Virtual experiments have become an interesting research topic in the field of education. However, we found that there are some limitations in the current virtual experiments: first, the researchers used the virtual effects of the simulation to represent the virtual experiments, which led to decrease the immersion of the user's simulated experiments; second, most of the virtual experiments are only mouse or touch screen interactive mode, which reduces the realism of user simulation experiments; third, students independently explore the experimental operation process and spend too much time simulating the experiment, which leads to problems such as overloading the operation and low interaction efficiency. In order to solve the above problems, we propose and implement a multimodal navigational interaction virtual and real fusion chemistry laboratory (MNIVRFCL). We design a new sensing structure intelligent equipment and propose a multimodal navigational interaction algorithm (MMNI) based on auditory and tactile channel, which are verified and applied in MNIVRFCL. The MMNI algorithm can detect user's specific behaviors to understand their behavioral intentions, and then the system guide and rectify users' current correct or incorrect behaviors in the form of voice navigation broadcasts. Finally, we achieve the purpose that students can use virtual and real fusion interactions through tactile and auditory channels, they can independently complete simulations and learning experiments based on experimental navigation. The experimental statistic results show that system's successful understanding of intention is 91.48%, and prove the MNIVRFCL operational load reduce by 23.81% compared to the pure virtual experiment, it can reduce the time consumption and improves the students' interaction efficiency.

At present, virtual-reality fusion smart experiments mostly employ visual perception devices to collect user behavior data, but this method faces the obstacles of distance, angle, occlusion, light, and a variety of other factors of indoor interactive input devices. Moreover, the essence of the traditional multimodal fusion algorithm (TMFA) is to analyze the user’s experimental intent serially using single-mode information, which cannot fully utilize the intent information of each mode. Therefore, this paper designs a multimodal fusion algorithm (hereinafter referred to as MFA, Algorithm 4) which focuses on the parallel fusion of user’s experimental intent. The essence of the MFA is the fusion of multimodal intent probability. At the same time, this paper designs a smart glove based on the virtual-reality fusion experiments, which can integrate multichannel data such as voice, visual, and sensor. This smart glove can not only capture user’s experimental intent but also navigate, guide, or warn user’s operation behaviors, and it has stronger perception capabilities compared to any other data glove or smart experimental device. The experimental results demonstrate that the smart glove presented in this paper can be widely employed in the chemical experiment teaching based on virtual-reality fusion.

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This paper proposes an optimal design for a surface-mounted permanent magnet motor (SPMM) to reduce torque pulsations, including cogging torque and torque ripple, by using multi-grade ferrite magnets. Based on a conventional SPMM with single-grade ferrite magnets, the proposed SPMM is designed with four-grade ferrite magnets and then optimized to minimize torque pulsations by maintaining the required torque, utilizing the Kriging method and a genetic algorithm. The results obtained by the finite element analysis show that the optimized SPMM with multi-grade ferrite magnets exhibits improved airgap flux density distribution with highly reduced cogging torque and torque ripple by maintaining the same average torque, as compared to the conventional SPMM. Furthermore, the analysis of the working points for the multi-grade ferrite magnets reveals that the optimized SPMM has good durability against the irreversible demagnetization.