Theses and Dissertations
Issuing Body
Mississippi State University
Advisor
Yu, Fei
Committee Member
Elder, Steven H.
Committee Member
Perkins, Andy D.
Committee Member
Waggoner, Charles A.
Date of Degree
5-1-2020
Document Type
Graduate Thesis - Open Access
Major
Biological Engineering
Degree Name
Master of Science
College
James Worth Bagley College of Engineering
Department
Department of Agricultural and Biological Engineering
Abstract
The quality of the spectral data collected by radiological survey systems depends on many factors including the survey environment, configuration of the system and its detectors, and the radionuclides in question. Algorithms in the field of machine learning have the potential to classify data that would be difficult and time-intensive for a human to analyze. Depleted and natural uranium spectra are of particular interest due to known contamination at domestic sites and world-wide. Several machine learning classifiers were developed with data collected from laboratory experiments. This thesis demonstrates the potential of machine learning algorithms to discriminate gamma-ray emitting sources using sparse, or low-count statistic, data. Effectiveness has been demonstrated for discriminating chemical forms of uranium, mixtures with differing uranium isotope distributions, and predicting source masses given certain detector geometries and a known target distribution. All activity has been supported by the U.S. Army Engineering Research and Development Center (ERDC).
URI
https://hdl.handle.net/11668/16953
Sponsorship
U.S. Army’s Corp of Engineers, Research, and Development Center (W912HZ-16-2-15).
Recommended Citation
Finney, Austin, "Anomaly detection and classification of sparse gamma-ray spectra using machine learning algorithms for depleted uranium remediation" (2020). Theses and Dissertations. 680.
https://scholarsjunction.msstate.edu/td/680