A GMM (Gaussian Mixture Model) based adaptive image restoration is proposed in this paper. The feature vectors of pixels are selected and extracted. Pixels are clustered into smooth,edge or detail texture region accor...A GMM (Gaussian Mixture Model) based adaptive image restoration is proposed in this paper. The feature vectors of pixels are selected and extracted. Pixels are clustered into smooth,edge or detail texture region according to variance-sum criteria function of the feature vectors. Then pa-rameters of GMM are calculated by using the statistical information of these feature vectors. GMM predicts the regularization parameter for each pixel adaptively. Hopfield Neural Network (Hopfield-NN) is used to optimize the objective function of image restoration,and network weight value matrix is updated by the output of GMM. Since GMM is used,the regularization parameters share properties of different kind of regions. In addition,the regularization parameters are different from pixel to pixel. GMM-based regularization method is consistent with human visual system,and it has strong gener-alization capability. Comparing with non-adaptive and some adaptive image restoration algorithms,experimental results show that the proposed algorithm obtains more preferable restored images.展开更多
Recently, the frequent extreme natural disasters made enormous damage to the electric grid leading to blackouts. The lifeline system aiming at providing continuous power supply for the important load in extreme natura...Recently, the frequent extreme natural disasters made enormous damage to the electric grid leading to blackouts. The lifeline system aiming at providing continuous power supply for the important load in extreme natural disasters was designed in that condition. In this paper, a developed model for planning of the transformer substation in lifeline system? which considered the effect of existing transformer substations, the motivated areas and punishment areas was proposed. The Hopfield NN was adopted to solve the feeders and the PSO was adopted to new the locations of the transformer substations based on the feeders. The planning result not only took fully use of the existing substation but also get the suitable location for new construction which was satisfactory.展开更多
文摘A GMM (Gaussian Mixture Model) based adaptive image restoration is proposed in this paper. The feature vectors of pixels are selected and extracted. Pixels are clustered into smooth,edge or detail texture region according to variance-sum criteria function of the feature vectors. Then pa-rameters of GMM are calculated by using the statistical information of these feature vectors. GMM predicts the regularization parameter for each pixel adaptively. Hopfield Neural Network (Hopfield-NN) is used to optimize the objective function of image restoration,and network weight value matrix is updated by the output of GMM. Since GMM is used,the regularization parameters share properties of different kind of regions. In addition,the regularization parameters are different from pixel to pixel. GMM-based regularization method is consistent with human visual system,and it has strong gener-alization capability. Comparing with non-adaptive and some adaptive image restoration algorithms,experimental results show that the proposed algorithm obtains more preferable restored images.
文摘Recently, the frequent extreme natural disasters made enormous damage to the electric grid leading to blackouts. The lifeline system aiming at providing continuous power supply for the important load in extreme natural disasters was designed in that condition. In this paper, a developed model for planning of the transformer substation in lifeline system? which considered the effect of existing transformer substations, the motivated areas and punishment areas was proposed. The Hopfield NN was adopted to solve the feeders and the PSO was adopted to new the locations of the transformer substations based on the feeders. The planning result not only took fully use of the existing substation but also get the suitable location for new construction which was satisfactory.
