Spelling suggestions: "subject:"hidden markov models"" "subject:"hidden darkov models""
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Estimação de modelos de Markov ocultos usando aritmética intervalar / Estimating hidden Markov model parameters using interval arithmeticTiago de Morais Montanher 24 April 2015 (has links)
Modelos de Markov ocultos (MMOs) são uma ferramenta importante em matemática aplicada e estatística. Eles se baseiam em dois processos estocásticos. O primeiro é uma cadeia de Markov, que não é observada diretamente. O segundo é observável e sua distribuição depende do estado na cadeia de Markov. Supomos que os processos são discretos no tempo e assumem um número finito de estados. Para extrair informações dos MMOs, é necessário estimar seus parâmetros. Diversos algoritmos locais têm sido utilizados nas últimas décadas para essa tarefa. Nosso trabalho estuda a estimação de parâmetros em modelos de Markov ocultos, do ponto de vista da otimização global. Desenvolvemos algoritmos capazes de encontrar, em uma execução bem sucedida, todos os estimadores de máxima verossimilhança globais de um modelo de Markov oculto. Para tanto, usamos aritmética intervalar. Essa aritmética permite explorar sistematicamente o espaço paramétrico, excluindo regiões que não contém soluções. O cálculo da função objetivo é feito através da recursão \\textit, descrita na literatura estatística. Modificamos a extensão intervalar natural dessa recursão usando programação linear. Nossa abordagem é mais eficiente e produz intervalos mais estreitos do que a implementação padrão. Experimentos mostram ganhos de 16 a 250 vezes, de acordo com a complexidade do modelo. Revisamos os algoritmos locais, tendo em vista sua aplicação em métodos globais. Comparamos os algoritmos de Baum-Welch, pontos interiores e gradientes projetados espectrais. Concluímos que o método de Baum-Welch é o mais indicado como auxiliar em otimização global. Modificamos o \\textit{interval branch and bound} para resolver a estimação de modelos com eficiência. Usamos as condições KKT e as simetrias do problema na construção de testes para reduzir ou excluir caixas. Implementamos procedimentos de aceleração da convergência, como o método de Newton intervalar e propagação de restrições e da função objetivo. Nosso algoritmo foi escrito em \\textit{C++}, usando programação genérica. Mostramos que nossa implementação dá resultados tão bons quanto o resolvedor global BARON, porém com mais eficiência. Em média, nosso algoritmo é capaz de resolver $50\\%$ mais problemas no mesmo período de tempo. Concluímos estudando aspectos qualitativos dos MMOs com mistura Bernoulli. Plotamos todos os máximos globais detectados em instâncias com poucas observações e apresentamos novos limitantes superiores da verossimilhança baseados na divisão de uma amostra grande em grupos menores. / Hidden Markov models(HMMs) are an important tool in statistics and applied mathematics. Our work deals with processes formed by two discrete time and finite state space stochastic processes. The first process is a Markov chain and is not directly observed. On the other hand, the second process is observable and its distribution depends on the current state of the hidden component. In order to extract conclusions from a Hidden Markov Model we must estimate the parameters that defines it. Several local algorithms has been used to handle with this task. We present a global optimization approach based on interval arithmetic to maximize the likelihood function. Interval arithmetic allow us to explore parametric space systematically, discarding regions which cannot contain global maxima. We evaluate the objective function and its derivatives by the so called backward recursion and show that is possible to obtain sharper interval extensions for such functions using linear programming. Numerical experiments shows that our approach is $16$ to $250$ times more efficient than standard implementations. We also study local optimization algorithms hidden Markov model estimation. We compare Baum-Welch procedure with interior points and spectral projected gradients. We conclude that Baum-Welch is the best option as a sub-algorithm in a global optimization framework. We improve the well known interval branch and bound algorithm to take advantages on the problem structure. We derive new exclusion tests, based on its KKT conditions and symmetries. We implement our approach in C++, under generic programming paradigm. We show that our implementation is compatible with global optimization solver BARON in terms of precision. We also show that our algorithm is faster than BARON. In average, we can handle with $50\\%$ more problems within the same amount of time. We conclude studying qualitative aspects of Bernoulli hidden Markov models. We plot all global maxima found in small observations instances and show a new upper bound of the likelihood based on splitting observations in small groups.
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Face recognition using Hidden Markov ModelsSamaria, Ferdinando Silvestro January 1995 (has links)
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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Estudo de um sistema de conversão texto-fala baseado em HMM / Study of a HMM-based text-to-speech systemCarvalho, Sarah Negreiros de, 1985- 22 August 2018 (has links)
Orientador: Fábio Violaro / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação / Made available in DSpace on 2018-08-22T07:58:43Z (GMT). No. of bitstreams: 1
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Previous issue date: 2013 / Resumo: Com o contínuo desenvolvimento da tecnologia, há uma demanda crescente por sistemas de síntese de fala que sejam capazes de falar como humanos, para integrá-los nas mais diversas aplicações, seja no âmbito da automação robótica, sejam para acessibilidade de pessoas com deficiências, seja em aplicativos destinados a cultura e lazer. A síntese de fala baseada em modelos ocultos de Markov (HMM) mostra-se promissora em suprir esta necessidade tecnológica. A sua natureza estatística e paramétrica a tornam um sistema flexível, capaz de adaptar vozes artificiais, inserir emoções no discurso e obter fala sintética de boa qualidade usando uma base de treinamento limitada. Esta dissertação apresenta o estudo realizado sobre o sistema de síntese de fala baseado em HMM (HTS), descrevendo as etapas que envolvem o treinamento dos modelos HMMs e a geração do sinal de fala. São apresentados os modelos espectrais, de pitch e de duração que constituem estes modelos HMM dos fonemas dependentes de contexto, considerando as diversas técnicas de estruturação deles. Alguns dos problemas encontrados no HTS, tais como a característica abafada e monótona da fala artificial, são analisados juntamente com algumas técnicas propostas para aprimorar a qualidade final do sinal de fala sintetizado / Abstract: With the continuous development of technology, there is a growing demand for text-to-speech systems that are able to speak like humans, in order to integrate them in the most diverse applications whether in the field of automation and robotics, or for accessibility of people with disabilities, as for culture and leisure activities. Speech synthesis based on hidden Markov models (HMM) shows to be promising in addressing this need. Their statistical and parametric nature make it a flexible system capable of adapting artificial voices, insert emotions in speech and get artificial speech of good quality using a limited amount of speech data for HMM training. This thesis presents the study realized on HMM-based speech synthesis system (HTS), describing the steps that involve the training of HMM models and the artificial speech generation. Spectral, pitch and duration models are presented, which form context-dependent HMM models, and also are considered the various techniques for structuring them. Some of the problems encountered in the HTS, such as the characteristic muffled and monotone of artificial speech, are analyzed along with some of the proposed techniques to improve the final quality of the synthesized speech signal / Mestrado / Telecomunicações e Telemática / Mestra em Engenharia Elétrica
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Zpracování signálů pomocí skrytých Markovových modelů / Signal processing by hidden Markov modelsHampl, Jindřich January 2010 (has links)
One of the most common methods for isolated words recognition is based on Hidden Markov models. Speech signal can be considered as a sequence of successive parts of the signal with specific statistical parameters. Hidden Markov model corresponds to the statistical model with the final number of states, which may be useful for signals such as speech. HTK module is a software tools, which is mostly used to work with hidden Markov models.
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Predikce homologních sekvencí proteinů / Prediction of Homolog Protein SequencesChlupová, Hana January 2015 (has links)
Prediction and searching for homologous protein sequences is one of important tasks which are currently being addressed in the area of bioinformatics. According to the determination of homologous sequences of unknown protein sequence it is often possible to determine its structure and function in the organism. For searching homologous sequences, the most frequently used tools are based on direct sequence comparison, profile comparison or on the use of hidden Markov models. There is no universal method better than all others. To satisfy user`s request on needed sequence identity between domains and error rate between founded true positive and false positive pairs, the selection of proper method and its settings is needed. This work is focused to create tool which will help user to choose the best method and its settings according to his requirements. It was created on the basis of the analysis of method results with different settings. In addition, the implemented application offers the possibility to run this method and show its results.
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Predikce vazebních míst proteinu p53 / Prediction of p53 Protein Binding SitesRadakovič, Jozef January 2015 (has links)
Protein p53 which is encoded by gene TP53 plays crucial role in cell cycle as a regulator of transcription of genes in cases when cell is under stress. Therefore p53 acts like tumor suppressor. Understanding the pathway of p53 regulation as well as predicting its binding sites on p53 regulated genes is one of the major concerns of modern research in genetics and bioinformatics. In first part of this project we aim to introduce basics from molecular biology to better understand the p53 protein pathway in gene transcription and introduction to analysis of prediction of p53 binding sites. Second part is about implementation and testing of tool which would be able to predict transcription factor binding sites for protein p53.
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Rozpoznávání rukou psaného textu / Handwriting RecognitionZouhar, David January 2012 (has links)
This diploma thesis deals with handwriting recognition in real-time. It describes the ways how the intput data are processed. It is also focused on the classi cation methods, which are used for the recognition. It especially describes hidden Markov models. It also present the evaluation of the success of the recognition based on implemented experiments. The alternative keyboard for MeeGo system was created for this thesis as well. The established system achieved the success above 96%.
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A cost-effective diagnostic methodology using probabilistic approaches for gearboxes operating under non-stationary conditionsSchmidt, Stephan January 2016 (has links)
Condition monitoring is very important for critical assets such as gearboxes used in
the power and mining industries. Fluctuating operating conditions are inevitable for
wind turbines and mining machines such as bucket wheel excavators and draglines
due to the continuous
uctuating wind speeds and variations in ground properties,
respectively. Many of the classical condition monitoring techniques have proven to
be ine ective under
uctuating operating conditions and therefore more sophisticated
techniques have to be developed. However, many of the signal processing tools that
are appropriate for
uctuating operating conditions can be di cult to interpret, with
the presence of incipient damage easily being overlooked.
In this study, a cost-e ective diagnostic methodology is developed, using machine learning
techniques, to diagnose the condition of the machine in the presence of
uctuating
operating conditions when only an acceleration signal, generated from a gearbox during
normal operation, is available. The measured vibration signal is order tracked to
preserve the angle-cyclostationary properties of the data. A robust tacholess order
tracking methodology is proposed in this study using probabilistic approaches. The
measured vibration signal is order tracked with the tacholess order tracking method
(as opposed to computed order tracking), since this reduces the implementation and
the running cost of the diagnostic methodology.
Machine condition features, which are sensitive to changes in machine condition, are extracted
from the order tracked vibration signal and processed. The machine condition
features can be sensitive to operating condition changes as well. This makes it difficult to ascertain whether the changes in the machine condition features are due to changes
in machine condition (i.e. a developing fault) or changes in operating conditions. This
necessitates incorporating operating condition information into the diagnostic methodology
to ensure that the inferred condition of the machine is not adversely a ected
by the
uctuating operating conditions. The operating conditions are not measured
and therefore representative features are extracted and modelled with a hidden Markov
model. The operating condition machine learning model aims to infer the operating
condition state that was present during data acquisition from the operating condition
features at each angle increment. The operating condition state information is used
to optimise robust machine condition machine learning models, in the form of hidden
Markov models.
The information from the operating condition and machine condition models are combined
using a probabilistic approach to generate a discrepancy signal. This discrepancy
signal represents the deviation of the current features from the expected behaviour of
the features of a gearbox in a healthy condition. A second synchronous averaging
process, an automatic alarm threshold for fault detection, a gear-pinion discrepancy
distribution and a healthy-damaged decomposition of the discrepancy signal are proposed
to provide an intuitive and robust representation of the condition of the gearbox
under
uctuating operating conditions. This allows fault detection, localisation as well
as trending to be performed on a gearbox during
uctuating operation conditions.
The proposed tacholess order tracking method is validated on seven datasets and the
fault diagnostic methodology is validated on experimental as well as numerical data.
Very promising results are obtained by the proposed tacholess order tracking method
and by the diagnostic methodology. / Dissertation (MEng)--University of Pretoria, 2016. / Mechanical and Aeronautical Engineering / MEng / Unrestricted
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Human and animal classification using Doppler radarVan Eeden, Willem Daniel January 2017 (has links)
South Africa is currently struggling to deal with a significant poaching and livestock theft problem. This work is concerned with the detection and classification of ground based targets using radar micro- Doppler signatures to aid in the monitoring of borders, nature reserves and farmlands. The research starts of by investigating the state of the art of ground target classification. Different radar systems are investigated with respect to their ability to classify targets at different operating frequencies. Finally, a Gaussian Mixture Model Hidden Markov Model based (GMM-HMM) classification approach is presented and tested in an operational environment. The GMM-HMM method is compared to methods in the literature and is shown to achieve reasonable (up to 95%) classification accuracy, marginally outperforming existing ground target classification methods. / Dissertation (MEng)--University of Pretoria, 2017. / Electrical, Electronic and Computer Engineering / MEng / Unrestricted
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A hidden Markov modelling approach to understanding Ancient Murrelet behaviour and foraging habitatPattison, Vivian 28 April 2020 (has links)
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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