FBG Sensor Signal Detection Technique Using Multilayer Perceptron Approach for Internet of Things (IoT) Application

Dehnaw, Amare Mulatie; Manie, Yibeltal Chanie; Chen, Y. Y.; Li, J. W.; Peng, Peng‐Chun

doi:10.1109/icce-taiwan49838.2020.9258213

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…Therefore, in order to improve the accuracy and reduce the detection time of spectral overlap detection, researchers have proposed various machine learning-based wavelength detection methods, such as extreme learning machine, 7 least squares support vector regression, 8 and multilayer perceptron (MLP). 9 Although machine learning methods have improved the speed and accuracy of spectral overlap detection, the accuracy of machine learning is still to be improved when the peak wavelengths overlap significantly, and the detection speed of machine learning methods is relatively low. More recently, several deep learning-based wavelength detection techniques have been proposed, such as long short-term memory (LSTM), 10 convolutional neural network (CNN) with wavelet adaptive threshold denoising, 11 and dilated convolutional neural network.…”

Section: Introductionmentioning

confidence: 99%

“…However, as the number of sensors in the network increases, these algorithms require a long computation time. Therefore, in order to improve the accuracy and reduce the detection time of spectral overlap detection, researchers have proposed various machine learning-based wavelength detection methods, such as extreme learning machine, 7 least squares support vector regression, 8 and multi-layer perceptron (MLP) 9 . Although machine learning methods have improved the speed and accuracy of spectral overlap detection, the accuracy of machine learning is still to be improved when the peak wavelengths overlap significantly, and the detection speed of machine learning methods is relatively low.…”

Section: Introductionmentioning

confidence: 99%

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Wavelength demodulation technique for serial WDM fiber Bragg grating sensors based on light gated recurrent unit neural network and CCD interrogator

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In a serial wavelength division multiplexing (WDM) fiber Bragg grating (FBG) sensor network, it is well known that there are challenges in separating overlapping signals, which require high precision and low delays. And using an optical spectrum analyzer as a data source result in demodulation models that are impractical for use in engineering applications. Therefore, an overlapping spectral demodulation model based on transfer learning using a charge-coupled device (CCD) interrogator and light gated recurrent unit (Li-GRU) neural networks is proposed. This model can achieve a low signal demodulation error, even when applied to data collected using a CCD interrogator with low spectral resolution and a high signal-to-noise ratio. We describe the operation principle of the Li-GRU neural network and discuss the impact of transfer learning and CNN feature extraction layers on demodulation performance. The experimental results show that lowest root mean square error of our proposed model is 1.93 pm, and the single inference time of the model on the CPU is <45 ms. This serial WDM fiber grating demodulation method can be effectively applied in temperature and strain measurement demodulation.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Wavelength demodulation technique for serial WDM fiber Bragg grating sensors based on light gated recurrent unit neural network and CCD interrogator

Jiao,

Ren,

Xia

et al. 2024

Opt. Eng.

View full text Add to dashboard Cite

show abstract

Demonstration of a fast-training feed-forward machine learning algorithm for studying key optical properties of FBG and predicting precisely the output spectrum

et al. 2022

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In this article, we propose and demonstrate a generalized machine learning (ML) approach to analyse the various optical properties of the Fiber Bragg grating (FBGs), namely effective refractive index, bandwidth, re ectivity and wavelength. For this purpose, three commonly used variants of FBG, namely conventional, π phase-shifted and chirped ones are investigated and the re ected spectra of the aforementioned FBGs are predicted using ab initio arti cial neural networks (ANNs). We implemented a simple and fast-training feed-forward ANN and established the e cacy of our model by predicting the output spectrum with minute details for unknown device parameters along with non-linear and complex behaviour of the spectrum. Thus, our proposed ANN model is capable of predicting various key optical properties and reproducing the exact spectrum accurately and quickly, providing a cost-effective solution for e cient and precise modelling.

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Self-healing integration of fiber/FSO communication and sensor network for improving survivability

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et al. 2022

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FBG Sensor Signal Detection Technique Using Multilayer Perceptron Approach for Internet of Things (IoT) Application

Cited by 3 publications

References 5 publications

Wavelength demodulation technique for serial WDM fiber Bragg grating sensors based on light gated recurrent unit neural network and CCD interrogator

Wavelength demodulation technique for serial WDM fiber Bragg grating sensors based on light gated recurrent unit neural network and CCD interrogator

Demonstration of a fast-training feed-forward machine learning algorithm for studying key optical properties of FBG and predicting precisely the output spectrum

Self-healing integration of fiber/FSO communication and sensor network for improving survivability

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