Radionuclide Identification Algorithm Based on Convolutional Neural Network
To realize the accurate identification of low count and multi-class complex radionuclides,the convolutional neural network is introduced to build a recognition model for low count and multi-class complex radionuclides.The radionuclide energy spectrum databaseconsisting of 241Am,133Ba,57Co,60Co,137Cs,152Eu,and 40Kwas established by the Monte Carlo simulation,which contained a total of 63 different radioactive nuclide sources.The simulation training set and simulation verification set samples were used to complete the training and hyperparameter optimization of convolutional neural networks.The test set samples were used to verify the model performance.The results demonstrate that the convolutional neural networks has good recognition performancein the identification of low count and multi-class complex radionuclides.
radionuclide recognitionconvolutional neural networkMonte Carlo simulation
国家科技期刊平台
NSTL
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