ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2019, Vol. 56 ›› Issue (9): 1859-1871.doi: 10.7544/issn1000-1239.2019.20180382

• 人工智能 • 上一篇    下一篇

光纤安防系统中振动信号的特征提取和识别

邹柏贤1, 许少武2,3, 苗军2,3, 逯燕玲1   

  1. 1(北京联合大学应用文理学院 北京 100191); 2(北京信息科技大学计算机学院 北京 100101); 3(网络文化与数字传播北京市重点实验室(北京信息科技大学) 北京 100101) (fjqunar@163.com)
  • 出版日期: 2019-09-10
  • 基金资助: 
    国家自然科学基金项目(41671165,61650201);北京市教委科技计划项目(KM201911232003);北京未来芯片技术高精尖创新中心科研基金项目(KYJJ2018004);北京市属高校高水平教师队伍建设支持计划高水平创新团队建设计划项目(IDHT20180515)

Feature Extraction and Recognition of Vibration Signals in Optical Fiber Security System

Zou Baixian1, Xu Shaowu2,3, Miao Jun2,3, Lu Yanling1   

  1. 1(College of Applied Arts and Science, Beijing Union University, Beijing 100191); 2(School of Computer Science, Beijing Information Science and Technology University, Beijing 100101); 3(Beijing Key Laboratory of Internet Culture and Digital Dissemination Research (Beijing Information Science and Technology University), Beijing 100101)
  • Online: 2019-09-10
  • Supported by: 
    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).

摘要: 利用光纤振动传感器可以实现分布式周界安防监测,进而实现自动报警.对周界安防监测信号的分析处理和识别受到业界关注.对光纤信号的特征提取和识别方法进行综述,这些特征提取方法通过对光纤振动信号的时域这个维度进行各种分解,从而提取各种信号的属性特征;对光纤振动信号的识别主要使用经验阈值、神经网络、支持向量机方法,目前这些方法对光纤入侵事件识别效果还不能令人满意.通过实验采集挖掘机挖掘、人工挖掘、汽车行驶、行人和自然环境噪声这5种入侵行为引起的光纤振动信号数据,并进行数据的3维图形可视化分析,提出一种安防监测信号在时域和空域这2个维度信息的特征提取方法;根据光纤振动入侵事件的重要程度分成4个阶段先后完成识别任务,采用2分类任务决策树模型和约束极速学习机算法识别入侵事件类型,提高了对各类事件的正确识别率.

关键词: 入侵事件, 实验样本, 基于类间样本差向量的约束极速学习机, 基于混合向量的约束极速学习机, 识别率

Abstract: 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.

Key words: intrusion event, experimental sample, constrained difference extreme learning machine (CDELM), constrained mixed extreme learning machine (CMELM), recognition rate

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