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Optimized support vector machine assisted BOTDA for temperature extraction with accuracy enhancement
IEEE Photonics Journal ( IF 2.4 ) Pub Date : 2020-02-01 , DOI: 10.1109/jphot.2019.2957410
Hongna Zhu , Lei Yu , Yufeng Zhang , Le Cheng , Zhenyu Zhu , Jiayin Song , Jinli Zhang , Bin Luo , Kai Yang

Brillouin optical time domain analyzer (BOTDA) assisted by optimized support vector machine (SVM) algorithm for accurate temperature extraction is presented and experimentally demonstrated. Three typical intelligent optimization algorithms, particle swarm optimization algorithm, genetic algorithm and firefly algorithm are explored to optimize the SVM parameters. The performances of optimized SVM algorithms for temperature extraction are investigated in both simulation and experiment under different conditions for Brillouin gain spectrum collection, resulting in the significant enhancement of sensing accuracy. In particular, the extraction accuracy (i.e., smaller root mean square error value) of temperature information is improved about 4 °C compared with the conventional SVM when the signal-to-noise ratio (SNR) as low as 2.5 dB and 40-ns pump pulse width are adopted in the experiment. In addition to the enhanced accuracy with good robustness, the optimized algorithms have faster processing speed than the curve fitting method, over 20-times improvement. That makes the optimized algorithms become a very promising candidate for high performance BOTDA sensors in the future.

中文翻译:

优化的支持向量机辅助 BOTDA 进行温度提取并提高精度

布里渊光时域分析仪 (BOTDA) 由优化的支持向量机 (SVM) 算法辅助,用于精确提取温度,并进行了实验证明。探索了三种典型的智能优化算法,粒子群优化算法、遗传算法和萤火虫算法来优化SVM参数。在布里渊增益谱采集的不同条件下,通过仿真和实验研究了优化的SVM温度提取算法的性能,显着提高了传感精度。特别是在信噪比(SNR)低至2时,温度信息的提取精度(即更小的均方根误差值)比传统的SVM提高了4°C左右。实验中采用 5 dB 和 40-ns 泵浦脉冲宽度。优化后的算法除了精度提高、鲁棒性好外,处理速度比曲线拟合法快20倍以上。这使得优化后的算法成为未来高性能 BOTDA 传感器非常有前途的候选者。
更新日期:2020-02-01
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