This study proposes a ladder gradient method for neutron and gamma-ray discrimination.The proposed method exhibited state-of-the-art performance with low time consumption,which incorporates two parts:information extra...This study proposes a ladder gradient method for neutron and gamma-ray discrimination.The proposed method exhibited state-of-the-art performance with low time consumption,which incorporates two parts:information extraction and discrimination factor calculation.A quasi-continuous spiking cortical model was proposed to extract information from the radiation pulse signals,thus generating an ignition map corresponding to each pulse signal.The ignition map can be used to calculate the discrimination factor.A ladder gradient calculation was introduced to obtain a discrimination factor with low computational complexity.The proposed method was compared with five other discrimination methods to evaluate its robustness and efficacy.Furthermore,the filter adaptability of the pulse-coupled neural network and ladder gradient methods was investigated.Possible reasons for adapting the conditions with different discrimination methods and filters were analyzed.Experiments were conducted in 20 filtering situations with 11 types of filters to determine the most suitable filters for discrimination methods.The experimental results revealed that the three most adaptive filters of the pulse-coupled neural networks and ladder gradient methods are the wavelet,elliptic,and median filters and the elliptic,moving average,and wavelet filters,respectively.展开更多
基金supported by the National Natural Science Foundation of China(Nos.U19A2086,41874121,12205078).
文摘This study proposes a ladder gradient method for neutron and gamma-ray discrimination.The proposed method exhibited state-of-the-art performance with low time consumption,which incorporates two parts:information extraction and discrimination factor calculation.A quasi-continuous spiking cortical model was proposed to extract information from the radiation pulse signals,thus generating an ignition map corresponding to each pulse signal.The ignition map can be used to calculate the discrimination factor.A ladder gradient calculation was introduced to obtain a discrimination factor with low computational complexity.The proposed method was compared with five other discrimination methods to evaluate its robustness and efficacy.Furthermore,the filter adaptability of the pulse-coupled neural network and ladder gradient methods was investigated.Possible reasons for adapting the conditions with different discrimination methods and filters were analyzed.Experiments were conducted in 20 filtering situations with 11 types of filters to determine the most suitable filters for discrimination methods.The experimental results revealed that the three most adaptive filters of the pulse-coupled neural networks and ladder gradient methods are the wavelet,elliptic,and median filters and the elliptic,moving average,and wavelet filters,respectively.
