The noise reduction problem of composite laminated cylindrical shells at low and medium frequencies from 150 Hz to 1000 Hz is addressed by using the noise control method of laying acoustic coverings and conducting noise reduction experiments in a cylindrical shell cavity by laying melamine foam, sound-absorbing cotton, and multilayer combination materials and obtaining the corresponding transmission loss curves. Additionally, based on the LMS Virtual Lab acoustic simulation software, finite element models corresponding to the noise reduction experiments are established, and the acoustic cavity’s simple positive frequency and acoustic response of the cavity are numerically calculated. Based on this, the influence law for a laid acoustic cover layer on the sound transmission loss of a cylindrical shell is investigated. The results show that the noise reduction of sound-absorbing cotton with the same thickness is about 1.26 times that of melamine foam, and the noise reduction of melamine foam with the same mass is about 1.42 times that of sound-absorbing cotton. For multilayer laying, the noise reduction of adding the same thickness of butyl rubber is about 6.18 times that of melamine foam, and the larger the laying ratio is, the better the noise reduction effect will be.

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.

The problem of person re-identification has attracted a lot of attention in the field of machine vision. In practice, the non-overlapping sample images change drastically and the sample size is small, which makes the metric model overfitting phenomenon. In this paper, based on the k-NN and the sample normality property, we propose a resampling linear discriminant analysis (LDA) algorithm to suppress the local constraints caused by small samples, then train it to obtain the person re-identification metric learning model. A semi-supervised LDA algorithm with semi-supervised characteristics is developed by optimizing the inter-class scatter for weighting. A joint distance metric-based approach is also proposed to learn both the Mahalanobis distance and Euclidean distance. The improved algorithm is tested on the VIPeR and CUHK01 datasets, and the results indicate that, despite the change in the total number of training samples, the algorithm in this paper shows high recognition accuracy.