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.
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Showing 1 to 20 of 84 for “"hybrid models"”.
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Lifted Inference for Relational Hybrid Models
Probabilistic Graphical Models (PGMs) promise to play a prominent role in many complex real-world systems. Probabilistic Relational Graphical Models (PRGMs) scale the representation and learning of PGMs. Answering questions using PRGMs enables many current and future applications, such as medical …
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Robust learning of probabilistic hybrid models
… that system. For this reason, accurate models are essential for continued advancements in the field of autonomy. Hybrid stochastic models, such as JMLS and LPHA, allow for representational accuracy of a general scope of problems. The goal of this thesis is to develop a robust method for …
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Hybrid Models for Representation of Imagery Data
… in textures by proposing a directional hybrid linear model, which combines linear hybrid models with directional filters and provides a deterministic representation of textures. Such a hybrid linear representation allows us to naturally deal with multiple textures that may appear in a …
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Diverse Behavior Prediction through Deep Hybrid Models
… Existing model-based prediction methods leverage hybrid reasoning techniques to predict qualitatively representative agent motions from a large prediction space, yet they often assume simple agent dynamics and fail to account for scene context. Recently, learning-based approaches have demonstrated …
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Hazard elimination using backwards reachability techniques in discrete and hybrid models
… that state cannot be reached. State machine models are very powerful, but also present greater challenges in terms of reachability, including the backwards reachability needed to implement the Hazard Automaton Reduction Algorithm. The key to solving the backwards reachability problem lies in …
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Estimation Of Hybrid Models For Real-time Crash Risk Assessment On Freeways
… that existing real-time crash 'prediction' models (classification or otherwise) are generic in nature, i.e., a single model has been used to identify all crashes (such as rear-end, sideswipe, or angle), even though traffic conditions preceding crashes are known to differ by type of crash. …
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Design of 3D swept wing hybrid models for icing wind tunnel tests
… blockage. This type of model is referred to as a hybrid and its biggest advantage lies in the fact that it is designed to produce full-scale ice shapes, while reducing or even eliminating the need for icing scaling. While a design method for a straight, untapered hybrid wing is well documented and …
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Model-Based Learning and Planning for Intelligent Manipulation Using Probabilistic Hybrid Models
… and lack interpretability in the learned policy models. To reduce the cost of learning closed-loop manipulation controllers and facilitate more transparency, we propose to a model-based reinforcement learning algorithm. Our algorithm learns deep probabilistic hybrid automata (DPHA), a novel …
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Mode identification using stochastic hybrid models with applications to conflict detection and resolution
Most models of aircraft trajectories are non-linear and stochastic in nature; and their internal parameters are often poorly defined. The ability to model, simulate and analyze realistic air traffic management conflict detection scenarios in a scalable, composable, multi-aircraft fashion is an …
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Hybrid models for combination of visual and textual features in context-based image retrieval.
… a large-scale Content-based Image Retrieval are models based on the Bag of Visual Words framework. Existing approaches, however, produce high dimensional and thus expensive representations for data storage and computation. Because the standard Bag of Visual Words framework disregards the …
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Incorporating sensor measurements using data assimilation and machine learning to improve the accuracy of thermal finite element models
… is required. Classically, such high fidelity models would be either physics-based or data-driven, though both of these approaches have disadvantages that may make them unsuitable for application in a digital twin. A hybrid model is a combination of physics-based and data-driven models that …
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Bayesian Neural Networks for Actuarial Mortality Modelling
… domain-specific prior information through hybrid structures. By embedding classical mortality laws directly into the neural network framework, we develop a suite of hybrid models capable of leveraging both the flexibility of deep learning, and the interpretability of parametric actuarial …
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Extraction of eigen-pairs from beam structures using an exact element based on a continuum formulation and the finite element method
… The purpose of this study is to investigate hybrid finite element models composed of standard finite elements and exact-elements for the prediction of higher structure eigenvalues and eigenvectors. An exact beam-element dynamic-stiffness formulation is presented for a plane Timoshenko beam …
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A verification framework for hybrid systems
… state transitions with differential equations, Hybrid system models provide an expressive formalism for describing software systems that interact with a physical environment. Automatically checking properties, such as invariance and stability, is extremely hard for general hybrid models, and …
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Computational Investigation of Spin Traps Using Hybrid Solvation Models.
… hydrogen bonding. Most dielectric solvation models such as the polarized continuum model and COSMO are incapable of direct determination of solvent-spin trap chemical interactions. To examine this, hybrid models incorporating COSMO for long range effects and discrete solvent molecules for …
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Estimating the Performance of Optical Fibre Communication Systems
… look into accurately estimating the latter using hybrid models which not only require less data than machine learning models, but are also interpretable. The hybrid models consist of a measurement-informed physical model, developed by systematically reducing the number of independent parameters …
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Modelling, simulation and multi-objective optimization of industrial hydrocrackers
… through first principles, data based and hybrid modeling techniques followed by its optimization by GA for single and multiple objectives. First principles model (FPM) adopts discrete lumped model approach to characterize feed and reaction mixture to pseudocomponents. Data based models …
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