Bundesanstalt für Materialforschung und-Prüfung, Unter den Eichen 87, 12205 Berlin, Germany.
Sensors (Basel). 2021 Apr 13;21(8):2724. doi: 10.3390/s21082724.
To our knowledge, this is the first report on a machine-learning-assisted Brillouin optical frequency domain analysis (BOFDA) for time-efficient temperature measurements. We propose a convolutional neural network (CNN)-based signal post-processing method that, compared to the conventional Lorentzian curve fitting approach, facilitates temperature extraction. Due to its robustness against noise, it can enhance the performance of the system. The CNN-assisted BOFDA is expected to shorten the measurement time by more than nine times and open the way for applications, where faster monitoring is essential.
据我们所知，这是第一篇关于使用机器学习辅助布里渊光频域分析（BOFDA）进行高效时间温度测量的报告。我们提出了一种基于卷积神经网络（CNN）的信号后处理方法，与传统的洛伦兹曲线拟合方法相比，它可以方便温度提取。由于其对噪声的鲁棒性，它可以提高系统的性能。预计 CNN 辅助的 BOFDA 将使测量时间缩短超过九倍，并为需要更快监测的应用开辟道路。
