The 252Cf source-driven verification system(SDVS)can recognize the enrichment of fissile material with the enrichment-sensitive autocorrelation functions of a detector signal in252Cf source-driven noise-analysis(SDNA)...The 252Cf source-driven verification system(SDVS)can recognize the enrichment of fissile material with the enrichment-sensitive autocorrelation functions of a detector signal in252Cf source-driven noise-analysis(SDNA)measurements.We propose a parallel and optimized genetic Elman network(POGEN)to identify the enrichment of235U based on the physical properties of the measured autocorrelation functions.Theoretical analysis and experimental results indicate that,for 4 different enrichment fissile materials,due to higher information utilization,more efficient network architecture,and optimized parameters,the POGEN-based algorithm can obtain identification results with higher recognition accuracy,compared to the integrated autocorrelation function(IAF)method.展开更多
Experiments were performed on a high-speed online random neutron analyzing system (HORNA system) with a 252Cf neutron source (up to 1 GHz sampling rate and 3 input data channel),to obtain timeand frequency dependent s...Experiments were performed on a high-speed online random neutron analyzing system (HORNA system) with a 252Cf neutron source (up to 1 GHz sampling rate and 3 input data channel),to obtain timeand frequency dependent signatures which are sensitive to changes in the composition,fissile mass and configuration of the fissile assembly.The data were acquired by three high-speed synchronized acquisition cards at different detector angles,source-detector distances and block sizes.According to the relationship between 252Cf source and the ratio of power spectral density,Rpsd,all the signatures were calculated and analyzed using correlation and periodogram methods.Based on the results,the simulated autocorrelation functions were utilized for identifying different fissile mass with Elman neural network.The experimental results show that the Rpsd almost remains at constant amplitude in frequency range of 0-100 MHz,and is only related to the angle and source-detector distance.The trained Elman neural network is able to distinguish the characteristics of autocorrelation function and identify different fissile mass.The average identification rate reached 90% with high robustness.展开更多
近期提出的单体相移深度神经网络(single phase-shift deep neural network,SPDNN),因其网络规模小、学习精度高,成为首个复杂中子共振截面拟合与评价的实用深度学习工具。在SPDNN学习共振截面的过程中,诸多因素显著影响网络的训练效果...近期提出的单体相移深度神经网络(single phase-shift deep neural network,SPDNN),因其网络规模小、学习精度高,成为首个复杂中子共振截面拟合与评价的实用深度学习工具。在SPDNN学习共振截面的过程中,诸多因素显著影响网络的训练效果、训练效率以及训练模型的泛化性。这些因素包括:决定网络相移层大小的共振截面频谱范围与频段宽度、隐藏层的数目、每层神经元的数目、激活函数、损失函数、训练步数和训练数据的预处理等。为了进一步提升SPDNN在共振截面研究中的实用性,详细考察了这些因素对网络拟合性能的影响。通过考察,确定了SPDNN在共振截面研究中适宜的网络构建和训练方法,助力推动SPDNN的广泛应用。展开更多
基金Supported by National Natural Science Foundation of China(Nos.61201346,61175005 and 61401049)the Fundamental Research Funds for the Central Universities(No.CDJZR14125501)
文摘The 252Cf source-driven verification system(SDVS)can recognize the enrichment of fissile material with the enrichment-sensitive autocorrelation functions of a detector signal in252Cf source-driven noise-analysis(SDNA)measurements.We propose a parallel and optimized genetic Elman network(POGEN)to identify the enrichment of235U based on the physical properties of the measured autocorrelation functions.Theoretical analysis and experimental results indicate that,for 4 different enrichment fissile materials,due to higher information utilization,more efficient network architecture,and optimized parameters,the POGEN-based algorithm can obtain identification results with higher recognition accuracy,compared to the integrated autocorrelation function(IAF)method.
基金Supported by Natural Science Foundation Project of CQ (CSTC2009BB2188)Fundamental Research Funds for Central Universities (No. CDJXS10120013)
文摘Experiments were performed on a high-speed online random neutron analyzing system (HORNA system) with a 252Cf neutron source (up to 1 GHz sampling rate and 3 input data channel),to obtain timeand frequency dependent signatures which are sensitive to changes in the composition,fissile mass and configuration of the fissile assembly.The data were acquired by three high-speed synchronized acquisition cards at different detector angles,source-detector distances and block sizes.According to the relationship between 252Cf source and the ratio of power spectral density,Rpsd,all the signatures were calculated and analyzed using correlation and periodogram methods.Based on the results,the simulated autocorrelation functions were utilized for identifying different fissile mass with Elman neural network.The experimental results show that the Rpsd almost remains at constant amplitude in frequency range of 0-100 MHz,and is only related to the angle and source-detector distance.The trained Elman neural network is able to distinguish the characteristics of autocorrelation function and identify different fissile mass.The average identification rate reached 90% with high robustness.
文摘近期提出的单体相移深度神经网络(single phase-shift deep neural network,SPDNN),因其网络规模小、学习精度高,成为首个复杂中子共振截面拟合与评价的实用深度学习工具。在SPDNN学习共振截面的过程中,诸多因素显著影响网络的训练效果、训练效率以及训练模型的泛化性。这些因素包括:决定网络相移层大小的共振截面频谱范围与频段宽度、隐藏层的数目、每层神经元的数目、激活函数、损失函数、训练步数和训练数据的预处理等。为了进一步提升SPDNN在共振截面研究中的实用性,详细考察了这些因素对网络拟合性能的影响。通过考察,确定了SPDNN在共振截面研究中适宜的网络构建和训练方法,助力推动SPDNN的广泛应用。
