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 ...
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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.
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 ...
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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.
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 identifi...
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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.
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 co...
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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.
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 pa...
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.
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 to...
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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 genet...
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In this paper a hierarchical architecture that combines a high degree of reconfigurability and long-term memory is proposed as a fault tolerant control algorithm for complex nonlinear systems. Dual heuristic programmi...
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In this paper a hierarchical architecture that combines a high degree of reconfigurability and long-term memory is proposed as a fault tolerant control algorithm for complex nonlinear systems. Dual heuristic programming (DHP) is used for adapting to faults as they occur for the first time in an effort to prevent the build up of a general failure and also as tuning device after switching to a known scenario. A dynamical database, initialized with as much information of the plant as available, oversees the DHP controller. The decisions of which environments to record, when to intervene and where to switch are autonomously taken based on specifically designed quality indexes. The results of the application of the complete algorithm to a proof-of-the-concept numerical example help to illustrate the fine interrelations between each of its subsystems.
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