TY - JOUR
T1 - Brillouin Optical Time-Domain Analyzer Assisted by Support Vector Machine for Ultrafast Temperature Extraction
AU - Wu, Huan
AU - Wang, Liang
AU - Guo, Nan
AU - Shu, Chester
AU - Lu, Chao
N1 - Funding Information:
Manuscript received May 6, 2017; revised July 8, 2017; accepted August 8, 2017. Date of publication August 13, 2017; date of current version September 5, 2017. This work was supported in part by the Research Grants Council of Hong Kong Project: CUHK GRF 416213, 14206614, 14238816, and PolyU 5208/13E; and in part by the National Natural Science Foundation of China: NSFC 61377093, 61435006. (Corresponding author: Liang Wang.) H. Wu, L. Wang, and C. Shu are with the Department of Electronic Engineering, Chinese University of Hong Kong, Sha Tin, Hong Kong (e-mail: [email protected]; [email protected]; [email protected]).
Publisher Copyright:
© 1983-2012 IEEE.
PY - 2017/10/1
Y1 - 2017/10/1
N2 - Brillouin optical time-domain analyzer (BOTDA) assisted by support vector machine (SVM) for ultrafast temperature extraction is proposed and experimentally demonstrated. The temperature extraction is treated as a supervised classification problem and the Brillouin gain spectrum (BGS) is classified into each temperature class according to the support vectors and hyperplane of the SVM model after training. Ideal pseudo-Voigt curve-based BGS is used to train the SVM to get the support vectors and hyperplane. The performance of SVM is investigated in both simulation and experiment under various conditions for BGS collection. Both simulation and experiment results show that SVM is more robust to a wide range of signal-to-noise ratios, averaging times, pump pulse widths, frequency scanning steps, and temperatures. In addition to better accuracy, the processing speed for temperature extraction using SVM is 100 times faster than that using conventional Lorentzian curve and pseudo-Voigt curve fitting techniques in our experiment. The fast processing speed together with good accuracy and robustness makes SVM a highly competitive candidate for future high-speed BOTDA sensors.
AB - Brillouin optical time-domain analyzer (BOTDA) assisted by support vector machine (SVM) for ultrafast temperature extraction is proposed and experimentally demonstrated. The temperature extraction is treated as a supervised classification problem and the Brillouin gain spectrum (BGS) is classified into each temperature class according to the support vectors and hyperplane of the SVM model after training. Ideal pseudo-Voigt curve-based BGS is used to train the SVM to get the support vectors and hyperplane. The performance of SVM is investigated in both simulation and experiment under various conditions for BGS collection. Both simulation and experiment results show that SVM is more robust to a wide range of signal-to-noise ratios, averaging times, pump pulse widths, frequency scanning steps, and temperatures. In addition to better accuracy, the processing speed for temperature extraction using SVM is 100 times faster than that using conventional Lorentzian curve and pseudo-Voigt curve fitting techniques in our experiment. The fast processing speed together with good accuracy and robustness makes SVM a highly competitive candidate for future high-speed BOTDA sensors.
KW - Brillouin optical time domain analyzer
KW - fiber optics sensors
KW - stimulated Brillouin scattering
KW - support vector machine
UR - http://www.scopus.com/inward/record.url?scp=85028473285&partnerID=8YFLogxK
U2 - 10.1109/JLT.2017.2739421
DO - 10.1109/JLT.2017.2739421
M3 - Journal article
SN - 0733-8724
VL - 35
SP - 4159
EP - 4167
JO - Journal of Lightwave Technology
JF - Journal of Lightwave Technology
IS - 19
M1 - 8010274
ER -