ANDRE GOMES LAMAS OTERO
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Artigo IPEN-doc 28179 A desktop application for automatic gamma spectroscopy analysis with deep learning2021 - OTERO, ANDRE G.L.; POTIENS JUNIOR, ADEMAR J.; LINO, JULIANA dos S.; MARUMO, JULIO T.Artigo IPEN-doc 27853 Comparing deep learning architectures on gamma-spectroscopy analysis for nuclear waste characterization2021 - OTERO, A.G.L.; POTIENS JUNIOR, A.J.; MARUMO, J.T.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.Artigo IPEN-doc 26211 Comparing deep learning architectures on gamma-spectroscopy analysis for nuclear waste characterization2019 - OTERO, ANDRE G.L.; POTIENS JUNIOR, ADEMAR J.; CALZETA, EDUARDO P.; MARUMO, JULIO T.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.