光纤布拉格光栅
光纤传感器
光纤
折射率
材料科学
光学
信号处理
数据处理
纤维
计算机科学
光电子学
作者
Eugeny D Chubchev,Kirill Tomyshev,Igor Nechepurenko,Alexander Dorofeenko,Oleg V. Butov
标识
DOI:10.1109/jlt.2022.3148533
摘要
Fiber optic sensors are applied in industry, remote sensing, environmental monitoring and healthcare. A special place is occupied by tilted fiber Bragg gratings, which can significantly expand the capabilities provided by standard Bragg sensors. But these gratings have complex spectral responses and data processing becomes critical task for achieving maximum performance. In this paper, machine learning methods for processing of spectral data of a plasmonic fiber sensor based on a tilted fiber Bragg grating were applied for the first time for the measurement of small refractive index changes. The responses of two similar but not identical sensors were measured in the two independent experiments. The model trained on the data of the first sensor was used to analyze data obtained with another sensor. The best resolution achieved in our experiments was 910-6 RIU.
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