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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Main Authors: 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.
Other Authors: 57198797134
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Published: Elsevier Ltd 2024
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spelling 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
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
topic 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
spellingShingle 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
description 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
author2 57198797134
author_facet 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.
format 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 13.214268