• Chinese Journal of Lasers
  • Vol. 46, Issue 3, 0304003 (2019)
Yong Chen1、*, Wangyue An1, Huanlin Liu2, Zhiqiang Liu1, and Lixin Zhou1
Author Affiliations
  • 1 Key Laboratory of Internet of Things and Networking Control Under Ministry of Education, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
  • 2 Key Laboratory of Fiber-Optic Communication Technology Under Ministry of Information Industry,Chongqing University of Posts and Telecommunications, Chongqing 400065, China
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    DOI: 10.3788/CJL201946.0304003 Cite this Article Set citation alerts
    Yong Chen, Wangyue An, Huanlin Liu, Zhiqiang Liu, Lixin Zhou. Application of Improved Empirical Mode Decomposition Algorithm in Fiber Bragg Grating Perimeter Intrusion Behaviors Classification[J]. Chinese Journal of Lasers, 2019, 46(3): 0304003 Copy Citation Text show less
    Schematic of matching wavelet calculation
    Fig. 1. Schematic of matching wavelet calculation
    Flow chart of improved EMD algorithm
    Fig. 2. Flow chart of improved EMD algorithm
    Short time average zero-crossing rate distribution of intrusion signal. (a) Intrusion perturbation signal; (b) distribution of short time average zero-crossing rate
    Fig. 3. Short time average zero-crossing rate distribution of intrusion signal. (a) Intrusion perturbation signal; (b) distribution of short time average zero-crossing rate
    Photo of perimeter security system
    Fig. 4. Photo of perimeter security system
    Intrusion signal and short time average zero-crossing rate distribution before zeroing. (a) Intrusion signal; (b) short time average zero-crossing rate
    Fig. 5. Intrusion signal and short time average zero-crossing rate distribution before zeroing. (a) Intrusion signal; (b) short time average zero-crossing rate
    Intrusion signal and short time average zero-crossing rate distribution after zeroing. (a) Intrusion signal; (b) short time average zero-crossing rate
    Fig. 6. Intrusion signal and short time average zero-crossing rate distribution after zeroing. (a) Intrusion signal; (b) short time average zero-crossing rate
    Extracted intrusion signals. (a) Climbing; (b) colliding; (c) shearing; (d) nonintrusive; (e) touching
    Fig. 7. Extracted intrusion signals. (a) Climbing; (b) colliding; (c) shearing; (d) nonintrusive; (e) touching
    Identification results of climbing signal. (a) IMF components; (b) frequency domain distribution
    Fig. 8. Identification results of climbing signal. (a) IMF components; (b) frequency domain distribution
    Identification results of shearing signal. (a) IMF components; (b) frequency domain distribution
    Fig. 9. Identification results of shearing signal. (a) IMF components; (b) frequency domain distribution
    Identification results of nonintrusive signal. (a) IMF components; (b) frequency domain distribution
    Fig. 10. Identification results of nonintrusive signal. (a) IMF components; (b) frequency domain distribution
    BehaviorExperimentnumberNumber of successful recognitionRecognition rate /%
    EMDEEMDProposed algorithmEMDEEMDProposed algorithm
    Climbing100788396788396
    Shearing100919598919598
    Colliding100859096859096
    Touching100808697808697
    Nonintrusive1009410010094100100
    Table 1. Identification results of five behaviors by three algorithms
    Noise level /dBSignal-to-noise ratio after denoising /dB
    EMDEEMDProposed algorithm
    522.168323.235624.9389
    1026.619627.081328.1797
    1533.642234.174234.3243
    2035.310536.581937.7545
    2536.672638.761840.2439
    Table 2. Denoising performance of three algorithms
    Yong Chen, Wangyue An, Huanlin Liu, Zhiqiang Liu, Lixin Zhou. Application of Improved Empirical Mode Decomposition Algorithm in Fiber Bragg Grating Perimeter Intrusion Behaviors Classification[J]. Chinese Journal of Lasers, 2019, 46(3): 0304003
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