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

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.

图1 卷积神经网络（Convolutional Neural Network, CNN）架构示意图
图2 不同峰计数率下的模拟60Co伽马射线能谱示例
图3 四种从矢量映射至矩阵的变换方法
图4 各变换方法的收敛曲线
图5 采用希尔伯特（Hilbert）曲线变换后的60Co伽马射线能谱示例
图6 预测结果的受试者工作特征（Receiver Operating Characteristic, ROC）曲线；其中子图(b)为子图(a)的细节展示
图7 预测结果的精确率-召回率（Precision-Recall, P-R）曲线；其中子图(b)为子图(a)的细节展示
图8 不同总计数的60Co能谱：(a) 总计数为9648，(b) 总计数为1884
图9 (a) 137Cs与60Co的混合能谱，(b) 238Pu、137Cs与60Co的混合能谱
图10 漂移伽马射线能谱与原始能谱示意图
图11 用于评估卷积神经网络（Convolutional Neural Network, CNN）与反向传播神经网络（Back Propagation Neural Network, BPNN）的实测137Cs伽马射线能谱
图12 (a) 反向传播神经网络（Back Propagation Neural Network, BPNN）与(b) 卷积神经网络（Convolutional Neural Network, CNN）的核素识别准确率
图13 低计数率能谱示例
main.m文件包含卷积神经网络（Convolutional Neural Network, CNN）与反向传播神经网络（Back Propagation Neural Network, BPNN）的训练代码
My2Dtransformation.m为用于二维变换的函数，涵盖希尔伯特（Hilbert）曲线、Z阶（Z-order）曲线变换，以及垂直扫描与水平扫描两种映射方式
trainingset.mat数据集包含用于卷积神经网络（Convolutional Neural Network, CNN）与反向传播神经网络（Back Propagation Neural Network, BPNN）训练的全部模拟能谱数据