Comparing deep learning architectures on gamma-spectroscopy analysis for nuclear waste characterization
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2019
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INTERNATIONAL NUCLEAR ATLANTIC CONFERENCE
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Resumo
Neural networks, particularly deep neural networks, are used nowadays with great success in
several tasks, such as image classification, image segmentation, translation, text to speech,
speech to text, achieving super-human performance. In this study, we explore the capabilities
of deep learning on a new field: gamma-spectroscopy analysis, comparing the classification
performance of different deep neural networks architectures. We choose VGG-16, VGG-19,
Xception, ResNet, InceptionV3 and MobileNet architectures which are available through the
Keras Deep Learning framework to identify several different radionuclides (Am-241, Ba-
133, Cd-109, Co-60, Cs-137, Eu-152, Mn-54, Na-24, and Pb-210). Using an HPGe detector
to acquire several gamma spectra, from different sealed sources to created a dataset that was
used for the training and validation of the neural networks comparison. This study
demonstrates the strengths and weakness of applying deep learning on gamma-spectroscopy
analysis for nuclear waste characterization.
Como referenciar
OTERO, ANDRE G.L.; POTIENS JUNIOR, ADEMAR J.; CALZETA, EDUARDO P.; MARUMO, JULIO T. Comparing deep learning architectures on gamma-spectroscopy analysis for nuclear waste characterization. In: INTERNATIONAL NUCLEAR ATLANTIC CONFERENCE, October 21-25, 2019, Santos, SP. Proceedings... Rio de Janeiro: Associação Brasileira de Energia Nuclear, 2019. p. 1278-1283. Disponível em: http://repositorio.ipen.br/handle/123456789/30561. Acesso em: 30 Dec 2025.
Esta referência é gerada automaticamente de acordo com as normas do estilo IPEN/SP (ABNT NBR 6023) e recomenda-se uma verificação final e ajustes caso necessário.