The Nantong blue calico pattern is a significant and indispensable part of China’s intangible cultural heritage, representing an artistic form of weaving and dyeing. However, existing research on blue calico patterns is not extensive, and few studies have focused on the construction of a database categorizing them or on recognizing the Nantong blue calico pattern. Obtaining good efficiency and accuracy through manual recognition has been the primary challenge in recognizing the Nantong blue calico pattern. In light of these challenges, this study proposes the use of deep learning network model to intelligently classify and recognize blue calico patterns.First, the patterns are classified to establish a Nantong blue calico pattern database, and the corresponding category labels are then manually assigned to each image. Second, based on the database and a backbone feature extraction network, the abilities of SSD (Single Shot Multibox Detector), Faster RCNN (Region-CNN), and You Only Look Once (to recognize the Nantong blue calico pattern were compared. The results show that the SSD model based on a VGG (Visual Geometry Group) backbone network has the best recognition accuracy of these three algorithms, with an average accuracy of 79.42%. On this basis, we selected the SSD model for parameter optimization and adjustment, and we replaced the backbone with mobilenetv2, a lighter backbone extraction network, to recognize the Nantong blue calico pattern. The results show that compared with the original SSD model, the optimized SSD model can improve the pattern recognition rate of Nantong blue calico pattern. Furthermore, this paper makes use of the characteristics of the VGG deep network, the backbone network of the SSD model, to efficiently extract the features of blue calico patterns, which provides a basis for designers to design innovative blue calico patterns.

This paper used solidworks to design solar powered electric bike structure. By comparing and verifying the possibility of several installation methods, the program determined that the solar panels is placed in front and rear frame and that the solar tracking device is installed. And the structure interfaced into the ANSYS through the graphical data conversion. The finite element model of the bike is generated by grid division. The finite element analysis of the solar panel installation location is carried out. The strength and stiffness characteristics of the vehicle body are calculated in the static analysis. The frequency of the vehicle body is calculated by modal analysis.

This paper discusses how to get the body contour and size measurement, when people are measured wearing daily clothes in natural environment. This paper aims to propose the method of curve segmentation, extraction of effective feature points and curve fitting to make body silhouette curves reappear on the premise of somatotype, gets the rule of the body silhouette curves through contrasts between the fitting curves and the body silhouettes. And, calculate the final size in the garment design and manufacture, and puts forward some suggestions and put the attitude measurement. Being different from other 3D non-contact measuring methods, this paper provides a new way to get the BWH measurements in the condition of daily natural wearing, which not only optimize the non-contact anthropometrics theory, but also break the static measuring mode. Furthermore, the bringing forth of the concept of ‗natural wearing state' and the laboratory experiments of this paper would be worthy for practical use.