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检索条件"机构=Center for Control Engineering and ComputationDepartment of Electrical and Computer Engineering"

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Speed control of an interior permanent magnet synchronous motor using belbic (brain emotional learning based intelligent controller)

Speed control of an interior permanent magnet synchronous mo...

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Proceedings of the Biannual World Automation Congress

作者： R. Mohammdi-Milasi C. Lucas B. Nadjar-Arrabi Center of Excellence for Control and Intelligent Processing Department of Electrical and Computer Engineering University of Tehran Tehran Iran Center of Excellence for Control and Intelligent Processing Department of Electrical and Computer Engineering University of Tehran and School of Cognitive Sciences (SCS) Tehran Iran Center of Excellence for Control and Intelligent Processing Department of Electrical and Coinputer Enginecring University of Tehran and School of Cognitive Scienccs (SCS) Tehran Iran

Among ac drives, the permanent magnet synchronous motor has been gaining popularity owing to its high torque to current ratio, large power to weight ratio, high efficiency, high power factor and robustness. Because of its technical and economic advantages, Permanent Magnet Synchronous Motor drive technology is a serious contender for replacing the existing technologies. In this paper we report the utilization of a novel controller (BELBlC) based on emotion processing mechanism in brain. Our results show superior control characteristics especially very fast response, simple implementation and robustness with respect to disturbances and manufacturing imperfections. Our proposed method enables the designer to shape the response in accordance with the multiple objectives of his/her choice.

关键词： Velocity control Permanent magnet motors Torque control Rotors AC motors Machine vector control Synchronous motors Process control Magnetic materials control theory

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A 3-D deformable surface method for automatic hippocampus-amygdala complex segmentation

A 3-D deformable surface method for automatic hippocampus-am...

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2004 Nuclear Science Symposium, Medical Imaging Conference, Symposium on Nuclear Power Systems and the 14th International Workshop on Room Temperature Semiconductor X- and Gamma- Ray Detectors

作者： Karimi, M.M. Batmanghelich, N. Soltanian-Zadeh, H. Lucas, C. Control and Intelligent Processing Center of Excellence Department of Electrical and Computer Engineering University of Tehran Tehran 14395-515 Iran Institute for Studies in Theoretical Physics and Mathematics Tehran Iran Department of Radiology Henry Ford Health System Detroit MI 48202 United States

In this paper, we propose an atlas-based method for hippocampus-amygdala complex segmentation. An atlas is registered on all subjects and its transformation is calculated for each subject. This transformation is applied to the structural segmentation of the complex in atlas to construct an initial surface for the hippocampus-amygdala complex of each subject. A possibility approach is introduced for the segmentation process. Two different kinds of deformation based on edges and information obtained from tissue segmentation are used to find different parts of the complex. A new energy is defined to use tissue information. This energy is adopted to expand the model to embed dominant gray matter points in the volume and also withdraw from dominant white matter and CSF points. The initial shape is divided into several parts. In the normal direction of the center of each part, we construct a profile which search for the best poult that maximizes this new energy. This algorithm is reliable for finding the overall shape of the complex. It overcomes the poor features of the complex such as weak edges and noise. The algorithm is examined on 5 different subjects and validated using two different validation methods. © 2004 IEEE.

关键词： Medical imaging

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Reactive tabu search for optimal power flow under constrained emission dispatch

Reactive tabu search for optimal power flow under constraine...

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IEEE Region 10 International Conference TENCON

作者： D. Nualhong S. Chusanapiputt S. Phomvuttisarn S. Jantarang Electrical Engineering Department Electrical and Control System Engineering Division Electricity Generating Authority of Thailand Bangkok Thailand Department of Electrical Power Engineering Faculty of Engineering Mahanakom University of Technology Bangkok Thailand Center of Excellence in Electrical Power Technology Faculty of Engineering Chulalongkom University Bangkok Thailand Department of Computer Engineering and Satellite Research Center Faculty of Engineering Mahanakom University of Technology Bangkok Thailand

This paper presents an efficient method to obtain the optimal power flow (OPF) problem under constrained emission dispatch by applying reactive tabu search (RTS) algorithm. The RTS is developed as a derivative-free optimization technique in solving constrained emission OPF problem significantly reduces the computational burden with the strategies that make the search process robust and fast. The effectiveness of the proposed approach has been demonstrated through the IEEE 30-bus, 6-generator, test system. The simulation results reveal that the proposed RTS can yield highly optimal solution and tan reduce computational execution time superior to a standard tabu search. Moreover, the proposed method provides better solution than previous literatures with promising results.

关键词： Load flow

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A novel controller for a power system based belbic (brain emotional learning based intelligent controller)

A novel controller for a power system based belbic (brain em...

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Proceedings of the Biannual World Automation Congress

作者： R. Mohammdi-Milasi C. Lucas B. Najar-Arrabi Center of Excellence for Control and Intelligent Processing Department of Electrical and Computer Engineering University of Tehran Tehran Iran Center of Excellence for Control and Intelligent Processing Department of Electrical and Computer Engineering School of Intelligent Systems (SIS) Institute for Studies in Theoretical Physics and Mathematics (IPM) University of Tehran Tehran Iran

In this paper we report the utilization of a novel controller (BELBIC) based un emotion processing mechanism in brain for power system. Using the BELBIC controller, both transient stability and voltage regulation of power systems are achieved. The special characteristic of this controller that makes it effective is its flexibility its five gain parameters that give good freedom for choosing favorite response. With this degree of freedom choosing of these parameters involves trade-off between overshoot and speed of response. The effectiveness of the proposed BELBIC controller is shown through some computer simulations on a (SMIB) power system.

关键词： control systems Power system control Power systems Power system stability Power system modeling Power system transients Power system faults Power system dynamics Voltage control Process control

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Digital PI fuzzy control strategies applied to an electric microgenerator system

Digital PI fuzzy control strategies applied to an electric m...

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IEEE/PES Transmission and Distribution Conference and Exposition: Latin America

作者： B.M. Campos C.T. da Costa W. Barra K.C.F. da Silva J.A.L. Barreiros Laboratory of Control and Systems (LACOS) Technological Center Electrical and Computer Engineering Department Federal University of Para Belem Para Brazil

This paper investigates digital modeling and control strategies applied to the voltage regulation of a microgenerator system, placed in the Electric Energy Generation Laboratory of Federal University of Para. Identification parametric techniques are used in order to obtain a representative model for the controller design. With a suitable model, controllers of proportional integral type are designed, based on the root locus and fuzzy systems strategies, in order to improve the voltage regulation of the microgenerator system.

关键词： Fuzzy control Voltage control Digital control Proportional control Fuzzy logic Regulators control systems DC motors Velocity control Laboratories

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Diversity control approach to ant colony optimization for unit commitment problem

Diversity control approach to ant colony optimization for un...

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IEEE Region 10 International Conference TENCON

作者： D. Nualhong S. Chusanapiputt S. Phomvuttisarn T. Saengsuwan S. Jantarang Electrical Engineering Department Electrical and Control System Engineering Division Electricity Generating Authority of Thailand Bangkok Thailand Department of Electrical Power Engineering Faculty of Engineering Mahanakorn University Bangkok Thailand Center of Excellence in Electrical Power Technology Faculty of Engineering Chulalongkom University Bangkok Thailand Department of Electrical Engineering Faculty of Engineering Kasetsart University Bangkok Thailand Department of Computer Engineering and Satellite Research Center Faculty of Engineering Mahanakom University of Technology Bangkok Thailand

This paper presents a new method to solve the constrained unit commitment problem by applying ant colony optimization (ACO) based on the diversity control approach. The pheromone updating rule is modified to control the diversification by adopting a simple mechanism for random selection in ACO. The proposed method is tested on the 10-unit test system with a scheduling time horizon of 24 hours. The numerical results show an economical saving in the total operating cost when compared to the previous literature results. Moreover, two types of the proposed diversity control technique have the features of easy implementation and a better convergence rate superior to a standard ACO.

关键词： Ant colony optimization

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Induction machine broken bar detection using neural networks based classification

Induction machine broken bar detection using neural networks...

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IEEE Conference on Cybernetics and Intelligent Systems

作者： M.R. Rafimanzelat B.N. Araabi E. Khosroshahli Niroo Research Institute Tehran Iran Control and Intelligent Processing Center of Excellence Department of Electrical and Computer Engineering University of Tehran Iran

This paper addresses the developing of a fault diagnosis system for detection of broken rotor bars, a common mechanical fault in cage induction machines, using efficient feature extraction techniques and a neural network classifier. The proposed algorithm uses the stator current and motor speed as inputs. Fast Fourier Transform is utilized to obtain the frequency spectrum of the current signal. An efficient algorithm is then used to extract suitable features out of the frequency spectrum of the signal. The relevance of the features for the purpose of fauIt detection is investigated and verified. A neural network classifier is then developed and applied to distinguish different motor conditions. A series of data collected from experiments on a three phase 3 hp cage induction machine performed in different load and fauIt conditions are used to provide data for training and then testing the classifier. Experimental results confirm the etkiency of the proposed algorithm for detection of broken bar faults.

关键词： Induction machines Neural networks Rotors Feature extraction Induction motors Thermal stresses Frequency Fault diagnosis Bars Fault detection

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Dynamic high gain scaling: an application to robust adaptive output feedback for feedforward systems

Dynamic high gain scaling: an application to robust adaptive...

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Asian control Conference

作者： P. Krishnamurthy F. Khorrami Control/Robotics Research Laboratory (CRRL) Department of Electrical and Computer Engineering Six Metrotech Center Polytechnic University Brooklyn NY USA

We propose an adaptive output feedback control design for global asymptotic stabilization of feedforward systems based on our recent results on dynamic high gain scaling based controller design for strict-feedback systems. The system is allowed to contain uncertain functions of all the states and the input as long as they satisfy certain bounds. Unknown parameters are allowed in the bounds assumed on uncertain functions. If the uncertain functions involve the input, then the output-dependent functions in the bounds need to be polynomially bounded. It is also shown that if the uncertain functions can be bounded by a function independent of the input, then the polynomial boundedness requirement can be relaxed. The designed controllers have a simple structure being essentially a linear feedback with state-dependent dynamic gains and do not involve any saturations or recursive computations. The observer utilized to estimate unmeasured states is similar to a Luenberger observer with dynamic observer gains. The Lyapunov functions are quadratic in state estimates, observer errors, and parameter estimation error. The stability analysis is based on our recent results on uniform solvability of coupled state-dependent Lyapunov equations. The controller provides strong robustness properties both with respect to uncertain parameters and additive disturbances. This robustness is the key to the output feedback controller design.

关键词： Robustness Output feedback Feedforward systems Observers Polynomials State estimation Robust control Programmable control Adaptive control control design

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A novel auto regression and fuzzy-neural combination method to identify cardiovascular dynamics

A novel auto regression and fuzzy-neural combination method ...

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Proceedings of the Biannual World Automation Congress

作者： Jingyu Liu Mo Jamshidi S. Pourbabak Center of Autonomous Control Engineering (ACE) and Department of Electrical and Computer Engineering University of New Mexico Albuquerque NM USA Department of Internal Medicine University of Massachusetts Worcester MA USA

In this paper cardiovascular dynamics, which refers to the dynamic relationship among the heart rate (HR), arterial blood pressure (ABP) and instantaneous lung volume (ILV), is identified through a novel combination approach that consists of a set of linear auto-regression (AR) equations and nonlinear fuzzy-neural inference. Based on linear assumption of cardiovascular system, auto-regressive and moving average method (ARMA) has been popular approaches to identify the complex cardio-system in recent years. Fuzzy set theory is very suitable to systems with uncertainties such as the cardiovascular dynamic system with expert knowledge. Fuzzy- Neural inference paradigm imports the auto-learning property into fuzzy logic engine, therefore extracts some knowledge from data automatically. An effective hybrid approach, which has parallel modular structure of AR and Fuzzy-neural inference, becomes feasible IO interpret physiologically linear component of heart function and nonlinear nervous regulation component respectively. Details of proposed combination method as well as subjects' study results are presented in this paper.

关键词： Cardiology Heart rate Nonlinear dynamical systems Cardiovascular system Arterial blood pressure Uncertainty Signal analysis Frequency domain analysis Lungs Data mining

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Multispectral Landsat image classification using a data clustering algorithm

Multispectral Landsat image classification using a data clus...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Yan Wang Mo Jamshidi P. Neville C. Bales S. Morain Department of Electrical and Computer Engineering and Autonomous Control Engineering (ACE) Center University of New Mexico Albuquerque NM USA The Earth Data Analysis Center (EDAC) University of New Mexico Albuquerque NM USA

This work presents a new application of a data-clustering algorithm in Landsat image classification, which improves on conventional classification methods. Neural networks have been widely used in Landsat image classification because they are unbiased by data distribution. However, they need long training times for the network to get satisfactory classification accuracy. The data-clustering algorithm is based on fuzzy inferences using radial basis functions and clustering in input space. It only passes training data once so it has a short training tune. It can also generate fuzzy classification, which is appropriate in the case of mixed, intermediate or complex cover pattern pixels. This algorithm is applied in the land cover classification of Landsat 7 ETM+ over the Rio Rancho area, New Mexico. It is compared with back-propagation neural network (BPNN) to illustrate its effectiveness and concluded that it can get a better classification using shorter training time.

关键词： Satellites Remote sensing Image classification Neural networks Clustering algorithms Humans Content addressable storage Gaussian distribution Self organizing feature maps Indexing

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