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
  • [11] Hidden Markov Models and Gaussian Mixture Models for bearing fault detection using fractals
    Marwala, T.
    Mahola, U.
    Nelwamondo, F. V.
    2006 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORK PROCEEDINGS, VOLS 1-10, 2006, : 3237 - +
  • [12] Nonlinear Mixture Autoregressive Hidden Markov Models For Speech Recognition
    Srinivasan, Sundar
    Ma, Tao
    May, Daniel
    Lazarou, Georgios
    Picone, Joseph
    INTERSPEECH 2008: 9TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2008, VOLS 1-5, 2008, : 960 - +
  • [13] Video Trajectory-based Event Recognition using Hidden Markov Models
    Hervieu, Alexandre
    Bouthemy, Patrick
    Le Cadre, Jean-Pierre
    TRAITEMENT DU SIGNAL, 2009, 26 (03) : 187 - 197
  • [14] ICA and IVA bounded multivariate generalized Gaussian mixture based hidden Markov models
    Al-gumaei, Ali H.
    Azam, Muhammad
    Amayri, Manar
    Bouguila, Nizar
    ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2023, 123
  • [15] Activity recognition based on Hidden Markov Models
    Huang, Weiyao
    Zhang, Jun
    Liu, Zhijing
    KNOWLEDGE SCIENCE, ENGINEERING AND MANAGEMENT, 2007, 4798 : 532 - 537
  • [16] An optic-fiber fence intrusion recognition system using mixture Gaussian hidden Markov models
    Ma, Zhixian
    Liang, Yao
    Zhu, Jie
    IEICE ELECTRONICS EXPRESS, 2017, 14 (05):
  • [17] Video event detection using ica mixture hidden Markov models
    Zhou, Jian
    Zhang, Xiao-Ping
    2006 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, ICIP 2006, PROCEEDINGS, 2006, : 3005 - +
  • [18] A Dynamic Time Sequence Recognition and Knowledge Mining Method Based on the Hidden Markov Models (HMMs) for Pipeline Safety Monitoring With Φ-OTDR
    Wu, Huijuan
    Liu, Xiangrong
    Xiao, Yao
    Rao, Yunjiang
    JOURNAL OF LIGHTWAVE TECHNOLOGY, 2019, 37 (19) : 4991 - 5000
  • [19] Orthogonal Mixture of Hidden Markov Models
    Safinianaini, Negar
    de Souza, Camila P. E.
    Bostrom, Henrik
    Lagergren, Jens
    MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2020, PT I, 2021, 12457 : 509 - 525
  • [20] Molecular multiplex network inference using Gaussian mixture hidden Markov models
    Velickovic, Petar
    Lio, Pietro
    JOURNAL OF COMPLEX NETWORKS, 2016, 4 (04) : 561 - 574