Parallel processing algorithm for railway signal fault diagnosis data based on cloud computing

被引：121
|
作者：
Cao, Yuan [1 ,2 ]
Li, Peng [2 ]
Zhang, Yuzhuo [2 ]
机构：
[1] Beijing Jiaotong Univ, Natl Engn Res Ctr Rail Transportat Operat & Contr, Beijing 100044, Peoples R China
[2] Beijing Jiaotong Univ, Sch Elect & Informat Engn, Beijing 100044, Peoples R China
来源：
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE | 2018年 / 88卷
基金：
中国国家自然科学基金;
关键词：
High speed railway; Cloud computing; Fault diagnosis; MapReduce; MAPREDUCE;
D O I：
10.1016/j.future.2018.05.038
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
To explore the data processing of high-speed railway fault signal diagnosis based on MapReduce algorithm, the partitioning strategy of data flow was improved, and Bias classification algorithm was used to model and classify data. In MapReduce parallelization process, the data partition matrix T-k was stored in line segmentation, the computing load was distributed in every node of cluster, and the time consumption of mobile data matrix and the consumption of partitioned matrix were calculated. Results show that the algorithm proposed could reduce the amount of computation in the execution process, greatly reduce the memory space consumption, and improve the counting speed in railway signal system. (C) 2018 Elsevier B.V. All rights reserved.
引用
收藏
页码：279 / 283
页数：5
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