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dc.contributor.authorDESTERRO, Filipe S. M.-
dc.contributor.authorALMEIDA, Adino A. H.-
dc.contributor.authorPEREIRA, Claudio M. N. A.-
dc.date.accessioned2018-02-08T13:42:45Z-
dc.date.available2018-02-08T13:42:45Z-
dc.date.issued2017-10-
dc.identifier.urihttp://carpedien.ien.gov.br:8080/handle/ien/2152-
dc.languageengpt_BR
dc.publisherInstituto de Engenharia Nuclearpt_BR
dc.rightsopenAccesspt_BR
dc.subjectnuclear accidentspt_BR
dc.titleIMPROVEMENT OF RADIATION DOSE ESTIMATION DUE TO NUCLEAR ACCIDENTS USING DEEP NEURAL NETWORK AND GPUpt_BR
dc.typeconferenceObjectpt_BR
dc.description.resumoRecently, the use of mobile devices has been proposed for dose assessment during nuclear accidents. The idea is to support field teams, providing an approximated estimation of the dose distribution map in the vicinity of the nuclear power plant (NPP), without needing to be connected to the NPP systems. In order to provide such stand-alone execution, the use of artificial neural networks (ANN) has been proposed in substitution of the complex and time consuming physical models executed by the atmospheric dispersion radionuclide (ADR) system. One limitation observed on such approach is the very time-consuming training of the ANNs. Moreover, if the number of input parameters increases the performance of standard ANNs, like Multilayer-Perceptron (MLP) with backpropagation training, is affected leading to unreasonable training time. To improve learning, allowing better dose estimations, more complex ANN architectures are required. ANNs with many layers (much more than a typical number of layers), referred to as Deep Neural Networks (DNN), for example, have demonstrating to achieve better results. On the other hand, the training of such ANNs is very much slow. In order to allow the use of such DNNs in a reasonable training time, a parallel programming solution, using Graphic Processing Units (GPU) and Computing Unified Device Architecture (CUDA) is proposed. This work focuses on the study of computational technologies for improvement of the ANNs to be used in the mobile application, as well as their training algorithms.pt_BR
dc.publisher.countryBrasilpt_BR
dc.publisher.initialsIENpt_BR
dc.citation.issue2017pt_BR
dc.creator.affiliationInstituto de Engenharia Nuclear (IEN)-
dc.creator.affiliationInstituto de Engenharia Nuclear (IEN)-
dc.creator.affiliationInstituto de Engenharia Nuclear (IEN)-
Aparece nas coleções:Aplicação de técnicas nucleares na indústria, saúde e meio ambiente - Trabalhos de Congresso

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