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Southeastern Symposium on System Theory

作者： S. Giles D.L. Stachowiak G. Adamovsky Electrical Engineering Department Tuskegee University Tuskegee AL USA Electrical Engineering and Computer Science Department University of Tolctlo and Eötvös Lárand University Toledo OH USA Instrumentation and Control Group NASA Glenn Research Center Cleveland OH USA

A thin, hollow, monocone antenna above a ground plane was constructed and studied. The antenna was used to roughly model the convergent region of a rocket exhaust nozzle. The input impedances and currents on the aluminum antenna were measured. The monocone field expansions were found for the exterior and interior regions of the antenna. Mode matching was used to determine the coefficients of expansion with an edge condition applied to the rim of the antenna. After the approximate electric and magnetic fields were found, the associated time-averaged Poynting vector was produced and mapped.

关键词： Electromagnetic fields USA Councils Impedance Frequency Antenna measurements Ground support Instruments nasa Electromagnetic radiation Bandwidth

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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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Design of a robust and adaptive neural network for flexible AC transmission systems

Design of a robust and adaptive neural network for flexible ...

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IEEE International Conference on Industrial Technology (ICIT)

作者： F. Rashidi M. Rashidi Center of Excellence for Control and Intelligent Processing Department of Electrical and Computer Engineering University of Tehran Iran Hormozgan Regional Electric Company Bandar-Abbas Iran

A neuro-control approach for flexible AC transmission systems (FACTS) based on temporal difference learning based multilayer perceptron neural network (TDMLP) is presented in this paper. The proposed scheme consists of a single neuron network whose input is derived from the active or reactive power or voltage derivation at the power system bus, where the FACTS device (in this case an unified power flow controller) is located. The performance and usefulness of this approach is tested and evaluated using both single-machine infinite-bus and two-machine power system subjected to various transient disturbances. It was found that the new intelligent controller for FACTS exhibits a superior dynamic performance in compensation to the existing classical control schemes. Its simple architecture reduces the computational overhead, thereby real-time implementation.

关键词： Robustness Adaptive systems Neural networks Flexible AC transmission systems Power system transients Power system dynamics Multilayer perceptrons Multi-layer neural network Neurons Reactive power control

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L/sup /spl infin//-bounded robust control of nonlinear cascade systems 5th

L/sup /spl infin//-bounded robust control of nonlinear casca...

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Asian control Conference

作者： Shoudong Huang M.R. James Z.P. Jiang Faculty of Engineering Centre of Excellence in Autonomous Systems The University of Technology Sydney NSW Sydney Australia Department of Engineering Australian National University Canberra Australia Department of Electrical and Computer Engineering Polytechnic University Six Metrotech Center Brooklyn NY U.S.A.

ISBN: (纸本)0780388739

In this paper, we consider the L/sup /spl infin//-bounded robust control problem for a class of nonlinear cascade systems with disturbances. Sufficient conditions are provided under which a hard bound is imposed on the system performance measure. The backstepping approach is used for controller design. Examples are provided to illustrate the method.

关键词： Robust control Nonlinear control systems Backstepping control systems Stability Closed loop systems System performance Dynamic programming Nonlinear equations Design methodology

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Adaptive compensation of morphing actuator failures

Adaptive compensation of morphing actuator failures

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American control Conference (ACC)

作者： S. Chen G. Tao J. Fei S.M. Joshi Department of Naval Architechlre and Marine Engineering University of Michigan Ann Arbor MI USA Department of Electrical and Computer Engineering University of Virginia Charlottesville VA USA NASA-Langley Research Center Hampton VA USA

We develop an adaptive approach for compensating uncertain failures of morphing actuators used for flight control of morphing aircraft. Morphing actuators are special actuators that operate at only two states: "on" and "off", and are used in a large quantity. Failures of such actuators are characterized by some actuators being stuck at one state and unable to be changed by applied control inputs. We present a novel failure model for such actuators, by considering the failure percentage as an uncertain parameter, and derive a piecewise-linear characteristic to describe such failures. We develop an adaptive inverse of such characteristic to compensate for the uncertain actuator failures. Updated from a parameter projection adaptive law and combined with a state feedback control law, such an adaptive inverse compensation scheme is able to ensure the desired closed-loop stability and tracking properties in the presence of uncertain actuator failures. An application of this compensation scheme to the control of an ICE aircraft model with morphing actuators is studied and the simulation results are presented to illustrate the effectiveness of the actuator failure compensation design.

关键词： Actuators Aerospace control Aircraft Adaptive control Programmable control Ice Solid modeling Geometry Shape Aerospace engineering

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Non linear system identification and control design using adaptive wavelet network

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IFAC Proceedings Volumes 2004年第13期37卷 507-512页

作者： H.R. Karimi B. Lohmann B. Moshiri P. Jabehdar Maralani Control and Intelligent Processing Center of Excellence. Department of Electrical and Computer Engineering Faculty of Engineering University of Tehran. Tehran. Iran Institute of Automation University of Bremen Otto-Hahn-Allee NW1 Bremen D-28359 Germany

This paper presents an application of wavelet networks in identification and control design for a class of non-linear dynamical systems. The technique of feedback linearization, supervisory control and H ∞ control are used to design an adaptive control law and also the parameters of the wavelet networks are adapted using a Lyapunov-based design. By some theorems, it will be proved that even in the presence of modelling errors, named network error, the stability of the overall identification scheme and the convergence of the network parameters and the boundedness of the state errors are guaranteed.

关键词： System Identification Supervisory control He control Stability Analysis Wavelet Network.

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Feature selection using genetic algorithm and it's application to face recognition

Feature selection using genetic algorithm and it's applicati...

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IEEE Conference on Cybernetics and Intelligent Systems

作者： M.T. Harandi M.N. Ahmadabadi B.N. Araabi C. Lucas Institute of Studies in Theoretical Physics and Mathematics Tehran Iran Control and Intelligent Processing Center of Excellence Department of Electrical and Computer Engineering University of Tehran Tehran Iran

A new method for feature selection based on generic algorithm and its application in face recognition is proposed. Simulation results show the superior performance of the proposed algorithm over the eigenface original method. In comparison to evolutionary pursuit method, the proposed method has a faster training phase and the ability of finding and searching more features using our novel fitness function

关键词： Genetic algorithms Face recognition Face detection Humans Principal component analysis Application software Analysis of variance Character recognition Process control Intelligent control

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Clustering-based framework for comparing fMRI analysis methods

Clustering-based framework for comparing fMRI analysis metho...

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IEEE International Symposium on Biomedical Imaging

作者： G.-A. Hossein-Zadeh A.-M. Golestani H. Soltanian-Zadeh Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department Faculty of Engineering University of Tehran Tehran Iran Image Analysis Laboratory Radiology Department Henry Ford Health System Detroit MI USA

In this paper, a cluster-based framework is introduced for comparing analysis methods of functional magnetic resonance images (fMRI). In the proposed framework, fMRI data is replaced with a feature space and each method considered as a clustering method in the new space. As a result, different methods can be compared by means of a cluster validity measure. The feature space is computed using a non-parametric method (principal component analysis-PCA). Four subjects have been analyzed with three methods and the proposed cluster-based framework has evaluated performance of the methods. The results are identical to those of the modified receiver operating characteristics (ROC). This validates the proposed approach.

关键词： Clustering algorithms Algorithm design and analysis Image analysis Clustering methods Magnetic analysis Testing Accuracy Reproducibility of results Radiology Magnetic resonance

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Multiresolution automatic segmentation of T1-weighted brain MR images

Multiresolution automatic segmentation of T1-weighted brain ...

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IEEE International Symposium on Biomedical Imaging

作者： M. Zeydabadi R.A. Zoroofi H. Soltanian-Zadeh Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department Faculty of Engineering University of Tehran Tehran Iran Radiology Department Henry Ford Health System Image Analysis and Communications Laboratory Detroit MI USA

Automatic segmentation of brain tissues is crucial to many medical imaging applications. We use a multi-resolution analysis and a power transform to extend the well-known Gaussian mixture model expectation maximization based algorithm for segmentation of white matter, gray matter, and cerebrospinal fluid from T1-weighted magnetic resonance images (MRI) of the brain. Experimental results with near 4000 synthetic and real images are included. The results illustrate that the proposed method outperforms six existing methods.

关键词： Image resolution Image segmentation Biomedical imaging Multiresolution analysis Image analysis Magnetic analysis Algorithm design and analysis Brain modeling Magnetic liquids Magnetic resonance

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