我院电气与自动化工程学院教师姚雨果博士与日本横滨国立大学Yosuke Mizuno准教授合作研究的成果“Neural network-assisted signal processing in Brillouin optical correlation-domain sensing for potential high-speed implementation”近日被《Optics Express》 （中国科学院二区, 3.894）接收，我校为第一完成单位。该论文中提出布里渊光相关域传感中使用神经网络方法实现被测参量的提取，通过理论与实验研究表明了所提方法对该项传感技术的响应速度和测量精度的提升。此项研究工作为后续实现实时的布里渊相关域传感打下坚实基础。
Y. Yao and Y. Mizuno, “Neural network-assisted signal processing in Brillouin optical correlation-domain sensing for potential high-speed implementation,” Opt. Express, in press.
摘要：
The general neural networks (NNs) based on classification convert the Brillouin frequency shift (BFS) extraction in Brillouin-based distributed sensing to a problem in which the possible BFS output of the sensing system belongs to a finite number of discrete values. In this paper, we demonstrate a method of applying NNs with adaptive BFS incremental steps to signal processing for Brillouin optical correlation-domain sensing and achieve higher accuracy and operation speed. The comparison with the conventional curving fitting method shows that the NN improves the BFS measurement accuracy by 2–3 times and accelerates the signal processing speed by 1000 times for simulated signals. The experimental results demonstrate the NN provides 1.6–2.7 times enhancement for BFS measurement accuracy and 5000 times acceleration for the BFS extraction speed. This method supplies a potential solution to online signal processing for real-time Brillouin sensing.
图1 于神经网络的布里渊光相关域BFS提取原理
图2 用5 MHz，1 MHz和0.2 MHz步长的神经网络分别提取得到的分布式BFS实验结果
