Repetitive processes are a distinct class of two-dimensional systems (i.e. information propagation in two independent directions) of both systems theoretic and applications interest. They cannot be controlled by direc...
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作者：
Kwang-Hyun ParkZeungnam BienDivision 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...
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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.
In this paper a method to estimate the initial rotor position of the switched reluctance motor (SRM) at the stand still condition is proposed. The proposed method can be implemented by the simple microcontroller based...
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In this paper a method to estimate the initial rotor position of the switched reluctance motor (SRM) at the stand still condition is proposed. The proposed method can be implemented by the simple microcontroller based system, and it is not necessary to know the magnetization curves. The impulse gate drive signal is applied and the winding current is detected and its approximate value is used for calculating the rotor position. The compensation method to reduce the estimation error thorough one cycle is also mentioned. The proposed method is implemented with the 6-stator-pole and 4- rotor-pole, 1.5 kW 3-phase switched reluctance motor.
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...
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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.
Water diffusion measurements have been shown to be sensitive to tissue cellular size, extra cellular volume, and membrane permeability. Therefore, diffusion tensor imaging (DTI) by MRI can be used to characterize high...
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ISBN:
(纸本)9781424406715;1424406714
Water diffusion measurements have been shown to be sensitive to tissue cellular size, extra cellular volume, and membrane permeability. Therefore, diffusion tensor imaging (DTI) by MRI can be used to characterize highly cellular regions of tumors versus acellular regions, distinguishing cystic regions from solid regions. An automatic segmentation method is proposed in this paper based on a multi-phase clustering algorithm to segment the brain tumors in a feature space extracted from DTI images. The algorithm is applied on images of a total of 20 patients with 4 different types of tumors. The tumor region segmentation was 92% accurate based on the segmentation results using anatomical images and 100% accurate based on biopsy results. In general, the segmentation results obtained by the proposed method revealed a strong agreement with the biopsy results and anatomical images, providing support for the accuracy and robustness of the proposed feature space and the segmentation procedure.
There are some fatigue damage estimation methods of the austenitic stainless steel that uses the martensitic transformation. For instance, they are the remanent magnetization method, the excitation method, and so on. ...
There are some fatigue damage estimation methods of the austenitic stainless steel that uses the martensitic transformation. For instance, they are the remanent magnetization method, the excitation method, and so on. Those two methods are being researched also in our laboratory now. In the remanent magnetization method, it is well known that the relation between fatigue damage and the remanent magnetization is simple, clear, and reproducible. However, this method has the disadvantage to need a special magnetizer. This method cannot be easily used on the site such as the factory. On the other hand, because the special magnetizer is unnecessary, the excitation method can use easily on the site. The output signal of this method is small. In this paper, two fatigue evaluation methods such as the remanent magnetization method and the excitation method are introduced. In addition, we report on the result of comparing the fatigue evaluation performances of two methods.
Scientific visualization evolved up to the stage where collaborative work in virtual coexistence of various experts becomes unavoidable to identify and solve specific visualization tasks. These problems encouraged us ...
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ISBN:
(纸本)9537138097
Scientific visualization evolved up to the stage where collaborative work in virtual coexistence of various experts becomes unavoidable to identify and solve specific visualization tasks. These problems encouraged us to research and define key aspects of visualization service which will enable collaborative work over the Internet. In this paper we define architecture of the whole system and unified interface for all requirements in such environment. Unified requests are sent to visualization processing unit to execute required visualization routine. Collaborative work on the volumetric medical data is especially demanding task, so we present development of VMI (Visualization of Medical data over Internet) system, where segmentation for visualization is based on joint histogram.
Self-organization is a phenomenon that has been observed in many real-life complex networks and the associated concepts have been utilized in various branches of science. As capacity requirements in transparent optica...
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Self-organization is a phenomenon that has been observed in many real-life complex networks and the associated concepts have been utilized in various branches of science. As capacity requirements in transparent optical networks (TONs) increase, so does their complexity. This in turn makes designing scalable security mechanisms critical, particularly due to the high speeds and transparency inherent in TONs. These security mechanisms should be able to handle unforseen attacks, as well as component faults. In this paper, we explore the possibilities of applying self-organizing concepts to failure management in transparent optical networks. With further investigation, we hope to develop a network architecture which can self-organize into a functional structure in the presence of failures and other changes in the environment. If successful, this could significantly improve the scalability and robustness of such networks.
作者：
Suresh KalyanasundaramEdwin K. P. ChongNess B. ShroffMotorola India Electronics Limited
No. 66/1 Plot 5 Bagmane Techpark C. V. Raman Nagar Post Bangalore 560 093 India. Department of Electrical and Computer Engineering
Colorado State University Fort Collins CO 80523-1373 USA. Professor Edwin K. P. Chong received the B.E.(Hons.) degree with First Class Honors from the University of Adelaide
South Australia in 1987 graduating top of his class and the M.A. and Ph.D. degrees in 1989 and 1991
respectively both from Princeton University where he held an IBM Fellowship. He joined the School of Electrical and Computer Engineering at Purdue University in 1991 where he was named a University Faculty Scholar in 1999 and promoted to Full Professor in 2001. Since August 2001 he has been a Professor of Electrical and Computer Engineering and Professor of Mathematics at Colorado State University. His current interests are in communication networks and optimization methods. He coauthored the best-selling book An Introduction to Optimization 2nd Edition Wiley-Interscience 2001. He received the NSF CAREER Award in 1995 and the ASEE Frederick Emmons Terman Award in 1998. He coauthored a paper that was awarded Best Paper in the journal Computer Networks 2003. Professor Chong is a Fellow of the IEEE. He was founding chairman of the IEEE Control Systems Society Technical Committee on Discrete Event Systems and until recently served as an IEEE Control Systems Society Distinguished Lecturer. He has been on the editorial board of the IEEE Transactions on Automatic Control. He is currently on the editorial board of the journal Computer Networks. He has also served on the organizing committees of several international conferences. He has been on the program committees for the IEEE Conference on Decision and Control the American Control Conference the IEEE International Symposium on Intelligent Control IEEE Symposium on Computers and Communications and the IEEE Global Telecommunications Conference. He has also served in the executive committees for the IEEE Co
Solution techniques for Markov decision problems rely on exact knowledge of the transition rates, which may be difficult or impossible to obtain. In this paper, we consider Markov decision problems with uncertain tran...
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Solution techniques for Markov decision problems rely on exact knowledge of the transition rates, which may be difficult or impossible to obtain. In this paper, we consider Markov decision problems with uncertain transition rates represented as compact sets. We first consider the problem of sensitivity analysis where the aim is to quantify the range of uncertainty of the average per-unit-time reward given the range of uncertainty of the transition rates. We then develop solution techniques for the problem of obtaining the max-min optimal policy, which maximizes the worst-case average per-unit-time reward. In each of these problems, we distinguish between systems that can have their transition rates chosen independently and those where the transition rates depend on each other. Our solution techniques are applicable to Markov decision processes with fixed but unknown transition rates and to those with time-varying transition rates.
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