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Research on Clustering Algorithm Based on Grid Density on Uncertain Data Stream
Tang Xianghong,Yang Quanwei,Zheng Yang 보안공학연구지원센터 2016 International Journal of Database Theory and Appli Vol.9 No.9
To solve the clustering algorithm based on grid density on uncertain data stream in adjustment cycle for clustering omissions, the paper proposed an algorithm, named GCUDS, to cluster uncertain data steam using grid structure. The concept of the data trend degree was defined to describe the grade of a data point belonging to some grid unit and the defect of information loss around grid units was removed in the GCUDS algorithm. The GCUDS algorithm obtained better results of clustering and higher time efficiency than other algorithms over uncertain data stream, through improving the traditional online clustering framework and maintaining three buffers of micro-cluster. Experimental results showed that the GCUDS algorithm could effectively cluster in different shape database and outperform existing methods in clustering quality and efficiency.
Performance Evaluation and Modeling Method Research Based on IaaS Cloud Platform
Jian Wan,Xianghong Yang,Zujie Ren,Zheng Ye 보안공학연구지원센터 2016 International Journal of Grid and Distributed Comp Vol.9 No.10
With the widespread use of cloud platforms, their performance evaluation tools also have become the research hot spot of academic circle. So far, many performance evaluation tools of the cloud platform have been designed in their corresponding application scenarios, which have brought much convenience on the performance evaluation and management of the cloud platform. In order to predict the maximum number of virtual machines that can be opened by the cloud platform, this paper integrates the current tools of performance evaluation and proposes a performance evaluation tool based on IaaS cloud platform. The key of the performance evaluation tool is that it not only can evaluate the performance of the cloud platform, but also can predict the maximum number of virtual machines that can be opened by the cloud platform when the configuration of the virtual machine and the workload of each virtual machine have been known. This special performance evaluation tool has not been put forward now. And, the prediction model has been introduced into this tool in this paper that is the most important and core part. Lastly, to test the effectiveness of cloud platform performance evaluation tool proposed in this paper, some tests have been done on the IaaS cloud platform. According to the contrast results of the forecast error among models, establishing support vector machine and neural network as single forecasting model. The results show combined model can be chosen as the prediction model of cloud platform performance evaluation tool.
Primary Amebic Meningoencephalitis: A Case Report
Minhua Chen,Wei Ruan,Lingling Zhang,Bangchuan Hu,Xianghong Yang 대한기생충학ㆍ열대의학회 2019 The Korean Journal of Parasitology Vol.57 No.3
Primary amebic encephalitis (PAM) is a devastating central nervous system infection caused by Naegleria fowl- eri, a free-living amoeba, which can survive in soil and warm fresh water. Here, a 43-year-old healthy male was exposed to warm freshwater 5 days before the symptom onset. He rapidly developed severe cerebral edema before the diagnosis of PAM and was treated with intravenous conventional amphotericin B while died of terminal cerebral hernia finally. Com- paring the patients with PAM who has similar clinical symptoms to those with other common types of meningoencephali- tis, this infection is probably curable if treated early and aggressively. PAM should be considered in the differential diagno- sis of purulent meningoencephalitis, especially in patients with recent freshwater-related activities during the hot season.