Virtual experiment teaching is an important part of higher education institutions and research institutions. It plays an important role in helping students develop basic experimental research capabilities and innovative experimental research concepts. Mechanical majors focus on the cultivation of students' practical hands-on ability, but the traditional mechanical experimental equipment update iteration is slower, the experimental cost is higher, and there are potential safety hazards. In order to solve the problems encountered in the above mechanical experiment teaching, this paper focuses on the characteristics of the mechanical disciplines, after analyzing the development of virtual experiments at home and abroad, using the B/S architecture, Bootstrap framework and Hibernate technology, and the virtual reality technology and augmented reality technology in Unity3D environment are used to design and implement the mechanical virtual experiment teaching platform. Users can select the corresponding experiment in the virtual platform. In the experiment, the parts can be viewed from different perspectives. After the experiment is completed, the experimental result information can be obtained, and the experiment report can be completed online, which effectively saves teachers and students time. Through the survey of 518 students and 52 teachers who used this platform, most of the teachers and students agreed with the rich multimedia resources, friendly interaction and convenient collaborative communication of the mechanical virtual experiment teaching platform.
The kernel function has been successfully utilized in the extreme learning machine (ELM) that provides a stabilized and generalized performance and greatly reduces the computational complexity. However, the selection and optimization of the parameters constituting the most common kernel functions are tedious and time-consuming. In this study, a set of new Hermit kernel functions derived from the generalized Hermit polynomials has been proposed. The significant contributions of the proposed kernel include only one parameter selected from a small set of natural numbers; thus, the parameter optimization is greatly facilitated and excessive structural information of the sample data is retained. Consequently, the new kernel functions can be used as optimal alternatives to other common kernel functions for ELM at a rapid learning speed. The experimental results showed that the proposed kernel ELM method tends to have similar or better robustness and generalized performance at a faster learning speed than the other common kernel ELM and support vector machine methods. Consequently, when applied to human action recognition by depth video sequence, the method also achieves excellent performance, demonstrating its time-based advantage on the video image data.
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