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Tytuł:
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Event Detection for Distributed Acoustic Sensing: Combining Knowledge-Based, Classical Machine Learning, and Deep Learning Approaches.
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Autorzy:
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Bublin M; FH Campus Wien, University of Applied Sciences, 1100 Vienna, Austria.
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Źródło:
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Sensors (Basel, Switzerland) [Sensors (Basel)] 2021 Nov 12; Vol. 21 (22). Date of Electronic Publication: 2021 Nov 12.
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Typ publikacji:
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Journal Article
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Język:
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English
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Imprint Name(s):
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Original Publication: Basel, Switzerland : MDPI, c2000-
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MeSH Terms:
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Deep Learning*
Acoustics ; Algorithms ; Machine Learning
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References:
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Opt Express. 2021 Feb 1;29(3):3269-3283. (PMID: 33770929)
Sensors (Basel). 2019 Aug 04;19(15):. (PMID: 31382706)
Opt Express. 2012 Jun 4;20(12):13138-45. (PMID: 22714342)
Int J Mol Sci. 2021 Aug 26;22(17):. (PMID: 34502160)
Neural Comput. 1999 Feb 15;11(2):305-45. (PMID: 9950734)
Opt Express. 2020 Sep 14;28(19):27277-27292. (PMID: 32988024)
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Contributed Indexing:
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Keywords: Distributed Acoustic Sensing; deep neural networks; machine learning; signal processing
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Entry Date(s):
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Date Created: 20211127 Date Completed: 20211130 Latest Revision: 20211130
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Update Code:
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20240104
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PubMed Central ID:
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PMC8618866
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DOI:
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10.3390/s21227527
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PMID:
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34833613
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Distributed Acoustic Sensing (DAS) is a promising new technology for pipeline monitoring and protection. However, a big challenge is distinguishing between relevant events, like intrusion by an excavator near the pipeline, and interference, like land machines. This paper investigates whether it is possible to achieve adequate detection accuracy with classic machine learning algorithms using simulations and real system implementation. Then, we compare classical machine learning with a deep learning approach and analyze the advantages and disadvantages of both approaches. Although acceptable performance can be achieved with both approaches, preliminary results show that deep learning is the more promising approach, eliminating the need for laborious feature extraction and offering a six times lower event detection delay and twelve times lower execution time. However, we achieved the best results by combining deep learning with the knowledge-based and classical machine learning approaches. At the end of this manuscript, we propose general guidelines for efficient system design combining knowledge-based, classical machine learning, and deep learning approaches.
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