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Strain FBG-Based Sensor for Detecting Fence Intruders Using Machine Learning and Adaptive Thresholding
This paper demonstrates an intruder detection system using a strain-based optical fiber Bragg grating (FBG), machine learning (ML), and adaptive thresholding to classify the intruder as no intruder, intruder, or wind at low levels of signal-to-noise ratio. We demonstrate the intruder detection syste...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10255305/ https://www.ncbi.nlm.nih.gov/pubmed/37299742 http://dx.doi.org/10.3390/s23115015 |
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author | Elleathy, Ahmad Alhumaidan, Faris Alqahtani, Mohammed Almaiman, Ahmed S. Ragheb, Amr M. Ibrahim, Ahmed B. Ali, Jameel Esmail, Maged A. Alshebeili, Saleh A. |
author_facet | Elleathy, Ahmad Alhumaidan, Faris Alqahtani, Mohammed Almaiman, Ahmed S. Ragheb, Amr M. Ibrahim, Ahmed B. Ali, Jameel Esmail, Maged A. Alshebeili, Saleh A. |
author_sort | Elleathy, Ahmad |
collection | PubMed |
description | This paper demonstrates an intruder detection system using a strain-based optical fiber Bragg grating (FBG), machine learning (ML), and adaptive thresholding to classify the intruder as no intruder, intruder, or wind at low levels of signal-to-noise ratio. We demonstrate the intruder detection system using a portion of a real fence manufactured and installed around one of the engineering college’s gardens at King Saud University. The experimental results show that adaptive thresholding can help improve the performance of machine learning classifiers, such as linear discriminant analysis (LDA) or logistic regression algorithms in identifying an intruder’s existence at low optical signal-to-noise ratio (OSNR) scenarios. The proposed method can achieve an average accuracy of 99.17% when the OSNR level is <0.5 dB. |
format | Online Article Text |
id | pubmed-10255305 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-102553052023-06-10 Strain FBG-Based Sensor for Detecting Fence Intruders Using Machine Learning and Adaptive Thresholding Elleathy, Ahmad Alhumaidan, Faris Alqahtani, Mohammed Almaiman, Ahmed S. Ragheb, Amr M. Ibrahim, Ahmed B. Ali, Jameel Esmail, Maged A. Alshebeili, Saleh A. Sensors (Basel) Article This paper demonstrates an intruder detection system using a strain-based optical fiber Bragg grating (FBG), machine learning (ML), and adaptive thresholding to classify the intruder as no intruder, intruder, or wind at low levels of signal-to-noise ratio. We demonstrate the intruder detection system using a portion of a real fence manufactured and installed around one of the engineering college’s gardens at King Saud University. The experimental results show that adaptive thresholding can help improve the performance of machine learning classifiers, such as linear discriminant analysis (LDA) or logistic regression algorithms in identifying an intruder’s existence at low optical signal-to-noise ratio (OSNR) scenarios. The proposed method can achieve an average accuracy of 99.17% when the OSNR level is <0.5 dB. MDPI 2023-05-24 /pmc/articles/PMC10255305/ /pubmed/37299742 http://dx.doi.org/10.3390/s23115015 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Elleathy, Ahmad Alhumaidan, Faris Alqahtani, Mohammed Almaiman, Ahmed S. Ragheb, Amr M. Ibrahim, Ahmed B. Ali, Jameel Esmail, Maged A. Alshebeili, Saleh A. Strain FBG-Based Sensor for Detecting Fence Intruders Using Machine Learning and Adaptive Thresholding |
title | Strain FBG-Based Sensor for Detecting Fence Intruders Using Machine Learning and Adaptive Thresholding |
title_full | Strain FBG-Based Sensor for Detecting Fence Intruders Using Machine Learning and Adaptive Thresholding |
title_fullStr | Strain FBG-Based Sensor for Detecting Fence Intruders Using Machine Learning and Adaptive Thresholding |
title_full_unstemmed | Strain FBG-Based Sensor for Detecting Fence Intruders Using Machine Learning and Adaptive Thresholding |
title_short | Strain FBG-Based Sensor for Detecting Fence Intruders Using Machine Learning and Adaptive Thresholding |
title_sort | strain fbg-based sensor for detecting fence intruders using machine learning and adaptive thresholding |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10255305/ https://www.ncbi.nlm.nih.gov/pubmed/37299742 http://dx.doi.org/10.3390/s23115015 |
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