Identification algorithm of low-count energy spectra under short-duration measurement based on heterogeneous sample transfer  

作　　者：Hao-Lin Liu Hai-Bo Ji Jiang-Mei Zhang Jing Lu 

机构地区：[1]School of Information Engineering,Southwest University of Science and Technology,Mianyang 621010,China [2]Department of Automation,University of Science and Technology of China,Hefei 230026,China [3]Fundamental Science on Nuclear Wastes and Environment Safety Laboratory,Southwest University of Science and Technology,Mianyang 621010,China [4]School of Automation and Information Engineering,Sichuan University of Science and Engineering,Zigong 643000,China

出　　处：《Nuclear Science and Techniques》2025年第3期12-26,共15页核技术(英文)

基　　金：supported by the National Defense Fundamental Research Project(No.JCKY2022404C005);the Nuclear Energy Development Project(No.23ZG6106);the Sichuan Scientific and Technological Achievements Transfer and Transformation Demonstration Project(No.2023ZHCG0026);the Mianyang Applied Technology Research and Development Project(No.2021ZYZF1005)。

摘　　要：In scenarios such as vehicle radiation monitoring and unmanned aerial vehicle radiation detection,rapid measurements using a NaI(Tl)detector often result in low photon counts,weak characteristic peaks,and significant statistical fluctuations.These issues can lead to potential failures in peak-searching-based identification methods.To address the low precision associated with short-duration measurements of radionuclides,this paper proposes an identification algorithm that leverages heterogeneous spectral transfer to develop a low-count energy spectral identification model.Comparative experiments demonstrated that transferring samples from 26 classes of simulated heterogeneous gamma spectra aids in creating a reliable model for measured gamma spectra.With only 10%of target domain samples used for training,the accuracy on real low-count spectral samples was 95.56%.This performance shows a significant improvement over widely employed full-spectrum analysis methods trained on target domain samples.The proposed method also exhibits strong generalization capabilities,effectively mitigating overfitting issues in low-count energy spectral classification under short-duration measurements.

关 键 词：Radionuclide identification Low-count Gamma energy spectral analysis HETEROGENEOUS Transfer learning 

分 类 号：TP1[自动化与计算机技术—控制理论与控制工程]