An Event Recognition Method for Φ-OTDR Based on the Gaussian Mixture Models and Hidden Markov Models

被引:0
|
作者
Ma, Lilong [1 ,2 ]
Xu, Tuanwei [1 ,2 ]
Yang, Kaiheng [1 ]
Li, Fang [1 ,2 ]
机构
[1] Chinese Acad Sci, Inst Semicond, Key Labs Transducer Technol, Beijing 100083, Peoples R China
[2] Univ Chinese Acad Sci, Coll Mat Sci & Optoelect Technol, Beijing 100089, Peoples R China
来源
基金
中国国家自然科学基金;
关键词
Phi-OTDR; DAS; FFT; GMMs-HMMs;
D O I
10.1117/12.2573624
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
Fiber optic distributed acoustic sensing (DAS) based on phase-sensitive optical time-domain reflectometry (Phi-OTDR) technology has been widely used in safety monitoring areas including monitoring of oil/gas pipes, communication or power cable, perimeters and so on, however it suffers from the high nuisance alarm rate (NAR) due to the non-stationarity characteristics of signal and the interference of external environment. In this paper, GMMs-HMMs is utilized to reduce nuisance alarm rate, we prove that short time signal unit of appropriate length can contain the main frequency domain characteristics of signal, GMMs-HMMs is efficient recognition method for frequency domain sequence of signal, the experience results show the average recognition accuracy rate is 88.89% for seven events.
引用
收藏
页数:8
相关论文
共 50 条
  • [1] STRANDED GAUSSIAN MIXTURE HIDDEN MARKOV MODELS FOR ROBUST SPEECH RECOGNITION
    Zhao, Yong
    Juang, Biing-Hwang
    2012 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2012, : 4301 - 4304
  • [2] An Gaussian-Mixture Hidden Markov Models for Action Recognition Based On Key Frame
    Li, Jinhong
    Lei, Tingsheng
    Zhang, Fengquan
    2018 11TH INTERNATIONAL CONGRESS ON IMAGE AND SIGNAL PROCESSING, BIOMEDICAL ENGINEERING AND INFORMATICS (CISP-BMEI 2018), 2018,
  • [3] Encrypted traffic classification based on Gaussian mixture models and Hidden Markov Models
    Yao, Zhongjiang
    Ge, Jingguo
    Wu, Yulei
    Lin, Xiaosheng
    He, Runkang
    Ma, Yuxiang
    JOURNAL OF NETWORK AND COMPUTER APPLICATIONS, 2020, 166
  • [4] Boosted Mixture Learning of Gaussian Mixture Hidden Markov Models Based on Maximum Likelihood for Speech Recognition
    Du, Jun
    Hu, Yu
    Jiang, Hui
    IEEE TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING, 2011, 19 (07): : 2091 - 2100
  • [5] Dynamic Grasp Recognition Using Time Clustering, Gaussian Mixture Models and Hidden Markov Models
    Ju, Zhaojie
    Liu, Honghai
    Zhu, Xiangyang
    Xiong, Youlun
    INTELLIGENT ROBOTICS AND APPLICATIONS, PT I, PROCEEDINGS, 2008, 5314 : 669 - +
  • [6] Dynamic Grasp Recognition Using Time Clustering, Gaussian Mixture Models and Hidden Markov Models
    Ju, Zhaojie
    Liu, Honghai
    Zhu, Xiangyang
    Xiong, Youlun
    ADVANCED ROBOTICS, 2009, 23 (10) : 1359 - 1371
  • [7] Driving event recognition by Hidden Markov Models
    Mitrovic, D
    TELSIKS '99: 4TH INTERNATIONAL CONFERENCE ON TELECOMMUNICATIONS IN MODERN SATELLITE, CABLE AND BROADCASTING SERVICES, PROCEEDINGS, VOLS 1 AND 2, 1999, : 110 - 113
  • [8] A survey of feature selection methods for Gaussian mixture models and hidden Markov models
    Adams, Stephen
    Beling, Peter A.
    ARTIFICIAL INTELLIGENCE REVIEW, 2019, 52 (03) : 1739 - 1779
  • [9] A survey of feature selection methods for Gaussian mixture models and hidden Markov models
    Stephen Adams
    Peter A. Beling
    Artificial Intelligence Review, 2019, 52 : 1739 - 1779
  • [10] Hidden Markov and Gaussian mixture models for automatic call classification
    Brown, Judith C.
    Smaragdis, Paris
    JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA, 2009, 125 (06): : EL221 - EL224