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Conferences > OFS > 2023 > W4 > W4.80

Improvement of Multiplexing Capability of Fiber Bragg Gratings Using Convolutional Neural Network

Tatsuya Yamaguchi, Hiroto Kawashima, Hiroki Matsuda, and Yukitaka Shinoda

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Abstract

A method for improving the multiplexing capability of fiber Bragg gratings (FBGs) based on wavelength division multiplexing by a convolutional neural network (CNN) is developed. Using the devised CNN model, the direct analysis of the peak wavelengths of the arbitrary strain responses of four overlapping FBGs at the same wavelength without the support of special optical devices is demonstrated for the first time. The standard deviation of the measurement resolution is < 2.8 pm.

© 2023 The Author(s)

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