• Laser & Optoelectronics Progress
  • Vol. 58, Issue 13, 1306022 (2021)
Zhilong Li1, Weihua Zhang2、**, Yimin Wang1, Yufeng Zhang1, Bin Luo3, and Hongna Zhu1、*
Author Affiliations
  • 1School of Physical Science and Technology, Southwest Jiaotong University, Chengdu , Sichuan 610031, China
  • 2College of Meteorology and Ocean, National University of Defense Technology, Changsha , Hunan 410073, China
  • 3School of Information Science and Technology, Southwest Jiaotong University, Chengdu , Sichuan 610031, China
  • show less
    DOI: 10.3788/LOP202158.1306022 Cite this Article Set citation alerts
    Zhilong Li, Weihua Zhang, Yimin Wang, Yufeng Zhang, Bin Luo, Hongna Zhu. Advances of Machine Learning in Brillouin Optical Time Domain Analysis Sensing Systems for Temperature Extraction[J]. Laser & Optoelectronics Progress, 2021, 58(13): 1306022 Copy Citation Text show less
    Diagram of optical fiber temperature sensing system based on BOTDA[9]
    Fig. 1. Diagram of optical fiber temperature sensing system based on BOTDA9
    Principle of using SVM to extract temperature information of BGS[29]
    Fig. 2. Principle of using SVM to extract temperature information of BGS29
    Schematic diagram of training and testing steps required to extract temperature information with optimized SVM algorithm[31]
    Fig. 3. Schematic diagram of training and testing steps required to extract temperature information with optimized SVM algorithm31
    Typical ANN structure diagram[28]
    Fig. 4. Typical ANN structure diagram28
    Schematic diagram of training and testing steps required to extract temperature information with ANN[28]
    Fig. 5. Schematic diagram of training and testing steps required to extract temperature information with ANN28
    Structure of BP neural network[35]
    Fig. 6. Structure of BP neural network35
    Schematic diagram of training and testing steps required to extract temperature information with BP neural network[35]
    Fig. 7. Schematic diagram of training and testing steps required to extract temperature information with BP neural network35
    Structure of DNN with 2 autoencoder hidden layers[37]
    Fig. 8. Structure of DNN with 2 autoencoder hidden layers37
    Principle of using DNN for simultaneous temperature and strain measurement[38]
    Fig. 9. Principle of using DNN for simultaneous temperature and strain measurement38
    Structure of DnCNN[39]
    Fig. 10. Structure of DnCNN39
    Structure of ELM with three layers[41]
    Fig. 11. Structure of ELM with three layers41
    Structure of ESN[41]
    Fig. 12. Structure of ESN41
    Zhilong Li, Weihua Zhang, Yimin Wang, Yufeng Zhang, Bin Luo, Hongna Zhu. Advances of Machine Learning in Brillouin Optical Time Domain Analysis Sensing Systems for Temperature Extraction[J]. Laser & Optoelectronics Progress, 2021, 58(13): 1306022
    Download Citation