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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： S.M. Shams G.A. Hossein-Zadeh H. Soltanian-Zadeh Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department Institute for Studies in Theoretical Physics and Mathematics University of Tehran School of Cognitive Sciences Tehran Iran Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department Institute for Studies in Theoretical Physics and Mathematics and Medical Image Analysis Laboratory Henry Ford Health System University of Tehran School of Cognitive Sciences Iran

A new method based on generalized likelihood ratio test (GLRT) for activation detection in multi-subject studies of functional MRI (fMRI) is proposed. In this method, we test the correlation between the fMRI time series of different subjects and the bases of a signal subspace which increases the flexibility of method in detecting different shapes of hemodynamic response. The proposed multivariate method can be applied to group studies where the conventional cross-correlation method cannot be used due to its univariate property. This method is applied to both experimental and simulated fMRI data and the results are compared to those of general linear model (GLM). We show that the proposed method detects more significant activated regions in analyzing experimental data and more true voxels in simulated data

关键词： Testing Hemodynamics Brain modeling Process control intelligent control Physics computing Mathematics Time series analysis Magnetic resonance imaging Data analysis

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Unified, periodic & sampled data H/sub /spl infin// control using the delta operator

Unified, periodic & sampled data H/sub /spl infin// control ...

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IEEE Conference on Decision and control

作者： B. Lennartson R. Middleton A.-K. Christiansson Control and Automation Laboratory Department of Signals and Systems Chalmers University of Technology Goteborg Sweden Complex Dynamic Systems & Control School of Electrical Engineering & Computer Science University of Newcastle Callaghan NSW Australia University of Trollhättan/Uddevalla Trollhattan Sweden

A unified and general framework is presented for H/sub /spl infin// control of mixed continuous-time and discrete-time time-varying (periodic) systems. Using the delta operator, a close relationship is shown between the continuous- and discrete-time solutions. No assumptions are made on certain system matrices being zero or normalized, which makes the approach general and easy to apply. A combined continuous/discrete-time lifting procedure is shown to be useful, especially for ill-conditioned systems. This procedure together with the delta formalism results in a numerically robust design method concerning both short and long sampling periods for systems with W-conditioned dynamics, including widely spread eigenvalues.

关键词： control systems Sampling methods Riccati equations Automatic control Robustness Design methodology Eigenvalues and eigenfunctions Differential equations Automation Laboratories

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Low order sampled data H/sub /spl infin// control using the delta operator and LMIs

Low order sampled data H/sub /spl infin// control using the ...

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IEEE Conference on Decision and control

作者： B. Lennartson R. Middleton A.-K. Christiansson T. McKelvey Control and Automation Laboratory Department of Signals and Systems Chalmers University of Technology Goteborg Sweden Complex Dynamic Systems & Control School of Electrical Engineering & Computer Science University of Newcastle Callaghan NSW Australia University of Trollhättan/Uddevalla Trollhattan Sweden Department of Signals and Systems Chalmers University of Technology Goteborg Sweden

A procedure for H/sub /spl infin// optimization of low order controllers for discrete-time and sampled-data systems is presented in this paper. Generally, low order H/sub /spl infin// controllers may be achieved by solving bilinear matrix inequalities (BMIs). In this paper an iterative alternation between two LMIs gives a suboptimal solution. To avoid local minima in this search the initial controller is obtained by a frequency weighted controller reduction scheme, where the closed loop properties of a full order controller is taken into account. A minimal number of parameters in the state space realization of the controller also reduces the complexity and improves numerical robustness. The complete presentation is based on delta operator models, which shows a close relationship between the continuous- and discrete-time solutions. The sensitivity of the ordinary discrete-time shift operator LMI formulation to small sampling periods is also analyzed.

关键词： control systems Linear matrix inequalities Sampling methods Automatic control Frequency Weight control Robust control Signal processing State-space methods Robustness

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Using a non-prior training active feature model

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2004年 3333卷 69-78页

作者： Kim, Sangjin Kang, Jinyoung Shin, Jeongho Lee, Seongwon Paik, Joonki Kang, Sangkyu Abidi, Besma Abidi, Mongi Department of Image Engineering Graduate School of Advanced Imaging Science Multimedia and Film Chung-Ang University 221 Huksuk-Dong Tongjak-Ku Seoul 156-756 Korea Republic of Imaging Robotics and Intelligent Systems Laboratory Department of Electrical and Computer Engineering University of Tennessee Knoxville TN 27996-2100 United States

This paper presents a feature point tracking algorithm using optical flow under the non-prior training active feature model (NPT-AFM) framework. The proposed algorithm mainly focuses on analysis of deformable objects, and provides real-time, robust tracking. The proposed object tracking procedure can be divided into two steps: (i) optical flow-based tracking of feature points and (ii) NPT-AFM for robust tracking. In order to handle occlusion problems in object tracking, feature points inside an object are estimated instead of its shape boundary of the conventional active contour model (ACM) or active shape model (ASM), and are updated as an element of the training set for the AFM. The proposed NPT-AFM framework enables the tracking of occluded objects in complicated background. Experimental results show that the proposed NPT-AFM-based algorithm can track deformable objects in real-time. © Springer-Verlag 2004.

关键词： Optical flows

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A SOM projection technique with the growing structure for visualizing high-dimensional data.

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International journal of neural systems 2003年第5期13卷 353-365页

作者： Wu, Zheng Yen, Gary G Intelligent Systems and Control Laboratory School of Electrical and Computer Engineering Oklahoma State University Stillwater 74078 OK United States

The Self-Organizing Map (SOM) is an efficient tool for visualizing high-dimensional data. In this paper, an intuitive and effective SOM projection method is proposed for mapping high-dimensional data onto the two-dimensional grid structure with a growing self-organizing mechanism. In the learning phase, a growing SOM is trained and the growing cell structure is used as the baseline framework. In the ordination phase, the new projection method is used to map the input vector so that the input data is mapped to the structure of the SOM without having to plot the weight values, resulting in easy visualization of the data. The projection method is demonstrated on four different data sets, including a 118 patent data set and a 399 checical abstract data set related to polymer cements, with promising results and a significantly reduced network size.

关键词： Self-Organizing Map documentation clustering dimensionality reduction growing cell structure

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AUTOMATIC FACIAL FEATURE EXTRACTION USING EDGE DISTRIBUTION AND GENETIC SEARCH

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International Journal of Computational Intelligence and Applications 2003年第1期3卷 89-100页

作者： GARY G. YEN NETHRIE NITHIANANDAN Intelligent Systems and Control Laboratory School of Electrical and Computer Engineering Oklahoma State University Stillwater OK 74074-5032 USA

A heuristic, automatic facial feature extraction approach is presented in this paper. The method is based on the edge density distribution of the image, containing non-occluded, nearly frontal view of one or more faces. In the preprocessing stage, a face is approximated to an ellipse, and genetic algorithm is applied to search for the best ellipse region match. In the feature extraction stage, genetic algorithm is applied to extract the facial features, such as the eyes (with eyebrows), nose and mouth, in the predefined subregions. The simulation results validates that the proposed method is capable of automatically extracting features from various video images effectively under natural lighting environments and in the presence of complex backgrounds, certain amount of artificial noise and of multi-face oriented with angles. This preliminary study advanced from a rich literature provides robust facial feature detection under certain variations in lighting conditions and backgrounds.

关键词： Facial recognition feature extraction genetic algorithm edge density

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Hierarchical Rank Density Genetic Algorithm for Radial-Basis Function Neural Network Design

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International Journal of Computational Intelligence and Applications 2003年第3期3卷 213-232页

作者： Gary G. Yen Haiming Lu Intelligent Systems and Control Laboratory School of Electrical and Computer Engineering Oklahoma State University Stillwater OK 74078-503 USA

In this paper, we propose a genetic algorithm based design procedure for a radial-basis function neural network. A Hierarchical Rank Density Genetic Algorithm (HRDGA) is used to evolve the neural network's topology and parameters simultaneously. Compared with traditional genetic algorithm based designs for neural networks, the hierarchical approach addresses several deficiencies highlighted in literature. In addition, the rank-density based fitness assignment technique is used to optimize the performance and topology of the evolved neural network to deal with the confliction between the training performance and network complexity. Instead of producing a single optimal solution, HRDGA provides a set of near-optimal neural networks to the designers so that they can have more flexibility for the final decision-making based on certain preferences. In terms of searching for a near-complete set of candidate networks with high performances, the networks designed by the proposed algorithm prove to be competitive, or even superior, to three other traditional radial-basis function networks for predicting Mackey–Glass chaotic time series.

关键词： Genetic algorithm multiobjective evolutionary algorithm neural network design RBF network hierarchical representation Pareto set

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A SOM projection technique with the growing structure for visualizing high-dimensional data

A SOM projection technique with the growing structure for vi...

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International Joint Conference on Neural Networks (IJCNN)

作者： Z. Wu G.G. Yen Intelligent Systems and Control Laboratory School of Electrical and Computer Engineering Oklahoma State University Stillwater OK USA

The Self-Organizing Map (SOM) is an efficient tool for visualizing high-dimensional data. In this paper, an intuitive and effective SOM projection method is proposed for mapping high-dimensional data onto the two-dimensional SOM structure with a growing self-organizing map. In the learning phase, a growing SOM is trained and the growing cell structure is used as the baseline framework. After the learning phase, the new projection method is used to map the input vector so that the input data is mapped to the structure of the SOM without having to plot the weight values, resulting in easy visualization of the data. The projection method is demonstrated on two data sets with promising results and a significantly reduced network size.

关键词： Data visualization Neurons Data structures Shape intelligent systems intelligent control control systems Laboratories Data engineering Data mining

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Multiple model fault tolerant control using Globalized Dual Heuristic Programming

Multiple model fault tolerant control using Globalized Dual ...

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IEEE International Symposium on intelligent control (ISIC)

作者： P.G. DeLima G.G. Yen Intelligent Systems and Control Laboratory School of Electrical and Computer Engineering Oklahoma State University Stillwater OK USA

An advanced reconfigurable controller improved by a multiple model architecture is proposed as a tool to achieve fault tolerance in complex nonlinear systems. The most complete adaptive critic design, Globalized Dual Heuristic Programming (GDHP), constitutes a highly flexible nonlinear adaptive controller responsible for the generation of new control solutions for novel plant dynamics introduced by unknown faults. Working on a higher hierarchical level, the proposed supervisor makes use of two quality indexes to perform fault detection, identification and isolation based on the knowledge stored in a dynamic model bank. In the event of abrupt known faults, such knowledge is then used to greatly reduce the reconfiguration time of the GDHP controller. The synergy of the proposed supervisor and GDHP goes beyond, as solutions designed by the controller to previously unknown faults are autonomously added to the model bank. The fine interrelations among the algorithm's subsystems are illustrated in a rich numerical simulation of a MIMO nonlinear system subject to different fault scenarios.

关键词： Fault tolerance Programmable control Adaptive control Neural networks Nonlinear control systems Dynamic programming Stability Function approximation Nonlinear systems Fault detection

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Monitoring seagrass health using neural networks

Monitoring seagrass health using neural networks

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International Joint Conference on Neural Networks (IJCNN)

作者： H. Ressom S.K. Fyfe P. Natarajan S. Srirangam Intelligent Systems Laboratory Department of Electrical and Computer Engineering University of Maine Orono ME USA School of Geosciences and Institute for Conservation Biology University of Wollongong Wollongong NSW Australia

Monitoring seagrass health gives vital clues about the estuarine water quality, which is crucial for the existence of many aquatic plants and animals. Photosynthetic efficiency is a measure of plant stress and can be used to monitor seagrass health. However, insitu measurements of photosynthetic efficiency are time consuming and expensive. In this paper, neural network-based models are developed to estimate photosynthetic efficiency of a seagrass species, Zostera capricorni, from spectral reflectance measurements. The proposed neural network-based approach can be extended for other seagrass species by combining an ensemble of neural networks with a classifier. After identifying the type of seagrass species using the classifier, the neural network model that corresponds to the identified species is used to estimate photosynthetic efficiency.

关键词： Neural networks Sea measurements Reflectivity Remote monitoring Principal component analysis computerized monitoring Stress Time measurement Input variables intelligent systems

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