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Dissertations / Theses on the topic 'Hidden Markov modelling'
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1
Erlwein, Christina. "Applications of hidden Markov models in financial modelling." Thesis, Brunel University, 2008. http://bura.brunel.ac.uk/handle/2438/7898.
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Various models driven by a hidden Markov chain in discrete or continuous time are developed to capture the stylised features of market variables whose levels or values constitute as the underliers of financial derivative contracts or investment portfolios. Since the parameters are switching regimes, the changes and developments in the economy as soon as they arise are readily reflected in these models. The change of probability measure technique and the EM algorithm are fundamental techniques utilised in the optimal parameter estimation. Recursive adaptive filters for the state of the Markov chain and other auxiliary processes related to the Markov chain are derived which in turn yield self-tuning dynamic financial models. A hidden Markov model (HMM)-based modelling set-up for commodity prices is developed and the predictability of the gold market under this setting is examined. An Ornstein-Uhlenbeck (OU) model with HMM parameters is proposed and under this set-up, we address two statistical inference issues: the sensitivity of the model to small changes in parameter estimates and the selection of the optimal number of states. The extended OU model is implemented on a data set of 30-day Canadian T-bill yields. An exponential of a Markov-switching OU process plus a compound Poisson process is put forward as a model for the evolution of electricity spot prices. Using a data set compiled by Nord Pool, we illustrate the vast improvements gained in incorporating regimes in the model. A multivariate HMM is employed as a framework in providing the solutions of two asset allocation problems; one involves the mean-variance utility function and the other entails the CVaR constraint. Finally, the valuation of credit default swaps highlights the important considerations necessitated by pricing in a regime-switching environment. Certain numerical schemes are applied to obtain approximations for the default probabilities and swap rates.
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Bai, Jiongjun. "Adaptive hidden Markov noise modelling for speech enhancement." Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/11158.
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A robust and reliable noise estimation algorithm is required in many speech enhancement systems. The aim of this thesis is to propose and evaluate a robust noise estimation algorithm for highly non-stationary noisy environments. In this work, we model the non-stationary noise using a set of discrete states with each state representing a distinct noise power spectrum. In this approach, the state sequence over time is conveniently represented by a Hidden Markov Model (HMM). In this thesis, we first present an online HMM re-estimation framework that models time-varying noise using a Hidden Markov Model and tracks changes in noise characteristics by a sequential model update procedure that tracks the noise characteristics during the absence of speech. In addition the algorithm will when necessary create new model states to represent novel noise spectra and will merge existing states that have similar characteristics. We then extend our work in robust noise estimation during speech activity by incorporating a speech model into our existing noise model. The noise characteristics within each state are updated based on a speech presence probability which is derived from a modified Minima controlled recursive averaging method. We have demonstrated the effectiveness of our noise HMM in tracking both stationary and highly non-stationary noise, and shown that it gives improved performance over other conventional noise estimation methods when it is incorporated into a standard speech enhancement algorithm.
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Ljolje, A. "Intonation and phonetic segmentation using hidden Markov models." Thesis, University of Cambridge, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.377219.
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Samaria, Ferdinando Silvestro. "Face recognition using Hidden Markov Models." Thesis, University of Cambridge, 1995. https://www.repository.cam.ac.uk/handle/1810/244871.
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This dissertation introduces work on face recognition using a novel technique based on Hidden Markov Models (HMMs). Through the integration of a priori structural knowledge with statistical information, HMMs can be used successfully to encode face features. The results reported are obtained using a database of images of 40 subjects, with 5 training images and 5 test images for each. It is shown how standard one-dimensional HMMs in the shape of top-bottom models can be parameterised, yielding successful recognition rates of up to around 85%. The insights gained from top-bottom models are extended to pseudo two-dimensional HMMs, which offer a better and more flexible model, that describes some of the twodimensional dependencies missed by the standard one-dimensional model. It is shown how pseudo two-dimensional HMMs can be implemented, yielding successful recognition rates of up to around 95%. The performance of the HMMs is compared with the Eigenface approach and various domain and resolution experiments are also carried out. Finally, the performance of the HMM is evaluated in a fully automated system, where database images are cropped automatically.
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Wong, Georges. "Improved speech hidden Markov modelling via an expectation-maximization framework." Thesis, University of Cambridge, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.259544.
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McLellan, Christopher Richard. "Statistical modelling of home range and larvae movement data." Thesis, University of Edinburgh, 2014. http://hdl.handle.net/1842/14202.
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In this thesis, we investigate two di erent approaches to animal movement modelling; nite mixture models, and di usion processes. These models are considered in two di erent contexts, rstly for analysis of data obtained in home range studies, and then, on a much smaller scale, modelling the movements of larvae. We consider the application of mixture models to home range movement data, and compare their performance with kernel density estimators commonly used for this purpose. Mixtures of bivariate normal distributions and bivariate t distributions are considered, and the latter are found to be good models for simulated and real movement data. The mixtures of bivariate t distributions are shown to provide a robust parametric approach. Subsequently, we investigate several measures of overlap for assessing site delity in home range data. Di usion processes for home range data are considered to model the tracks of animals. In particular, we apply models based on a bivariate Ornstein-Uhlenbeck process to recorded coyote movements. We then study modelling in a di erent application area involving tracks. Di usion models for the movements of larvae are used to investigate their behaviour when exposed to chemical compounds in a scienti c study. We nd that the tted models represent the movements of the larvae well, and correctly distinguish between the behaviour of larvae exposed to attractant and repellent compounds. Mixtures of di usion processes and Hidden Markov models provide more exible alternatives to single di usion processes, and are found to improve upon them considerably. A Hidden Markov model with 4 states is determined to be optimal, with states accounting for directed movement, localized movement and stationary observations. Models incorporating higherorder dependence are investigated, but are found to be less e ective than the use of multiple states for modelling the larvae movements.
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Zhang, X. "A semi-hidden Markov model and its application to speech recognition." Thesis, Swansea University, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.378829.
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Leggetter, Christopher John. "Improved acoustic modelling for HMMs using linear transformations." Thesis, University of Cambridge, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.361709.
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Henderson, Daniel Adrian. "Modelling and analysis of non-coding DNA sequence data." Thesis, University of Newcastle Upon Tyne, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.299427.
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Germain, Sarah Elizabeth. "Bayesian spatio-temporal modelling of rainfall through non-homogenous hidden Markov models." Thesis, University of Newcastle Upon Tyne, 2010. http://hdl.handle.net/10443/1045.
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Multi-site statistical models for daily rainfall should account for spatial and temporal dependence amongst measurements and also allow for the event of no rain. Recent research into climate change and variability has sparked interest in the relationship between rainfall and climate, stimulating the development of statistical models that relate large-scale atmospheric variables to local precipitation. Although modelling daily rainfall presents a challenging and topical problem, there have been few attempts taking a subjective Bayesian approach. This thesis is concerned with developing hidden Markov models (HMMs) for the spatio-temporal analysis of rainfall data, within a Bayesian framework. In these models, daily rainfall patterns are driven by a finite number of unobserved states, interpreted as weather states, that evolve in time as a first order Markov chain. The weather states explain space time structure in the data so that reasonably simple models can be adopted within states. Throughout this thesis, the models and procedures are illustrated using data from a small dense network of six sites situated in Yorkshire, UK. First we study a simple (homogeneous) HMM in which rainfall occurrences and amounts, given occurrences, are conditionally independent in space and time, given the weather state, and have Bernoulli and gamma distributions, respectively. We compare methods for approximating the posterior distribution for the number of weather states. This simple model does not incorporate atmospheric information and appears not to capture the observed spatio-temporal structure. We therefore investigate two non-homogeneous hidden Markov models (NHMMs) in which we allow the transition probabilities between weather states to depend on time-varying atmospheric variables and successively relax the conditional independence assumptions. The first NHMM retains the simple conditional model for non-zero rainfall amounts but allows occurrences to form a Markov chain of autologistic models, given the weather state. The second introduces latent multivariate normal random variables to form a hierarchical NHMM in which neither rainfall occurrences nor non-zero amounts are conditionally spatially or temporally independent, given the weather state. Throughout this thesis, we emphasise the elicitation of prior distributions that convey genuine initial beliefs. For each hidden Markov model studied we demonstrate techniques to assist in this task.
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Chis, Tiberiu. "Performance modelling with adaptive hidden Markov models and discriminatory processor sharing queues." Thesis, Imperial College London, 2016. http://hdl.handle.net/10044/1/39049.
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In modern computer systems, workload varies at different times and locations. It is important to model the performance of such systems via workload models that are both representative and efficient. For example, model-generated workloads represent realistic system behaviour, especially during peak times, when it is crucial to predict and address performance bottlenecks. In this thesis, we model performance, namely throughput and delay, using adaptive models and discrete queues. Hidden Markov models (HMMs) parsimoniously capture the correlation and burstiness of workloads with spatiotemporal characteristics. By adapting the batch training of standard HMMs to incremental learning, online HMMs act as benchmarks on workloads obtained from live systems (i.e. storage systems and financial markets) and reduce time complexity of the Baum-Welch algorithm. Similarly, by extending HMM capabilities to train on multiple traces simultaneously it follows that workloads of different types are modelled in parallel by a multi-input HMM. Typically, the HMM-generated traces verify the throughput and burstiness of the real data. Applications of adaptive HMMs include predicting user behaviour in social networks and performance-energy measurements in smartphone applications. Equally important is measuring system delay through response times. For example, workloads such as Internet traffic arriving at routers are affected by queueing delays. To meet quality of service needs, queueing delays must be minimised and, hence, it is important to model and predict such queueing delays in an efficient and cost-effective manner. Therefore, we propose a class of discrete, processor-sharing queues for approximating queueing delay as response time distributions, which represent service level agreements at specific spatiotemporal levels. We adapt discrete queues to model job arrivals with distributions given by a Markov-modulated Poisson process (MMPP) and served under discriminatory processor-sharing scheduling. Further, we propose a dynamic strategy of service allocation to minimise delays in UDP traffic flows whilst maximising a utility function.
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Duong, Thi V. T. "Efficient duration modelling in the hierarchical hidden semi-Markov models and their applications." Curtin University of Technology, Dept. of Computing, 2008. http://espace.library.curtin.edu.au:80/R/?func=dbin-jump-full&object_id=18610.
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Modeling patterns in temporal data has arisen as an important problem in engineering and science. This has led to the popularity of several dynamic models, in particular the renowned hidden Markov model (HMM) [Rabiner, 1989]. Despite its widespread success in many cases, the standard HMM often fails to model more complex data whose elements are correlated hierarchically or over a long period. Such problems are, however, frequently encountered in practice. Existing efforts to overcome this weakness often address either one of these two aspects separately, mainly due to computational intractability. Motivated by this modeling challenge in many real world problems, in particular, for video surveillance and segmentation, this thesis aims to develop tractable probabilistic models that can jointly model duration and hierarchical information in a unified framework. We believe that jointly exploiting statistical strength from both properties will lead to more accurate and robust models for the needed task. To tackle the modeling aspect, we base our work on an intersection between dynamic graphical models and statistics of lifetime modeling. Realizing that the key bottleneck found in the existing works lies in the choice of the distribution for a state, we have successfully integrated the discrete Coxian distribution [Cox, 1955], a special class of phase-type distributions, into the HMM to form a novel and powerful stochastic model termed as the Coxian Hidden Semi-Markov Model (CxHSMM). We show that this model can still be expressed as a dynamic Bayesian network, and inference and learning can be derived analytically.Most importantly, it has four superior features over existing semi-Markov modelling: the parameter space is compact, computation is fast (almost the same as the HMM), close-formed estimation can be derived, and the Coxian is flexible enough to approximate a large class of distributions. Next, we exploit hierarchical decomposition in the data by borrowing analogy from the hierarchical hidden Markov model in [Fine et al., 1998, Bui et al., 2004] and introduce a new type of shallow structured graphical model that combines both duration and hierarchical modelling into a unified framework, termed the Coxian Switching Hidden Semi-Markov Models (CxSHSMM). The top layer is a Markov sequence of switching variables, while the bottom layer is a sequence of concatenated CxHSMMs whose parameters are determined by the switching variable at the top. Again, we provide a thorough analysis along with inference and learning machinery. We also show that semi-Markov models with arbitrary depth structure can easily be developed. In all cases we further address two practical issues: missing observations to unstable tracking and the use of partially labelled data to improve training accuracy. Motivated by real-world problems, our application contribution is a framework to recognize complex activities of daily livings (ADLs) and detect anomalies to provide better intelligent caring services for the elderly.
Coarser activities with self duration distributions are represented using the CxHSMM. Complex activities are made of a sequence of coarser activities and represented at the top level in the CxSHSMM. Intensive experiments are conducted to evaluate our solutions against existing methods. In many cases, the superiority of the joint modeling and the Coxian parameterization over traditional methods is confirmed. The robustness of our proposed models is further demonstrated in a series of more challenging experiments, in which the tracking is often lost and activities considerably overlap. Our final contribution is an application of the switching Coxian model to segment education-oriented videos into coherent topical units. Our results again demonstrate such segmentation processes can benefit greatly from the joint modeling of duration and hierarchy.
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Li, Shikun. "Network Exceptions Modelling Using Hidden Markov Model : A Case Study of Ericsson’s DroppedCall Data." Thesis, Uppsala universitet, Statistiska institutionen, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-251382.
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In telecommunication, the series of mobile network exceptions is a processwhich exhibits surges and bursts. The bursty part is usually caused by systemmalfunction. Additionally, the mobile network exceptions are often timedependent. A model that successfully captures these aspects will make troubleshootingmuch easier for system engineers. The Hidden Markov Model(HMM) is a good candidate as it provides a mechanism to capture both thetime dependency and the random occurrence of bursts. This thesis focuses onan application of the HMM to mobile network exceptions, with a case study ofEricsson’s Dropped Call data. For estimation purposes, two methods of maximumlikelihood estimation for HMM, namely, EM algorithm and stochasticEM algorithm, are used.
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梁舜德 and Shun Tak Albert Leung. "A study of some variations on the hidden Markov modelling approach to speaker independent isolated word speech recognition." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1990. http://hub.hku.hk/bib/B31209294.
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Leung, Shun Tak Albert. "A study of some variations on the hidden Markov modelling approach to speaker independent isolated word speech recognition /." [Hong Kong : University of Hong Kong], 1990. http://sunzi.lib.hku.hk/hkuto/record.jsp?B12782956.
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Kaval, Katsiaryna. "Aspects of link-save trading in markets with frictions and financial modelling using hidden Markov models." Thesis, University of Glasgow, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.419174.
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Van, Heerden Charl Johannes. "Phoneme duration modelling for speaker verification." Diss., Pretoria : [s.n.], 2009. http://upetd.up.ac.za/thesis/available/etd-06262009-150945/.
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Huss, Anders. "Hybrid Model Approach to Appliance Load Disaggregation : Expressive appliance modelling by combining convolutional neural networks and hidden semi Markov models." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-179200.
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The increasing energy consumption is one of the greatest environmental challenges of our time. Residential buildings account for a considerable part of the total electricity consumption and is further a sector that is shown to have large savings potential. Non Intrusive Load Monitoring (NILM), i.e. the deduction of the electricity consumption of individual home appliances from the total electricity consumption of a household, is a compelling approach to deliver appliance specific consumption feedback to consumers. This enables informed choices and can promote sustainable and cost saving actions. To achieve this, accurate and reliable appliance load disaggregation algorithms must be developed. This Master's thesis proposes a novel approach to tackle the disaggregation problem inspired by state of the art algorithms in the field of speech recognition. Previous approaches, for sampling frequencies Den ökande energikonsumtionen är en stor utmaning för en hållbar utveckling. Bostäder står för en stor del av vår totala elförbrukning och är en sektor där det påvisats stor potential för besparingar. Non Intrusive Load Monitoring (NILM), dvs. härledning av hushållsapparaters individuella elförbrukning utifrån ett hushålls totala elförbrukning, är en tilltalande metod för att fortlöpande ge detaljerad information om elförbrukningen till hushåll. Detta utgör ett underlag för medvetna beslut och kan bidraga med incitament för hushåll att minska sin miljöpåverakan och sina elkostnader. För att åstadkomma detta måste precisa och tillförlitliga algoritmer för el-disaggregering utvecklas. Denna masteruppsats föreslår ett nytt angreppssätt till el-disaggregeringsproblemet, inspirerat av ledande metoder inom taligenkänning. Tidigare angreppsätt inom NILM (i frekvensområdet
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Dosis, Aristotelis. "Modelling and assessment of surgical dexterity in laparoscopic and robotically assisted surgery using synchronized video-motion analysis and hidden Markov models." Thesis, Imperial College London, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.424908.
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Andrés, Ferrer Jesús. "Statistical approaches for natural language modelling and monotone statistical machine translation." Doctoral thesis, Universitat Politècnica de València, 2010. http://hdl.handle.net/10251/7109.
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Esta tesis reune algunas contribuciones al reconocimiento de formas estadístico y, más especícamente, a varias tareas del procesamiento del lenguaje natural. Varias técnicas estadísticas bien conocidas se revisan en esta tesis, a saber: estimación paramétrica, diseño de la función de pérdida y modelado estadístico. Estas técnicas se aplican a varias tareas del procesamiento del lenguajes natural tales como clasicación de documentos, modelado del lenguaje natural
y traducción automática estadística.
En relación con la estimación paramétrica, abordamos el problema del suavizado proponiendo una nueva técnica de estimación por máxima verosimilitud con dominio restringido (CDMLEa ). La técnica CDMLE evita la necesidad de la etapa de suavizado que propicia la pérdida de las propiedades del estimador máximo verosímil. Esta técnica se aplica a clasicación de documentos mediante el clasificador Naive Bayes. Más tarde, la técnica CDMLE se extiende a la estimación por máxima verosimilitud por leaving-one-out aplicandola al suavizado de modelos de lenguaje. Los resultados obtenidos en varias tareas de modelado del lenguaje natural, muestran una mejora en términos de perplejidad.
En a la función de pérdida, se estudia cuidadosamente el diseño de funciones de pérdida diferentes a la 0-1. El estudio se centra en aquellas funciones de pérdida que reteniendo una complejidad de decodificación similar a la función 0-1, proporcionan una mayor flexibilidad. Analizamos y presentamos varias funciones de pérdida en varias tareas de traducción automática y con varios modelos de traducción. También, analizamos algunas reglas de traducción que destacan por causas prácticas tales como la regla de traducción directa; y, así mismo, profundizamos en la comprensión de los modelos log-lineares, que son de hecho, casos particulares de funciones de pérdida.
Finalmente, se proponen varios modelos de traducción monótonos basados en técnicas de modelado estadístico .Andrés Ferrer, J. (2010). Statistical approaches for natural language modelling and monotone statistical machine translation [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/7109
Palancia
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Economou, Theodoros. "Bayesian modelling of recurrent pipe failures in urban water systems using non-homogeneous Poisson processes with latent structure." Thesis, University of Exeter, 2010. http://hdl.handle.net/10036/111499.
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Recurrent events are very common in a wide range of scientific disciplines. The majority of statistical models developed to characterise recurrent events are derived from either reliability theory or survival analysis. This thesis concentrates on applications that arise from reliability, which in general involve the study about components or devices where the recurring event is failure. Specifically, interest lies in repairable components that experience a number of failures during their lifetime. The goal is to develop statistical models in order to gain a good understanding about the driving force behind the failures. A particular counting process is adopted, the non-homogenous Poisson process (NHPP), where the rate of occurrence (failure rate) depends on time. The primary application considered in the thesis is the prediction of underground water pipe bursts although the methods described have more general scope. First, a Bayesian mixed effects NHPP model is developed and applied to a network of water pipes using MCMC. The model is then extended to a mixture of NHPPs. Further, a special mixture case, the zero-inflated NHPP model is developed to cope with data involving a large number of pipes that have never failed. The zero-inflated model is applied to the same pipe network. Quite often, data involving recurrent failures over time, are aggregated where for instance the times of failures are unknown and only the total number of failures are available. Aggregated versions of the NHPP model and its zero-inflated version are developed to accommodate aggregated data and these are applied to the aggregated version of the earlier data set. Complex devices in random environments often exhibit what may be termed as state changes in their behaviour. These state changes may be caused by unobserved and possibly non-stationary processes such as severe weather changes. A hidden semi-Markov NHPP model is formulated, which is a NHPP process modulated by an unobserved semi-Markov process. An algorithm is developed to evaluate the likelihood of this model and a Metropolis-Hastings sampler is constructed for parameter estimation. Simulation studies are performed to test implementation and finally an illustrative application of the model is presented. The thesis concludes with a general discussion and a list of possible generalisations and extensions as well as possible applications other than the ones considered.
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Laurinčiukaitė, Sigita. "Acoustic modelling of Lithuanian speech recognition." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2008. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2008~D_20080626_121551-77545.
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This paper is devoted to an acoustic modelling of Lithuanian speech recognition. Word-, syllable-, contextual syllable-, phoneme- and contextual phoneme-based speech recognition was investigated. Investigations were performed for isolated words and continuous speech. The most popular sub-word units in Lithuanian speech recognition are phonemes and contextual phonemes, and research on other sub-word units is omitted. This paper aims to compare capacity of linguistic sub-word units to model speech and to demonstrate that investigation of sub-word units suggest using alternative sub-word units to phoneme and contextual phoneme.
The dissertation proposes a new methodology for acoustic modelling of syllables and phonemes, new sub-word unit – pseudo-syllable; technologies for acoustic modelling of separate sub-word units, including developed schemes, tools and recommendations. Speech corpus of isolated words was prepared and two versions of corpus of continuous speech LRN were developed for experimental research.
Investigation of recognition of isolated words and construction of acoustic models for words showed that a size of training set of acoustic models, a content of training set in regard to number of speakers have an influence on speech recognition accuracy. The recommendations for word-based acoustic modelling are given.
Investigation of recognition of isolated words and construction of acoustic models for words, syllables and phonemes showed that the best recognition... [to full text]Darbas „Lietuvių šnekos atpažinimo akustinis modeliavimas“ yra skirtas lietuvių šnekos atpažinimo akustiniam modeliavimui. Darbe buvo tirtas žodžiais, skiemenimis, kontekstiniais skiemenimis, fonemomis ir kontekstinėmis fonemomis grįstas šnekos atpažinimas. Tyrimai atlikti izoliuotiems žodžiams ir ištisinei šnekai. Iki šiol lietuvių šnekos atpažinime populiariausi kalbos vienetai buvo fonema ir kontekstinė fonema, o kitų kalbos vienetų analizė nebuvo atliekama. Šiame darbe siekiama palyginti lingvistinio tipo kalbos vienetų gebėjimą modeliuoti šneką ir parodyti, kad kalbos vienetų analizė siūlo alternatyvius fonemai ir kontekstinei fonemai kalbos vienetus. Darbe pasiūlyta metodika mišriam skiemenų ir fonemų akustiniam modeliavimui, naujas kalbos vienetas – pseudo-skiemuo; technologijos atskirų kalbos vienetų akustiniam modeliavimui (schemos, įrankiai, rekomendacijos). Eksperimentiniams tyrimams atlikti paruoštas izoliuotų žodžių garsynas ir sukurtos dvi ištisinės šnekos garsyno LRN versijos. Ištyrus izoliuotų žodžių atpažinimą, akustinius modelius konstruojant žodžiams, nustatyta, kad modelių mokymo aibės dydis, akustinių modelių mokymo aibės turinys daro įtaką šnekos atpažinimo tikslumui. Pateikiamos rekomendacijos akustiniam modeliavimui žodžių pagrindu. Ištyrus izoliuotų žodžių atpažinimą, akustinius modelius konstruojant žodžiams, skiemenims ir fonemoms, gauti rezultatai 98 ±1,8 % tikslumu siejami su skiemens tipo kalbos vienetais. Dėl skiemenų akustinio modeliavimo... [toliau žr. visą tekstą]
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Touron, Augustin. "Modélisation multivariée de variables météorologiques." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLS264/document.
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La production d'énergie renouvelable et la consommation d'électricité dépendent largement des conditions météorologiques : température, précipitations, vent, rayonnement solaire... Ainsi, pour réaliser des études d'impact sur l'équilibre offre-demande, on peut utiliser un générateur de temps, c'est-à-dire un modèle permettant de simuler rapidement de longues séries de variables météorologiques réalistes, au pas de temps journalier. L'une des approches possibles pour atteindre cet objectif utilise les modèles de Markov caché : l'évolution des variables à modéliser est supposée dépendre d'une variable latente que l'on peut interpréter comme un type de temps. En adoptant cette approche, nous proposons dans cette thèse un modèle permettant de simuler simultanément la température, la vitesse du vent et les précipitations, en tenant compte des non-stationnarités qui caractérisent les variables météorologiques. D'autre part, nous nous intéressons à certaines propriétés théoriques des modèles de Markov caché cyclo-stationnaires : nous donnons des conditions simples pour assurer leur identifiabilité et la consistance forte de l'estimateur du maximum de vraisemblance. On montre aussi cette propriété de l'EMV pour des modèles de Markov caché incluant des tendances de long terme sous forme polynomialeRenewable energy production and electricity consumption both depend heavily on weather: temperature, precipitations, wind, solar radiation... Thus, making impact studies on the supply/demand equilibrium may require a weather generator, that is a model capable of quickly simulating long, realistic times series of weather variables, at the daily time step. To this aim, one of the possible approaches is using hidden Markov models : we assume that the evolution of the weather variables are governed by a latent variable that can be interpreted as a weather type. Using this approach, we propose a model able to simulate simultaneously temperature, wind speed and precipitations, accounting for the specific non-stationarities of weather variables. Besides, we study some theoretical properties of cyclo-stationary hidden Markov models : we provide simple conditions of identifiability and we show the strong consistency of the maximum likelihood estimator. We also show this property of the MLE for hidden Markov models including long-term polynomial trends
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Starke, Martin, Benjamin Beck, Denis Ritz, Frank Will, and Jürgen Weber. "Frequency based efficiency evaluation - from pattern recognition via backwards simulation to purposeful drive design." Technische Universität Dresden, 2020. https://tud.qucosa.de/id/qucosa%3A71072.
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The efficiency of hydraulic drive systems in mobile machines is influenced by several factors, like the operators’ guidance, weather conditions, material respectively loading properties and primarily the working cycle. This leads to varying operation points, which have to be performed by the drive system. Regarding efficiency analysis, the usage of standardized working cycles gained through measurements or synthetically generated is state of the art. Thereby, only a small extract of the real usage profile is taken into account. This contribution deals with process pattern recognition (PPR) and frequency based efficiency evaluation to gain more precise information and conclusion for the drive design of mobile machines. By the example of an 18 t mobile excavator, the recognition system using Hidden – Markov - Models (HMM) and the efficiency evaluation process by means of backwards simulation of measured operation points will be described.
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Rosser, Gabriel A. "Mathematical modelling and analysis of aspects of bacterial motility." Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:1af98367-aa2f-4af3-9344-8c361311b553.
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The motile behaviour of bacteria underlies many important aspects of their actions, including pathogenicity, foraging efficiency, and ability to form biofilms. In this thesis, we apply mathematical modelling and analysis to various aspects of the planktonic motility of flagellated bacteria, guided by experimental observations. We use data obtained by tracking free-swimming Rhodobacter sphaeroides under a microscope, taking advantage of the availability of a large dataset acquired using a recently developed, high-throughput protocol. A novel analysis method using a hidden Markov model for the identification of reorientation phases in the tracks is described. This is assessed and compared with an established method using a computational simulation study, which shows that the new method has a reduced error rate and less systematic bias. We proceed to apply the novel analysis method to experimental tracks, demonstrating that we are able to successfully identify reorientations and record the angle changes of each reorientation phase. The analysis pipeline developed here is an important proof of concept, demonstrating a rapid and cost-effective protocol for the investigation of myriad aspects of the motility of microorganisms. In addition, we use mathematical modelling and computational simulations to investigate the effect that the microscope sampling rate has on the observed tracking data. This is an important, but often overlooked aspect of experimental design, which affects the observed data in a complex manner. Finally, we examine the role of rotational diffusion in bacterial motility, testing various models against the analysed data. This provides strong evidence that R. sphaeroides undergoes some form of active reorientation, in contrast to the mainstream belief that the process is passive.
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Panton, Mark Stuart. "Off-line signature verification using ensembles of local Radon transform-based HMMs." Thesis, Stellenbosch : Stellenbosch University, 2011. http://hdl.handle.net/10019.1/6903.
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Thesis (MSc)--Stellenbosch University, 2011.ENGLISH ABSTRACT: An off-line signature verification system attempts to authenticate the identity of an individual by examining his/her handwritten signature, after it has been successfully extracted from, for example, a cheque, a debit or credit card transaction slip, or any other legal document. The questioned signature is typically compared to a model trained from known positive samples, after which the system attempts to label said signature as genuine or fraudulent. Classifier fusion is the process of combining individual classifiers, in order to construct a single classifier that is more accurate, albeit computationally more complex, than its constituent parts. A combined classifier therefore consists of an ensemble of base classifiers that are combined using a specific fusion strategy. In this dissertation a novel off-line signature verification system, using a multi-hypothesis approach and classifier fusion, is proposed. Each base classifier is constructed from a hidden Markov model (HMM) that is trained from features extracted from local regions of the signature (local features), as well as from the signature as a whole (global features). To achieve this, each signature is zoned into a number of overlapping circular retinas, from which said features are extracted by implementing the discrete Radon transform. A global retina, that encompasses the entire signature, is also considered. Since the proposed system attempts to detect high-quality (skilled) forgeries, it is unreasonable to assume that samples of these forgeries will be available for each new writer (client) enrolled into the system. The system is therefore constrained in the sense that only positive training samples, obtained from each writer during enrolment, are available. It is however reasonable to assume that both positive and negative samples are available for a representative subset of so-called guinea-pig writers (for example, bank employees). These signatures constitute a convenient optimisation set that is used to select the most proficient ensemble. A signature, that is claimed to belong to a legitimate client (member of the general public), is therefore rejected or accepted based on the majority vote decision of the base classifiers within the most proficient ensemble. When evaluated on a data set containing high-quality imitations, the inclusion of local features, together with classifier combination, significantly increases system performance. An equal error rate of 8.6% is achieved, which compares favorably to an achieved equal error rate of 12.9% (an improvement of 33.3%) when only global features are considered. Since there is no standard international off-line signature verification data set available, most systems proposed in the literature are evaluated on data sets that differ from the one employed in this dissertation. A direct comparison of results is therefore not possible. However, since the proposed system utilises significantly different features and/or modelling techniques than those employed in the above-mentioned systems, it is very likely that a superior combined system can be obtained by combining the proposed system with any of the aforementioned systems. Furthermore, when evaluated on the same data set, the proposed system is shown to be significantly superior to three other systems recently proposed in the literature.
AFRIKAANSE OPSOMMING: Die doel van ’n statiese handtekening-verifikasiestelsel is om die identiteit van ’n individu te bekragtig deur sy/haar handgeskrewe handtekening te analiseer, nadat dit suksesvol vanaf byvoorbeeld ’n tjek,’n debiet- of kredietkaattransaksiestrokie, of enige ander wettige dokument onttrek is. Die bevraagtekende handtekening word tipies vergelyk met ’n model wat afgerig is met bekende positiewe voorbeelde, waarna die stelsel poog om die handtekening as eg of vervals te klassifiseer. Klassifiseerder-fusie is die proses waardeer individuele klassifiseerders gekombineer word, ten einde ’n enkele klassifiseerder te konstrueer, wat meer akkuraat, maar meer berekeningsintensief as sy samestellende dele is. ’n Gekombineerde klassifiseerder bestaan derhalwe uit ’n ensemble van basis-klassifiseerders, wat gekombineer word met behulp van ’n spesifieke fusie-strategie. In hierdie projek word ’n nuwe statiese handtekening-verifikasiestelsel, wat van ’n multi-hipotese benadering en klassifiseerder-fusie gebruik maak, voorgestel. Elke basis-klassifiseerder word vanuit ’n verskuilde Markov-model (HMM) gekonstrueer, wat afgerig word met kenmerke wat vanuit lokale gebiede in die handtekening (lokale kenmerke), sowel as vanuit die handtekening in geheel (globale kenmerke), onttrek is. Ten einde dit te bewerkstellig, word elke handtekening in ’n aantal oorvleulende sirkulêre retinas gesoneer, waaruit kenmerke onttrek word deur die diskrete Radon-transform te implementeer. ’n Globale retina, wat die hele handtekening in beslag neem, word ook beskou. Aangesien die voorgestelde stelsel poog om hoë-kwaliteit vervalsings op te spoor, is dit onredelik om te verwag dat voorbeelde van hierdie handtekeninge beskikbaar sal wees vir elke nuwe skrywer (kliënt) wat vir die stelsel registreer. Die stelsel is derhalwe beperk in die sin dat slegs positiewe afrigvoorbeelde, wat bekom is van elke skrywer tydens registrasie, beskikbaar is. Dit is egter redelik om aan te neem dat beide positiewe en negatiewe voorbeelde beskikbaar sal wees vir ’n verteenwoordigende subversameling van sogenaamde proefkonynskrywers, byvoorbeeld bankpersoneel. Hierdie handtekeninge verteenwoordig ’n gereieflike optimeringstel, wat gebruik kan word om die mees bekwame ensemble te selekteer. ’n Handtekening, wat na bewering aan ’n wettige kliënt (lid van die algemene publiek) behoort, word dus verwerp of aanvaar op grond van die meerderheidstem-besluit van die basis-klassifiseerders in die mees bekwame ensemble. Wanneer die voorgestelde stelsel op ’n datastel, wat hoë-kwaliteit vervalsings bevat, ge-evalueer word, verhoog die insluiting van lokale kenmerke en klassifiseerder-fusie die prestasie van die stelsel beduidend. ’n Gelyke foutkoers van 8.6% word behaal, wat gunstig vergelyk met ’n gelyke foutkoers van 12.9% (’n verbetering van 33.3%) wanneer slegs globale kenmerke gebruik word. Aangesien daar geen standard internasionale statiese handtekening-verifikasiestelsel bestaan nie, word die meeste stelsels, wat in die literatuur voorgestel word, op ander datastelle ge-evalueer as die datastel wat in dié projek gebruik word. ’n Direkte vergelyking van resultate is dus nie moontlik nie. Desnieteenstaande, aangesien die voorgestelde stelsel beduidend ander kenmerke en/of modeleringstegnieke as dié wat in bogenoemde stelsels ingespan word gebruik, is dit hoogs waarskynlik dat ’n superieure gekombineerde stelsel verkry kan word deur die voorgestelde stelsel met enige van bogenoemde stelsels te kombineer. Voorts word aangetoon dat, wanneer op dieselfde datastel geevalueerword, die voorgestelde stelstel beduidend beter vaar as drie ander stelsels wat onlangs in die literatuur voorgestel is.
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Laurinčiukaitė, Sigita. "Lietuvių šnekos atpažinimo akustinis modeliavimas." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2008. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2008~D_20080626_121701-05189.
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Darbas „Lietuvių šnekos atpažinimo akustinis modeliavimas“ yra skirtas lietuvių šnekos atpažinimo akustiniam modeliavimui. Darbe buvo tirtas žodžiais, skiemenimis, kontekstiniais skiemenimis, fonemomis ir kontekstinėmis fonemomis grįstas šnekos atpažinimas. Tyrimai atlikti izoliuotiems žodžiams ir ištisinei šnekai. Iki šiol lietuvių šnekos atpažinime populiariausi kalbos vienetai buvo fonema ir kontekstinė fonema, o kitų kalbos vienetų analizė nebuvo atliekama. Šiame darbe siekiama palyginti lingvistinio tipo kalbos vienetų gebėjimą modeliuoti šneką ir parodyti, kad kalbos vienetų analizė siūlo alternatyvius fonemai ir kontekstinei fonemai kalbos vienetus.
Darbe pasiūlyta metodika mišriam skiemenų ir fonemų akustiniam modeliavimui, naujas kalbos vienetas – pseudo-skiemuo; technologijos atskirų kalbos vienetų akustiniam modeliavimui (schemos, įrankiai, rekomendacijos). Eksperimentiniams tyrimams atlikti paruoštas izoliuotų žodžių garsynas ir sukurtos dvi ištisinės šnekos garsyno LRN versijos.
Ištyrus izoliuotų žodžių atpažinimą, akustinius modelius konstruojant žodžiams, nustatyta, kad modelių mokymo aibės dydis, akustinių modelių mokymo aibės turinys daro įtaką šnekos atpažinimo tikslumui. Pateikiamos rekomendacijos akustiniam modeliavimui žodžių pagrindu.
Ištyrus izoliuotų žodžių atpažinimą, akustinius modelius konstruojant žodžiams, skiemenims ir fonemoms, gauti rezultatai 98 ±1,8 % tikslumu siejami su skiemens tipo kalbos vienetais. Dėl skiemenų akustinio modeliavimo... [toliau žr. visą tekstą]This paper is devoted to an acoustic modelling of Lithuanian speech recognition. Word-, syllable-, contextual syllable-, phoneme- and contextual phoneme-based speech recognition was investigated. Investigations were performed for isolated words and continuous speech. The most popular sub-word units in Lithuanian speech recognition are phonemes and contextual phonemes, and research on other sub-word units is omitted. This paper aims to compare capacity of linguistic sub-word units to model speech and to demonstrate that investigation of sub-word units suggest using alternative sub-word units to phoneme and contextual phoneme. The dissertation proposes a new methodology for acoustic modelling of syllables and phonemes, new sub-word unit – pseudo-syllable; technologies for acoustic modelling of separate sub-word units, including developed schemes, tools and recommendations. Speech corpus of isolated words was prepared and two versions of corpus of continuous speech LRN were developed for experimental research. Investigation of recognition of isolated words and construction of acoustic models for words showed that a size of training set of acoustic models, a content of training set in regard to number of speakers have an influence on speech recognition accuracy. The recommendations for word-based acoustic modelling are given. Investigation of recognition of isolated words and construction of acoustic models for words, syllables and phonemes showed that the best recognition... [to full text]
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Knab, Bernhard. "Erweiterungen von Hidden-Markov-Modellen zur Analyse ökonomischer Zeitreihen." [S.l. : s.n.], 2000. http://deposit.ddb.de/cgi-bin/dokserv?idn=96144195X.
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Oberle, Stefan. "Detektion und Estimation von akustischen Signalen mit Hidden-Markov-Modellen /." [S.l.] : [s.n.], 1998. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=12936.
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Koserski, Jan. "Analyse der Ratingmigrationen interner Ratingsysteme mit Markov-Ketten, Hidden-Markov-Modellen und Neuronalen Netzen /." Aachen : Shaker, 2006. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=015604019&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.
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Koserski, Jan [Verfasser]. "Analyse der Ratingmigrationen interner Ratingsysteme mit Markov-Ketten, Hidden-Markov-Modellen und Neuronalen Netzen / Jan Koserski." Aachen : Shaker, 2006. http://d-nb.info/1166515184/34.
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Carlsson, Anders, and Linus Lauri. "Modellering av finansiella data med dolda markovmodeller / Analysis of Financial Data with Hidden Markov Models." Thesis, KTH, Matematisk statistik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-105526.
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The prediction and understanding of market fluctuations are of great interest in today’s society. A common tool for analyzing financial data is the use of different statistical models. This report will focus on examining the stability of a financial data sequence using a statistical model. The sequence that will be used in the report is the logarithmic return of OMXS30 index between the 30th of March 2005 and the 6th of March 2009. The statistical model that will be used is a HMM ( Hidden Markov Model). This model consists essentially of two stochastic processes: A non-observable Markov chain in a finite state space. A state-dependent process with a superimposed white noise. The latter of these two processes is generally known. Therefore, the key is to find how the hidden Markov chain behaves. This will be solved with the so-called EM-algorithm, which is an iterative method to get the model to converge. An optimization of the model with respect to the number of states will be made with the BIC (Bayesian Information Criterion). Thereafter, a validation of the model is done by graphically comparing the quantiles of the model distribution function and the given data. This study shows that by employing an HMM it is possible to describe how the return on the index varies, by examining the probability of changes between the Markov chains volatility states.I dagens samhälle finns det ett stort intresse i att kunna analysera finansiella data och skapa sig en uppfattning om hur marknaden utveck-las. Olika statistiska modeller är de vanligaste verktygen för att kunna göra denna analys. Den här rapporten fokuserar på att med hjälp av en statistisk modell undersöka stabiliteten på en finansiell datasekvens. Datasekvensen kommer i rapporten vara de logaritmiska dagsavkastningarna på OMXS30-index mellan den 30e mars 2005 och den 6e mars 2009. Den statistiska modellen som kommer användas är en så kallad dold Markovmodell eller HMM ( Hidden Markov Model). Modellen består huvudsakligen av två stokastiska processer: En icke observerbar Markovkedja i ett ändligt tillståndsrum. En tillståndsberoende process med ett pålagt vitt brus. Den senare av dessa två processer är vanligtvis känd. Problemet blir därför att försöka hitta hur den dolda Markovkedjan uppför sig. Detta löses med den så kallade EM-algoritmen, vilket är en iterativ metod för att få modellen att konvergera. Därefter genomförs en optimering med avseende på antal tillstånd med BIC ( Bayesian Information Criterion), varefter en validering av modellen utförs, genom att grafiskt jämföra kvantilerna för modellens fördelningsfunktion med den observerade datamängden. Studien visar att det med hjälp av en HMM är möjligt att beskriva hur avkastningen på index varierar. Detta genom att undersöka hur sanno-likt det är för förändringar mellan Markovkedjans volatilitetstillstånd.
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Bieler, Kaspar. "Vergleich von Multilayer Perzeptron, Kohonen Maps und Hidden Markov Modellen für die Fehlerdiagnose in elektrischen Energieübertragungssystemen /." [S.l.] : [s.n.], 1994. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=10861.
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Balasubramanian, Vijay. "Equivalence and Reduction of Hidden Markov Models." 1993. http://hdl.handle.net/1721.1/6801.
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This report studies when and why two Hidden Markov Models (HMMs) may represent the same stochastic process. HMMs are characterized in terms of equivalence classes whose elements represent identical stochastic processes. This characterization yields polynomial time algorithms to detect equivalent HMMs. We also find fast algorithms to reduce HMMs to essentially unique and minimal canonical representations. The reduction to a canonical form leads to the definition of 'Generalized Markov Models' which are essentially HMMs without the positivity constraint on their parameters. We discuss how this generalization can yield more parsimonious representations of stochastic processes at the cost of the probabilistic interpretation of the model parameters.
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Whiting, Julian Peter. "Identification and modelling of hydrological persistence with hidden Markov models." 2006. http://hdl.handle.net/2440/37842.
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Hydrological observations are characterised by wet and dry cycles, a characteristic that is termed hydrological persistence. Interactions between global climate phenomena and the hydrological cycle result in rainfall and streamflow data clustering into wetter and drier states. These states have implications for the management and planning of water resources. Statistical tests constructed from the theory of wet and dry spells indicate that evidence for persistence in monthly observations is more compelling than at an annual scale. This thesis demonstrates that examination of monthly data yields spatially - consistent patterns of persistence across a range of hydrological variables. It is imperative that time series models for rainfall and streamflow replicate the observed fluctuations between the climate regimes. Monthly time series are generally represented with linear models such as ARMA variants ; however simulations from such models may underestimate the magnitude and frequency of persistence. A different approach to modelling these data is to incorporate shifting levels in the broader climate with a tendency to persist within these regimes. Hidden Markov models ( HMMs ) provide a strong conceptual basis for describing hydrological persistence, and are shown to provide accurate descriptions of fluctuating climate states. These models are calibrated here with a full Bayesian approach to quantify parameter uncertainty. A range of novel variations to standard HMMs are introduced, in particular Autoregressive HMMs and hidden semi - Markov models which have rarely been used to model monthly rainfall totals. The former model combines temporal persistence within observations with fluctuations between persistent climate states, and is particularly appropriate for modelling streamflow time series. The latter model extends the modelling capability of HMMs by fitting explicit probability distributions for state durations. These models have received little attention for modelling persistence at monthly scale. A non - parametric ( NP ) HMM, which overcomes the major shortcomings of standard parametric HMMs, is also described. Through removing the requirement to assume parametric forms of conditional distributions prior to model calibration, the innovative NP HMM framework provides an improved estimation of persistence in discrete and continuous data that remains unaffected by incorrect parametric assumptions about the state distributions. Spatially - consistent persistence is identified across Australia with the NP HMM, showing a tendency toward stronger persistence in low-rainfall regions. Coherent signatures of persistence are also identified across time series of total monthly rainfall, numbers of rain - days each month, and the intensities of the most extreme rain events recorded each month over various short durations, illustrating that persistent climate states modulate both the numbers of rain events and the amount of moisture contained within these events. These results provide a new interpretation of the climatic interactions that underlie hydrological persistence. The value of HMMs to water resource management is illustrated with the accurate simulation of a range of hydrologic data, which in each case preserves statistics and spell properties over a range of aggregations. Catchment - scale rainfall for the Warragamba Reservoir is simulated accurately with HMMs, and rainfall - runoff transformations from these simulations provide reservoir inflows of lower drought risk than provided from ARMA models.Thesis (Ph.D.)--School of Civil and Environmental Engineering, 2006.
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Pattison, Vivian. "A hidden Markov modelling approach to understanding Ancient Murrelet behaviour and foraging habitat." Thesis, 2020. http://hdl.handle.net/1828/11698.
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Seabird species are increasingly threatened around the world due to a range of anthropogenic impacts affecting at-sea and breeding habitat. One such species is the Ancient Murrelet, an Alcid species nesting on the Pacific Coast of Canada. Ancient Murrelets are an important species in Canadian waters as approximately 50 % of the world’s breeding population nest in a small region of the British Columbia coast. Ancient Murrelets are listed as a species of Special Concern, due to threats in their breeding colonies; threats to their at-sea habitat, such as disturbance from shipping traffic, oil pollution, and fisheries bycatch, are currently poorly- documented due to the challenges associated with studying seabirds in their offshore environments. Conservation efforts to protect this species require information on movements and habitat use at sea. Therefore, there exists a critical need for research that provides new knowledge on where murrelets are travelling and the habitats in which they are foraging.
The objective of this thesis research is to investigate movement behaviour and at-sea habitat of Ancient Murrelets during breeding season foraging trips. Movement modelling using hidden Markov models differentiated the tracks into behaviour states, and identified foraging locations at sea. Foraging locations were used in regression modelling to investigate the degree to which variability in Ancient Murrelet foraging locations could be explained by seafloor depth, slope and tidal current, and spatial measures such as distance from the breeding colony. From characteristics of movement paths, hidden Markov models identified three movement behaviour states, which were interpreted as transit, resting, and foraging behaviours. Logistic regression models suggested that depth, seafloor slope, tidal speed, and distance from the colony exhibited a negative influence on locations where birds chose to forage. Nevertheless, of the locations where foraging took place, foraging intensity was found to be higher in deeper areas suggesting Ancient Murrelets may be focusing efforts in areas of higher prey abundance. The combination of individual movement analysis and habitat analysis provides an important first step in gaining a greater understanding of Ancient Murrelet behaviour and foraging habitat at sea. These findings can inform marine management planning in this region and conservation of this vulnerable species.Graduate
2021-04-17
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Frost, Andrew James. "Spatio-temporal hidden Markov models for incorporating interannual variability in rainfall." 2004. http://hdl.handle.net/1959.13/24868.
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Two new spatio-temporal hidden Markov models (HMM) are introduced in this thesis, with the purpose of capturing the persistent, spatially non-homogeneous nature of climate influence on annual rainfall series observed in Australia. The models extend the two-state HMM applied by Thyer (2001) by relaxing the assumption that all sites are under the same climate control. The Switch HMM (SHMM) allows at-site anomalous states, whilst still maintaining a regional control. The Regional HMM (RHMM), on the other hand, allows sites to be partitioned into different Markovian state regions. The analyses were conducted using a Bayesian framework to explicitly account for parameter uncertainty and select between competing hypotheses. Bayesian model averaging was used for comparison of the HMM and its generalisations. The HMM, SHMM and RHMM were applied to four groupings of four sites located on the Eastern coast of Australia, an area that has previously shown evidence of interannual persistence. In the majority of case studies, the RHMM variants showed greatest posterior weight, indicating that the data favoured the multiple region RHMM over the single region HMM or the SHMM variants. In no cases does the HMM produce the maximum marginal likelihood when compared to the SHMM and RHMM. The HMM state series and preferred model variants were sensitive to the parameterisation of the small-scale site-to-site correlation structure. Several parameterisations of the small-scale Gaussian correlation were trialled, namely Fitted Correlation, Exponential Decay Correlation, Empirical and Zero Correlation. Significantly, it was shown that annual rainfall data outliers can have a large effect on inference for a model that uses Gaussian distributions. The practical value of this modelling is demonstrated by the conditioning of the event based point rainfall model DRIP on the hidden state series of the HMM variants. Short timescale models typically underestimate annual variability because there is no explicit structure to incorporate long-term persistence. The two-state conditioned DRIP model was shown to reproduce the annual variability observed to a greater degree than the single state DRIP.PhD Doctorate
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Soleimani, Morteza, I. Felician Campean, and Daniel Neagu. "Integration of Hidden Markov Modelling and Bayesian Network for Fault Detection and Prediction of Complex Engineered Systems." 2021. http://hdl.handle.net/10454/18518.
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yesThis paper presents a methodology for fault detection, fault prediction and fault isolation based on the integration of hidden Markov modelling (HMM) and Bayesian networks (BN). This addresses the nonlinear and non-Gaussian data characteristics to support fault detection and prediction, within an explainable hybrid framework that captures causality in the complex engineered system. The proposed methodology is based on the analysis of the pattern of similarity in the log-likelihood (LL) sequences against the training data for the mixture of Gaussians HMM (MoG-HMM). The BN model identifies the root cause of detected/predicted faults, using the information propagated from the HMM model as empirical evidence. The feasibility and effectiveness of the presented approach are discussed in conjunction with the application to a real-world case study of an automotive exhaust gas Aftertreatment system. The paper details the implementation of the methodology to this case study, with data available from real-world usage of the system. The results show that the proposed methodology identifies the fault faster and attributes the fault to the correct root cause. While the proposed methodology is illustrated with an automotive case study, its applicability is much wider to the fault detection and prediction problem of any similar complex engineered system.
The full text will be available at the end of the publisher's embargo: 28th May 2023
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Thyer, Mark Andrew. "Modelling Long-Term Persistence in Hydrological Time Series." 2001. http://hdl.handle.net/1959.13/24891.
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The hidden state Markov (HSM) model is introduced as a new conceptual framework for modelling long-term persistence in hydrological time series. Unlike the stochastic models currently used, the conceptual basis of the HSM model can be related to the physical processes that influence long-term hydrological time series in the Australian climatic regime. A Bayesian approach was used for model calibration. This enabled rigourous evaluation of parameter uncertainty, which proved crucial for the interpretation of the results. Applying the single site HSM model to rainfall data from selected Australian capital cities provided some revealing insights. In eastern Australia, where there is a significant influence from the tropical Pacific weather systems, the results showed a weak wet and medium dry state persistence was likely to exist. In southern Australia the results were inconclusive. However, they suggested a weak wet and strong dry persistence structure may exist, possibly due to the infrequent incursion of tropical weather systems in southern Australia. This led to the postulate that the tropical weather systems are the primary cause of two-state long-term persistence. The single and multi-site HSM model results for the Warragamba catchment rainfall data supported this hypothesis. A strong two-state persistence structure was likely to exist in the rainfall regime of this important water supply catchment. In contrast, the single and multi-site results for the Williams River catchment rainfall data were inconsistent. This illustrates further work is required to understand the application of the HSM model. Comparisons with the lag-one autoregressive [AR(1)] model showed that it was not able to reproduce the same long-term persistence as the HSM model. However, with record lengths typical of real data the difference between the two approaches was not statistically significant. Nevertheless, it was concluded that the HSM model provides a conceptually richer framework than the AR(1) model.PhD Doctorate
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Hladky, Stephen Michael. "Predicting opponent locations in first-person shooter video games." Master's thesis, 2009. http://hdl.handle.net/10048/600.
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Thesis (M. Sc.)--University of Alberta, 2009.Title from PDF file main screen (viewed on Oct. 2, 2009). "A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Master of Science, Department of Computing Science, University of Alberta." Includes bibliographical references.
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Jiang, Rui. "System Availability Maximization and Residual Life Prediction under Partial Observations." Thesis, 2011. http://hdl.handle.net/1807/31792.
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Many real-world systems experience deterioration with usage and age, which often leads to low product quality, high production cost, and low system availability. Most previous maintenance and reliability models in the literature do not incorporate condition monitoring information for decision making, which often results in poor failure prediction for partially observable deteriorating systems. For that reason, the development of fault prediction and control scheme using condition-based maintenance techniques has received considerable attention in recent years. This research presents a new framework for predicting failures of a partially observable deteriorating system using Bayesian control techniques. A time series model is fitted to a vector observation process representing partial information about the system state. Residuals are then calculated using the fitted model, which are indicative of system deterioration. The deterioration process is modeled as a 3-state continuous-time homogeneous Markov process. States 0 and 1 are not observable, representing healthy (good) and unhealthy (warning) system operational conditions, respectively. Only the failure state 2 is assumed to be observable. Preventive maintenance can be carried out at any sampling epoch, and corrective maintenance is carried out upon system failure. The form of the optimal control policy that maximizes the long-run expected average availability per unit time has been investigated. It has been proved that a control limit policy is optimal for decision making. The model parameters have been estimated using the Expectation Maximization (EM) algorithm. The optimal Bayesian fault prediction and control scheme, considering long-run average availability maximization along with a practical statistical constraint, has been proposed and compared with the age-based replacement policy. The optimal control limit and sampling interval are calculated in the semi-Markov decision process (SMDP) framework. Another Bayesian fault prediction and control scheme has been developed based on the average run length (ARL) criterion. Comparisons with traditional control charts are provided. Formulae for the mean residual life and the distribution function of system residual life have been derived in explicit forms as functions of a posterior probability statistic. The advantage of the Bayesian model over the well-known 2-parameter Weibull model in system residual life prediction is shown. The methodologies are illustrated using simulated data, real data obtained from the spectrometric analysis of oil samples collected from transmission units of heavy hauler trucks in the mining industry, and vibration data from a planetary gearbox machinery application.
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Malladi, Raghuram. "Automatic signature verification system." 2013. http://hdl.handle.net/11394/3617.
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Philosophiae Doctor - PhDIn this thesis, we explore dynamic signature verification systems. Unlike other signature models, we use genuine signatures in this project as they are more appropriate in real world applications. Signature verification systems are typical examples of biometric devices that use physical and behavioral characteristics to verify that a person really is who he or she claims to be. Other popular biometric examples include fingerprint scanners and hand geometry devices. Hand written signatures have been used for some time to endorse financial transactions and legal contracts although little or no verification of signatures is done. This sets it apart from the other biometrics as it is well accepted method of authentication. Until more recently, only hidden Markov models were used for model construction. Ongoing research on signature verification has revealed that more accurate results can be achieved by combining results of multiple models. We also proposed to use combinations of multiple single variate models instead of single multi variate models which are currently being adapted by many systems. Apart from these, the proposed system is an attractive way for making financial transactions more secure and authenticate electronic documents as it can be easily integrated into existing transaction procedures and electronic communications
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Knab, Bernhard [Verfasser]. "Erweiterungen von Hidden-Markov-Modellen zur Analyse ökonomischer Zeitreihen / vorgelegt von Bernhard Knab." 2000. http://d-nb.info/96144195X/34.
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