The problem of distributed detection with consulting sensors in the presence of communication cost associated with any exchange of information (consultation) between sensors is considered. We consider a system of two ...
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The problem of distributed detection with consulting sensors in the presence of communication cost associated with any exchange of information (consultation) between sensors is considered. We consider a system of two sensors, S1 and S2, in which S1 is the primary sensor responsible for the final decision u0, while S2 is a consulting sensor capable of relaying its decision u2 to S1 when requested by S1. In the scenario that is considered, the final decision u0 is based either on the raw data available to S1 only, or it may, under certain request conditions, also take into account the decision u2 of sensor S2. Random and nonrandom request schemes are analyzed and numerical results are presented and compared for Gaussian and slow-fading Rayleigh channels. For each decision making scheme, an associated optimization problem is formulated whose solution is shown to satisfy certain a priori set design criteria that we consider essential for sensor fusion.
Two identification methods;"stepby-step method" and "simultaneous method" which takes advantage of the instrumental variable method are experimentary examined to estimate the parameters of the dyna...
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An effective method for visual pattern recognition using morphological techniques is presented. It is shown that it can be successfully used for the recognition of deformed letters. The method extracts morphological i...
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Church’s problem and the emptiness problem for Rabin automata on infinite trees, which represent basic paradigms for program synthesis and logical decision procedures, are formulated as a control problem for automata...
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An iteration method is presented for determining the largest singular value (2 - norm) of a matrix, and its corresponding singular vectors. Connections with the power method, and Bernoulli's method are presented. ...
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As a first step to the control of paraplegic gait by functional electrical stimulation (FES), the control of the swinging lower leg is being studied. This paper deals with a neural control system, that has been develo...
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As a first step to the control of paraplegic gait by functional electrical stimulation (FES), the control of the swinging lower leg is being studied. This paper deals with a neural control system, that has been developed for this case. The control system has been tested for a model of the swinging lower leg using computer simulations. The neural controller was trained by supervised learning (SL) and by backpropagation through time (BTT). The performance of the controller with random initial weights was poor after training with BTT and fair after SL. BTT training of the neural controller with weights, which had been initialized by SL, resulted in good control. Training with BTT thus improved the performance of the controller that initially had been trained by SL. An adaptive neural control system based on BTT has been proposed and partially tested. The controller adapted relatively fast to the change of an important model parameter.
As a first step to the control of paraplegic gait by functional electrical stimulation (FES), the control of the swinging lower leg is being studied. This paper deals with a neural control system, that has been develo...
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As a first step to the control of paraplegic gait by functional electrical stimulation (FES), the control of the swinging lower leg is being studied. This paper deals with a neural control system, that has been developed for this case. The control system has been tested for a model of the swinging lower leg using computer simulations. The neural controller was trained by supervised learning (SL) and by backpropagation through time (BTT). The performance of the controller with random initial weights was poor after training with BTT and fair after SL. BTT training of the neural controller with weights, which had been initialized by SL, resulted in good control. Training with BTT thus improved the performance of the controller that initially had been trained by SL. An adaptive neural control system based on BTT has been proposed and partially tested. The controller adapted relatively fast to the change of an important model parameter.
The authors consider the design of a discrete time model reference adaptive control system (MRACS) for motion controlsystems with unknown nonlinear friction. From a control system viewpoint, the static and Coulomb fr...
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The authors consider the design of a discrete time model reference adaptive control system (MRACS) for motion controlsystems with unknown nonlinear friction. From a control system viewpoint, the static and Coulomb frictions are characterized by a nonlinear dead-zone element and constant value disturbance for the control input signal, respectively. The discrete MRACS is considered for a nonlinear system which is a cascade combination of an unknown dead-zone block and a linear dynamic block with disturbance. In order to verify the validity of the theory, the design method described is applied to a servo system influenced by friction. Experimental results show the high control performance of the constructed MRACS and the effectiveness of the proposed method.< >
The discrimination of gas-liquid two-phase flow patterns has so far been made mainly by visual observation; and various flow parameters characterizing the flow, such as fluctuating pressures, void fraction etc., have ...
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The discrimination of gas-liquid two-phase flow patterns has so far been made mainly by visual observation; and various flow parameters characterizing the flow, such as fluctuating pressures, void fraction etc., have been employed as quantitative tools for this purpose. It would be useful for the prediction and prevention of accidents experienced in thermal engineering plants dealing with the flow if it could be found the effective techniques for analyzing void fraction signals detected for diagnosing plant operation states. This paper presents pattern recognition and clustering techniques for analyzing the cross-sectional mean void fraction signals in gas-liquid two-phase flow based on an AR-model with bias components. The pattern recognition method based on the Bayes decision rule was applied for the recognition of flow regimes which were determined by visual observations. The clustering methods based on K-mean algorithm and mixture probability algorithm were applied for the determination of the new boundaries on a superficial gas-liquid velocities diagram. The effectiveness of the discussed techniques were confirmed.
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