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2008 Mediterranean Conference on control Automation, vol.1

作者： M. Mirmomeni C. Lucas M. Shafiee B. N. Araabi Islamic Azad University슠 Tehran Iran Control and Intelligent Processing Center of Excellence School of Electrical and Computer Eng University of Tehran Tehran Iran School of Cognitive Sciences Institute of studies in theoretical Physics and Mathematics Tehran Iran Electrical Engineering Department Amirkabir University of Technology슠 Tehran Iran

Singular systems have been the subject of interest over the last two decades due to their many practical applications. But it has to be said that system identification of such system is still a challenging area because of the difficulty of identification of such systems for their complex structures. In addition, it seems that by developing a useful method for identification of singular system, one can use the useful property of such systems in describing the natural complex phenomena. This paper presents a novel methodology for identifying nonlinear singular systems from empirical data. Singular systems are idealized models for systems with slow and quick modes of change. However, their identification is a challenging problem even for the linear case. A new learning method, generalized locally linear model tree (GLoLiMoT) algorithm is introduced. The contribution of this paper is to provide a method for adjusting the parameters of fuzzy descriptor model, e.g. the splitting ratio and the standard deviation, the number of locally linear neurons or the number of linear singular systems for the consequent part in fuzzy descriptor model as well as the order of the singular system. By these modifications an accurate model of nonlinear singular system is obtained which is compared with several other methods in two case studies. Results depict the power of the proposed approach in describing nonlinear complex phenomena.

关键词： Automation Conferences

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A Parametric Lyapunov Equation Approach to Low Gain Feedback Design for Discrete-time Systems

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IFAC Proceedings Volumes 2008年第2期41卷 6289-6294页

作者： Bin Zhou Zongli Lin Guangren Duan Center for Control Theory and Guidance Technology Harbin Institute of Technology P.O. Box 416 Harbin 150001 P. R. China Charles L. Brown Department of Electrical and Computer Engineering University of Virginia P.O. Box 400743-4743 Charlottesville VA 22904-4743 U.S.A.

Low gain feedback, a parameterized family of stabilizing state feedback gains whose magnitudes approach zero as the parameter decreases to zero, has found several applications in constrained control systems, robust control and nonlinear control. In the continuous-time setting, there are currently three ways of constructing low gain feedback laws the eigenstructure assignment approach, the parametric ARE based approach and the parametric Lyapunov equation based approach. The eigenstructure assignment approach leads to feedback gains explicitly parameterized in the low gain parameter. The parametric ARE based approach results in a Lyapunov function along with the feedback gain, but requires the solution of an ARE for each value of the parameter. The parametric Lyapunov equation based approach possesses the advantages of the first two approaches and results both an explicitly parameterized feedback gains and a Lyapunov function. The first two approaches have been extended to discrete-time setting. This paper develops the parametric Lyapunov equation based approach to low gain feedback design for discrete-time systems.

关键词： Pole shift circle-symmetry parametric Lyapunov matrix equation low gain feedback actuator saturation semi-global stabilization

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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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The Short Winding Path Coupled Inductor Voltage Regulators

The Short Winding Path Coupled Inductor Voltage Regulators

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23rd Annual IEEE Applied Power Electronics Conference and Exposition (APEC 2008), vol.3

作者： Yan Dong Yugang Yang Fred. C. Lee Ming Xu Center for Power Electronics Systems The Bradley Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State University Blacksburg VA USA College of Electrical & Control Engineering Liaoning Technical University Huludao Liaoning China

In today's CPU Voltage Regulator (VR) industry, coupled inductor VRs have begun to replace the non-coupled VRs for their better performances. However, today's coupled inductor implementations have long winding paths, which is not preferred in the high current CPU VR application. In this paper, several short winding path coupled inductor structures for 2 and more phase coupled inductors are proposed. Their precise reluctance models are illustrated and their design guidelines based on their precise reluctance models are also shown. The experimental results verify the proposed structures, their models and their design procedures.

关键词： Voltage regulator (VR) Coupled inductor Short winding path

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2008 IEEE International Symposium on Circuits and Systems (ISCAS 2008), vol.4

作者： Chin-Teng Lin Hong-Zhang Lin Tzai-Wen Chiu Chih-Feng Chao Yu-Chieh Chen Sheng-Fu Liang Li-Wei Ko Department of Electrical and Control Engineering National Chiao-Tung University Hsinchu Taiwan Brain Research Center University System of Taiwan Hsinchu Taiwan Department of Computer Science and Information Engineering National Cheng-Kung University Tainan Taiwan

Driver distraction has been recognized as a significant cause of traffic incidents. Therefore, the aim of this study was to investigate Electroencephalography (EEG) dynamics in response to distraction during driving. To study human cognition under specific driving task, we used Virtual Reality (VR) based driving simulation to simulate events including unexpected car deviations and mathematics questions (math) in real driving. For further assessing effects of the stimulus onset asynchrony (SOA) between the deviation onset and math presented on the EEG dynamics, we designed five cases with different SOA. The scalp-recorded EEG channel signals were first separated into independent brain sources by Independent Component Analysis (ICA). Then, the Event-Related-Spectral-Perturbations (ERSP) measuring changes of EEG power spectra were used to evaluate the brain dynamics in time-frequency domains. Results showed that increases of theta band (5approx7.8 Hz) and beta band (12.2approx17 Hz) power were observed in the frontal cortex. Results demonstrated that reaction time and multiple cortical EEG sources responded to the driving deviations and math occurrences differentially in the stimulus onset asynchrony. Results also suggested that the theta band power increase in frontal area could be used as the distracted indexes for early detecting driver's inattention in the future.

关键词： Electroencephalography Virtual reality Brain modeling Discrete event simulation Semiconductor optical amplifiers Independent component analysis Traffic control Humans Cognition Mathematics

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Mechatronics, robotics and components for automation and control IFAC milestone report

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IFAC Proceedings Volumes 2008年第2期41卷 10800-10809页

作者： S. Boverie Dongil Dan Cho H. Hashimoto M. Tomizuka Wang Wei D. Zühlke Continental Automotive France BP 83649 1 Ave.Paul Ourliac. F-31036 Toulouse CEDEX. France School of Electrical Engineering and Computer Science Seoul National University Kwanak-ku Seoul 151-742 Korea Institute of Industrial Science University of Tokyo 4-6-1 Komaba Meguro-ku Tokyo 153-8505 JAPAN Department of Mechanical Engineering University of California Berkeley CA 94720-1740 USA Research Center of Information and Control Dalian University of Technology Dalian 116024 P.R. China German Research Center for Artificial Intelligence DFKI GmbH Center for Human-Machine-Interaction PO Box 3049 D-67653 Kaiserslautern Germany

This paper presents a general overview of the technological fields of mechatronic, robotic, components for automation and control. Five technical areas are considered: component and instruments, mechatronic, robotics, cost oriented automation and human-machine systems. The paper addresses their current key problems and the recent major accomplishments. At last the most promising forecasted development and applications are considered.

关键词： Component for control Mechatronic systems Robotics Cost oriented automation Human-machine systems

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Unit commitment by selective self-adaptive ACO with relativity pheromone updating approach

Unit commitment by selective self-adaptive ACO with relativi...

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IPEC 2007 Meeting

作者： Chusanapiputt, Songsak Nualhong, Dulyatat Jantarang, Sujate Phoomvuthisarn, Sukumvit Department of Electrical Power Engineering Faculty of Engineering Mahanakorn University of Technology Thailand Electrical Engineer Department Electrical and Control System Engineering Division Electricity Generating Authority of Thailand (EGAT) Department of Computer Engineering Faculty of Engineering Mahanakorn University of Technology Thailand Center of Excellence in Electrical Power Technology Faculty of Engineering Chulalongkorn University Thailand IEEE

ISBN: (纸本)9789810594237

This paper presents a novel approach to solve the constrained unit commitment problem using the Selective Self-Adaptive Ant Colony Optimization (SSACO) for improving search performance by automatically adapting ant populations and their transition probability parameters, which cooperates with the Candidate Path Management Module (CPMM) and the Effective Repairing Heuristic Module (ERHM) in reducing search space and recovering a feasible optimality region so that a high quality solution can be acquired in a very early iterative. A new concept of the Relativity Pheromone Updating (RPU) is also introduced to provide a reasonable evaluation of the pheromone trail intensity among the agents. The proposed SSACO method not only enhances the convergence of search process, but also provides a suitable number of the population sharing which conducts a good guidance for trading-off between the importance of the visibility and the pheromone trail intensity. The proposed method has been performed on a test system up to 100 generating units with a scheduling time horizon of 24 hours. The numerical results show the most economical saving in the total operating cost when compared to the previous literature results. Moreover, the proposed SSACO topology can remarkably speed up the computational time of ant colony optimization, which is favorable for a large-scale UC problem implementation.

关键词： Ant Colony Optimization unit commitment problem optimization method

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Three-phase full-bridge converters applied to switched reluctance motor drives with a modified switching strategy

Three-phase full-bridge converters applied to switched reluc...

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International Conference on electrical Machines and Systems

作者： Somsiri, P. Tungpimonrut, K. Aree, P. Industrial Control and Automation Lab. National Electronics and Computer Technology Center Thailand Department of Electrical Engineering Thammasat University Thailand

ISBN: (纸本)9788986510089

This paper presents an improved switching strategy for three-phase full-bridge converters applied to switched reluctance motor (SRM) drives. The modified switching strategy is based on the idea that in every electrical cycle the two switches in same leg of the converter alternate their commutating without using dead time. This drive method has several advantages including low cost, compactness, no dead time required, reduced number of switching pulses, and consequently reduced switching loss. The proposed method is analyzed and the experimental is investigated on 1.5 kW, three-phase 6/4 poles SRM which is designed for small electric vehicles by using standard three-phase full-bridge converter. The experimental results have confirmed an acceptable performance of this proposed technique.

关键词： switched reluctance motor three-phase full-bridge converter switching strategy two-phase excitation

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An EEG spike prescreening algorithm using RBF ANN with multi-channel correlation

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Journal of Medical and Biological engineering 2007年第4期27卷 199-205页

作者： Ko, Cheng-Wen Lin, Yue-Der Jan, Gwo-Jen Chung, Hsiao-Wen Sung, Shing-Ming Department of Computer Science and Engineering National Sun Yat-Sen University Kaohsiung 804 Taiwan Department of Automatic Control Engineering Feng-Chia University Taichung 407 Taiwan Department of Electrical Engineering National Taiwan University Taipei 106 Taiwan Department of Neurology Taipei City Psychiatric Center Taipei 103 Taiwan

This study presents an algorithm for multi-channel electroencephalographic (EEG) spike prescreening based on radial basis function (RBF) artificial neural network. Sixteen-channel EEG records from twenty-eight patients with epilepsy were examined online. The single-channel recognition network consisted of input-layer neurons receiving three EEG waveform parameters (peak angle, amplitude, and velocity), ten hidden-layer neurons, and one output neuron. An optimal network model was chosen according to receiver operating characteristics analysis. Identification of multi-channel geometric correlation was performed with an incidence matrix. Sensitivity and selectivity cross-over value was found to be 80% for the single-channel RBF network. Compared with error back-propagation, a substantial improvement in training efficiency by at least two to three orders of magnitude was obtained for the RBF network. Validation with visual analysis showed 87.3% sensitivity using the proposed online multi-channel classification algorithm. The computation time required for spike detection was significantly less than that needed for online display of the EEG record. The proposed algorithm was able to achieve classification accuracy similar to previous artificial neural network schemes based on error back-propagation, while the training needed for RBF network was substantially less. Therefore, the RBF neural network is potentially an effective tool in real-time prescreening for epileptic spikes.

关键词： Radial basis function networks

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