ESPRIT is an algorithm for determining the fixed directions of arrival of a set of narrowband signals at an array of sensors. Its computational burden makes it unsuitable for real-time processing of signals with time-...
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ESPRIT is an algorithm for determining the fixed directions of arrival of a set of narrowband signals at an array of sensors. Its computational burden makes it unsuitable for real-time processing of signals with time-varying directions of arrival. The authors develop a new implementation of ESPRIT that has potential for real-time processing. It is based on a rank-revealing URV decomposition, rather than the eigendecomposition or singular value decomposition (SVD) used in previous ESPRIT algorithms. Its performance is demonstrated on simulated data representing both constant and time-varying signals. It is shown that the URV-based ESPRIT algorithm is effective for estimating time-varying directions-of-arrival at considerable computational savings over the SVD-based algorithm.< >
Chang and Wu have proposed a letter-oriented perfect hashing scheme based on sparse matrix compression. We present a method which is a refinement of the Chang-Wu scheme. By experimental evaluation, we show that the ha...
Chang and Wu have proposed a letter-oriented perfect hashing scheme based on sparse matrix compression. We present a method which is a refinement of the Chang-Wu scheme. By experimental evaluation, we show that the hashing of our refinement has more efficient storage utilization than Chang-Wu's method. Our refinement is valuable in practical implementations of hashing for large sets of keys.
This paper presents a new stochastic approach called annealing-genetic algorithm for the one-dimensional bin-packing problem. This approach incorporates genetic algorithms into simulated annealing to improve the perfo...
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An artificial vision system with spatio-chromatic channels is proposed. A dynamic neural network is used for the spatial and chromatic information of a scene. The spatio-chromatic information is transmitted into two c...
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The authors present a novel stochastic approach called the annealing-genetic algorithm for the one-dimensional bin-packing problem. This approach incorporates genetic algorithms into simulated annealing (SA) to improv...
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The authors present a novel stochastic approach called the annealing-genetic algorithm for the one-dimensional bin-packing problem. This approach incorporates genetic algorithms into simulated annealing (SA) to improve the performance of SA. The genetic approach to SA seems to facilitate the exhaustive and parallel treatment of the problem and to increase the probability of finding global minima. The empirical results show that the quality of the solution obtained with this approach is better than or equal to that of the FFD (first-fit-decreasing) in the average cases but is better than that of the FFD in all the known worst cases. Unlike the FFD, no nonmonotone anomaly has been found in the proposed approach.< >
The neural-network model based on the theory proposed by Wilson and Cowan has been simulated by using digitized real images. Mathematically, the model is based on coupled nonlinear differential equations that describe...
Problems concerning the choice of the optimum sampling frequency value in system recursive-type identifications are analyzed. The links between optimum test conditions and some of the characteristic quantities of the ...
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Problems concerning the choice of the optimum sampling frequency value in system recursive-type identifications are analyzed. The links between optimum test conditions and some of the characteristic quantities of the system under analysis are identified through experimental tests carried out on a number of different passive electrical networks. These confirmed that parameter estimation accuracy depends also on the input signal characteristics. Suitable links were identified between both time and frequency domain system properties (settling time and transfer function, respectively) and optimum conditions. Procedures are described which allow test conditions for the identification of unknown systems to be automatically and optimally evaluated.< >
The neural network model based on the theory proposed by Wilson-Cowan has been simulated using digitized real images. The Wilson-Cowan net can operate in different modes depending on the parameter selection, and it is...
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The increasing complexity of process plants and their reliability has encouraged industry to look for new approaches for detecting and diagnosing process abnormalities. One such approach is the use of knowledge-based ...
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The increasing complexity of process plants and their reliability has encouraged industry to look for new approaches for detecting and diagnosing process abnormalities. One such approach is the use of knowledge-based system techniques. On the contrary to many recent attempts using this technology where diagnostic analysis is based solely on measurable and observable data, in this work we consider the adaptive inclusion of a state and/or parameter estimation module in the diagnostic reasoning loop, in addition to employing information based on measurable data. The design methodology is a new layered knowledge base that houses compiled/qualitative knowledge in the high-levels and process-general estimation knowledge in the low-levels of a hierarchical knowledge structure. The compiled knowledge is used to narrow the diagnostic search space and provide an effective way of employing estimation modules. The purpose of this paper is to present the failure detection issues for the deaerator control subsystem of a coal fired power plant. The main emphasis is placed upon the model-based redundancy methods which create the low-levels of the hierarchical knowledge base. Due to the highly-nonlinear and mixed-mode nature of the power plant dynamics, the modified extended Kalman filter is used in designing the local detection filters.
Fault diagnosis in the area of process operations is critical for modern production and is receiving increasing theoretical and practical attention. In spite of many research and practical attempts, process fault diag...
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Fault diagnosis in the area of process operations is critical for modern production and is receiving increasing theoretical and practical attention. In spite of many research and practical attempts, process fault diagnosis remains a rather complex task. In this work, we present a diagnostic methodology in which the symbolic reasoning of knowledge-based systems techniques is integrated with quantitative analysis of analytical redundancy methods. The system first performs a diagnosis by means of a compiled knowledge structure, and then attempts to build a detailed explanation by using proper fault models (adaptive filters). It also performs various statistical analyses to determine the process condition and check the validity of models. This unified approach increases the completeness and reliability of the diagnostic system.
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