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Nuclear Criticality Safety
NCSD provides communication among nuclear criticality safety professionals through the development of standards, the evolution of training methods and materials, the presentation of technical data and procedures, and the creation of specialty publications. In these ways, the division furthers the exchange of technical information on nuclear criticality safety with the ultimate goal of promoting the safe handling of fissionable materials outside reactors.
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2024 ANS Annual Conference
June 16–19, 2024
Las Vegas, NV|Mandalay Bay Resort and Casino
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The Nuclear Family: Empowering parents and caregivers
The Diversity and Inclusion in ANS Committee is hosting a webinar today to celebrate the contributions of parents in the nuclear industry while fostering diversity and inclusion within the community.
Register now: The webinar, from 1:00-2:00 pm ET, will highlight how the nuclear industry supports caregivers, new parents, and new mothers, and will focus on life transitions and parental responsibilities.
Byoungil Jeon, Jinhwan Kim, Myungkook Moon
Nuclear Technology | Volume 209 | Number 1 | January 2023 | Pages 1-14
Technical Paper | doi.org/10.1080/00295450.2022.2096389
Articles are hosted by Taylor and Francis Online.
Radioisotope identification (RIID) is a representative application of deep learning for radiation measurements. Deep learning-based RIID models have been implemented in various types of radiation detectors; however, very few of these models have been interpreted using explainable artificial intelligence (XAI) methods. This paper presents an explanation of a deep learning–based RIID model for a plastic scintillation detector. The RIID task is defined as a multilabel binary classification problem, and the dataset is generated using a random sampling procedure. The identification performance is verified using experimental data. The experimental results demonstrate that the performance of the RIID models increased with the increase in the total counts of the dataset. Additionally, XAI methods are implemented, and their explanatory performance is verified for the spectral input. The domain knowledge of RIID for the plastic scintillation detector is that patterns near the Compton edge can be used as evidence for the existence of radioisotopes. Among the implemented XAI methods, integrated gradient and layerwise relevance propagation exhibited concurrence with the domain knowledge, with the Shapley value explanation method presenting the most reliable results.