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Improved Spectral Interrogation of Tilted Fiber Bragg Grating Refractometer Using Residual Convolutional Neural Networks

This paper proposes a new approach of demodulating the spectra of tilted fiber Bragg grating (TFBG) based refractive-index (RI) sensor by employing convolutional neural networks (CNN) with residual blocks. The proposed residual CNN model was trained and tested by over 5500 samples recorded at different concentrations of sodium chloride solution, whose RI ranged from 1.3350 to 1.3790 RIU, and the raw spectral data can be directly utilized to train the residual CNN without any pre-processing. Results show that the coefficient of determination could reach 99.82% and the mean square error (MSE) achieves 2.818 × 10 −7 RIU. The well-trained model could be applied to demodulate other similar sensors through transfer learning, which demonstrates its good generalization capability of the proposed method. Additionally, the proposed method could maintain high MSE (i.e., 2.938 × 10 −7 RIU) although the interrogator with poorer resolution (20 times less) was utilized, showing high potential to reduce the cost of the interrogation hardware.

Ketersediaan

Informasi Detail

Judul Seri

JOURNAL OF LIGHTWAVE TECHNOLOGY; Vol.40 No.22 November 15, 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 7403-7411

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

CONVOLUTIONAL NEURAL NETWORKS
FIBER-OPTIC SENSOR
TILTED FIBER BRAGG GRATING
DEEP LEARNING

Info Detail Spesifik

DOI: 10.1109/JLT.2022.3200999

Pernyataan Tanggungjawab

Zihan Cao, Titi Xia, Shengqi Zhang, Shuhe Zhang, Yongchang Mei, Zhengyong Liu, Tuan Guo, Zhaohui Li

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