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- 저자 : Liu Aiyong (검색결과 2 건)
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Configuration of WLAN and Ad Hoc Network Access Point and Research on Internet Topology Control
Aiyong Liu,Hong Chen 보안공학연구지원센터 2016 International Journal of Future Generation Communi Vol.9 No.9
Wireless Mesh network, a typical wireless ad hoc network, can effectively solve the “last one thousand meters” problem of broadband access. It, with features of self-forming, self-healing and high bandwidth, provides its users with better service by integrating the advantages of WLAN and ad hoc network and fully utilizing and combining with WiMAX, WiFi and other wireless technologies. In network planning, the effective configuration of access point is of great importance for network set-up cost control. A good topological structure, in addition, plays a decisive role in network throughput capacity. The research stated in the thesis studied the configuration of wireless Mesh network access point and internet topology control, simulating a pure Mesh network connecting to external networks. By studying the relationships between such parameters as gateway connection and broadcast interval with transfer rate of packets, end to end delay and system overhead, their relationships were able to be confirmed and the correctness of the analysis results was tested by the comparative studies of the simulation results.
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Song Jinling,Huang Liming,Gao Yan,Liu Aiyong,Liu Haibin 보안공학연구지원센터 2016 International Journal of u- and e- Service, Scienc Vol.9 No.5
To ameliorate the limitations of traditional collaborative filtering technologies and enhance the recommendation quality of agricultural science and technology information, a collaborative filtering recommendation method based on synthetic strategy is proposed. Firstly, filter the user set and user-item rating matrix according to the location of target user, which can solve the regional problem. Then, predict ratings of items according to the similarity of users or item content, which can relax the impact of the sparse rating. In addition, add the rating time to the user-item rating matrix to distinguish the timeliness of the user preference, and add user preference shifting in the similarity formula as a factor which can express the similarity of users or item content better. Our method can not only guarantee the recommended information is local and suit to current season of agricultural production, but also ensure the recommending precision under sparse rating data.
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