Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 20 of 24 for “"Dynamic Bayesian Network"”.
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Modelling the Baltic Sea food web with a Dynamic Bayesian Network with hidden variables
… that have a major effect on the ecosystem dynamics. These changes may be driven by unobserved variables, i.e. ecosystem components that we do not have data on. This thesis fits a Dynamic Bayesian Network (DBN) model to one such ecosystem, the Baltic Sea. Three versions of a DBN of the …
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Safety of Flight Prediction for Small Unmanned Aerial Vehicles Using Dynamic Bayesian Networks
This thesis compares three variations of the Bayesian network as an aid for decision-making using uncertain information. After reviewing the basic theory underlying probabilistic graphical models and Bayesian estimation, the thesis presents a user-defined static Bayesian network, a static Bayesian …
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Context Aware Pre-Crash System for Vehicular ad hoc Networks Using Dynamic Bayesian Model
… of wireless communications and mobile ad hoc networks has led to improvements in intelligent transportation systems heightening these systems’ safety. Vehicular ad hoc Networks comprise an important technology; included within intelligent transportation systems, they use dedicated short-range …
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Beyong lexical meaning : probabilistic models for sign language recognition
… in multiple data streams. We propose a novel dynamic Bayesian network structure -- the Multichannel Hierarchical Hidden Markov Model which models the hierarchical, sequential and parallel organization in signing while requiring synchronization between parallel data streams at sign boundaries.
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Data Mining Approches to Complex Environmental Problems
… data-driven models (e.g. artificial neural networks) and dynamic Bayesian network (DBN) models of the sensor data stream. All of the developed methods perform fast, incremental evaluation of data as it becomes available; scale to large quantities of data; and require no a priori information, …
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Probabilistic Approximation and Analysis Techniques for Bio-Pathway Models
Quantitative modeling of bio-pathway dynamics is crucial to the system-level understanding of cellular functions and behavior. Currently, a common method of representing bio-pathways is through a system of ordinary differential equations (ODEs). However, calibrating and analyzing large ODE-based …
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Aircraft collision avoidance using Monte Carlo Real-Time Belief Space Search
… using an encounter model formulated as a dynamic Bayesian network that is based on radar feeds covering U.S. airspace. MC-RTBSS leverages statistical information from the airspace model to predict future intruder behavior and inform its maneuvers. Use of the POMDP formulation permits the …
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Relating an archive of in situ vertical chlorophyll-a profiles to concurrent remotely sensed surface data
… to global scales but little information on the dynamics below the surface. As a result estimates of global production tend to use regional profile averages but these methods oversimplify the smaller scale dynamics, particularly in coastal regions where productivity is highly variable on time …
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Articulatory features for robust visual speech recognition
… Vector Machines, and then incorporated in a Dynamic Bayesian Network to obtain the final word hypothesis. Preliminary experiments show that our approach increases viseme classification rates in visually noisy conditions, and improves visual word recognition through feature-based context …
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Development of an innovative business-oriented probability-based maintenance (BOPM) methodology for ship machinery systems
… the system. This PAU model uses an innovative Dynamic Bayesian Network (DBN) with first order Markov Chains to predict the future probabilistic pattern of each system monitored from the vessel. Afterward, net cost analysis is performed using cost values modified by company MPIs inside utility …
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Novel models and algorithms for systems reliability modeling and optimization
… in particular the Markov chains method and the Dynamic Bayesian Network approach, by incorporating a Continuous Time Bayesian Network framework for more effective modeling of sub-system/component interactions, dependencies, and various repair policies. We also propose a multi-object optimization …
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Banking regulation: a Bayesian network approach to risk management
… basis against which a novel and comprehensive Bayesian network (BN) methodology for producing VaR and ES forecasts, and those of their stressed counterparts, is assessed in the context of banking regulations, using four learning algorithms. The forecasts generated by the BNs are not found to …
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Banking regulation: a bayesian network approach to risk management
… basis against which a novel and comprehensive Bayesian network (BN) methodology for producing VaR and ES forecasts, and those of their stressed counterparts, is assessed in the context of banking regulations, using four learning algorithms. The forecasts generated by the BNs are not found to …
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Word based off-line handwritten Arabic classification and recognition. Design of automatic recognition system for large vocabulary offline handwritten Arabic words using machine learning approaches.
… as K nearest neighbour classifier (k-NN), neural network classifier (NN), Hidden Markov models (HMMs), and the Dynamic Bayesian Network (DBN). To test this concept, the particular pattern recognition problem studied is the classification of 32492 words using ii the IFN/ENIT database. The results …
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Deception detection in dialogues
… background knowledge in the form of a relational dynamic Bayesian network structure.
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Identifying evolving multivariate dynamics in individual and cohort time series, with application to physiological control systems
… multivariate time-series often exhibit rich dynamical patterns, which are altered under pathological conditions. However, model identification for physiological systems is complicated by measurement artifacts and changes between operating regimes. The overall aim of this thesis is to develop …
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Algorithms for modeling and simulation of biological systems; applications to gene regulatory networks
… level. The reverse-engineering of biochemical networks from experimental data has become a central focus in systems biology. A variety of methods have been proposed for the study and identification of the system's structure and/or dynamics. The objective of this dissertation is to introduce and …
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A Context Aware Classification System for Monitoring Driver’s Distraction Levels
… three-phase Fast Recurrent Convolutional Neural Network (Fast-RCNN) architecture addresses the physiological attributes. Secondly, a novel two-tier FRCNN-LSTM framework is devised to classify the severity of driver distraction. Thirdly, a Dynamic Bayesian Network (DBN) for the prediction of …
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Investigating Tafheet as a Unique Driving Style Behaviour
… and reckless driving behaviour. Thus, the dynamic Bayesian Network (DBN) framework was applied to perform reasoning relating to the uncertainty associated with driver’s behaviour and to deduce the possible combinations of the driver’s behaviour based on the information gathered by the …
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A Bayesian latent time-series model for switching temporal interaction analysis
We introduce a Bayesian discrete-time framework for switching-interaction analysis under uncertainty, in which latent interactions, switching pattern and signal states and dynamics are inferred from noisy and possibly missing observations of these signals. We propose reasoning over posterior …
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