A Gaussian Mixture Model-Hidden Markov Model (GMM-HMM)-based fiber optic surveillance system for pipeline integrity threat detection

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Authors
Tejedor Noguerales, JavierUniversity of Alcalá Author; Macías Guarasa, JavierUniversity of Alcalá Author; Fidalgo Martins, HugoUniversity of Alcalá Author; Martín López, SoniaUniversity of Alcalá Author; González Herráez, MiguelUniversity of Alcalá Author
Identifiers
Permanent link (URI): http://hdl.handle.net/10017/36467
DOI: 10.1364/OFS.2018.WF36
Publisher
OSA Publishing
Date
2018-09-24
Embargo end date
2019-12-01
Academic Departments
Universidad de Alcalá. Departamento de Electrónica
Research group
Ingeniería Fotónica
Funders
European Commission
Ministerio de Economía y Competitividad
Comunidad de Madrid
Universidad de Alcalá
Bibliographic citation
Tejedor, J., Macías-Guarasa, J., Martins, H. F., Martin-López, S. & González-Herráez, M. 2018, "A Gaussian Mixture Model-Hidden Markov Model (GMM-HMM)-based fiber optic surveillance system for pipeline integrity threat detection", in 26th International Conference on Optical Fiber Sensors, OSA Technical Digest (Optical Society of America, 2018), paper WF36.
Keywords
Fiber optics sensors
Pattern Recognition
Description / Notes
26th International Conference on Optical Fiber Sensors OFS-26, 24/09/2018-28/09/2018, Lausanne, Suiza.
Project
info:eu-repo/grantAgreement/EC/FP7/307441/EU/Ubiquitous optical FIbre NErves/U-FINE
info:eu-repo/grantAgreement/EC/H2020/722509/EU/Fibre Nervous Sensing Systems/FINESSE
info:eu-repo/grantAgreement/EC/H2020/WaterJPI-JC-2015-04/EU/Dikes and Debris Flows Monitoring by Novel Optical Fiber Sensors/DOMINO
info:eu-repo/grantAgreement/MINECO//TEC2015-71127-C2-2-R/ES/REDUCCION DE LOS EFECTOS DE RUIDO EN SISTEMAS DE FIBRA OPTICA NO LINEALES
info:eu-repo/grantAgreement/MINECO//TIN2013-47630-C2-1-R/ES/Supervisión de patrones de comportamiento humano mediante múltiples sensores
info:eu-repo/grantAgreement/MINECO//TIN2016-75982-C2-1-R/ES/DETECCION SEMANTICA MULTISENSORIAL DE SITUACIONES ANOMALAS EN ENTORNOS SIN RESTRICCIONES
info:eu-repo/grantAgreement/UAH//CCGP2017-EXP%2F025/ES/RE-IDENTIFICACIÓN DE PERSONAS EN REDES DE SENSORES NO SOLAPADOS, UTILIZANDO ÚNICAMENTE INFORMACIÓN DE PROFUNDIDAD/JANO
info:eu-repo/grantAgreement/CAM//S2009%2FMIT2790/ES/Sensores e INstrumentación en tecnologías FOTÓNicas/SINFOTON
Document type
info:eu-repo/semantics/conferenceObject
Version
info:eu-repo/semantics/acceptedVersion
Publisher's version
http://dx.doi.org/10.1364/OFS.2018.WF36
Rights
Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
(c) OSA, 2018
Access rights
info:eu-repo/semantics/openAccess
Abstract
A pipeline integrity threat detection system using Distributed Acoustic Sensing and Artificial Intelligence (AI) is presented. The AI uses a combination of Gaussian Mixture Models and Hidden Markov Models (GMMs-HMMs), outperforming our former GMM-based system.

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Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
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