Human activities classification based on ?-OTDR system by utilizing gammatone filter cepstrum coefficient envelope using support vector machine
Intrusion into the critical energy assets area is a serious problem that may cause essential operations to be disrupted. Besides using closed-circuit television to detect early intrusion, perimeter fencing using fiber optic distributed acoustic sensing is becoming popular. This paper proposes a phas...
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my.uniten.dspace-340812024-10-14T11:17:53Z Human activities classification based on ?-OTDR system by utilizing gammatone filter cepstrum coefficient envelope using support vector machine Saleh N.L. Faisal B. Yusri M.S. Sulaiman A.H. Ismail M.F. Nik Zulkefli N.A.H.A. Muhamud-Kayat S. Ismail A. Abdullah F. Jamaludin M.Z. Aripin N.M. 57198797134 57209973264 57480859600 36810678100 57211721986 58191841700 55027311200 36023817800 56613644500 58071849900 35092180800 Distributed acoustic sensor Energy infrastructure Gammatone filter cepstral coefficient Phase optical time-domain reflectometry Support vector machine Classification (of information) Feature extraction Permittivity measurement Reflection Reflectometers Signal to noise ratio Time domain analysis Acoustic Sensors Cepstral coefficients Distributed acoustic sensor Energy infrastructures Gammatone filter cepstral coefficient Gammatone filters Human activities Optical time domain reflectometry Phase optical time-domain reflectometry Support vectors machine Support vector machines Intrusion into the critical energy assets area is a serious problem that may cause essential operations to be disrupted. Besides using closed-circuit television to detect early intrusion, perimeter fencing using fiber optic distributed acoustic sensing is becoming popular. This paper proposes a phase-sensitive optical time-domain reflectometry system-based classification of human activities using a coexisting support vector machine which the Gammatone filter cepstrum coefficient envelope as input features. The detection and classification campaign consists of four phases: detection, feature extraction, classification, and evaluation. A combination of wavelet and normalized differential methods is used for detection to improve the signal-to-noise ratio. Simple dataset management methods that use envelope-wrapped, local maxima, averaging, truncation, rearrangement, and random permutation are beneficial for reducing the dimensionality of problems with lower computational power. The classification performance was considered good, which is higher than 95 %. Despite using the legacy classifier algorithm, an improvement in dataset management presented in this paper proves that it is still realistic to implement and does not require demanding computational power to achieve a good classification result. Also, the way datasets are managed in this paper is made to be flexible enough to work with other legacy classifiers. � 2023 Elsevier Ltd Final 2024-10-14T03:17:53Z 2024-10-14T03:17:53Z 2023 Article 10.1016/j.optlastec.2023.109417 2-s2.0-85153316700 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153316700&doi=10.1016%2fj.optlastec.2023.109417&partnerID=40&md5=95795227857eb1f3f2e3745feead305f https://irepository.uniten.edu.my/handle/123456789/34081 164 109417 Elsevier Ltd Scopus |
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Distributed acoustic sensor Energy infrastructure Gammatone filter cepstral coefficient Phase optical time-domain reflectometry Support vector machine Classification (of information) Feature extraction Permittivity measurement Reflection Reflectometers Signal to noise ratio Time domain analysis Acoustic Sensors Cepstral coefficients Distributed acoustic sensor Energy infrastructures Gammatone filter cepstral coefficient Gammatone filters Human activities Optical time domain reflectometry Phase optical time-domain reflectometry Support vectors machine Support vector machines |
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Distributed acoustic sensor Energy infrastructure Gammatone filter cepstral coefficient Phase optical time-domain reflectometry Support vector machine Classification (of information) Feature extraction Permittivity measurement Reflection Reflectometers Signal to noise ratio Time domain analysis Acoustic Sensors Cepstral coefficients Distributed acoustic sensor Energy infrastructures Gammatone filter cepstral coefficient Gammatone filters Human activities Optical time domain reflectometry Phase optical time-domain reflectometry Support vectors machine Support vector machines Saleh N.L. Faisal B. Yusri M.S. Sulaiman A.H. Ismail M.F. Nik Zulkefli N.A.H.A. Muhamud-Kayat S. Ismail A. Abdullah F. Jamaludin M.Z. Aripin N.M. Human activities classification based on ?-OTDR system by utilizing gammatone filter cepstrum coefficient envelope using support vector machine |
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Intrusion into the critical energy assets area is a serious problem that may cause essential operations to be disrupted. Besides using closed-circuit television to detect early intrusion, perimeter fencing using fiber optic distributed acoustic sensing is becoming popular. This paper proposes a phase-sensitive optical time-domain reflectometry system-based classification of human activities using a coexisting support vector machine which the Gammatone filter cepstrum coefficient envelope as input features. The detection and classification campaign consists of four phases: detection, feature extraction, classification, and evaluation. A combination of wavelet and normalized differential methods is used for detection to improve the signal-to-noise ratio. Simple dataset management methods that use envelope-wrapped, local maxima, averaging, truncation, rearrangement, and random permutation are beneficial for reducing the dimensionality of problems with lower computational power. The classification performance was considered good, which is higher than 95 %. Despite using the legacy classifier algorithm, an improvement in dataset management presented in this paper proves that it is still realistic to implement and does not require demanding computational power to achieve a good classification result. Also, the way datasets are managed in this paper is made to be flexible enough to work with other legacy classifiers. � 2023 Elsevier Ltd |
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57198797134 |
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57198797134 Saleh N.L. Faisal B. Yusri M.S. Sulaiman A.H. Ismail M.F. Nik Zulkefli N.A.H.A. Muhamud-Kayat S. Ismail A. Abdullah F. Jamaludin M.Z. Aripin N.M. |
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Article |
author |
Saleh N.L. Faisal B. Yusri M.S. Sulaiman A.H. Ismail M.F. Nik Zulkefli N.A.H.A. Muhamud-Kayat S. Ismail A. Abdullah F. Jamaludin M.Z. Aripin N.M. |
author_sort |
Saleh N.L. |
title |
Human activities classification based on ?-OTDR system by utilizing gammatone filter cepstrum coefficient envelope using support vector machine |
title_short |
Human activities classification based on ?-OTDR system by utilizing gammatone filter cepstrum coefficient envelope using support vector machine |
title_full |
Human activities classification based on ?-OTDR system by utilizing gammatone filter cepstrum coefficient envelope using support vector machine |
title_fullStr |
Human activities classification based on ?-OTDR system by utilizing gammatone filter cepstrum coefficient envelope using support vector machine |
title_full_unstemmed |
Human activities classification based on ?-OTDR system by utilizing gammatone filter cepstrum coefficient envelope using support vector machine |
title_sort |
human activities classification based on ?-otdr system by utilizing gammatone filter cepstrum coefficient envelope using support vector machine |
publisher |
Elsevier Ltd |
publishDate |
2024 |
_version_ |
1814061165022543872 |
score |
13.209306 |