期刊文献+

一种用于光纤链路振动信号模式识别的规整化复合特征提取方法 被引量:18

A regular composite feature extraction method for vibration signal pattern recognition in optical fiber link system
原文传递
导出
摘要 相位光时域反射链路监测系统是一种利用光纤作为传感介质的传感系统,能够监测、定位、识别入侵信号.模式识别模块是其重要组成部分,实时智能区分安全扰动和危险入侵.本文提出一种用于光纤链路振动信号模式识别的复合特征提取方法.利用改进的双门限方法确定有效信号段的起止位置,结合最大能量与最高信噪比挑选出采样周期内主要入侵扰动的特征段.综合利用特征段时域持续时间和小波包能量谱提取复合特征向量,使用支持向量机进行模式识别.实验表明,基于本文提出的规整化特征提取方法的模式识别准确率有了显著提高. Phase optical time-domain reflectometer link monitoring system is a kind of sensor system which uses optical fiber as sensing medium. It can detect, recognize and locate invasive signals. The module of pattern recognition, which is one of the important parts of the system, can intelligently and instantly distinguish dangerous intrusions from safe disturbances. This paper proposes a regular composite feature extraction method which can be used for vibrational signal pattern recognition in an optical fiber monitoring system. This method applies an improved double-threshold method to detect the start-stop positions of the valid signal segments, and then combines the maximum energy and maximum signal noise ratio to select the main invasion feature segment within a sampling period. The composite eigenvectors, which are extracted by using the last time of the feature segment and wavelet packet energy spectrum, can be used by the support vector machine to recognize the signal patterns. Experiment results show that the accuracy rate of the pattern recognition has been improved significantly based on the proposed method in this paper.
出处 《物理学报》 SCIE EI CAS CSCD 北大核心 2015年第5期243-249,共7页 Acta Physica Sinica
基金 江苏省产学研前瞻性项目(批准号:BY2009155 BY2013073)资助的课题~~
关键词 光纤振动信号 小波包 复合特征提取 模式识别 optical fiber vibration signal wavelet package composite feature extraction pattern recognition
  • 相关文献

参考文献5

二级参考文献31

  • 1薛建中,闫相国,郑崇勋.用核学习算法的意识任务特征提取与分类[J].电子学报,2004,32(10):1749-1753. 被引量:10
  • 2杨立才,李佰敏,李光林,贾磊.脑-机接口技术综述[J].电子学报,2005,33(7):1234-1241. 被引量:69
  • 3高克芳,陈亚光.基于小波的模拟自然阅读事件相关电位的单次提取[J].电子学报,2006,34(10):1856-1859. 被引量:2
  • 4Weston J,Watkins C.Multi-class support vector machines,Technical Report : CSD-TR-98-04[R].Royal Holloway : University of London, 1998. 被引量:1
  • 5Schwenker F.Hierarchical support vector machines for muhi-class pattern recognition[C]//Proceedings of the Fourth International conference on Knowledge-Based Intelligent Engineering Systems & Allied Technologies, 2000,2 : 561-565. 被引量:1
  • 6Platt J C,Cristianini N,Shawe-Taylor J.Large margin DAGs for muhiclass classification[J].Advances in Neural Information Processing Systems, 2000,12( 12 ) : 547-553. 被引量:1
  • 7Fei B,Jiu J.Binary Tree of SVM:A new fast multiclass training and classification algorithm[J].IEEE Transactions on Neural Networks, 2006, 17(3 ) :696-704. 被引量:1
  • 8Gexiang Z.Support vector machines with huffman tree architecture for multiclass classification[J].Lecture Notes in Computer Science. 2005 : 24-33. 被引量:1
  • 9Schwenker F.Solving multi-class pattern recognition problems with tree-structured support vector machines[C]//DAGM-Symposium,2001: 283-290. 被引量:1
  • 10Do M N,Vetterli M.Contourlets:A directional multisolution image representation[C]//Proc of IEEE International Conference on Image Processing.Rochester, NY, 2002 : 357-360. 被引量:1

共引文献2434

同被引文献147

引证文献18

二级引证文献142

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部