Proceedings Article10.1109/icocn59242.2023.10236357
Neural network-based FBG array sensor demodulation system with sparse data
Sufen Ren,Shengchao Chen,Haoyang Xu,Xuan Hou,Guanjun Wang,Mengxing Huang +5 more
- 31 Jul 2023
pp 1-3
TL;DR: A neural network-based FBG array sensor demodulation system with sparse data achieves high interrogation precision using an augmented algorithm.
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Abstract: An efficient and practical neural network-based demodulation system for FBG array sensors is investigated, which fusing a augmentated algorthm so that system can achieve excellent interrogation precision under sparse data.
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References
Interrogating fiber Bragg grating sensors by thermally scanning a demultiplexer based on arrayed waveguide gratings.
TL;DR: Results show that the Bragg wavelength of fiber Bragg grating sensors can be precisely interrogated by thermally scanning an AWG-based demultiplexer, potentially offering a low-cost, compact, and high-performance solution for the interrogation of FBG distributed sensors and multisensor arrays.
88
Cost-effective improvement of the performance of AWG-based FBG wavelength interrogation via a cascaded neural network.
Sheng‐Hao Chen,Fei Yao,Sufen Ren,Guanjun Wang,Mengxing Huang +4 more
TL;DR: A high-performance, low-cost wavelength interrogation method for FBG sensors, constructed by cascading a convolutional neural network and a residual backpropagation neural network, and validated in experiments.
45
High-efficiency FBG array sensor interrogation system via a neural network working with sparse data.
Sufen Ren,Shengchao Chen,Jianli Yang,Jiahao Wang,Qian Yang,Chenyang Xue,Guanjun Wang,Mengxing Huang +7 more
TL;DR: In this paper , a cost-effective demodulation system for FBG array sensors based on a Neural Network (NN) is proposed for multi-point monitoring of large structures due to their excellent optical multiplexing capability.
17
Reconstruction of Fabry-Perot Interferometric Sensor Spectrum From Extremely Sparse Sampling Points Using Dense Neural Network
15 Dec 2022
TL;DR: In this paper , a neural network-based systematic scheme was proposed to achieve fiber optic sensor spectral reconstruction with high-frequency and high-resolution potential, which can reconstruct the FPI sensor spectrum consisting of hundreds of points based on extremely sparse (even single-digit) sampling points, with MHz-level frequency, and supports custom tuning the reconstructed wavelength range.
15
Reconstruction of Fabry-Perot Interferometric Sensor Spectrum From Extremely Sparse Sampling Points Using Dense Neural Network
TL;DR: In this paper , a neural network-based systematic scheme was proposed to achieve fiber optic sensor spectral reconstruction with high-frequency and high-resolution potential, which can reconstruct the FPI sensor spectrum consisting of hundreds of points based on extremely sparse (even single-digit) sampling points, with MHz-level frequency.
6