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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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AUTOMATIC BRAIN TUMOR SEGMENTATION USING TISSUE DIFFISIVITY CHARACTERISTICS

AUTOMATIC BRAIN TUMOR SEGMENTATION USING TISSUE DIFFISIVITY ...

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IEEE International Symposium on Biomedical Imaging

作者： Azadeh Yazdan-Shahmorad Hesamoddin Jahanian Suresh Patel Hamid Soltanian-Zadeh Radiology Department Henry Ford Health System Medical Image Analysis Laboratory Detroit MI USA Electrical and Computer Engineering Department Wayne State University Detroit MI USA Biomedical Engineering Department University of Michigan Ann Arbor MI USA Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department Faculty of Engineering University of Tehran Tehran Iran

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.

关键词： Neoplasms Image segmentation Diffusion tensor imaging Clustering algorithms Biopsy Permeability measurement Volume measurement Size measurement Biomembranes Magnetic resonance imaging

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NONPARAMETRIC ENTROPY-BASED COUPLED MULTI-SHAPE MEDICAL IMAGE SEGMENTATION

NONPARAMETRIC ENTROPY-BASED COUPLED MULTI-SHAPE MEDICAL IMAG...

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IEEE International Symposium on Biomedical Imaging

作者： Alireza Akhoundi-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

ISBN: (纸本)9781424406715;1424406714

We propose a 3D nonparametric, entropy-based, coupled, multishape approach for the segmentation of subcortical brain structures in magnetic resonance images (MRI). Our method uses PCA to capture structures variability. Because of complex relationships of pose and shape of the coupled structures, we only use their shape and size relation. To this end, we apply separate registrations of the structures. For each structure, we consider a similarity transform using seven parameters. In addition, to generate most accurate results, we estimate probability density functions (pdf) iteratively. The proposed method minimizes an entropy-based energy function using quasi-Newton algorithm. To improve the results, we use analytical derivatives. Sample results are given for the segmentation of putamen, thalamus and caudate illustrating the impact of coupling on the accuracy of the results.

关键词： Biomedical imaging Image segmentation Shape Couplings Brain Magnetic resonance Magnetic resonance imaging Principal component analysis Probability density function Iterative algorithms

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Integrated Guidance and control of AUVs Using Shrinking Horizon Model Predictive control

Integrated Guidance and Control of AUVs Using Shrinking Hori...

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OCEANS

作者： Charmane V. Caldwell Emmanuel G. Collins Srinivas Palanki Center for Intelligent System Control and Robotics epartment of Electrical and Computer Engineering Florida A&M University-Florida State University Tallahassee FL USA Center for Intelligent System Control and Robotics Department of Mechanical Engineerin Florida A&M University-Florida State University Tallahassee FL USA Center for Intelligent System Control and Robotics Department of Chemical and Biomedical Engineering Florida A&M University-Florida State University Tallahassee FL USA

There are problems controlling autonomous underwater vehicles (AUVs) because they are nonlinear and coupled and have external environmental disturbances acting on the vehicle. More difficulties arise when the vehicle tasks require precise positioning and control as in military missions. This paper presents an integrated guidance and control method for an AUV. The algorithm is based on an optimal control method called shrinking horizon model predictive control (SHMPC). Scenarios for reconnaissance and reacquisition in mine countermeasures missions are explored applying SHMPC. In addition, there are scenarios with obstacle avoidance. Simulations of this method show the control method is able to systematically handle disturbances and constraints to successfully maneuver through volatile areas

关键词： Predictive models Predictive control Underwater vehicles Remotely operated vehicles Nonlinear control systems intelligent vehicles Mobile robots intelligent robots control system synthesis Biomedical engineering

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Complexity and Size Analysis of Hybrid system Modeling with Mixed Logical Dynamical Approach

Complexity and Size Analysis of Hybrid System Modeling with ...

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Mediterranean Conference on control and Automation (MED)

作者： Hamid Mahboubi Jalal Habibi Behzad Moshiri Ali Khaki-Sedigh School of ECE University of Tehran Tehran Iran Control and Intelligent Processing Center of Excellence School of ECE University of Tehran Tehran Iran Control System Engineering School of Electrical Engineering Khaje Nasir Toosi University of Technology Tehran Iran

Recently, a great amount of interest has been shown in the field of modeling and control of hybrid systems. One of the efficient methods in this area utilizes the mixed logical-dynamical (MLD) systems in the modeling. In this method, the system constraints are transformed into mixed-integer inequalities by defining some logic statements. In this paper, a system containing three tanks is modeled as a nonlinear switched system using the MLD framework. Regarding this three-tank modeling, an n-tank system is modeled and number of binary and continuous auxiliary variables and also number of mixed-integer inequalities are obtained in terms of n. Then, the system size and complexity due to increase in number of tanks are considered. It is concluded that as the number of tanks increases, the system size and complexity increase exponentially which hampers control of the system. Therefore, methods should be found which result in fewer variables

关键词： Logic Power system modeling Size control control system synthesis Switched systems control systems Automatic control Automata Difference equations Design methodology

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High SNR flexible top hat monopole probe for 1.5 T MRI

High SNR flexible top hat monopole probe for 1.5 T MRI

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作者： Mohammadzadeh, M. Shahabadi, M. Soltanian-Zadeh, H. Tavakoli, A. Rafiei, B. Javidnia, D. Oghabian, M.A. Department of Physics Amir-Kabir University of Technology Tehran Iran School of Electrical and Computer Engineering University of Tehran Tehran Iran Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran Image Analysis Laboratory Department of Radiology Henry Ford Health System Detroit MI United States Department of Electrical Engineering Amir-Kabir University of Technology Tehran Iran Medical Imaging Center Imam Khomeyni Hospital Tehran Iran Research Center for Science and Technology in Medicine Tehran University of Medical Sciences Tehran Iran

In this paper, design principles and application of a thin and flexible intravascular top hat monopole probe with increased signal-to-noise ratio (SNR) and improved longitudinal and radial coverage are described and compared with the conventional monopole probe. The top hat probe is constructed by removing a segment of the shield of a coaxial cable. A flexible thin copper foil in contact with the inner conductor of the cable is added to the end section of the cable. The copper foil acts as an antenna top hat load increasing the uniformity of the current distribution on the probe and establishes a uniform region of SNR distribution around the probe. Improvement of the SNR distribution of the top hat probe relative to the conventional monopole probe is evaluated using quantitative indices. Experimental results show that for the top hat probe, the ratio of the maximum image SNR to its relative standard deviation (SR) is 2.73 times higher than that of a conventional probe. © 2006 IOP Publishing Ltd.

关键词： Medical imaging

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Multiscale Face Detection: A New Approach to Robust Face Detection

Multiscale Face Detection: A New Approach to Robust Face Det...

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IEEE International Conference on Fuzzy systems (FUZZ-IEEE)

作者： M. Aghagolzadeh H. Soltanian-Zadeh B.N. Araabi Department of Electrical and Computer Engineering University of Tehran Tehran Iran Radiologyimageanalaysis laboratory Henry Ford Health System Detroit MI USA Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department University of Tehran Tehran Iran

This paper presents a novel method for detecting multiple frontal faces in still images using multi-scale processing. The main characteristic of this algorithm is its stability in detecting faces with seldom false detections and a high correct detection rate. The novelty of this work comes from the utilization of multiscale detection and using two classifiers to reduce false detections. The algorithm generally has two stages: in the first stage, a face is detected in a unique scale and in the second stage, only the faces that are located in the neighbor scales are accepted as real faces. Consequently, a still image is first resized and scanned block wise, and then each enhanced block is tested for being face. One dimensional Harr wavelet is used for feature extraction, which gives appropriate discriminating features between the face and nonface classes. Detection results at each scale are accumulated in an internal database, so the ultimate detection is prepared based on the mutual detection information between consequent scales. To parameterize both the Bayesian and the simple proposed classifier, 2,643 faces were congregated from famous face databases and more than 10,000 non-face samples were selected from nature images. Experimental results using images gathered from known databases like MIT-CMU show great ability of the proposed algorithm in detecting faces.

关键词： Face detection Robustness Image databases Bayesian methods Humans Application software Stability Feature extraction Spatial databases Computer vision

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Rule-based decision-making framework for knowledge-based anatomical landmark localization (K-BALL)

Rule-based decision-making framework for knowledge-based ana...

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IEEE International Symposium on Biomedical Imaging

作者： M.-R. Siadat H. Soltanian-Zadeh K. Elisevich Henry Ford Health System Radiology Image Analysis Laboratory Detroit MI USA Control and Intelligent Processing Center of Excellence Department of Electrical and Computer Engineering University of Tehran Tehran Iran Department of Neurosurgery Henry Ford Health System Detroit MI USA

K-BALL is a general method for localization of anatomical phenomena of the same origin with natural discrepancies distributed over a reference space, e.g., human brain anatomical structures. In this paper, we focus on information analysis step (2nd step) of K-BALL during which landmarks extracted in its first step are evaluated. We provide a framework in which rules are automatically generated based on estimated and derived models. We show that the rules based on the derived models can improve the overall success rate of K-BALL. Each rule evaluates the extracted points by producing an intermediate confidence factor (ICNF). A total confidence factor is calculated using ICNF's to facilitate the acceptance or rejection of a set of points as landmarks of interest. Using the rules merely based on the estimated models, simulation study produced an overall success rate of 91.8%. Using the rules based on both of the estimated and derived models, this rate increased to 92.5%

关键词： Decision making Hippocampus Information analysis Data mining Humans Radiology Image analysis Radio control Process control control systems

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