Abstract
Through the study of scattered gamma beam intensity, material density could be obtained. Most important factor in this densitometry method is determining a relation between recorded intensity by detector and target material density. Such situation needs many experiments over materials with different densities. In this paper, using two different artificial neural networks, intensity of scattered gamma is obtained for whole densities. Mean relative error percentage for test data using best method is 1.27% that shows good agreement between the proposed artificial neural network model and experimental results.
Highlights
The gamma-ray photons lose their energy in a stopping medium by these processes: photoelectric effect, Compton effect, pair production, and photonuclear effect
In [7], the void fraction has been predicted without using artificial neural network (ANN); the error is considerable
These results show the applicability of ANN as an accurate and reliable model for the prediction of density according to the counted gamma photons
Summary
The gamma-ray photons lose their energy in a stopping medium by these processes: photoelectric effect, Compton effect, pair production, and photonuclear effect. Experimental data have been obtained from a density measurement tomography system [8] These data were used for training the ANN. In this study because the scattering method is used and the number of counts in this method is less in comparison with the transmission method, the measurement time should be increased in order to decrease the stochastic error. In this study, registered counts in the scattering detector, which are obtained for some known materials with various densities by the experiments, are used as the input of the ANN. Two optimum structures with low error for using in densitometry system are proposed Using these feedforward multilayer perceptron ANNs, numbers of detected photons were obtained for some unknown materials
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