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：
ADVANCED SENSOR SYSTEMS AND APPLICATIONS X | 2020年 / 11554卷
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
  • [31] On a class of finite mixture models that includes hidden Markov models
    Bartolucci, Francesco
    Pandolfi, Silvia
    Pennoni, Fulvia
    JOURNAL OF MULTIVARIATE ANALYSIS, 2025, 208
  • [32] A method for degradation prediction based on Hidden semi-Markov models with mixture of Kernels
    Yang, Tianji
    Zheng, Zeyu
    Qi, Liang
    COMPUTERS IN INDUSTRY, 2020, 122
  • [33] A new method for fuzzy hidden Markov models in speech recognition
    Tarihi, MR
    Taheri, A
    Bababeyk, H
    IEEE: 2005 INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES, PROCEEDINGS, 2005, : 36 - 40
  • [34] Inference of collective Gaussian hidden Markov models
    Singh, Rahul
    Chen, Yongxin
    2021 60TH IEEE CONFERENCE ON DECISION AND CONTROL (CDC), 2021, : 1637 - 1643
  • [35] Bayesian face recognition based on Gaussian mixture models
    Wang, XG
    Tang, XO
    PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 4, 2004, : 142 - 145
  • [36] License plate recognition based on Gaussian mixture models
    College of Electronics and Information Engineering, Sichuan University, Chengdu 610064, China
    Guangdianzi Jiguang, 2007, 4 (487-490):
  • [37] Spectral Learning of Mixture of Hidden Markov Models
    Subakan, Y. Cem
    Traa, Johannes
    Smaragdis, Paris
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 27 (NIPS 2014), 2014, 27
  • [38] MIXTURE OF HIDDEN MARKOV MODELS FOR ACCELEROMETER DATA
    de Chaumaray, Marie Du Roy
    Marbac, Matthieu
    Navarro, Fabien
    ANNALS OF APPLIED STATISTICS, 2020, 14 (04): : 1834 - 1855
  • [39] Learning hidden Markov models for linear Gaussian systems with applications to event-based state estimation
    Zheng, Kaikai
    Shi, Dawei
    Shi, Ling
    AUTOMATICA, 2021, 128
  • [40] Mixture Hidden Markov Models in Finance Research
    Dias, Jose G.
    Vermunt, Jeroen K.
    Ramos, Sofia
    ADVANCES IN DATA ANALYSIS, DATA HANDLING AND BUSINESS INTELLIGENCE, 2010, : 451 - +
12345