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Classical dictionary learning algorithms that rely on a single source of information have been successfully used for the discriminative tasks. However, exploiting multiple sources has demonstrated its effectiveness in solving challenging real-world situations. We propose a new framework for feature fusion to achieve better classification performance as compared to the case where individual sources...
Compressive sensing (CS) theory indicates that the optimal reconstruction of an unknown sparse signal can be achieved from limited noisy measurements by solving a sparsity-driven optimization problem. For inverse synthetic aperture radar (ISAR) imagery, the scattering field of the target is usually composed of only a limited number of strong scattering centers, representing strong spatial sparsity...
In this paper, we proposed a hybrid algorithm to solve unit commitment optimization problem, which is composed of operation ant colony optimization (ACO) algorithm and Lambda-iteration method. By means of operation encoding, space complexity of ACO algorithm for solving UCP is reduced. Moreover, space complexity can be regulated by adjusting the number of maximum allowable operation in a single time...
By introducing effective method, a global optimization algorithm is proposed for solving a class of multiplicative programming problems which arise in many fields such as engineering, finance, economics and other fields.
Multi-robot system can improve the efficiency of mapping and exploration. One of the key problems is when and how to merge the partial maps acquired by robots independently to share environmental information between robots. This paper studies the problem of fusing two partial maps without common reference frames and relative position information of robots. On the basis of the similarity metric, the...
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