Comparing deep learning architectures on gamma-spectroscopy analysis for nuclear waste characterization
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2021
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Brazilian Journal of Radiation Sciences
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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, the capabilities of deep learning are explored on a new field: gamma-spectroscopy analysis, comparing the classification performance of different deep neural network architectures. The following architectures where tested: VGG-16, VGG-19, Xception, ResNet, InceptionV3, and MobileNet, which are available through the Keras Deep Learning framework to identify several different radionuclides (Am-241, Ba133, 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 create a dataset used for the training and validation of the neural network's comparison. This study demonstrates the strengths and weaknesses of applying deep learning on gamma-spectroscopy analysis for nuclear waste characterization.
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OTERO, A.G.L.; POTIENS JUNIOR, A.J.; MARUMO, J.T. Comparing deep learning architectures on gamma-spectroscopy analysis for nuclear waste characterization. Brazilian Journal of Radiation Sciences, v. 9, n. 1A, p. 1-8, 2021. DOI: 10.15392/bjrs.v9i1A.1257. Disponível em: http://repositorio.ipen.br/handle/123456789/32082. Acesso em: 18 Apr 2024.
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.