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- 저자 : ( Zihao Shen ) (검색결과 3 건)
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Jiaxing Shen,Zihao Pan,Ping Xu,Yinghua Yu 대한기계학회 2022 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.36 No.7
The thermal performance of a steel-basalt fiber polymer concrete machine tool joint surface has a key influence on a machine tool. Based on virtual material theory, the equivalent thermal model was established and the thermal performance parameters of virtual materials were deduced by theoretical calculation. The calculation method of actual contact area ratio of steel - basalt fiber polymer concrete joint surface was established based on discrete principle and its accuracy was verified. To determine the influence of preload pressure on the actual contact area ratio, a prediction model was established. The thermal conductivity of the material was measured by the quasi-steady state method. A new systematic simulation analysis method that synthesizes virtual material theory and finite element simulation analysis was proposed, and its accuracy was verified. The results show that the relative error is only 5.01 %, which proves the method’s effectiveness.
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IKPCA-ELM-based Intrusion Detection Method
( Hui Wang ),( Chengjie Wang ),( Zihao Shen ),( Dengwei Lin ) 한국인터넷정보학회 2020 KSII Transactions on Internet and Information Syst Vol.14 No.7
An IKPCA-ELM-based intrusion detection method is developed to address the problem of the low accuracy and slow speed of intrusion detection caused by redundancies and high dimensions of data in the network. First, in order to reduce the effects of uneven sample distribution and sample attribute differences on the extraction of KPCA features, the sample attribute mean and mean square error are introduced into the Gaussian radial basis function and polynomial kernel function respectively, and the two improved kernel functions are combined to construct a hybrid kernel function. Second, an improved particle swarm optimization (IPSO) algorithm is proposed to determine the optimal hybrid kernel function for improved kernel principal component analysis (IKPCA). Finally, IKPCA is conducted to complete feature extraction, and an extreme learning machine (ELM) is applied to classify common attack type detection. The experimental results demonstrate the effectiveness of the constructed hybrid kernel function. Compared with other intrusion detection methods, IKPCA-ELM not only ensures high accuracy rates, but also reduces the detection time and false alarm rate, especially reducing the false alarm rate of small sample attacks.
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Enhancing Location Privacy through P2P Network and Caching in Anonymizer
Peiqian Liu,Shangchen Xie,Zihao Shen,Hui Wang 한국인터넷정보학회 2022 KSII Transactions on Internet and Information Syst Vol.16 No.5
The fear that location privacy may be compromised greatly hinders the development of location-based service. Accordingly, some schemes based on the distributed architecture in peer-to-peer network for location privacy protection are proposed. Most of them assume that mobile terminals are mutually trusted, but this does not conform to realistic scenes, and they cannot make requirements for the level of location privacy protection. Therefore, this paper proposes a scheme for location attribute-based security authentication and private sharing data group, so that they trust each other in peer-to-peer network and the trusted but curious mobile terminal cannot access the initiator's query request. A new identifier is designed to allow mobile terminals to customize the protection strength. In addition, the caching mechanism is introduced considering the cache capacity, and a cache replacement policy based on deep reinforcement learning is proposed to reduce communications with location-based service server for achieving location privacy protection. Experiments show the effectiveness and efficiency of the proposed scheme.
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