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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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Ensemble neural network methods for satellite-derived estimation of chlorophyll /spl alpha/

Ensemble neural network methods for satellite-derived estima...

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International Joint Conference on Neural Networks (IJCNN)

作者： W.H. Slade R.L. Miller H. Ressom P. Natarajan Department of Electrical and Computer Engineering Intelligent Systems Laboratory University of Maine Orono ME USA Earth Science Applications Directorate Stennis Space Centet NASA MS USA

In this paper, neural network-based methods incorporating ensemble learning techniques are presented that estimate chlorophyll /spl alpha/ (chl /spl alpha/) concentration in the coastal waters of the Gulf of Maine (GOM). A dataset was constructed consisting of in situ chl measurements from the GOM matched with satellite data from the sea-viewing wide-field-of-view sensor (SeaWiFS). These data were used to develop models using diverse neural network ensembles for estimation of chl /spl alpha/ concentration from satellite-retrieved ocean reflectances. Results indicate that the models are able to generalize across geographical and temporal variation, and are resilient to uncertainty such as that introduced by poor atmospheric correction, or radiance contributions from non-chl /spl alpha/ components in case 2 waters.

关键词： Neural networks Oceans Biomedical optical imaging Optical network units Optical sensors Sea measurements Atmospheric modeling Uncertainty Water Sea surface

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Evolutionary multi-criterion optimization

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Lecture Notes in computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2003年 2632卷

作者： Fonseca, Carlos M. Fleming, Peter J. Zitzler, Eckart Deb, Kalyanmoy Thiele, Lothar University of the Algarve Centre for Intelligent Systems Campus de Gambelas Faro Portugal University of Sheffield Department of Automatic Control and Systems Engineering Mappin Street Sheffield United Kingdom Swiss Federal Institute of Technology Department of Electrical Engineering Computer Engineering and Networks Laboratory Gloriastrabe 35 Zurich Switzerland Indian Institute of Technology Department of Mechanical Engineering Kanpur Genetic Algorithms Laboratory Kanpur India

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Automatic face region tracking for highly accurate face recognition in unconstrained environments

Automatic face region tracking for highly accurate face reco...

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IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS)

作者： Y.-O. Kim J. Paik Jingu Heo A. Koschan B. Abidi M. Abidi Korea Electronics and Technology Institute Puchon Gyeonggi South Korea Image Processing Laboratory Department of Image Engineering Graduate School of Advanced Imaging Science Multimedia and Film Chung-Ang University Seoul South Korea Imaging Robotics and Intelligent Systems Laboratory Department of Electrical and Computer Engineering University of Tennessee Knoxville USA

We present a combined real-time face region tracking and highly accurate face recognition technique for an intelligent surveillance system. High-resolution face images are very important to achieving accurate identification of a human face. Conventional surveillance or security systems, however, usually provide poor image quality because they use only fixed cameras to record scenes passively. We have implemented a real-time surveillance system that tracks a moving face using four pan-tilt-zoom (PTZ) cameras. While tracking, the region-of-interest (ROI) can be obtained by using a low-pass filter and background subtraction with the PTZ. Color information in the ROI is updated to extract features for optimal tracking and zooming. FaceIt/sup /spl reg//, which is one of the most popular face recognition software packages, is evaluated and then used to recognize the faces from the video signal. Experimentation with real human faces showed highly acceptable results in the sense of both accuracy and computational efficiency.

关键词： Face recognition Surveillance Real time systems Humans Cameras intelligent systems Security Image quality Layout Low pass filters

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Preface

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Lecture Notes in computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2003年 2632卷 v页

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Reconfigurable control system design for fault diagnosis and accommodation.

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International journal of neural systems 2002年第6期12卷 497-520页

作者： Ho, Liang-Wei Yen, Gary G Intelligent Systems and Control Laboratory School of Electrical and Computer Engineering Oklahoma State University United States

The growing demand in system reliability and survivability under failures has urged ever-increasing research effort on the development of fault diagnosis and accommodation. In this paper, the on-line fault tolerant control problem for dynamic systems under unanticipated failures is investigated from a realistic point of view without any specific assumption on the type of system dynamical structure or failure scenarios. The sufficient conditions for system on-line stability under catastrophic failures have been derived using the discrete-time Lyapunov stability theory. Based upon the existing control theory and the modern computational intelligence techniques, an on-line fault accommodation control strategy is proposed to deal with the desired trajectory-tracking problems for systems suffering from various unknown and unanticipated catastrophic component failures. Theoretical analysis indicates that the control problem of interest can be solved on-line without a complete realization of the unknown failure dynamics provided an on-line estimator satisfies certain conditions. Through the on-line estimator, effective control signals to accommodate the dynamic failures can be computed using only the partially available information of the faults. Several on-line simulation studies have been presented to demonstrate the effectiveness of the proposed strategy. To investigate the feasibility of using the developed technique for unanticipated fault accommodation in hardware under the real-time environment, an on-line fault tolerant control test bed has been constructed to validate the proposed technology. Both on-line simulations and the real-time experiment show encouraging results and promising futures of on-line real-time fault tolerant control based solely upon insufficient information of the system dynamics and the failure dynamics.

关键词： Fault tolerant control reconfigurable control fault diagnosis Lyapunov stability fault accommodation

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Hierarchical genetic algorithm for near optimal feedforward neural network design.

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International journal of neural systems 2002年第1期12卷 31-43页

作者： Yen, Gary Lu, Haiming Intelligent Systems and Control Laboratory School of Electrical and Computer Engineering Stillwater 74078 OK United States

In this paper, we propose a genetic algorithm based design procedure for a multi layer feed forward neural network. A hierarchical genetic algorithm is used to evolve both the neural networks topology and weighting parameters. Compared with traditional genetic algorithm based designs for neural networks, the hierarchical approach addresses several deficiencies, including a feasibility check highlighted in literature. A multi objective cost function is used herein to optimize the performance and topology of the evolved neural network simultaneously. In the prediction of Mackey Glass chaotic time series, the networks designed by the proposed approach prove to be competitive, or even superior, to traditional learning algorithms for the multi layer Perceptron networks and radial basis function networks. Based upon the chosen cost function, a linear weight combination decision making approach has been applied to derive an approximated Pareto optimal solution set. Therefore, designing a set of neural networks can be considered as solving a two objective optimization problem.

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Hierarchical rank density genetic algorithm for radial-basis function neural network design

Hierarchical rank density genetic algorithm for radial-basis...

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2002 Congress on Evolutionary Computation, CEC 2002

作者： Yen, Gary G. Lu, Haiming Intelligent Systems and Control Laboratory School of Electrical and Computer Engineering Oklahoma State University Stillwater OK 74078-5032 United States

ISBN: (纸本)0780372824

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 both the neural network's topology and parameters. 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 network, HRDGA provides a set of near-optimal neural networks to the designers or the decision makers so that they can have more flexibility for the final decision-making based on their 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 selected traditional radial-basis function networks for predicting Mackey-Glass chaotic time series. © 2002 IEEE.

关键词： Genetic algorithms

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Facial feature extraction using genetic algorithm

Facial feature extraction using genetic algorithm

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2002 Congress on Evolutionary Computation, CEC 2002

作者： Yen, Gary G. Nithianandan, Nethrie Intelligent Systems and Control Laboratory School of Electrical and Computer Engineering Oklahoma State University Stillwater OK 74074-5032 United States

ISBN: (纸本)0780372824

An automatic facial feature extraction method is presented in this paper. The method is based on the edge density distribution of the image. In the preprocessing stage a face is approximated to an ellipse, and a genetic algorithm is applied to search for the best ellipse region match. In the feature extraction stage, the genetic algorithm is applied to extract the facial features, such as the eyes, nose and mouth, in the predefined sub regions. 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 certain amount of artificial noise and of multi-face oriented with angles. © 2002 IEEE.

关键词： Genetic algorithms

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Enabling iterative software architecture derivation using early non-functional property evaluation

Enabling iterative software architecture derivation using ea...

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IEEE International Conference on Automated Software engineering (ASE)

作者： K.S. Barber T. Graser J. Holt Laboratory for Intelligent Processes and Systems Electrical and Computer Engineering University of Technology Austin TX USA

The structure of a software architecture strongly influences the architecture's ability to prescribe systems satisfying functional requirements, non functional requirements, and overall qualities such as maintainability, reusability, and performance. Achieving an acceptable architecture requires an iterative derivation and evaluation process that allows refinement based on a series of tradeoffs. Researchers at the University of Texas at Austin are developing a suite of processes and supporting tools to guide architecture derivation from requirements acquisition through system design. The various types of decisions needed for concurrent derivation and evaluation demand a synthesis of evaluation techniques, because no single technique is suitable for all concerns of interest. Two tools in this suite, RARE and ARCADE, cooperate to enable iterative architecture derivation and architecture property evaluation. RARE guides derivation by employing a heuristics knowledge base, and evaluates the resulting architecture by applying static property evaluation based on structural metrics. ARCADE provides dynamic property evaluation leveraging simulation and model-checking. This paper presents a study whereby RARE and ARCADE were employed in the early stages of an industrial project to derive a Domain Reference Architecture (DRA), a high-level architecture capturing domain functionality, data, and timing. The discussion emphasizes early evaluation of performance qualities, and illustrates how ARCADE and RARE cooperate to enable iterative derivation and evaluation. These evaluations influenced DRA refinement as well as subsequent design decisions involving application implementation and computing platform selection.

关键词： Software architecture computer architecture Yarn Application software Connectors Laboratories intelligent systems intelligent structures Maintenance engineering Timing

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