Tesis sobre el tema "Bayesian Inference Damage Detection"
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Goi, Yoshinao. "Bayesian Damage Detection for Vibration Based Bridge Health Monitoring". Kyoto University, 2018. http://hdl.handle.net/2433/232013.
Texto completoLebre, Sophie. "Stochastic process analysis for Genomics and Dynamic Bayesian Networks inference". Phd thesis, Université d'Evry-Val d'Essonne, 2007. http://tel.archives-ouvertes.fr/tel-00260250.
Texto completoFirst we study a parsimonious Markov model called Mixture Transition Distribution (MTD) model which is a mixture of Markovian transitions. The overly high number of constraints on the parameters of this model hampers the formulation of an analytical expression of the Maximum Likelihood Estimate (MLE). We propose to approach the MLE thanks to an EM algorithm. After comparing the performance of this algorithm to results from the litterature, we use it to evaluate the relevance of MTD modeling for bacteria DNA coding sequences in comparison with standard Markovian modeling.
Then we propose two different approaches for genetic regulation network recovering. We model those genetic networks with Dynamic Bayesian Networks (DBNs) whose edges describe the dependency relationships between time-delayed genes expression. The aim is to estimate the topology of this graph despite the overly low number of repeated measurements compared with the number of observed genes.
To face this problem of dimension, we first assume that the dependency relationships are homogeneous, that is the graph topology is constant across time. Then we propose to approximate this graph by considering partial order dependencies. The concept of partial order dependence graphs, already introduced for static and non directed graphs, is adapted and characterized for DBNs using the theory of graphical models. From these results, we develop a deterministic procedure for DBNs inference.
Finally, we relax the homogeneity assumption by considering the succession of several homogeneous phases. We consider a multiple changepoint
regression model. Each changepoint indicates a change in the regression model parameters, which corresponds to the way an expression level depends on the others. Using reversible jump MCMC methods, we develop a stochastic algorithm which allows to simultaneously infer the changepoints location and the structure of the network within the phases delimited by the changepoints.
Validation of those two approaches is carried out on both simulated and real data analysis.
Ko, Kyungduk. "Bayesian wavelet approaches for parameter estimation and change point detection in long memory processes". Diss., Texas A&M University, 2004. http://hdl.handle.net/1969.1/2804.
Texto completoReichl, Johannes y Sylvia Frühwirth-Schnatter. "A Censored Random Coefficients Model for the Detection of Zero Willingness to Pay". Springer, 2011. http://epub.wu.ac.at/3707/1/WU_epub_(2).pdf.
Texto completoZhang, Hanze. "Bayesian inference on quantile regression-based mixed-effects joint models for longitudinal-survival data from AIDS studies". Scholar Commons, 2017. https://scholarcommons.usf.edu/etd/7456.
Texto completoOsborne, Michael A. "Bayesian Gaussian processes for sequential prediction, optimisation and quadrature". Thesis, University of Oxford, 2010. http://ora.ox.ac.uk/objects/uuid:1418c926-6636-4d96-8bf6-5d94240f3d1f.
Texto completoSuvorov, Anton. "Molecular Evolution of Odonata Opsins, Odonata Phylogenomics and Detection of False Positive Sequence Homology Using Machine Learning". BYU ScholarsArchive, 2018. https://scholarsarchive.byu.edu/etd/7320.
Texto completoZhang, Fan. "Statistical Methods for Characterizing Genomic Heterogeneity in Mixed Samples". Digital WPI, 2016. https://digitalcommons.wpi.edu/etd-dissertations/419.
Texto completoAsgrimsson, David Steinar. "Quantifying uncertainty in structural condition with Bayesian deep learning : A study on the Z-24 bridge benchmark". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-251451.
Texto completoEn maskininlärningsmetod för strukturell skadedetektering av broar presenteras. Metoden valideras på det kända referensdataset Z-24, där en sensor-instrumenterad trespannsbro stegvist skadats. Ett Bayesianskt neuralt nätverk med autoenkoders tränas till att rekonstruera råa sensordatasekvenser, med osäkerhetsgränser i förutsägningen. Rekonstrueringsavvikelsen jämförs med avvikelsesfördelningen i oskadat tillstånd och sekvensen bedöms att komma från ett skadad eller icke skadat tillstånd. Flera realistiska stegvisa skadetillstånd upptäcktes, vilket gör metoden användbar i ett databaserat skadedetektionssystem för en bro i full storlek. Detta är ett lovande steg mot ett helt operativt databaserat skadedetektionssystem.
Kennedy, Justin M. "Wave-induced marine craft motion estimation and control". Thesis, Queensland University of Technology, 2021. https://eprints.qut.edu.au/213481/1/Justin_Kennedy_Thesis.pdf.
Texto completoStanaway, Mark Andrew. "Hierarchical Bayesian models for estimating the extent of plant pest invasions". Thesis, Queensland University of Technology, 2011. https://eprints.qut.edu.au/40852/1/Mark_Stanaway_Thesis.pdf.
Texto completoSaade, Alaa. "Spectral inference methods on sparse graphs : theory and applications". Thesis, Paris Sciences et Lettres (ComUE), 2016. http://www.theses.fr/2016PSLEE024/document.
Texto completoIn an era of unprecedented deluge of (mostly unstructured) data, graphs are proving more and more useful, across the sciences, as a flexible abstraction to capture complex relationships between complex objects. One of the main challenges arising in the study of such networks is the inference of macroscopic, large-scale properties affecting a large number of objects, based solely on he microscopic interactions between their elementary constituents. Statistical physics, precisely created to recover the macroscopic laws of thermodynamics from an idealized model of interacting particles, provides significant insight to tackle such complex networks.In this dissertation, we use methods derived from the statistical physics of disordered systems to design and study new algorithms for inference on graphs. Our focus is on spectral methods, based on certain eigenvectors of carefully chosen matrices, and sparse graphs, containing only a small amount of information. We develop an original theory of spectral inference based on a relaxation of various meanfield free energy optimizations. Our approach is therefore fully probabilistic, and contrasts with more traditional motivations based on the optimization of a cost function. We illustrate the efficiency of our approach on various problems, including community detection, randomized similarity-based clustering, and matrix completion
Decelle, Aurélien. "Statistical physics of disordered networks - Spin Glasses on hierarchical lattices and community inference on random graphs". Phd thesis, Université Paris Sud - Paris XI, 2011. http://tel.archives-ouvertes.fr/tel-00653375.
Texto completoHarlé, Flore. "Détection de ruptures multiples dans des séries temporelles multivariées : application à l'inférence de réseaux de dépendance". Thesis, Université Grenoble Alpes (ComUE), 2016. http://www.theses.fr/2016GREAT043/document.
Texto completoThis thesis presents a method for the multiple change-points detection in multivariate time series, and exploits the results to estimate the relationships between the components of the system. The originality of the model, called the Bernoulli Detector, relies on the combination of a local statistics from a robust test, based on the computation of ranks, with a global Bayesian framework. This non parametric model does not require strong hypothesis on the distribution of the observations. It is applicable without modification on gaussian data as well as data corrupted by outliers. The detection of a single change-point is controlled even for small samples. In a multivariate context, a term is introduced to model the dependencies between the changes, assuming that if two components are connected, the events occurring in the first one tend to affect the second one instantaneously. Thanks to this flexible model, the segmentation is sensitive to common changes shared by several signals but also to isolated changes occurring in a single signal. The method is compared with other solutions of the literature, especially on real datasets of electrical household consumption and genomic measurements. These experiments enhance the interest of the model for the detection of change-points in independent, conditionally independent or fully connected signals. The synchronization of the change-points within the time series is finally exploited in order to estimate the relationships between the variables, with the Bayesian network formalism. By adapting the score function of a structure learning method, it is checked that the independency model that describes the system can be partly retrieved through the information given by the change-points, estimated by the Bernoulli Detector
Teixeira, Josiele da Silva. "Identificação de danos estruturais via método de Monte Carlo com cadeias de Markov". Universidade do Estado do Rio de Janeiro, 2014. http://www.bdtd.uerj.br/tde_busca/arquivo.php?codArquivo=6733.
Texto completoO presente trabalho apresenta um estudo referente à aplicação da abordagem Bayesiana como técnica de solução do problema inverso de identificação de danos estruturais, onde a integridade da estrutura é continuamente descrita por um parâmetro estrutural denominado parâmetro de coesão. A estrutura escolhida para análise é uma viga simplesmente apoiada do tipo Euler-Bernoulli. A identificação de danos é baseada em alterações na resposta impulsiva da estrutura, provocadas pela presença dos mesmos. O problema direto é resolvido através do Método de Elementos Finitos (MEF), que, por sua vez, é parametrizado pelo parâmetro de coesão da estrutura. O problema de identificação de danos é formulado como um problema inverso, cuja solução, do ponto de vista Bayesiano, é uma distribuição de probabilidade a posteriori para cada parâmetro de coesão da estrutura, obtida utilizando-se a metodologia de amostragem de Monte Carlo com Cadeia de Markov. As incertezas inerentes aos dados medidos serão contempladas na função de verossimilhança. Três estratégias de solução são apresentadas. Na Estratégia 1, os parâmetros de coesão da estrutura são amostrados de funções densidade de probabilidade a posteriori que possuem o mesmo desvio padrão. Na Estratégia 2, após uma análise prévia do processo de identificação de danos, determina-se regiões da viga potencialmente danificadas e os parâmetros de coesão associados à essas regiões são amostrados a partir de funções de densidade de probabilidade a posteriori que possuem desvios diferenciados. Na Estratégia 3, após uma análise prévia do processo de identificação de danos, apenas os parâmetros associados às regiões identificadas como potencialmente danificadas são atualizados. Um conjunto de resultados numéricos é apresentado levando-se em consideração diferentes níveis de ruído para as três estratégias de solução apresentadas.
This work presents a study on the application of Bayesian approach as a technique for solving the inverse problem of structural damage identification, where the integrity of the structure is continuously described by a structural cohesion parameter. The structure chosen for analysis is a simply supported Euler - Bernoulli beam. The damage identification is based on changes in the impulse response of the structure caused by the presence thereof. The direct problem is solved by the finite element method (FEM), which, in turn, is parameterized by the cohesion parameter of the structure. The problem of identifying damages is formulated as an inverse problem, whose solution, from the Bayesian framework, is a posteriori probability distribution of the cohesion parameter, obtained using the sampling methodology of Monte Carlo with Markov Chain. The uncertainties inherent to the measured data will be included in the likelihood function. Three solution strategies are presented. In the Strategy 1, the cohesion parameters of the structure are sampled from probability density functions a posteriori that have the same standard deviation. In the Strategy 2, after a previous analysis of the damage identification process, are determined potentially damaged regions and the cohesion parameters associated with these regions are sampled from probability density functions a posteriori that have different deviations. In the Strategy 3, after a preliminary analysis of the damage identification process, only the parameters associated with regions identifed as potentially damaged are updated. A set of numerical results are presented taking into account different noise levels for the three considered strategies.
Rozas, Rony. "Intégration du retour d'expérience pour une stratégie de maintenance dynamique". Thesis, Paris Est, 2014. http://www.theses.fr/2014PEST1112/document.
Texto completoThe optimization of maintenance strategies is a major issue for many industrial applications. It involves establishing a maintenance plan that ensures security levels, security and high reliability with minimal cost and respecting any constraints. The increasing number of works on optimization of maintenance parameters in particular in scheduling preventive maintenance action underlines the importance of this issue. A large number of studies on maintenance are based on a modeling of the degradation of the system studied. Probabilistic Models Graphics (PGM) and especially Markovian PGM (M-PGM) provide a framework for modeling complex stochastic processes. The issue with this approach is that the quality of the results is dependent on the model. More system parameters considered may change over time. This change is usually the result of a change of supplier for replacement parts or a change in operating parameters. This thesis deals with the issue of dynamic adaptation of a maintenance strategy, with a system whose parameters change. The proposed methodology is based on change detection algorithms in a stream of sequential data and a new method for probabilistic inference specific to the dynamic Bayesian networks. Furthermore, the algorithms proposed in this thesis are implemented in the framework of a research project with Bombardier Transportation. The study focuses on the maintenance of the access system of a new automotive designed to operate on the rail network in Ile-de-France. The overall objective is to ensure a high level of safety and reliability during train operation
Qin, Yingying. "Early breast anomalies detection with microwave and ultrasound modalities". Electronic Thesis or Diss., université Paris-Saclay, 2021. http://www.theses.fr/2021UPASG058.
Texto completoImaging of the breast for early detec-tion of tumors is studied by associating microwave (MW) and ultrasound (US) data. No registration is enforced since a free pending breast is tackled. A 1st approach uses prior information on tissue boundaries yielded from US reflection data. Regularization incorporates that two neighboring pixels should exhibit similar MW properties when not on a boundary while a jump allowed otherwise. This is enforced in the distorted Born iterative and the contrast source inversion methods. A 2nd approach involves deterministic edge preserving regularization via auxiliary variables indicating if a pixel is on an edge or not, edge markers being shared by MW and US parameters. Those are jointly optimized from the last parameter profiles and guide the next optimization as regularization term coefficients. Alternate minimization is to update US contrast, edge markers and MW contrast. A 3rd approach involves convolutional neural networks. Estimated contrast current and scattered field are the inputs. A multi-stream structure is employed to feed MW and US data. The network outputs the maps of MW and US parameters to perform real-time. Apart from the regression task, a multi-task learning strategy is used with a classifier that associates each pixel to a tissue type to yield a segmentation image. Weighted loss assigns a higher penalty to pixels in tumors when wrongly classified. A 4th approach involves a Bayesian formalism where the joint posterior distribution is obtained via Bayes’ rule; this true distribution is then approximated by a free-form separable law for each set of unknowns to get the estimate sought. All those solution methods are illustrated and compared from a wealth of simulated data on simple synthetic models and on 2D cross-sections of anatomically-realistic MRI-derived numerical breast phantoms in which small artificial tumors are inserted
Tiomoko, ali Hafiz. "Nouvelles méthodes pour l’apprentissage non-supervisé en grandes dimensions". Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLC074/document.
Texto completoSpurred by recent advances on the theoretical analysis of the performances of the data-driven machine learning algorithms, this thesis tackles the performance analysis and improvement of high dimensional data and graph clustering. Specifically, in the first bigger part of the thesis, using advanced tools from random matrix theory, the performance analysis of spectral methods on dense realistic graph models and on high dimensional kernel random matrices is performed through the study of the eigenvalues and eigenvectors of the similarity matrices characterizing those data. New improved methods are proposed and are shown to outperform state-of-the-art approaches. In a second part, a new algorithm is proposed for the detection of heterogeneous communities from multi-layer graphs using variational Bayes approaches to approximate the posterior distribution of the sought variables. The proposed methods are successfully applied to synthetic benchmarks as well as real-world datasets and are shown to outperform standard approaches to clustering in those specific contexts
Monteiro, João Filipe Gonçalves. "Modelo combinado captura-recaptura e transectos lineares: uma abordagem bayesiana". Doctoral thesis, Universidade de Évora, 2010. http://hdl.handle.net/10174/17969.
Texto completoNarasimha, Rajesh. "Application of Information Theory and Learning to Network and Biological Tomography". Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/19889.
Texto completoSahin, Serdar. "Advanced receivers for distributed cooperation in mobile ad hoc networks". Thesis, Toulouse, INPT, 2019. http://www.theses.fr/2019INPT0089.
Texto completoMobile ad hoc networks (MANETs) are rapidly deployable wireless communications systems, operating with minimal coordination in order to avoid spectral efficiency losses caused by overhead. Cooperative transmission schemes are attractive for MANETs, but the distributed nature of such protocols comes with an increased level of interference, whose impact is further amplified by the need to push the limits of energy and spectral efficiency. Hence, the impact of interference has to be mitigated through with the use PHY layer signal processing algorithms with reasonable computational complexity. Recent advances in iterative digital receiver design techniques exploit approximate Bayesian inference and derivative message passing techniques to improve the capabilities of well-established turbo detectors. In particular, expectation propagation (EP) is a flexible technique which offers attractive complexity-performance trade-offs in situations where conventional belief propagation is limited by computational complexity. Moreover, thanks to emerging techniques in deep learning, such iterative structures are cast into deep detection networks, where learning the algorithmic hyper-parameters further improves receiver performance. In this thesis, EP-based finite-impulse response decision feedback equalizers are designed, and they achieve significant improvements, especially in high spectral efficiency applications, over more conventional turbo-equalization techniques, while having the advantage of being asymptotically predictable. A framework for designing frequency-domain EP-based receivers is proposed, in order to obtain detection architectures with low computational complexity. This framework is theoretically and numerically analysed with a focus on channel equalization, and then it is also extended to handle detection for time-varying channels and multiple-antenna systems. The design of multiple-user detectors and the impact of channel estimation are also explored to understand the capabilities and limits of this framework. Finally, a finite-length performance prediction method is presented for carrying out link abstraction for the EP-based frequency domain equalizer. The impact of accurate physical layer modelling is evaluated in the context of cooperative broadcasting in tactical MANETs, thanks to a flexible MAC-level simulator
Pepi, Chiara. "Suitability of dynamic identification for damage detection in the light of uncertainties on a cable stayed footbridge". Doctoral thesis, 2019. http://hdl.handle.net/2158/1187384.
Texto completoLiu, Che-Hsun y 劉哲勳. "A Novel Android Malware Detection Using Bayesian Inference". Thesis, 2015. http://ndltd.ncl.edu.tw/handle/16037116098850344753.
Texto completo國立臺灣大學
電機工程學研究所
103
Android malware detection has been a popular research topic due to non-negligible amount of malware targeting the Android operating system. In particular, the naive Bayes generative classifier is a common technique widely adopted in many papers. However, we found that the naive Bayes classifier performs badly in Contagio Malware Dump dataset, which could result from the assumption that no feature dependency exists. In this paper, we propose a lightweight method for Android malware detection, which improves the performance of Bayesian classification on the Contagio Malware Dump dataset. It performs static analysis to gather malicious features from an application, and applies principal component analysis to reduce the dependencies among them. With the hidden naive Bayes model, we can infer the identity of the application. In an evaluation with 15,573 normal applications and 3,150 malicious samples, our work detects 94.5% of the malware with a false positive rate of 1.0%. The experiment also shows that our approach is feasible on smartphones.
Bhattacharya, Archan. "Inference for controlled branching process, Bayesian inference for zero-inflated count data and Bayesian techniques for hairline fracture detection and reconstruction". 2007. http://purl.galileo.usg.edu/uga%5Fetd/bhattacharya%5Farchan%5F200705%5Fphd.
Texto completoGonzalez, Ruben. "Bayesian Methods for On-Line Gross Error Detection and Compensation". Master's thesis, 2010. http://hdl.handle.net/10048/1541.
Texto completoProcess Control
Ratto, Christopher Ralph. "Nonparametric Bayesian Context Learning for Buried Threat Detection". Diss., 2012. http://hdl.handle.net/10161/5413.
Texto completoThis dissertation addresses the problem of detecting buried explosive threats (i.e., landmines and improvised explosive devices) with ground-penetrating radar (GPR) and hyperspectral imaging (HSI) across widely-varying environmental conditions. Automated detection of buried objects with GPR and HSI is particularly difficult due to the sensitivity of sensor phenomenology to variations in local environmental conditions. Past approahces have attempted to mitigate the effects of ambient factors by designing statistical detection and classification algorithms to be invariant to such conditions. These methods have generally taken the approach of extracting features that exploit the physics of a particular sensor to provide a low-dimensional representation of the raw data for characterizing targets from non-targets. A statistical classification rule is then usually applied to the features. However, it may be difficult for feature extraction techniques to adapt to the highly nonlinear effects of near-surface environmental conditions on sensor phenomenology, as well as to re-train the classifier for use under new conditions. Furthermore, the search for an invariant set of features ignores that possibility that one approach may yield best performance under one set of terrain conditions (e.g., dry), and another might be better for another set of conditions (e.g., wet).
An alternative approach to improving detection performance is to consider exploiting differences in sensor behavior across environments rather than mitigating them, and treat changes in the background data as a possible source of supplemental information for the task of classifying targets and non-targets. This approach is referred to as context-dependent learning.
Although past researchers have proposed context-based approaches to detection and decision fusion, the definition of context used in this work differs from those used in the past. In this work, context is motivated by the physical state of the world from which an observation is made, and not from properties of the observation itself. The proposed context-dependent learning technique therefore utilized additional features that characterize soil properties from the sensor background, and a variety of nonparametric models were proposed for clustering these features into individual contexts. The number of contexts was assumed to be unknown a priori, and was learned via Bayesian inference using Dirichlet process priors.
The learned contextual information was then exploited by an ensemble on classifiers trained for classifying targets in each of the learned contexts. For GPR applications, the classifiers were trained for performing algorithm fusion For HSI applications, the classifiers were trained for performing band selection. The detection performance of all proposed methods were evaluated on data from U.S. government test sites. Performance was compared to several algorithms from the recent literature, several which have been deployed in fielded systems. Experimental results illustrate the potential for context-dependent learning to improve detection performance of GPR and HSI across varying environments.
Dissertation
Xun, Xiaolei. "Statistical Inference in Inverse Problems". Thesis, 2012. http://hdl.handle.net/1969.1/ETD-TAMU-2012-05-10874.
Texto completoSu, Wanhua. "Efficient Kernel Methods for Statistical Detection". Thesis, 2008. http://hdl.handle.net/10012/3598.
Texto completoMustafa, Ghulam. "High fidelity micromechanics-based statistical analysis of composite material properties". Thesis, 2016. http://hdl.handle.net/1828/7100.
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0548
enginer315@gmail.com
Kolba, Mark Philip. "Information-Based Sensor Management for Static Target Detection Using Real and Simulated Data". Diss., 2009. http://hdl.handle.net/10161/1313.
Texto completoIn the modern sensing environment, large numbers of sensor tasking decisions must be made using an increasingly diverse and powerful suite of sensors in order to best fulfill mission objectives in the presence of situationally-varying resource constraints. Sensor management algorithms allow the automation of some or all of the sensor tasking process, meaning that sensor management approaches can either assist or replace a human operator as well as ensure the safety of the operator by removing that operator from a dangerous operational environment. Sensor managers also provide improved system performance over unmanaged sensing approaches through the intelligent control of the available sensors. In particular, information-theoretic sensor management approaches have shown promise for providing robust and effective sensor manager performance.
This work develops information-theoretic sensor managers for a general static target detection problem. Two types of sensor managers are developed. The first considers a set of discrete objects, such as anomalies identified by an anomaly detector or grid cells in a gridded region of interest. The second considers a continuous spatial region in which targets may be located at any point in continuous space. In both types of sensor managers, the sensor manager uses a Bayesian, probabilistic framework to model the environment and tasks the sensor suite to make new observations that maximize the expected information gain for the system. The sensor managers are compared to unmanaged sensing approaches using simulated data and using real data from landmine detection and unexploded ordnance (UXO) discrimination applications, and it is demonstrated that the sensor managers consistently outperform the unmanaged approaches, enabling targets to be detected more quickly using the sensor managers. The performance improvement represented by the rapid detection of targets is of crucial importance in many static target detection applications, resulting in higher rates of advance and reduced costs and resource consumption in both military and civilian applications.
Dissertation
Huang, Qindan. "Adaptive Reliability Analysis of Reinforced Concrete Bridges Using Nondestructive Testing". Thesis, 2010. http://hdl.handle.net/1969.1/ETD-TAMU-2010-05-7920.
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