In this paper,a genetic-algorithm-based artificial neural network(GAANN)model radioactivity prediction is proposed,which is verified by measuring results from Long Range Alpha Detector(LRAD).GAANN can integrate capabilities of approximation of Artificial Neural Networks(ANN)and of global optimization of Genetic Algorithms(GA)so that the hybrid model can enhance capability of generalization and prediction accuracy,theoretically.With this model,both the number of hidden nodes and connection weights matrix in ANN are optimized using genetic operation.The real data sets are applied to the introduced method and the results are discussed and compared with the traditional Back Propagation(BP)neural network,showing the feasibility and validity of the proposed approach.
WANG LeiTUO XianguoYAN YuchengLIU MingzheCHENG YiLI Pingchuan
We present the design and optimization of a prompt γ-ray neutron activation analysis(PGNAA) thermal neutron output setup based on Monte Carlo simulations using MCNP5 computer code. In these simulations, the moderator materials, reflective materials, and structure of the PGNAA252 Cf neutrons of thermal neutron output setup are optimized. The simulation results reveal that the thin layer paraffin and the thick layer of heavy water moderating effect work best for the252 Cf neutron spectrum. Our new design shows a significantly improved performance of the thermal neutron flux and flux rate, that are increased by 3.02 times and 3.27 times, respectively,compared with the conventional neutron source design.
描述了一种基于现场可编程逻辑阵列(FPGA)的高速核信号采集系统的设计方案。FPGA作为控制核心,实现对高速Analog-to-Digital Converter(ADC)和Universal Serial Bus(USB)的逻辑控制和数字信号的采样、滤波、甄别、存储、传输处理,并使用异步First In First Out(FIFO)实现ADC数据采集模块和USB数据传输模块2个不同时钟域之间的数据传输,提高数据的吞吐率。最后利用上位机软件进行数据处理和绘图显示。测试结果表明,该系统能够实现核信号的实时、高效采集。
