Variational Mode Decomposition-Based Threat Classification for Fiber Optic Distributed Acoustic Sensing

dc.authorid Kara, Ali/0000-0002-9739-7619
dc.authorscopusid 57217113920
dc.authorscopusid 51763497600
dc.authorscopusid 57211496508
dc.authorscopusid 7102824862
dc.authorwosid ABUFANA, SALEH/AAA-6134-2022
dc.authorwosid Kara, Ali/R-8038-2019
dc.contributor.author Abufana, Saleh A.
dc.contributor.author Dalveren, Yaser
dc.contributor.author Aghnaiya, Alghannai
dc.contributor.author Kara, Ali
dc.contributor.other Department of Electrical & Electronics Engineering
dc.date.accessioned 2024-07-05T15:41:06Z
dc.date.available 2024-07-05T15:41:06Z
dc.date.issued 2020
dc.department Atılım University en_US
dc.department-temp [Abufana, Saleh A.; Kara, Ali] Atilim Univ, Dept Elect & Elect Engn, TR-06830 Ankara, Turkey; [Dalveren, Yaser] Norwegian Univ Sci & Technol, Fac Informat Technol & Elect Engn, Dept Elect Syst, N-2815 Gjovik, Norway; [Dalveren, Yaser] Atilim Univ, Dept Avion, TR-06830 Ankara, Turkey; [Aghnaiya, Alghannai] Coll Elect Technol, Dept Commun Engn, Bani Waled, Libya en_US
dc.description Kara, Ali/0000-0002-9739-7619 en_US
dc.description.abstract In this study, a novel method is proposed to detect and classify the threats for fiber optic distributed acoustic sensing (DAS) systems. In the study, phase-sensitive optical time-domain reflectometry (phase-OTDR) is realized for the sensing system. The proposed method is consisted of three main stages. In the first stage, Wavelet denoising method is applied for noise reduction in the measured signal, and difference in time domain approach is used to perform high-pass filtering. Autocorrelation is then used for comparing the signal with itself over time in each bin to remove uncorrelated signals. Next, the power of the correlated signals at each bin is calculated and sorted where maximum valued bins are considered as the event signal. In the second stage, Variational Mode Decomposition (VMD) technique is used to decompose the detected event signals into a series of band-limited modes from which the event signals are reconstructed. From the reconstructed event signals, higher order statistical (HOS) features including variance, skewness, and kurtosis are extracted. In the last stage, the threats are discriminated by implementing Linear Support Vector Machine (LSVM)-based classification approach to the extracted features. In order to evaluate the effects of proposed method on the classification performance, different types of activities such as digging with hammer, pickaxe, and shovel collected from various points of a buried fiber optic cable have been used under different Signal-to-Noise Ratio (SNR) levels (& x2212;4 to & x2212;18 dB). It has observed that the classification accuracy at high/moderate (& x2212;4 to & x2212;8 dB) and low (& x2212;8 to & x2212;18 dB) SNR levels are 79.5 & x0025; and 75.2 & x0025;, respectively. To the best of authors & x2019; knowledge, this research study is the first report to use VMD technique for threat classification in phase-OTDR-based DAS systems. en_US
dc.identifier.citationcount 31
dc.identifier.doi 10.1109/ACCESS.2020.2997941
dc.identifier.endpage 100158 en_US
dc.identifier.issn 2169-3536
dc.identifier.scopus 2-s2.0-85086308997
dc.identifier.scopusquality Q1
dc.identifier.startpage 100152 en_US
dc.identifier.uri https://doi.org/10.1109/ACCESS.2020.2997941
dc.identifier.uri https://hdl.handle.net/20.500.14411/3420
dc.identifier.volume 8 en_US
dc.identifier.wos WOS:000541127800099
dc.identifier.wosquality Q2
dc.institutionauthor Dalveren, Yaser
dc.institutionauthor Kara, Ali
dc.language.iso en en_US
dc.publisher Ieee-inst Electrical Electronics Engineers inc en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.scopus.citedbyCount 38
dc.subject Feature extraction en_US
dc.subject Optical fibers en_US
dc.subject Time-domain analysis en_US
dc.subject Optical fiber cables en_US
dc.subject Noise reduction en_US
dc.subject Sensors en_US
dc.subject Distributed acoustic sensing en_US
dc.subject optical time-domain reflectometry en_US
dc.subject Rayleigh backscattering light en_US
dc.subject support vector machine en_US
dc.subject threat classification en_US
dc.subject variational mode decomposition en_US
dc.title Variational Mode Decomposition-Based Threat Classification for Fiber Optic Distributed Acoustic Sensing en_US
dc.type Article en_US
dc.wos.citedbyCount 36
dspace.entity.type Publication
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