会议专题

Fault Diagnosis Analysis in Large-Scale Computing Environments

This paper issues the problem of fault diagnosis in high computing system. In order to solve this problem, i.e., correctly and efficiently detecting the anomaly nodes during the system operation, which is very similar to the principle of pattern recognition research work, thus we try to use some pattern recognition methods to analysis and solve fault diagnosis problem in this paper. And also we do some experiment and compare the results and finally get some useful conclusion to show that Kernel Eigenface and Kernel Fisherface methods achieve lower error rates than the ICA and PCA approaches in anomaly nodes detection.

fault diagnosis pattern recognition feature extraciton PCA KPCA ICA FDA KFDA

Yan Xue Xuefang Zhu

Dept. of Information Management in Nanjing University Nanjing, Jiangsu

国际会议

2010年IEEE多媒体信息网络与安全国际会议

南京

英文

551-554

2010-11-01(万方平台首次上网日期,不代表论文的发表时间)