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Acta Optica Sinica, Volume. 42, Issue 20, 2006003(2022)

Optical Fiber Multi-Parameter Measurement Based on Machine Learning

Zehang Ma1, Rui Gong1, Bin Li2, Li Pei1, and Huai Wei1、*
Author Affiliations
  • 1Key Laboratory of All Optical Network and Advanced Telecommunication Network, Ministry of Education, Institute of Lightwave Technology, Beijing Jiaotong University, Beijing 100044, China
  • 2School of Information and Communication Engineering, Communication University of China, Beijing 100024, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (16)
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    References (23)
    Cited By (2)
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    Figures & Tables(16)
    Evolution of optical pulse in optical fiber. (a) Input and output power in time domain; (b) relative intensity of input and output power spectral density in frequency domain; (c) pulse evolution in time domain; (d) pulse evolution in frequency domain

    Fig. 1. Evolution of optical pulse in optical fiber. (a) Input and output power in time domain; (b) relative intensity of input and output power spectral density in frequency domain; (c) pulse evolution in time domain; (d) pulse evolution in frequency domain

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    Schematic of detecting fiber parameters using neural network

    Fig. 2. Schematic of detecting fiber parameters using neural network

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    Schematic of optical fiber parameter measurement using different methods. (a) Complete signal measurement method; (b) power spectrum measurement method

    Fig. 3. Schematic of optical fiber parameter measurement using different methods. (a) Complete signal measurement method; (b) power spectrum measurement method

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    Output spectra corresponding to input pulses with different peak powers. (a) 600 W; (b) 800 W; (c) 1000 W

    Fig. 4. Output spectra corresponding to input pulses with different peak powers. (a) 600 W; (b) 800 W; (c) 1000 W

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    Structure diagram of convolutional neural network

    Fig. 5. Structure diagram of convolutional neural network

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    Measurement results of optical fiber parameters using all information in decision tree algorithm. (a) Measurement results of γ; (b) measurement results of D

    Fig. 6. Measurement results of optical fiber parameters using all information in decision tree algorithm. (a) Measurement results of γ; (b) measurement results of D

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    Measurement results of optical fiber parameters using all information in K-nearest neighbor algorithm. (a) Measurement results of γ; (b) measurement results of D

    Fig. 7. Measurement results of optical fiber parameters using all information in K-nearest neighbor algorithm. (a) Measurement results of γ; (b) measurement results of D

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    Measurement results of optical fiber parameters using all information in random forest algorithm. (a) Measurement results of γ; (b) measurement results of D

    Fig. 8. Measurement results of optical fiber parameters using all information in random forest algorithm. (a) Measurement results of γ; (b) measurement results of D

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    Measurement results of optical fiber parameters using all information in FCN algorithm. (a) Measurement results of γ;(b) measurement results of D

    Fig. 9. Measurement results of optical fiber parameters using all information in FCN algorithm. (a) Measurement results of γ;(b) measurement results of D

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    Measurement results of optical fiber parameters using all information in CNN algorithm. (a) Measurement results of γ;(b) measurement results of D

    Fig. 10. Measurement results of optical fiber parameters using all information in CNN algorithm. (a) Measurement results of γ;(b) measurement results of D

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    Measurement results of optical fiber parameters using power spectrum in decision tree algorithm. (a) Measurement results of γ; (b) measurement results of D

    Fig. 11. Measurement results of optical fiber parameters using power spectrum in decision tree algorithm. (a) Measurement results of γ; (b) measurement results of D

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    Measurement results of optical fiber parameters using power spectrum in K-nearest neighbor algorithm. (a) Measurement results of γ; (b) measurement results of D

    Fig. 12. Measurement results of optical fiber parameters using power spectrum in K-nearest neighbor algorithm. (a) Measurement results of γ; (b) measurement results of D

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    Measurement results of optical fiber parameters using power spectrum in random forest algorithm. (a) Measurement results of γ; (b) measurement results of D

    Fig. 13. Measurement results of optical fiber parameters using power spectrum in random forest algorithm. (a) Measurement results of γ; (b) measurement results of D

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    Measurement results of optical fiber parameters using power spectrum in FCN algorithm. (a) Measurement results of γ;(b) measurement results of D

    Fig. 14. Measurement results of optical fiber parameters using power spectrum in FCN algorithm. (a) Measurement results of γ;(b) measurement results of D

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    Measurement results of optical fiber parameters using power spectrum in CNN algorithm. (a) Measurement results of γ;(b) measurement results of D

    Fig. 15. Measurement results of optical fiber parameters using power spectrum in CNN algorithm. (a) Measurement results of γ;(b) measurement results of D

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    • Table 1. Loss of five machine learning algorithms in test set

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      Table 1. Loss of five machine learning algorithms in test set

      Algorithms of machine learning

      Loss of full information /%

      Loss of power spectrum information /%

      Decision tree algorithm

      0. 7405

      0. 6594

      Random forest algorithm

      0. 2705

      0. 1918

      K-nearest neighbor algorithm

      1.2716

      0. 3289

      FCN algorithm

      0.8544

      0. 2910

      CNN algorithm

      1.5244

      0. 6045

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    Zehang Ma, Rui Gong, Bin Li, Li Pei, Huai Wei. Optical Fiber Multi-Parameter Measurement Based on Machine Learning[J]. Acta Optica Sinica, 2022, 42(20): 2006003

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    Paper Information

    Category: Fiber Optics and Optical Communications

    Received: Feb. 9, 2022

    Accepted: Apr. 27, 2022

    Published Online: Oct. 18, 2022

    The Author Email: Huai Wei (hwei@bjtu.edu.cn)

    DOI:10.3788/AOS202242.2006003

    Topics

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