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Articles by Wei Song
Total Records ( 4 ) for Wei Song
  Wei Song , Shi Tong Wang and Cheng Hua Li

This paper proposes two modified evolutionary computing methods for genetic algorithms (GAs) and proves an effective content-based feature selection approach to improve clustering performance. The conventional GAs suffer from the problem of slow learning and are prone to be trapped into a local minimum due to a high dimensional exploration space. In this paper, we propose a parametric and a nonparametric evolutionary algorithms to properly adjust the operators of GA. In the parametric approach, several fuzzy control parameters are artificially defined to adaptively optimize the GA behaviors. By contrast, they are automatically adjusted by GA itself in the nonparametric approach. Moreover, a content-based feature selection (CFS) approach is demonstrated to create a robust semantic space and reduce the number of dimension which accelerates the speed of evolutionary computing. We take advantage of a parallel computing technology to improve the efficiency of clustering. The experimental results show that our methods enhance the performance of the standard GA and are more efficient than those implemented on a single processor. The CFS approach not only reduces the document dimension, but also indirectly advances clustering efficiency.

  Hongwei Ge , Shitong Wang and Wei Song
  In order to determine the optimal choice of the parameter h (i.e., the threshold value used to measure degree of fit) in Possibilistic Linear Model (PLM) with the existence of noisy input, the dependency between h and the input noise is studied. PLM is first extended to its regularized version, i.e., Regularized Possibilistic Linear Model RPLM; then RPLM is interpreted as the corresponding equivalent Maximum a Posteriori (MAP) problem; finally, the approximately inverse proportional dependency relationships that the parameter h with Laplacian noisy input and Uniform noisy input should follow are derived, respectively. At the same time, the emulation results also proved the conclusion. Obviously, this important conclusion is very helpful for the practical applications of both PLM and RPLM.
  Hongwei Ge and Wei Song
  Based on possibility concepts, various Possibilistic Linear Models (PLMs) have been proposed and their pivotal role in fuzzy modeling and the associated applications have been established. The Regularized Possibilistic Linear Model (RPLM) is a regularized version of PLM which can enhance the generalization capability of PLM. In present study, a novel Adaptive RPLMs Based Median Filter (ARBMF) is proposed for improving the performance of median-based filters, preserving image details while effectively suppressing impulsive noises. The proposed filter achieves its effect through the linear combinations of the weighted output of the median filter and the related weighted input signal and the weights are set based on regularized possibilistic linear models concerning the states of the input signal sequence. Experimental results for benchmark images demonstrate that the proposed filter outperforms a number of extensively-used median-based filters. Moreover, the proposed filter also provides excellent robustness with respect to various percentages of impulse noise in our testing examples.
  Wei Song and Ping Zou
  We demonstrate how to perform quantum phase gate with cavity QED system in decoherence-free subspace by using only linear optics elements and photon detectors. The qubits are encoded in the singlet state of the atoms in cavities among spatially separated nodes, and the quantum interference of polarized photons decayed from the optical cavities is used to realized the desired quantum operation among distant nodes. In comparison with previous schemes, the distinct advantage is that the gate fidelity could not only resist collective noises, but also immune from atomic spontaneous emission, cavity decay, and imperfection of the photodetectors. We also discuss the experimental feasibility of our scheme.
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