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Publication Citation: Neutron Tomography of a Fuel Cell: Statistical Learning Implementation of a Penalized Likelihood Method

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Author(s): Kevin J. Coakley; Dominic F. Vecchia; Daniel S. Hussey; David L. Jacobson;
Title: Neutron Tomography of a Fuel Cell: Statistical Learning Implementation of a Penalized Likelihood Method
Published: October 15, 2013
Abstract: At the NIST Neutron Imaging Facility, we collect neutron projection data for both the dry and wet states of a Proton-Exchange-Membrane (PEM) fuel cell. Transmitted neutrons captured in a scintillator doped with Lithium-6 produce scintillation light that is detected by an amorphous silicon detector. Based on joint analysis of the dry and wet state projection data, we reconstruct a residual neutron attenuation image with a penalized likelihood method. We use a Huber penalty function and select its regularization parameters by two-fold cross-validation. Before reconstruction, we transform the projection data so that the variance-to-mean ratio is approximately one. For the measured projection data, the Penalized likelihood method reconstruction is visually sharper than a reconstruction yielded by Filtered Back Projection. In an idealized simulation experiment, we demonstrate that the cross-validation procedure selects regularization parameters that yield a reconstruction that is nearly optimal according to a root mean-square prediction error criterion.
Citation: IEEE Transactions on Nuclear Science
Volume: 60
Pages: pp. 3945 - 3953
Keywords: Bayesian;cross-validation;fuel cell;Huber penalty;method of surrogates;neutron attenuation;neutron transmission tomography; penalized likelihood; PEM fuel cell; statistical learning; water density
Research Areas: Statistics