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2007 9th European control Conference, ECC 2007

作者： Galvez-Carrillo, M. De Keyser, R. Ionescu, C. Department of Control Engineering and System Analysis Université Libre de Bruxelles Av. F.D. Roosevelt 50 - CP 165/55 BrusselsB-1050 Belgium EeSA Department of Electrical Energy Systems and Automation Ghent University Technologiepark 913 GentB-9052 Belgium

ISBN: (纸本)9783952417386

This work presents the comparison between the results obtained from applying three different control strategies for regulating the temperature in a distributed solar collector (DSC) field. A nonlinear predictive control strategy, the Nonlinear Extended Prediction Self-Adaptive control (Nonlinear EPSAC or NEPSAC), is compared with a linear predictive controller and a PI controller. All control strategies include a Smith Predictor and use a model of the process for the tuning of the controllers. Simulations on a model based on physical and geometric properties of the solar field are performed and controllers are validated on real data measurements. © 2007 EUCA.

关键词： Solar energy

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Application of a Smith Predictor based nonlinear predictive controller to a Solar Power Plant

Application of a Smith Predictor based nonlinear predictive ...

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作者： Gálvez-Carrillo, Manuel De Keyser, Robin Ionescu, Clara Department of Control Engineering and System Analysis Université Libre de Bruxelles Av. F.D. Roosevelt 50-CP165/55 B1050 Brussels Belgium Department of Electrical Energy Systems and Automation Ghent University Technologiepark 913 B9052 Gent Belgium

ISBN: (纸本)9783902661289

Renewable energies are gaining space in the energy generation panorama, thanks to technological advances and policy support. To take profit of these energies in an optimal and sustained way, research of new control strategies becomes imperative. This work presents the study and application of a nonlinear control strategy, where a Smith Predictor is added to the Nonlinear Extended Prediction Self-Adaptive control (NEPSAC), for the control of a Solar Power Plant. Different simulations are performed to study the effect of the design parameters in the dynamic behavior of the system. Copyright © 2007 IFAC.

关键词： Solar energy

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Comparison between model based controllers for a distributed solar collector field

Comparison between model based controllers for a distributed...

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European control Conference (ECC)

作者： M. Gálvez-Carrillo R. De Keyser C. Ionescu Department of Control Engineering and System Analysis Universite Libre de Bruxelles Brussels Belgium EeSA - department of Electrical energy Systems and Automation Ghent University Gent Belgium

关键词： Mathematical model Computational modeling Predictive models Feedforward neural networks Optical filters Tuning Solar radiation

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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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Dynamic Event analysis using Synchronized PMUs via 220V Wall Outlets

Dynamic Event Analysis using Synchronized PMUs via 220V Wall...

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2007 International Power engineering Conference (IPEC2007), vol.2

作者： Issarachai Ngamroo Panlert Lappanakul Nitus Voraphonpiput Sanchai Dechanupaprittha Yasunori Mitani Jareerat Sakulrat Department of Electrical Engineering Faculty of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Power System Analysis Department Control and Protection Electricity Generating Authority of Thailand Thailand Department of Electrical Engineering Kyushu Institute of Technology Fukuoka Japan Department of Civil Engineering Prince of Songkla University Songkla Thailand

This paper presents the dynamic analysis results of a system event in Thailand power network using a wide-area monitoring system based on synchronized phasor measurement units (PMUs). The salient feature of the proposed monitoring system is the convenient installation of PMU at 220V wall power outlets. The event considering here is a 80MW load transferring from Thailand to Malaysia systems through a weak 115kV tie-line. To perform load transferring, a synchronization between both systems is required. This causes a problem of frequency osculation in a southern area of Thailand. Besides, this event severely excites the inter-area oscillation in a 230kV tie-line between central and southern areas of Thailand. Analyzed results based on discrete wavelet decomposition and short-time Fourier Transform provide valuable information of the system dynamic behavior in both frequency and time domains.

关键词： Synchronized Phasor measurement units Wide-area monitoring Inter-area oscillation Discrete wavelet decomposition Least-squares regression Short-time Fourier Transform

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control system DESIGN CONSIDERING A TRADEOFF BETWEEN EVALUATED UNCERTAINTY RANGES AND control PERFORMANCE

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Asian Journal of control 2008年第1期1卷

作者： Y. Wakasa Y. Yamamoto Dept. of Applied Analysis and Complex Dynamical Systems Graduate School of Informatics Kyoto University Kyoto Japan. Yuji Wakasa was born in Okayama Japan in 1968. He received the B.S. and M.S. degrees in engineering from Kyoto university Japan in 1992 and 1994 respectively. From 1994 to 1998 he was a Research Associate in the Department of Information Technology Okayama University. Since April 1998 he has been a Research Associate in the Graduate School of Informatics Kyoto University. His current research interests include robust control and control system design via mathematical programming. Yutaka Yamamoto received his B.S. and M.S. degrees in engineering from Kyoto University Kyoto Japan in 1972 and 1974 respectively and the M.S. and Ph.D. degree in mathematics from the University of Florida in 1976 and 1978 respectively. From 1978 to 1987 he was with Department of Applied Mathematics and Physics Kyoto University and from 1987 to 1997 with Department of Applied System Science. Since 1998 he is a professor at the current position. His current research interests include realization and robust control of distributed parameter systems learning control sampled-data systems and digital signal processing. Dr. Yamamoto is a receipient of the Sawaragi memorial paper award (1985) the Outstanding Paper Award of SICE (1987) Best Author Award of SICE (1990) the George Axelby Outstanding Paper Award of IEEE CSS in 1996 Takeda Paper Prize of SICE in 1997. He is a Fellow of IEEE. He was an associate editor of Automatica. He is currently an associate editor of IEEE Transactions on Automatic Control Systems and Control Letters and Mathematics of Control Signals and Systems. He is a member of the IEEE the Society of Instrument and Control Engineers (SICE) and the Institute of Systems Control and Information Engineers.

This paper presents a design method of control systems such that a designer can flexibly take account of tradeoffs between evaluated uncertainty ranges and the level of control performance. The problem is reduced to a BMI problem and approximately solved by LMIs. The convergence of the proposed approximation is proved in a modified sense. A numerical example shows the effectiveness of the proposed method in comparison with the standard robust control.

关键词： control system design LMI BMI tradeoff curves

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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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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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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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