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RISS 인기검색어
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- 저자 : Congcong Xiong (검색결과 4 건)
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A Data Placement Algorithm for Data Intensive Applications in Cloud
Qing Zhao,Congcong Xiong,Kunyu Zhang,Yang Yue,Jucheng Yang 보안공학연구지원센터 2016 International Journal of Grid and Distributed Comp Vol.9 No.2
Data layout is an important issue which aims at reducing data movements among data centers to improve the efficiency of the entire cloud system. This paper proposes a data-intensive application oriented data layout algorithm. It is based on hierarchical data correlation clustering and the PSO algorithm. The datasets with fixed location have been considered, and both the offline strategy and the online strategy for data layout have been given. As this proposed strategy is aimed at reducing the global amount of data transmissions, and the special permission of the datasets has been introduced, the cost of data transmission can be measured more reasonable. Simulation results show that compared with two classical strategies, our algorithm can reduce the amount of data transmission more effectively.
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An Online Learning Model of Mobile User Preference Based on Context Quantification
Yancui Shi,Congcong Xiong,Jucheng Yang,Yarui Chen,Jianhua Cao 보안공학연구지원센터 2015 International Journal of Hybrid Information Techno Vol.8 No.9
In mobile network, the mobile user has the strict requirement for the performance of accessing the information. In order to provide the personalized service for mobile user timely and accurately, an online learning model of mobile user preference based on context quantification is proposed. In the model, a context quantification method is proposed, which can enhance the accuracy of learned mobile user preference; and the sliding window and the online extreme leaning machine (O-ELM) are introduced to realize the online learning. Firstly, it needs to judge whether the mobile user preference is affected by the context through analyzing the mobile user behaviors. Secondly, the context is quantified according to the context relevancy and the context similarity. And then, the sliding window is employed to select the samples that need to be learned when updating the mobile user preference. Finally, O-ELM is employed to learn the mobile user preference. The experimental results show that the proposed method surpasses the existing methods in the performance.
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Reliability Analysis of Unrepairable Systems with Uncertain Lifetimes
Ying Liu,Xiaozhong Li,Congcong Xiong 보안공학연구지원센터 2015 International Journal of Security and Its Applicat Vol.9 No.12
The topic of unrepairable system is an important content in system reliability theory. There are many reasons cannot be repaired, some because of technical reasons, cannot repair, some because of economic reasons, not worth to repair, and some because of making repairable system simplification. So it is essential to pay attention to unrepairable systems. In this paper, the lifetimes of unrepairable systems are considered as uncertain variables. Based on that, the fundamental mathematical models of series systems, parallel systems, series-parallel systems and parallel-series systems are established, respectively. Furthermore, we make reliability analysis of above four unrepairable systems, respectively. Some numerical examples are also given for illustration.
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An Improved method of Two Stage Linear Discriminant Analysis
( Yarui Chen ),( Xin Tao ),( Congcong Xiong ),( Jucheng Yang ) 한국인터넷정보학회 2018 KSII Transactions on Internet and Information Syst Vol.12 No.3
The two-stage linear discrimination analysis (TSLDA) is a feature extraction technique to solve the small size sample problem in the field of image recognition. The TSLDA has retained all subspace information of the between-class scatter and within-class scatter. However, the feature information in the four subspaces may not be entirely beneficial for classification, and the regularization procedure for eliminating singular metrics in TSLDA has higher time complexity. In order to address these drawbacks, this paper proposes an improved two-stage linear discriminant analysis (Improved TSLDA). The Improved TSLDA proposes a selection and compression method to extract superior feature information from the four subspaces to constitute optimal projection space, where it defines a single Fisher criterion to measure the importance of single feature vector. Meanwhile, Improved TSLDA also applies an approximation matrix method to eliminate the singular matrices and reduce its time complexity. This paper presents comparative experiments on five face databases and one handwritten digit database to validate the effectiveness of the Improved TSLDA.
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