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 18 of 18 for “"gaussian process model"”.
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Modelling of Dynamic Computer Experiments with Both Qualitative and Quantitative Variables
… responses. Different dynamic computer models have been proposed to emulate the relationship between the time-series responses and the corresponding quantitative factors. Qualitative factors are also widely used and show important effects in many scientific problems. Different models …
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Neural Network Gaussian Process considering Input Uncertainty and Application to Composite Structures Assembly
… is promising for composite structures assembly process. It requires accurate predictive analysis on deformation of the composite structures to improve production quality and efficiency of composite structures assembly. The novel composite structures assembly involves two challenges: (i) the …
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Spatial optimization of an existing, low-cost sensor network for air pollution in London
… In this paper we combine two different Gaussian process methods to optimize spatially an existing low-cost sensor network for air pollution in London. We demonstrate the practical utility of these combined algorithms using a cross-validation approach, applied to air pollution data …
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Probabilistic Modelling in Function Space
Gaussian processes have established themselves as powerful tools for inferring functions from data. They provide a flexible framework for defining distributions over functions, enabling closed form solutions and principled handling of uncertainty. However, their application is often hindered by the …
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Efficient Bayesian active learning and matrix modelling
… framework we develop new techniques for active Gaussian process modelling and adaptive quantum tomography. The latter has been shown, in both simulation and laboratory experiments, to yield faster learning rates than any non-adaptive design. Numerous datasets can be represented as matrices. …
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Immersion cooled environmental monitoring and prediction system for the meerKAT imager
… involves a case study of the MeerKAT Science Processor that is responsible for the MeerKAT imaging pipeline. Immersion cooling brings a coolant into direct physical contact with the chips and the circuit board by directly immersing computing equipment into a bath of cooling fluid. According to …
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Methods for Control in Robotic Excavation
… robotic excavation arising from terramechanics modeling. In particular, methods are presented to tackle the soil interaction problem in the context of three tasks typically encountered in robotic excavation. Firstly, we address the efficient bulk removal of material by introducing an approach …
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Bayesian Optimization for Engineering Design and Quality Control of Manufacturing Systems
… and oftentimes constrained by physical laws. Modeling and approximation of their underly- ing response surface functions are extremely challenging. Bayesian optimization is a great statistical tool, based on Bayes rule, used to optimize and model these expensive-to-evaluate functions. Bayesian …
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Reconstruction of three-dimensional facial geometric features related to fetal alcohol syndrome using adult surrogates
… single 2D image of the face using a 3D morphable model (3DMM) were explored in this research study. The research project was accomplished in several steps. 3D facial data were obtained from the publicly available BU-3DFE database, developed by the State University of New York. The 3D face scans in …
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Statistical Methods for Non-Linear Profile Monitoring
… and extensive research in the monitoring of a process over time whose characteristics are represented mathematically in functional forms such as profiles. Most of the current techniques require all of the data for each profile to determine the state of the process. Thus, quality engineers from …
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Suspension Controls and Parameter Estimation Using Accelerometer Based Intelligent Tires
… suspensions and vehicle stability. A parametric model of an automotive monotube damper is developed and several control algorithms for semi-active suspensions have been developed. An extensive comparison of different control algorithms has been done. Skyhook, Groundhook, Hybrid, …
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Modeling of the fundamental mechanical interactions of unit load components during warehouse racking storage
… and their interactions during the pallet design process, the structure of pallets can be optimized. This, in turn, will reduce the material consumption required to support the pallet industry. In order to understand the mechanical interactions between stacked boxes and pallet decks, and how these …
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Using population ecology to inform the conservation of Nevada's rare plants
… designs to account for heterogeneity, however model-based approaches could also be useful to estimate population size of heterogeneously distributed species but have so far not been examined. In chapter 1, I tested the ability of a model-based approach to accurately estimate population size. I …
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Performance-Based Coastal Engineering Framework
… the implementation of a probabilistic graphical model in the form of Bayesian networks (BNs) and dynamic Bayesian networks (DBNs). BNs and DBNs allow modeling the causal dependence among the variables, offering an efficient sampling strategy and facilitating the incorporation of expert knowledge. …
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Modeling methods for merging computational and experimental aerodynamic pressure data
<p>This research describes a process to model surface pressure data sets as a function of wing geometry from computational and wind tunnel sources and then merge them into a single predicted value. The described merging process will enable engineers to integrate these data sets with the goal of …
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Implementation of gaussian process models for non-linear system identification
… is concerned with investigating the use of Gaussian Process (GP) models for the identification of nonlinear dynamic systems. The Gaussian Process model is a non-parametric approach to system identification where the model of the underlying system is to be identified through the application …
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Data-driven modeling and transportation data analytics
… research. Unfortunately, existing traffic models, though developed and practiced for decades, are not data driven and therefore inherently incapable of analyzing modern traffic data from multiple sources with different time resolution and spatial coverage. A new paradigm centered on …
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Hierarchical Gaussian Processes for Spatially Dependent Model Selection
In this dissertation, we develop a model selection and estimation methodology for nonstationary spatial fields. Large, spatially correlated data often cover a vast geographical area. However, local spatial regions may have different mean and covariance structures. Our methodology accomplishes three …