文献类型：期刊文献

中文题名：光纤安防系统中振动信号的特征提取和识别

英文题名：Feature Extraction and Recognition of Vibration Signals in Optical Fiber Security System

作者：Zou, Baixian[1]; Xu, Shaowu[2,3]; Miao, Jun[2,3]; Lu, Yanling[1]

第一作者：邹柏贤

机构：[1] College of Applied Arts and Science, Beijing Union University, Beijing, 100191, China; [2] School of Computer Science, Beijing Information Science and Technology University, Beijing, 100101, China; [3] Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, Beijing Information Science and Technology University, Beijing, 100101, China

第一机构：北京联合大学应用文理学院

年份：2019

卷号：56

期号：9

起止页码：1859-1871

外文期刊名：Jisuanji Yanjiu yu Fazhan/Computer Research and Development

收录：EI(收录号：20194607675867);Scopus(收录号：2-s2.0-85074710639)

基金：This work was supported by the National Natural Science Foundation of China (41671165, 61650201), the Beijing Municipal Education Commission Project (KM201911232003), the Research Fund from Beijing Innovation Center for Future Chips (KYJJ2018004), and the Funding Project for Academic Human Resources Development in Institutions of Higher Learning Under the Jurisdiction of Beijing Municipality (IDHT20180515).

语种：中文

外文关键词：Data visualization - Decision trees - Extraction - Feature extraction - Knowledge acquisition - Learning algorithms - Optical fibers - Support vector machines - Time domain analysis - Trees (mathematics) - Vibration analysis

摘要：Optical fiber vibration sensor is widely used in the new generation of security monitoring system. The feature extraction and recognition methods of optical fiber vibration signal have become a research hotspot in the field of pattern recognition. The feature extraction and recognition methods of various optical fiber signals are summarized. These feature extraction methods decompose optical fiber vibration signals from the perspective of time domain, so different attribute characteristics of signals can be extracted. The empirical thresholds, neural networks and support vector machines are used to identify optical fiber vibration signals. Up to now, there is still a problem that the correct recognition rate of optical fiber intrusion events is not high. Vibration signal data of five kinds of optical fiber vibration signals, such as excavator mining, artificial digging, vehicle walking, personnel walking and noise, are visually analyzed. An effective method for feature selection of optical fiber vibration signal is proposed. According to the importance of optical fiber vibration intrusion events, identification tasks are completed in four stages, and the two-class task decision tree model and the constrained extreme learning machine algorithm are used to identify the type of intrusion events, which improves the correct recognition rate of all kinds of events. ? 2019, Science Press. All right reserved.