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- 저자 : Youzhong Ma (검색결과 3 건)
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Hongsheng Xu,Ruiling Zhang,Chunjie Lin,Youzhong Ma 보안공학연구지원센터 2016 International Journal of Hybrid Information Techno Vol.9 No.4
The paper firstly describes some typical manual and automatic semantic annotation, then analyses strategy of semantic annotation based on ontology. Secondly, this paper proposes semantic hierarchical structure of fuzzy ontology based on Variable Precision Rough Set and concept lattice in order to make up the shortage of traditional semantic annotation methods. Novel algorithm of semantic annotation based on fuzzy ontology for domain Webpage is presented in the paper by comparing ontology data of extractive webpage. Finally, experiments show that this presented algorithm is better than the traditional method in semantic annotation rate of support and recall.
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Hongsheng Xu,Ruiling Zhang,Chunjie Lin,Youzhong Ma 보안공학연구지원센터 2016 International Journal of Database Theory and Appli Vol.9 No.10
Intelligent call center is also known as the customer service center or telephone service center, it is a kind of integrated information service system. This paper analyzes the classification method based on cost sensitive variable precision rough set. And in this paper, we use the attribute weighted cost sensitive rough set classification method based on established and customer level, customer history records, agents business related dynamic queuing strategy. In addition, this paper improves the multi fractal BP neural network algorithm for the call center customer classification. Improved algorithm is able to use multifractal fluctuation and BP neural network to predict the call center traffic and seat allocation. The paper presents construction of intelligent call center system based on cost sensitive variable precision rough set and multi fractal BP neural network. Experimental results show that novel method proposed can classify customers, reduce the impact of missing data and noise data, and improve the efficiency of customer satisfaction and intelligent call.
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Hongsheng Xu,Ruiling Zhang,Chunjie Lin,Youzhong Ma 보안공학연구지원센터 2016 International Journal of Future Generation Communi Vol.9 No.4
Nodes of wireless sensor network (WSN) will appear various faults, because the influence of many unavoidable factors and environment is very complex and harsh. Rough set can deal with incomplete information, especially in the data reduction, and it is easy to realize low energy consumption problem of on-line fault diagnosis based on WSN node energy Co. This paper adopts attribute reduction algorithm by integrate rough set with neural network model to eliminate WSN node failure, so as to achieve data reduction and to improve the accuracy and efficiency of fault diagnosis purpose. The paper makes use of rough set and neural network to the failure phenomenon of WSN node by using knowledge reduction of discernibility matrix and logic operation, eliminating the redundant attribute WSN node fault. Then, fault decision complex table is built by he classified fault, and finally determine the fault location corresponding to fault phenomenon and repair of the final decision table. The experimental results show that this method improves the robustness of the fault diagnosis, and enhances the practicability of WSN limited energy.
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