Tesi sul tema "Hidden Markov process"
Cita una fonte nei formati APA, MLA, Chicago, Harvard e in molti altri stili
Vedi i top-29 saggi (tesi di laurea o di dottorato) per l'attività di ricerca sul tema "Hidden Markov process".
Accanto a ogni fonte nell'elenco di riferimenti c'è un pulsante "Aggiungi alla bibliografia". Premilo e genereremo automaticamente la citazione bibliografica dell'opera scelta nello stile citazionale di cui hai bisogno: APA, MLA, Harvard, Chicago, Vancouver ecc.
Puoi anche scaricare il testo completo della pubblicazione scientifica nel formato .pdf e leggere online l'abstract (il sommario) dell'opera se è presente nei metadati.
Vedi le tesi di molte aree scientifiche e compila una bibliografia corretta.
Jin, Chao. "A Sequential Process Monitoring Approach using Hidden Markov Model for Unobservable Process Drift". University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1445341969.
Testo completoMattila, Robert. "Hidden Markov models : Identification, control and inverse filtering". Licentiate thesis, KTH, Reglerteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-223683.
Testo completoQC 20180301
Chamroukhi, Faicel. "Hidden process regression for curve modeling, classification and tracking". Compiègne, 2010. http://www.theses.fr/2010COMP1911.
Testo completoThis research addresses the problem of diagnosis and monitoring for predictive maintenance of the railway infrastructure. In particular, the switch mechanism is a vital organ because its operating state directly impacts the overall safety of the railway system and its proper functioning is required for the full availability of the transportation system; monitoring it is a key task within maintenance team actions. To monitor and diagnose the switch mechanism, the main available data are curves of electric power acquired during several switch operations. This study therefore focuses on modeling curve-valued or functional data presenting regime changes. In this thesis we propose new probabilistic generative machine learning methodologies for curve modeling, classification, clustering and tracking. First, the models we propose for a single curve or independent sets of curves are based on specific regression models incorporating a flexible hidden process. They are able to capture non-stationary (dynamic) behavior within the curves and address the problem of missing information regarding the underlying regimes, and the problem of complex shaped classes. We then propose dynamic models for learning from curve sequences to make decision and prediction over time. The developed approaches rely on autoregressive dynamic models governed by hidden processes. The learning of the models is performed in both a batch mode (in which the curves are stored in advance) and an online mode as the learning proceeds (in which the curves are analyzed one at a time). The obtained results on both simulated curves and the real world switch operation curves demonstrate the practical use of the ideas introduced in this thesis
Balali, Samaneh. "Incorporating expert judgement into condition based maintenance decision support using a coupled hidden markov model and a partially observable markov decision process". Thesis, University of Strathclyde, 2012. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=19510.
Testo completoWong, Wee Chin. "Estimation and control of jump stochastic systems". Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31775.
Testo completoCommittee Chair: Jay H. Lee; Committee Member: Alexander Gray; Committee Member: Erik Verriest; Committee Member: Magnus Egerstedt; Committee Member: Martha Grover; Committee Member: Matthew Realff. Part of the SMARTech Electronic Thesis and Dissertation Collection.
Löhr, Wolfgang. "Models of Discrete-Time Stochastic Processes and Associated Complexity Measures". Doctoral thesis, Universitätsbibliothek Leipzig, 2010. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-38267.
Testo completoDamian, Camilla, Zehra Eksi-Altay e Rüdiger Frey. "EM algorithm for Markov chains observed via Gaussian noise and point process information: Theory and case studies". De Gruyter, 2018. http://dx.doi.org/10.1515/strm-2017-0021.
Testo completoLöhr, Wolfgang. "Models of Discrete-Time Stochastic Processes and Associated Complexity Measures". Doctoral thesis, Max Planck Institut für Mathematik in den Naturwissenschaften, 2009. https://ul.qucosa.de/id/qucosa%3A11017.
Testo completoCarvalho, Walter Augusto Fonsêca de 1964. "Processos de renovação obtidos por agregação de estados a partir de um processo markoviano". [s.n.], 2014. http://repositorio.unicamp.br/jspui/handle/REPOSIP/306196.
Testo completoTese (doutorado) - Universidade Estadual de Campinas, Instituto de Matemática Estatística e Computação Científica
Made available in DSpace on 2018-08-24T12:54:22Z (GMT). No. of bitstreams: 1 Carvalho_WalterAugustoFonsecade_D.pdf: 1034671 bytes, checksum: 25dd72305f343655bedfde62a785a259 (MD5) Previous issue date: 2014
Resumo: Esta tese é dedicada ao estudo dos processos de renovação binários obtidos como agregação de estados a partir de processos Markovianos com alfabeto finito. Na primeira parte, utilizamos uma abordagem matricial para obter condições sob as quais o processo agregado pertence a cada uma das seguintes classes: (1) Markoviano de ordem finita, (2) processo de ordem infinita com probabilidades de transição contínuas, (3) processo Gibbsiano. A segunda parte trata da distância d entre processos de renovação binários. Obtivemos condições sob as quais esta distância pode ser atingida entre tais processos
Abstract: This thesis is devoted to the study of binary renewal processes obtained as aggregation of states from Markov processes with finite alphabet. In the rst part, we use a matrix approach to obtain conditions under which the aggregated process belongs to each of the following classes: (1) Markov of finite order, (2) process of infinite order with continuous transition probabilities, (3) Gibbsian process. The second part deals with the distance d between binary renewal processes. We obtain conditions under which this distance can be achieved between these processes
Doutorado
Estatistica
Doutor em Estatística
Starke, Martin, Benjamin Beck, Denis Ritz, Frank Will e 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.
Testo completoTang, Man. "Statistical methods for variant discovery and functional genomic analysis using next-generation sequencing data". Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/104039.
Testo completoDoctor of Philosophy
The development of high-throughput next-generation sequencing (NGS) techniques produces massive amount of data and bring out innovations in biology and medicine. A greater concentration is needed in developing novel, powerful, and efficient tools for NGS data analysis. In this dissertation, we mainly focus on three problems closely related to NGS and its applications: (1) how to improve variant calling accuracy, (2) how to model transcription factor (TF) binding patterns, and (3) how to quantify of the contribution of TF binding on gene expression. We develop novel statistical methods to identify sequence variants, find TF binding patterns, and explore the relationship between TF binding and gene expressions. We expect our findings will be helpful in promoting a better understanding of disease causality and facilitating the design of personalized treatments.
Almeida, Gustavo Matheus de. "Detecção de situações anormais em caldeiras de recuperação química". Universidade de São Paulo, 2006. http://www.teses.usp.br/teses/disponiveis/3/3137/tde-01122006-155750/.
Testo completoThe greatest challenge faced by the area of process monitoring in chemical industries still resides in the fault detection task, which aims at developing reliable systems. One may say that a system is reliable if it is able to perform early fault detection and, at the same time, to reduce the generation of false alarms. Once there is a reliable system available, it can be employed to help operators, in factories, in the decisionmaking process. The aim of this study is presenting a methodology, based on the Hidden Markov Model (HMM) technique, suggesting its use in the detection of abnormal situations in chemical recovery boilers. The most successful applications of HMM are in the area of speech recognition. Some of its advantages are: probabilistic reasoning, explicit modeling and the identification based on process history data. This study discusses two applications. The first one is on a benchmark of a multiple evaporation system in a sugar factory. A HMM representative of the normal operation was identified, in order to detect five abnormal situations at the actuator responsible for controlling the syrup flow to the first evaporator. The detection result for the three abrupt situations was immediate, since the HMM was capable of detecting the statistical changes on the signal of the monitored variable as soon as they occurred. Regarding to the two incipient situations, the detection was done at an early stage. For both events, the value of vector f (responsible for representing the strength of an abnormal event over time), at the time it occurred, was near zero, equal to 2.8 and 2.1%, respectively. The second case study deals with the application of HMM in a chemical recovery boiler, belonging to a cellulose mill, in Brazil. The aim is monitoring the accumulation of ash deposits over the equipments of the convective heat transfer section, through pressure drop measures. This is one of the main challenges to be overcome nowadays, bearing in mind the interest that exists in increasing the operational efficiency of this equipment. Initially, a HMM for high values of pressure drop was identified. With this model, it was possible to check its capacity to inform the current state, and consequently, the tendency of the system (similarly as a predictor). It was also possible to show the utility of defining control limits, in order to inform the operator the relative distance between the current state of the system and the alarm levels of pressure drop.
White, Nicole. "Bayesian mixtures for modelling complex medical data : a case study in Parkinson’s disease". Thesis, Queensland University of Technology, 2011. https://eprints.qut.edu.au/48202/1/Nicole_White_Thesis.pdf.
Testo completoBaysse, Camille. "Analyse et optimisation de la fiabilité d'un équipement opto-électrique équipé de HUMS". Phd thesis, Université Sciences et Technologies - Bordeaux I, 2013. http://tel.archives-ouvertes.fr/tel-00986112.
Testo completoPadilla, Pérez Nicolás. "Heterogeneidad de estados en Hidden Markov models". Tesis, Universidad de Chile, 2014. http://www.repositorio.uchile.cl/handle/2250/129971.
Testo completoIngeniero Civil Industrial
Hidden Markov models (HMM) han sido ampliamente usados para modelar comportamientos dinámicos tales como atención del consumidor, navegación en internet, relación con el cliente, elección de productos y prescripción de medicamentos por parte de los médicos. Usualmente, cuando se estima un HMM simultáneamente para todos los clientes, los parámetros del modelo son estimados asumiendo el mismo número de estados ocultos para cada cliente. Esta tesis busca estudiar la validez de este supuesto identificando si existe un potencial sesgo en la estimación cuando existe heterogeneidad en el número de estados. Para estudiar el potencial sesgo se realiza un extenso ejercicio de simulación de Monte Carlo. En particular se estudia: a) si existe o no sesgo en la estimación de parámetros, b) qué factores aumentan o disminuyen el sesgo, y c) qué métodos pueden ser usados para estimar correctamente el modelo cuando existe heterogeneidad en el número de estados. En el ejercicio de simulación, se generan datos utilizando un HMM con dos estados para el 50% de clientes y un HMM con tres estados para el 50% restante. Luego, se utiliza un procedimiento MCMC jerárquico Bayesiano para estimar los parámetros de un HMM con igual número de estados para todos los clientes. En cuanto a la existencia de sesgo, los resultados muestran que los parámetros a nivel individual son recuperados correctamente, sin embargo los parámetros a nivel agregado correspondientes a la distribución de heterogeneidad de los parámetros individuales deben ser reportados cuidadosamente. Esta dificultad es generada por la mezcla de dos segmentos de clientes con distinto comportamiento. En cuanto los factores que afectan el sesgo, los resultados muestran que: 1) cuando la proporción de clientes con dos estados aumenta, el sesgo de los resultados agregados también aumenta; 2) cuando se incorpora heterogeneidad en las probabilidades condicionales, se generan estados duplicados para los clientes con 2 estados y los estados no representan lo mismo para todos los clientes, incrementando el sesgo a nivel agregado; y 3) cuando el intercepto de las probabilidades condicionales es heterogéneo, incorporar variables exógenas puede ayudar a identificar los estados igualmente para todos los clientes. Para reducir los problemas mencionados se proponen dos enfoques. Primero, usar una mezcla de Gaussianas como distribución a priori para capturar heterogeneidad multimodal, y segundo usar un modelo de clase latente con HMMs de distintos número de estados para cada clase. El primer modelo ayuda en representar de mejor forma los resultados agregados. Sin embargo, el modelo no evita que existan estados duplicados para los clientes con menos estados. El segundo modelo captura la heterogeneidad en el número de estados, identificando correctamente el comportamiento a nivel agregado y evitando estados duplicados para clientes con dos estados. Finalmente, esta tesis muestra que en la mayoría de los casos estudiados, el supuesto de un número fijo de estados no genera sesgo a nivel individual cuando se incorpora heterogeneidad. Esto ayuda a mejorar la estimación, sin embargo se deben tomar precauciones al realizar conclusiones usando los resultados agregados.
Le, Hai-Son Phuoc. "Probabilistic Models for Collecting, Analyzing, and Modeling Expression Data". Research Showcase @ CMU, 2013. http://repository.cmu.edu/dissertations/245.
Testo completoRosser, 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.
Testo completoBerberovic, Adnan, e Alexander Eriksson. "A Multi-Factor Stock Market Model with Regime-Switches, Student's T Margins, and Copula Dependencies". Thesis, Linköpings universitet, Produktionsekonomi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-143715.
Testo completoTong, Xiao Thomas. "Statistical Learning of Some Complex Systems: From Dynamic Systems to Market Microstructure". Thesis, Harvard University, 2013. http://dissertations.umi.com/gsas.harvard:10917.
Testo completoStatistics
YOU, GUO-HUI, e 游國輝. "Hardware implementation of hidden Markov model scoring process". Thesis, 1988. http://ndltd.ncl.edu.tw/handle/41607730896590372987.
Testo completoChang, Wang-Jung, e 張旺榮. "Improving Performance of Process Monitoring Using Hidden Markov Tree Model". Thesis, 2004. http://ndltd.ncl.edu.tw/handle/abw47w.
Testo completo中原大學
化學工程研究所
92
Wavelet-based hidden Markov tree (HMT) models is proposed to improve the conventional time-scale only statistical process model (SPC) for process monitoring. HMT in the wavelet domain can not only analyze the measurements at multiple scales in time and frequency but also capture the statistical behavior of real world measurements in these different scales. The former can provide better noise reduction and less signal distortion than conventional filtering methods; the latter can extract the statistical characteristics of the unmeasured dynamic disturbances, like the clustering and persistence of the practical data which are not considered in SPC. Based on HMT, a univariate and a multivariate SPC are respectively developed. Initially, the HMT model is trained in the wavelet domain using the data obtained from the normal operation regions. The model parameters are trained by the expectation maximization algorithm. After extracting the past operating information, the proposed method, like the philosophy of the traditional SPC, can generate simple monitoring charts, easily tracking and monitoring the occurrence of observable upsets. The comparisons of the existing SPC methods that explain the advantages of the properties of the newly proposed method are shown. They indicate that the proposed method can lead to more accurate results when the unmeasured disturbance series are getting strong correlation. Data from the monitoring practice in the industrial problems are presented to help readers delve into the matter.
Guo, Jia-Liang, e 郭家良. "Process Discovery using Rule-Integrated Trees Hidden Semi-Markov Models". Thesis, 2017. http://ndltd.ncl.edu.tw/handle/456975.
Testo completo國立中山大學
資訊管理學系研究所
105
To predict or to explain? With the dramatical growth of the volume of information generated from various information systems, data science has become popular and important in recent years while machine learning algorithms provide a very strong support and foundation for various data applications. Many data applications are based on black-box models. For example, a fraud detection system can predict which person will default but we cannot understand how the system consider it’s fraud. While white-box models are easy to understand but have relatively poor predictive performance. Hence, in this thesis, we propose a novel grafted tree algorithm to integrate trees of random forests. The model attempt to find a balance between a decision tree and a random forest. That is, the grafted tree have better interpretability and the performance than a single decision tree. With the decision tree is integrated from a random forest, it will be applied to Hidden semi-Markov models (HSMM) to build a Classification Tree Hidden Semi- Markov Model (CTHSMM) in order to discover underlying changes of a system. The experimental result shows that our proposed model RITHSMM is better than a simple decision tree based on Classification and Regression Trees and it can find more states/leaves so as to answer a kind of questions, “given a sequence of observable sequence, what are the most probable/relevant sequence of changes of a dynamic system?”.
Frost, Andrew James. "Spatio-temporal hidden Markov models for incorporating interannual variability in rainfall". Thesis, 2004. http://hdl.handle.net/1959.13/24868.
Testo completoPhD Doctorate
Frost, Andrew James. "Spatio-temporal hidden Markov models for incorporating interannual variability in rainfall". 2004. http://hdl.handle.net/1959.13/24868.
Testo completoPhD Doctorate
Obado, Victor Owino. "A hidden Markov model process for wormhole attack detection in a localised underwater wireless sensor network". 2012. http://encore.tut.ac.za/iii/cpro/DigitalItemViewPage.external?sp=1000399.
Testo completoHines, Keegan. "Bayesian approaches for modeling protein biophysics". Thesis, 2014. http://hdl.handle.net/2152/26016.
Testo completotext
"A MULTI-FUNCTIONAL PROVENANCE ARCHITECTURE: CHALLENGES AND SOLUTIONS". Thesis, 2013. http://hdl.handle.net/10388/ETD-2013-12-1419.
Testo completoKim, Michael J. "Optimal Control and Estimation of Stochastic Systems with Costly Partial Information". Thesis, 2012. http://hdl.handle.net/1807/32792.
Testo completoJiang, Rui. "System Availability Maximization and Residual Life Prediction under Partial Observations". Thesis, 2011. http://hdl.handle.net/1807/31792.
Testo completo