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Ensemble Learning with Stochastic Configuration Network for Noisy Optical Fiber Vibration Signal Recognition
Optical fiber pre-warning systems (OFPS) based on Φ-OTDR are applied to many different scenarios such as oil and gas pipeline protection. The recognition of fiber vibration signals is one of the most important parts of this system. According to the characteristics of small sample set, we choose stoc...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6695753/ https://www.ncbi.nlm.nih.gov/pubmed/31357489 http://dx.doi.org/10.3390/s19153293 |
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author | Qu, Hongquan Feng, Tingliang Zhang, Yuan Wang, Yanping |
author_facet | Qu, Hongquan Feng, Tingliang Zhang, Yuan Wang, Yanping |
author_sort | Qu, Hongquan |
collection | PubMed |
description | Optical fiber pre-warning systems (OFPS) based on Φ-OTDR are applied to many different scenarios such as oil and gas pipeline protection. The recognition of fiber vibration signals is one of the most important parts of this system. According to the characteristics of small sample set, we choose stochastic configuration network (SCN) for recognition. However, due to the interference of environmental and mechanical noise, the recognition effect of vibration signals will be affected. In order to study the effect of noise on signal recognition performance, we recognize noisy optical fiber vibration signals, which superimposed analog white Gaussian noise, white uniform noise, Rayleigh distributed noise, and exponentially distributed noise. Meanwhile, bootstrap sampling (bagging) and AdaBoost ensemble learning methods are combined with original SCN, and Bootstrap-SCN, AdaBoost-SCN, and AdaBoost-Bootstrap-SCN are proposed and compared for noisy signals recognition. Results show that: (1) the recognition rates of two classifiers combined with AdaBoost are higher than the other two methods over the entire noise range; (2) the recognition for noisy signals of AdaBoost-Bootstrap-SCN is better than other methods in recognition of noisy signals. |
format | Online Article Text |
id | pubmed-6695753 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-66957532019-09-05 Ensemble Learning with Stochastic Configuration Network for Noisy Optical Fiber Vibration Signal Recognition Qu, Hongquan Feng, Tingliang Zhang, Yuan Wang, Yanping Sensors (Basel) Article Optical fiber pre-warning systems (OFPS) based on Φ-OTDR are applied to many different scenarios such as oil and gas pipeline protection. The recognition of fiber vibration signals is one of the most important parts of this system. According to the characteristics of small sample set, we choose stochastic configuration network (SCN) for recognition. However, due to the interference of environmental and mechanical noise, the recognition effect of vibration signals will be affected. In order to study the effect of noise on signal recognition performance, we recognize noisy optical fiber vibration signals, which superimposed analog white Gaussian noise, white uniform noise, Rayleigh distributed noise, and exponentially distributed noise. Meanwhile, bootstrap sampling (bagging) and AdaBoost ensemble learning methods are combined with original SCN, and Bootstrap-SCN, AdaBoost-SCN, and AdaBoost-Bootstrap-SCN are proposed and compared for noisy signals recognition. Results show that: (1) the recognition rates of two classifiers combined with AdaBoost are higher than the other two methods over the entire noise range; (2) the recognition for noisy signals of AdaBoost-Bootstrap-SCN is better than other methods in recognition of noisy signals. MDPI 2019-07-26 /pmc/articles/PMC6695753/ /pubmed/31357489 http://dx.doi.org/10.3390/s19153293 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Qu, Hongquan Feng, Tingliang Zhang, Yuan Wang, Yanping Ensemble Learning with Stochastic Configuration Network for Noisy Optical Fiber Vibration Signal Recognition |
title | Ensemble Learning with Stochastic Configuration Network for Noisy Optical Fiber Vibration Signal Recognition |
title_full | Ensemble Learning with Stochastic Configuration Network for Noisy Optical Fiber Vibration Signal Recognition |
title_fullStr | Ensemble Learning with Stochastic Configuration Network for Noisy Optical Fiber Vibration Signal Recognition |
title_full_unstemmed | Ensemble Learning with Stochastic Configuration Network for Noisy Optical Fiber Vibration Signal Recognition |
title_short | Ensemble Learning with Stochastic Configuration Network for Noisy Optical Fiber Vibration Signal Recognition |
title_sort | ensemble learning with stochastic configuration network for noisy optical fiber vibration signal recognition |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6695753/ https://www.ncbi.nlm.nih.gov/pubmed/31357489 http://dx.doi.org/10.3390/s19153293 |
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