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Research on Optimization Adjustment Strategy for SaaS Multi-tenant Data Placement
Li Xiaona,Li Qingzhong,Zhu Weiyi,Li Hui 보안공학연구지원센터 2015 International Journal of Grid and Distributed Comp Vol.8 No.2
In order to meet the requirements for data access by tenants and management by service providers, the multi-tenant data stored in cloud using replica technology must be reasonably placed. For the outweight nodes and the ultra light nodes, according to characteristics of the multi-tenant data and the load of nodes, through adjusting the number and position of the replicas to maintenance and optimization the strategy so that meet the SLA requirements meanwhile minimize the overall cost. Experimental results through comparison with random placement strategy and greedy placement policy demonstrate the feasibility and effectiveness of the proposed strategy.
Improved PCA method for sensor fault detection and isolation in a nuclear power plant
Wei Li,Minjun Peng,Qingzhong Wang 한국원자력학회 2019 Nuclear Engineering and Technology Vol.51 No.1
An improved principal component analysis (PCA) method is applied for sensor fault detection andisolation (FDI) in a nuclear power plant (NPP) in this paper. Data pre-processing and false alarm reducingmethods are combined with general PCA method to improve the model performance in practice. In datapre-processing, singular points and random fluctuations in the original data are eliminated with varioustechniques respectively. In fault detecting, a statistics-based method is proposed to reduce the falsealarms of T2 and Q statistics. Finally, the effects of the proposed data pre-processing and false alarmreducing techniques are evaluated with sensor measurements from a real NPP. They are proved to begreatly beneficial to the improvement on the reliability and stability of PCA model. Meanwhile varioussensor faults are imposed to normal measurements to test the FDI ability of the PCA model. Simulationresults show that the proposed PCA model presents favorable performance on the FDI of sensors nomatter with major or small failures.
Exploiting User Behavior Changes in Privacy Disclosure by Modified Clustering Technique
Hongchen Wu,Xinjun Wang,Zhaohui Peng,Qingzhong Li 보안공학연구지원센터 2015 International Journal of Security and Its Applicat Vol.9 No.5
The analysis of user behaviors has been an important subject in recommending research recently. This paper proposes a modified clustering technique, showing that users privacy disclosure may change when they are answering the information requests, and we argues that their attitudes, including risk, useful, appropriate, played an important role behind those changes. We presented the new data structure in our dataset that would be loaded to experiment, e.g. personal information requests, users’ answers to those requests, and most importantly, users cluster and attitude for later analysis. Our modified clustering technique would not only locate users privacy disclosure change by comparing the results from learning their past disclosure behaviors and from learning their current disclosures, but also exploit the relationship between the inconsistence in those two results and their attitudes. The data containing users’ answers to a questionnaire with personal information requests was integrated to analyze their disclosure behaviors and attitude with the proposed clustering technique. We indeed find some interesting connections between their privacy disclosure change and attitudes, and the exploration of this paper could benefit to any researchers and online community owners who focusing on user-centered strategies and personal-information-requesting issues.