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基于深度学习的252Cf源驱动核材料浓度识别技术

陈乐林 魏彪 李鹏程 冯鹏 周密

陈乐林, 魏彪, 李鹏程, 等. 基于深度学习的252Cf源驱动核材料浓度识别技术[J]. 强激光与粒子束, 2018, 30: 096001. doi: 10.11884/HPLPB201830.170487
引用本文: 陈乐林, 魏彪, 李鹏程, 等. 基于深度学习的252Cf源驱动核材料浓度识别技术[J]. 强激光与粒子束, 2018, 30: 096001. doi: 10.11884/HPLPB201830.170487
Chen Lelin, Wei Biao, Li Pengcheng, et al. 252Cf-source-driven nuclear material concentration identification based on deep learning[J]. High Power Laser and Particle Beams, 2018, 30: 096001. doi: 10.11884/HPLPB201830.170487
Citation: Chen Lelin, Wei Biao, Li Pengcheng, et al. 252Cf-source-driven nuclear material concentration identification based on deep learning[J]. High Power Laser and Particle Beams, 2018, 30: 096001. doi: 10.11884/HPLPB201830.170487

基于深度学习的252Cf源驱动核材料浓度识别技术

doi: 10.11884/HPLPB201830.170487
基金项目: 

国家自然科学青年基金项目 11605017

详细信息
    作者简介:

    陈乐林(1991-), 男,硕士研究生,从事信号检测及信号处理研究;1369932914@qq.com

  • 中图分类号: TP301.6

252Cf-source-driven nuclear material concentration identification based on deep learning

  • 摘要: 针对核武器/核材料识别系统中核材料浓度识别的关键技术问题,采用Monte Carlo方法,通过建立252Cf源驱动核材料裂变中子信号样本库,模拟分析了随探测器距离和角度及核材料浓度变化的裂变脉冲中子信号特点,基于深度学习之卷积神经网络,构建了一种252Cf源驱动核材料浓度识别方法,实现了对测试样本浓度的识别,且还与BP神经网络和K最近邻方法进行了对比试验研究。结果表明,卷积神经网络算法进行核材料浓度识别,得到了高达92.05%识别准确率,不仅解决了因探测器距离和角度变化时对核材料浓度识别准确率影响的难题,而且还获得了优于BP神经网络和K最近邻算法对核材料浓度识别的认识,这为252Cf源驱动核材料浓度识别提供了一种新的途径。
  • 图  1  252Cf源驱动的三通道裂变中子脉冲信号测量系统示意图

    Figure  1.  Measurement model of 252Cf source spectrum measurement system

    图  2  卷积神经网络构建流程原理框图

    Figure  2.  Construction flow chart of convolutional neural network

    图  3  核材料浓度识别流程原理框图

    Figure  3.  Flow chart of nuclear material concentration identification

    图  4  训练样本计数时域分布随角度变化示意图

    Figure  4.  Count distribution of training samples varies with angle

    图  5  训练样本计数时域分布随浓度变化示意图

    Figure  5.  Count distribution of training samples varies with concentration

    图  6  训练样本计数时域分布随距离变化示意图

    Figure  6.  Count distribution of training samples varies with distance

    图  7  分帧处理原理图

    Figure  7.  Frame processing

    图  8  初始构建的卷积神经网络示意图

    Figure  8.  Initial construction of convolutional neural network

    图  9  权值调节因子取不同值的分类错误率

    Figure  9.  Classification error rate varies with weight adjustment factor

    图  10  学习率取不同值的分类错误率

    Figure  10.  Classification error rate varies with learning rate

    图  11  卷积核取不同个数的分类错误率

    Figure  11.  Classification error rate varies with number of kernels

    图  12  损失函数曲线

    Figure  12.  Curve of loss function

    表  1  不同实验得到的分类准确率一览表

    Table  1.   Classification accuracy of different experiments

    experiment error/%
    convolutional neural network 7.95
    BP neural network feature Ⅰ 37.95
    BP neural network feature Ⅱ 12.73
    K-nearest neighbor 37.5
    下载: 导出CSV
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出版历程
  • 收稿日期:  2017-12-04
  • 修回日期:  2018-04-18
  • 刊出日期:  2018-09-15

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