Propuesta metodológica para el procesamiento de señales y videos aplicada a la detección y caracterización de la multiplicidad de descargas eléctricas atmosféricas

dc.contributor.advisorBolaños Martínez, Freddy
dc.contributor.advisorHerrera Murcia, Javier Gustavo
dc.contributor.authorOrozco Gómez, Diego Hernando
dc.contributor.researchgroupGrupo de Automática de la Universidad Nacional Gaunalspa
dc.date.accessioned2022-04-06T18:51:41Z
dc.date.available2022-04-06T18:51:41Z
dc.date.issued2022-04-06
dc.description.abstractActualmente diversas investigaciones se han enfocado en analizar a partir de videos de alta velocidad, características de las descargas eléctricas atmosféricas con el fin de adquirir mejor comprensión del fenómeno, que conlleva entre otros aspectos el desarrollo de sistemas de protección robustos. La mayoría de las investigaciones han requerido de un observador que ante el suceso del evento provea un disparo manual a la cámara permitiendo almacenar la información visual del fenómeno. Por tanto, este trabajo se orientó en proponer una metodología para la detección de las descargas utilizando dos implementaciones basadas en procesamiento de señales y visión computacional, con el propósito que el sistema autónomamente sea el que suministre el disparo, apartando al observador de la realización de esta tarea. El sistema de detección basado en técnicas de procesamiento de imágenes requirió la adecuación de métodos de segmentación, representación, descripción y clasificación. El algoritmo de reconocimiento con visión computacional se implementó mediante la red neuronal convolucional EfficientNetB4. Fuera de línea, las técnicas basadas en procesamiento de imágenes suministraron una precisión del 81.81%, mientras que haciendo uso de visión computacional la precisión fue de 71.63%. Con el objeto de evaluar el desempeño en tiempo real, las técnicas de procesamiento se adaptaron en un ordenador de placa reducida correspondiente a la Raspberry Pi 3 modelo B+ obteniéndose una precisión de 86.95%. Adicionalmente, se evaluó la característica de multiplicidad la cual corresponde al número de descargas subsecuentes presentes en el canal de la descarga logrando una precisión de 66.66%. (Texto tomado de la fuente)spa
dc.description.abstractCurrently, several researches have conducted in processing high speed videos, in order to analyze lightning features and acquire a better phenomenon comprehension, which might lead to development of more robust protection systems. Most of the investigations have required a human observer, who, in the occurrence of the event, provides a manual trigger to the camera allowing the visual information of the phenomenon to be stored. Therefore, this work was aimed at proposing a methodology for the lightning detection using two implementations based on signal processing and computer vision, with the purpose that the system autonomously provides the trigger, avoiding the need of a human observer for performing this task. The detection system based on image processing techniques required the adaptation of segmentation, representation, description and classification methods. The computer vision recognition algorithm was implemented using the EfficientNetB4 convolutional neural network. Off-line, the techniques based on image processing provided an accuracy of 81.81%, using computer vision the accuracy was 71.63%. In order to evaluate the performance in real time, the processing techniques were adapted in a single-board computer corresponding to the Raspberry Pi 3 model B+, obtaining an accuracy of 86.95%. Additionally, the lightning multiplicity that refers to the number of strokes in a flash was evaluated, achieving an accuracy of 66.66%.eng
dc.description.curricularareaÁrea Curricular de Ingeniería Eléctrica e Ingeniería de Controlspa
dc.description.degreelevelMaestríaspa
dc.description.degreenameMagister en Ingeniería – Automatización Industrialspa
dc.description.researchareaProcesamiento de Señales Visión Artificialspa
dc.format.extentxii, 130 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/81443
dc.language.isospaspa
dc.publisherUniversidad Nacional de Colombiaspa
dc.publisher.branchUniversidad Nacional de Colombia - Sede Medellínspa
dc.publisher.departmentDepartamento de Ingeniería Eléctrica y Automáticaspa
dc.publisher.facultyFacultad de Minasspa
dc.publisher.placeMedellín, Colombiaspa
dc.publisher.programMedellín - Minas - Maestría en Ingeniería - Automatización Industrialspa
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dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.licenseReconocimiento 4.0 Internacionalspa
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/spa
dc.subject.armarcImage processing
dc.subject.armarcRedes neuronales (computadores)
dc.subject.ddc620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingenieríaspa
dc.subject.lembProcesamiento de imágenesspa
dc.subject.lembProcesamiento de señalesspa
dc.subject.proposalDescarga eléctrica atmosféricaspa
dc.subject.proposalProcesamiento imágenesspa
dc.subject.proposalRed neuronal convolucionalspa
dc.subject.proposalSegmentaciónspa
dc.subject.proposalConvolutional neural networkeng
dc.subject.proposalDetectioneng
dc.subject.proposalImage processingeng
dc.subject.proposalLightningeng
dc.subject.proposalMultiplicityeng
dc.subject.proposalSegmentationeng
dc.titlePropuesta metodológica para el procesamiento de señales y videos aplicada a la detección y caracterización de la multiplicidad de descargas eléctricas atmosféricasspa
dc.title.translatedMethodological proposal for the signals and video processing applied to detection and multiplicity characterization of lightningeng
dc.typeTrabajo de grado - Maestríaspa
dc.type.coarhttp://purl.org/coar/resource_type/c_bdccspa
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/masterThesisspa
dc.type.redcolhttp://purl.org/redcol/resource_type/TMspa
dc.type.versioninfo:eu-repo/semantics/acceptedVersionspa
dcterms.audience.professionaldevelopmentEstudiantesspa
dcterms.audience.professionaldevelopmentInvestigadoresspa
dcterms.audience.professionaldevelopmentMaestros, Investigadoresspa
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2spa

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