Supplementary materials for a paper in IEEE Trans. Nuclear Science entitled "High Performance Remote Radioactive Material Identification of Mixtures"

Citation Author(s):
Chiman
Kwan
Signal Processing, Inc.
Submitted by:
Chiman Kwan
Last updated:
Fri, 11/19/2021 - 11:14
DOI:
10.21227/nt7m-9h53
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Abstract 

Detecting radioactive materials in mixtures is challenging due to low concentration, environmental factors, sensor noise, and others. This paper presents new results on nuclear material identification and mixing ratio estimation for mixtures of materials in which there are multiple isotopes present. Conventional and deep learning-based machine learning algorithms were compared. Both simulated and actual experimental data were used in the comparative studies. It was observed that some newly developed machine learning and deep learning methods have better performance than a conventional method based on region of interest (ROI). Extensive experiments also demonstrated that trained models using low signal-to-background ratio (SBR) data can better generalize to other SBR conditions.

Instructions: 

This is a pdf file containing supporting materials to a paper in IEEE Trans. Nuclear Science.