Demodulation of Light Sensing Overlapping Spectral Signal by Improved Particle Swarm Optimization Algorithm

Yong, 陈勇 Chen; Yanan, 程亚男 Cheng; Huanlin, 刘焕淋 Liu

doi:10.3788/cjl201845.0710003

Cited by 2 publications

(3 citation statements)

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“…However, due to the restricted bandwidth of the light source and the limited number of work areas, the number of FBG multiplexing is limited 11 . In order to increase the multiplexing of FBGs in a simple sensing network, spectral shape multiplexing 12 – 14 has been proposed. This method uses the shape information of the spectrum to achieve demodulation so that the spectrum overlaps and multiple FBGs can be multiplexed in one working area.…”

Section: Introductionmentioning

confidence: 99%

“…used an improved dynamic multiparticle swarm optimizer to demodulate the FBG center wavelength for a multiplexed sensing network of FBG sensors with four different reflectance spectral shapes. Chen et al 12 . presented a dynamic PSO algorithm, which improved the weight factor and learning factor of PSO.…”

Section: Introductionmentioning

confidence: 99%

“…Guo et al 15 used an improved dynamic multiparticle swarm optimizer to demodulate the FBG center wavelength for a multiplexed sensing network of FBG sensors with four different reflectance spectral shapes. Chen et al 12 presented a dynamic PSO algorithm, which improved the weight factor and learning factor of PSO. Bao et al 16 took advantage of an adaptive mutation quantum PSO algorithm to solve the problem of partial or complete spectral overlap in the multiplexing of FBG sensors with different center wavelengths.…”

Section: Introductionmentioning

confidence: 99%

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Overlapped spectral demodulation of fiber Bragg grating using convolutional time-domain audio separation network

Shan

Xia

et al. 2023

Opt. Eng.

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In fiber Bragg grating (FBG) sensor networks, the highly overlapped spectral signals can lead to considerable errors in center wavelength demodulation. To tackle this problem, we utilize the fully convolutional time-domain audio separation network (Conv-TasNet) model to produce a distinct spectral signal, which is then demodulated using the dual-weight centroid approach to determine the spectral signal's center wavelength. Specifically, we first demonstrate the theoretical feasibility of the Conv-TasNet model on simulated data. Experimental results show that the Conv-TasNet model can separate the signals of three FBG sensors. After that, we collect the spectral data and further train and validate the model based on the pretrained model of the simulated data to see how it performs on the real data. The experiments consistently illustrate superior performance of our Conv-TasNet model that can also separate actual spectrum signals. The same performance can be achieved by applying the pretrained model but with less training data. The model obtains a competitive performance compared to currently available methods. Moreover, the method provides a solution for improving the multiplexing performance of the FBG sensor network.

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