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Monte Carlo simulation applicable for predictive algorithm analysis in aerospace

dc.creatorBautista Hernández, Jorgees
dc.creatorMartín Prats, María de los Ángeleses
dc.date.accessioned2023-08-28T10:18:39Z
dc.date.available2023-08-28T10:18:39Z
dc.date.issued2023
dc.identifier.citationBautista Hernández, J. y Martín Prats, M.d.l.Á. (2023). Monte Carlo simulation applicable for predictive algorithm analysis in aerospace. En 14th IFIP WG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2023 (243-256), Caparica: Springer.
dc.identifier.isbn978-303136006-0es
dc.identifier.issn1868-4238es
dc.identifier.urihttps://hdl.handle.net/11441/148534
dc.descriptionThis chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.es
dc.description.abstractSafety investigations about electrical wiring harness caused by failures in electrical systems establish that origin of these accidents are related to electrical installation. Predictive techniques which mitigate and reduce risk of the occurrence of errors to enhance safety shall be considered. The development of machine learning has evolved towards the creation of innovative predictive algorithms which show high performance in data analysis and making predictions in the context of artificial intelligence. The Monte Carlo approach is used to validate the model performance. In this paper, Monte Carlo simulation was used to evaluate the level of the uncertainty of the selected parameters over 1000 runs. This study analyzes the reliability of the predictive algorithm in order to be implemented as an automatic error predictor in aerospace. The results obtained are within the expected range suggesting that the model used is accurate and reliable.es
dc.formatapplication/pdfes
dc.format.extent14 p.es
dc.language.isoenges
dc.publisherSpringeres
dc.relation.ispartof14th IFIP WG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2023 (2023), pp. 243-256.
dc.rightsAtribución 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectMonte Carlo Simulationes
dc.subjectPredictive Algorithmses
dc.subjectSensitivity Analysises
dc.subjectSystem Reliabilityes
dc.subjectAutomatic Error Predictores
dc.titleMonte Carlo simulation applicable for predictive algorithm analysis in aerospacees
dc.typeinfo:eu-repo/semantics/conferenceObjectes
dcterms.identifierhttps://ror.org/03yxnpp24
dc.type.versioninfo:eu-repo/semantics/publishedVersiones
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Ingeniería Electrónicaes
dc.relation.publisherversionhttps://link.springer.com/chapter/10.1007/978-3-031-36007-7_18es
dc.identifier.doi10.1007/978-3-031-36007-7_18es
dc.contributor.groupUniversidad de Sevilla. TIC109: Tecnología Electrónicaes
dc.publication.initialPage243es
dc.publication.endPage256es
dc.eventtitle14th IFIP WG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2023es
dc.eventinstitutionCaparicaes

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