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2008 American control Conference (ACC), vol.9

作者： Junyan Yu Long Wang Mei Yu Yingmin Jia Jie Chen Intelligent Control Laboratory Center for Systems and Control College of Engineering Peking University Beijing China Department of Automation North China Electric Power University Beijing China Key Laboratory of Complex System Intelligent Control and Decision Ministry of Education Beijing Institute of Technology Beijing China

We consider the output feedback stabilizability of networked control systems with bounded packet loss. A packet-loss dependent Lyapunov function is adopted to design packet-loss dependent stabilizing output feedback controllers by resolving some linear matrix inequalities. Moreover, two types of packet-loss processes are discussed: one is the arbitrary packet-loss process, and the other is the Markovian packet-loss process. A numerical example and some simulations are worked out to demonstrate the effectiveness of the proposed design technique.

关键词： Networked control systems Output feedback control systems Switched systems State feedback Lyapunov method Linear matrix inequalities Electrical equipment industry Industrial control Sufficient conditions

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Packet-loss dependent controller design for networked control systems via switched system approach

Packet-loss dependent controller design for networked contro...

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IEEE Conference on Decision and control

作者： Junyan Yu Long Wang Mei Yu Yingmin Jia Jie Chen Intelligent Control Laboratory Center for Systems and Control College of Engineering Peking University Beijing China Department of Automation North China Electric Power University Zhuxinzhuang Beijing China The Seventh Research Division Beihang University Beijing China Beijing Institute of Technology Key Laboratory of Complex System Intelligent Control and Decision Ministry of Education Beijing China

ISBN: (纸本)9781424431236

The stabilization problem of networked control systems with bounded packet loss is addressed. We model such networked control systems as a class of switched systems, and present sufficient conditions for the stabilization by using a packet-loss dependent Lyapunov function. Moreover, different from existing results, we propose the design for packet-loss dependent stabilizing controllers for two types of packet-loss processes: one is a arbitrary packet-loss process, and the other is a Markovian packet-loss process. Several numerical examples and simulations are worked out to demonstrate the effectiveness of the proposed design technique.

关键词： control systems Networked control systems Switched systems Sufficient conditions Optimal control Delay effects Communication system control Communication system traffic control Feedback control Electrical equipment industry

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Feature fusion as a practical solution toward noncooperative iris recognition

Feature fusion as a practical solution toward noncooperative...

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International Conference on Information Fusion

作者： Nima Tajbakhsh Babak N. Araabi Hamid Soltanianzadeh Control & Intelligent Processing Center of Excellence School of Electrical & Computer Engineering University of Tehran Tehran Iran School of Cognitive Sciences Institute of Studies in Theoretical Physics and Mathematics Tehran Iran Image Analysis Laboratory Radiology Department Henry Ford Health System Detroit MI USA

In noncooperative Iris recognition one should deal with uncontrolled behavior of the subject as well as uncontrolled lighting conditions. That means imperfect focus, contrast, brightness, and orientation among the others. To cope with this situation we propose to take iris images at both near infrared (NIR) and visible light (VL) and use them simultaneously for recognition. In this paper, a novel approach for iris recognition is proposed so that extracted features of NIR and VL images are fused to improve the recognition rate. When the images do not have enough quality due to focus, contrast, etc., effects of feature fusion is more pronounced. This is the situation in UTIRIS database, which is used in our experiments. Experimental results show that the proposed approach, especially in small training samples, leads to a remarkable improvement on recognition rate compared with either NIR or VL recognition.

关键词： Iris Feature extraction Discrete cosine transforms Image recognition Databases Training Humans

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Constrained optimization of nonparametric entropy-based segmentation of brain structures

Constrained optimization of nonparametric entropy-based segm...

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IEEE International Symposium on Biomedical Imaging

作者： Alireza Akhondi Asl Hamid Soltanian-Zadeh School of Cognitive Sciences Institute of Studies in Theoretical Physics and Mathematics Tehran Iran Control and Intelligent Processing Center of Excellence (CIPCE) School of Electrical and Computer Engineering University of Tehran Tehran Iran Image Analysis Laboratory Radiology Department Henry Ford Health System Detroit MI USA

We propose a constrained, three-dimensional, nonparametric, entropy-based, coupled, multi-shape approach to segment subcortical brain structures from magnetic resonance images (MRI). The proposed method uses PCA to develop shape models that capture structural variability. It integrates geometrical relationship between different structures into the algorithm by coupling them (limiting their independent deformations). On the other hand, to allow variations among coupled structures, it registers each structure separately when building the shape models. It defines an entropy-based energy function, which is minimized using quasi-Newton algorithm. To this end, probability density functions (pdf) are estimated iteratively using nonparametric Parzen window method. In the optimization algorithm, constraints are used to improve segmentation quality. These constraints are extracted from training data. Sample results are given for the segmentation of caudate, hippocampus, and putamen, illustrating highly superior performance of the proposed method compared to the most similar methods in the literature.

关键词： Constraint optimization Brain Iterative algorithms Image segmentation Shape Couplings Magnetic resonance Magnetic resonance imaging Principal component analysis Buildings

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Towards an inclusive computational model of visual cortex

Towards an inclusive computational model of visual cortex

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IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： M. Gheiratmand H. Soltanian-Zadeh H. Khaloozadeh Electrical Engineering Department K.N. Toosi University of Technology Tehran Iran Radiology Department Henry Ford Health System Image Analysis and Communications Laboratory Detroit MI USA School of Cognitive Sciences Institute for Studies Theoretical Physics and Mathematics (IPM) Tehran Iran Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department University of Tehran Tehran Iran

Understanding the primatespsila visual system has been one of the challenging problems of different groups of scientists for years. Though many studies, from physiology and neuroscience to computer vision, are done on different aspects of visual processing in the cortex, a comprehensive computational model of visual cortex is still missing. We have implemented a computational model of object recognition in ventral visual pathway in our previous work. This hierarchical model covers visual areas V1/V2, V4/PIT, and AIT sending inputs to the Prefrontal Cortex (PFC) for categorization. To extend our model, in this work, we have added a simple model of motion detection in neurons of areas V1 and MT of the dorsal stream to our previous model. This has enabled the model to perform another principal function of the visual cortex, i.e., motion perception.

关键词： Brain modeling Computational modeling Neurons Biological system modeling Object recognition Motion analysis Solid modeling Visual system Computer vision Motion detection

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An Indoor Localization Algorithm for Lighting control using RFID

An Indoor Localization Algorithm for Lighting Control using ...

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IEEE Energy 2030 Conference, ENERGY

作者： Zi-Ning Zhen Qing-Shan Jia Chen Song Xiaohong Guan Department of Automation Tsinghua University Beijing China Center for Intelligent and Networked Systems (CFINS) Tsinghua National Laboratory for Information Science and Technology Department of Automation Tsinghua University Beijing China United Technologies Research Center China and the Tsinghua-UTC Research Institute for Integrated Building Energy Safety and Control Systems Beijing 100084 P. R. China (phone: +86-10-62772183 fax: +86-10-62770554 e-mail: songc@***) SKLMS Laboratory and System Engineering Institute Xi''an Jiaotong University Xi'an China

Occupant's identity and location are important information for lighting control in order to reduce the energy consumption while increasing livelihood. While active RFID system provides occupant's identity, it is nontrivial to localize the occupant's location in an indoor environment due to the multipath effect, the changing environment, and the unreliable communication link. In this paper, we implement a system with multiple active RFID readers, and develop a localization algorithm based on support vector machine (SVM). The algorithm uses round-robin comparison to localize the occupant to one of the multiple regions in a floor. The geometric relationship among the rooms and the historical localization data are used to further improve the localization accuracy. Numerical results demonstrate a high localization accuracy of this algorithm. We hope this work sheds insight on lighting control for energy saving and an increased livelihood.

关键词： Lighting control Radiofrequency identification Energy consumption Active RFID tags Educational institutions Indoor environments Round robin Support vector machines Humans Safety

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Research on rough set theory and applications in China

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Lecture Notes in Computer Science

作者： Wang, Guoyin Zhang, Qinghua Huang, Houkuan Ye, Dongyi Hu, Qinghua Hu, Xuegang Shi, Zhongzhi Li, Yongli Shang, Lin An, Liping Sai, Ying Chen, Shanben Liang, Jiye Qin, Keyun Zeng, Huanglin Xie, Keming Miao, Duoqian Min, Fan Wu, Zhaocong Wu, Weizhi Dai, Jianhua Institute of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing 400065 China School of Information Science and Technology Southwest Jiaotong University Chengdu 610031 China School of Computer and Information Technology Beijing Jiaotong University Beijing 100044 China College of Math. and Computer Fuzhou University Fuzhou 350002 China Harbin Institute of Technology PO Box 458 Harbin 150006 China School of Computer and Information Hefei University of Technology Hefei 230009 China Institute of Computing Technology Chinese Academy of Sciences Beijing 100080 China Lanzhou University No.222 Tianshui South Rd. Lanzhou 730000 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing 210093 China Management Science and Engineering Department Business School Nankai University Tianjin 300071 China Shandong University of Finance No.40 Shun-geng Road Jinan 250014 China Welding Engineering Institute Shanghai Jiaotong University Shanghai 200030 China School of Computer and Information Technology Shanxi University Taiyuan 030006 China Intelligent Control Development Center Southwest Jiaotong University Chengdu 610031 China Artificial Intelligence of Key Laboratory Sichuan University of Science and Engineering Zigong 643000 China Department of Automation College of Information Engineering Taiyuan University of Technology Taiyuan 030024 China 4800 CaoAn Highway Shanghai 201804 China School of Computer Science and Technology University of Electronic Science and Technology of China Chengdu 610054 China School of Remote Sensing Information Engineering Wuhan University Wuhan 430079 China Institute for Information and System Sciences Faculty of Science Xi'an Jiaotong University Xi'an 710049 China Information College Zhejiang Ocean University Zhejiang 316004 China Institute of Artificial Intelligence Zhejiang University Hangzhou 310027 China

ISBN: (纸本)3540850635

This article gives a capsule view of research on rough set theory and applications ongoing at universities and laboratories in China. Included in this capsule view of rough set research is a brief description of the following things: Chinese research groups on rough set with their URLs for web pages, names of principal researchers (supervisors), numbers of graduate students, and topics being investigated. Statistical summaries showing the growth in the research on rough set theory and application in China are included. In addition, an introduction summarizing the research interests of Chinese researchers is included in this article. The contribution of this article is a complete overview of the principal research directions in rough set theory and its applications in China. © 2008 Springer-Verlag Berlin Heidelberg.

关键词： Rough set theory

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A Study On Iterative Learning control With Adjustment Of Learning Interval For Monotone Convergence In The Sense Of Sup-Norm

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Asian Journal of control 2008年第1期4卷

作者： Kwang-Hyun Park Zeungnam Bien Division of EE Department of EECS Korea Advanced Institute of Science and Technology 373–1 Kusong-dong Yusong-gu Taejon 305–701 Korea. Zeungname Bien:received the B.S. degree in electronics engineering from Seoul National University Seoul Korea in 1969 and the M.S. and Ph.D. degrees in electrical engineering from the University of Iowa Iowa City Iowa U.S.A. in 1972 and 1975 respectively. During 1976–1977 academic years he taught as assistant professor at the Department of Electrical Engineering University of Iowa. Then Dr. Bien joined Korea Advanced Institute of Science and Technology summer 1977 and is now Professor of Control Engineering at the Department of Electrical Engineering and Computer Science KAIST. Dr. Bien was the president of the Korea Fuzzy Logic and Intelligent Systems Society during 1990–1995 and also the general chair of IFSA World Congress 1993 and for FUZZ-IEEE99 respectively. He is currently co-Editor-in-Chief for International Journal of Fuzzy Systems (IJFS) Associate Editor for IEEE Transactions on Fuzzy Systems and a regional editor for the International Journal of Intelligent Automation and Soft Computing. He has been serving as Vice President for IFSA since 1997 and is now Chief Chairman of Institute of Electronics Engineers of Korea and Director of Humanfriendly Welfare Robot System Research Center. His current research interests include intelligent control methods with emphasis on fuzzy logic systems service robotics and rehabilitation engineering and large-scale industrial control systems. Kwang-Hyun Park:received the B.S. M.S. and Ph.D. degrees in electrical engineering and computer science from KAIST Korea in 1994 19997 and 2001 respectively. He is now a researcher at Human-friendly Welfare Robot System Research Center. His research interests include learning control machine learning human-friendly interfaces and service robotics.

It has been found that some huge overshoot in the sense of sup-norm may be observed when typical iterative learning control (ILC) algorithms are applied to LTI systems, even though monotone convergence in the sense of λ-norm is guaranteed. In this paper, a new ILC algorithm with adjustment of learning interval is proposed to resolve such an undesirable phenomenon, and it is shown that the output error can be monotonically converged to zero in the sense of sup-norm when the proposed ILC algorithm is applied. A numerical example is given to show the effectiveness of the proposed algorithm.

关键词： Iterative learning control monotone convergence learning interval sup-norm

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A Hierarchical Clustering Based on Mutual Information Maximization

A Hierarchical Clustering Based on Mutual Information Maximi...

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IEEE International Conference on Image Processing

作者： M. Aghagolzadeh H. Soltanian-Zadeh B. Araabi A. Aghagolzadeh Control and Intelligent Processing Center of Excellence Department of Electrical and Computer Engineering University of Tehran Tehran Iran Radiology Image Analysis Laboratory Henry Ford Health System Detroit MI USA Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran

Mutual information has been used in many clustering algorithms for measuring general dependencies between random data variables, but its difficulties in computing for small size datasets has limited its efficiency for clustering in many applications. A novel clustering method is proposed which estimates mutual information based on information potential computed pair-wise between data points and without any prior assumptions about cluster density function. The proposed algorithm increases the mutual information in each step in an agglomerative hierarchy scheme. We have shown experimentally that maximizing mutual information between data points and their class labels will lead to an efficient clustering. Experiments done on a variety of artificial and real datasets show the superiority of this algorithm, besides its low computational complexity, in comparison to other information based clustering methods and also some ordinary clustering algorithms.

关键词： Mutual information Clustering algorithms Clustering methods Data mining Data structures Density functional theory Computational complexity Entropy Higher order statistics Probability density function

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Data modeling for content-based support environment (C-BASE): Application on epilepsy data mining

Data modeling for content-based support environment (C-BASE)...

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17th IEEE International Conference on Data Mining Workshops, ICDM Workshops 2007

作者： Siadat, Mohammad-Reza Soltanian-Zadeh, Hamid Fotouhi, Farshad Eetemadi, Ameen Elisevich, Kost Department ofComputer Science and Engineering Oakland University Rochester MI 48309 United States Radiology Image Analysis Lab. Henry Ford Health System Detroit MI 48202 United States Control and Intelligent Proc. Center of Excellence Dept. of Elec. and Comp. Eng. Univ. of Tehran Tehran 14395-515 Iran Department of Computer Science Wayne State University Detroit MI 48202 United States Department of Neurosurgery Henry Ford Health System Detroit MI 48202 United States

ISBN: (纸本)0769530192

Data Modeling is an essential first step for data preparation in any data mining procedure. Conventional entity-relational (E-R) data modeling is lossy, irreproducible, and time-consuming especially when dealing with unstructured image data associated with complex systems like the human brain. We propose a methodological framework for more objective E-R data modeling by eliminating the structured content-dependent metadata associated with the unstructured data. The proposed method is applied to epilepsy-related image data and a system called the human brain image database system (HBIDS) is developed accordingly. Supported with navigation, segmentation, data fusion, and feature extraction modules, HBIDS provides a content-based support environment (C-BASE). Such an environment potentially provides an unlimited (ad hoc) query support with a reproducible and efficient database schema. Switching between different modalities of data, while confining the feature extractors within the object(s) of interest, HBIDS yields anatomically specific query results. The price of such scheme is large storage requirements and relatively high computational cost. Examples of navigation through unstructured image data and content-based retrieval are presented in this paper. The results show the potential of HBIDS in content-based data management for decision support systems in real life medical applications. © 2007 IEEE.

关键词： Data mining

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