ADEMAR JOSE POTIENS JUNIOR
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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 26887 Use of plasma reactor to viabilise the volumetric reduction of radioactive wastes2020 - PRADO, E.S.P.; MIRANDA, F.S.; PETRACONI, G.; POTIENS JUNIOR, A.J.Nuclear reactors, hospitals, industries and research institutes generate considerable amounts of radioactive waste every day. To dispose this waste in a safe and costeffective manner, it must be treated by immobilising the radionuclides and, for better stocking capacity, it must be volumetrically reduced as much as possible. To this end, plasma technology, among other promising technologies for radioactive waste treatment, exposes radioactive waste to temperatures above 1400 °C, thereby substantially reducing its volume. In the planning and managing of radioactive waste, the challenges related to plasma technology are presented as a motivation factor for the possible implantation of plasma reactors in nuclear plants and research centres, thereby improving radioactive waste management. In this study, a thermal plasma treatment process was established, and a plasma reactor was used for compactable waste processing. After 30 min of thermal plasma treatment, the volume reduction factor reached 1:99. The results demonstrate the viability of using a thermal plasma process for the volumetric reduction of radioactive waste in a safe and cost-effective manner.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.Resumo IPEN-doc 21480 Caracterização primária de rejeitos radioativos líquidos da GRR2015 - COUVO, NATHALIA da S.; POTIENS JUNIOR, ADEMAR J.Artigo IPEN-doc 15245 Characterization of radioactive wastes - spent ion-exchange resins and charcoal filter beds2009 - SILVA, ROZILENE E.; ISIKI, VERA L.K.; GOES, MARCOS M. de; POTIENS JUNIOR, ADEMAR J.; DELLAMANO, JOSE C.; VICENTE, ROBERTOArtigo IPEN-doc 19397 Monte Carlo method to characterize radioactive waste drums2013 - LIMA, JOSENILSON B.; DELLAMANO, JOSE C.; POTIENS JUNIOR, ADEMAR J.