Optical fiber intrusion signal recognition method based on TSVD-SCN

Sheng, Zhiyong; Zeng, Zhiqiang; Qu, Hongquan; Zhang, Yuwei

doi:10.1016/j.yofte.2019.01.023

Cited by 11 publications

(3 citation statements)

References 21 publications

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“…where X ls is the required solution, that is, β in the SCN network, A is the n * t-dimensional coefficient matrix, that is, the hidden layer output matrix of the SCN network, H, and y is the label of data [16].…”

Section: Introduction To the Tsvd-scn Algorithmmentioning

confidence: 99%

Electronic Information Signal Recognition Based on a Stochastic Neural Network Algorithm

Wang

2022

Journal of Control Science and Engineering

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In order to improve the recognition accuracy of SCN for optical fiber data, a method of optical fiber intrusion signal recognition based on SCN (TSVD-SCN) based on truncated singular value decomposition (TSVD) is proposed in this paper. TSVD-SCN performs SVD decomposition on the hidden layer output of the network and sets a threshold to remove the smaller singular values, so as to reduce the number of conditions of the hidden layer output matrix and improve the network recognition rate. This paper uses the method of duty cycle, average amplitude difference function, and FFT to calculate the energy duty cycle for feature extraction and uses TSVD-SCN algorithm to classify and recognize different intrusion vibration feature vectors. The experimental results show that the root mean square errors of TSVD-SCN and SCN networks are significantly less than RVFL. After the hidden layer node L = 20 , the training error decline speed of RVFL tends to be gentle. When LRVFL = L max , the learning effect is the best, and RMSERVFL = 0.3 . With the continuous increase of L, the training error of SCN network and TSVD-SCN network will be reduced to very small, and the training error of TSVD-SCN network is also less than SCN. Conclusion. The accuracy of the algorithm model proposed in this paper is higher than that of the SCN model. It can accurately identify the types of optical fiber intrusion signals, which is of great significance to improve the classification accuracy of the SCN network in practical applications.

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Section: Introduction To the Tsvd-scn Algorithmmentioning

confidence: 99%

Electronic Information Signal Recognition Based on a Stochastic Neural Network Algorithm

Wang

2022

Journal of Control Science and Engineering

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“…Machine learning methods. (Kabir, Sadiq, and Tesfamariam 2016;Guo et al 2018) used Bayesian network and its variant for PSEW, and (Sheng et al 2019) updated the stochastic configuration network (SCN) proposed by (Wang and Li 2017) based on truncation singular value decomposition, calling it TSVD-SCN. (Wu et al 2019) applied hidden Markov model (HMM) to extract the event areas and judge event categories.…”

Section: Related Work and Solutions For Psewmentioning

confidence: 99%

Early Safety Warnings for Long-Distance Pipelines: A Distributed Optical Fiber Sensor Machine Learning Approach

Yang

Zhang

et al. 2021

AAAI

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Automated pipeline safety early warning (PSEW) systems are designed to automatically identify and locate third-party damage events on oil and gas pipelines. They are intended to replace traditional, inefficient manual inspection methods. However, current PSEW methods cannot achieve universality for various complex environments because they are sensitive to the spatiotemporal stability of the signal obtained by its distributed sensors at various locations and times. Our research aimed to improve the accuracy of long-distance oil–gas PSEW systems through machine learning. In this paper, we propose a novel real-time action recognition method for long-distance PSEW systems based on a coherent Rayleigh scattering distributed optical fiber sensor. More specifically, we put forward two complementary feature calculation methods to describe signals and build a new action recognition deep learning network based on those features. Encouraging empirical results on the data collected at a real location confirm that the features can effectively describe signals in an environment with strong noise and weak signals, and the entire approach can identify and locate third-party damage events quickly under various hardware conditions with accuracies of 99.26% (500 Hz) and 97.20% (100 Hz). More generically, our method can be applied to other fields as well.

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“…To address prediction interval estimation problems, the corresponding deep, ensemble, robust, and sparse versions of SCNs were developed [17][18][19]. In addition to the above theoretical studies, SCNs have successful applications in many fields, such as optical fiber pre-warning system [20], industrial process [21], concrete defect recognition [22], and so on.…”

Section: Introductionmentioning

confidence: 99%

A Lightweight Learning Method for Stochastic Configuration Networks Using Non-Inverse Solution

et al. 2022

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Stochastic configuration networks (SCNs) face time-consuming issues when dealing with complex modeling tasks that usually require a mass of hidden nodes to build an enormous network. An important reason behind this issue is that SCNs always employ the Moore–Penrose generalized inverse method with high complexity to update the output weights in each increment. To tackle this problem, this paper proposes a lightweight SCNs, called L-SCNs. First, to avoid using the Moore–Penrose generalized inverse method, a positive definite equation is proposed to replace the over-determined equation, and the consistency of their solution is proved. Then, to reduce the complexity of calculating the output weight, a low complexity method based on Cholesky decomposition is proposed. The experimental results based on both the benchmark function approximation and real-world problems including regression and classification applications show that L-SCNs are sufficiently lightweight.

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Optical fiber intrusion signal recognition method based on TSVD-SCN

Cited by 11 publications

References 21 publications

Electronic Information Signal Recognition Based on a Stochastic Neural Network Algorithm

Electronic Information Signal Recognition Based on a Stochastic Neural Network Algorithm

Early Safety Warnings for Long-Distance Pipelines: A Distributed Optical Fiber Sensor Machine Learning Approach

A Lightweight Learning Method for Stochastic Configuration Networks Using Non-Inverse Solution

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