Researching

Journals
  • Advanced Photonics
  • Photonics Insights
  • Advanced Photonics Nexus
  • Photonics Research
  • Advanced Imaging
  • View All Journals
  • Chinese Optics Letters
  • High Power Laser Science and Engineering
    • Articles
  • Optics
  • Physics
  • Geography
  • View All Subjects
    • Conferences
  • CIOP
  • HPLSE
  • AP
  • View All Events
    • News
    About CLP
    Search by keywords or author
    Journals >Acta Optica Sinica >Volume 42 >Issue 20 >Page 2006003 > Article
    • Acta Optica Sinica
    • Vol. 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
    • show less
    DOI: 10.3788/AOS202242.2006003 Cite this Article Set citation alerts
    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 Copy Citation Text show less
    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
    Download full size
    Schematic of detecting fiber parameters using neural network
    Fig. 2. Schematic of detecting fiber parameters using neural network
    Download full size
    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
    Download full size
    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
    Download full size
    Structure diagram of convolutional neural network
    Fig. 5. Structure diagram of convolutional neural network
    Download full size
    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
    Download full size
    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
    Download full size
    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
    Download full size
    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
    Download full size
    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
    Download full size
    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
    Download full size
    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
    Download full size
    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
    Download full size
    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
    Download full size
    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
    Download full size

    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

    Table 1. Loss of five machine learning algorithms in test set
    Abstract
    Get PDF(in Chinese)
    Figures&Tables (16)
    Equations (0)
    References (23)
    Get Citation
    Copy Citation Text
    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
    Download Citation
    Set citation alerts for the article
    Tools
    Share
    Set citation alerts for the article
    Save the article for my favorites
    Paper Information
    Recommended Topics
    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology