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 12 of 12 for “"model fusion"”.
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Behavioural Model Fusion
In large-scale model-based development, developers periodically need to combine collections of interrelated models. These models may capture different features of a system, describe alternative perspectives on a single feature, or express ways in which different features alter one another's …
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Predictive Model Fusion: A Modular Approach to Big, Unstructured Data
… disparate sources inhibits joint processing and modeling. Rather modular segmentation is required, in which a set of models process (potentially overlapping) partitions of the data to independently construct predictions. This framework enables individuals models to be tailored for specific …
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Model fusion for improving hypoxia forecasts in Corpus Christi Bay, TX, USA: A study of boosting and historical scenario modeling
… in this work involves a suite of data-driven model fusion techniques such as historical scenario modeling and boosting both a k-nearest neighbor (KNN) algorithm and the historical scenario model. Existing data-driven k-nearest neighbor and physics-based valve models are used as the basis for …
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Prognostic modelling for residual useful life prediction
… thesis studies new approaches for prognostic modelling of residualuseful life prediction using condition monitoring information.First, stochastic filtering models are applied for residual useful life prediction, andboth failure and censored data are utilized for model parameterization. Then, …
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Modeling volcanic unrest by data assimilation
… multiple observations and advanced physical models allows us to explore the response of the surrounding host rock to changes in the physical condition in a magmatic system. This work focuses on developing and applying a robust data-model fusion framework to investigate the mechanisms involved …
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Prognostics and health management of light emitting diodes
… to harsh operating conditions. Data driven, model driven and fusion prognostics approaches are developed to monitor and identify LED failures, based on the requirement for the light output power. The approaches adopted in this work are validated and can be used to assess the life of an LED …
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Electrospray Fundamentals and Non-Covalent Peptide-Lipid Interactions as Studied by Fourier Transform Ion Cyclotron Reonance Mass Spectrometry
… noncovalent associations between lipids and fusion peptides. Detailed binding specificities between selected phospholipids and model fusion peptides were investigated. Strong evidence has been compiled to demonstrate the importance of the initial hydrophobic interaction to the observation of …
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Neural networks as a tool for statistical modeling
… often as alternatives to traditional statistical models. As a result, their performance needs to be examined in a statistical framework. Following a brief overview of many types of neural networks, details concerning the implementation of the single hidden layer feedforward neural network (SHLFNN) …
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Atomistic Simulation Of Plasma Interaction With Plasma Facing Components In Fusion Reactors
<p>The interaction between plasma and fusion relevant materials is one of the critical issues in successfully using those materials in Tokamak reactors. This research uses molecular dynamics, kinetic Monte Carlo and binary collision approximation methods to model fusion relevant material bombarded …
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Surface Ozone and Population Health
… make the parametrisation for numerical modelling a chasing-deer challenge (Chapter 1). Original research starts from the Coupled Model Inter-comparison Project Phase 6 (CMIP6), which collates an ensemble of Earth system models to simulate the global surface ozone for the purpose of …
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Model Composition and Aggregation in Macromolecular Regulatory Networks
Mathematical models of regulatory networks become more difficult to construct and understand as they grow in size and complexity. Large regulatory network models can be built up from smaller models, representing subsets of reactions within the larger network. This dissertation focuses on novel …
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Toward Robust and Generalizable Spatiotemporal Modeling for Tasks beyond Forecasting and Classification
In spatiotemporal data mining, building models that are robust and generalizable across complex, non-ideal conditions is crucial for real-world deployment. While many existing methods perform well on benchmark datasets, they often assume clean, stationary, and uniformly sampled data, limiting their …