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Tong, Yan, Zhang, Jian, Qin, Tao.  2016.  Security Problems Analysis and Solving Policy Design for Mobile Agents Running Platform. Proceedings of the 2016 International Conference on Intelligent Information Processing. :24:1–24:6.

Security mechanism of the mobile agent running platform is very important for mobile agent system operation and stability running. In this paper we mainly focus on the security issues related with the mobile agent running platform and we proposed a cross validation mechanism mixed with encryption algorithm to solve the security problems during the migration and communication of mobile agents. Firstly, we employ the cross-validation mechanism to authenticate the nodes mobile agents will be visiting. Secondly, we employ the hybrid encryption mechanism, which combines the advantages of the symmetric encryption and asymmetric encryption, to encrypt the mobile agents and ensure the transferring process of data. Finally, we employ the EMSSL socket communication method to encrypt the content of transmission, in turn to enhance the security and robustness of the mobile agent system. We implement several experiments in the simulation environment and the experimental results verify the efficiency and accuracy of the proposed methods.

Tong, Yan, Ku, Zhaoyu, Chen, Nanxin, Sheng, Hu.  2022.  Research on Mechanical Fault Diagnosis of Vacuum Circuit Breaker Based on Deep Belief Network. 2022 2nd International Conference on Electrical Engineering and Mechatronics Technology (ICEEMT). :259–263.
VCB is an important component to ensure the safe and smooth operation of the power system. As an important driving part of the vacuum circuit breaker, the operating mechanism is prone to mechanical failure, which leads to power grid accidents. This paper offers an in-depth analysis of the mechanical faults of the operating mechanism of vacuum circuit breaker and their causes, extracts the current signal of the opening and closing coil strongly correlated with the mechanical faults of the operating mechanism as the characteristic information to build a Deep Belief Network (DBN) model, trains each data set via Restricted Boltzmann Machine(RBM) and updates the model parameters. The number of hidden layer nodes, the structure of the network layer, and the learning rate are determined, and the mechanical fault diagnosis system of vacuum circuit breaker based on the Deep Belief Network is established. The results show that when the network structure is 8-110-110-6 and the learning rate is 0.01, the recognition accuracy of the DBN model is the highest, which is 0.990871. Compared with BP neural network, DBN has a smaller cross-entropy error and higher accuracy. This method can accurately diagnose the mechanical fault of the vacuum circuit breaker, which lays a foundation for the smooth operation of the power system.