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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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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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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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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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Image Processing-Based Mine Detection Techniques: A Review

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Subsurface Sensing Technologies and Applications 2002年第3期3卷 153-202页

作者： Paik, Joonki Lee, Cheolha P. Abidi, Mongi A. Imaging Robotics and Intelligent Systems Laboratory Department of Electrical and Computer Engineering The University of Tennessee Knoxville

Various mine detection techniques are reviewed with particular emphasis on signal and image processing methods. Based on the target, mines are classified into two types; anti-tank mine (ATM) and anti-personnel mine (APM). Because of the variety of mine types, current mine detection techniques are diversified. The assumption is made that most mine detection techniques consist of sensor, signal processing, and decision processes. For the sensor part, ground penetration radar (GPR), infrared (IR), and ultrasound (US) sensors are reviewed and their characteristics are summarized for the corresponding output signals. For the signal processing and decision parts, a set of image processing techniques including filtering, enhancement, feature extraction, and segmentation are surveyed. Segmentation is used to extract mine signal from various competing signals. For most image processing techniques covered by this paper, mine detection related experimental results are included or reproduced from existing works.

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Integration of topological and metric maps for indoor mobile robot path planning and navigation 2nd

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2nd Hellenic Conference on Artificial Intelligence, SETN 2002

作者： Zavlangas, Panagiotis G. Tzafestas, Spyros G. Intelligent Robotics and Automation Laboratory Signals Control and Robotics Division Department of Electrical and Computer Engineering National Technical University of Athens Zografou Athens15773 Greece

ISBN: (纸本)3540434720

Autonomous mobile robots need to use spatial information about the environment in order to effectively plan and execute navigation tasks. The information can be represented at different levels of abstraction, ranging from detailed geometric maps to coarse topological maps. Each level is adequate for some sub-task, but not for others. In this paper, we consider the representation of spatial knowledge at two different levels of abstraction, which are commonly considered in the robotics literature: the geometric level, and the topological level. We propose to represent the environment by local metric maps connected into a topological network. This technique allows us to use maps that are not metrically consistent on the global scale, although they are metrically consistent locally. The structure allows also the combination of abstract global reasoning and precise local geometric computations. Moreover, this structure reflects the typical organization of indoor environments, where rooms and hallways define independent but connected local working spaces. To navigate in the environment, the robot uses the topological information to plan a sequence of sectors to traverse, and uses the metric information in each sector to locally move within the sector and to the next one. The functioning of the proposed system with respect to omnidirectional mobile robots and results of simulated experiments are presented. © Springer-Verlag Berlin Heidelberg 2002.

关键词： Abstracting

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NeuroFAST: High accuracy neuro-fuzzy modeling

NeuroFAST: High accuracy neuro-fuzzy modeling

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IEEE International Conference on Artificial Intelligence systems, ICAIS 2002

作者： Tzafestas, S.G. Zikidis, K.C. Intelligent Robotics and Automation Laboratory Electrical and Computer Engineering Department National Technical University of Athens Zographou Athens15773 Greece

ISBN: (纸本)0769517331

Most fuzzy modeling algorithms rely either on simplistic (grid type) or off-line (trial-and-error type) structure identification methods. The proposed neurofuzzy modeling architecture, NeuroFAST, is an on-line, structure and parameter learning algorithm, featuring high function approximation accuracy. It is based on the first order Takagi-Sugeno-Kang (TSK) model (functional reasoning), where the consequence part of each fuzzy rule is a linear equation of the input variables. Fuzzy rules are allocated as learning evolves by a modified Fuzzy ART (Adaptive Resonance Theory) mechanism, assisted by fuzzy rule splitting and adding procedures (structure learning). The well known δ-rule continuously tunes learning weights on both premise and consequence parts (parameter identification). Tested on the Box-Jenkins gas furnace process modeling and the Mackey-Glass chaotic time series prediction, NeuroFAST yields very good results in terms of approximation accuracy, outperforming all known approaches. © 2002 IEEE.

关键词： Fuzzy rules

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