanisms. In this paper, we 

adopt a minimum neuron model, design the Wash-out filter from a physiological 

view, and achieve the transition between different action potential 

initial dynamic mechanisms. Finally, we demonstrate the physiological 

basis of Wash-out filter, which is affecting the result of competition 

between currents with different dynamics in the sub-threshold potential. 

SuB09 15:50–17:50 Room 311A 

Invited Session: Data-based control, modeling and optimization 

Chair: Liu, Derong 

Co-Chair: He, Haibo 



◮ SuB09-1 15:50–16:10 

Data-Based Approach for the Control of a Class of Nonlinear Affine 


Wang, Zhuo 

Liu, Derong 

Univ. of Illinois at Chicago 

CASIA 

In this paper, a data-based output feedback control method is developed 

for a class of nonlinear affine systems. This method requires little 

priori knowledge about the system. It does not need to know or to build 

the mathematical model of the system. We apply a fast sampling technique 

to measure the output signal, which contains information about 

the plant. The zero-order hold (ZOH) and the control switch techniques 

are also applied for information collection. Then, the feedback gain matrix 

is calculated and adjusted according to these sampled data. Computer 

simulation results demonstrate the feasibility of this data-based 

control method. 

◮ SuB09-2 16:10–16:30 

Data-Driven Learning and Control with Multiple Critic Networks, 

pp.523–527 

He, Haibo 

Ni, Zhen 





Abstract - In this paper, we extend our previous work of a three-network 

adaptive dynamic programming design [1] to be a multiple critic networks 

design for online learning and control. The key idea of this approach 

is to develop a hierarchical internal goal representation to facilitate 

the online learning with detailed and informative internal value 

signal representations. We present our learning architecture in detail, 

and also demonstrate its performance on the popular cart-pole balancing 

benchmark. Simulation results demonstrate the effectiveness of our 

approach. We also present discussions of further research directions 

along this topic. 

◮ SuB09-3 16:30–16:50 

Data-driven Model Free Adaptive Control for Block-Connected Systems, 

pp.2827–2832 

Zhu, Yuanming 


Jin, Shangtai 




Data-driven model free adaptive control (MFAC) is presented for three 

kinds of block-connected discrete-time nonlinear systems, describing 

by cascaded connection, parallel connection and feedback connection. 

The proposed data-driven MFAC means that the controller is designed 

merely by the measured input-output data of the controlled system without 

any explicit or implicit use of the plant model. The stability of the 

data-driven MFAC is guaranteed by rigorous theoretical analysis and 

the effectiveness is verified by simulation results. 

◮ SuB09-4 16:50–17:10 

Integration of Fuzzy Controller with AdaptiveDynamic Programming, 

pp.310–315 

Zhu, Yuanheng 


He, Haibo 




Adaptive dynamic programming (ADP) is an effective method for learning 

while fuzzy controller has been put into use in many applications 

because of its simplicity and no need of accurate mathematic modeling. 

The combination of ADP and fuzzy control has been studied a 

lot. Before this paper, we have studied using ADP to learn the fuzzy 

rules of a Monotonic controller, which shows good performance. In this 

paper, a hyperbolic fuzzy model is adopted to make an improvement. 

In this way, both membership function and fuzzy rules are learned. 

With ADP algorithm, fuzzy controller has the capacity of learning and 

adapting. Simulations on a single cart-pole plant and a rotational inverted 

pendulum are implemented to observe the performance, even with 

uncertainties and disturbances. 

◮ SuB09-5 17:10–17:30 

Learning Control of a Bioreactor System Using Kernel-based Heuristic 

Dynamic Programming, pp.316–321 

Lian, Chuanqiang 

Xu, Xin 



To solve the learning control problem of a bioreactor system, a novel 

framework of heuristic dynamic programming (HDP) with sparse kernel 

machines is presented, which integrates kernel methods into critic 

learning of HDP. As a class of adaptive critic designs (ACDs), HDP 

has been used to realize online learning control of dynamical systems, 

where neural networks are commonly employed to approximate the value 

functions or policies. However, there are still some difficulties in the 

design and implementation of HDP such as that the learning efficiency 

and convergence of HDP greatly rely on the empirical design of the critic 

and so on. In this paper, by using the sparse kernel machines, Kernel 

HDP (KHDP) is proposed and its performance is analyzed both theoretically 

and empirically. Due to the representation learning and nonlinear 

approximation ability of sparse kernel machines, KHDP can obtain better 

performance than previous HDP method with manually designed 

neural networks. Simulation results demonstrate the effectiveness of 


SuB10 15:50–17:50 Room 311B 

Invited Session: Wireless Sensor Networks 

Chair: Xiao, Wendong 

Co-Chair: Wu, Jian Kang 



◮ SuB10-1 15:50–16:10 

221


A comparison study on TDOA based localization algorithms for sensor 

networks, pp.4490–4495 

Qu, Xiaomei 

Xie, Lihua 



For the purpose of source localization, we have proposed two recursive 

algorithms in our companion paper, which use time difference of arrival 

(TDOA) measurements received from sensors by accounting for random 

uncertainties in sensor positions. This paper is devoted to presenting 

a comparative analysis on the two recursive localization algorithms. 

The first algorithm is called recursive localization algorithm, which uses 

the current estimate of source position to form a new measurement 

equation of the unknown source position. The second algorithm firstly 

estimates an auxiliary variable and then rearranges the nonlinear T- 

DOA equation into a linear measurement equation. By employing the 

update covariance of the update localization of the two algorithms, it is 

shown that the second algorithm outperforms the first one. An illustrative 

example is included to validate our theoretic results. 

◮ SuB10-2 16:10–16:30 

Robust Stabilization of Networked Control Systems with Multiple-packet 

Transmission via Jump System Approach, pp.4496–4501 

Ding, Nan 

Yu, Mei 

Yuan, Xiaodan 

Li, Jie 

Yu, Junyan 





Univ. of Electronic Sci. ang Tech. in China 

A jump system approach to stabilization and robust stabilization of networked 

control systems (NCSs) with multiple-packet transmissions are 

addressed. We focus our attention on the case that the packets are 

transmitted via limited capacity communication channels. Sufficient 

conditions on the mean square stabilization of NCSs are obtained in 

terms of linear matrix inequalities (LMIs). Non-fragile state feedback 

controller can be constructed directly via LMIs. A numerical example is 

worked out to demonstrate the effectiveness of the proposed method. 

◮ SuB10-3 16:30–16:50 

Distributed fusion filter for multi-rate multi-sensor systems with packet 

dropouts, pp.4502–4506 

Ma, Jing 

Sun, Shuli 



This paper is concerned with the distributed fusion filtering problem for 

a class of asynchronous multi-rate multi-sensor systems with different 

packet dropout rates, where the system is described at the highest 

sampling rate and different sensors may have different measurement 

sampling rates. Firstly, the multi-rate fusion estimation problem is 

transformed into an equivalent single rate fusion estimation problem 

using a “dummy”measurement. Then, the optimal distributed fusion 

filter is given based on the well-known optimal fusion algorithm weighted 

by matrices in the linear minimum variance sense. The filtering error 

cross-covariance matrix is derived between any two subsystems. Simulation 

research verifies the effectiveness of the proposed filter. 

◮ SuB10-4 16:50–17:10 

Self-learning sensor scheduling for target tracking in wireless sensor 

networks based on adaptive dynamic programming, pp.1056–1061 

Xiao, Wendong 

Song, Ruizhuo 



This paper proposes a novel self-learning sensor scheduling scheme, 

which makes the sensor energy consumption and sensor tracking error 

optimal over the system operational horizon. The scheme employs 

Kalman filter estimation technique to predict the tracking accuracy. A 

performance index function is established based on the predicted energy 

consumption and tracking error. A self-learning scheduling method 

is proposed based on adaptive dynamic programming algorithm. The 

numerical example shows the effectiveness of the proposed approach. 

◮ SuB10-5 17:10–17:30 

A New Approach to Quantized Stabilization of Stochastic System with 

Multiplicative Noise, pp.2382–2387 

Wei, Li 




A new quantization dependent Lyapunov function is proposed to analysis 

the quantized feedback stabilization problem of discrete-time system 

with multiplicative noises. For convenience of the proof, only singleinput 

case is considered (which can be generalized to multi-input channel), 

conditions for the system to be mean-square poly-quadratic stabilized 

are derived as well as the analysis of H-inf performance and controller 

design for the given stochastic system with logarithmic quantizer. 

The most significant feature is the utilization of a quantization dependent 

Lyapunov function, leading to less conservative results, which is 

shown both theoretically and through numerical examples. 

◮ SuB10-6 17:30–17:50 

Outlier Detection in Heart Rate Signal using Activity information, 

pp.4511–4516 

Yang, Yuanjing 

Ji, Lianying 

Wu, Jian Kang 

Graduated Univ. of Chinese Acad. of Sci. 

Graduate Univ. of Chinese Acad. of Sci. 


During exercise, heart rate will increase and its distribution will be different 

from that in stationary statement. Moreover, activity introduces 

several outliers into heart rate series. Heart rate variability analysis 

under exercise conditions can’t be conducted identically to the traditional 

methods. A heart rate distribution analysis method is proposed 

to fuse the activity information and heart rate signals which will be used 

to make heart rate analysis under exercise conditions. Firstly we use 

Gaussian function to fit RR intervals under various intensity activities, 

and then establish dynamic model for the parameter 𝛍 and 

𝛔 which is changed with activity intensity. With the distribution 

of heart rate determined, outliers in RR interval can be detected 

and replaced according to possibility distribution. In the last, the validity 

of outlier detection algorithm and the influence of outliers to HRV are 

verified. 

SuB11 16:10–17:50 Room 311C 

Invited Session: Hypersonic flight control 

Chair: Chen, Mou 


NUAA 

Beihang Univ. 

◮ SuB11-1 16:10–16:30 

Modeling and Control for Near-Space Vehicles With Oblique Wing, 

pp.1773–1778 

Pang, Jie 

Rong, Mei 

Chen, Mou 

NUAA 


NUAA 

In this paper, the flight motion model for near-space vehicle with an 

oblique wing is presented, and the flight control system is developed 

using dynamic inversion method. Firstly, based on its asymmetry 

configuration and special aerodynamic characteristic, the six-degreeof-freedom 

nonlinear equations of flight motion are derived using Newton’s 

second law and law of inertia via considering the influence of 

oblique wing. Secondly, the dynamic inversion control scheme is designed 

for the near-space vehicle with an oblique wing. Finally, simulation 

results are presented to illustrate the effectiveness of the proposed 

flight control scheme. 

◮ SuB11-2 16:30–16:50 

Attitude Quaternion Control in the Final Approach Phase of Rendezvous 

and Docking by Sliding Mode Control, pp.2177–2181 


Liu, Zhenghua 

Ren, Yan 

Li, Nan 

Beihang Univ. 


Beihang Univ. 

Beihang Univ. 

For the relative attitude control problem in phasing period of rendezvous 

and docking, attitude quaternion was adopted to describe the dynamic 

model and the kinematics equation of spacecraft. Then attitude quaternion 

PD feedback was designed to control the relative attitude successfully. 

On this basis, the sliding mode control was researched and 

used in relative attitude control to improve accuracy and build up anti- 

222



jamming ability. The simulation results demonstrate that sliding mode 

controller is more superior in rapidity and robustness. 

◮ SuB11-3 16:50–17:10 

Design of 3-D Discrete Sliding Mode Variable Structure Guidance Law 

for Air Missile, pp.2204–2208 

Chang, Le 

Liu, Zhenghua 


Beihang Univ. 


Beihang Univ. 

Based on kinematics relationship model of missile and target in threedimensional 

space,this assignment will extend 2-D plane to realistic 

3-D space. Firstly, it will deduce 3-D Guidance Law Model between 

air missile and maneuvering target. Then this assignment will design 

suitable discrete sliding mode variable structure guidance law through 

the robustness characteristic controlled by sliding mode. The guidance 

features will be subsequently analyzed. Finally, the result will confirm 

the feasibility of the designed guidance law through the mathematical 

simulation. 

◮ SuB11-4 17:10–17:30 

Nonlinear Flight Control Design Using Sliding Mode Disturbance 

Observer-Based Constraint Backstepping, pp.1818–1825 

Zhang, Chao 

Chen, Zongji 

Wei, Chen 




This paper presents a sliding mode disturbance observer-based constraint 

backstepping control approach (CBS/SMDO) to control a cargo 

aircraft during heavy cargo airdrop operation. The CBS/SMDO controller 

is capable of dealing with parametric uncertainties, external disturbances, 

and unmodeled dynamics in the presence of state/control 

constraints. The super-twisting second order sliding mode disturbance 

observer (SOSMDO) is utilized to compensate for the overall uncertainties, 

avoiding high control gains. The SOSMDO is much simpler than 

the neural networks-based estimator and could provide asymptotic convergence 

of the estimation error to zero in finite time. The closed-loop 

stability is guaranteed in the sense of Lyapunov. The proposed CB- 

S/SMDO controller is applied to the airdrop flight control design on a 

nonlinear six-degrees-of-freedom transport aircraft model in a low attitude 

tandem extraction airdrop scenario. Simulation results demonstrate 

the feasibility of the CBS/SMDO method. 

◮ SuB11-5 17:30–17:50 

Neural Adaptive Back Stepping Flight Controller for a Ducted Fan UAV, 

pp.2370–2375 

Wang, Jianliang 

Sundaram, Suresh 

RAJASHEKARAN, ARUNESHWARAN 


NanYang Technological Univ. 

Nanyang Technologial 

Univ. 

In this paper, we present a neural adaptive back-stepping flight controller 

for a ducted fan UAV whose dynamics is characterized by uncertainties 

and highly coupled nonlinearities. The proposed neural adaptive 

back-stepping controller can handle unknown nonlinearities, unmodeled 

dynamics and external wind disturbances. A single layer radial 

basis function network is used to approximate the virtual control 

law derived using back stepping approach, which provides necessary 

stability and tracking performances. The neural controller parameters 

are adapted online using Lyapunov based update laws. The proposed 

controller is evaluated using nonlinear desktop simulation model 

of a typical ducted fan UAV performing bop-up maneuver. Three neural 

adaptive controllers are implemented to handle attitude command altitude 

hold system, one in each body axis. A separate neural controller is 

implemented to track the height command for autonomous takeoff and 

landing. The results indicate that the proposed controller can stabilize 

the ducted fan UAV and provide necessary tracking performance. 

223


224


Author Index 

Author Index 

(C=Chair, CC=Co-Chair) 

A 

Afshar, Puya . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB05-6 161 

AL-BAYATI, AHMAD HUSSAIN . . . . . . . . . . . . . . . . . . . . . . PFrC-11 136 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA06-6 153 

Ali, Zahid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-11 180 

An, Bao-ran . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-34 127 

An, Hongwen. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrC-14 137 

An, Hui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA01-4 205 

An, Jiancheng. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA07-5 153 

An, Peng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-86 190 

An, Yao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-26 195 

Ao, Chaohua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-12 168 

Arminski, Krzysztof . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA06-4 96 

B 

Bai, Baodong. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrC-48 141 

Bai, Danyu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA04 CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA04-1 150 

Bai, Meng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB06-2 105 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB06-6 105 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA03-6 207 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB03-2 216 

Bai, Rui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-57 130 

Bai, Weimin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-26 113 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-51 117 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-52 117 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-46 172 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-47 172 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-48 173 

Bai, Xue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB02-3 102 

Bai, Yan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-33 127 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-33 139 

Bao, Leping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB01-4 214 

Bao, Yuqing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrB04-3 103 

Bao, Zhejing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-79 133 

Bi, Haiyun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB03-5 103 

Bi, Jianchao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-82 146 

Bian, Mingkun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-62 175 

BIAN, Xinqian. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrA05-6 96 

Bin, Yang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-03 123 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB05-4 161 

Bing, Zhigang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-19 137 

Botmart, Thongchai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB01-1 214 

Bou Matar, Amjad . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-10 193 

Bourque, François-Alex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-91 191 

Bragin, Mikhail . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA04-4 208 

Brdys, Mietek . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA06-4 96 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB02 C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB02-3 158 

Brown, Martin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA07-6 154 

Bu, Xuhui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA03-5 150 

C 

Cai, Chenxiao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB05-2 104 

Cai, Chenxiao. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA10-6 213 

Cai, Jian Xian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-39 115 

Cai, Jun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-05 167 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB04-5 160 

Cai, Tao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-28 183 

Cai, Wen-Jian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-44 128 

Cai, Yunze . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-10 180 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA02 C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA02-5 206 

Cai, Zi-xing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-79 202 

Cao, Hui. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrA-64 119 

Cao, Liulin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-48 129 

Cao, Luguang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA07-5 153 

Cao, Ping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-66 143 

Cao, Shouqi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-42 128 

Cao, Wenjing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB02-2 158 

Cao, Xiaoqing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA09-1 155 

Cao, Yuchao. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrB-07 123 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-01 167 

Cao, Zhiqiang. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrA09-4 99 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-65 131 

Cen, Wei. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA01-3 148 

Chai, Li . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB01-4 101 

Chai, Tian-you . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA09-4 155 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB05-6 161 

Chai, Tianyou. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PD-2 205 

Chai, Wei. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrA-48 116 

Chai, Xiuli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB06-5 105 

Chakravorty, Jhelum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-16 137 

Chang, Jian. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrA01-1 91 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA07-5 97 

Chang, Le . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB11-3 223 

Chao, Tao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB01-5 101 

Che, Guolin. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrB11-3 109 

Che, Jun. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaC-45 197 

Che, Weiwei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-83 177 

Che, Yan-Qiu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-12 193 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-13 193 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB08-2 220 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB08-4 220 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB08-6 221 

Chen, Ben M. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB09-6 108 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PD-1 205 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA08 C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA10-6 213 

Chen, Ben M. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PL-4 C 

Chen, Binglong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-81 190 

Chen, Cailian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-10 168 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB06 C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB06-1 161 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB06-3 162 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB06-4 162 

Chen, ChangJia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-67 175 

Chen, Chen. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrB11-1 109 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB11-5 110 

Chen, Dongyan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB03-5 103 

225


. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA01-1 205 

Chen, Dongzhi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA03-1 149 

Chen, Fang. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrA-64 119 

Chen, Gang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-86 203 

Chen, Gang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-32 139 

Chen, Ge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA08-2 154 

Chen, Guangmou . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB11-2 166 

Chen, Guanrong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA10-1 99 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA10-3 99 

Chen, Guohai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-81 133 

Chen, Guojin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-16 169 

Chen, Haiyong. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrB07-5 106 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-57 199 

Chen, Han-Fu. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA08-4 154 

Chen, Haotian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA07-2 97 

Chen, Heping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB02-5 102 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB09-1 164 

Chen, Hong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-30 126 

Chen, Hong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PL-2 91 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-15 181 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA05-5 152 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA09 CC 

Chen, Hongmei. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrA-82 121 

Chen, Hua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB10-3 108 

Chen, Huan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-63 131 

Chen, Jianmin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA07-4 153 

Chen, Jianye . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB04-5 104 

Chen, Jie. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrC-77 145 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-87 146 

Chen, Jie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA11 

C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB11 C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-04 110 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-52 198 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA09 C 

Chen, Lanlan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB05-4 218 

Chen, Liping. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrA-88 121 

Chen, LongSheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-71 119 

Chen, Luonan. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB08-4 164 

Chen, Michael Z. Q. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA10 

CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA10-2 99 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB01 C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB01-1 157 

Chen, Mou . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB11 

C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB11-1 222 

Chen, Ning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-86 203 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA01 CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA01-6 205 

Chen, Pengnian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-33 171 

Chen, Qijuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-12 137 

Chen, Qijun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB11-2 166 

Chen, Qingwei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA05-1 95 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-34 139 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-41 140 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-41 172 

Chen, Rong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-70 119 

Chen, Shi-jun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB11-5 110 

Chen, Siyun. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA04-2 150 

Chen, Songhang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-61 131 

Chen, Tao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-52 173 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-39 184 

Chen, Wanmi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-73 132 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-72 189 

Chen, Wei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-64 175 

Chen, Wenjie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-48 198 

Chen, Xi. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrC-38 140 

Chen, Xi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB05-1 218 

Chen, Xi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB06 

CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB06-1 105 

Chen, Xian-gan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-14 181 

Chen, Xiaodu. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrC-34 139 

Chen, Xiaofang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA06-3 96 

Chen, Xiaolong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-45 197 

Chen, Xiaotian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA11-1 100 

Chen, Xijun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA06-5 210 

Chen, Xing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-82 177 

Chen, Yan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-43 184 

Chen, Yangzhou . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-21 182 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-22 182 

Chen, Yao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA11-2 156 

Chen, Yefei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA06-3 209 

Chen, Yen-Lun. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaC-54 199 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA09-3 155 

Chen, Yucheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-73 132 

Chen, Yueyan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-61 118 

Chen, Yun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-40 115 

Chen, Yuzhong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA02-4 93 

Chen, Zengqiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA09 

C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA09-1 98 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA06-3 152 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB01-5 157 

Chen, Zhenfeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-90 134 

Chen, Zhigang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-43 128 

Chen, Zhiliang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-07 167 

Chen, Zhiyong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA11-1 156 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB09-5 165 

Chen, Ziyin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA05-6 95 

Chen, Zongji . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB11-4 223 

Cheng, Changjian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-51 173 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-55 174 

Cheng, Daizhan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA08-5 98 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB09-5 108 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA10-2 156 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA02-1 206 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA08-5 211 

Cheng, Hongtai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB09-1 164 

Cheng, Hui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-15 169 

Cheng, Li . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-13 181 

Cheng, Liangliang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-01 179 

Cheng, Lina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-39 115 

Cheng, Long. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrA09 

CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA09-4 99 

Cheng, S.-Y. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-63 187 

Cheng, Yu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA03-4 207 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB05-5 218 

Cheng, Zekai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-26 138 

Chi, Dongnan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-57 174 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-29 195 

Chi, Xiaobo. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrC-40 140 

Chiu, Pei-Ling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-63 187 

Chu, Tianguang. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrA08 

CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA08-2 98 

Chu, Zhuang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-73 201 

Chua, Leon O. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PL-5 148 

Chun, Xiong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-47 185 

Cong, Shuang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-14 193 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA11 CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA11-2 213 

226


Author Index 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA11-3 213 

Cui, Baotong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-27 114 

Cui, Guangzhao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA02-3 93 

Cui, Guangzhao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB08-5 221 

Cui, Guimei. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrB-92 134 

Cui, Haiyue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB01-6 101 

Cui, Han. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrA-76 120 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-77 120 

CUI, Pingyuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-34 196 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-47 198 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-59 199 

Cui, Wei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA11-6 214 

Cui, Wenhua. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaA-59 174 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-61 187 

Cui, Wenxia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-88 134 

Cui, Xiwang. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaA-54 173 

Cui, Yan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-66 143 

Cui, Zhe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-27 126 

CUI, Zhiqiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA07-2 210 

D 

Dai, Guiping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-92 191 

Dai, Lizhen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA03-5 94 

Dai, Shaowu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-87 146 

Dai, Shuguang. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaC-75 201 

DAI, Wen-zhan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-83 146 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-72 201 

Dai, Yeming. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-74 189 

Dai, Yi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB01-2 157 

DAI, Yuanming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-94 122 

Daigle, Bernie. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB08-6 164 

Dang, Zhaohui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA01 

CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA01-5 92 

Debeljkovic, Dragutin. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB01-3 214 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB01-6 215 

Deng, Bin. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaC-12 193 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-13 193 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB08-2 220 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB08-4 220 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB08-6 221 

Deng, Chao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-57 142 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-56 174 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-57 174 

Deng, Fang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB11-1 109 

Deng, Guanlong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-27 126 

Deng, Liang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-76 145 

Deng, Ling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB07-1 162 

Deng, Wenbo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA05-2 151 

Deng, Xiangyang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-91 122 

Deng, Xiaogang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-60 118 

Deng, Zhidong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-31 126 

Deng, Zili . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB03-5 216 

Dezert, Jean . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA03-1 93 

Dimitrijevic, Nebojsa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB01-3 214 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB01-6 215 

Ding, Baocang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA07-5 211 

Ding, Chengjun. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrC-43 140 

Ding, Feng. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaA-09 168 

DING, Jian-bo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-30 139 

Ding, Jinliang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB05-6 161 

Ding, Nan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB10-2 222 

Ding, Qiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-30 126 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-55 130 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-56 130 

Ding, Qingqing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-32 183 

Ding, Shenping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-23 125 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-17 181 

Ding, Shihong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB10-4 109 

Ding, Weilong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-24 138 

Ding, Xilun. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrA07-3 97 

Ding, Yanhui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA01-1 205 

Ding, Zhengtao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB05-1 160 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB11-4 166 

DOAN, THANH NGHI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-28 126 

Dong, Daoyi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA11-1 213 

Dong, Fan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-06 123 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-12 124 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA04-1 207 

Dong, Feng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB08-2 220 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB08-4 220 

Dong, Guocheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-66 143 

Dong, Hairong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-39 196 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA11-2 156 

Dong, Haolong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-03 123 

Dong, Hongli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA06-1 152 

Dong, Jie. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrC-09 136 

Dong, Jixiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB09-4 165 

Dong, Lin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB03-6 159 

Dong, Mingyu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA04-5 151 

Dong, Qiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB03-3 159 

Dong, Xin-zhuang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB03-6 103 

Dong, Xisong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-06 123 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-12 124 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA04 CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA04-1 207 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA04-6 208 

Dong, Yao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-89 122 

Dong, Ying . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-15 112 

Dong, Yuehong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA03-1 149 

Dong, Zhe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB04-3 160 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB01 CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB01-2 214 

Dou, Jinmei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-47 116 

Doyle, Francis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PL-1 91 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB08-6 164 

Du, Chunyan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-62 118 

Du, Fei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-36 196 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-37 196 

Du, Feng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-77 133 

Du, Haibo. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrB10-1 108 

Du, Jialu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-16 193 

Du, Jingjing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrB-02 123 

Du, Junping. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrA03-4 94 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-88 121 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB03-5 159 

Du, Mingli. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaA-41 172 

Du, Puze . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB11-3 109 

Du, Rong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-10 168 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB06-3 162 

Du, Ronghua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-77 176 

Du, Shengzhi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-60 200 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-61 200 

Du, Wenli. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrA-20 113 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-52 129 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-23 170 

Du, Xiuxia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-21 113 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-55 117 

Du, Yanhua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB04-2 217 

Du, Zhanzhan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-54 174 

Duan, Guang-Ren . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA 

C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-18 194 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB01-4 157 

227


. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB09 C 

Duan, Haibin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA10 

CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA10-2 212 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB11 CC 

Duan, Haillong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-07 167 

Duan, Hailong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-19 112 

Duan, JianMin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-78 202 

Duan, Jianmin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-81 202 

Duan, Jianming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-78 177 

Duan, Junhua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-69 132 

Duan, Ping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-41 115 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-41 128 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-43 140 

Duan, Shukai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB11-6 167 

Duan, Wenyong. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrB05-2 104 

DUAN, Xingguang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-50 141 

Duan, Zhisheng. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrA10-1 99 

E 

Emharuethai, Chanikan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA02-1 149 

F 

Fan, Guoliang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA06-2 96 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA09-5 155 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB09-6 165 

Fan, Hongbiao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA08-2 211 

Fan, Rui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA02-3 93 

Fan, Weihua. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrC-34 139 

Fan, Xiaoping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-48 185 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-58 187 

Fan, Xinyi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-10 168 

Fan, Yuling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-04 179 

Fang, Chun-Hsiung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA02-6 149 

Fang, Hai-Tao. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrB06-1 105 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB03-3 216 

Fang, Haijun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB05-4 104 

Fang, Hao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB11-2 109 

Fang, Huajing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-72 132 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-35 139 

Fang, Jun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-67 119 

Fang, Liying . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA03-2 93 

Fang, Qiang. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB09-4 165 

Fang, Xiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-87 203 

Fang, Xiaoke . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-44 172 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-25 195 

Fang, Xueyi. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-34 183 

Fang, Yangwang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-38 171 

Fei, Minrui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB08-5 107 

Fei, Qing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-92 204 

Fei, Shumin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB01-4 214 

Fei, Shumin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-44 197 

FENG, Aixiang. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrB02-6 102 

Feng, Gang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB09-1 107 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PD-1 C 

Feng, Jian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-02 179 

FENG, JIN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-69 144 

Feng, Jun-e . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA08 

C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA08-2 211 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA08-3 211 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA08-5 211 

Feng, Nenglian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-03 123 

Feng, Shulin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB05-3 104 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB11-4 109 

Feng, Wei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-27 114 

Feng, Xiaoliang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB06-6 162 

Feng, Xin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA03 

C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA03-6 94 

Feng, Yong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB10-6 109 

Feng, Zhihui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-52 117 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-46 172 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-47 172 

Feng, Zhimin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-72 176 

Feng, Zuren . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB11-6 110 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-05 192 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA02-2 206 

Fu, Bo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-07 192 

Fu, Chen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-85 134 

Fu, Deqian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA10-4 213 

Fu, Dongmei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB03-6 159 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA03-5 207 

Fu, Junjie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-05 167 

Fu, Li-Chen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PD-1 205 

Fu, Mengyin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA10-4 99 

FU, Qin. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrB-80 133 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-68 144 

Fu, Xingjian. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaA-18 169 

Fu, Yanan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA03-4 207 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB05-5 218 

Fukuda, Toshio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-09 168 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB04-6 160 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA06-2 209 

G 

Gai, Shaoyan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-37 171 

Gan, Ming Gang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-24 113 

Gan, Xun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-92 179 

Gao, Bingtuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB04-3 103 

Gao, Bingzhao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-15 181 

Gao, Cheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-77 202 

Gao, Dayuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-49 116 

GAO, Fei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-45 128 

Gao, Feng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA04-5 151 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA04-6 151 

Gao, Feng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-53 129 

Gao, Furong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB05-3 218 

GAO, Hongwei. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-74 189 

Gao, Huating . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB01-1 101 

GAO, Hui-bin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-58 118 

Gao, Huijun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA06-1 152 

Gao, Ming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-28 170 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-29 170 

Gao, Nan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-31 183 

Gao, Qian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-37 140 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-20 194 

Gao, Qian. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA10-1 212 

Gao, Qian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-29 139 

Gao, Rong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-42 140 

Gao, Rong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-79 145 

Gao, Rui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB08-5 164 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB08-8 164 

Gao, Shigen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-39 196 

Gao, Xiaoteng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB08-7 164 

GAO, Xue-jin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-13 181 

Gao, Xuejun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB05-2 160 

Gao, Xueshan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-50 141 

Gao, Yan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-76 133 

Gao, Yuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB03-5 216 

Gao, Yuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-30 195 

Gao, Yuanyuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB02-2 102 

Gao, Zengliang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-02 167 

Gao, Zhifeng. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaA-37 171 

Gao, Zhiqi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-02 191 

Gao, Zhiqiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB07 

C 

228


Author Index 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB07-1 219 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB07-2 219 

Ge, Bin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-61 143 

Ge, Jing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA03-3 207 

Ge, Li . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-29 114 

Ge, Xiaohua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB05-6 104 

GE, Xinsheng. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrB04-1 103 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-11 124 

Ge, Yanrong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-21 182 

Geng, Qingbo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-92 204 

Geng, Yunhai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-81 190 

Ghazali, Rozaimi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB07-3 162 

Gong, Daoxiong. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA05-2 151 

Gong, Dunwei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA11-2 100 

Gong, Huarong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-81 145 

Gu, Jason . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-64 200 

Gu, Kaikai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-26 113 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-51 117 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-52 117 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-46 172 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-47 172 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-48 173 

Gu, Mingqin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-79 202 

Gu, Shusheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-44 172 

Gu, Xingsheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-27 126 

Guan, Ping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-17 112 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-18 112 

Guan, Xiaohong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA04-2 150 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA04-5 151 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA04-6 151 

Guan, Xinping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-11 111 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-10 168 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA05-1 151 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB06-1 161 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB06-3 162 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB06-4 162 

Guan, Xinping. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA01-2 148 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB02-2 215 

Guan, Yu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-43 116 

Guan, Zhi-Hong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA01-5 148 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA01-6 149 

Gui, Weihua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA06-3 96 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA01-6 206 

GUO, Chen. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrB-89 134 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-36 171 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-27 182 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-16 193 

Guo, Cheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-77 133 

Guo, Dapeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA05-5 152 

Guo, Dongsheng. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrA03-3 93 

Guo, Fang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB07-2 106 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB07-3 106 

Guo, Gang. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaA-70 176 

Guo, Hongyan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA05-5 152 

Guo, Jian Wen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-23 170 

GUO, Jiang. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrA-26 113 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-51 117 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-52 117 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-46 172 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-47 172 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-48 173 

Guo, Jian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-21 169 

Guo, Jian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA10-1 155 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA10-4 156 

Guo, Jin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-62 131 

Guo, Jing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA03 

C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA03-2 150 

Guo, Junyan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB06-5 105 

Guo, Lanlei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-02 135 

Guo, Lei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-47 128 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB07-3 219 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB07-4 220 

Guo, Lei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA04-3 94 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB04-4 217 

Guo, Liquan. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB09-4 165 

Guo, Liyong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-83 121 

Guo, Qing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA05-5 95 

Guo, Ruijun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-77 176 

GUO, Tian-tai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-82 190 

Guo, Wanli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-57 130 

Guo, Wei. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB07 

CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB07-1 162 

Guo, Xuan. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrA-20 113 

Guo, Yajuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-89 178 

Guo, Yang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA10-1 155 

Guo, Yi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB11-1 166 

Guo, Yu. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrA05-1 95 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-41 140 

Guo, Yuguang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-81 133 

Guo, Yunfei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-06 180 

Guo, Yuqian. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA01 

C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA01-5 205 

GUO, ZIWEI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-63 118 

H 

Han, Chao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB07-4 220 

Han, Chunyan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB05-3 104 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB01 CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB01-3 157 

Han, Deqiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA03 

CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA03-1 93 

Han, Dongying . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-54 117 

Han, Fei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-65 131 

Han, Huawei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB04-3 217 

Han, Jianda . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-56 142 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-26 170 

Han, Jing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB04 

C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB04-3 217 

Han, Lin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-73 201 

Han, Pengcheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA03-4 94 

Han, Qiaoni . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA01-2 148 

Han, Qing-Long. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrB05 

C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB05-6 104 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA07-4 153 

Han, Shaoze . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-69 119 

Han, Xiaojuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-61 118 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-64 119 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-54 173 

Han, Xingguang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-22 182 

Han, Yanling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-42 128 

Hao, Weidong. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA09-3 155 

Hao, Xiao Ni . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA02-6 93 

Hao, Xue-Jun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA04-5 208 

Hao, Zulong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB01-3 101 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-14 112 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-55 186 

He, Chenlong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA02-2 206 

He, Dezhi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-40 140 

He, Fenghua. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-16 181 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA10 C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA10-3 156 

He, Guannan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-04 135 

He, Haibo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB09 

CC 

229


. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB09-2 221 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB09-4 221 

He, Hengpan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-56 199 

He, Jingyuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-68 132 

HE, Kunpeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-70 144 

HE, Li Ming. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrC-83 146 

He, Minjia. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaA-22 169 

He, Naibao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-37 140 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-20 194 

He, Pengfei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB11-6 166 

He, Xiao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA10-5 100 

He, Yang. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-30 183 

He, Yi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-39 140 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-37 184 

He, Yiheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-08 192 

He, Ying. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrC-13 137 

He, Zhijun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-49 198 

He, Zhonghe. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-22 182 

Heng, Qinghai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-07 136 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA07 C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA07-2 153 

Hirano, Yutaka . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB10-3 166 

Hong, Chengqiu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-10 111 

Hong, Yiguang. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrA08-4 98 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA09-2 98 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA08-5 154 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA02-3 206 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA02-6 206 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA04-3 208 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB02-4 215 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB03-3 216 

Horng, Wen-Ren . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA02-6 149 

Hou, Guolian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-46 116 

Hou, Lifeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-04 123 

Hou, Linlin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA01-3 205 

Hou, Ming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-18 169 

Hou, Ranran . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB04-4 160 

Hou, Ting. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrB-10 124 

Hou, Wenbo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB03-2 159 

Hou, Xuyang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA05-4 151 

Hou, Yuan-bin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-31 114 

Hou, Zengguang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA09-4 99 

Hou, Zhongsheng. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrB 

CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB09-3 221 

Hou, Zhongsheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA03-5 150 

Hu, Chen. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrC-24 138 

Hu, Chuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB07-4 163 

Hu, Chuxiong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-05 180 

Hu, Guoqiang. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrB09-3 108 

Hu, Haijing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-34 196 

Hu, Ji . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB09-4 165 

Hu, Jianfeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-78 177 

Hu, Jinchun. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-05 180 

Hu, Kaihang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-68 188 

Hu, Lingyan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA09-2 155 

Hu, Nai-Ping. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaC-33 196 

Hu, Rong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB11-3 109 

Hu, Shiguo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-38 171 

Hu, Wei. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-75 189 

Hu, Weili. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrA05-1 95 

Hu, Xiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-10 111 

Hu, Xiangyang. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrA-60 118 

Hu, Xiaodong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-50 173 

Hu, Xiaoming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA09-6 99 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PD-1 205 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA03-3 150 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB02-4 215 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB02-6 216 

Hu, Yanpu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-15 112 

Hu, Ying . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-18 125 

Hu, Yinlong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB01-1 157 

Hu, Zhiqiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-93 147 

Hua, Changchun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA05 

C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA05-1 151 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB05-5 161 

Hua, Cunqing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB06-1 161 

Huang, Can . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-03 192 

Huang, Chaodong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA04 

CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA04-3 94 

Huang, Chun E . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-31 195 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-32 196 

Huang, Congzhi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-33 127 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-33 139 

Huang, Fuzhen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-21 125 

Huang, Guoping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB07-2 106 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB07-3 106 

Huang, Haibo. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrA-34 115 

Huang, Hsing-Hisn . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA05-4 209 

Huang, Jian. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaA-09 168 

Huang, Jie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PL-4 148 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA08-3 154 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB09-5 165 

Huang, Junjie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-68 175 

Huang, Kaiqi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB04-6 160 

Huang, Lei. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaA-92 179 

Huang, Miao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA04-6 95 

HUANG, Qiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-50 141 

Huang, Qian. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrB-95 135 

Huang, Qiulan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-45 141 

Huang, Rui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-02 179 

Huang, Shujuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-69 132 

Huang, Shuzi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-63 131 

Huang, Tingwen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB05-2 160 

Huang, Wenjun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-81 177 

Huang, Xinhan. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaC-43 197 

Huang, Xun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-10 136 

Huang, Xuncheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-07 111 

Huang, Yalou . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA07-2 97 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-63 131 

Huang, Yan Guo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-80 120 

Huang, Yi. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaC-69 201 

Huang, Yu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB03-4 103 

Huo, Wei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA 

CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaC C 

Huo, Yifeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-65 119 

I 

ILYAS, MUHAMMAD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB07 

CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB07-4 106 

INOUSSA, GARBA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA06 

C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA06-6 96 

J 

Jen, Fu-Hua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB03 

C 

230


Author Index 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB03-6 216 

Jhang, Seong Tai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA10-4 213 

Ji, Haibo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-24 182 

Ji, Jing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-04 135 

Ji, Lianying. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB10-6 222 

Ji, Lujun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-91 203 

Ji, Qin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-03 135 

Ji, Wen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-44 172 

Ji, Zhicheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-41 184 

Jia, Baozhu. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrA-37 115 

Jia, Chao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-62 118 

Jia, Chenhui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB08-4 220 

Jia, Heming. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrA05-6 96 

Jia, Heming. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaC-29 195 

Jia, Jie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB05-3 161 

Jia, Lei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-44 128 

Jia, Lihao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA06-2 209 

Jia, Pengxiao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-87 178 

Jia, Qing-Shan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA04 

C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA04-2 150 

Jia, Qingxian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-43 116 

Jia, Xiaohong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB05-3 161 

Jia, Xinchun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-40 140 

Jia, Zhixian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-82 202 

Jiang, Aipeng. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrB-30 126 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-55 130 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-56 130 

Jiang, Bin. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaA-66 175 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA06-2 152 

Jiang, Dawei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA06-5 96 

Jiang, DeSheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-20 182 

JIANG, Guangwen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-45 128 

JIANG, Huirong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB04-1 159 

JIANG, Jie-wei. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-82 190 

Jiang, Ling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaA-52 173 

Jiang, Min. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA11-1 213 

Jiang, Xiefu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB05-6 104 

Jiang, Xin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-79 145 

Jiang, Ye . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB08-3 107 

Jiang, Yi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-13 124 

Jiang, Ying . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-48 141 

Jiang, Yu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-43 116 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-22 194 

Jiang, Yuanshu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-83 121 

JIANG, Zhe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-26 170 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-26 182 

Jiang, Zhong-Ping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PD-1 205 

Jiang, Zhongmin. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrB-08 123 

Jiang, Zhongping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB09-4 108 

Jiao, Xiaohong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA03-1 149 

Jie, Feiran . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-44 197 

JIN, Chengjun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-67 131 

Jin, Hongzhang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-38 184 

Jin, Jing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-22 194 

JIN, Lizuo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-44 197 

Jin, Lu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-65 131 

Jin, Qitao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-12 193 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-13 193 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB08-2 220 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB08-6 221 

Jin, Shangtai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB09-3 221 

Jin, Wen. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrC-38 140 

Jin, Wen Zhou . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA02-6 93 

Jin, Xiao-zheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-83 177 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-84 177 

Jing, Sifeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-51 173 

Johansson, Karl Henrik . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA08-5 154 

Ju, He-Hua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-66 143 

Ju, Hehua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-94 135 

K 

Kang, Chaohai. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-76 189 

Kang, Yifei. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaC-53 198 

Kang, Yu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA09-1 154 

Ke, Liangjun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB11-6 110 

Ke, Zibo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-53 142 

Khoja, Shakeel. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB05-6 218 

Kong, Bing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-82 121 

Kong, Qingkai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-90 203 

Kong, Xiangdong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-60 174 

Kong, Zhi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-60 130 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-23 138 

Kosuge, Kazuhiro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PD-2 205 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA05-1 208 

Kou, Zhitao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-08 136 

Kragic, Danica . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA09-6 99 

Kuang, Sen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA11-2 213 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA11-3 213 

L 

La-inchua, Teerapong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA02-2 149 

Lan, Weiyao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB02-1 158 

Langowski, Rafal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB02-3 158 

Lao, Dazhong. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaA-40 172 

Lee, Hua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB06-1 161 

Lee, JangMyung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA03-4 94 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB03-5 159 

Lee, Kai-Hui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-63 187 

Lee, Tong Heng. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrB09-6 108 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA10-6 213 

Leghmizi, Said . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-23 113 

Lei, Yang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-11 193 

Lei, Yang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-02 135 

LEI, Yinghui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-52 198 

Lei, Zhengling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-27 182 

Li, Aiping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-06 110 

Li, Baokui. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaC-92 204 

LI, Baopu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-52 186 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA06-1 209 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB08 CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB08-3 220 

Li, Benyin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-56 174 

Li, Bin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-89 178 

Li, Bin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA01-1 91 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA07-5 97 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-22 113 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-13 168 

Li, Bing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-90 178 

Li, Binglin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-04 167 

Li, Bo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-70 176 

Li, Bo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA11-1 100 

Li, Changbin. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrC-39 140 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-37 184 

Li, Changli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-02 167 

Li, Changxian. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrB-84 134 

Li, Chaochun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-15 169 

231


. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-60 174 

Li, Chen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-58 199 

Li, Chunwen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA11-5 214 

Li, Da-yu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-86 146 

Li, Dalei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-01 167 

Li, Dazi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB01 

CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB01-2 101 

Li, Dequan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB02-5 215 

Li, Dewei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB09-2 107 

Li, Donghai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-40 172 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-31 195 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-32 196 

Li, Guangchun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-93 204 

Li, Haitao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-66 188 

Li, Haitao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA08-1 98 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB08-2 163 

Li, Haotao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB07-2 106 

Li, Hongchao. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA02-4 149 

Li, Hongliang. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA09-6 155 

Li, Huazhong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-91 178 

LI, Hui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-40 127 

Li, Hui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-94 135 

Li, Huifeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA10-5 213 

Li, Huijia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB08-4 164 

Li, Huimin. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrA03-2 93 

Li, Huiyan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-12 193 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-13 193 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB08-2 220 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB08-6 221 

LI, Jiajia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB08-3 107 

Li, Jian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-19 181 

Li, Jiangyun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-09 136 

Li, Jianyong. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-32 183 

Li, Jie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB10-2 222 

Li, Jing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-86 146 

LI, Jinna . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-35 127 

Li, Jinyu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-77 133 

Li, Jizhao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-54 117 

Li, Jr-Shin. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA08-6 154 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB05 CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB05-2 218 

Li, Juan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-08 123 

Li, Jun. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrC-75 145 

Li, Jun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-84 203 

Li, Junjun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-88 203 

Li, Junyuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB04-3 103 

Li, Kang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB08 

CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB08-4 107 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB08-5 107 

Li, Kangshun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-43 184 

Li, Kun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA06 

CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA06-5 210 

Li, Lei-Min . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-80 202 

LI, Li. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaC-80 202 

LI, Li-ting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-28 195 

LI, Lian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB02-2 158 

Li, Li . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-39 196 

Li, Li . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-90 178 

Li, Liangliang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB03-4 103 

Li, Lixue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA07-1 153 

LI, Mengwei. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA10-2 212 

Li, Meng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-07 167 

Li, Ming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-19 125 

Li, Ming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB03-1 216 

Li, Mingai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA06-7 210 

Li, Minghui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-35 127 

Li, Minhua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB06-2 105 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB06-6 105 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA03-6 207 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB03-2 216 

Li, Munan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-65 143 

Li, Nan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA10-5 213 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB11-2 222 

Li, NanNan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-54 199 

Li, Ning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-31 114 

Li, Ning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-57 118 

Li, Pei-xi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-80 202 

Li, Peiran . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-79 133 

Li, Peng. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB09-4 165 

Li, Peng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-67 200 

Li, Pengfei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-46 141 

Li, Ping. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-74 144 

Li, Pingkang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-21 113 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-55 117 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-05 167 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB04 C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB04-5 160 

Li, Qing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-09 136 

Li, Qingling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-58 142 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-53 198 

Li, Ruonan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-21 113 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-55 117 

Li, Shaobao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB02-2 215 

Li, Shaoyuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB06-4 105 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB08 C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB08-5 107 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-14 124 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA07-5 211 

Li, Shengwei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB06-6 162 

Li, Shi. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB01-4 157 

Li, Shihua. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrB10 

C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB10-1 108 

Li, Shuai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB11-1 166 

Li, Shuang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-24 182 

Li, Shulun. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrA07-2 97 

Li, Shun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA04-5 94 

Li, Shurong. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrC-02 135 

Li, Tao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA09-3 98 

Li, Teng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA03-4 207 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB05-5 218 

Li, Tongying. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrA02-2 92 

Li, Weihong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-18 181 

Li, Wen Qiang. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB02-2 158 

Li, Xiangjun. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrA-64 119 

Li, Xiaohua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-09 124 

232


Author Index 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-24 125 

Li, Xin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-48 198 

Li, Xin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-64 175 

Li, Xinyuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-30 183 

LI, Xiuliang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-74 144 

Li, Xiumei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-84 134 

Li, Xuejing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-74 132 

Li, Xuezhe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-78 189 

Li, Xun. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-46 185 

Li, Yalei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-08 168 

Li, Yan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB04-4 217 

Li, Yanbin. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrA-46 116 

Li, Yang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-04 123 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA01-4 205 

Li, Yang. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaC-65 200 

Li, Yangmin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB06-1 218 

Li, Yani . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-92 135 

Li, Yanjun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA01-6 92 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB04-1 217 

Li, Yanping. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaA-84 177 

Li, Yanshu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-22 169 

Li, Yanzhao. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrC-90 147 

Li, Yingchun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-18 169 

Li, Yongzhou . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-58 187 

Li, Yuanchun. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrA07-1 97 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB07-1 105 

Li, Yue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-54 142 

LI, ZHI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-45 185 

Li, Zhigang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-27 138 

Li, Zhijun. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA09-1 155 

Li, Zhiqiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA08-4 211 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA08-6 212 

Li, Zhongjuan. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrB-81 133 

Li, Zhongkui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA09-5 99 

Li, Zhuang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB01-6 101 

Li, Zihao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA04-2 94 

Li, Zilong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-76 202 

Lian, Chuanqiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB09-5 221 

Lian, Guan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-15 193 

Lian, Xiaoyan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB11-1 109 

Liang, Fujun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-67 144 

Liang, Guolong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-38 184 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-39 184 

Liang, Jinling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA06-1 152 

Liang, Meiyu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB03-5 159 

Liang, TianCai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-40 197 

Liang, You-ming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-13 112 

Liang, Yutao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-78 120 

Liao, Fucheng. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA06-4 209 

Lin, Chuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA09-3 155 

Lin, Hai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA08-4 98 

Lin, Jia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-03 192 

Lin, Jiajun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-40 127 

Lin, Jinxing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-37 171 

Lin, Peiqun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-73 189 

Lin, Qing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-32 139 

Lin, Rui. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrC-28 138 

Lin, Weiwei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-30 126 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-55 130 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-56 130 

Lin, Yan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA03-4 150 

LIN, Yining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-94 122 

Lin, Zhiwei. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrB-62 131 

Lin, Zhiyun. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrA07-6 97 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA05-2 208 

Lin, Zongli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB09-2 108 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB C 

Liu, Aidong. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB03-3 159 

Liu, Bai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-65 175 

Liu, Bin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-22 169 

Liu, Bin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-54 117 

Liu, Changyou . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-78 120 

Liu, Chenglin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA01-3 92 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-13 112 

Liu, Cong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-51 198 

Liu, Dan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-35 127 

Liu, Derong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB09 

C 

Liu, Derong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA08-1 154 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA09-6 155 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB09-1 221 

Liu, Dongliang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB04-5 217 

Liu, Dongpo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-53 173 

Liu, Fang. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB03-4 159 

Liu, Fei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-13 112 

Liu, Fei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA01-3 92 

Liu, Feng. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA01-5 148 

Liu, Fucai. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrA-47 116 

Liu, Fuchun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA10 

CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA10-5 156 

LIU, Guang-sheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-13 181 

Liu, Gui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-75 189 

Liu, Guoqing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrB-15 124 

Liu, Haihua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-14 181 

Liu, Haixia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-86 178 

Liu, Haiying . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA03-4 207 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB05-5 218 

Liu, Han . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-54 129 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-60 187 

Liu, Helong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-17 137 

Liu, Hezhuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-43 184 

Liu, Hongbo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-44 128 

Liu, Hongjun. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrA-02 110 

Liu, Houyi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-88 203 

Liu, Huan. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrA-76 120 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-77 120 

Liu, Huan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA04-4 151 

Liu, Hugh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA10-3 156 

Liu, Jiangbo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB09-5 108 

Liu, Jing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-53 173 

Liu, Jingwei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA03-2 93 

LIU, Jinkun. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA07 

CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA07-2 210 

Liu, Jiuming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-91 134 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-78 145 

LIU, Jizhen. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA07-2 210 

Liu, Ju . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA03-3 207 

Liu, Juan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-14 181 

Liu, Jun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB05-2 160 

Liu, Jun. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaC-89 203 

Liu, Keping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB07-1 105 

Liu, Kun. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA04-5 151 

Liu, Liang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA09-2 212 

Liu, Limin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-58 187 

Liu, Lingxia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-15 124 

Liu, Min . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-10 136 

Liu, Mingyao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-08 180 

Liu, Ning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrB07-5 106 

Liu, Pei-Lan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA08-6 154 

Liu, Qiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-88 178 

233


. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB07-2 162 

LIU, QIN. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrB-16 125 

Liu, Qingqian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-81 121 

Liu, Qipeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-59 130 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB02-5 215 

Liu, Quanli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-62 175 

Liu, Ruilan. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrA-53 117 

Liu, Shaoqiang. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-48 185 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-58 187 

LIU, Sheng. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA04-1 207 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA04-2 207 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA04-6 208 

Liu, Shengchun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-83 190 

Liu, Shijie. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrB-91 134 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-78 145 

Liu, Shirong. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrB04 

C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB04-5 104 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB09-2 165 

Liu, Shuaishi. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaC-46 197 

Liu, Shuang. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaA-22 169 

Liu, Shuhua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-64 131 

Liu, Shujie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA06-5 152 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB07-5 163 

Liu, Shuyang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA07-1 97 

Liu, Song . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-32 196 

Liu, Tao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA07-6 211 

Liu, Tong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-71 176 

Liu, Wei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-53 117 

Liu, WeiYi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-82 121 

Liu, Wending . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA03-4 150 

Liu, Wenqi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-70 132 

Liu, Xi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA06 

CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA06-1 96 

Liu, Xiangdong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA09-5 99 

Liu, Xiao He . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-17 112 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-18 112 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-18 169 

LIU, Xiao-He . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-78 145 

Liu, Xiao-he. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-31 183 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-30 195 

Liu, Xiaobing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaA-59 174 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-61 187 

Liu, Xiaohua. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrC-79 145 

Liu, Xiaohui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-85 178 

Liu, Xiaoping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB05-5 161 

Liu, Xijing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrA-49 116 

Liu, Xin-wen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-86 146 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-90 147 

LIU, Xinhua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-56 186 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-59 187 

LIU, Xiwei. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaA-51 173 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-55 174 

Liu, Xueliang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-63 143 

LIU, Xueqin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB08-5 107 

Liu, Xuezhen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-25 182 

Liu, Yajin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-26 113 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-51 117 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-52 117 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-46 172 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-47 172 

Liu, Yang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-27 182 

Liu, Yang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-89 134 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-36 171 

Liu, Yang. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB07-4 163 

LIU, Yanhong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-32 183 

Liu, Yaxiong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB06-4 162 

Liu, Yi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-02 167 

Liu, Yi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-75 132 

Liu, Yi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA01-6 92 

Liu, Yi. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA02-4 149 

Liu, Yibing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-13 137 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-14 137 

Liu, Yongchao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-49 129 

Liu, Yudong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB02-1 101 

Liu, Yuliang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-19 112 

Liu, Yun-Hui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-50 141 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB06 C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB06-2 219 

Liu, Yungang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA09-5 212 

Liu, Yunjing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-60 130 

Liu, Yunlong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-47 128 

Liu, Yuyan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA05-4 95 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB01-3 101 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-14 112 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA07-3 153 

Liu, Zhenbin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB08-2 163 

Liu, Zhenghua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA10-5 213 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB11-2 222 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB11-3 223 

Liu, Zhenxing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-39 127 

Liu, Zhi-Ping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB08-4 164 

Liu, Zhi-Xin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA08-2 154 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA11-3 156 

Liu, Zhigang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB06 

CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB06-5 162 

Liu, Zhixin. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA01-2 148 

Liu, Zhongxin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA09-1 98 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA06-3 152 

Liu, Zhunga. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrA03-1 93 

Liu, Zilin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-71 132 

Liu, Zunren . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-66 131 

Long, Hui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-48 185 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-58 187 

Long, Junfeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-05 167 

Long, Yue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-01 123 

Lou, Xuyang. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrA-27 114 

Lou, Youcheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA08-5 154 

Lou, Zhengzheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-11 193 

Lu, Chanchan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA06-7 210 

Lu, Di . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB09-6 165 

Lu, Guizhang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-03 179 

Lu, Hongli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-20 182 

Lu, Huimin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-47 141 

Lu, Jing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-07 136 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA07-2 153 

Lu, Jinghua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-52 198 

Lu, Jinhu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA10-3 99 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA11 C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA11-2 156 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA11-5 157 

Lu, Jinyun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-87 134 

Lu, Kai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-91 134 

Lu, Na . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-05 192 

Lu, Peizhong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA06-6 210 

Lu, W.-S.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA03-4 207 

Lu, Yang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-07 136 

234


Author Index 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA07-2 153 

Lu, Yazhou . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB06-6 162 

Luan, Fan. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrB01-4 101 

Lueth, Tim C. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PD-2 205 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PL-3 91 

Luh, Peter B. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PD-2 205 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB08 C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA04-4 208 

Luo, Ming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA06-5 152 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB07-5 163 

Luo, Qiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-80 145 

Luo, Xiaoyuan. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB02-2 215 

LUO, Xiong-lin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB02-6 102 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB03-2 102 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB05-1 104 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-01 135 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-05 136 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB04-1 159 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA07-1 210 

Luo, Xiong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB02-3 102 

Luo, Yudong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB06-4 219 

Luo, Yunhui. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrB-44 128 

Luo, Zhen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-35 139 

Lv, Haotun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA05-6 152 

Lv, Hong-tao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB03-3 103 

Lv, Hongli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA08 

CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA08-5 211 

Lv, Qiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB03-3 103 

Lv, Weijie. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrC-38 140 

Lv, Wenjun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-11 124 

Lv, Yisheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-06 123 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-61 131 

Lv, Yong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-68 119 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-95 122 

Lv, Yuegang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-56 117 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-30 170 

M 

Ma, Bibo. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-77 189 

Ma, Chao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA06-4 210 

Ma, Fengning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrA-89 122 

Ma, Fengying. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-50 129 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-57 186 


. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-44 185 

Ma, Fengying . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-44 185 


Ma, Hao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-80 189 

Ma, Hongbin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA10-4 99 

Ma, Hongji. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrB-10 124 

Ma, Hui-xia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-24 113 

Ma, Huijie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-86 203 

Ma, Jiachen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-88 178 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB07-2 162 

Ma, Jing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB10-3 222 

Ma, Jingcong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-88 146 

Ma, Kai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA01-2 148 

Ma, Tao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-93 204 

Ma, Weiwei. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrC-40 140 

Ma, Xiaoping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-50 198 

Ma, Xin Jun. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaC-40 197 

Ma, Yulan. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-35 184 

Ma, Yuxin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB08-2 107 

Mao, Lifei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-09 168 

Mao, Qing-tang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB03-6 103 

Mao, Ying . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB11-2 166 

Mao, Yuwen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-53 186 

Markdahl, Johan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA09-6 99 

Matsuno, Takayuki . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-09 168 

Memon, Qurban . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-11 180 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-10 193 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB05-6 218 

Men, Xiyao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-02 110 

Meng, Fancheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-55 142 

Meng, Fangfang. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA11-3 213 

Meng, Li . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-39 171 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-40 184 

Meng, Max, Q.-H.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PD-2 

C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PL-3 C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-14 168 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-52 186 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB08-5 164 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA03-4 207 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA06-5 210 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB05-5 218 

Meng, Wei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-36 171 

Meng, Xiang-zhong. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrC-86 146 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-90 147 

Mi, Wenjun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-49 141 

MIAO, Ling-juan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-63 118 

Min, Huasong. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaA-92 179 

Ming, Zhiyuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-01 191 

Mo, Hong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB02 

C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB02-4 102 

Mo, Lei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-23 182 

Mooring, Ben . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB09-1 164 

Mu, Ping’an. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaC-75 201 

Mu, Xiaojiang. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrA-29 114 

Mu, Zhichun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-49 198 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-51 198 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-58 199 

N 

Naiborhu, Janson . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA03-6 150 

Nguyen, Bao. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-91 191 

Ni, Jiacheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA11-5 100 

Ni, Jing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-16 169 

Ni, Pingqiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB08-3 220 

Ni, Wei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-36 127 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-47 185 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-23 194 

Ni, Zhen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB09-2 221 

Niamsup, Piyapong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA02-1 149 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA02-2 149 

235


. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA01-2 205 

Nie, Donghu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB01-6 101 

Nie, Junlian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-22 138 

Nie, Qi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB01-6 157 

Ning, Bin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-39 196 

NING, Shu-Rong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-28 138 

Niu, Ran . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA03-1 206 

Niu, Xiaobing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB02-1 101 

Niu, Ying . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB08-5 221 

Niu, Yugang. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA07-4 210 

Nong, Mengsong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-20 125 

Nourafza, Nasim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA02-1 92 

O 

OHATA, AKIRA. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB10 

CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB10-1 165 

Ou, Yongsheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA05-3 209 

Ouyang, Fan. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrB07-6 106 

P 

Pan, Baogui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-12 111 

Pan, Changpeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-70 119 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-77 145 

Pan, Haining. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaC-59 199 

Pan, Haipeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-87 134 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-74 201 

Pan, Li . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA04-5 151 

Pan, Qi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA05-2 151 

Pan, Shuang. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-87 190 

Pan, Yu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA11-6 214 

Pan, Zheng. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrC-08 136 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA07-3 210 

Pang, Chee Khiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB11 

C 

Pang, Jie. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB11-1 222 

Pei, Binbin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB04-5 217 

Peng, Daogang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-40 127 

Peng, Dongliang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-12 111 

Peng, Hui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA06-6 96 

Peng, Hui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-64 143 

Peng, Junmin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB02-3 215 

Peng, K.-W. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-63 187 

Peng, Kui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-67 188 

Peng, Qinke . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-43 172 

PENG, Xiafu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-94 179 

Peng, Zhihong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA11-1 100 

Peng, Zhouhua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB02-1 158 

Petzold, Linda. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB08-6 164 

Phang, Swee King . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA10-6 213 

Popov, Dejan. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB01-3 214 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB01-6 215 

Pu, Yuanyuan. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaC-62 200 

Pu, Zhiqiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA09-5 155 

Pu, Zhonghao. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB09-3 165 

Q 

Qi, Chenkun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB08-3 107 

Qi, Guoqing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-93 191 

Qi, Hongsheng. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaA-74 176 

Qi, Huan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-07 111 

Qi, Rongbin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-20 113 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-15 169 

Qi, Ruiyun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB02-3 158 

Qi, Shengbiao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-67 144 

Qi, Shiqing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-23 138 

Qi, Shuhu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-39 196 

Qi, Wenyuan. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrB01-2 101 

Qian, Bin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB11-3 109 

Qian, Chunjiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB10-1 108 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-36 139 

Qian, Feng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA04-6 95 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-20 113 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-52 129 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-15 169 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-23 170 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-60 174 

Qian, Jinwu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-49 141 

Qian, Wenhua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-62 200 

Qian, Xu. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrA-90 122 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB06-2 161 

Qiang, Yanhui. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaA-76 176 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-69 188 

Qiao, Gang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB01-6 101 

Qiao, Hong. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB04-6 160 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA06-2 209 

Qiao, Jihong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA04-2 94 

Qiao, Jinghui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA09-4 155 

Qiao, Meiying . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-50 198 

Qiao, Yanfeng. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrA07-1 97 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB07-1 105 

Qiao, Yupeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-24 170 

Qiao, Zongmin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-73 144 

Qin, Chang-ming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-45 185 

Qin, Feng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-26 138 

Qin, Hong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-71 144 

Qin, Huashu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-33 171 

Qin, Ling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-24 194 

Qin, Shoutong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-07 123 

Qin, Xiaoyu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-43 172 

Qiu, Chunxin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA05-6 209 

Qiu, Quan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-56 142 

Qiu, Sihai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA06-3 152 

Qiu, Zhanzhi. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrB-49 129 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-51 129 

Qiu, Zhenzhen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-21 138 

Qiu, Zhihui. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrB10-5 109 

Qiu, Zhipeng. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaC-90 203 

QU, Feng-Lin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA01-6 149 

Qu, Xiaomei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB10-1 222 

Qu, Yanhua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-71 144 

Qu, Zhihua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-48 185 

R 

RAJASHEKARAN, ARUNESHWARAN . . . . . . . . . . . . . . SuB11-5 223 

Ren, Jia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-77 133 

Ren, Jun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-41 140 

Ren, Li-hong. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrB03-2 102 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB05 CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB05-1 104 

Ren, Xiudong. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrA-38 115 

236


Author Index 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-25 126 

Ren, Yan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB11-2 222 

Ren, Zhang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB07-4 106 

Ren, Zhigang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA02-2 206 

Ren, Ziwu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-05 110 

Ricanek, Karl . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA03-2 207 

Rojsiraphisal, Thaned . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA02-5 149 

Rong, Mei. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB11-1 222 

ROY, TUSHAR KANTI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA05 

CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA05-2 95 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB02-6 158 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB08-1 163 

Ru, Hai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA04-6 151 

Ruan, Xiaogang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA03-5 94 

Ruan, Xiaogang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-08 168 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-67 188 

Ruan, Xiaogang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB02-2 102 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-03 192 

Ruan, Xiaogang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-84 203 

Ruan, Xiaogang. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA05-4 151 

RUAN, Xiu-kai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-17 125 

Ruan, Yue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-85 190 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-91 203 

S 

Sam, Yahaya . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB07 

C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB07-3 163 

Sang, Zi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-90 203 

Sauvik, Das Gupta . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB02-5 102 

Setayeshi, Saeed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA02-1 92 

Shakeri, Moein . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB06-3 219 

Shan, Mingguang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-83 190 

Shang, Hong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA01-1 91 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA07-5 97 

Shang, Ke . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB11-6 110 

Shang, Xiuqin. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaA-51 173 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-55 174 

Shang, Zengguang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-48 198 

Shao, Bin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-16 169 

Shao, Cheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA07-6 211 

Shao, Genfu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-12 111 

Shao, Weiming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-12 181 

Shao, Xuesong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-89 178 

SHAO, Zhijiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-34 183 

Shao, Zhiwei. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaA-79 177 

Shen, Dong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA07-7 211 

Shen, Gongzhang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA05-3 95 

Shen, Hao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-31 171 

Shen, Jie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-50 117 

SHEN, Jun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-63 118 

Shen, Qikun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-66 175 

Shen, Tielong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB10 

C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB10-4 166 

Shen, Tielong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PL-2 

C 

Shen, Ting’ao. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-54 186 

Shen, Xiaoyu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA01-6 205 

Shen, Yanlin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-07 180 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB03-4 216 

Shen, Yantao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB06-4 219 

Shen, Yi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-32 114 

Shen, Yi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-43 116 

Shen, Ying . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-59 118 

Shen, Zhipeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-16 193 

Sheng, Jie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-24 182 

Sheng, Li . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-28 170 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-29 170 

Sheng, Weihua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB02-5 102 

Shi, Bing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-90 191 

Shi, Dalong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA10-2 212 

Shi, Guodong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA08-5 154 

Shi, Hongbo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB08-1 107 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB08-2 107 

Shi, Hongyu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB10-6 109 

Shi, Jianjun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-22 182 

Shi, Jin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-89 122 

SHI, Juan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB04 

CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB04-4 104 

Shi, Lei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-51 117 

Shi, Lin. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-27 138 

Shi, Mengting. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrB-70 132 

Shi, Mina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-82 133 

Shi, Peiming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-54 117 

Shi, Peng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA06-2 152 

Shi, Rongqi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA05-3 151 

SHI, Shiying . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-88 203 

Shi, Wuxi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-75 120 

Shi, Xiaocheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB01-1 101 

Shi, Xiaowei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-01 110 

Shi, Yu. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaA-50 173 

Shi, Yujing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA01-1 205 

Shi, Zhiguo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-68 188 

Shi, ZhongKe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-69 201 

Shimohara, Katsunori. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrA-88 121 

Shumsky, Alexey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA06-2 152 

Si, Wenhui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-75 176 

SONG, Bo. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrC-76 145 

Song, Chao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-87 146 

Song, Cheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB09-1 107 

Song, Chunyue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-02 123 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-03 135 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-08 136 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA07-3 210 

Song, Dandan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-92 122 

Song, Fangzhen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-76 145 

Song, Ge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB06-1 161 

Song, Hongjun. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrB02-2 102 

Song, Hongting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA11-6 214 

Song, Jianmei. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA05-3 151 

SONG, Jingyu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-45 128 

Song, Jinli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA08-4 211 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA08-6 212 

Song, Juan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-18 137 

Song, Liping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-49 185 

Song, Maoqiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-88 121 

Song, Meng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA07-2 97 

Song, Pei. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrB-94 135 

Song, Qi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-26 170 

Song, Ruizhuo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB10-4 222 

Song, Wenjun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB02-4 215 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB02-6 215 

SONG, YANG. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaA-81 177 

Song, Yan. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA01-4 148 

Song, Yiming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-32 171 

Song, Ying . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB07-1 162 

SONG, Yong-jie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-44 116 

Song, Yu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-58 142 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-53 198 

Song, Zhihuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-04 192 

Srinivasan, Haritha . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB02-5 102 

Stern, Harold . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB09-1 164 

Stojanovic, Sreten. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB01-3 214 

237


. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB01-6 215 

Su, Baili . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB08-3 107 

Su, Boxian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-13 137 

Su, Dan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-07 180 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB03-1 216 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB03-4 216 

Su, Gang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-70 188 

Su, Housheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA10-2 99 

Su, Jianbo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB07-7 106 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA06-3 209 

Su, Jianhua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB04-6 160 

SU, Jinlong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-58 174 

Su, Jin. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaC-43 197 

Su, JinXia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-72 189 

SU, Tao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB02-2 158 

SU, Yongzhen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-86 146 

Su, Youfeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA08-3 154 

Sun, Changchun. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaC-63 200 

Sun, Changyin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-27 170 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA03-2 207 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB01-5 214 

Sun, Dihua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-56 199 

Sun, Fengchi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-63 131 

Sun, Fengchi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA07-2 97 

SUN, Gang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB02-1 158 

Sun, Hexu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-79 120 

Sun, Hexu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB07-5 106 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-30 114 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-57 199 

Sun, Jing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA11-2 100 

Sun, Jinsheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-06 136 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-36 196 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-37 196 

Sun, Jitao. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrA08-3 98 

Sun, Junwei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-32 114 

Sun, Lei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-49 129 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-51 129 

Sun, Li-Ying . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-75 132 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-72 144 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-91 147 

Sun, Liang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA01-4 92 

Sun, Mingzhu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-03 179 

Sun, Qu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB04-2 103 

Sun, Shuli. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB10-3 222 

Sun, Suqin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-07 111 

Sun, Wei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-90 122 

Sun, Weijie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-24 170 

Sun, Weiwei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA02 

CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA02-3 149 

Sun, Xiaoyan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA11-2 100 

Sun, Xubin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-06 123 

Sun, Xue. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA02-4 206 

Sun, Yajie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB05-1 160 

Sun, Yannan. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrB-84 134 

Sun, Yazhen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-63 200 

Sun, Yong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-18 194 

Sun, Youxian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB05-3 218 

Sun, Yu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-48 173 

Sun, Zongxin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB01-6 101 

Sundaram, Suresh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB11-5 223 

Suo, Jinghui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA08-3 98 

Suo, Xudong. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaA-24 170 

T 

Tacnet, Jean-marc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA03-1 93 

Tan, Cheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA09-3 212 

TAN, Libo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-72 201 

Tan, Min . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA09-4 99 

Tan, Xiangmin. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA09-5 155 

Tan, Yongqian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-62 200 

Tan, Zongkai. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-58 187 

Tang, Cheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA05-3 209 

Tang, Chong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA05-2 208 

Tang, Gao-hui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-69 176 

Tang, Hui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-89 191 

Tang, Jie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA10-3 212 

Tang, Qiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-45 197 

Tang, Shuangze . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-87 203 

Tang, Xianjuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-26 138 

Tang, Xiufang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-42 184 

Tang, Yi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-56 199 

Tang, Ying . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-85 190 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-91 204 

Tang, Yirui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB06-1 218 

Tang, Yutao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA09-2 98 

Tao, Lili . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-15 169 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-60 174 

Tao, Quan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-04 167 

Tao, Xiaoliang. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-08 180 

Tarn, Tzyh-Jong. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PL-5 

C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA08-6 154 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB08-5 164 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA11-5 214 

Thunberg, Johan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB02 

C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB02-4 215 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB02-6 215 

Tian, Bailing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-17 137 

Tian, Guohui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-52 142 

Tian, Jianyan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-35 115 

Tian, Liguo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-07 167 

Tian, Lingling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-31 195 

Tian, Wan-hu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB03-6 103 

Tian, Xue-Min . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-60 118 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-12 181 

Tian, Yantao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-46 197 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-65 200 

Tian, Ye. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-30 183 

Tiwari, Shanaz. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrB09-4 108 

Tong, Chaonan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-09 136 

Tong, Guofeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB03 

C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB03-2 159 

Tong, Ru-qiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-80 202 

Tu, Chunling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-60 199 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-61 200 

Tu, Jun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-68 188 

Tu, Yaqing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-07 180 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-54 186 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB03-1 216 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB03-4 216 

Tu, Yaqing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-53 186 

Tutsoy, Onder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA07-6 154 

V 

Valeyev, Najl . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA10-2 99 

Van Wyk, Barend . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-60 199 

238


Author Index 

W 

Wan, Chuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-46 197 

Wan, Jiaona . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-34 183 

Wang, Baocheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-59 143 

Wang, Baolei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB11-3 166 

WANG, Beiyang. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA11-5 157 

Wang, Bei. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB05-1 218 

Wang, Biao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB09-6 108 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA10 C 

Wang, Bin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-15 181 

Wang, Bing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-53 173 

Wang, Bo. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrB-23 125 

Wang, Bo. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrA-81 121 

Wang, Bohua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-22 169 

Wang, Caifeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA06-4 209 

Wang, Chao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-49 116 

Wang, Chao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-54 199 

Wang, Chaogang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA04-5 208 

Wang, Chaoli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB10-3 108 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB11-3 166 

Wang, Chengmin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-61 118 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-54 174 

Wang, Chun-lin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-30 126 

Wang, Cuihong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-17 194 

Wang, Dai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA04-2 150 

Wang, Dan. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB02-1 158 

Wang, Dan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-83 177 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-84 177 

Wang, Deji . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-20 138 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-31 139 

Wang, Ding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA09-6 155 

WANG, Dong-yun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB02-4 158 

Wang, Dong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-07 123 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-01 167 

Wang, Dongxiao. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrC-50 141 

Wang, Fan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA05 

CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA05-3 209 

Wang, Fei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA05 

CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA05-5 152 

Wang, Fei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB09-6 108 

Wang, Fei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-81 202 

Wang, Guangchen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA09 

CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA09-4 212 

Wang, GuangHui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-28 183 

Wang, Guanwei. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrA01-4 92 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA03-5 94 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-68 200 

WANG, Guirong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-74 189 

Wang, Guo-sheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB03 

C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB03-3 103 

WANG, GUODONG . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-31 126 

Wang, Guohua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-15 124 

Wang, Hai-Hong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-33 196 

Wang, Haito . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-70 176 

Wang, Hanlei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB02 

CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB02-1 215 

Wang, Hao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-36 115 

Wang, Hao. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB02-1 158 

Wang, Heng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-01 123 

WANG, Hongjian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA05-6 95 

Wang, Hong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-11 136 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA06-6 153 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB05-1 160 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB05-6 161 

Wang, Hongbin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-77 176 

Wang, Hongbo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA05-1 208 

Wang, Hongrui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-46 141 

Wang, Hongxia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA09-1 212 

Wang, Hongxin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-85 203 

Wang, Hongyan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA04-2 94 

Wang, Hua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-11 168 

Wang, Hua O. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA01-5 148 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA02-5 206 

Wang, Huan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-59 199 

Wang, Huangang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB04-4 160 

Wang, Hui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA11-4 213 

Wang, Hui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-08 123 

Wang, Jian-an . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-83 133 

WANG, Jianguo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-94 147 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-93 179 

Wang, Jiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-12 193 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-13 193 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB08 C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB08-2 220 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB08-4 220 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB08-6 221 

Wang, Jianhui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-25 195 

Wang, Jianhui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-44 172 

Wang, Jiankui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB10 

CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB10-5 109 

Wang, Jianliang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB11-5 223 

Wang, Jianwen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-46 185 

Wang, Jie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-17 137 

Wang, Jie-sheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-38 115 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-25 126 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-59 174 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-61 187 

Wang, Jing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-29 183 

Wang, Jing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-82 145 

Wang, Jing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-48 129 

Wang, Jing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-31 171 

Wang, Jingcheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB05-5 104 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA01-4 148 

Wang, Jinhuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-30 114 

Wang, Jinkuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB06-5 162 

Wang, Jinping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-94 204 

WANG, Kai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB06-2 219 

Wang, Kai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-49 198 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-51 198 

WANG, KEJUN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-55 199 

Wang, Kexin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-34 183 

WANG, LI. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaA-20 169 

Wang, Li . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-51 186 

Wang, Li. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrA-50 117 

Wang, Li . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-27 170 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB01-5 214 

Wang, Liangshun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-72 132 

Wang, Lidan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB11-6 166 

Wang, Lifu. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrB-60 130 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-23 138 

Wang, Lijun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-09 136 

Wang, Liling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-46 141 

Wang, Lin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-25 138 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB09-3 165 

Wang, Lin. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrA09-6 99 

Wang, Ling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA11 

CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA11-3 100 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA11-4 100 

239


. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB11 CC 

Wang, Liwei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-57 199 

Wang, Lu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-93 204 

Wang, Lujia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-14 168 

Wang, Mengling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB08-1 107 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB08-2 107 

Wang, Min . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-43 197 

WANG, NA. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB07-3 219 

Wang, Nan. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA04-5 208 

Wang, Nana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-87 121 

Wang, Ning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB02 

CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB02-1 101 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB02-1 158 

Wang, Pan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA02-3 149 

Wang, Peijin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA01 

C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA01-2 92 

Wang, Peng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-49 173 

Wang, Peng. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB04-5 160 

Wang, Ping. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrC-67 144 

Wang, Pu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA03-2 93 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-08 111 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-09 111 

WANG, Qiangde . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-34 171 

Wang, Qi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-40 140 

Wang, Qiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-90 147 

Wang, Rui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-15 124 

Wang, Ruihua. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA01-3 205 

Wang, Sai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-18 112 

Wang, Shengyao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA11-3 100 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA11-4 100 

Wang, Shi Ming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-67 175 

Wang, Shi-gang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-73 120 

Wang, Shiming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-80 145 

Wang, Shiming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-91 134 

WANG, Shoufeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-73 176 

Wang, Shouqiang. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaC-83 203 

Wang, Shu’en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-47 116 

Wang, Shuai. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaC-58 199 

Wang, Shubin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-01 135 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-05 136 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA07-1 210 

Wang, Shuoyu. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrC-48 141 

Wang, Songyan. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrB01-5 101 

Wang, Wei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA06-6 210 

Wang, Wei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-62 175 

Wang, Wei. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrB05-3 104 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB11-4 109 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB01-3 157 

Wang, Wei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-89 178 

Wang, Wei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA01 

C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA01-1 148 

Wang, Wei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-35 184 

Wang, Weihong. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-94 191 

Wang, Weihua. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrA-34 115 

Wang, Weiping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-50 173 

Wang, Weiqun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-18 181 

Wang, Weixing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaA-51 173 

Wang, Xiao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-03 110 

Wang, Xiaobo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-14 124 

Wang, Xiaofan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB08-3 163 

Wang, Xiaofan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA10-2 99 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-59 130 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB02-5 215 

Wang, Xiaofeng. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrA10-5 100 

Wang, Xiaoli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-36 127 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-47 185 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-23 194 

Wang, Xiaoyan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-86 134 

Wang, Xiaoyi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-42 116 

Wang, Xiaozhe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-94 204 

WANG, Xin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA04-6 95 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA07 CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA07-1 153 

Wang, Xin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-66 200 

Wang, Xin. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB04-3 217 

Wang, Xing-yu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB05-1 218 

Wang, Xingang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-60 130 

Wang, Xingcheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA09-2 155 

Wang, Xingdan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA10-4 156 

Wang, Xingxuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA04 

C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA04-4 94 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA04-4 151 

Wang, Xinmin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-86 134 

Wang, Xiuhong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-29 139 

Wang, Xuan. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA09-1 212 

Wang, Xuejie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA01-6 92 

Wang, Ya-hui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB04-6 104 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-65 119 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA04 C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA04-5 208 

WANG, Yan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-25 113 

Wang, Yan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-41 184 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-42 184 

Wang, Yanfeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA02-3 93 

Wang, Yanwei. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB07-5 163 

Wang, Yijing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA02 

C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA02-4 149 

WANG, Yong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB09-1 107 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-28 114 

Wang, Yong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-15 112 

Wang, Yong Ming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-85 121 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-93 122 

Wang, Yongchu. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrA-66 119 

Wang, Yongyu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB04-2 103 

Wang, Youqing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB08-7 164 

Wang, Yu. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrC-14 137 

Wang, Yuan. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrB09 

C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB09-4 108 

Wang, Yuehua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-04 123 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-05 123 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA01-4 205 

Wang, Yunpeng. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA09-4 99 

Wang, Yuzhen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA08 

C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA08-1 98 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA03-3 150 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB08-2 163 

Wang, Zaiying. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB04 

CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB04-2 160 

Wang, Zhang-quan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-85 190 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-91 204 

Wang, Zhaojie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA04-5 151 

Wang, Zhengjie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB06-2 161 

Wang, Zhengyuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-15 124 

Wang, Zhenlei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA04-6 95 

Wang, Zhi-qian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA07-1 97 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB07-1 105 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-28 195 

WANG, Zhiliang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-68 188 

Wang, Zhiying . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA07 

C 

240


Author Index 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA07-3 97 

Wang, Zhongwei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-74 120 

Wang, Zhuo. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB09-1 221 

Wang, Zhuping. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB11-2 166 

Wang, Zidong. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrA10-5 100 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA06-1 152 

Warakorn, Sudsanguan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA02-5 149 

Weera, Wajaree. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA01-2 205 

Wei, Airong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA03 

CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA03-3 150 

Wei, Bin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-62 187 

Wei, Changming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA05-5 209 

Wei, Chen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA10-3 212 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB11-4 223 

WEI, Chunling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-34 171 

Wei, Dongfang. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrA-33 114 

Wei, Hua. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB01-2 157 

Wei, Jianping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-75 176 

WEI, Junming. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-68 188 

Wei, Kongming. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB07-5 220 

Wei, Li . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB10-5 222 

Wei, Ming. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrA02-6 93 

Wei, Qinglai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA08-1 154 

Wei, Ruoyan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-08 168 

Wei, Shaoqing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrB-05 123 

Wei, Wei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-94 122 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB04-1 217 

Wei, Xile. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaC-12 193 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-13 193 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB08-2 220 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB08-4 220 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB08-6 221 

Wei, Xinjiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-29 139 

Wei, Yanhui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-70 144 

WEI, Yiheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-28 114 

Wen, Chuanbo. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-10 180 

Wen, Guanghui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA10-1 99 

Wen, Jie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-14 193 

Wen, Sufang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-25 138 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB09-3 165 

Wen, Xiao-Qin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-63 175 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-50 185 

Weng, Ninglong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB03-2 159 

Wu, Aiguo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-39 140 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-37 184 

Wu, Ban . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB07-7 106 

Wu, Chengdong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA01-1 91 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA07-5 97 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-22 113 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-13 168 

Wu, Gengfeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-66 131 

Wu, Haitao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-21 125 

Wu, Hao. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrC-52 142 

Wu, Jian Kang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB10 

CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB10-6 222 

Wu, Jiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA04-2 150 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA04-5 151 

Wu, Jianhua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-71 144 

Wu, Jie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB08-1 220 

Wu, Jin Ping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA11-1 100 

Wu, Jun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-45 141 

Wu, Junfeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-61 175 

Wu, Maogang. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaA-82 177 

Wu, Mengzhou . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-87 203 

Wu, Minjie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-75 201 

Wu, Nian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-26 113 

Wu, Ning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-40 197 

Wu, Qian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-12 168 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB04-6 217 

Wu, Rebing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA11-5 214 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA11-7 214 

Wu, Weiling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-79 189 

Wu, Weirong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-23 125 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-17 181 

Wu, Wen-Yen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-42 197 

Wu, Xiangjun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB06-5 105 

Wu, Xinyu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-54 199 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA09-3 155 

Wu, Xiuli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA02 

CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA02-5 93 

Wu, Xueli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-04 123 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA01-4 205 

Wu, Yanrui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-38 171 

Wu, Yifei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-41 172 

WU, Ying . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-82 190 

Wu, Youli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-38 171 

Wu, Yunjie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-67 200 

Wu, Zhaoxia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-60 130 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-23 138 

Wu, Zhen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA09-4 212 

Wu, Zhifang. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaA-48 173 

Wu, Zhizheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-49 141 

Wu, Zhong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA05-6 152 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB07-5 220 

Wu, Zizhang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB03-2 159 

X 

Xi, Huiling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-25 170 

Xi, Zairong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA11 

C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA11-4 213 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA11-6 214 

Xia, Chao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-62 131 

XIA, Jianwei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA03-2 207 

Xia, Wen. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-70 188 

Xia, Zhifeng. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrB04-6 104 

Xian, Bin. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrB07 

C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB07-2 106 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB07-3 106 

Xiang, Ji . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB04 

CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB04-1 217 

Xiang, Tao. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrA-10 111 

Xiang, Weilong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA07-5 210 

Xiao, Huimin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA08-4 211 

Xiao, Jianan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-87 121 

Xiao, Jinzhuang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-46 141 

Xiao, Li . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-79 120 

Xiao, Lin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA03-3 93 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-06 167 

Xiao, Lingfei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-42 172 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-38 196 

Xiao, MingQing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB03-4 103 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB05 C 

241


. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB05-2 160 

Xiao, Peng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-92 134 

Xiao, Qian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-27 195 

XIAO, WEI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-07 180 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB03-4 216 

Xiao, Wendong. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB10 

C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB10-4 222 

Xiao, Yingchao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB04-4 160 

Xiao, Yunqi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-56 117 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-30 170 

Xie, Changwu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-76 133 

Xie, Daocheng. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrA-74 120 

Xie, FangMing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-90 147 

Xie, Jihua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB04-3 103 

Xie, Lihua. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrA09-3 98 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA10-6 100 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB06-3 105 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB09 CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB09-3 108 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB10-2 108 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PL-1 C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA01-5 205 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB10-1 222 

Xie, Min . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-54 186 

Xie, Mujun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA07-1 97 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-28 195 

Xie, Nan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-06 111 

Xie, Qing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-59 143 

Xie, Qing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-60 143 

Xie, Qiuyan. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrC-78 145 

Xie, Ronghua. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrC-34 139 

XIE, Wei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB03 

CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB03-1 102 

Xie, Xue-Jun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA09-2 212 

Xin, Weidong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-13 137 

Xing, Guansheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB07-5 106 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-62 118 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-57 199 

Xing, Zhiwei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-15 193 

Xing, Zhiwei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-71 201 

Xiong, Dan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-47 141 

XIONG, Gang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-06 123 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-12 124 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-61 131 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-51 173 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-55 174 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA04-1 207 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA04-6 208 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA07-7 211 

Xiong, Ji-ming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-69 176 

Xiong, Jie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA09-4 212 

Xiong, Lingchun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-35 184 

Xiong, Rong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-45 141 

Xiong, Weiqing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-22 125 

XU, Bao-chang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-44 116 

Xu, Bugong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-63 143 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-23 182 

Xu, Changqing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-09 180 

Xu, Chuanzhong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-66 119 

Xu, Dan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-62 200 

Xu, Dengguo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-17 169 

Xu, Dexin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-70 144 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-93 204 

XU, Fen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-65 188 

Xu, Feng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-84 203 

Xu, Haojun. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB04-5 217 

Xu, Hongxia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-51 141 

Xu, Jian-min . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-91 134 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-78 145 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-67 175 

Xu, Jianmin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-73 189 

Xu, Jianmin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-80 145 

Xu, Jintao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-05 192 

Xu, Jinxue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-36 115 

Xu, Lifeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-24 138 

Xu, Liyun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-06 110 

Xu, Longjian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-19 169 

Xu, Peng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-92 204 

Xu, Qicheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-63 200 

Xu, Qin. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-06 136 

Xu, Qinghan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-44 197 

Xu, Sen. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-85 190 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-91 204 

Xu, Shengyuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-85 134 

Xu, Shouhuai. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrC-36 139 

Xu, Wenli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB04-4 160 

Xu, Xiangru . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA08-4 98 

Xu, Xiaoming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-10 180 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA02-5 206 

Xu, Xin. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB09-5 221 

Xu, Xing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-28 183 

Xu, Yangsheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PD-2 205 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA05-3 209 

Xu, Yanying . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-09 124 

Xu, Ye . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA11-3 100 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA11-4 100 

Xu, Yong. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-01 179 

Xu, Yongjun. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrB07-1 105 

Xu, Zhanbo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA04-2 150 

Xu, Zhishu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-28 183 

Xu, Zhuojun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-65 200 

Xue, Anke . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-06 180 

Xue, Li . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-17 112 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-18 112 

Xue, Mantian. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB05-3 161 

Xue, ShiBei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA11-7 214 

Y 

YAN, Aijun. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrA-08 111 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-09 111 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-49 173 

Yan, Deli. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaC-53 198 

Yan, Fang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-93 135 

Yan, Gang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-69 176 

Yan, Gangfeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA05-2 208 

Yan, Gangfeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-11 168 

Yan, Gangfeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA07-6 97 

Yan, Jiangang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-77 145 

Yan, Jianshu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB06-1 161 

Yan, Jing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA05-1 151 

Yan, Joseph. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA04-4 208 

YAN, Junwei. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrC-10 136 

Yan, Jun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-70 176 

Yan, Keguo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-14 137 

YAN, TAO. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaC-55 199 

Yan, Tao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-54 174 

Yan, Tingfang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA03-4 207 

242


Author Index 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB05-5 218 

Yan, Wenjun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-79 133 

Yan, Yan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB04-4 104 

Yan, Zheping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-29 126 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-57 142 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-52 173 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-56 174 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-57 174 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-39 184 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-29 195 

YAN, Zhi-gang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-32 126 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-15 137 

Yang, Aiping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-83 146 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-72 201 

Yang, Baoqing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA10-1 155 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA10-4 156 

Yang, Bo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-11 111 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB06-3 162 

Yang, Chenguang. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrA10-4 99 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-59 143 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-60 143 

Yang, Chunhua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB09 

CC 

Yang, Dedong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-30 114 

Yang, Fan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA10-3 213 

Yang, Fang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB10-3 108 

Yang, Fuwen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA07-4 153 

Yang, GuiGen. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaA-67 175 

YANG, Hong Tao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-58 118 

Yang, Hong-yong. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB07-4 220 

Yang, Hua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB06 

C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB06-4 105 

Yang, Huan. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrC-14 137 

Yang, Hui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA07-1 153 

Yang, Huiyue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-53 186 

YANG, JIAN. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB07-3 219 

Yang, Jie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-36 127 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-47 185 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-23 194 

Yang, Jie. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaA-02 167 

Yang, Jinliang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB08-5 164 

Yang, Jizhi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA03-2 150 

Yang, Jun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB01-4 101 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-33 114 

YANG, Le-ping. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrA04-1 94 

Yang, Lei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA11-2 100 

Yang, Li . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-19 112 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-90 178 

Yang, Lingling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-90 134 

Yang, Lingyu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA05-3 95 

Yang, Meng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA08-2 98 

YANG, Min . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-41 197 

Yang, Ming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB01-5 101 

Yang, Peng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-30 114 

Yang, Qiong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA01-4 92 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-68 201 

Yang, Ruoting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB08 

CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB08-6 164 

Yang, Sen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-86 134 

Yang, Shizhong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB10-1 108 

Yang, Tao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB03-6 159 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA03-5 207 

Yang, Tao. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrA-92 122 

Yang, Tianqi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-86 121 

Yang, Wankou . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA03 

C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA03-2 207 

Yang, Weichao. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrB10-5 109 

Yang, WenFu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-15 112 

Yang, Xian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA05-1 151 

Yang, Xiaoyi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-12 168 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB04-6 217 

Yang, Xu. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-81 190 

Yang, Yana. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB05-5 161 

Yang, Yang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-16 193 

Yang, Yanhua. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrB-37 127 

Yang, Yong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-21 169 

Yang, Yuanjing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB10-6 222 

Yang, Yuequan. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrB01-1 101 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-65 131 

YANG, Yunchun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB07-4 106 

Yang, Yun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-10 111 

Yang, Yungao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB07-3 106 

Yang, Zhenguang. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrC-29 139 

YAO, Jian-shi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA04-2 207 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA04-6 208 

Yao, Jie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-84 146 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-92 147 

Yao, Kaixue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-19 169 

Yao, Lina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA06 

CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA06-4 152 

Yao, Lu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-75 132 

Yao, Pengfei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA04-5 95 

Yao, Qingming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-61 131 

Yao, Wen-ji . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-85 190 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-91 204 

Yao, Yu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-16 181 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA10-1 155 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA10-3 156 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA10-4 156 

Yasuda, Gen Ichi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-61 143 

Yasui, Yuji . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB10-2 165 

Ye, Dan. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrB-01 123 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-19 181 

Ye, Peijun. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrB-61 131 

Ye, Qian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-27 114 

Ye, Wen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-45 172 

Ye, WenJun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-60 143 

Ye, Xudong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB02-3 215 

Ye, Yangdong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-09 192 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-11 193 

Yi, Jian-qiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-89 178 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA09-5 155 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB09-6 165 

Yi, Jianqiang. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrA06-2 96 

Yi, Peng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-54 186 

Yi, Yang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA07 

CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA07-6 97 

YIn, Fengjie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-38 127 

Yin, Hong Li . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-85 121 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-93 122 

Yin, Yixin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-09 136 

Yin, Yixin. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB07-6 163 

Yin, YongHua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA03-3 93 

Yin, Zhi-gang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-83 133 

YIN, Ziqiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB03 

CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB03-1 158 

You, Keyou . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA10 

C 

243


. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA10-6 100 

You, Xiazhu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-52 129 

Yu, Chengpu. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrB06-3 105 

Yu, Chunxuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-87 203 

Yu, Dehong. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrA-16 112 

Yu, Fashan. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA03-5 150 

YU, FEI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-69 144 

Yu, Fusheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-03 110 

Yu, Haibin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-35 127 

Yu, Haisheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-35 196 

Yu, Haixin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-04 110 

Yu, Hangchen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB06-4 162 

Yu, Hongxiao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-78 202 

Yu, Jianjun. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrA01-4 92 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA03-5 94 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-68 200 

Yu, Juanyi. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB05-2 218 

Yu, Jue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-16 112 

Yu, Junyan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB10-2 222 

Yu, Junzhi. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA05-5 209 

Yu, Lei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB09-4 165 

Yu, Liye. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaC-25 195 

Yu, Mei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB10-2 222 

Yu, Naigong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-03 192 

Yu, Naigong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-84 203 

Yu, Ruihong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-39 115 

Yu, Shanshan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB05-1 218 

Yu, Shouyuan. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-52 186 

Yu, Shun. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-84 190 

Yu, Tao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-09 180 

Yu, Wenlong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-91 122 

Yu, Wenwu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA10-3 99 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA11-4 156 

Yu, Xiao. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB03-6 159 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA03 CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA03-5 207 

Yu, Xiaotian. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrA03-3 93 

Yu, Xinghuo. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrB04-4 104 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB10-6 109 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA11 CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA11-2 156 

Yu, Xuefeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-28 126 

Yu, Yang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA02-4 93 

Yu, Zhengshi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-47 198 

Yu, Zhiqiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-53 142 

Yu, Zhongqing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-66 131 

Yuan, Fushun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA01-6 149 

Yuan, GuiLi. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrC-85 146 

Yuan, Guosen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-73 201 

Yuan, Jing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-63 131 

Yuan, Shengyun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-28 138 

Yuan, Xiaodan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB10-2 222 

Yuan, Youchen. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaA-79 177 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-80 177 

Yue, Hanqi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-15 181 

Yue, Jianting. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrA-11 111 

Yuling, Pei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB04-6 217 

Z 

Zeng, Bing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-26 113 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-51 117 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-52 117 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-46 172 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-47 172 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-48 173 

Zeng, Hui. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaC-51 198 

Zeng, Ming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-86 190 

ZENG, Qi-jie. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrB-90 134 

Zeng, Qingjun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-86 178 

Zhai, Chao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA02-6 206 

Zhai, Dihua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA09-1 155 

Zhai, Feng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA01 

CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA01-3 148 

Zhai, Junyong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-36 139 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB01-4 214 

Zhai, Li . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA04-3 150 

Zhai, Qiaozhu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA04-5 151 

Zhan, Xisheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA01-6 149 

Zhan, Zhenyu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-70 132 

Zhang, Ailong. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-03 179 

Zhang, Bo-Qi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-91 147 

Zhang, Botao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB09-2 165 

Zhang, Boyang. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA03-3 207 

Zhang, Cai-Yan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-75 201 

Zhang, Chao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB11-4 223 

Zhang, Chun-xiao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-08 111 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-09 111 

Zhang, Cishen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB06-3 105 

Zhang, Danfeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-22 113 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-13 168 

Zhang, Daosong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-74 201 

Zhang, Desheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-75 145 

Zhang, Dongkai. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrB10-3 108 

Zhang, Feng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-58 199 

Zhang, Gang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-72 176 

Zhang, Guanyu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-35 115 

ZHANG, Gui-chen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-37 115 

Zhang, Guo-you . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA07-4 97 

Zhang, Guofeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-76 133 

Zhang, Guoshan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB10-5 109 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-19 137 

Zhang, Guoxiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-21 182 

Zhang, Hai-Tao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA11-1 156 

Zhang, Handong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-31 171 

Zhang, Hao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA04-5 151 

Zhang, Hao. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrB-40 127 

Zhang, Hao. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrA-61 118 

Zhang, Hong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB06-3 219 

Zhang, Hong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-75 145 

Zhang, Honghan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-29 126 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-38 184 

Zhang, Hongwei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-04 192 

Zhang, Houwu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-19 169 

Zhang, Huanshui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB11-4 109 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA08 CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB01-3 157 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA09 C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA09-1 212 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB10-5 222 

Zhang, Hui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-71 201 

Zhang, Hui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-59 118 

Zhang, Huiduan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-19 194 

Zhang, Huiqing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-01 110 

244


Author Index 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA07-5 153 

Zhang, Huiqun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-38 140 

Zhang, Huo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-45 185 

Zhang, Jiajie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-27 138 

Zhang, Jian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB05-4 218 

Zhang, Jian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA09-5 212 

Zhang, Jiangbo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA04-3 208 

Zhang, Jiangyan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB10-4 166 

Zhang, Jianhua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-46 116 

Zhang, Jie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA04-4 94 

Zhang, Jing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA11-5 214 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA11-7 214 

Zhang, Jing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA05-3 95 

Zhang, Jinggang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-89 146 

Zhang, Jingjing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB08-4 107 

Zhang, Jingmei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-27 170 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB01-5 215 

Zhang, Juan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB11-1 109 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB11-5 110 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-04 110 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-52 198 

Zhang, Junjie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-24 125 

Zhang, Kan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-94 191 

Zhang, Ke. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA06 

C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA06-2 152 

Zhang, Kefei. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrA-51 117 

Zhang, Kuize. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA08-1 211 

Zhang, Lei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB02-4 158 

Zhang, Lei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB03-4 216 

Zhang, Lequn . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA08-2 211 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA08-3 211 

Zhang, Li . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-65 200 

Zhang, Lieping. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaA-73 176 

Zhang, Lihong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-10 111 

Zhang, Lijun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-04 179 

Zhang, Lijun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA08-1 211 

Zhang, Limin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB01-5 157 

Zhang, Limin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-91 122 

Zhang, Meijun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-70 176 

Zhang, Ming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB01-2 157 

Zhang, Minjie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-73 189 

Zhang, Ning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-73 189 

Zhang, Pei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-73 144 

ZHANG, PENG-Yu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-69 144 

Zhang, Peng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-16 181 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA10-1 155 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA10-3 156 

Zhang, Pengtao. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaA-77 176 

Zhang, Qi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB07-2 162 

Zhang, Qi. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-52 186 

Zhang, Qian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA10-5 156 

Zhang, Qizhi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-44 140 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB07-6 163 

Zhang, Rui. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-33 183 

Zhang, Rui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-91 147 

Zhang, Ruihua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-86 121 

Zhang, Shaoru . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-21 194 

ZHANG, SHENG . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-83 202 

Zhang, Shuqi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-16 181 

Zhang, Shuzheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-20 182 

Zhang, Tao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-84 121 

Zhang, Tianping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB01 

C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB01-1 101 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-65 131 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-66 175 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-26 195 

Zhang, Tie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB07-6 106 

Zhang, Tie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-84 190 

Zhang, Tiejun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-34 183 

Zhang, Tong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB04-6 104 

Zhang, Tuo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-07 123 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-01 167 

Zhang, Wei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-17 112 

Zhang, Weidong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB05 

CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB05-3 161 

Zhang, Weihai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-10 124 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA09-3 212 

Zhang, Weimin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-50 141 

Zhang, Weiping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-71 188 

Zhang, Wenjing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA03-6 94 

Zhang, Wenle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-25 195 

Zhang, Xiang-Sun. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB08-4 164 

Zhang, Xianxia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB08-3 107 

Zhang, Xiaodong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-02 135 

Zhang, Xiaomei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-36 184 

Zhang, Xilin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-61 118 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-64 119 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-54 173 

Zhang, Xinghui. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB01-5 157 

Zhang, Xining. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrA01-3 92 

Zhang, Xinliang. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrB-02 123 

Zhang, Xinyu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-15 124 

Zhang, Xinzheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-81 133 

Zhang, Xiyu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-73 201 

Zhang, Xu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB11-2 109 

Zhang, XueFeng. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrB-78 133 

Zhang, Xun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-38 184 

Zhang, Xuncai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA02 

C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA02-3 93 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB08-5 221 

Zhang, Xuxi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA10-2 156 

Zhang, Yang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-28 114 

Zhang, Yang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB08-1 107 

Zhang, Yanqiong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA02-3 206 

Zhang, Yating . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA06-5 96 

Zhang, Ye . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA06-5 96 

Zhang, Ying . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-89 122 

Zhang, Ying . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-33 183 

Zhang, Ying . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-45 116 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-03 167 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-64 188 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-06 192 

Zhang, Yingchun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-43 116 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-22 194 

Zhang, Yong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-39 127 

ZHANG, Yuan-wen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA04-1 94 

Zhang, Yu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-77 133 

Zhang, Yumei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-71 144 

Zhang, Yumin. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrB-47 128 

Zhang, Yun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-90 134 

Zhang, Yunfan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-40 172 

Zhang, Yunlong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-06 180 

Zhang, Yunong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA03-3 93 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-06 167 

Zhang, Yuping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-24 138 

Zhang, Yuren . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB04-6 160 

ZHANG, Zebang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-45 128 

Zhang, Zhenning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA02 

CC 

245


. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA02-1 206 

Zhang, Zhenwei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-15 181 

Zhang, Zhi-kai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA05-5 95 

Zhang, Zhongyuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-28 126 

Zhao, Bing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA01-3 148 

Zhao, Chengtao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-86 178 

Zhao, Chunhui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB05 

C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB05-3 218 

Zhao, Dong-bin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB09-2 221 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB09-4 221 

Zhao, Dongwei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-54 117 

Zhao, Dongya. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrB10-2 108 

Zhao, Guang Zhou . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-85 121 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-93 122 

Zhao, Guanglei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB05-5 104 

Zhao, Hongxia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA04-2 207 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA04-6 208 

Zhao, Hui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA10-1 155 

Zhao, Hui. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrA-08 111 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-09 111 

Zhao, Jiangpeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-70 144 

Zhao, Liang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-26 126 

Zhao, Liang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-52 129 

ZHAO, Lili . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-74 144 

Zhao, Min. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaC-56 199 

Zhao, Ni . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB02-5 158 

Zhao, Ping. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaC-23 194 

Zhao, Qianchuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC 

CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaA C 

ZHAO, Qingjiang. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaC-70 201 

Zhao, Rongchang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-48 129 

Zhao, Shen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB07-2 219 

Zhao, Tingting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB07-2 106 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB07-3 106 

Zhao, Wen-Xiao. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA08-4 154 

Zhao, Xianyong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA06-3 96 

Zhao, Xiaobing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA05-6 209 

Zhao, Xiaoguang. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB06-2 161 

Zhao, Xiaoping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-42 116 

Zhao, Xin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-03 179 

Zhao, Xingang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-19 181 

Zhao, Yali . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA10-4 99 

Zhao, Yanlong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-62 131 

Zhao, Yin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA08-5 98 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB09-5 108 

Zhao, You-gang. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrB-08 123 

Zhao, Yu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA10-1 99 

Zhao, Yufei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-29 126 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-57 142 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-52 173 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-38 184 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-39 184 

Zhao, Yun-Bo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA08-5 98 

ZHAO, ZHENG . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-20 169 

Zhao, Zhe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-05 123 

Zhao, Zhicheng. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrC-89 146 

Zhao, Zhigang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-05 167 

Zhao, Zhong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB07-4 163 

Zhao, Zixiang. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrC-19 137 

Zhen, Ran . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-05 123 

ZHENG, Banggui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-81 202 

Zheng, Fei Feng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA04-6 151 

Zheng, Fu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-35 171 

Zheng, Kai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-13 124 

Zheng, Qing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB07-1 219 

Zheng, Qinling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB07-2 219 

Zheng, Suiwu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA06 

C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA06-2 209 

Zheng, Wei Xing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB10-4 109 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-40 115 

Zheng, Wengang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-56 142 

Zheng, Xiaotong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-10 111 

Zheng, Xuetao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-49 185 

Zheng, Yang. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrC-88 146 

ZHENG, Yihui. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA07-1 153 

Zheng, Yi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-14 124 

Zheng, Yi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-86 134 

Zheng, Ying . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA06-5 152 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB07-5 163 

Zheng, Zhihui. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrA-81 121 

Zheng, Zhiqiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-47 141 

Zhou, Bin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB09-2 107 

Zhou, Chuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-41 140 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-41 172 

ZHOU, Duan. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrA05-1 95 

Zhou, Fengqi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-20 182 

Zhou, Fengxu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-06 110 

Zhou, Fengyu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-52 142 

Zhou, Hui. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrB-77 133 

Zhou, Jiajia. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrC-57 142 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-57 174 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-29 195 

Zhou, Jianping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-31 171 

Zhou, Jianping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-72 119 

Zhou, Jie. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB08-3 163 

Zhou, Jiehua. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaA-94 179 

Zhou, Jing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrC-95 147 

Zhou, Jing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB11-5 166 

Zhou, Jun-jing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-78 202 

Zhou, Lei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-39 127 

Zhou, Li . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB04-5 217 

Zhou, Li . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB07-1 162 

Zhou, Lihua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-82 121 

Zhou, Min. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrB02-4 102 

Zhou, Minghao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB10-6 109 

Zhou, Rui. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrA-57 118 

Zhou, Rujuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-36 184 

ZHOU, Shiliang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA05 

C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA05-4 95 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB01-3 101 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-14 112 

246


Author Index 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA07-3 153 

Zhou, Wei. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB01-2 157 

Zhou, Wei. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB02-3 215 

Zhou, Xianzhong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-50 117 

ZHOU, Xiaolong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-05 136 

Zhou, Xingxing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB-09 180 

Zhou, Yali . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-44 141 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB07-6 163 

Zhou, Yeli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-46 116 

Zhou, Yingjiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-27 170 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB01 C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB01-5 214 

Zhou, Yinzuo. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB08-3 163 

Zhou, Youjun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-46 128 

Zhou, Yulan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-53 142 

Zhou, Zhenwei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB03 CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB03-3 216 

Zhu, Baicheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-26 195 

Zhu, Fenghua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-12 124 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-61 131 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA04-1 207 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA04-2 207 

Zhu, Hai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-49 116 

Zhu, Jiandong. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB02 CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB02-5 158 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA02-4 206 

Zhu, Jihong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-30 183 

Zhu, Lei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-85 146 

Zhu, Min. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrA-28 114 

Zhu, Quan-Min . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB10-2 108 

Zhu, Shanying . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-10 168 

Zhu, Shengying. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaC-34 196 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-47 198 

Zhu, Xianghe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-07 111 

Zhu, Xiaoqing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaA-08 168 

Zhu, Xiaorui. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA05 C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA05-6 209 

Zhu, Xingzheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-11 111 

Zhu, Xuemei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaA-81 177 

Zhu, Xun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-95 147 

Zhu, Yakun. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB02-2 215 

Zhu, Yanwei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA04-1 94 

Zhu, Yaodong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-68 119 

Zhu, Yi-an . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB-69 132 

Zhu, Yinggu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA06-2 96 

Zhu, Yingli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-88 191 

Zhu, Yu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-05 180 

Zhu, Yuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-28 139 

Zhu, Yuanheng. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB09-4 221 

Zhu, Yuanming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB09-3 221 

Zhu, Yue. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaA-42 172 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-38 196 

Zhu, Zhenfeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-09 192 

Zhuan, Xiangtao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-04 179 

Zhuan, Yifu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-70 119 

Zhuang, Jian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA-16 112 

Zi, Lingling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB03-5 159 

Zong, Guangdeng. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA01-3 205 

Zong, Qun. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrC-17 137 

Zong, Xiaoxiao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA10-5 213 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB11-2 222 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB11-3 223 

Zou, Dexuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-23 138 

Zou, Fengxing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB01-2 157 

Zou, Hui. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrC-62 143 

Zou, Jingfeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA05-3 95 

Zou, Junzhong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB05-4 218 

Zou, Kui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB04-1 103 

Zou, Meikui. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrC-69 144 

Zou, Tao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB08-3 107 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA07-5 210 

Zou, Xuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC-61 143 

Zou, Xubo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-14 193 

Zou, Yuanyuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA07 C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA07-4 210 

Zou, Yun. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrB05-2 104 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaC-90 203 

Zubowicz, Tomasz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA06-4 96 

Zuo, Guoyu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA05-2 151 

Zuo, Guoyu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PSaB-67 188 

Zuo, Zhiqiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA02-4 149 

247


248


Chair/Co-Chair Index 

Chair/Co-Chair Index 

(C=Chair, CC=Co-Chair) 

B 

J 

Bai, Danyu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA04 

Brdys, Mietek . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB02 

CC 

C 

Jen, Fu-Hua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB03 

Jia, Qing-Shan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA04 

C 

C 

C 

Cai, Yunze . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA02 C 

Chen, Ben M. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA08 C 

Chen, Ben M. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PL-4 C 

Chen, Cailian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB06 C 

Chen, Hong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA09 CC 

Chen, Jie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA11 C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB11 C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA09 C 

Chen, Michael Z. Q. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA10 CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB01 C 

Chen, Mou . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB11 C 

Chen, Ning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA01 CC 

Chen, Xi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB06 CC 

Chen, Zengqiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA09 C 

Cheng, Long. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrA09 CC 

Chu, Tianguang. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrA08 CC 

Cong, Shuang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA11 CC 

D 

Dang, Zhaohui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA01 CC 

Dong, Xisong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA04 CC 

Dong, Zhe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB01 CC 

Duan, Guang-Ren . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB09 C 

Duan, Haibin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA10 CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB11 CC 

Feng, Gang. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PD-1 

Feng, Jun-e . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA08 

Feng, Xin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA03 

Gao, Zhiqiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB07 

Guo, Jing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA03 

Guo, Wei. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB07 

Guo, Yuqian. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA01 

F 

G 

H 

C 

C 

C 

C 

C 

CC 

C 

Han, Chunyan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB01 CC 

Han, Deqiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA03 CC 

Han, Jing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB04 C 

Han, Qing-Long. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrB05 C 

He, Fenghua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA10 C 

He, Haibo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB09 CC 

Heng, Qinghai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA07 C 

Hou, Zhongsheng. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PFrB CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaB C 

Hua, Changchun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA05 C 

Huang, Chaodong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA04 CC 

Huo, Wei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrA CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaC C 

ILYAS, MUHAMMAD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB07 

INOUSSA, GARBA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA06 

I 

CC 

C 

L 

LI, Baopu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB08 CC 

Li, Dazi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB01 CC 

Li, Jr-Shin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB05 CC 

Li, Kang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB08 CC 

Li, Kun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA06 CC 

Li, Pingkang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB04 C 

Li, Shaoyuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB08 C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC C 

Li, Shihua. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrB10 C 

Lin, Zongli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrB C 

Liu, Derong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB09 C 

Liu, Fuchun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA10 CC 

LIU, Jinkun. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA07 CC 

Liu, Shirong. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrB04 C 

Liu, Xi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA06 CC 

Liu, Yun-Hui. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB06 C 

Liu, Zhigang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB06 CC 

Lu, Jinhu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA11 C 

Luh, Peter B. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB08 C 

Lv, Hongli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA08 CC 

M 

Meng, Max, Q.-H.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PD-2 C 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PL-3 C 

Mo, Hong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB02 C 

OHATA, AKIRA. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB10 

Pang, Chee Khiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB11 

Ren, Li-hong. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrB05 

ROY, TUSHAR KANTI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA05 

Sam, Yahaya . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB07 

Shen, Tielong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB10 

Shen, Tielong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PL-2 

SHI, Juan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB04 

Sun, Weiwei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA02 

Tarn, Tzyh-Jong. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PL-5 

Thunberg, Johan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB02 

Tong, Guofeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB03 

Wang, Biao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA10 

Wang, Fan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA05 

Wang, Fei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA05 

Wang, Guangchen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA09 

Wang, Guo-sheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB03 

Wang, Hanlei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB02 

Wang, Jiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB08 

Wang, Jiankui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB10 

O 

P 

R 

S 

T 

W 

CC 

C 

CC 

CC 

C 

C 

C 

CC 

CC 

C 

C 

C 

C 

CC 

CC 

CC 

C 

CC 

C 

CC 

249


Wang, Ling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA11 CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB11 CC 

Wang, Ning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB02 CC 

Wang, Peijin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA01 C 

Wang, Wei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA01 C 

WANG, Xin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA07 CC 

Wang, Xingxuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA04 C 

Wang, Ya-hui. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA04 C 

Wang, Yijing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA02 C 

Wang, Yuan. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrB09 C 

Wang, Yuzhen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA08 C 

Wang, Zaiying. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB04 CC 

Wang, Zhiying . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA07 C 

Wei, Airong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA03 CC 

Wu, Jian Kang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB10 CC 

Wu, Xiuli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA02 CC 

X 

Xi, Zairong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA11 C 

Xian, Bin. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrB07 C 

Xiang, Ji . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB04 CC 

Xiao, MingQing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB05 C 

Xiao, Wendong. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuB10 C 

Xie, Lihua. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .FrB09 CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PL-1 C 

XIE, Wei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB03 CC 

Yang, Chunhua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB09 

Y 

CC 

Yang, Hua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB06 

Yang, Ruoting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB08 

Yang, Wankou . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA03 

Yao, Lina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA06 

Yi, Yang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA07 

YIN, Ziqiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB03 

You, Keyou . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA10 

Yu, Xiao. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA03 

Yu, Xinghuo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA11 

Z 

C 

CC 

C 

CC 

CC 

CC 

C 

CC 

CC 

Zhai, Feng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaA01 CC 

Zhang, Huanshui. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA08 CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA09 C 

Zhang, Ke. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaA06 C 

Zhang, Tianping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrB01 C 

Zhang, Weidong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SaB05 CC 

Zhang, Xuncai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA02 C 

Zhang, Zhenning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA02 CC 

Zhao, Chunhui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB05 C 

Zhao, Qianchuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFrC CC 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .PSaA C 

Zheng, Suiwu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA06 C 

ZHOU, Shiliang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FrA05 C 

Zhou, Yingjiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB01 C 

Zhou, Zhenwei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuB03 CC 

Zhu, Jiandong. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SaB02 CC 

Zhu, Xiaorui. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .SuA05 C 

Zou, Yuanyuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SuA07 C 

250

Conference Program of WCICA 2012

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