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Prediction Interval Estimation Techniques For Empirical Modeling Strategies And Their Applications To Signal Validation Tasks, Brandon Peter Rasmussen
Prediction Interval Estimation Techniques For Empirical Modeling Strategies And Their Applications To Signal Validation Tasks, Brandon Peter Rasmussen
Doctoral Dissertations
The basis of this work was to evaluate both parametric and non-parametric empirical modeling strategies applied to signal validation or on-line monitoring tasks. On-line monitoring methods assess signal channel performance to aid in making instrument calibration decisions, enabling the use of condition-based calibration schedules. The three non-linear empirical modeling strategies studied were: artificial neural networks (ANN), neural network partial least squares (NNPLS), and local polynomial regression (LPR). These three types are the most common nonlinear models for applications to signal validation tasks. Of the class of local polynomials (for LPR), two were studied in this work: zero-order (kernel regression), and …
Monte Carlo Simulation Of Neutron Detectors, Andrew Curtis Stephan
Monte Carlo Simulation Of Neutron Detectors, Andrew Curtis Stephan
Doctoral Dissertations
Neutron detectors are simulated using Monte Carlo methods in order to gain insight into how they work and optimize their performance. Simulated results for a Micromegas neutron beam monitor using a custom computer code are compared with published experimental data to verify the accuracy of the simulation. Different designs (e.g. neutron converter material, gas chamber width, gas pressure) are tested to assess their impact on detector performance. It is determined that a 10B converter foil and 1 mm drift gap width work best for a neutron beam monitor. The Micromegas neutron beam monitor neutronics are evaluated using the computer …
Learning From Data With Localized Regression And Differential Evolution, Mark A. Buckner
Learning From Data With Localized Regression And Differential Evolution, Mark A. Buckner
Doctoral Dissertations
Learning from data is fast becoming the rule rather than the exception for many science and engineering research problems, particularly those encountered in nuclear engineering. Problems associated with learning from data fall under the more general category of inverse problems. A data-drive inverse problem involves constructing a predictive model of a target system from a collection of input/output observations. One of the difficulties associated with constructing a model that approximates such unknown causes based solely on observations of their effects is that collinearities in the input data result in the problem being ill-posed. Ill-posed problems cause models obtained by …