Evaluating the performance of two neutron spectrum unfolding codes based on iterative procedures and artificial neural networks

2013 ◽  
Author(s):  
J. M. Ortiz-Rodríguez ◽  
A. Reyes Alfaro ◽  
A. Reyes Haro ◽  
L. O. Solís Sánches ◽  
R. Castañeda Miranda ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Neutron Spectrum ◽  
Spectrum Unfolding ◽  
Neutron Spectrum Unfolding ◽  
Artificial Neural ◽  
Iterative Procedures

A neutron spectrum unfolding computer code based on artificial neural networks

2014 ◽  
Vol 95 ◽  
pp. 428-431 ◽  
Author(s):  
J.M. Ortiz-Rodríguez ◽  
A. Reyes Alfaro ◽  
A. Reyes Haro ◽  
J.M. Cervantes Viramontes ◽  
H.R. Vega-Carrillo
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Neutron Spectrum ◽  
Computer Code ◽  
Spectrum Unfolding ◽  
Neutron Spectrum Unfolding ◽  
Artificial Neural

A neutron spectrum unfolding code based on generalized regression artificial neural networks

2016 ◽  
Vol 117 ◽  
pp. 8-14 ◽  
Author(s):  
Ma. del Rosario Martinez-Blanco ◽  
Gerardo Ornelas-Vargas ◽  
Celina Lizeth Castañeda-Miranda ◽  
Luis Octavio Solís-Sánchez ◽  
Rodrigo Castañeda-Miranada ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Neutron Spectrum ◽  
Spectrum Unfolding ◽  
Neutron Spectrum Unfolding ◽  
Artificial Neural ◽  
Generalized Regression

scholarly journals Synapse. A Neutron Spectrum Unfolding Code Based on Generalized Regression Artificial Neural Networks

2020 ◽  
Vol 7 (24) ◽  
pp. 166667
Author(s):  
Ma. Martinez-Blanco ◽  
Arturo Serrano-Muñoz ◽  
Hector Vega-Carrillo ◽  
Marco de Sousa-Lacerda ◽  
Roberto Mendez-Villafañe ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Neutron Spectrum ◽  
Spectrum Unfolding ◽  
Neutron Spectrum Unfolding ◽  
Artificial Neural ◽  
Generalized Regression

NSDann2BS, a neutron spectrum unfolding code based on neural networks technology and two bonner spheres

2013 ◽  
Author(s):  
J. M. Ortiz-Rodríguez ◽  
A. Reyes Alfaro ◽  
A. Reyes Haro ◽  
L. O. Solís Sánches ◽  
R. Castañeda Miranda ◽  
...  
Keyword(s):  
Neural Networks ◽  
Neutron Spectrum ◽  
Spectrum Unfolding ◽  
Neutron Spectrum Unfolding

Unfolding the neutron spectrum of a NE213 scintillator using artificial neural networks

2009 ◽  
Vol 67 (10) ◽  
pp. 1912-1918 ◽  
Author(s):  
A. Sharghi Ido ◽  
M.R. Bonyadi ◽  
G.R. Etaati ◽  
M. Shahriari
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Neutron Spectrum ◽  
Artificial Neural

scholarly journals EVALUATION OF ARTIFICIAL NEURAL NETWORKS EFFECTIVENESS FOR UNFOLDING GAMMA-SPECTRUM OF 137CS

2021 ◽  
Vol 2 ◽  
pp. 44-54
Author(s):  
Aleksander N. Nikitin ◽  
◽  
Egor V. Mischenko ◽  
Olga A. Shurankova ◽  
◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Radiation Source ◽  
Γ Radiation ◽  
Spectrometry Analysis ◽  
Convolution Neural Networks ◽  
Spectrum Unfolding ◽  
The Common ◽  
Artificial Neural ◽  
Fully Connected

Development of machine learning methods for spectrum processing is one of the most promising ways for gamma- spectrometry automation and accuracy improvement. Effectiveness of fully connected and convolution neural networks for quantitative γ-spectrometry analysis using scintillation detector NaI(Tl) and lead shielding is presented in the article. Semi-synthetic spectrums were used for the models training; the semi-synthetic spectrums are in channels additions of random spectrums measured at a short duration. The analysis shows advantages of artificial neural networks compare to the common analytical method of spectrum unfolding. The mean square error of activity evaluation is 2–4 times lower than the common method if measuring time is equal to 100 s. In highly standardized conditions of measuring, the advantages of convolution neural networks appear with increasing radiation source activity. Validation with sources not used in training of neural networks has shown fully connected and convolution neural networks can have advantages over the standard method when activity of γ-radiation source is relatively high.

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