Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 6 of 6 for “"Hidden Markov Modelling"”.
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Integration of Hidden Markov Modelling and Bayesian Networks for fault analysis of complex systems. Development of a hybrid diagnostics methodology based on the integration of hidden Markov modelling and Bayesian networks for fault detection, prediction and isolation of complex automotive systems
… an integrated data-driven methodology in which hidden Markov modelling (HMM) and Bayesian network (BN) were employed to detect, predict and isolate faults in a system. The fault detection and prediction were based on comparing and exploiting pattern similarity in the data via the loglikelihood …
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Integration of Hidden Markov Modelling and Bayesian Networks for fault analysis of complex systems. Development of a hybrid diagnostics methodology based on the integration of hidden Markov modelling and Bayesian networks for fault detection, prediction and isolation of complex automotive systems
… an integrated data-driven methodology in which hidden Markov modelling (HMM) and Bayesian network (BN) were employed to detect, predict and isolate faults in a system. The fault detection and prediction were based on comparing and exploiting pattern similarity in the data via the loglikelihood …
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Integration of Hidden Markov Modelling and Bayesian Networks for fault analysis of complex systems. Development of a hybrid diagnostics methodology based on the integration of hidden Markov modelling and Bayesian networks for fault detection, prediction and isolation of complex automotive systems
… an integrated data-driven methodology in which hidden Markov modelling (HMM) and Bayesian network (BN) were employed to detect, predict and isolate faults in a system. The fault detection and prediction were based on comparing and exploiting pattern similarity in the data via the loglikelihood …
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Discriminative training of hidden Markov Models for gesture recognition
… to this end. Gesture recognition is a problem of modelling temporal data. Non-temporal models can be used for gesture recognition, but require that the signals be adapted to the models. For example, the requirement of fixed-length inputs for support-vector machine classification. Hidden Markov …
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Modelling chromatin dynamics in neural stem cell quiescence and reactivation
… of Drosophila neural stem cells with the use of hidden Markov modelling and found that chromatin becomes more accessible upon quiescence induction. Moreover, quiescent cells gain more Trithorax chromatin domains and revert to a less accessible state upon reactivation. I also showed that genes …
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Characterising In Vivo Brain Function Following Mild Traumatic Brain Injury
… the dynamics of brain connectivity using hidden Markov modelling. Here I found a shift in dynamics: patients with mTBI spend less time in a state dominated by connections between subcortical-cortical regions, shifting to a state characterised by cortico-cortical connections. To establish …