Research Article
Synapse. A Neutron Spectrum Unfolding Code Based on Generalized Regression Artificial Neural Networks
@ARTICLE{10.4108/eai.21-10-2020.166667, author={Ma. del Rosario Martinez-Blanco and Arturo Serrano-Mu\`{o}oz and Hector Rene Vega-Carrillo and Marco Aurelio de Sousa-Lacerda and Roberto Mendez-Villafa\`{o}e and Eduardo Gallego and Antonio del Rio de Santiago and Luis Octavio Solis-Sanchez and Jose Manuel Ortiz-Rodriguez}, title={Synapse. A Neutron Spectrum Unfolding Code Based on Generalized Regression Artificial Neural Networks}, journal={EAI Endorsed Transactions on Industrial Networks and Intelligent Systems}, volume={7}, number={24}, publisher={EAI}, journal_a={INIS}, year={2020}, month={10}, keywords={Neutron spectrometry and dosimetry, Artificial intelligence, Generalized regression artificial neural networks, unfolding code, programming}, doi={10.4108/eai.21-10-2020.166667} }
- Ma. del Rosario Martinez-Blanco
Arturo Serrano-Muñoz
Hector Rene Vega-Carrillo
Marco Aurelio de Sousa-Lacerda
Roberto Mendez-Villafañe
Eduardo Gallego
Antonio del Rio de Santiago
Luis Octavio Solis-Sanchez
Jose Manuel Ortiz-Rodriguez
Year: 2020
Synapse. A Neutron Spectrum Unfolding Code Based on Generalized Regression Artificial Neural Networks
INIS
EAI
DOI: 10.4108/eai.21-10-2020.166667
Abstract
In its broadest sense, the term artificial intelligence indicates the ability of an artifact to perform the same types of functions that characterize human thought. The goal of AI is to use algorithms, heuristics and methodologies based on the ways in which the human brain solves problems. Artificial neural networks recreate the structure of the human brain imitating the learning process. The Artificial neural networks theory has provided an alternative to classical computing for those problems in which traditional methods have delivered results that are not very convincing or not very convenient such as in the case of the neutron spectrometry and dosimetry problem for radiation protection purposes, using the Bonner spheres spectrometer as measurement system, mainly because many problems are encountered when trying to determine the neutron energy spectrum of a measured data. The most delicate part of the spectrometry based on this system is the unfolding process, for which several neutron spectrum unfolding codes have being developed. However, these codes require an initial guess spectrum in order to initiate the unfolding process. Their poor availability and their not easy management for the end user are other associated problems. Artificial Intelligence technology, is an alternative technique that is gaining popularity among researchers in neutron spectrometry research area, since it offers better results compared with the traditional solution methods. In this work, "Synapse", a neutron spectrum unfolding code based on Generalized Regression Artificial Neural Networks technology is presented. The Synapse code is capable to unfold the neutron spectrum and to calculate 15 dosimetric quantities using the count rates, coming from a BSS as the only entrance information. The results obtained show that the Synapse code, based on GRANN technology, is a promising and innovative technological alternative for solving the neutron spectrometry and dosimetry problems.
Copyright © 2020 M.R. Martinez-Blanco et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.