Laser & Optoelectronics Progress, Volume. 58, Issue 13, 1306010(2021)
Research Status of Brillouin Signal Analysis Method Based on Machine Learning
Fig. 1. Change curves of BGS and LCF
Fig. 2. Schematic of ANN[30]
Fig. 3. Fiber temperature distribution diagram extracted by ANN under different frequency sweep intervals, where temperature of fiber at 41 m of tail is (a) 21.00 ℃ (room temperature), (b) 29.90 ℃, (c) 39.14 ℃, and (d) 48.63 ℃[30]
Fig. 4. Ratio of time spent in processing BGS data between LCF and ANN[30]
Fig. 5. Schematic of SVM extraction temperature[32]
Fig. 6. Measured BGS distribution and temperature distribution extracted by SVM-0.5 °C[32]
Fig. 7. Uncertainty and RMSE comparison curves under different SNR[32]. (a1) (a2) 11.5 dB; (b1) (b2) 6.1 dB
Fig. 8. Structure and extraction principle of DNN[35]. (a) DNN structure; (b) schematic of extracting temperature and stress at the same time by DNN
Fig. 9. Experimental device of LEAF and comparison curves of extraction results of different methods[35]. (a) Experimental device; (b) comparison curves of temperature and stress under DNN and equation solving method
Fig. 10. BGS signal processing flow based on K-SVD algorithm [37]
Fig. 11. Temperature error curves of K-SVD algorithm and LCF algorithm[37]
Fig. 12. ELM training and testing process[38]
Fig. 13. CNN structure for extracting BFS [40]
Fig. 14. Comparison of BFS results after CNN and LCF algorithm processing[40]
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Liang Wang, Hao Wu, Ming Tang, Deming Liu. Research Status of Brillouin Signal Analysis Method Based on Machine Learning[J]. Laser & Optoelectronics Progress, 2021, 58(13): 1306010
Category: Fiber Optics and Optical Communications
Received: Feb. 26, 2021
Accepted: May. 28, 2021
Published Online: Jul. 14, 2021
The Author Email: Wu Hao (wuhaoboom@qq.com), Tang Ming (tangming@mail.hust.edu.cn)