Tesi sul tema "Semi-Markov Models"
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1
Bulla, Jan. "Application of Hidden Markov and Hidden Semi-Markov Models to Financial Time Series". Doctoral thesis, [S.l. : s.n.], 2006. http://swbplus.bsz-bw.de/bsz260867136inh.pdf.
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Yeo, Sungchil. "On estimation for a combined Markov and semi-Markov model with censoring /". The Ohio State University, 1987. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487586889187169.
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Stenberg, Fredrik. "Semi-Markov models for insurance and option rewards /". Västerås : Department of Mathematics and Physics, Mälardalen University, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-170.
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Choi, K. M. "Semi-Markov and delay time models of maintenance". Thesis, University of Salford, 1997. http://usir.salford.ac.uk/2158/.
Testo completoAbstract (sommario):
This thesis is concerned with modelling inspection policies of facilities which Qraduallv deteriorate in time. The context of inspection policies lends itself readily to probabilistic modelling. Indeed, many of the published theoretical models to be found in the literature adopt a Markov approach, where states are usually 'operating', 'operating but fault present', and 'failed'. However, most of these models fail to discuss the 'fit' of the model to data,a nd virtually no exampleso f actual applications or case-studiesa re to be found. hi a series of recent papers dating from 1984, a robust approach to solve these problems has been introduced and developed as the Delay Time Model (DTM). The central concept for this model is the delay time, h, of a fault which is the time lapse from when a fault could first be noticed until the time when its repair can be delayed no longer because of unacceptable consequences. The bottle neck in delay time modelling is how to estimate the delay time distribution parameters. Two methods for estimating these parameters have been developed. namely the subjective method and the objective method. Markov models have the advantage of an extensive body of theory. 'fliere are, however. difficulties of definition, measurement, and calculation when applying Markov models to real-world situations within a maintenance context. Indeed. this problem has motivated the current research which ainis to explore the two modelling methodologies in cases where comparison is valid, and also to gain an insight as to how robust Markov inspection models can be as decision-aids where Markovian properties are not strictly satisfied. It Nvill be seen that a class of inspection problems could be solved by a serni- Markov model using the delay time concept. In this thesis, a typical senii-i%Ia, rkov inspection model based upon the delay time concept is presented for a complex repairable systein that may fail during the course of its service lifetime and the results are compared. Finally, a case study of the senii-Markov inspection model and the delay time model is discussed.
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Huang, Xuedong. "Semi-continuous hidden Markov models for speech recognition". Thesis, University of Edinburgh, 1989. http://hdl.handle.net/1842/14125.
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Forsyth, Mark Eric. "Semi-continuous hidden Markov models for automatic speaker verification". Thesis, University of Edinburgh, 1995. http://hdl.handle.net/1842/10903.
Testo completoAbstract (sommario):
This thesis investigates the use of semi-continuous hidden Markov models (HMM) for automatic speaker verification (ASV) over a telephone channel. The system which was implemented is evaluated on a large database of isolated digits recorded over the British telephone network. The goal of the work is to improve performance of the ASV system under the constraints of limited enrolment data (5 tokens of each digit) and realistic computational and storage requirements. Experiments are conducted on the combined use of several standard feature sets under a common state segmentation, multiple codebook architecture. The feature sets investigated are linear predictive cepstral coefficients, mel-frequency cepstral coefficients and their respective first order differences. New algorithms which are proposed and evaluated include the weighting of digits scores according to their usefulness to the verification task and using Gaussian state duration probabilities as an additional information source in the verification decision. The most important contribution of this thesis is the development of a method for the construction of discriminating HMMs without the need for discriminative training. This new form of model, known as a discriminating observation probability (DOP) HMM involves the combination of standard HMMs to form a discriminating model. The DOP models are more flexible and perform better than the speaker normalisation techniques which are currently favoured in the literature. DOP models have potential application to many binary classification tasks using HMMs. The equal error rate (EER) using speaker specific thresholds on a series of 12 isolated digits was 0.17% using multiple codebook DOP models, compared to 1.93% using single codebook conventional HMM models. This represents a reduction in EER of 91%.
7
Bulla, Jan. "Computational Advances and Applications of Hidden (Semi-)Markov Models". Habilitation à diriger des recherches, Université de Caen, 2013. http://tel.archives-ouvertes.fr/tel-00987183.
Testo completoAbstract (sommario):
The document is my habilitation thesis, which is a prerequisite for obtaining the "habilitation à diriger des recherche (HDR)" in France (https://fr.wikipedia.org/wiki/Habilitation_universitaire#En_France). The thesis is of cumulative form, thus providing an overview of my published works until summer 2013.
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Dingle, Nicholas John. "Parallel computation of response time densities and quantiles in large Markov and semi-Markov models". Thesis, Imperial College London, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.412007.
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Bondesson, Carl. "Modelling of Safety Concepts for Autonomous Vehicles using Semi-Markov Models". Thesis, Uppsala universitet, Signaler och System, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-353060.
Testo completoAbstract (sommario):
Autonomous vehicles is soon a reality in the every-day life. Though before it is used commercially the vehicles need to be proven safe. The current standard for functional safety on roads, ISO 26262, does not include autonomous vehicles at the moment, which is why in this project an approach using semi-Markov models is used to assess safety. A semi-Markov process is a stochastic process modelled by a state space model where the transitions between the states of the model can be arbitrarily distributed. The approach is realized as a MATLAB tool where the user can use a steady-state based analysis called a Loss and Risk based measure of safety to assess safety. The tool works and can assess safety of semi-Markov systems as long as they are irreducible and positive recurrent. For systems that fulfill these properties, it is possible to draw conclusions about the safety of the system through a risk analysis and also about which autonomous driving level the system is in through a sensitivity analysis. The developed tool, or the approach with the semi-Markov model, might be a good complement to ISO 26262.
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The, Yu-Kai. "Analysis of ion channels with hidden Markov models". [S.l.] : [s.n.], 2005. http://deposit.ddb.de/cgi-bin/dokserv?idn=976048744.
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11
Akbar, Ihsan Ali. "Markov Modeling of Third Generation Wireless Channels". Thesis, Virginia Tech, 2003. http://hdl.handle.net/10919/32421.
Testo completoAbstract (sommario):
Wireless has proved to be one of the most important and fastest growing fields of communications especially during last few decades. To achieve reliable communication, we model a wireless system to analyze its performance and to find ways to improve the reliability of a particular system. Extensive research is being done to accurately model wireless systems, and to achieve better performance. Simulation techniques have been in use for many years to support the design and evaluation of electronic communication systems. Over the past few decades, Computer Aided Design (CAD) techniques (including both computerized analytical techniques and simulation) have matured, and are now usually applied at some point in the system design/development process.
The aim of this thesis is to find efficient algorithms that can model third generation wireless channels in a discrete sense. For modeling these channels, mathematical tools known as hidden Markov models are used. These models have proved themselves to be very efficient in many areas of electrical engineering including speech recognition, pattern recognition, artificial intelligence, wavelets and queuing theory. Wideband Code Division Multiple Access (W-CDMA) wireless communication parameters including channels fading statistics, Bit Error Rate (BER) performance and interval distribution of errors are modeled using different Markov models, and their results are tested and validated.
Four algorithms for modeling error sources are implemented, and their results are discussed. Both hidden Markov models and semi-hidden Markov models are used in this thesis, and their results are validated for the W-CDMA environment. The state duration distributions for these channels are also approximated using Phase-Type (PH) distribution.Master of Science
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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.
Testo completoAbstract (sommario):
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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Akbar, Ihsan Ali. "Statistical Analysis of Wireless Systems Using Markov Models". Diss., Virginia Tech, 2007. http://hdl.handle.net/10919/26089.
Testo completoAbstract (sommario):
Being one of the fastest growing fields of engineering, wireless has gained the attention of researchers and commercial businesses all over the world. Extensive research is underway to improve the performance of existing systems and to introduce cutting edge wireless technologies that can make high speed wireless communications possible.
The first part of this dissertation deals with discrete channel models that are used for simulating error traces produced by wireless channels. Most of the time, wireless channels have memory and we rely on discrete time Markov models to simulate them. The primary advantage of using these models is rapid experimentation and prototyping. Efficient estimation of the parameters of a Markov model (including its number of states) is important to reproducing and/or forecasting channel statistics accurately. Although the parameter estimation of Markov processes has been studied extensively, its order estimation problem has been addressed only recently. In this report, we investigate the existing order estimation techniques for Markov chains and hidden Markov models. Performance comparison with semi-hidden Markov models is also discussed. Error source modeling in slow and fast fading conditions is also considered in great detail.
Cognitive Radio is an emerging technology in wireless communications that can improve the utilization of radio spectrum by incorporating some intelligence in its design. It can adapt with the environment and can change its particular transmission or reception parameters to execute its tasks without interfering with the licensed users. One problem that CR network usually faces is the difficulty in detecting and classifying its low power signal that is present in the environment. Most of the time traditional energy detection techniques fail to detect these signals because of their low SNRs. In the second part of this thesis, we address this problem by using higher order statistics of incoming signals and classifying them by using the pattern recognition capabilities of HMMs combined with cased-based learning approach.
This dissertation also deals with dynamic spectrum allocation in cognitive radio using HMMs. CR networks that are capable of using frequency bands assigned to licensed users, apart from utilizing unlicensed bands such as UNII radio band or ISM band, are also called Licensed Band Cognitive Radios. In our novel work, the dynamic spectrum management or dynamic frequency allocation is performed by the help of HMM predictions. This work is based on the idea that if Markov models can accurately model spectrum usage patterns of different licensed users, then it should also correctly predict the spectrum holes and use these frequencies for its data transmission. Simulations have shown that HMMs prediction results are quite accurate and can help in avoiding CR interference with the primary licensed users and vice versa. At the same time, this helps in sending its data over these channels more reliably.Ph. D.
14
Wan, Lijie. "CONTINUOUS TIME MULTI-STATE MODELS FOR INTERVAL CENSORED DATA". UKnowledge, 2016. http://uknowledge.uky.edu/statistics_etds/19.
Testo completoAbstract (sommario):
Continuous-time multi-state models are widely used in modeling longitudinal data of disease processes with multiple transient states, yet the analysis is complex when subjects are observed periodically, resulting in interval censored data. Recently, most studies focused on modeling the true disease progression as a discrete time stationary Markov chain, and only a few studies have been carried out regarding non-homogenous multi-state models in the presence of interval-censored data. In this dissertation, several likelihood-based methodologies were proposed to deal with interval censored data in multi-state models.
Firstly, a continuous time version of a homogenous Markov multi-state model with backward transitions was proposed to handle uneven follow-up assessments or skipped visits, resulting in the interval censored data. Simulations were used to compare the performance of the proposed model with the traditional discrete time stationary Markov chain under different types of observation schemes. We applied these two methods to the well-known Nun study, a longitudinal study of 672 participants aged ≥ 75 years at baseline and followed longitudinally with up to ten cognitive assessments per participant.
Secondly, we constructed a non-homogenous Markov model for this type of panel data. The baseline intensity was assumed to be Weibull distributed to accommodate the non-homogenous property. The proportional hazards method was used to incorporate risk factors into the transition intensities. Simulation studies showed that the Weibull assumption does not affect the accuracy of the parameter estimates for the risk factors. We applied our model to data from the BRAiNS study, a longitudinal cohort of 531 subjects each cognitively intact at baseline.
Last, we presented a parametric method of fitting semi-Markov models based on Weibull transition intensities with interval censored cognitive data with death as a competing risk. We relaxed the Markov assumption and took interval censoring into account by integrating out all possible unobserved transitions. The proposed model also allowed for incorporating time-dependent covariates. We provided a goodness-of-fit assessment for the proposed model by the means of prevalence counts. To illustrate the methods, we applied our model to the BRAiNS study.
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Pertsinidou, Christina Elisavet. "Stochastic models for the estimation of the seismic hazard". Thesis, Compiègne, 2017. http://www.theses.fr/2017COMP2342.
Testo completoAbstract (sommario):
Dans le premier chapitre, la notion d'évaluation des risques sismiques est définie et les caractéristiques sismotectoniques de la région d'étude sont brièvement présentés. Un examen rigoureux des modèles stochastiques, appliqués au domaine de la sismologie est fourni. Dans le chapitre 2, différents modèles semi-Markoviens sont développés pour étudier la sismicité des îles Ioniennes centrales ainsi que le Nord de la mer Egée (Grèce). Les quantités telles que le noyau semi-Markovien et les probabilités de destination sont évaluées, en considérant que les temps de séjour suivent les distributions géométrique, discrète Weibull et Pareto. Des résultats utiles sont obtenus pour l'estimation de la sismicité. Dans le troisième chapitre un nouvel algorithme de Viterbi pour les modèles semi-Markoviens cachés est construit, dont la complexité est une fonction linéaire du nombre d'observations et une fonction quadratique du nombre d'états cachés, la plus basse existante dans la littérature. Une extension de ce nouvel algorithme est développée pour le cas où une observation dépend de l'état caché correspondant, mais aussi de l'observation précédente (cas SM1-M1). Dans le chapitre 4 les modèles semi-Markoviens cachés sont appliquées pour étudier la sismicité du Nord et du Sud de la mer Égée. La séquence d'observation est constituée des magnitudes et des positions d’un tremblement de terre et le nouvel algorithme de Viterbi est mis en œuvre afin de décoder les niveaux des tensions cachés qui sont responsables pour la sismogenèse. Les phases précurseurs (variations des tensions cachées) ont été détectées en avertissant qu’un tremblement de terre pourrait se produire. Ce résultat est vérifié pour 70 sur 88 cas (le score optimal). Les temps de séjour du processus caché étaient supposés suivre les distributions Poisson, logarithmique ou binomiale négative, tandis que les niveaux de tensions cachés ont été classés en 2, 3 ou 4 états. Les modèles de Markov caché ont également été adaptés sans présenter des résultats intéressants concernant les phases précurseurs. Dans le chapitre 5 un algorithme de Viterbi généralisé pour les modèles semi-Markoviens cachés, est construit dans le sens que les transitions au même état caché sont autorisées et peuvent également être décodées. De plus, une extension de cet algorithme généralisé dans le contexte SM1-M1 est présentée. Dans le chapitre 6 nous modifions de manière convenable le modèle Cramér-Lundberg y compris des sinistres négatifs et positifs, afin de décrire l'évolution avec le temps des changements de contraintes de Coulomb (valeurs ΔCFF) calculées pour sept épicentres (M ≥ 6) du Nord de la mer Egée. Formules pour les probabilités de ruine sont définies sous une forme générale. Corollaires sont également formulés pour la distribution exponentielle et Pareto. L'objectif est de mettre en lumière la question suivante qui pose la problématique dans la Sismologie: Au cours d'une année pourquoi un tremblement de terre s’est produit dans une position précise et pas dans une autre position, aux régions sismotectoniquement homogènes ayant valeurs ΔCFF positives. Les résultats montrent que les nouvelles formules de probabilité peuvent contribuer à répondre au problème susmentionnéIn the first chapter the definition of the seismic hazard assessment is provided, the seismotectonic features of the study areas are briefly presented and the already existing mathematical models applied in the field of Seismology are thoroughly reviewed. In chapter 2, different semi-Markov models are developed for studying the seismicity of the areas of the central Ionian Islands and the North Aegean Sea (Greece). Quantities such as the kernel and the destination probabilities are evaluated, considering geometric, discrete-Weibull and Pareto distributed sojourn times. Useful results are obtained for forecasting purposes. In the third chapter a new Viterbi algorithm for hidden semi-Markov models is developed, whose complexity is a linear function of the number of observations and a quadratic function of the number of hidden states, the lowest existing in the literature. Furthermore, an extension of this new algorithm is introduced for the case that an observation depends on the corresponding hidden state but also on the previous observation (SM1-M1 case). In chapter 4, different hidden semi-Markov models (HSMMs) are applied for the study of the North and South Aegean Sea. The earthquake magnitudes and locations comprise the observation sequence and the new Viterbi algorithm is implemented in order to decode the hidden stress field associated with seismogenesis. Precursory phases (variations of the hidden stress field) were detected warning for an anticipated earthquake occurrence for 70 out of 88 cases (the optimal model’s score). The sojourn times of the hidden process were assumed to follow Poisson, logarithmic or negative binomial distributions, whereas the hidden stress levels were classified into 2, 3 or 4 states. HMMs were also adapted without presenting significant results as for the precursory phases. In chapter 5 a generalized Viterbi algorithm for HSMMs is constructed in the sense that now transitions to the same hidden state are allowed and can also be decoded. Furthermore, an extension of this generalized algorithm in the SM1-M1 context is given. In chapter 6 we modify adequately the Cramér-Lundberg model considering negative and positive claims, in order to describe the evolution in time of the Coulomb failure function changes (ΔCFF values) computed at the locations of seven strong (M ≥ 6) earthquakes of the North Aegean Sea. Ruin probability formulas are derived and proved in a general form. Corollaries are also formulated for the exponential and the Pareto distribution. The aim is to shed light to the following problem posed by the seismologists: During a specific year why did an earthquake occur at a specific location and not at another location in seismotectonically homogeneous areas with positive ΔCFF values (stress enhanced areas). The results demonstrate that the new probability formulas can contribute in answering the aforementioned question
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Anger, Christoph [Verfasser], Uwe [Akademischer Betreuer] Klingauf e Tobias [Akademischer Betreuer] Melz. "Hidden semi-Markov Models for Predictive Maintenance of Rotating Elements / Christoph Anger ; Uwe Klingauf, Tobias Melz". Darmstadt : Universitäts- und Landesbibliothek Darmstadt, 2018. http://d-nb.info/1164592424/34.
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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.
Testo completoAbstract (sommario):
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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Henriques, Bruno M. "Hybrid galaxy evolution modelling : Monte Carlo Markov Chain parameter estimation in semi-analytic models of galaxy formation". Thesis, University of Sussex, 2010. http://sro.sussex.ac.uk/id/eprint/2334/.
Testo completoAbstract (sommario):
We introduce a statistical exploration of the parameter space of the Munich semi-analytic model built upon the Millennium dark matter simulation. This is achieved by applying a Monte Carlo Markov Chain (MCMC) method to constrain the 6 free parameters that define the stellar mass function at redshift zero. The model is tested against three different observational data sets, including the galaxy K-band luminosity function, B −V colours, and the black hole-bulge mass relation, to obtain mean values, confidence limits and likelihood contours for the best fit model. We discuss how the model parameters affect each galaxy property and find that there are strong correlations between them. We analyze to what extent these are simply reflections of the observational constraints, or whether they can lead to improved understanding of the physics of galaxy formation. When all the observations are combined, the need to suppress dwarf galaxies requires the strength of the supernova feedback to be significantly higher in our best-fit solution than in previous work. We interpret this fact as an indication of the need to improve the treatment of low mass objects. As a possible solution, we introduce the process of satellite disruption, caused by tidal forces exerted by central galaxies on their merging companions. We apply similar MCMC sampling techniques to the new model, which allows us to discuss the impact of disruption on the basic physics of the model. The new best fit model has a likelihood four times better than before, reproducing reasonably all the observational constraints, as well as the metallicity of galaxies and predicting intra-cluster light. We interpret this as an indication of the need to include the new recipe. We point out the remaining limitations of the semi-analytic model and discuss possible improvements that might increase its predictive power in the future.
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McGarity, Michael Computer Science & Engineering Faculty of Engineering UNSW. "Heterogeneous representations for reinforcement learning control of dynamic systems". Awarded by:University of New South Wales. School of Computer Science and Engineering, 2004. http://handle.unsw.edu.au/1959.4/19350.
Testo completoAbstract (sommario):
Intelligent agents are designed to interact with, and learn about, their environment so that they can act purposefully towards a goal. One class of problems encountered in building such agents is learning how to respond to dynamic systems with a continuous state space. The goals of this dissertation are to develop a framework for understanding the behaviour of partitioned dynamic systems with continuous underlying state and to translate this framework into algorithms which adaptively form a partition of the continuous space such that the partitioned system is more easily learned and controlled, and such that the control law may be easily explained in intuitive ways. Currently, algorithms which learn a control policy for partitioned continuous state space systems treat the partitioned system as an approximation to a Markov chain. I give conditions for the partitioned system to be a Markov chain, a semi-Markov process and a new class of system, a weak-semi-Markov process. The weak-semi-Markov model is shown to model partitioned dynamic systems with greater economy than other surveyed models. The behaviour of a partitioned state space system in the area around the region boundaries is also considered. I use the theory of sliding surfaces, and some heuristic arguments to recommend region boundary shape and position. The concept of 'staying on the boundary' then becomes a robust and relatively easy subgoal within the control algorithm. The concept of 'reaching the sliding surface' as a subgoal is used as the basis for an intuitive explanation of the learnt controller. I present an algorithm based on this concept which explains the behaviour of a learnt controller in ways not previously available to a machine learning algorithms. Finally, the Markov Property and the theory of Sliding Mode Control are used as the basis of a class of recursive algorithms. These algorithms adaptively find a partition, and simultaneously use this partition in conjunction with one of five reinforcement learning algorithms to find a control policy based on that partition. This technique is shown to work very well in learning, controlling and explaining a variety of physical systems, from a monorail to a container crane.
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Kapetanakis, Venediktos. "Investigation of ageing using semi-Markov multi-state models in the presence of left-, right-, and interval-censoring". Thesis, University of Cambridge, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.610246.
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Wei, Shaoceng. "MULTI-STATE MODELS FOR INTERVAL CENSORED DATA WITH COMPETING RISK". UKnowledge, 2015. http://uknowledge.uky.edu/statistics_etds/10.
Testo completoAbstract (sommario):
Multi-state models are often used to evaluate the effect of death as a competing event to the development of dementia in a longitudinal study of the cognitive status of elderly subjects. In this dissertation, both multi-state Markov model and semi-Markov model are used to characterize the flow of subjects from intact cognition to dementia with mild cognitive impairment and global impairment as intervening transient, cognitive states and death as a competing risk.
Firstly, a multi-state Markov model with three transient states: intact cognition, mild cognitive impairment (M.C.I.) and global impairment (G.I.) and one absorbing state: dementia is used to model the cognitive panel data. A Weibull model and a Cox proportional hazards (Cox PH) model are used to fit the time to death based on age at entry and the APOE4 status. A shared random effect correlates this survival time with the transition model.
Secondly, we further apply a Semi-Markov process in which we assume that the wait- ing times are Weibull distributed except for transitions from the baseline state, which are exponentially distributed and we assume no additional changes in cognition occur between two assessments. We implement a quasi-Monte Carlo (QMC) method to calculate the higher order integration needed for the likelihood based estimation.
At the end of this dissertation we extend a non-parametric “local EM algorithm” to obtain a smooth estimator of the cause-specific hazard function (CSH) in the presence of competing risk.
All the proposed methods are justified by simulation studies and applications to the Nun Study data, a longitudinal study of late life cognition in a cohort of 461 subjects.
22
Votsi, Irène. "Evaluation des risques sismiques par des modèles markoviens cachés et semi-markoviens cachés et de l'estimation de la statistique". Thesis, Compiègne, 2013. http://www.theses.fr/2013COMP2058.
Testo completoAbstract (sommario):
Le premier chapitre présente les axes principaux de recherche ainsi que les problèmes traités dans cette thèse. Plus précisément, il expose une synthèse sur le sujet, en y donnant les propriétés essentielles pour la bonne compréhension de cette étude, accompagnée des références bibliographiques les plus importantes. Il présente également les motivations de ce travail en précisant les contributions originales dans ce domaine. Le deuxième chapitre est composé d’une recherche originale sur l’estimation du risque sismique, dans la zone du nord de la mer Egée (Grèce), en faisant usage de la théorie des processus semi-markoviens à temps continue. Il propose des estimateurs des mesures importantes qui caractérisent les processus semi-markoviens, et fournit une modélisation dela prévision de l’instant de réalisation d’un séisme fort ainsi que la probabilité et la grandeur qui lui sont associées. Les chapitres 3 et 4 comprennent une première tentative de modélisation du processus de génération des séismes au moyen de l’application d’un temps discret des modèles cachés markoviens et semi-markoviens, respectivement. Une méthode d’estimation non paramétrique est appliquée, qui permet de révéler des caractéristiques fondamentales du processus de génération des séismes, difficiles à détecter autrement. Des quantités importantes concernant les niveaux des tensions sont estimées au moyen des modèles proposés. Le chapitre 5 décrit les résultats originaux du présent travail à la théorie des processus stochastiques, c’est- à-dire l’étude et l’estimation du « Intensité du temps d’entrée en temps discret (DTIHT) » pour la première fois dans des chaînes semi-markoviennes et des chaînes de renouvellement markoviennes cachées. Une relation est proposée pour le calcul du DTIHT et un nouvel estimateur est présenté dans chacun de ces cas. De plus, les propriétés asymptotiques des estimateurs proposés sont obtenues, à savoir, la convergence et la normalité asymptotique. Le chapitre 6 procède ensuite à une étude de comparaison entre le modèle markovien caché et le modèle semi-markovien caché dans un milieu markovien et semi-markovien en vue de rechercher d’éventuelles différences dans leur comportement stochastique, déterminé à partir de la matrice de transition de la chaîne de Markov (modèle markovien caché) et de la matrice de transition de la chaîne de Markov immergée (modèle semi-markovien caché). Les résultats originaux concernent le cas général où les distributions sont considérées comme distributions des temps de séjour ainsi que le cas particulier des modèles qui sont applique´s dans les chapitres précédents où les temps de séjour sont estimés de manière non-paramétrique. L’importance de ces différences est spécifiée à l’aide du calcul de la valeur moyenne et de la variance du nombre de sauts de la chaîne de Markov (modèle markovien caché) ou de la chaîne de Markov immergée (modèle semi-markovien caché) pour arriver dans un état donné, pour la première fois. Enfin, le chapitre 7 donne des conclusions générales en soulignant les points les plus marquants et des perspectives pour développements futursThe first chapter describes the definition of the subject under study, the current state of science in this area and the objectives. In the second chapter, continuous-time semi-Markov models are studied and applied in order to contribute to seismic hazard assessment in Northern Aegean Sea (Greece). Expressions for different important indicators of the semi- Markov process are obtained, providing forecasting results about the time, the space and the magnitude of the ensuing strong earthquake. Chapters 3 and 4 describe a first attempt to model earthquake occurrence by means of discrete-time hidden Markov models (HMMs) and hidden semi-Markov models (HSMMs), respectively. A nonparametric estimation method is followed by means of which, insights into features of the earthquake process are provided which are hard to detect otherwise. Important indicators concerning the levels of the stress field are estimated by means of the suggested HMM and HSMM. Chapter 5 includes our main contribution to the theory of stochastic processes, the investigation and the estimation of the discrete-time intensity of the hitting time (DTIHT) for the first time referring to semi-Markov chains (SMCs) and hidden Markov renewal chains (HMRCs). A simple formula is presented for the evaluation of the DTIHT along with its statistical estimator for both SMCs and HMRCs. In addition, the asymptotic properties of the estimators are proved, including strong consistency and asymptotic normality. In chapter 6, a comparison between HMMs and HSMMs in a Markov and a semi-Markov framework is given in order to highlight possible differences in their stochastic behavior partially governed by their transition probability matrices. Basic results are presented in the general case where specific distributions are assumed for sojourn times as well as in the special case concerning the models applied in the previous chapters, where the sojourn time distributions are estimated non-parametrically. The impact of the differences is observed through the calculation of the mean value and the variance of the number of steps that the Markov chain (HMM case) and the EMC (HSMM case) need to make for visiting for the first time a particular state. Finally, Chapter 7 presents concluding remarks, perspectives and future work
23
Cuvillier, Philippe. "On temporal coherency of probabilistic models for audio-to-score alignment". Thesis, Paris 6, 2016. http://www.theses.fr/2016PA066532/document.
Testo completoAbstract (sommario):
Cette thèse porte sur l'alignement automatique d'un enregistrement audio avec la partition de musique correspondante. Nous adoptons une approche probabiliste et proposons une démarche théorique pour la modélisation algorithmique de ce problème d'alignement automatique. La question est de modéliser l'évolution temporelle des événements par des processus stochastiques. Notre démarche part d'une spécificité de l'alignement musical : une partition attribue à chaque événement une durée nominale, qui est une information a priori sur la durée probable d'occurrence de l'événement. La problématique qui nous occupe est celle de la modélisation probabiliste de cette information de durée. Nous définissons la notion de cohérence temporelle à travers plusieurs critères de cohérence que devrait respecter tout algorithme d'alignement musical. Ensuite, nous menons une démarche axiomatique autour du cas des modèles de semi-Markov cachés. Nous démontrons que ces critères sont respectés lorsque des conditions mathématiques particulières sont vérifiées par les lois a priori du modèle probabiliste de la partition. Ces conditions proviennent de deux domaines mathématiques jusqu'ici étrangers à la question de l'alignement : les processus de Lévy et la totale positivité d'ordre deux. De nouveaux résultats théoriques sont démontrés sur l'interrelation entre ces deux notions. En outre, les bienfaits pratiques de ces résultats théoriques sont démontrés expérimentalement sur des algorithmes d'alignement en temps réelThis thesis deals with automatic alignment of audio recordings with corresponding music scores. We study algorithmic solutions for this problem in the framework of probabilistic models which represent hidden evolution on the music score as stochastic process. We begin this work by investigating theoretical foundations of the design of such models. To do so, we undertake an axiomatic approach which is based on an application peculiarity: music scores provide nominal duration for each event, which is a hint for the actual and unknown duration. Thus, modeling this specific temporal structure through stochastic processes is our main problematic. We define temporal coherency as compliance with such prior information and refine this abstract notion by stating two criteria of coherency. Focusing on hidden semi-Markov models, we demonstrate that coherency is guaranteed by specific mathematical conditions on the probabilistic design and that fulfilling these prescriptions significantly improves precision of alignment algorithms. Such conditions are derived by combining two fields of mathematics, Lévy processes and total positivity of order 2. This is why the second part of this work is a theoretical investigation which extends existing results in the related literature
24
Li, Haoyu. "Recent hidden Markov models for lower limb locomotion activity detection and recognition using IMU sensors". Thesis, Lyon, 2019. http://www.theses.fr/2019LYSEC041.
Testo completoAbstract (sommario):
Le contexte de la thèse est celui du quantified-self, un mouvement né en Californie qui consiste à mieux se connaître en mesurant les données relatives à son corps et à ses activités. Les travaux de recherche ont consisté à développer des algorithmes d'analyse des signaux d'un capteur IMU (\textit{Inertial Measurement Unit}) placé sur la jambe pour reconnaître différentes activités de mouvement telles que la marche, la course, la montée d’escalier.... Ces activités sont reconnaissables grâce à la forme des signaux d'accélération et de vitesse angulaire du capteur, tous triaxiaux, pendant le mouvement des jambes lors du cycle de marche. Pour résoudre ce problème de reconnaissance, les travaux de thèse ont permis la construction d'un modèle de chaîne de Markov cachée particulier, appelé chaîne triplet semi-Markov, qui combine un modèle semi-Markov et un modèle de mélange gaussien dans un modèle de Markov triplet. Ce nouveau modèle est adapté à la fois à la nature du cycle de marche et à l'enchaînement des activités que l’on peut réaliser dans la vie quotidienne. Pour adapter les paramètres du modèle aux différences de morphologie et de comportement humain, nous avons développé des algorithmes d'estimation des paramètres en ligne et hors ligne.Pour établir les performances d'apprentissage et de classification des algorithmes, nous avons mené des expériences sur la base d'enregistrements recueillis pendant la thèse et d'un ensemble de données publiques. Les résultats sont systématiquement comparés aux algorithmes de reconnaissance actuelsThe thesis context is that of the quantified self, a movement born in California that consists in getting to know oneself better by measuring data relating to one’s body and activities. The research work consisted in developing algorithms for analyzing signals from an IMU (Inertial Measurement Unit) sensor placed on the leg to recognize different movement activities such as walking, running, stair climbing... These activities are recognizable by the shape of the sensor’s acceleration and angular velocity signals, both tri-axial, during leg movement and gait cycle.To address the recognition problem, the thesis work resulted in the construction of a particular hidden Markov chain, called semi-triplet Markov chain, which combines a semi-Markov model and a Gaussian mixture model in a triplet Markov model. This model is both adapted to the nature of the gait cycle, and to the sequence of activities as it can be carried out in daily life. To adapt the model parameters to the differences in human morphology and behavior, we have developed algorithms for estimating parameters both off-line and on-line.To establish the classification and learning performance of the algorithms, we conducted experiments on the basis of recordings collected during the thesis and on public dataset. The results are systematically compared with state-of-the-art algorithms
25
Petersson, Mikael. "Perturbed discrete time stochastic models". Doctoral thesis, Stockholms universitet, Matematiska institutionen, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-128979.
Testo completoAbstract (sommario):
In this thesis, nonlinearly perturbed stochastic models in discrete time are considered. We give algorithms for construction of asymptotic expansions with respect to the perturbation parameter for various quantities of interest. In particular, asymptotic expansions are given for solutions of renewal equations, quasi-stationary distributions for semi-Markov processes, and ruin probabilities for risk processes.At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 4: Manuscript. Paper 5: Manuscript. Paper 6: Manuscript.
26
Lin, Yushun. "PARAMETRIC ESTIMATION IN COMPETING RISKS AND MULTI-STATE MODELS". UKnowledge, 2011. http://uknowledge.uky.edu/statistics_etds/1.
Testo completoAbstract (sommario):
The typical research of Alzheimer's disease includes a series of cognitive states. Multi-state models are often used to describe the history of disease evolvement. Competing risks models are a sub-category of multi-state models with one starting state and several absorbing states.
Analyses for competing risks data in medical papers frequently assume independent risks and evaluate covariate effects on these events by modeling distinct proportional hazards regression models for each event. Jeong and Fine (2007) proposed a parametric proportional sub-distribution hazard (SH) model for cumulative incidence functions (CIF) without assumptions about the dependence among the risks. We modified their model to assure that the sum of the underlying CIFs never exceeds one, by assuming a proportional SH model for dementia only in the Nun study. To accommodate left censored data, we computed non-parametric MLE of CIF based on Expectation-Maximization algorithm. Our proposed parametric model was applied to the Nun Study to investigate the effect of genetics and education on the occurrence of dementia. After including left censored dementia subjects, the incidence rate of dementia becomes larger than that of death for age < 90, education becomes significant factor for incidence of dementia and standard errors for estimates are smaller.
Multi-state Markov model is often used to analyze the evolution of cognitive states by assuming time independent transition intensities. We consider both constant and duration time dependent transition intensities in BRAiNS data, leading to a mixture of Markov and semi-Markov processes. The joint probability of observing a sequence of same state until transition in a semi-Markov process was expressed as a product of the overall transition probability and survival probability, which were simultaneously modeled. Such modeling leads to different interpretations in BRAiNS study, i.e., family history, APOE4, and sex by head injury interaction are significant factors for transition intensities in traditional Markov model. While in our semi-Markov model, these factors are significant in predicting the overall transition probabilities, but none of these factors are significant for duration time distribution.
27
Weining, Wang. "Adaptive methods for risk calibration". Doctoral thesis, Humboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät, 2012. http://dx.doi.org/10.18452/16585.
Testo completoAbstract (sommario):
Dieser Artikel enthält vier Kapitel. Das erste Kapitel ist berechtigt, '''' lokalen Quantil Regression"und seine Zusammenfassung: Quantil Regression ist eine Technik, bedingte Quantil Kurven zu schätzen. Es bietet ein umfassendes Bild über ein Antwort-Kontingent auf erklärenden Variablen. In einem Rahmen flexible Modellierung ist eine besondere Form der bedingten Quantil-Kurve nicht von vornherein festgelegt. Dies motiviert eine lokale parametrische anstatt einer globalen feste Modell passend Ansatz. Eine nichtparametrische Glättung Schätzung der bedingte Quantil Kurve erfordert, zwischen lokalen Krümmung und stochastische auszugleichen Variabilität. In den ersten Essay empfehlen wir eine lokale Modellauswahl Technik, die eine adaptive Schätzung der bedingte bietet Quantil-Regression-Kurve bei jedem Entwurf-Punkt. Theoretische Ergebnisse behaupten, dass das vorgeschlagene adaptive Verfahren als führt gut als Orakel die würde das Risiko der lokalen Abschätzung für die Aufgabenstellung minimieren. Wir veranschaulichen die Leistung der Trolle.This article includes four chapters. The first chapter is entitled ``Local Quantile Regression", and its summary: Quantile regression is a technique to estimate conditional quantile curves. It provides a comprehensive picture of a response contingent on explanatory variables. In a flexible modeling framework, a specific form of the conditional quantile curve is not a priori fixed. This motivates a local parametric rather than a global fixed model fitting approach. A nonparametric smoothing estimate of the conditional quantile curve requires to balance between local curvature and stochastic variability. In the first essay, we suggest a local model selection technique that provides an adaptive estimate of the conditional quantile regression curve at each design point. Theoretical results claim that the proposed adaptive procedure performs as good as an oracle which would minimize the local estimation risk for the problem at hand. We illustrate the performance of the procedure by an extensive simulation study and consider a couple of applications: to tail dependence analysis for the Hong Kong stock market and to analysis of the distributions of the risk factors of temperature dynamics.
28
Rosales, Claudia R. "Technology Enabled New Inventory Control Policies in Hospitals". University of Cincinnati / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1299178847.
Testo completo
29
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 completoAbstract (sommario):
Many complexity measures are defined as the size of a minimal representation in
a specific model class. One such complexity measure, which is important because
it is widely applied, is statistical complexity. It is defined for
discrete-time, stationary stochastic processes within a theory called
computational mechanics. Here, a mathematically rigorous, more general version
of this theory is presented, and abstract properties of statistical complexity
as a function on the space of processes are investigated. In particular, weak-*
lower semi-continuity and concavity are shown, and it is argued that these
properties should be shared by all sensible complexity measures. Furthermore, a
formula for the ergodic decomposition is obtained.
The same results are also proven for two other complexity measures that are
defined by different model classes, namely process dimension and generative
complexity. These two quantities, and also the information theoretic complexity
measure called excess entropy, are related to statistical complexity, and this
relation is discussed here.
It is also shown that computational mechanics can be reformulated in terms of
Frank Knight's prediction process, which is of both conceptual and technical
interest. In particular, it allows for a unified treatment of different
processes and facilitates topological considerations. Continuity of the Markov
transition kernel of a discrete version of the prediction process is obtained as
a new result.
30
Wang, Chao. "Exploiting non-redundant local patterns and probabilistic models for analyzing structured and semi-structured data". Columbus, Ohio : Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1199284713.
Testo completo
31
Soueidan, Hayssam. "Discrete event modeling and analysis for systems biology models". Thesis, Bordeaux 1, 2009. http://www.theses.fr/2009BOR13916/document.
Testo completoAbstract (sommario):
Les travaux effectués durant cette thèse portent sur la spécification, l'analyse et l'application de systèmes a événements discrets pour la modélisation de processus biologiques stochastiques en biologie des systèmes. Le point de départ de cette thèse est le langage de modélisation AltaRica, que nous étendons afin de permettre de décrire des événements temporisés selon des distributions de probabilités quelconques (dégénérées, discrètes et continues). Nous définissons ensuite la sémantique de ce langage en terme d'automates de mode stochastiques et présentons trois opérations de compositions permettant de modéliser des systèmes hiérarchiques avec événements synchronisés et partage de valeurs via un mécanisme de connexion. Nous donnons ensuite au automates de mode stochastiques une sémantique en termes de systèmes de transitions dont les transitions sont étiquetées par des distributions de probabilités et des probabilités de transitions instantanées. Nous caractérisons ensuite 6 sous classes de ces systèmes de transitions et donnons pour chacune de ces classes un algorithme de simulation ainsi qu'une mesure de probabilité sur les chemins finis. Nous montrons que pour certaines de ces classes, notre sémantique est conforme avec les mesures de probabilité de chemin usuellement associées aux chaînes de Markov a temps discret, a temps continu et aux processus semi-Markoviens généralisés. Nous abordons ensuite le problème de la réutilisation de modèles continus existant dans un système discret. Nous donnons une méthode d'abstraction permettant de représenter un ensemble de trajectoires bornées ou non d'un modèle continu sous forme d'un système de transition stochastique fini. A travers des exemples tirés de la littérature, nous montrons que notre abstraction préserve les propriétés "qualitatives" (par exemple oscillations, hystérie) des modèles continus et qu'une comparaison entre trajectoires basée sur leurs représentations en termes de systèmes de transitions permet de regrouper les trajectoires en fonction de comportements qualitatifs plus fins que ceux permis par la théorie des bifurcations. Finalement, nous étudions a l'aide de ces modèles des processus liés a la division cellulaire chez les levures. En particulier, nous définissons un modèle pour le vieillissement cellulaire dans une population de levure où le comportement individuel d'une cellule est régi par une équation différentielle ordinaire et où le processus de division est régi par un système de transition. Nous montrons a l'aide de ce modèle que la survie d'une population de levure de type Schizosaccharomyces Pombe, qui se divisent par une fission médiane, n'est possible que grâce a un mécanisme de distribution non symétrique des dégâts oxydatifs entre la progéniture et la cellule souche. Cette hypothèse fut validée expérimentalement lors d'une collaboration avec le laboratoire de micro-biologie de GöteborgA general goal of systems biology is to acquire a detailed understanding of the dynamics of living systems by relating functional properties of whole systems with the interactions of their constituents. Often this goal is tackled through computer simulation. A number of different formalisms are currently used to construct numerical representations of biological systems, and a certain wealth of models is proposed using ad hoc methods. There arises an interesting question of to what extent these models can be reused and composed, together or in a larger framework. In this thesis, we propose BioRica as a means to circumvent the difficulty of incorporating disparate approaches in the same modeling study. BioRica is an extension of the AltaRica specification language to describe hierarchical non-deterministic General Semi-Markov processes. We first extend the syntax and automata semantics of AltaRica in order to account for stochastic labeling. We then provide a semantics to BioRica programs in terms of stochastic transition systems, that are transition systems with stochastic labeling. We then develop numerical methods to symbolically compute the probability of a given finite path in a stochastic transition systems. We then define algorithms and rules to compile a BioRica system into a stand alone C++ simulator that simulates the underlying stochastic process. We also present language extensions that enables the modeler to include into a BioRica hierarchical systems nodes that use numerical libraries (e.g. Mathematica, Matlab, GSL). Such nodes can be used to perform numerical integration or flux balance analysis during discrete event simulation. We then consider the problem of using models with uncertain parameter values. Quantitative models in Systems Biology depend on a large number of free parameters, whose values completely determine behavior of models. Some range of parameter values produce similar system dynamics, making it possible to define general trends for trajectories of the system (e.g. oscillating behavior) for some parameter values. In this work, we defined an automata-based formalism to describe the qualitative behavior of systems’ dynamics. Qualitative behaviors are represented by finite transition systems whose states contain predicate valuation and whose transitions are labeled by probabilistic delays. We provide algorithms to automatically build such automata representation by using random sampling over the parameter space and algorithms to compare and cluster the resulting qualitative transition system. Finally, we validate our approach by studying a rejuvenation effect in yeasts cells population by using a hierarchical population model defined in BioRica. Models of ageing for yeast cells aim to provide insight into the general biological processes of ageing. For this study, we used the BioRica framework to generate a hierarchical simulation tool that allows dynamic creation of entities during simulation. The predictions of our hierarchical mathematical model has been validated experimentally by the micro-biology laboratory of Gothenburg
32
Liu, Chong. "Reinforcement learning with time perception". Thesis, University of Manchester, 2012. https://www.research.manchester.ac.uk/portal/en/theses/reinforcement-learning-with-time-perception(a03580bd-2dd6-4172-a061-90e8ac3022b8).html.
Testo completoAbstract (sommario):
Classical value estimation reinforcement learning algorithms do not perform very well in dynamic environments. On the other hand, the reinforcement learning of animals is quite flexible: they can adapt to dynamic environments very quickly and deal with noisy inputs very effectively. One feature that may contribute to animals' good performance in dynamic environments is that they learn and perceive the time to reward. In this research, we attempt to learn and perceive the time to reward and explore situations where the learned time information can be used to improve the performance of the learning agent in dynamic environments. The type of dynamic environments that we are interested in is that type of switching environment which stays the same for a long time, then changes abruptly, and then holds for a long time before another change. The type of dynamics that we mainly focus on is the time to reward, though we also extend the ideas to learning and perceiving other criteria of optimality, e.g. the discounted return, so that they can still work even when the amount of reward may also change. Specifically, both the mean and variance of the time to reward are learned and then used to detect changes in the environment and to decide whether the agent should give up a suboptimal action. When a change in the environment is detected, the learning agent responds specifically to the change in order to recover quickly from it. When it is found that the current action is still worse than the optimal one, the agent gives up this time's exploration of the action and then remakes its decision in order to avoid longer than necessary exploration. The results of our experiments using two real-world problems show that they have effectively sped up learning, reduced the time taken to recover from environmental changes, and improved the performance of the agent after the learning converges in most of the test cases compared with classical value estimation reinforcement learning algorithms. In addition, we have successfully used spiking neurons to implement various phenomena of classical conditioning, the simplest form of animal reinforcement learning in dynamic environments, and also pointed out a possible implementation of instrumental conditioning and general reinforcement learning using similar models.
33
Löhr, 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 completoAbstract (sommario):
Many complexity measures are defined as the size of a minimal representation in
a specific model class. One such complexity measure, which is important because
it is widely applied, is statistical complexity. It is defined for
discrete-time, stationary stochastic processes within a theory called
computational mechanics. Here, a mathematically rigorous, more general version
of this theory is presented, and abstract properties of statistical complexity
as a function on the space of processes are investigated. In particular, weak-*
lower semi-continuity and concavity are shown, and it is argued that these
properties should be shared by all sensible complexity measures. Furthermore, a
formula for the ergodic decomposition is obtained.
The same results are also proven for two other complexity measures that are
defined by different model classes, namely process dimension and generative
complexity. These two quantities, and also the information theoretic complexity
measure called excess entropy, are related to statistical complexity, and this
relation is discussed here.
It is also shown that computational mechanics can be reformulated in terms of
Frank Knight''s prediction process, which is of both conceptual and technical
interest. In particular, it allows for a unified treatment of different
processes and facilitates topological considerations. Continuity of the Markov
transition kernel of a discrete version of the prediction process is obtained as
a new result.
34
Dillon, Joshua V. "Stochastic m-estimators: controlling accuracy-cost tradeoffs in machine learning". Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/42913.
Testo completoAbstract (sommario):
m-Estimation represents a broad class of estimators, including least-squares and maximum likelihood, and is a widely used tool for statistical inference. Its successful application however, often requires negotiating physical resources for desired levels of accuracy. These limiting factors, which we abstractly refer as costs, may be computational, such as time-limited cluster access for parameter learning, or they may be financial, such as purchasing human-labeled training data under a fixed budget. This thesis explores these accuracy- cost tradeoffs by proposing a family of estimators that maximizes a stochastic variation of the traditional m-estimator.
Such "stochastic m-estimators" (SMEs) are constructed by stitching together different m-estimators, at random. Each such instantiation resolves the accuracy-cost tradeoff differently, and taken together they span a continuous spectrum of accuracy-cost tradeoff resolutions. We prove the consistency of the estimators and provide formulas for their asymptotic variance and statistical robustness. We also assess their cost for two concerns typical to machine learning: computational complexity and labeling expense.
For the sake of concreteness, we discuss experimental results in the context of a variety of discriminative and generative Markov random fields, including Boltzmann machines, conditional random fields, model mixtures, etc. The theoretical and experimental studies demonstrate the effectiveness of the estimators when computational resources are insufficient or when obtaining additional labeled samples is necessary. We also demonstrate that in some cases the stochastic m-estimator is associated with robustness thereby increasing its statistical accuracy and representing a win-win.
35
Lecuelle, Guillaume. "Analyse et modélisation de la Dominance Temporelle des Sensations à l'aide de processus stochastiques". Thesis, Bourgogne Franche-Comté, 2019. http://www.theses.fr/2019UBFCK031/document.
Testo completoAbstract (sommario):
La Dominance Temporelle des Sensations (DTS) est une méthode d’analyse sensorielle qui mesure la perception temporelle d’un produit au cours de sa dégustation. Pour un panéliste, la DTS consiste à choisir parmi une liste de descripteurs lequel est dominant à chaque instant. Ce travail a pour but la modélisation des données DTS à l’aide de processus stochastiques et propose d’utiliser les processus semi-markoviens (PSM), une généralisation des chaînes de Markov qui permet de modéliser librement les durées de dominance. Le modèle obtenu peut être utilisé pour comparer des échantillons DTS en réalisant un rapport de vraisemblance. Étant donné que les probabilités de transition entre les descripteurs peuvent dépendre du temps, nous proposons d’utiliser des modèles différents par période et nous proposons un algorithme pour déterminer le nombre et les frontières de ces périodes de manière optimale. Le modèle est représenté sous forme d’un graphe montrant les transitions entre descripteurs les plus observées. Finalement, ce travail introduit les modèles de mélange de processus semi-markoviens afin de segmenter le panel en fonction des différences de perception interindividuelles.Les méthodes développées sont appliquées à des jeux de données DTS variés : chocolats, fromages frais et Goudas. Les résultats montrent que la modélisation par un PSM apporte de nouvelles informations sur la perception temporelle, en particulier sur la variabilité de perception au sein d’un panel, alors que les méthodes classiques se focalisent sur une vision moyenne de la perception du panel. De plus, à notre connaissance, ce travail est le premier à proposer l’identification d’un modèle de mélange de processus semi-markoviensTemporal Dominance of Sensations (TDS) is a technique to measure temporal perception of food product during tasting. For a panelist, it consists in choosing in a list of attributes which one is dominant at any time. This work aims to model TDS data with a stochastic process and proposes to use semi-Markov processes (SMP), a generalization of Markov chains which allows dominance durations to be modeled by any type of distribution. The model can then be used to compare TDS samples based on likelihood ratio. Because probabilities of transition from one attribute to another one can also depend on time, we propose to model TDS by period and we propose a method to select optimally the number of periods and the frontiers between periods. Graphs built upon the stochastic pattern can be plotted to represent main chronological transitions between attributes. Finally, this work introduces new statistical models based on finite mixtures of semi-Markov processes in order to derive consumer segmentation based on individual differences in temporal perception of a product.The methods are applied to various TDS datasets: chocolates, fresh cheeses and Gouda cheeses. Results show that SMP modeling gives new information about temporal perception compared to classical methods. It particularly emphasizes the existence of several perceptions for a same product in a panel, whereas classical methods only provide a mean panel overview. Furthermore, as far as we know, this work is the first one that considers mixtures of semi-Markov processes
36
Duong, Quoc Bao. "Approche probabiliste pour l'estimation dynamique de la confiance accordée à un équipement de production : vers une contribution au diagnostic de services des SED". Thesis, Grenoble, 2012. http://www.theses.fr/2012GRENT102/document.
Testo completoAbstract (sommario):
Le travail que nous présentons dans ce mémoire apporte sa contribution au domaine dela surveillance et de la supervision en ligne des systèmes à événements discrets complexes.Il se place dans un contexte perturbé par l’occurrence d’aléas de fonctionnement d’une partieopérative au sein duquel nous visons à mettre à disposition des équipes de maintenance desoutils pour les aider à localiser rapidement les équipements à l’origine probable de défautsproduits : localiser mieux pour maintenir mieux et donc minimiser encore davantage les tempsde dérives équipements. Si les équipements de production étaient en mesure de détecterde telles dérives, le problème pourrait être considéré comme simple, cependant, la présenced’équipements de métrologie montre le contraire. Aussi, partant du constat que les équipementsde production ne peuvent être dotés d’un système de captage couvrant de manière exhaustivel’ensemble des paramètres à observer, que la fiabilité des capteurs est variable dans le temps,que les contextes de production sont particulièrement stressants, nous avons proposé ici dedévelopper une approche probabiliste basée sur un raisonnement Bayésien permettant d’estimeren temps réel la confiance qui peut être accordée aux opérations réalisées par les équipementsde productionThe work that we present in this paper contributes to the field of supervision, monitoringand control of complex discrete event systems services. It is placed in the context of randomfailure occurrence of operative parts where we focus on providing tools to maintenance teamsby locating the possible origin of potential defect products: better locate to better maintain, soeffectively to minimize more equipment’s time drift. If the production equipment were able todetect such drifts, the problem could be considered simple; however, metrology equipment addsto the complexity. In addition, because of an impossibility to equip the production equipment witha sensor system which comprehensively covers all parameters to be observed, a variable sensorreliability in time and a stressed production environments, we propose a probabilistic approachbased on Bayesian network to estimate real time confidence, which can be used for productionequipment?s operation
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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.
Testo completoAbstract (sommario):
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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Joo, Arakawa Rocío. "A behavioral ecology of fishermen : hidden stories from trajectory data in the Northern Humboldt Current System". Thesis, Montpellier 2, 2013. http://www.theses.fr/2013MON20224/document.
Testo completoAbstract (sommario):
Ce travail propose une contribution originale à la compréhension du comportement spatial des pêcheurs, basée sur les paradigmes de l'écologie comportementale et de l'écologie du mouvement. En s'appuyant sur des données du 'Vessel Monitoring System', nous étudions le comportement des pêcheurs d'anchois du Pérou à des échelles différentes: (1) les modes comportementaux au sein des voyages de pêche (i.e. recherche, pêche et trajet), (2) les patrons comportementaux parmi les voyages de pêche, (3) les patrons comportementaux par saison de pêche conditionnés par des scénarios écosystémiques et (4) les patrons spatiaux des positions de modes comportementaux, que nous utilisons pour la création de cartes de probabilité de présence d'anchois. Pour la première échelle, nous comparons plusieurs modèles Markoviens (modèles de Markov et semi-Markov cachés) et discriminatifs (forêts aléatoires, machines à vecteurs de support et réseaux de neurones artificiels) pour inférer les modes comportementaux associés aux trajectoires VMS. L'utilisation d'un ensemble de données pour lesquelles les modes comportementaux sont connus (grâce aux données collectées par des observateurs embarqués), nous permet d'entraîner les modèles dans un cadre supervisé et de les valider. Les modèles de semi-Markov cachés sont les plus performants, et sont retenus pour inférer les modes comportementaux sur l'ensemble de données VMS. Pour la deuxième échelle, nous caractérisons chaque voyage de pêche par plusieurs descripteurs, y compris le temps passé dans chaque mode comportemental. En utilisant une analyse de classification hiérarchique, les patrons des voyages de pêche sont classés en groupes associés à des zones de gestion, aux segments de la flottille et aux personnalités des capitaines. Pour la troisième échelle, nous analysons comment les conditions écologiques donnent forme au comportement des pêcheurs à l'échelle d'une saison de pêche. Via des analyses de co-inertie, nous trouvons des associations significatives entre les dynamiques spatiales des pêcheurs, des anchois et de l'environnement, et nous caractérisons la réponse comportementale des pêcheurs selon des scénarios environnementaux contrastés. Pour la quatrième échelle, nous étudions si le comportement spatial des pêcheurs reflète dans une certaine mesure la répartition spatiale de l'anchois. Nous construisons un indicateur de la présence d'anchois à l'aide des modes comportementaux géo-référencés inférés à partir des données VMS. Ce travail propose enfin une vision plus large du comportement de pêcheurs: les pêcheurs ne sont pas seulement des agents économiques, ils sont aussi des fourrageurs, conditionnés par la variabilité dans l'écosystème. Pour conclure, nous discutons de la façon dont ces résultats peuvent avoir de l'importance pour la gestion de la pêche, des analyses de comportement collectif et des modèles end-to-endThis work proposes an original contribution to the understanding of fishermen spatial behavior, based on the behavioral ecology and movement ecology paradigms. Through the analysis of Vessel Monitoring System (VMS) data, we characterized the spatial behavior of Peruvian anchovy fishermen at different scales: (1) the behavioral modes within fishing trips (i.e., searching, fishing and cruising); (2) the behavioral patterns among fishing trips; (3) the behavioral patterns by fishing season conditioned by ecosystem scenarios; and (4) the computation of maps of anchovy presence proxy from the spatial patterns of behavioral mode positions. At the first scale considered, we compared several Markovian (hidden Markov and semi-Markov models) and discriminative models (random forests, support vector machines and artificial neural networks) for inferring the behavioral modes associated with VMS tracks. The models were trained under a supervised setting and validated using tracks for which behavioral modes were known (from on-board observers records). Hidden semi-Markov models performed better, and were retained for inferring the behavioral modes on the entire VMS dataset. At the second scale considered, each fishing trip was characterized by several features, including the time spent within each behavioral mode. Using a clustering analysis, fishing trip patterns were classified into groups associated to management zones, fleet segments and skippers' personalities. At the third scale considered, we analyzed how ecological conditions shaped fishermen behavior. By means of co-inertia analyses, we found significant associations between fishermen, anchovy and environmental spatial dynamics, and fishermen behavioral responses were characterized according to contrasted environmental scenarios. At the fourth scale considered, we investigated whether the spatial behavior of fishermen reflected to some extent the spatial distribution of anchovy. Finally, this work provides a wider view of fishermen behavior: fishermen are not only economic agents, but they are also foragers, constrained by ecosystem variability. To conclude, we discuss how these findings may be of importance for fisheries management, collective behavior analyses and end-to-end models
39
Gupta, Vivek. "Probability of SLA Violation for Semi-Markov Availability". Ohio University / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1235610777.
Testo completo
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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.
Testo completo
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Hoglin, Phillip J. "Survival analysis and accession optimization of prior enlisted United States Marine Corps officers". Thesis, Monterey, California. Naval Postgraduate School, 2004. http://hdl.handle.net/10945/1673.
Testo completoAbstract (sommario):
Approved for public release, distribution is unlimitedThe purpose of this thesis is to firstly analyze the determinants on the survival of United States Marine Corps Officers, and secondly, to develop the methodology to optimize the accessions of prior and non-prior enlisted officers. Using data from the Marine Corps Officer Accession Career file (MCCOAC), the Cox Proportional Hazards Model is used to estimate the effects of officer characteristics on their survival as a commissioned officer in the USMC. A Markov model for career transition is combined with fiscal data to determine the optimum number of prior and non-prior enlisted officers under the constraints of force structure and budget. The findings indicate that prior enlisted officers have a better survival rate than their non-prior enlisted counterparts. Additionally, officers who are married, commissioned through MECEP, graduate in the top third of their TBS class, and are assigned to a combat support MOS have a better survival rate than officers who are unmarried, commissioned through USNA, graduate in the middle third of their TBS class, and are assigned to either combat or combat service support MOS. The findings also indicate that the optimum number of prior enlisted officer accessions may be considerably lower than recent trends and may differ across MOS. Based on the findings; it is recommended that prior enlisted officer accession figures be reviewed.
Major, Australian Army
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Wanneveich, Mathilde. "Estimations et projections d’indicateurs de santé pour maladies chroniques et prise en compte de l’impact d’interventions". Thesis, Bordeaux, 2016. http://www.theses.fr/2016BORD0262/document.
Testo completoAbstract (sommario):
De nos jours, la Santé Publique porte de plus en plus d’intérêts aux maladies chroniques neuro-dégénératives liées au vieillissement telles que la démence ou la maladie de Parkinson. Ces pathologies ne peuvent ni être évitées ni guéries et occasionnent une détérioration progressive de la santé des malades requérant donc des soins spécifiques. Le contexte démographique actuel laisse entrevoir un vieillissement de la population et un allongement de l’espérance de vie continus, ce qui aura pour conséquence majeure d’aggraver le fardeau économique, social et démographique de ces maladies dans les années à venir. C’est pourquoi, dans l’optique d’anticiper les problèmes à venir, il est important de développer des modèles statistiques permettant de faire des projections et d’évaluer le fardeau futur de ces maladies via divers indicateurs de santé. De plus, il est intéressant de pouvoir donner des projections,en fonction de scénarios potentiels, qui pourraient être mis en place (e.g. un nouveau traitement), afin d’évaluer l’impact qu’aurait une telle intervention de Santé Publique, mais aussi de prendre en compte une variation de l’incidence due, par exemple, à des changements de comportement. Une approche utilisant le modèle illness-death sous une hypothèse Markovienne a été proposée par Joly et al. 2013 pour répondre à cet objectif. Cette approche est particulièrement adaptée dans ce contexte, car elle permet, notamment, de prendre en compte le risque compétitif qui existe entre devenir malade ou décéder (pour un individu non-malade). L’utilisation de ce modèle pour faire des projections a l’avantage, d’une part, de faire intervenir des projections démographiques nationales pour mieux capter l’évolution de la mortalité au cours du temps, et d’autre part, de proposer une modélisation adaptée de la mortalité (en distinguant la mortalité des malades, des non-malades et générale). C’est sur la base de ce modèle que découle tout le travail de cette thèse. Dans une première partie, les hypothèses du modèle existant ont été améliorées ou modifiées dans le but de considérer l’évolution de l’incidence de la maladie au cours du temps,puis de passer d’un modèle Markovien à un modèle semi-Markovien afin de modéliser la mortalité des malades en fonction de la durée passée avec la maladie. Dans une deuxième partie, la méthode initiale, permettant de considérer l’impact d’une intervention mais avec des hypothèses restrictives, a été développée et généralisée pour prendre en compte des interventions plus flexibles. Puis, les expressions mathématiques/statistiques d’indicateurs de santé pertinents ont été développées dans ce contexte dans le but d’avoir un panel de projections permettant une meilleure évaluation du fardeau futur de la maladie. L’application principale de ce travail a porté sur des projections concernant la démence. Cependant, en appliquant ces modèles à la maladie de Parkinson, nous avons proposé des méthodes permettant d’adapter notre approche à d’autres types de donnéesNowadays, Public Health is more and more interested in the neuro-degenerative chronic diseases related to the ageing such as Dementia or Parkinson’s disease. These pathologies cannot be prevented or cured and cause a progressive deterioration of health, requiring specific cares. The current demographic situation suggests a continuous ageing of the population and a rise of the life expectancy. As consequence of this, the economic, social and demographic burden related to these diseases will worsen in years to come. That is why, developing statistical models, which allow to make projections and to estimate the future burden of chronic diseases via several health indicators, is becoming of paramount importance. Furthermore, it would be interesting to give projections, according to hypothetical scenarios, which could be set up (e.g. a new treatment), to estimate the impact of such Public health intervention, but also to take into account modifications in disease incidence due, for example, to behavior changes. To attend this objective, an approach was proposed by Joly et al. 2013, using the illness-death model under Markovian hypothesis. Such approach has shown to be particularly adapted in this context, since it allows to consider the competive risk existing between the risk of death and the risk to develop the disease (for anon-diseased subject). On one hand, projections made by using this model take advantage of including national demographic projections to better consider the mortality trends over time. Moreover, the approach proposes to carefully model mortality, distinguishing overall mortality, non-diseased and diseased mortality trends. All the work of this thesis have been developed based on this model. In a first part, the hypotheses of the existing model are improved or modified in order to both :consider the evolution of disease incidence over time ; pass from a Markov to a semi-Markov hypothesis, which allows to model the mortality among diseased subjects depending on the time spent with the disease. In a second part, the initial method, allowing to take into account the impact of an intervention, but with many restrictive assumptions, is developed and generalized for more flexible interventions. Then, the mathematical/statistical expressions of relevant health indicators are developed in this context to have a panel of projections giving a better assessment of the future disease burden. The main application of this work concerns projections of Dementia. However, by applying these models to Parkinson’s disease, we propose methods which allows to adapt our approach to other types of data
43
Kempken, Sebastian. "Modellierung und verifizierte Analyse von zeitkorreliertem Datenverkehr im Internet". Düsseldorf VDI-Verl, 2009. http://d-nb.info/999487949/04.
Testo completo
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Jagbrant, Gustav. "Autonomous Crop Segmentation, Characterisation and Localisation". Thesis, Linköpings universitet, Institutionen för systemteknik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-97374.
Testo completoAbstract (sommario):
Orchards demand large areas of land, thus they are often situated far from major population centres. As a result it is often difficult to obtain the necessary personnel, limiting both growth and productivity. However, if autonomous robots could be integrated into the operation of the orchard, the manpower demand could be reduced. A key problem for any autonomous robot is localisation; how does the robot know where it is? In agriculture robots, the most common approach is to use GPS positioning. However, in an orchard environment, the dense and tall vegetation restricts the usage to large robots that reach above the surroundings. In order to enable the use of smaller robots, it is instead necessary to use a GPS independent system. However, due to the similarity of the environment and the lack of strong recognisable features, it appears unlikely that typical non-GPS solutions will prove successful. Therefore we present a GPS independent localisation system, specifically aimed for orchards, that utilises the inherent structure of the surroundings. Furthermore, we examine and individually evaluate three related sub-problems. The proposed system utilises a 3D point cloud created from a 2D LIDAR and the robot’s movement. First, we show how the data can be segmented into individual trees using a Hidden Semi-Markov Model. Second, we introduce a set of descriptors for describing the geometric characteristics of the individual trees. Third, we present a robust localisation method based on Hidden Markov Models. Finally, we propose a method for detecting segmentation errors when associating new tree measurements with previously measured trees. Evaluation shows that the proposed segmentation method is accurate and yields very few segmentation errors. Furthermore, the introduced descriptors are determined to be consistent and informative enough to allow localisation. Third, we show that the presented localisation method is robust both to noise and segmentation errors. Finally it is shown that a significant majority of all segmentation errors can be detected without falsely labeling correct segmentations as incorrect.Eftersom fruktodlingar kräver stora markområden är de ofta belägna långt från större befolkningscentra. Detta gör det svårt att finna tillräckligt med arbetskraft och begränsar expansionsmöjligheterna. Genom att integrera autonoma robotar i drivandet av odlingarna skulle arbetet kunna effektiviseras och behovet av arbetskraft minska. Ett nyckelproblem för alla autonoma robotar är lokalisering; hur vet roboten var den är? I jordbruksrobotar är standardlösningen att använda GPS-positionering. Detta är dock problematiskt i fruktodlingar, då den höga och täta vegetationen begränsar användandet till större robotar som når ovanför omgivningen. För att möjliggöra användandet av mindre robotar är det istället nödvändigt att använda ett GPS-oberoende lokaliseringssystem. Detta problematiseras dock av den likartade omgivningen och bristen på distinkta riktpunkter, varför det framstår som osannolikt att existerande standardlösningar kommer fungera i denna omgivning. Därför presenterar vi ett GPS-oberoende lokaliseringssystem, speciellt riktat mot fruktodlingar, som utnyttjar den naturliga strukturen hos omgivningen.Därutöver undersöker vi och utvärderar tre relaterade delproblem. Det föreslagna systemet använder ett 3D-punktmoln skapat av en 2D-LIDAR och robotens rörelse. Först visas hur en dold semi-markovmodell kan användas för att segmentera datasetet i enskilda träd. Därefter introducerar vi ett antal deskriptorer för att beskriva trädens geometriska form. Vi visar därefter hur detta kan kombineras med en dold markovmodell för att skapa ett robust lokaliseringssystem.Slutligen föreslår vi en metod för att detektera segmenteringsfel när nya mätningar av träd associeras med tidigare uppmätta träd. De föreslagna metoderna utvärderas individuellt och visar på goda resultat. Den föreslagna segmenteringsmetoden visas vara noggrann och ge upphov till få segmenteringsfel. Därutöver visas att de introducerade deskriptorerna är tillräckligt konsistenta och informativa för att möjliggöra lokalisering. Ytterligare visas att den presenterade lokaliseringsmetoden är robust både mot brus och segmenteringsfel. Slutligen visas att en signifikant majoritet av alla segmenteringsfel kan detekteras utan att felaktigt beteckna korrekta segmenteringar som inkorrekta.
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Olivier, Brice. "Analyse conjointe de traces oculométriques et d'EEG à l'aide de modèles de Markov cachés couplés". Thesis, Université Grenoble Alpes (ComUE), 2019. http://www.theses.fr/2019GREAM030/document.
Testo completoAbstract (sommario):
Cette thèse consiste à analyser conjointement des signaux de mouvement des yeux et d’électroencéphalogrammes (EEG) multicanaux acquis simultanément avec des participants effectuant une tâche de lecture de recueil d'informations afin de prendre une décision binaire - le texte est-il lié à un sujet ou non? La recherche d'informations textuelles n'est pas un processus homogène dans le temps - ni d'un point de vue cognitif, ni en termes de mouvement des yeux. Au contraire, ce processus implique plusieurs étapes ou phases, telles que la lecture normale, le balayage, la lecture attentive - en termes d'oculométrie - et la création et le rejet d'hypothèses, la confirmation et la décision - en termes cognitifs.Dans une première contribution, nous discutons d'une méthode d'analyse basée sur des chaînes semi-markoviennes cachées sur les signaux de mouvement des yeux afin de mettre en évidence quatre phases interprétables en termes de stratégie d'acquisition d'informations: lecture normale, lecture rapide, lecture attentive et prise de décision.Dans une deuxième contribution, nous lions ces phases aux changements caractéristiques des signaux EEG et des informations textuelles. En utilisant une représentation en ondelettes des EEG, cette analyse révèle des changements de variance et de corrélation des coefficients inter-canaux, en fonction des phases et de la largeur de bande. En utilisant des méthodes de plongement des mots, nous relions l’évolution de la similarité sémantique au sujet tout au long du texte avec les changements de stratégie.Dans une troisième contribution, nous présentons un nouveau modèle dans lequel les EEG sont directement intégrés en tant que variables de sortie afin de réduire l’incertitude des états. Cette nouvelle approche prend également en compte les aspects asynchrones et hétérogènes des donnéesThis PhD thesis consists in jointly analyzing eye-tracking signals and multi-channel electroencephalograms (EEGs) acquired concomitantly on participants doing an information collection reading task in order to take a binary decision - is the text related to some topic or not ? Textual information search is not a homogeneous process in time - neither on a cognitive point of view, nor in terms of eye-movement. On the contrary, this process involves several steps or phases, such as normal reading, scanning, careful reading - in terms of oculometry - and creation and rejection of hypotheses, confirmation and decision - in cognitive terms.In a first contribution, we discuss an analysis method based on hidden semi-Markov chains on the eye-tracking signals in order to highlight four interpretable phases in terms of information acquisition strategy: normal reading, fast reading, careful reading, and decision making.In a second contribution, we link these phases with characteristic changes of both EEGs signals and textual information. By using a wavelet representation of EEGs, this analysis reveals variance and correlation changes of the inter-channels coefficients, according to the phases and the bandwidth. And by using word embedding methods, we link the evolution of semantic similarity to the topic throughout the text with strategy changes.In a third contribution, we present a new model where EEGs are directly integrated as output variables in order to reduce the state uncertainty. This novel approach also takes into consideration the asynchronous and heterogeneous aspects of the data
46
Ter-Hovhannisyan, Vardges. "Unsupervised and semi-supervised training methods for eukaryotic gene prediction". Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/26645.
Testo completoAbstract (sommario):
Thesis (Ph.D)--Biology, Georgia Institute of Technology, 2009.Committee Chair: Mark Borodovky; Committee Member: Jung H. Choi; Committee Member: King Jordan; Committee Member: Leonid Bunimovich; Committee Member: Yury Chernoff. Part of the SMARTech Electronic Thesis and Dissertation Collection.
47
Samara, Marko. "Limit Theorems for the Rotational Isomeric State Model". The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1322578686.
Testo completo
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Nishikimi, Ryo. "Generative, Discriminative, and Hybrid Approaches to Audio-to-Score Automatic Singing Transcription". Doctoral thesis, Kyoto University, 2021. http://hdl.handle.net/2433/263772.
Testo completo
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Le, Thanh Trung. "Contribution to deterioration modeling and residual life estimation based on condition monitoring data". Thesis, Université Grenoble Alpes (ComUE), 2015. http://www.theses.fr/2015GREAT099/document.
Testo completoAbstract (sommario):
La maintenance prédictive joue un rôle important dans le maintien des systèmes de production continue car elle peut aider à réduire les interventions inutiles ainsi qu'à éviter des pannes imprévues. En effet, par rapport à la maintenance conditionnelle, la maintenance prédictive met en œuvre une étape supplémentaire, appelée le pronostic. Les opérations de maintenance sont planifiées sur la base de la prédiction des états de détérioration futurs et sur l'estimation de la vie résiduelle du système. Dans le cadre du projet européen FP7 SUPREME (Sustainable PREdictive Maintenance for manufacturing Equipment en Anglais), cette thèse se concentre sur le développement des modèles de détérioration stochastiques et sur des méthodes d'estimation de la vie résiduelle (Remaining Useful Life – RUL en anglais) associées pour les adapter aux cas d'application du projet. Plus précisément, les travaux présentés dans ce manuscrit sont divisés en deux parties principales. La première donne une étude détaillée des modèles de détérioration et des méthodes d'estimation de la RUL existant dans la littérature. En analysant leurs avantages et leurs inconvénients, une adaptation d’une approche de l'état de l'art est mise en œuvre sur des cas d'études issus du projet SUPREME et avec les données acquises à partir d’un banc d'essai développé pour le projet. Certains aspects pratiques de l’implémentation, à savoir la question de l'échange d'informations entre les partenaires du projet, sont également détaillées dans cette première partie. La deuxième partie est consacrée au développement de nouveaux modèles de détérioration et les méthodes d'estimation de la RUL qui permettent d'apporter des éléments de solutions aux problèmes de modélisation de détérioration et de prédiction de RUL soulevés dans le projet SUPREME. Plus précisément, pour surmonter le problème de la coexistence de plusieurs modes de détérioration, le concept des modèles « multi-branche » est proposé. Dans le cadre de cette thèse, deux catégories des modèles de type multi-branche sont présentées correspondant aux deux grands types de modélisation de l'état de santé des système, discret ou continu. Dans le cas discret, en se basant sur des modèles markoviens, deux modèles nommés Mb-HMM and Mb-HsMM (Multi-branch Hidden (semi-)Markov Model en anglais) sont présentés. Alors que dans le cas des états continus, les systèmes linéaires à sauts markoviens (JMLS) sont mis en œuvre. Pour chaque modèle, un cadre à deux phases est implémenté pour accomplir à la fois les tâches de diagnostic et de pronostic. A travers des simulations numériques, nous montrons que les modèles de type multi-branche peuvent donner des meilleures performances pour l'estimation de la RUL par rapport à celles obtenues par des modèles standards mais « mono-branche »Predictive maintenance plays a crucial role in maintaining continuous production systems since it can help to reduce unnecessary intervention actions and avoid unplanned breakdowns. Indeed, compared to the widely used condition-based maintenance (CBM), the predictive maintenance implements an additional prognostics stage. The maintenance actions are then planned based on the prediction of future deterioration states and residual life of the system. In the framework of the European FP7 project SUPREME (Sustainable PREdictive Maintenance for manufacturing Equipment), this thesis concentrates on the development of stochastic deterioration models and the associated remaining useful life (RUL) estimation methods in order to be adapted in the project application cases. Specifically, the thesis research work is divided in two main parts. The first one gives a comprehensive review of the deterioration models and RUL estimation methods existing in the literature. By analyzing their advantages and disadvantages, an adaption of the state of the art approaches is then implemented for the problem considered in the SUPREME project and for the data acquired from a project's test bench. Some practical implementation aspects, such as the issue of delivering the proper RUL information to the maintenance decision module are also detailed in this part. The second part is dedicated to the development of innovative contributions beyond the state-of-the-are in order to develop enhanced deterioration models and RUL estimation methods to solve original prognostics issues raised in the SUPREME project. Specifically, to overcome the co-existence problem of several deterioration modes, the concept of the "multi-branch" models is introduced. It refers to the deterioration models consisting of different branches in which each one represent a deterioration mode. In the framework of this thesis, two multi-branch model types are presented corresponding to the discrete and continuous cases of the systems' health state. In the discrete case, the so-called Multi-branch Hidden Markov Model (Mb-HMM) and the Multi-branch Hidden semi-Markov model (Mb-HsMM) are constructed based on the Markov and semi-Markov models. Concerning the continuous health state case, the Jump Markov Linear System (JMLS) is implemented. For each model, a two-phase framework is carried out for both the diagnostics and prognostics purposes. Through numerical simulations and a case study, we show that the multi-branch models can help to take into account the co-existence problem of multiple deterioration modes, and hence give better performances in RUL estimation compared to the ones obtained by standard "single branch" models
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Holmes, Daniel G. "Semi-hidden markov models for visible light communication channels". Thesis, 2018. https://hdl.handle.net/10539/26477.
Testo completoAbstract (sommario):
A dissertation submitted to the Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg, in fulfillment of the requirements for the degree of Master of Science in Engineering, Johannesburg 2018Visible Light Communication (VLC) is an emerging field in optical wireless communication that uses light emitting diodes (LEDs) for data transmission. LEDs are being widely adopted both indoors and outdoors due to their low cost, long lifespan and high efficiency. Furthermore, LEDs can be modulated to provide both illumination and wireless communication. There is also potential for VLC to be incorporated into future smart lighting systems. One of the current challenges in VLC is being able to deal with noise and interference; including interference from other dimmed, Pulse-Width Modulated (PWM) LEDs. Other noise includes natural light from the sun and artificial light from other non-modulating light sources. Modelling these types of channels is one of the first steps in understanding the channel and eventually designing techniques for mitigating the effects of noise and interference. This dissertation presents a semi-hidden Markov model, known as the Fritchman model, that discretely models the effects of as well as errors introduced from noise and interference in on-off keying modulated VLC channels. Models have been developed for both the indoor and outdoor environments and can be used for VLC simulations and designing error mitigation techniques. Results show that certain channels are able to be better modelled than others. Experimental error distributions shows insights into the impact that PWM interference has on VLC channels. This can be used for assisting in the development of error control codes and interference avoidance techniques in standalone VLC systems, as well as systems where VLC and smart lighting coexist. The models developed can also be used for simulations of VLC channels under different channel conditions
XL2019