Partially-Hidden Markov Models

Ramasso, Emmanuel; Denœux, Thierry; Zerhouni, Noureddine

doi:10.1007/978-3-642-29461-7_42

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…More recently, the procedures of inference and training in Hidden Markov Models (Rabiner, 1989) were extended to the partially-supervised case and the resulting model, called a Partially-Hidden Markov Model (PHMM) (Ramasso, Denoeux, & Zerhouni, 2012), is able to perform clustering and classification. This model appears to be well-suited for datadriven identification and monitoring of damages in composites, in particular because:…”

Section: Problem Statement and Contributionmentioning

confidence: 99%

Health Assessment of Composite Structures in Unconstrained Environments Using Partially Supervised Pattern Recognition Tools

Ramasso,

Placet,

Gouriveau

et al. 2012

PHM_CONF

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The health assessment of composite structures from acoustic emission data is generally tackled by the use of clustering techniques. In this paper, the K-means clustering and the newly proposed Partially-Hidden Markov Model (PHMM) are exploited to analyse the data collected during mechanical tests on composite structures. The health assessment considered in this paper is made difficult by working in unconstrained environments. The presence of the noise is illustrated in several examples and is shown to distort strongly the results of clustering. A solution is proposed to filter out the noisy partition provided by the clustering methods. After filtering, the PHMM provides results which appeared closer to the expectations than the K-means. The PHMM offers the possibility to use uncertain and imprecise labels on the possible states, and thus covers supervised and unsupervised learning as special cases which makes it suitable for real applications.

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Section: Problem Statement and Contributionmentioning

confidence: 99%

Health Assessment of Composite Structures in Unconstrained Environments Using Partially Supervised Pattern Recognition Tools

Ramasso,

Placet,

Gouriveau

et al. 2012

PHM_CONF

View full text Add to dashboard Cite

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Second-Order Belief Hidden Markov Models

Park¹,

Chebbah²,

Jendoubi³

et al. 2014

Belief Functions: Theory and Applications

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Abstract. Hidden Markov Models (HMMs) are learning methods for pattern recognition. The probabilistic HMMs have been one of the most used techniques based on the Bayesian model. First-order probabilistic HMMs were adapted to the theory of belief functions such that Bayesian probabilities were replaced with mass functions. In this paper, we present a second-order Hidden Markov Model using belief functions. Previous works in belief HMMs have been focused on the first-order HMMs. We extend them to the second-order model.

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Partially-Hidden Markov Models

Cited by 2 publications

References 11 publications

Health Assessment of Composite Structures in Unconstrained Environments Using Partially Supervised Pattern Recognition Tools

Health Assessment of Composite Structures in Unconstrained Environments Using Partially Supervised Pattern Recognition Tools

Second-Order Belief Hidden Markov Models

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