An AWG-Based FBG Interrogation Approach Using Convolutional Neural Networks

We propose a low-cost and high-accuracy FBG interrogation approach based on an AWG by employing CNN. The achieved accuracy of network trained on experimental data could reach 99.9967% with a RMSE of 0.1575°C.

Authors

Zhengyong Liu (ORCID: https://orcid.org/0000-0002-0737-1782)
Juanli Li (ORCID: https://orcid.org/0000-0002-1190-9119)
Ziqi Liu (ORCID: https://orcid.org/0009-0005-3566-5824)
Xiaoliang Cao
Haoen Cai (ORCID: https://orcid.org/0009-0009-6388-808X)
Chang Liu (ORCID: https://orcid.org/0000-0003-0650-1773)

Institutions

Sun Yat-sen University (CN)

Publication Details

Published: 2024-11-02
DOI: https://doi.org/10.1109/acp/ipoc63121.2024.10809493
Primary Topic: PAPR reduction in OFDM
Type: article
Field-Weighted Citation Impact: 0.00

Funders

National Natural Science Foundation of China
Fundamental Research Funds for the Central Universities

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article

An AWG-Based FBG Interrogation Approach Using Convolutional Neural Networks

Zhengyong Liu, Juanli Li, Ziqi Liu, Xiaoliang Cao et al.

Nov 2, 2024

PAPR reduction in OFDM

article

An AWG-Based FBG Interrogation Approach Using Convolutional Neural Networks

Zhengyong Liu, Juanli Li, Ziqi Liu, Xiaoliang Cao, Haoen Cai, Chang Liu

November 2, 2024article en

Abstract

We propose a low-cost and high-accuracy FBG interrogation approach based on an AWG by employing CNN. The achieved accuracy of network trained on experimental data could reach 99.9967% with a RMSE of 0.1575°C.

Sun Yat-sen University (CN)

National Natural Science Foundation of China, Fundamental Research Funds for the Central Universities

Openalex Percentile: Top 77%

PAPR reduction in OFDM

DOI Source OpenAlex

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