Application of artificial neural networks for the prediction of volume fraction using spectra of gamma rays backscattered by three-phase flows

2017 ◽  
Vol 132 (12) ◽  
Author(s):  
R. Gholipour Peyvandi ◽  
S. Z. Islami Rad
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Gamma Rays ◽  
Volume Fraction ◽  
Three Phase ◽  
Artificial Neural

Stator fault analysis of three-phase induction motors using information measures and artificial neural networks

2017 ◽  
Vol 143 ◽  
pp. 347-356 ◽  
Author(s):  
Gustavo Henrique Bazan ◽  
Paulo Rogério Scalassara ◽  
Wagner Endo ◽  
Alessandro Goedtel ◽  
Wagner Fontes Godoy ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Induction Motors ◽  
Fault Analysis ◽  
Information Measures ◽  
Three Phase ◽  
Artificial Neural ◽  
Stator Fault

scholarly journals Artificial Neural Networks Investigation of Indentation Force Effects on Nano- and Microhardness of Dual Phase Steels

2014 ◽  
Vol 2014 ◽  
pp. 1-11
Author(s):  
A. Fotovati ◽  
J. Kadkhodapour ◽  
S. Schmauder
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Volume Fraction ◽  
Nanoindentation Test ◽  
Dual Phase ◽  
Test Results ◽  
Indentation Force ◽  
Input Parameters ◽  
Artificial Neural ◽  
Selection Of

Nanoindentation test results on different grain sizes of dual phase (DP) steels are used to train artificial neural networks (ANNs). With selection of ferrite and martensite grain size, martensite volume fraction (MVF), and indentation force as input and microhardness, ferrite, and martensite nanohardness as outputs, six different ANNs are trained according to normalized datasets to predict hardness and their tolerances. A graphical user interface (GUI) is developed for a better investigation of the trained ANN prediction. The response of the ANN is analyzed in five case studies. In each case the variation of two input parameters on the output is analyzed when the other input parameters are kept constant. Reliable and reasonable results of ANN predictions are achieved in each case.

scholarly journals Pulse Shape Discrimination of Neutrons and Gamma Rays Using Kohonen Artificial Neural Networks

2013 ◽  
Vol 3 (2) ◽  
pp. 77-88 ◽  
Author(s):  
Tatiana Tambouratzis ◽  
Dina Chernikova ◽  
Imre Pzsit
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Gamma Rays ◽  
Pulse Shape ◽  
Energy Levels ◽  
Pulse Shape Discrimination ◽  
Shape Discrimination ◽  
Discrimination Ability ◽  
Artificial Neural ◽  
Self Organising Map

Abstract The potential of two Kohonen artificial neural networks I ANNs) - linear vector quantisa - tion (LVQ) and the self organising map (SOM) - is explored for pulse shape discrimination (PSD), i.e. for distinguishing between neutrons (n's) and gamma rays (γ’s). The effect that la) the energy level, and lb) the relative- of the training and lest sets, have on iden- tification accuracy is also evaluated on the given PSD datasel The two Kohonen ANNs demonstrate compfcmentary discrimination ability on the training and test sets: while the LVQ is consistently mote accurate on classifying the training set. the SOM exhibits higher n/γ identification rales when classifying new paltms regardless of the proportion of training and test set patterns at the different energy levels: the average tint: for decision making equals ∼100 /e in the cax of the LVQ and ∼450 μs in the case of the SOM.

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