Volume 32 Issue 10
Sep.  2020
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Wang Yao, Liu Zhiming, Wan Yaping, et al. Energy spectrum nuclide recognition method based on long short-term memory neural network[J]. High Power Laser and Particle Beams, 2020, 32: 106001. doi: 10.11884/HPLPB202032.200118
Citation: Wang Yao, Liu Zhiming, Wan Yaping, et al. Energy spectrum nuclide recognition method based on long short-term memory neural network[J]. High Power Laser and Particle Beams, 2020, 32: 106001. doi: 10.11884/HPLPB202032.200118

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

doi: 10.11884/HPLPB202032.200118
  • Received Date: 2020-05-12
  • Rev Recd Date: 2020-08-28
  • Publish Date: 2020-09-29
  • 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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  • [1]
    Liang Chen, Yi Xiangwei. Nuclide identification algorithm based on K–L transform and neural networks[J]. Nuclear Inst and Methods in Physics Research A, 2009, 598(2): 450-453.
    [2]
    王一鸣, 魏义祥. 基于模糊逻辑的γ能谱核素识别[J]. 清华大学学报(自然科学版), 2012, 52(12):1736-1740. (Wang Yiming, Wei Yixiang. Fuzzy logic based nuclide identification for γ ray spectra[J]. Journal of Tsinghua University(Science and Technology), 2012, 52(12): 1736-1740
    [3]
    问斯莹, 王百荣, 肖刚, 等. 基于序贯贝叶斯方法的核素识别算法研究[J]. 核电子学与探测术, 2016, 36(2):179-183. (Wen Siying, Wang Bairong, Xiao Gang, et al. The study on nuclide identification algorithm based on sequential Bayesian analysis[J]. Nuclear Electronics and Detection Technology, 2016, 36(2): 179-183
    [4]
    张江梅, 季海波, 冯兴华, 等. 基于稀疏表示的核素能谱特征提取及核素识别[J]. 强激光与粒子束, 2018, 30:046003. (Zhang Jiangmei, Ji Haibo, Feng Xinghua, et al. Nuclide spectrum feature extraction and nuclide identification based on sparse representation[J]. High Power Laser and Particle Beams, 2018, 30: 046003
    [5]
    胡浩行, 张江梅, 王坤朋, 等. 卷积神经网络在复杂核素识别中的应用[J]. 传感器与微系统, 2019, 38(10):154-156, 160. (Hu Haohang, Zhang Jiangmei, Wang Kunpeng, et al. Application of convolutional neural networks in identification of complex nuclides[J]. Transducer and Microsystem Technologies, 2019, 38(10): 154-156, 160
    [6]
    Hochreiter S, Schmidhuber J. Long short-term memory[J]. Neural Computation, 1997, 9: 1735-1780. doi: 10.1162/neco.1997.9.8.1735
    [7]
    Graves A, Jaitly N, Mohamed A. Hybrid speech recognition with deep bidirectional LSTM[C]//IEEE Workshop on Automatic Speech Recognition and Understanding. 2013.
    [8]
    Hayashi T, Watanabe S, Toda T, et al. Duration-controlled LSTM for polyphonic sound event detection[J]. IEEE ACM Transactions on Audio, Speech and Language Processing, 2017, 25(11): 2059-2070.
    [9]
    任智慧, 徐浩煜, 封松林, 等. 基于LSTM网络的序列标注中文分词法[J]. 计算机应用研究, 2017, 34(5):1321-1324, 1341. (Ren Zhihui, Xu Haoyu, Feng Songlin, et al. Sequence labeling Chinese word segmentation method based on LSTM networks[J]. Application Research of Computers, 2017, 34(5): 1321-1324, 1341
    [10]
    Ran J. A self-attention based LSTM network for text classification[J]. Journal of Physics: Conference Series, 2019, 1207: 12008. doi: 10.1088/1742-6596/1207/1/012008
    [11]
    梁军, 柴玉梅, 原慧斌, 等. 基于极性转移和LSTM递归网络的情感分析[J]. 中文信息学报, 2015, 29(5):152-159. (Liang Jun, Chai Yumei, Yuan Huibin, et al. Polarity shifting and LSTM based recursive networks for sentiment analysis[J]. Journal of Chinese Information Processing, 2015, 29(5): 152-159
    [12]
    季学武, 费聪, 何祥坤, 等. 基于LSTM网络的驾驶意图识别及车辆轨迹预测[J]. 中国公路学报, 2019, 32(6):34-42. (Ji Xuewu, Fei Cong, He Xiangkun, et al. Intention recognition and trajectory prediction for vehicles using LSTM network[J]. China Journal of Highway and Transport, 2019, 32(6): 34-42
    [13]
    祝强, 李少康, 徐臻. LM算法求解大残差非线性最小二乘问题研究[J]. 中国测试, 2016, 42(3):12-16. (Zhu Qiang, Li Shaokang, Xu Zhen. Study of solving nonlinear least squares under large residual based on Levenberg-Marquardt algorithm[J]. China Measurement and Test, 2016, 42(3): 12-16
    [14]
    高伟伟, 王广龙, 陈建辉, 等. 多尺度变步长最小均方自适应算法在光纤陀螺数据处理中的应用[J]. 强激光与粒子束, 2014, 26:071002. (Gao Weiwei, Wang Guanglong, Cheng Jianhui, et al. Application of multiple-scale variable step least mean square adaptive algorithm to fiber optic gyroscope data processing[J]. High Power Laser and Particle Beams, 2014, 26: 071002 doi: 10.3788/HPLPB20142607.71002
    [15]
    Li Q, Huang Y, Song X, et al. Moving window smoothing on the ensemble of competitive adaptive reweighted sampling algorithm[J]. Spectrochimica Acta. Part A, Molecular And Biomolecular Spectroscopy, 2019, 214: 129-138. doi: 10.1016/j.saa.2019.02.023
    [16]
    Bolstad B M, Irizarry R A, Astrand M, et al. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias[J]. Bioinformatics, 2003, 19(2): 185-193. doi: 10.1093/bioinformatics/19.2.185
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