Data for: Rapid nuclide identification algorithm based on convolutional neural network

Published: 06-06-2019| Version 1 | DOI: 10.17632/jwxkrzh6fg.1
Contributor:
Xiaobin Tang

Description

Figure 1. Illustrate of CNN architecture Figure 2. Examples of the simulated 60Co gamma-ray spectra with different count rates of peaks Figure. 3. Four transformation methods from vector mapping to a matrix Figure. 4. Convergence curves for each transformation method Figure. 5. Example of the transformed 60Co gamma-ray spectrum using Hilbert curve Figure. 6. ROC curve for the predicted results; (b) shows the details of (a) Figure. 7. P-R curve for the predicted results; (b) shows the details of (a) Figure. 8. 60Co spectra of different gross counts. (a) 9648 gross counts and (b) 1884 gross counts. Figure. 9. (a) Mixed spectrum of 137Cs and 60Co and (b) mixed spectrum of 238Pu, 137Cs, and 60Co Figure. 10. Illustration of drift gamma-ray spectra and original spectrum Figure. 11. Measured 137Cs gamma-ray spectra for evaluating CNN and BPNN Figure. 12. Accuracy of nuclide identification for (a) BPNN and (b) CNN Figure. 13. Examples spectra with a low count rate. main.m contains CNN and BPNN training. My2Dtransformation.m is a funtion for 2D transiformation, contains Hilbert and Z-order curves, vertical and horizon scanning. trainingset.mat contain all training simulated spectra for CNN and BPNN.

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