基于长短时记忆神经网络的能谱核素识别方法

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。

Energy spectrum nuclide recognition method based on long short-term memory neural network

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.