Pattern representations for classifying (non-)metric (non-)vectorial data with applications in Structural Health Monitoring and geotechnical/natural-hazard engineering

gráficos, tablas

Autores:: Ospina Dávila, Yesid Mauricio

Tipo de recurso:: Doctoral thesis

Fecha de publicación:: 2022

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: eng

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dc.title.eng.fl_str_mv	Pattern representations for classifying (non-)metric (non-)vectorial data with applications in Structural Health Monitoring and geotechnical/natural-hazard engineering
dc.title.translated.spa.fl_str_mv	Representaciones de patrones para la clasificación de datos (no-)vectoriales (no-)métricos con aplicaciones en el Monitoreo de Salud Estructural y la ingeniería de amenazas geotécnicas/naturales
title	Pattern representations for classifying (non-)metric (non-)vectorial data with applications in Structural Health Monitoring and geotechnical/natural-hazard engineering
spellingShingle	Pattern representations for classifying (non-)metric (non-)vectorial data with applications in Structural Health Monitoring and geotechnical/natural-hazard engineering 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores Classifier system design Data-driven surrogates Dissimilarity pattern recognition Landslides Pattern representation Structural health monitoring Slope stability Deslizamientos Diseño de sistemas de clasificación Estabilidad de taludes Monitoreo de salud estructural Reconocimiento de patrones basado en disimilitudes Representación de patrones Sustitutos basados en datos Ingeniería de la construcción Construction engineering
title_short	Pattern representations for classifying (non-)metric (non-)vectorial data with applications in Structural Health Monitoring and geotechnical/natural-hazard engineering
title_full	Pattern representations for classifying (non-)metric (non-)vectorial data with applications in Structural Health Monitoring and geotechnical/natural-hazard engineering
title_fullStr	Pattern representations for classifying (non-)metric (non-)vectorial data with applications in Structural Health Monitoring and geotechnical/natural-hazard engineering
title_full_unstemmed	Pattern representations for classifying (non-)metric (non-)vectorial data with applications in Structural Health Monitoring and geotechnical/natural-hazard engineering
title_sort	Pattern representations for classifying (non-)metric (non-)vectorial data with applications in Structural Health Monitoring and geotechnical/natural-hazard engineering
dc.creator.fl_str_mv	Ospina Dávila, Yesid Mauricio
dc.contributor.advisor.none.fl_str_mv	Orozco-Alzate, Mauricio
dc.contributor.author.none.fl_str_mv	Ospina Dávila, Yesid Mauricio
dc.subject.ddc.spa.fl_str_mv	000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores
topic	000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores Classifier system design Data-driven surrogates Dissimilarity pattern recognition Landslides Pattern representation Structural health monitoring Slope stability Deslizamientos Diseño de sistemas de clasificación Estabilidad de taludes Monitoreo de salud estructural Reconocimiento de patrones basado en disimilitudes Representación de patrones Sustitutos basados en datos Ingeniería de la construcción Construction engineering
dc.subject.proposal.eng.fl_str_mv	Classifier system design Data-driven surrogates Dissimilarity pattern recognition Landslides Pattern representation Structural health monitoring Slope stability
dc.subject.proposal.spa.fl_str_mv	Deslizamientos Diseño de sistemas de clasificación Estabilidad de taludes Monitoreo de salud estructural Reconocimiento de patrones basado en disimilitudes Representación de patrones Sustitutos basados en datos
dc.subject.unesco.none.fl_str_mv	Ingeniería de la construcción Construction engineering
description	gráficos, tablas
publishDate	2022
dc.date.accessioned.none.fl_str_mv	2022-10-11T17:04:20Z
dc.date.available.none.fl_str_mv	2022-10-11T17:04:20Z
dc.date.issued.none.fl_str_mv	2022
dc.type.spa.fl_str_mv	Trabajo de grado - Doctorado
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/doctoralThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.coar.spa.fl_str_mv	http://purl.org/coar/resource_type/c_db06
dc.type.content.spa.fl_str_mv	Image Text
format	http://purl.org/coar/resource_type/c_db06
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/82365
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/82365 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	eng
language	eng
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dc.rights.license.spa.fl_str_mv	Atribución-NoComercial-SinDerivadas 4.0 Internacional
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dc.format.extent.spa.fl_str_mv	xiv, 85 páginas
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dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Manizales - Ingeniería y Arquitectura - Doctorado en Ingeniería - Automática
dc.publisher.department.spa.fl_str_mv	Departamento de Ingeniería Eléctrica y Electrónica
dc.publisher.faculty.spa.fl_str_mv	Facultad de Ingeniería y Arquitectura
dc.publisher.place.spa.fl_str_mv	Manizales, Colombia
dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Manizales
institution	Universidad Nacional de Colombia
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spelling	Atribución-NoComercial-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Orozco-Alzate, Mauricio854bb7da044fd757cf5087bb23930d6d600Ospina Dávila, Yesid Mauricio77edae32e4fa6591301d95ae010006282022-10-11T17:04:20Z2022-10-11T17:04:20Z2022https://repositorio.unal.edu.co/handle/unal/82365Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/gráficos, tablasNowadays, data-driven modelling in structural and geo-engineering problems using Statistical Pattern Recognition and Machine Learning provides powerful and more versatile tools within a predictive framework. In contrast to the mainstream orientations of the state-of-art in data-driven structural and geo-engineering surrogates, which are based on advanced and (hyper-)parametrized classifiers, this thesis is focused on data representation issues. Firstly, for vectorial slope/landslide data, feature-based vector spaces are enriched and enhanced according to the Occam’s razor principle, which is achieved through three simple but powerful existing variants of a transparent classifier as the nearest neighbor rule. Secondly, for non-vectorial SHM data, powerful and highly discriminant dissimilarity-vector spaces are built-up using spectral/time-frequency information from structural states, adopting a proximity-based learning scheme. In both cases, the results show the importance of a proper data representation and its key role in a bottom-up design for surrogate modelling. (Texto tomado de la fuente)Actualmente, el Reconocimiento de Patrones Estadístico y el Aprendizaje de Máquinas proveen herramientas poderosas y versátiles para el modelamiento predictivo de problemas de estructuras civiles, mecánicas y de la geo-ingeniería. A diferencia de las principales tendencias en el estado del arte en los sustitutos basados en datos en problemas de estructuras y de geo-ingeniería, esta tesis se enfoca en la representación de los datos. Primero, para datos vectoriales de taludes/deslizamientos, los espacios vectoriales basados en características son enriquecidos y mejorados de acuerdo al principio de la navaja de Occam o de parsimonia, el cual se logra mediante tres simples pero poderosos variantes ya existentes del clasificador de vecinos más cercanos. Segundo, para datos no-vectoriales pertenecientes al Monitoreo de Salud Estructural, son construidos, poderosos y altamente discriminantes, espacios de disimilitudes usando información espectral/tiempo-frecuencia, tomando un esquema de aprendizaje basado en proximidades. En ambos casos, los resultados demuestran la importancia de una apropiada representación de datos y su influencia en el diseño incremental de modelos sustitutos.DoctoradoDoctor en IngenieríaStatistical Pattern Recognition, Machine Learning and Signal ProcessingEléctrica, Electrónica, Automatización Y Telecomunicacionesxiv, 85 páginasapplication/pdfengUniversidad Nacional de ColombiaManizales - Ingeniería y Arquitectura - Doctorado en Ingeniería - AutomáticaDepartamento de Ingeniería Eléctrica y ElectrónicaFacultad de Ingeniería y ArquitecturaManizales, ColombiaUniversidad Nacional de Colombia - Sede Manizales000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadoresClassifier system designData-driven surrogatesDissimilarity pattern recognitionLandslidesPattern representationStructural health monitoringSlope stabilityDeslizamientosDiseño de sistemas de clasificaciónEstabilidad de taludesMonitoreo de salud estructuralReconocimiento de patrones basado en disimilitudesRepresentación de patronesSustitutos basados en datosIngeniería de la construcciónConstruction engineeringPattern representations for classifying (non-)metric (non-)vectorial data with applications in Structural Health Monitoring and geotechnical/natural-hazard engineeringRepresentaciones de patrones para la clasificación de datos (no-)vectoriales (no-)métricos con aplicaciones en el Monitoreo de Salud Estructural y la ingeniería de amenazas geotécnicas/naturalesTrabajo de grado - Doctoradoinfo:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_db06ImageTextJamal A. 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ICPR 2004, volume 3, pages 446–449, Aug 2004.BibliotecariosEstudiantesInvestigadoresMaestrosPúblico generalLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/82365/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL75092593.2022.pdf75092593.2022.pdfDoctorado en Ingeniería - Automáticaapplication/pdf8746227https://repositorio.unal.edu.co/bitstream/unal/82365/2/75092593.2022.pdf519a43dc2b03a65c061d9d2018480417MD52THUMBNAIL75092593.2022.pdf.jpg75092593.2022.pdf.jpgGenerated Thumbnailimage/jpeg4874https://repositorio.unal.edu.co/bitstream/unal/82365/3/75092593.2022.pdf.jpg99cf310339207af25594f8495157dc0bMD53unal/82365oai:repositorio.unal.edu.co:unal/823652023-08-09 23:04:32.511Repositorio Institucional Universidad Nacional de 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Pattern representations for classifying (non-)metric (non-)vectorial data with applications in Structural Health Monitoring and geotechnical/natural-hazard engineering

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