False-alarm and non-detection probabilities for on-line quality control via HMM

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dc.contributor.authorDorea, C.C.Y.
dc.contributor.authorGonçalves, C.R.
dc.contributor.authorMedeiros, P.G.
dc.contributor.authorSantos, W.B.
dc.date.accessioned2022-12-08T20:18:22Z
dc.date.available2022-12-08T20:18:22Z
dc.date.issued2012
dc.description.resumoOn-line quality control during production calls for monitoring produced items according to some prescribed strategy. It is reasonable to assume the existence of system internal non-observable variables so that the carried out monitoring is only partially reliable. In this note, under the setting of a Hidden Markov Model (HMM) and assuming that the evolution of the internal state changes are governed by a two-state Markov chain, we derive estimates for false-alarm and non-detection malfunctioning probabilities. Kernel density methods are used to approximate the stable regime density and the stationary probabilities. As a side result, alternative monitoring strategies are proposed.pt_BR
dc.identifier.citationDOREA, C. C. Y.; et al. False-Alarm and non-detection probabilities for on-line quality control via HMM. International Journal of mathematical analysis, v. 6, p. 1153-1162, 2012. Disponível em: http://www.m-hikari.com/ijma/ijma-2012/ijma-21-24-2012/doreaIJMA21-24-2012.pdf . Acesso em: 11 dez. 2017pt_BR
dc.identifier.issn1312-8876
dc.identifier.urihttps://repositorio.ufrn.br/handle/123456789/50046
dc.languageenpt_BR
dc.publisherInternational Journal of Mathematical Analysispt_BR
dc.rightsAcesso Abertopt_BR
dc.subjectHidden Markov modelpt_BR
dc.subjectFalse-alarmpt_BR
dc.subjectKernel density methodspt_BR
dc.titleFalse-alarm and non-detection probabilities for on-line quality control via HMMpt_BR
dc.typearticlept_BR

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