光纤布拉格光栅
探地雷达
克里金
温度测量
高斯过程
计算机科学
光纤
高斯分布
过程(计算)
信号(编程语言)
信号处理
声学
材料科学
电子工程
光学
机器学习
工程类
物理
雷达
电信
操作系统
量子力学
程序设计语言
作者
Martin S. E. Djurhuus,Stefan Werzinger,Bernhard Schmauß,A.T. Clausen,Darko Zibar
出处
期刊:IEEE Photonics Technology Letters
[Institute of Electrical and Electronics Engineers]
日期:2019-04-30
卷期号:31 (12): 939-942
被引量:37
标识
DOI:10.1109/lpt.2019.2913992
摘要
This letter proposes an alternative approach to the signal processing of temperature measurements based on fiber Bragg gratings (FBGs) using the machine learning tool Gaussian process regression (GPR). The experimental results show that for a majority of the cases under consideration, the reported technique provides a more accurate calculation of the temperature than the conventional methods. Furthermore, the GPR can give the uncertainty of an estimate together with the estimate itself, which for example is useful when it is important to know the worst-case scenario of a measurand. The GPR also has the potential to improve the measurement speed of FBG-based temperature sensing compared to current standards.
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