The statistical distribution of natural phenomena is of great significance in studying the laws of nature. In order to study the statistical characteristics of a random pulse signal, a random process model is proposed...The statistical distribution of natural phenomena is of great significance in studying the laws of nature. In order to study the statistical characteristics of a random pulse signal, a random process model is proposed theoretically for better studying of the random law of measured results. Moreover, a simple random pulse signal generation and testing system is designed for studying the counting distributions of three typical objects including particles suspended in the air, standard particles, and background noise. Both normal and lognormal distribution fittings are used for analyzing the experimental results and testified by chi-square distribution fit test and correlation coefficient for comparison. In addition, the statistical laws of three typical objects and the relations between them are discussed in detail. The relation is also the non-integral dimension fractal relation of statistical distributions of different random laser scattering pulse signal groups.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
文摘The statistical distribution of natural phenomena is of great significance in studying the laws of nature. In order to study the statistical characteristics of a random pulse signal, a random process model is proposed theoretically for better studying of the random law of measured results. Moreover, a simple random pulse signal generation and testing system is designed for studying the counting distributions of three typical objects including particles suspended in the air, standard particles, and background noise. Both normal and lognormal distribution fittings are used for analyzing the experimental results and testified by chi-square distribution fit test and correlation coefficient for comparison. In addition, the statistical laws of three typical objects and the relations between them are discussed in detail. The relation is also the non-integral dimension fractal relation of statistical distributions of different random laser scattering pulse signal groups.
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.
