A topology optimization method of robot lightweight design based on the finite element model of assembly and its applications.

Topology optimization is a widely used lightweight design method for structural design of the collaborative robot. In this article, a topology optimization method for the robot lightweight design is proposed based on finite element analysis of the assembly so as to get the minimized weight and to avoid the stress analysis distortion phenomenon that compared the conventional topology optimization method by adding equivalent confining forces at the analyzed part's boundary. For this method, the stress and deformation of the robot's parts are calculated based on the finite element analysis of the assembly model. Then, the structure of the parts is redesigned with the goal of minimized mass and the constraint of maximum displacement of the robot's end by topology optimization. The proposed method has the advantages of a better lightweight effect compared with the conventional one, which is demonstrated by a simple two-linkage robot lightweight design. Finally, the method is applied on a 5 degree of freedom upper-limb exoskeleton robot for lightweight design. Results show that there is a 10.4% reduction of the mass compared with the conventional method.

A Multi-Branch 3D Convolutional Neural Network for EEG-Based Motor Imagery Classification.

One of the challenges in motor imagery (MI) classification tasks is finding an easy-handled electroencephalogram (EEG) representation method which can preserve not only temporal features but also spatial ones. To fully utilize the features on various dimensions of EEG, a novel MI classification framework is first introduced in this paper, including a new 3D representation of EEG, a multi-branch 3D convolutional neural network (3D CNN) and the corresponding classification strategy. The 3D representation is generated by transforming EEG signals into a sequence of 2D array which preserves spatial distribution of sampling electrodes. The multi-branch 3D CNN and classification strategy are designed accordingly for the 3D representation. Experimental evaluation reveals that the proposed framework reaches state-of-the-art classification kappa value level and significantly outperforms other algorithms by 50% decrease in standard deviation of different subjects, which shows good performance and excellent robustness on different subjects. The framework also shows great performance with only nine sampling electrodes, which can significantly enhance its practicality. Moreover, the multi-branch structure exhibits its low latency and a strong ability in mitigating overfitting issues which often occur in MI classification because of the small training dataset.

[Study on the rejuvenating by isolation and the immobilization of Thiobacillus ferrooxidans].

A strain of Thiobacillus ferrooxidation T1 with stronger oxidation ability and more exuberant vitality was isolated from the liquid with regressive Thiobacillus ferrooxidans by method of dilution butteron on plate. A fixed-bed bioreactor was constructed by immobilized cells of Thiobacillus ferrooxidans with H-2 carriers. In the state of bioreactor operation, a maximum Fe2+ oxidation rate of 7.67g[Fe2+] x h(-1) x L(-1) was attained. We studied the precipitates formed on the surface of the carriers during the operation of the bioreactor. By X-ray diffraction analyses we proved the precipitates were jarosite.