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:2019,28(7):199-205
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基于机器学习的铁路工务人员行为识别方法
(西南交通大学 电气工程学院, 成都 611756)
Railway Engineering Staff Behavior Recognition Method Based on Machine Learning
(School of Electrical Engineering, Southwest Jiaotong University, Chengdu 611756, China)
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投稿时间:2018-12-26    修订日期:2019-01-18
中文摘要: 针对现如今铁路工务人员在作业时得不到实时监控,安全事故时有发生的情形.以铁路探伤工为例,分析了工务人员的7种主要行为.每个工务人员佩戴集成了加速度传感器的嵌入式设备,采集其行为数据并提取特征,采用C4.5决策树、随机森林、KNN、SVM四种分类算法做了实验,结果表明:SVM分类识别率最高,行为识别准确率达到了99.2%.该研究为消除铁路现场作业人员的行为安全隐患具有一定工程应用价值.
Abstract:In view of the fact that today's railway engineering staff cannot be real-time monitored during operation, and safety incidents occur from time to time, seven main behaviors of them are analyzed by taking railway inspectors as an example. An embedded device integrated with an accelerometer is worn by every worker, collects their behavior data and extracts features, and uses four kinds of classifiers, which are C4.5 decision tree, random forest, KNN, and SVM, to carry out experiments. The results show that classifier SVM performs the best, the behavioral recognition accuracy rate reaches 99.2%. This research has certain engineering application value for eliminating the safety hazards of railway field engineering staffs.
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引用文本:
杜成飞.基于机器学习的铁路工务人员行为识别方法.计算机系统应用,2019,28(7):199-205
DU Cheng-Fei.Railway Engineering Staff Behavior Recognition Method Based on Machine Learning.COMPUTER SYSTEMS APPLICATIONS,2019,28(7):199-205

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