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Laser & Optoelectronics Progress, Volume. 59, Issue 13, 1307004(2022)

Recognition and Classification Method for Fiber Optical Vibration Signal Using AdaBoost Ensemble Learning

Hongquan Qu1、*, Xiang Ji1, Zhiyong Sheng1, Hongbin Qu2, and Ling Wang3
Author Affiliations
  • 1School of Information Science and Technology, North China University of Technology, Beijing 100144, China
  • 2International Business Department, China Petroleum Pipeline Bureau Engineering Co., Ltd, Langfang 065000, Hebei , China
  • 3Asia Pacific Branch of China Petroleum Pipeline Bureau Engineering Co., Ltd.Langfang 065000, Hebei , China
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    AbstractGet PDF(in Chinese)
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    References (1)
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    Figures & Tables(11)
    Original signals and reconstructed signals by LMD. (a1) Car cross original signal; (a2) car cross reconstructed signal;(b1) running original signal; (b2) running reconstructed signal; (c1) noise original signal; (c2) noise reconstructed signal; (d1) pickaxe original signal; (d2) pickaxe reconstructed signal; (e1) tapping original signal; (e2) tapping reconstructed signal

    Fig. 1. Original signals and reconstructed signals by LMD. (a1) Car cross original signal; (a2) car cross reconstructed signal;(b1) running original signal; (b2) running reconstructed signal; (c1) noise original signal; (c2) noise reconstructed signal; (d1) pickaxe original signal; (d2) pickaxe reconstructed signal; (e1) tapping original signal; (e2) tapping reconstructed signal

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    Three-dimensional feature map of five different signals

    Fig. 2. Three-dimensional feature map of five different signals

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    Flow chart of ensemble learning classification

    Fig. 3. Flow chart of ensemble learning classification

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    Implementation of AdaBoost

    Fig. 4. Implementation of AdaBoost

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    Learning curves of decision tree and its AdaBoost classifier under different parameters. (a) Max_depth of decision tree; (b) number of base classifiers

    Fig. 5. Learning curves of decision tree and its AdaBoost classifier under different parameters. (a) Max_depth of decision tree; (b) number of base classifiers

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    SVM learning curves. (a) Penalty coefficient C; (b) core parameter γ

    Fig. 6. SVM learning curves. (a) Penalty coefficient C; (b) core parameter γ

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    Precision, recall and F1-score for 10-fold cross-validation with different classifiers

    Fig. 7. Precision, recall and F1-score for 10-fold cross-validation with different classifiers

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    Experimental flowchart

    Fig. 8. Experimental flowchart

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    Confusion matrixes of test samples. (a) SVM; (b) DTC; (c) AdaBoost-DTC

    Fig. 9. Confusion matrixes of test samples. (a) SVM; (b) DTC; (c) AdaBoost-DTC

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    Fiber optical identification true positive rates based on three different classifiers

    Fig. 10. Fiber optical identification true positive rates based on three different classifiers

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    • Table 1. Important parameters and optimal parameter values of different classifiers

      View table

      Table 1. Important parameters and optimal parameter values of different classifiers

      ClassifierDTCAdaBoost-DTCSVM
      Parameterfeature selection criteriafeature divide criteriamax-depthnumber of weak classifiersCγ
      Parameter rangegini/entropybest/random1-301-300.1-1000.1-1
      Best parameterentropybest1020800.63
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    Hongquan Qu, Xiang Ji, Zhiyong Sheng, Hongbin Qu, Ling Wang. Recognition and Classification Method for Fiber Optical Vibration Signal Using AdaBoost Ensemble Learning[J]. Laser & Optoelectronics Progress, 2022, 59(13): 1307004

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    Paper Information

    Category: Fourier Optics and Signal Processing

    Received: Aug. 2, 2021

    Accepted: Sep. 10, 2021

    Published Online: Jun. 9, 2022

    The Author Email: Hongquan Qu (qhqphd@ncut.edu.cn)

    DOI:10.3788/LOP202259.1307004

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology