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| Main Author: | |
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| Format: | Artículo científico |
| Language: | en |
| Published: |
Sociedade Brasileira de Proteção Radiológica - SBPR
2021
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| Subjects: | |
| Online Access: | https://www.redalyc.org/articulo.oa?id=722280970002 |
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Table of Contents:
- Radionuclide classification based on gamma spectroscopy and artificial intelligence M. M. A Silva R. K Morales W. V Nunes D.O Cardoso Biología Gamma Spectroscopy Artificial Intelligence Artificial Neural Networks Currently, in almost all segments of the production chain, automation is a requirement for productivity improvement. With respect to nuclear facilities, active online monitoring is one of best practices for nuclear security and safety maintenance, to prevent incidents that could compromise a particular installation. In this context, spectral signature monitoring automation can be explored, aiming at the rapid identification of adverse events, such as radiological accidents. The main objective of this work was an automated radionuclides classification technique establishment, using an Artificial Neural Networks (ANN) architecture. The methodology used consisted basically of simulating the geometry of an established experimental apparatus, using the MCNP5 code, obtaining the simulated gamma spectral signature for the studied nuclides. The simulated spectra were used to compose the ANN training and testing data set, while the experimental spectra were subjected to the artificial intelligence model classification, in order to allow the neural network quality assessment. The final developed architecture of ANN was correct to recognize the experimental spectra of 60Co, 137Cs and 152Eu. Therefore, the results were satisfactory and proved automation technique development viable. 2021 artículo científico 2319-0612 https://www.redalyc.org/articulo.oa?id=722280970002 en http://www.redalyc.org/revista.oa?id=7222 Brazilian Journal of Radiation Sciences application/pdf Sociedade Brasileira de Proteção Radiológica - SBPR Brazilian Journal of Radiation Sciences (Brasil) Num.3 Vol.9