解调
光时域反射计
均方根
均方误差
波长
可靠性(半导体)
计算机科学
信号(编程语言)
电子工程
声学
光学
遥感
工程类
电信
光纤传感器
物理
光纤
数学
电气工程
统计
频道(广播)
光纤分路器
地质学
功率(物理)
量子力学
程序设计语言
作者
Hong Jiang,Chenyang Wang,Yihan Zhao,Rui Tang
标识
DOI:10.1016/j.yofte.2023.103458
摘要
To improve the rapidity and reliability of the subway tunnel fire monitoring system and realize the monitoring of small fire sources, the method of regional subsection demodulation is used to group several adjacent nearly identical ultra-weak fiber Bragg gratings (UWFBGs) sensors into a group, and the 1-D dilated convolutional neural network (1-DDCNN) is used to demodulate the same group of highly overlapping sensing signals. The well-trained 1-DDCNN model can achieve extremely low signal demodulation error. The experiment shows that the UWFBG demodulation scheme proposed in this paper improves the detection accuracy of the subway tunnel fire monitoring system and shortens the detection time. The root-mean-square error (RMSE) of four highly overlapping peak wavelengths of sensing signals is less than 1.5 pm, and the average demodulation time is less than 30 ms.
科研通智能强力驱动
Strongly Powered by AbleSci AI