Acoustic radiation through a system of double-walled shells, lined with porous foams, and stiffened by annular plates simultaneously is studied. Based on modeling, the porous foams as absorbent fluid property, acoustic characteristics of structure are presented for various sandwich construction by means of vibro-acoustic finite element method with automatic matching layer technology. It is noted that equipping porous foams and annular plates simultaneously enhances the acoustic insulation of structure in the entire frequency domain. The overall sound power level is modified by the density of shell. Moreover, the increase of structural stiffness is shown to effectively reduce the acoustic radiation via rising the thickness of inner shell and the number of annular plates. The foam cores decrease the peak value of structural sound power level through using polyurethane foam cores and increasing filling ratio.

Person re-identification is a challenging task in the field of computer vision in recent years. The image samples of pedestrians undergo with drastic appearance variations across camera views. The training data of the existing dataset is unable to describe the complex appearance changes, which leads to over-fitting problem of the metric model. In order to solve this problem, based on the statistical and topological characteristics of multi-view paired pedestrian images, a resampled linear discriminant analysis (LDA) method was proposed. This method utilized sample normality and k-nearest neighbours to form potential positive pairs. The potential positive pairs are used to improve the metric model and generalize the metric model to the test data. By optimizing the inter-class divergence method, a semi-supervised re-sampling LDA person re-identification algorithm was established. It was then tested on the VIPeR, CUHK01 and Market 1501datasets. The results show that the proposed method achieves the best performance compared to some available methods. Especially, the proposed method outplays the best comparison method by 0.6% and 5.76% at rank-1 identification rate on the VIPeR and CUHK01 datasets respectively. At the same time, the improved LDA algorithm has improved the rank-1 identification accuracy of traditional LDA method by 9.36% and 32.11% on these two datasets respectively. Besides, the proposed method is limited to Market-1501 dataset when the test data is of large size.