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

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dc.contributor.advisorOrozco-Alzate, Mauricio
dc.contributor.authorOspina Dávila, Yesid Mauricio
dc.date.accessioned2022-10-11T17:04:20Z
dc.date.available2022-10-11T17:04:20Z
dc.date.issued2022
dc.descriptiongráficos, tablasspa
dc.description.abstractNowadays, 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)eng
dc.description.abstractActualmente, 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.spa
dc.description.curricularareaEléctrica, Electrónica, Automatización Y Telecomunicacionesspa
dc.description.degreelevelDoctoradospa
dc.description.degreenameDoctor en Ingenieríaspa
dc.description.researchareaStatistical Pattern Recognition, Machine Learning and Signal Processingspa
dc.format.extentxiv, 85 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.identifier.instnameUniversidad Nacional de Colombiaspa
dc.identifier.reponameRepositorio Institucional Universidad Nacional de Colombiaspa
dc.identifier.repourlhttps://repositorio.unal.edu.co/spa
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/82365
dc.language.isoengspa
dc.publisherUniversidad Nacional de Colombiaspa
dc.publisher.branchUniversidad Nacional de Colombia - Sede Manizalesspa
dc.publisher.departmentDepartamento de Ingeniería Eléctrica y Electrónicaspa
dc.publisher.facultyFacultad de Ingeniería y Arquitecturaspa
dc.publisher.placeManizales, Colombiaspa
dc.publisher.programManizales - Ingeniería y Arquitectura - Doctorado en Ingeniería - Automáticaspa
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dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.licenseAtribución-NoComercial-SinDerivadas 4.0 Internacionalspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/spa
dc.subject.ddc000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadoresspa
dc.subject.proposalClassifier system designeng
dc.subject.proposalData-driven surrogateseng
dc.subject.proposalDissimilarity pattern recognitioneng
dc.subject.proposalLandslideseng
dc.subject.proposalPattern representationeng
dc.subject.proposalStructural health monitoringeng
dc.subject.proposalSlope stabilityeng
dc.subject.proposalDeslizamientosspa
dc.subject.proposalDiseño de sistemas de clasificaciónspa
dc.subject.proposalEstabilidad de taludesspa
dc.subject.proposalMonitoreo de salud estructuralspa
dc.subject.proposalReconocimiento de patrones basado en disimilitudesspa
dc.subject.proposalRepresentación de patronesspa
dc.subject.proposalSustitutos basados en datosspa
dc.subject.unescoIngeniería de la construcción
dc.subject.unescoConstruction engineering
dc.titlePattern representations for classifying (non-)metric (non-)vectorial data with applications in Structural Health Monitoring and geotechnical/natural-hazard engineeringeng
dc.title.translatedRepresentaciones 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/naturalesspa
dc.typeTrabajo de grado - Doctoradospa
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dc.type.driverinfo:eu-repo/semantics/doctoralThesisspa
dc.type.versioninfo:eu-repo/semantics/acceptedVersionspa
dcterms.audience.professionaldevelopmentBibliotecariosspa
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dcterms.audience.professionaldevelopmentInvestigadoresspa
dcterms.audience.professionaldevelopmentMaestrosspa
dcterms.audience.professionaldevelopmentPúblico generalspa
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2spa

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