Date of Award
12-2017
Degree Type
Thesis
Degree Name
Master of Science in Nuclear Engineering
Department
Nuclear Engineering
Committee Chair
Lefteri H. Tsoukalas
Committee Member 1
Chan K. Choi
Committee Member 2
Mary Comer
Committee Member 3
Miltiadis Alamaniotis
Abstract
At first, simulated γ-ray spectra for a set of 25 radionuclides, have been produced using the “Gamma Detector Response and Analysis Software (GADRAS)”. For each of these profiles (counts/kev vs energy), a Gaussian “Radial Basis Function” (RBF) network has been trained to represent it by an analytic closed form expression. Hence a library consisting of 25 RBF-networks, for the corresponding radionuclides, has been built. Secondly, a method for identifying the presence of radionuclides in the spectrum of an unknown source has been developed, assuming that the source contains a mixture of the considered radionuclides only. A linear combination of the library profiles is compared to the actual spectrum, and constrained optimization techniques are applied to minimize the deviation in a least-squares sense.
Recommended Citation
Lagari, Pola-Lydia, ""An RBF Neural Network Approach in Radionuclide Identification of unknown sources utilizing gamma-ray spectra"" (2017). Open Access Theses. 1298.
https://docs.lib.purdue.edu/open_access_theses/1298