Energy spectrum nuclide recognition method based on long short-term memory neural network
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摘要: 针对新兴的能谱核素识别方法在混合放射性核素的噪声环境中存在识别速度慢、准确率较低等问题，提出了基于长短时记忆神经网络（LSTM）的能谱核素识别方法。实验使用溴化镧（LaBr3）晶体探测器，分别对环境中60Co、137Cs放射性源分组测量得到能谱数据集，首先使用数据平滑方法和归一化方法进行数据预处理，然后将能谱数据按时间序列分组以获得可用的输入序列数组，最后训练LSTM模型得到预测结果。通过基于BP神经网络和卷积神经网络（CNN）的两个能谱识别模型进行对比，得到在测试集中平均识别率分别为83.45%和86.21%，而LSTM能谱识别模型平均识别率为93.04%，实验结果表明，该能谱模型在核素识别效果中表现较好，可用于快速的能谱核素识别设备上。Abstract: Energy spectrum data analysis is the main source of nuclide identification. Aiming at the slow recognition speed and low accuracy of the emerging energy spectrum nuclide identification method in the noisy environment of mixed radionuclides, an energy spectrum nuclide recognition method based on long short-term memory neural network (LSTM) is proposed. In the experiment, a LaBr3 crystal detector was used to measure the 60Co and 137Cs radioactive sources in the environment to obtain a gamma spectrum data set. First, the experiment used data smoothing and normalization methods for data preprocessing. Then, the energy spectrum data was grouped in time series to obtain a usable input sequence array. Finally, the prediction results were obtained through the LSTM model. By comparing two energy spectrum recognition models based on BP neural network and convolutional neural network (CNN), the average recognition rates in the test set are 83.45% and 86.21% respectively, while the average recognition rate of the LSTM model is 93.04%. The experimental results show that the energy spectrum model has performed well in the nuclide identification and can be used in fast energy spectrum nuclide identification equipment.
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Key words:
- energy spectrum data /
- long short-term memory /
- nuclide identification /
- data smoothing /
- normalization
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图 2 基于长短时记忆神经网络的能谱核素识别模型
Figure 2. Spectral nuclide recognition model based on long short-term memory neural network
图 3 LSTM能谱模型的不同学习率收敛曲线
Figure 3. Different learning rate convergence curves of LSTM energy spectrum model
图 5 BP和CNN能谱模型的学习率收敛曲线图
Figure 5. The learning rate convergence curve of BP and CNN energy spectrum models
图 6 各模型的训练集损失曲线和准确率
Figure 6. The training set loss curve and accuracy curve of each model
表 1 LSTM最后一个时间节点的能谱预测结果
Table 1. Energy spectrum prediction results of the last time node of LSTM
sample prediction 60Co 137Cs 60Co＋137Cs no radioactive source 60Co 17.25354004 −13.38463688 5.48915672 −8.59954166 137Cs −3.00819635 6.40356064 0.29465187 2.9503994 60Co＋137Cs 0.48095784 2.74640751 8.61173725 −5.41806173 no radioactive source −0.71746081 −1.06083262 −3.47033572 8.91043949 
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表 2 相同测量时间的训练集和相同测量时间的测试集结果
Table 2. Results of the training set with the same measurement time and the test set with the same measurement time
sample measurement time/s size of training set data size of test set data average accuracy/% 5 160 40 90.15 10 160 40 92.26 20 160 40 92.66
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表 3 混合测量时间的训练集和混合测量时间的测试集结果
Table 3. Results of training set and mixed measurement time test set with mixed measurement time
sample measurement time/s size of training set data size of test set data average accuracy/% 5 s，10 s and 20 s mixed 480 120 92.37 360 120 89.60 240 120 90.96
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表 4 混合测量时间的训练集和连续测量时间的测试集结果
Table 4. Results of mixed measurement time training set and continuous measurement time test set
sample measurement time/s size of training set data size of test set data average accuracy/% 5 s，10 s and 20 s mixed 480 40 92.07 360 40 91.57 240 40 88.05
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表 5 准确率达到100%所需训练步数和训练时长
Table 5. Training steps and training time required to achieve 100% accuracy
model name training steps training time /min BP 67 73.10 CNN 66 72.78 LSTM 40 35.26
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表 6 各模型识别准确率
Table 6. Recognition accuracy of each model
model name accuracy of data set 1/% accuracy of data set 2/% accuracy of data set 3/% BP 83.74 83.59 83.04 CNN 86.05 85.83 86.73 LSTM 93.56 92.13 93.44
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