Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/7362
Title: Real Time Classification Analysis in Distributed Acoustic Sensing Systems
Authors: Maral, Hakan
Akgün, Toygar
Aktaş, Metin
Keywords: Distributed acoustic sensing
phase-OTDR
deep learning
convolutional neural networks
CNN
threat detection
threat classification
real-time processing
Issue Date: 2018
Publisher: IEEE
Source: 26th IEEE Signal Processing and Communications Applications Conference (SIU) -- MAY 02-05, 2018 -- Izmir, TURKEY
Series/Report no.: Signal Processing and Communications Applications Conference
Abstract: In this paper, we present a real time treat classification approach to be used in a distributed acoustic sensing system that is developed for monitoring linear assets with a maximum length of 50 kms. The Convolutional Neuaral Network (CNN) based deep learning approach is used for treat classification. The classification accuracies and execution times for neural networks with different architecture and complexity are measured. The proposed approach for classifying all the detected treats without decreasing the detection accuracy is introduced. The maximum allowable execution time for the network structure that is appropriate for the proposed approach is analyzed for the worst case scenario. Hence, the most appropriate network architecture selection can be performed based on classification accuracy and also applicability in real-time criterion.
URI: https://hdl.handle.net/20.500.11851/7362
ISBN: 978-1-5386-1501-0
ISSN: 2165-0608
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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