A Novel Method for Brillouin Scattering Spectrum of Distributed Sensing Systems Based on Radial Basis Function Neural Networks to Extract Features

Yin, 刘银 Liu; Guangwei, 付广伟 Fu; Yanjun, 张燕君 Zhang; Weihong, 毕卫红 Bi

doi:10.3788/aos201232.0206002

Cited by 2 publications

(3 citation statements)

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“…In formula (7): η is the learning rate; The value of ε is 10 -8 ; β1，β2ϵ[0,1)represent the exponential decay rates estimated by the first and second-moment methods, respectively; vt is the non-central variance value of the second moment of the gradient; mt is the average value of the first moment of the gradient.…”

Section: B Extraction Of Brillouin Frequency Shift Based On Thementioning

confidence: 99%

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Research on Distributed Fiber Optical Strain Testing Technology Based on the Adam Algorithm

Duan,

Liu,

et al. 2024

IEEE Photonics J.

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Distributed Optical Fiber Sensing Technology, with advantages such as long detection distance, resistance to electromagnetic interference, and easy maintenance, is widely applied in various fields including security monitoring, electrical safety, and aerospace. However, the current Distributed Optical Fiber Sensing Technology faces challenges such as short localization distance, limited range for stress measurement, low accuracy, and time-consuming processes. This study proposes a novel approach for extracting Brillouin frequency shift signals using an adaptive gradient descent algorithm (Adam Algorithm). A Brillouin strain testing system based on heterodyne coherent detection is also constructed. Experimental results show that the distributed strain testing system using the Adam Algorithm can achieve accurate and fast measurement of maximum strain up to 9500με within a range of 10 kilometers. The average strain measurement deviation is 32.88με, and the time required for frequency shift extraction is less than 18.5ms. This method provides a theoretical and experimental basis for the application of BOTDR Distributed Optical Fiber Sensing Technology.

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Section: B Extraction Of Brillouin Frequency Shift Based On Thementioning

confidence: 99%

“…For instance, Y Zhang et al utilized the LM algorithm to adjust the weights' radial direction in order to extract features of Brillouin scattering using neural networks. This approach enhances the accuracy of Brillouin scattering signal extraction, albeit at the cost of time-consuming and complex neural network training [6], [7]. M.A.…”

Section: Introductionmentioning

confidence: 99%

Research on Distributed Fiber Optical Strain Testing Technology Based on the Adam Algorithm

Duan,

Liu,

et al. 2024

IEEE Photonics J.

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“…At last, the LM method is used to extract the characteristics of Brillouin scattering spectrum after denoising. So the optical fiber strain can be obtained based on the linear relation between Brillouin scattering spectrum frequency shift and strain [17] . Its expression is shown in formula (6).…”

Section: System Design and Experimentsmentioning

confidence: 99%

The research of optical fiber Brillouin spectrum denoising based on wavelet transform and neural network

Zhang

Yan

et al. 2013

SPIE Proceedings

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The center frequency of Brillouin scattering spectrum is easily influenced by the noise and the measurement accuracy of optical fiber strain is reduced. So a novel denoising method which can be applied in the Brillouin scattering spectrum is developed in this article. The Brillouin scattering spectrum is decomposed into multi-scale detail coefficients and approximation coefficients by using the wavelet transform. The wavelet decomposition detail coefficients are threshold quantified by utilizing the threshold algorithm. At the same time, the wavelet decomposition approximation coefficients are trained and simulated by using the BP neural network in order to remove noise hided in the approximation coefficients. So the novel method can reduce the wavelet decomposition scales. The Brillouin scattering spectrum which has a better denoising effect can be gained by using the inverse wavelet transform, and the measurement accuracy of optical fiber strain is enhanced also. The results of simulation and experiment demonstrate that the proposed method can suppress noise better; accordingly, the new method can gain more precision optical fiber strain and reduce the wavelet decomposition scales effectively than the conventional wavelet denoising method. Theory analysis and experiment show that the method is reasonable and efficient.

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A Novel Method for Brillouin Scattering Spectrum of Distributed Sensing Systems Based on Radial Basis Function Neural Networks to Extract Features

Cited by 2 publications

References 0 publications

Research on Distributed Fiber Optical Strain Testing Technology Based on the Adam Algorithm

Research on Distributed Fiber Optical Strain Testing Technology Based on the Adam Algorithm

The research of optical fiber Brillouin spectrum denoising based on wavelet transform and neural network

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