Deep learning classification and regression models for temperature values on a simulated fibre specklegram sensor

Los sensores de specklegram de fibra óptica utilizan el patrón de interferencia modal (o specklegram) para determinar la magnitud de una perturbación. Los métodos de interrogación más utilizados para estos sensores se han centrado en mediciones puntuales de la intensidad o en correlaciones entre spe...

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Autores:: Universidad Cooperativa de Colombia
Velez Hoyos, Francisco Javier
Aristizabal Tique, Víctor Hugo
Herrera-Ramirez, Jorge
Arango, Juan David
Gómez, Jorge Alberto
Quijano, Jairo Camilo

Tipo de recurso:: Article of investigation

Fecha de publicación:: 2021

Institución:: Universidad Cooperativa de Colombia

Repositorio:: Repositorio UCC

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repository_id_str
dc.title.spa.fl_str_mv	Deep learning classification and regression models for temperature values on a simulated fibre specklegram sensor
title	Deep learning classification and regression models for temperature values on a simulated fibre specklegram sensor
spellingShingle	Deep learning classification and regression models for temperature values on a simulated fibre specklegram sensor sensores de fibra óptica, métodos electromagnéticos computacionales, aproximación numérica y análisis, detección y sensores ópticos, interferometría de Speckle. fiber optics sensors, computational electromagnetic methods, numerical approximation and analysis, optical sensing and sensors, speckle interferometry.
title_short	Deep learning classification and regression models for temperature values on a simulated fibre specklegram sensor
title_full	Deep learning classification and regression models for temperature values on a simulated fibre specklegram sensor
title_fullStr	Deep learning classification and regression models for temperature values on a simulated fibre specklegram sensor
title_full_unstemmed	Deep learning classification and regression models for temperature values on a simulated fibre specklegram sensor
title_sort	Deep learning classification and regression models for temperature values on a simulated fibre specklegram sensor
dc.creator.fl_str_mv	Universidad Cooperativa de Colombia Velez Hoyos, Francisco Javier Aristizabal Tique, Víctor Hugo Herrera-Ramirez, Jorge Arango, Juan David Gómez, Jorge Alberto Quijano, Jairo Camilo
dc.contributor.author.none.fl_str_mv	Universidad Cooperativa de Colombia Velez Hoyos, Francisco Javier Aristizabal Tique, Víctor Hugo Herrera-Ramirez, Jorge Arango, Juan David Gómez, Jorge Alberto Quijano, Jairo Camilo
dc.subject.spa.fl_str_mv	sensores de fibra óptica, métodos electromagnéticos computacionales, aproximación numérica y análisis, detección y sensores ópticos, interferometría de Speckle.
topic	sensores de fibra óptica, métodos electromagnéticos computacionales, aproximación numérica y análisis, detección y sensores ópticos, interferometría de Speckle. fiber optics sensors, computational electromagnetic methods, numerical approximation and analysis, optical sensing and sensors, speckle interferometry.
dc.subject.other.spa.fl_str_mv	fiber optics sensors, computational electromagnetic methods, numerical approximation and analysis, optical sensing and sensors, speckle interferometry.
description	Los sensores de specklegram de fibra óptica utilizan el patrón de interferencia modal (o specklegram) para determinar la magnitud de una perturbación. Los métodos de interrogación más utilizados para estos sensores se han centrado en mediciones puntuales de la intensidad o en correlaciones entre specklegrams, con limitaciones en cuanto a sensibilidad y rango de medición útil. Para investigar métodos alternativos de interrogación de specklegrams que mejoren el rendimiento de los sensores de specklegrams de fibra, implementamos y comparamos dos modelos de aprendizaje profundo: un modelo de clasificación y un modelo de regresión. Para probar y entrenar los modelos, utilizamos modelos físico-ópticos y simulaciones por el método de elementos finitos para crear una base de datos de imágenes de specklegram, que cubren el rango de temperatura entre 0 °C y 100 °C. Con las pruebas de predicción, demostramos que ambos modelos pueden cubrir el de temperatura propuesto y alcanzar una precisión del 99,5%, para el modelo de clasificación y un error absoluto medio de 2,3 °C en el modelo de regresión. Creemos que estos resultados que las estrategias implementadas pueden mejorar las capacidades metrológicas de este tipo de sensor.
publishDate	2021
dc.date.issued.none.fl_str_mv	2021-12-09
dc.date.accessioned.none.fl_str_mv	2022-09-13T13:55:41Z
dc.date.available.none.fl_str_mv	2022-09-13T13:55:41Z
dc.type.none.fl_str_mv	Artículos Científicos
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dc.identifier.issn.spa.fl_str_mv	17426588, 17426596
dc.identifier.uri.spa.fl_str_mv	10.1088/1742-6596/2139/1/012001
dc.identifier.uri.none.fl_str_mv	https://hdl.handle.net/20.500.12494/46365
dc.identifier.bibliographicCitation.spa.fl_str_mv	Arango et al. Deep learning classification and regression models for temperature values on a simulated fibre specklegram sensor Reino Unido, Journal of Physics: Conference Series ISSN: 1742-6596, 2021 vol:2139 fasc: 1 págs: 12001 - 12005, DOI:10.1088/1742-6596/2139/1/012001 Autores: VICTOR HUGO ARISTIZABAL TIQUE, FRANCISCO JAVIER VELEZ HOYOS, JORGE ALBERTO GOMEZ LOPEZ, JORGE ALEXIS HERRERA RAMIREZ, JAIRO CAMILO QUIJANO PEREZ, JUAN DAVID ARANGO MORENO, JUAN FELIPE CARRASQUILLA ALVAREZ
identifier_str_mv	17426588, 17426596 10.1088/1742-6596/2139/1/012001 Arango et al. Deep learning classification and regression models for temperature values on a simulated fibre specklegram sensor Reino Unido, Journal of Physics: Conference Series ISSN: 1742-6596, 2021 vol:2139 fasc: 1 págs: 12001 - 12005, DOI:10.1088/1742-6596/2139/1/012001 Autores: VICTOR HUGO ARISTIZABAL TIQUE, FRANCISCO JAVIER VELEZ HOYOS, JORGE ALBERTO GOMEZ LOPEZ, JORGE ALEXIS HERRERA RAMIREZ, JAIRO CAMILO QUIJANO PEREZ, JUAN DAVID ARANGO MORENO, JUAN FELIPE CARRASQUILLA ALVAREZ
url	https://hdl.handle.net/20.500.12494/46365
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dc.relation.conferenceplace.spa.fl_str_mv	San José de Cúcuta, Colombia
dc.relation.ispartofconference.spa.fl_str_mv	8th International Week of Science, Technology & Innovation (8th IWSTI)
dc.relation.ispartofjournal.spa.fl_str_mv	Journal of Physics: Conference Series
dc.relation.references.spa.fl_str_mv	[1] Fujiwara E, dos Santos M F M, Suzuki C K 2017 Optical fiber specklegram sensor analysis by speckle pattern division Applied Optics 56(6) 1585 [2] Castaño L F, et al. 2018 Temperature measurement by means of fiber specklegram sensors (FSS) Optica Pura y Aplicada 51(3) 50306 [3] Hoyos A, Gómez N D, Gómez J A 2013 Fiber specklegram sensors (FSS) for measuring high frequency mechanical perturbations 8th Ibero American Optics Meeting/11th Latin American Meeting on Optics, Lasers, and Applications (Porto: Society of Photo-Optical Instrumentation Engineers) [4] Liu Y, Li G, Qin Q, Tan Z, Wang M, Yan F 2020 Bending recognition based on the analysis of fiber specklegrams using deep learning Optics & Laser Technology 131 106424 [5] Krohn D A, MacDougall T W, Mendez A 2015 Fiber Optic Sensors: Fundamentals and Applications (Bellingham: SPIE press) [6] Efendioglu H 2017 A Review of fiber-optic modal modulated sensors: specklegram and modal power distribution sensing IEEE Sensors Journal 17(7) 2055 [7] Fujiwara E, Ri Y, Wu Y T, Fujimoto H, Suzuki C K 2018 Evaluation of image matching techniques for optical fiber specklegram sensor analysis Applied Optics 57(33) 9845 [8] Gubarev F, Li L, Klenovskii M, Glotov A 2016 Speckle pattern processing by digital image correlation MATEC Web of Conferences 48 04003 [9] Crammond G, Boyd S W, Dulieu-Barton J M 2013 Speckle pattern quality assessment for digital image correlation Optics and Lasers in Engineering 51(12) 1368 [10] Wei M, Tang G, Liu J, Zhu L, Liu J, Huang C, Zhang J, Shen L, Yu S 2021 Neural network based perturbation-location fiber specklegram sensing system towards applications with limited number of training samples Journal of Lightwave Technology 39(19) 6315 [11] Razmyar S, Mostafavi M T 2020 Deep learning for estimating deflection direction of a multimode fiber from specklegram Journal of Lightwave Technology 39(6) 1850 [12] Arístizabal V H, et al. 2016 Numerical modeling of fiber specklegram sensors by using finite element method (FEM) Optics Express 24 27225 [13] Torres P, Aristizábal V H, Andrés M V. 2011 Modeling of photonic crystal fibers from the scalar wave equation with a purely transverse linearly polarized vector potential Journal of the Optical Society of America B 28(4) 787 [14] Arango J D, et al. 2021 Numerical study using finite element method for the thermal response of fiber specklegram sensors with changes in the length of the sensing zone Computer Optics 45(4) 534
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dc.coverage.temporal.spa.fl_str_mv	2139
dc.publisher.spa.fl_str_mv	IOP Publishing Ltd. Universidad Cooperativa de Colombia sede Medellín, Facultad de Ingeniería
dc.publisher.program.spa.fl_str_mv	Ingeniería Civil
dc.publisher.place.spa.fl_str_mv	Medellín
institution	Universidad Cooperativa de Colombia
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spelling	Universidad Cooperativa de ColombiaVelez Hoyos, Francisco JavierAristizabal Tique, Víctor HugoHerrera-Ramirez, JorgeArango, Juan DavidGómez, Jorge AlbertoQuijano, Jairo Camilo21392022-09-13T13:55:41Z2022-09-13T13:55:41Z2021-12-0917426588, 1742659610.1088/1742-6596/2139/1/012001https://hdl.handle.net/20.500.12494/46365Arango et al. Deep learning classification and regression models for temperature values on a simulated fibre specklegram sensor Reino Unido, Journal of Physics: Conference Series ISSN: 1742-6596, 2021 vol:2139 fasc: 1 págs: 12001 - 12005, DOI:10.1088/1742-6596/2139/1/012001 Autores: VICTOR HUGO ARISTIZABAL TIQUE, FRANCISCO JAVIER VELEZ HOYOS, JORGE ALBERTO GOMEZ LOPEZ, JORGE ALEXIS HERRERA RAMIREZ, JAIRO CAMILO QUIJANO PEREZ, JUAN DAVID ARANGO MORENO, JUAN FELIPE CARRASQUILLA ALVAREZLos sensores de specklegram de fibra óptica utilizan el patrón de interferencia modal (o specklegram) para determinar la magnitud de una perturbación. Los métodos de interrogación más utilizados para estos sensores se han centrado en mediciones puntuales de la intensidad o en correlaciones entre specklegrams, con limitaciones en cuanto a sensibilidad y rango de medición útil. Para investigar métodos alternativos de interrogación de specklegrams que mejoren el rendimiento de los sensores de specklegrams de fibra, implementamos y comparamos dos modelos de aprendizaje profundo: un modelo de clasificación y un modelo de regresión. Para probar y entrenar los modelos, utilizamos modelos físico-ópticos y simulaciones por el método de elementos finitos para crear una base de datos de imágenes de specklegram, que cubren el rango de temperatura entre 0 °C y 100 °C. Con las pruebas de predicción, demostramos que ambos modelos pueden cubrir el de temperatura propuesto y alcanzar una precisión del 99,5%, para el modelo de clasificación y un error absoluto medio de 2,3 °C en el modelo de regresión. Creemos que estos resultados que las estrategias implementadas pueden mejorar las capacidades metrológicas de este tipo de sensor.Fiber optic specklegram sensors use the modal interference pattern (or specklegram) to determine the magnitude of a disturbance. The most used interrogation methods for these sensors have focused on point measurements of intensity or correlations between specklegrams, with limitations in sensitivity and useful measurement range. To investigate alternative methods of specklegram interrogation that improve the performance of the fiber specklegram sensors, we implemented and compared two deep learning models: a classification model and a regression model. To test and train the models, we use physical-optical models and simulations by the finite element method to create a database of specklegram images, covering the temperature range between 0 °C and 100 °C. With the prediction tests, we showed that both models can cover the entire proposed temperature range and achieve an accuracy of 99.5%, for the classification model, and a mean absolute error of 2.3 °C, in the regression model. We believe that these results show that the strategies implemented can improve the metrological capabilities of this type of sensor.1. Introduction, 2. Methodology and materials, 3. Results and discussions, 4. Conclusionshttps://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000448222https://orcid.org/0000-0002-4267-042Xhttps://scienti.minciencias.gov.co/gruplac/jsp/visualiza/visualizagr.jsp?nro=00000000005961francisco.velezh@campusucc.edu.covictor.aristizabalt@campusucc.edu.cohttps://scholar.google.com/citations?user=CLkAM5AAAAAJ&hl=es12001 - 12005IOP Publishing Ltd.Universidad Cooperativa de Colombia sede Medellín, Facultad de IngenieríaIngeniería CivilMedellínhttps://iopscience.iop.org/article/10.1088/1742-6596/2139/1/012001/metaSan José de Cúcuta, Colombia8th International Week of Science, Technology & Innovation (8th IWSTI)Journal of Physics: Conference Series[1] Fujiwara E, dos Santos M F M, Suzuki C K 2017 Optical fiber specklegram sensor analysis by speckle pattern division Applied Optics 56(6) 1585 [2] Castaño L F, et al. 2018 Temperature measurement by means of fiber specklegram sensors (FSS) Optica Pura y Aplicada 51(3) 50306 [3] Hoyos A, Gómez N D, Gómez J A 2013 Fiber specklegram sensors (FSS) for measuring high frequency mechanical perturbations 8th Ibero American Optics Meeting/11th Latin American Meeting on Optics, Lasers, and Applications (Porto: Society of Photo-Optical Instrumentation Engineers) [4] Liu Y, Li G, Qin Q, Tan Z, Wang M, Yan F 2020 Bending recognition based on the analysis of fiber specklegrams using deep learning Optics & Laser Technology 131 106424 [5] Krohn D A, MacDougall T W, Mendez A 2015 Fiber Optic Sensors: Fundamentals and Applications (Bellingham: SPIE press) [6] Efendioglu H 2017 A Review of fiber-optic modal modulated sensors: specklegram and modal power distribution sensing IEEE Sensors Journal 17(7) 2055 [7] Fujiwara E, Ri Y, Wu Y T, Fujimoto H, Suzuki C K 2018 Evaluation of image matching techniques for optical fiber specklegram sensor analysis Applied Optics 57(33) 9845 [8] Gubarev F, Li L, Klenovskii M, Glotov A 2016 Speckle pattern processing by digital image correlation MATEC Web of Conferences 48 04003 [9] Crammond G, Boyd S W, Dulieu-Barton J M 2013 Speckle pattern quality assessment for digital image correlation Optics and Lasers in Engineering 51(12) 1368 [10] Wei M, Tang G, Liu J, Zhu L, Liu J, Huang C, Zhang J, Shen L, Yu S 2021 Neural network based perturbation-location fiber specklegram sensing system towards applications with limited number of training samples Journal of Lightwave Technology 39(19) 6315 [11] Razmyar S, Mostafavi M T 2020 Deep learning for estimating deflection direction of a multimode fiber from specklegram Journal of Lightwave Technology 39(6) 1850 [12] Arístizabal V H, et al. 2016 Numerical modeling of fiber specklegram sensors by using finite element method (FEM) Optics Express 24 27225 [13] Torres P, Aristizábal V H, Andrés M V. 2011 Modeling of photonic crystal fibers from the scalar wave equation with a purely transverse linearly polarized vector potential Journal of the Optical Society of America B 28(4) 787 [14] Arango J D, et al. 2021 Numerical study using finite element method for the thermal response of fiber specklegram sensors with changes in the length of the sensing zone Computer Optics 45(4) 534sensores de fibra óptica, métodos electromagnéticos computacionales, aproximación numérica y análisis, detección y sensores ópticos, interferometría de Speckle.fiber optics sensors, computational electromagnetic methods, numerical approximation and analysis, optical sensing and sensors, speckle interferometry.Deep learning classification and regression models for temperature values on a simulated fibre specklegram sensorArtículos Científicoshttp://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionAtribucióninfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2PublicationORIGINAL2021-ART_Deep learning classification and regression.pdf2021-ART_Deep learning classification and regression.pdfapplication/pdf985086https://repository.ucc.edu.co/bitstreams/e7d37fbe-12d0-4c61-9bda-0fc4ae664c0f/download80756c39d6710a2fe4fea03c9200bec6MD51LICENSElicense.txtlicense.txttext/plain; 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Deep learning classification and regression models for temperature values on a simulated fibre specklegram sensor

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