Aplica??o de redes neurais artificiais para classificar padr?es de ru?do eletromagn?tico conduzido nas linhas de alimenta??o de circuitos integrados
Ano de defesa: | 2022 |
---|---|
Autor(a) principal: | |
Orientador(a): | |
Banca de defesa: | |
Tipo de documento: | Dissertação |
Tipo de acesso: | Acesso aberto |
Idioma: | por |
Instituição de defesa: |
Pontif?cia Universidade Cat?lica do Rio Grande do Sul
|
Programa de Pós-Graduação: |
Programa de P?s-Gradua??o em Engenharia El?trica
|
Departamento: |
Escola Polit?cnica
|
País: |
Brasil
|
Palavras-chave em Português: | |
Palavras-chave em Inglês: | |
Área do conhecimento CNPq: | |
Link de acesso: | https://tede2.pucrs.br/tede2/handle/tede/10166 |
Resumo: | With the increasing use of embedded systems in our daily lives and the increasing level of electromagnetic noise in the environment in which these systems are exposed, the need for reliable operation is paramount. When the vital components of these systems are exposed to a large amount of noise, which can be both conducted and radiated, these noises can lead to serious and unavoidable failures, so it is essential to analyze and reliably test these components. In this scenario, this work presents a study on the use of machine learning techniques (artificial neural networks) to carry out the identification and classification in the field of different types of electromagnetic noise conducted in the power lines of integrated circuits (ICs), according to with a specific set of IEC standards. This work details the development of a methodology in which the waveforms of the phenomena contained in the standards are simulated through software due to the time available for a master's work plus the pandemic bias and the difficulty of accessing the laboratories due to this scenario, and also by the risk involving the time and complexity to obtain these waveforms in hardware using a microprocessor, for example. Experimental results obtained in this methodology suggest that the proposed approach is very effective to achieve the objective of identifying the types of electromagnetic noise conducted. In this way, the methodology developed allows the insertion of artificial intelligence in the context of tests, allowing the developers of systems and integrated circuits a new approach to assess the susceptibility to conducted EMI, enabling the development of new techniques to increase the reliability and robustness of the projects. |
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Vargas, Fabian Luishttp://lattes.cnpq.br/9050311050537919http://lattes.cnpq.br/0531963360882387Borba, Douglas2022-04-28T13:47:34Z2022-01-31https://tede2.pucrs.br/tede2/handle/tede/10166With the increasing use of embedded systems in our daily lives and the increasing level of electromagnetic noise in the environment in which these systems are exposed, the need for reliable operation is paramount. When the vital components of these systems are exposed to a large amount of noise, which can be both conducted and radiated, these noises can lead to serious and unavoidable failures, so it is essential to analyze and reliably test these components. In this scenario, this work presents a study on the use of machine learning techniques (artificial neural networks) to carry out the identification and classification in the field of different types of electromagnetic noise conducted in the power lines of integrated circuits (ICs), according to with a specific set of IEC standards. This work details the development of a methodology in which the waveforms of the phenomena contained in the standards are simulated through software due to the time available for a master's work plus the pandemic bias and the difficulty of accessing the laboratories due to this scenario, and also by the risk involving the time and complexity to obtain these waveforms in hardware using a microprocessor, for example. Experimental results obtained in this methodology suggest that the proposed approach is very effective to achieve the objective of identifying the types of electromagnetic noise conducted. In this way, the methodology developed allows the insertion of artificial intelligence in the context of tests, allowing the developers of systems and integrated circuits a new approach to assess the susceptibility to conducted EMI, enabling the development of new techniques to increase the reliability and robustness of the projects.Com o crescente uso de sistemas embarcados em nosso dia a dia e o aumento do n?vel de ru?do eletromagn?tico no ambiente em que esses sistemas est?o expostos, a necessidade de uma opera??o confi?vel ? primordial. Quando os componentes vitais desses sistemas ficam expostos a uma grande quantidade de ru?dos, que podem ser tanto conduzidos como radiados, esses ru?dos podem acarretar em falhas graves e incontorn?veis, por isso torna-se imprescind?vel a an?lise e testes confi?veis em cima destes componentes. Nesse cen?rio, este trabalho apresenta um estudo sobre o uso de t?cnicas de aprendizado de m?quina (redes neurais artificiais) para realizar a identifica??o e classifica??o em campo de diferentes tipos de ru?do eletromagn?tico conduzido nas linhas de alimenta??o de circuito integrados (CIs), de acordo com um conjunto espec?fico das normas IEC. Este trabalho detalha o desenvolvimento de uma metologia o qual as formas de onda dos fen?menos contidos nas normas s?o simuladas atrav?s de software devido ao tempo disposto para um trabalho de mestrado acrescido do vi?s de pandemia e a dificuldade de acesso aos laborat?rios por conta deste cen?rio, e tamb?m pelo risco envolvendo o tempo e complexidade para obten??o dessas formas de onda em hardware utilizando um microprocessador, por exemplo. Resultados experimentais obtidos nesta metodologia, sugerem que a abordagem proposta ? muito eficaz para atingir o objetivo de identificar os tipos de ru?dos eletromagn?ticos conduzidos. Dessa forma, a metodologia desenvolvida possibilita a inser??o da intelig?ncia artificial no contexto de testes, permitindo aos desenvolvedores de sistemas e circuitos integrados uma nova abordagem para avaliar a susceptibilidade ? EMI conduzido, possibilitando o desenvolvimento de novas t?cnicas para o aumento da confiabilidade e robustez dos projetos.Submitted by PPG Engenharia El?trica (engenharia.pg.eletrica@pucrs.br) on 2022-04-25T13:20:20Z No. of bitstreams: 1 DOUGLAS_BORBA_DIS.pdf: 2180170 bytes, checksum: 7bf95073f28b4335a9600f3bb4a7bb59 (MD5)Approved for entry into archive by Sheila Dias (sheila.dias@pucrs.br) on 2022-04-28T13:41:43Z (GMT) No. of bitstreams: 1 DOUGLAS_BORBA_DIS.pdf: 2180170 bytes, checksum: 7bf95073f28b4335a9600f3bb4a7bb59 (MD5)Made available in DSpace on 2022-04-28T13:47:34Z (GMT). No. of bitstreams: 1 DOUGLAS_BORBA_DIS.pdf: 2180170 bytes, checksum: 7bf95073f28b4335a9600f3bb4a7bb59 (MD5) Previous issue date: 2022-01-31Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior - CAPESapplication/pdfhttps://tede2.pucrs.br/tede2/retrieve/183726/DOUGLAS_BORBA_DIS.pdf.jpgporPontif?cia Universidade Cat?lica do Rio Grande do SulPrograma de P?s-Gradua??o em Engenharia El?tricaPUCRSBrasilEscola Polit?cnicaRu?do ConduzidoInterfer?ncia Eletromagn?ticaRede Neural ArtificialSistema Embarcado RobustoAnaconda NavigatorTensor FlowConducted NoiseElectromagnetic InterferenceMachine LearningDeep Neural NetworkRobust Embedded SystemAnaconda NavigatorTensor FlowENGENHARIASAplica??o de redes neurais artificiais para classificar padr?es de ru?do eletromagn?tico conduzido nas linhas de alimenta??o de circuitos integradosinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisTrabalho n?o apresenta restri??o para publica??o-26605041092728202950050060045189710564848268253590462550136975366info:eu-repo/semantics/openAccessreponame:Biblioteca Digital de Teses e Dissertações da PUC_RSinstname:Pontifícia Universidade Católica do Rio Grande do Sul (PUCRS)instacron:PUC_RSTHUMBNAILDOUGLAS_BORBA_DIS.pdf.jpgDOUGLAS_BORBA_DIS.pdf.jpgimage/jpeg5484https://tede2.pucrs.br/tede2/bitstream/tede/10166/3/DOUGLAS_BORBA_DIS.pdf.jpg1e7e8ffe1a65276ac9e166f5f1361cffMD53TEXTDOUGLAS_BORBA_DIS.pdf.txtDOUGLAS_BORBA_DIS.pdf.txttext/plain160840https://tede2.pucrs.br/tede2/bitstream/tede/10166/4/DOUGLAS_BORBA_DIS.pdf.txt581dc987ff76d6dd39ae5eba659d2167MD54ORIGINALDOUGLAS_BORBA_DIS.pdfDOUGLAS_BORBA_DIS.pdfapplication/pdf2180170https://tede2.pucrs.br/tede2/bitstream/tede/10166/2/DOUGLAS_BORBA_DIS.pdf7bf95073f28b4335a9600f3bb4a7bb59MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-8590https://tede2.pucrs.br/tede2/bitstream/tede/10166/1/license.txt220e11f2d3ba5354f917c7035aadef24MD51tede/101662022-04-28 20:00:17.704oai:tede2.pucrs.br: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Biblioteca Digital de Teses e Dissertaçõeshttp://tede2.pucrs.br/tede2/PRIhttps://tede2.pucrs.br/oai/requestbiblioteca.central@pucrs.br||opendoar:2022-04-28T23:00:17Biblioteca Digital de Teses e Dissertações da PUC_RS - Pontifícia Universidade Católica do Rio Grande do Sul (PUCRS)false |
dc.title.por.fl_str_mv |
Aplica??o de redes neurais artificiais para classificar padr?es de ru?do eletromagn?tico conduzido nas linhas de alimenta??o de circuitos integrados |
title |
Aplica??o de redes neurais artificiais para classificar padr?es de ru?do eletromagn?tico conduzido nas linhas de alimenta??o de circuitos integrados |
spellingShingle |
Aplica??o de redes neurais artificiais para classificar padr?es de ru?do eletromagn?tico conduzido nas linhas de alimenta??o de circuitos integrados Borba, Douglas Ru?do Conduzido Interfer?ncia Eletromagn?tica Rede Neural Artificial Sistema Embarcado Robusto Anaconda Navigator Tensor Flow Conducted Noise Electromagnetic Interference Machine Learning Deep Neural Network Robust Embedded System Anaconda Navigator Tensor Flow ENGENHARIAS |
title_short |
Aplica??o de redes neurais artificiais para classificar padr?es de ru?do eletromagn?tico conduzido nas linhas de alimenta??o de circuitos integrados |
title_full |
Aplica??o de redes neurais artificiais para classificar padr?es de ru?do eletromagn?tico conduzido nas linhas de alimenta??o de circuitos integrados |
title_fullStr |
Aplica??o de redes neurais artificiais para classificar padr?es de ru?do eletromagn?tico conduzido nas linhas de alimenta??o de circuitos integrados |
title_full_unstemmed |
Aplica??o de redes neurais artificiais para classificar padr?es de ru?do eletromagn?tico conduzido nas linhas de alimenta??o de circuitos integrados |
title_sort |
Aplica??o de redes neurais artificiais para classificar padr?es de ru?do eletromagn?tico conduzido nas linhas de alimenta??o de circuitos integrados |
author |
Borba, Douglas |
author_facet |
Borba, Douglas |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Vargas, Fabian Luis |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/9050311050537919 |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/0531963360882387 |
dc.contributor.author.fl_str_mv |
Borba, Douglas |
contributor_str_mv |
Vargas, Fabian Luis |
dc.subject.por.fl_str_mv |
Ru?do Conduzido Interfer?ncia Eletromagn?tica Rede Neural Artificial Sistema Embarcado Robusto Anaconda Navigator Tensor Flow |
topic |
Ru?do Conduzido Interfer?ncia Eletromagn?tica Rede Neural Artificial Sistema Embarcado Robusto Anaconda Navigator Tensor Flow Conducted Noise Electromagnetic Interference Machine Learning Deep Neural Network Robust Embedded System Anaconda Navigator Tensor Flow ENGENHARIAS |
dc.subject.eng.fl_str_mv |
Conducted Noise Electromagnetic Interference Machine Learning Deep Neural Network Robust Embedded System Anaconda Navigator Tensor Flow |
dc.subject.cnpq.fl_str_mv |
ENGENHARIAS |
description |
With the increasing use of embedded systems in our daily lives and the increasing level of electromagnetic noise in the environment in which these systems are exposed, the need for reliable operation is paramount. When the vital components of these systems are exposed to a large amount of noise, which can be both conducted and radiated, these noises can lead to serious and unavoidable failures, so it is essential to analyze and reliably test these components. In this scenario, this work presents a study on the use of machine learning techniques (artificial neural networks) to carry out the identification and classification in the field of different types of electromagnetic noise conducted in the power lines of integrated circuits (ICs), according to with a specific set of IEC standards. This work details the development of a methodology in which the waveforms of the phenomena contained in the standards are simulated through software due to the time available for a master's work plus the pandemic bias and the difficulty of accessing the laboratories due to this scenario, and also by the risk involving the time and complexity to obtain these waveforms in hardware using a microprocessor, for example. Experimental results obtained in this methodology suggest that the proposed approach is very effective to achieve the objective of identifying the types of electromagnetic noise conducted. In this way, the methodology developed allows the insertion of artificial intelligence in the context of tests, allowing the developers of systems and integrated circuits a new approach to assess the susceptibility to conducted EMI, enabling the development of new techniques to increase the reliability and robustness of the projects. |
publishDate |
2022 |
dc.date.accessioned.fl_str_mv |
2022-04-28T13:47:34Z |
dc.date.issued.fl_str_mv |
2022-01-31 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
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info:eu-repo/semantics/masterThesis |
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masterThesis |
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publishedVersion |
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https://tede2.pucrs.br/tede2/handle/tede/10166 |
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https://tede2.pucrs.br/tede2/handle/tede/10166 |
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por |
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Programa de P?s-Gradua??o em Engenharia El?trica |
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PUCRS |
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Escola Polit?cnica |
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Pontif?cia Universidade Cat?lica do Rio Grande do Sul |
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