The rapid growth of image archives increases the need for efficient and fast tools that can retrieve and search through large amount of visual data. In this paper we propose an efficient method of extracting the image...
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The rapid growth of image archives increases the need for efficient and fast tools that can retrieve and search through large amount of visual data. In this paper we propose an efficient method of extracting the image color content, which can serve as an image digital signature, allowing the efficient indexing and retrieval of large Internet-based multimedia databases. We applied the proposed method using the images from two Internet databases containing a collection of images of fine arts and a database of low resolution images, and show that the new method of image color representation is robust to image distorsions caused by resizing and compression and can be incorporated into existing web-based retrieval systems, that exploit the information on color content of digital images.
It is well-known that for linear systems internal asymptotic stability implies external stability in the sense that when the external input is in L/sub p/ then also the state will be in L/sub p/. However, for the cont...
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It is well-known that for linear systems internal asymptotic stability implies external stability in the sense that when the external input is in L/sub p/ then also the state will be in L/sub p/. However, for the control of linear systems with saturation where the controlled system is nonlinear this implication is no longer directly applicable. Several people have studied the effect of external inputs in L/sub p/ either directly or in the context of ISS as introduced by Sontag. In this paper we study the effect of external stochastic disturbances on linear systems with input saturation and we establish that when we can achieve internal global asymptotic stability then we can also achieve a bounded variance for the state.
The rapid growth of image archives increases the need for efficient and fast tools that can retrieve and search through large amount of visual data. In this paper we propose an efficient method of extracting the image...
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In this paper a novel class of filters designed for the removal of impulsive noise in color images is presented. The proposed filter class is based on the nonparametric estimation of the density probability function i...
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This paper presents an efficient method to obtain the optimal power flow (OPF) problem under constrained emission dispatch by applying reactive tabu search (RTS) algorithm. The RTS is developed as a derivative-free op...
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This paper presents an efficient method to obtain the optimal power flow (OPF) problem under constrained emission dispatch by applying reactive tabu search (RTS) algorithm. The RTS is developed as a derivative-free optimization technique in solving constrained emission OPF problem significantly reduces the computational burden with the strategies that make the search process robust and fast. The effectiveness of the proposed approach has been demonstrated through the IEEE 30-bus, 6-generator, test system. The simulation results reveal that the proposed RTS can yield highly optimal solution and tan reduce computational execution time superior to a standard tabu search. Moreover, the proposed method provides better solution than previous literatures with promising results.
This paper reports an industrial application of principle component analysis to process abnormality detection in a sugar mill. The process under investigation is a continuous pan which is one of the most crucial proce...
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This paper reports an industrial application of principle component analysis to process abnormality detection in a sugar mill. The process under investigation is a continuous pan which is one of the most crucial processes in sugar production. Two sets of experimental results are obtained in this work: one is related to an artificially induced fault by a stuck valve; the other captures a naturally occurred fault due to equipment failure. The principle component analysis algorithm successfully detects both faults. Only the latter case is reported in this paper.
We study the problem of removing users from congested cells. We consider two different objectives for the removal algorithms. The first objective is to maximize the throughput. The second objective is to minimize the ...
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We study the problem of removing users from congested cells. We consider two different objectives for the removal algorithms. The first objective is to maximize the throughput. The second objective is to minimize the number of removed connections. The optimal removing problem can be classified as a combinatorial optimization problem. It is well known that there is no general closed form solution for this type of problem. Furthermore, finding the optimal solutions in medium and large sizes of these problems is usually very exhausting. Consequently, we use heuristics to solve the problem. Our proposed heuristic algorithm is very simple to implement and gives a solution close to the optimum in many different cases.
Parameter estimation of an autoregressive movmg average (ARMA) model is discussed in this paper by using bounding approach. Bounds on the model structure error are assumed unknown, or known but conservative. To reduce...
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Parameter estimation of an autoregressive movmg average (ARMA) model is discussed in this paper by using bounding approach. Bounds on the model structure error are assumed unknown, or known but conservative. To reduce this conservatism, a point-parametric model concept is proposed, where there exist a set of model parameters and structure error corresponding to each input. Feasible parameter sets are defined for point-parametric model. Bounded values on the model parameters and structure error can then be computed jointly by tightening the feasible set using observations under deliberately designed input excitations. Finally, a constantly bounded parameter model is established, which can be used for robust control.
With the objective of facilitating improved productivity and process control, this paper investigates the use of modular neural networks (MNNs) for malfunction diagnosis in reactive ion etching (RIE) using optical emi...
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With the objective of facilitating improved productivity and process control, this paper investigates the use of modular neural networks (MNNs) for malfunction diagnosis in reactive ion etching (RIE) using optical emission spectroscopy (OES) data. OES data acquisition is performed for 49 experimental trials in the etching of SiLK/spl trade/ (a low dielectric constant polymer). The data collected is subsequently used for MNN modeling. MNNs consist of a number of local experts and a "gating" network, where the former map different regions of the input data space under the supervision of the gating network using a combination of supervised and unsupervised learning. 0.56% and 2.81% of errors were achieved from training and testing data set respectively: therefore, MNNs are found to be useful for diagnosis using OES data.
This paper presents a new method to solve the constrained unit commitment problem by applying ant colony optimization (ACO) based on the diversity control approach. The pheromone updating rule is modified to control t...
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This paper presents a new method to solve the constrained unit commitment problem by applying ant colony optimization (ACO) based on the diversity control approach. The pheromone updating rule is modified to control the diversification by adopting a simple mechanism for random selection in ACO. The proposed method is tested on the 10-unit test system with a scheduling time horizon of 24 hours. The numerical results show an economical saving in the total operating cost when compared to the previous literature results. Moreover, two types of the proposed diversity control technique have the features of easy implementation and a better convergence rate superior to a standard ACO.
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