Novel algorithm for detection and identification of radioactive materials in an urban environment  

作　　者：Hao-Lin Liu Hai-Bo Ji Jiang-Mei Zhang Jing Lu Cao-Lin Zhang Xing-Hua Feng 

机构地区：[1]Department of Automation,University of Science and Technology of China,Hefei 230026,China [2]School of Information Engineering,Southwest University of Science and Technology,Mianyang 621010,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》2023年第10期103-116,共14页核技术（英文）

基　　金：supported by the National Defense Fundamental Research Projects (Nos. JCKY2020404C004 and JCKY2022404C005);Sichuan Science and Technology Program (No. 22NSFSC0044)。

摘　　要：This study introduces a novel algorithm to detect and identify radioactive materials in urban settings using time-series detector response data. To address the challenges posed by varying backgrounds and to enhance the quality and reliability of the energy spectrum data, we devised a temporal energy window. This partitioned the time-series detector response data, resulting in energy spectra that emphasize the vital information pertaining to radioactive materials. We then extracted characteristic features of these energy spectra, relying on the formation mechanism and measurement principles of the gammaray instrument spectrum. These features encompassed aggregated counts, peak-to-flat ratios, and peak-to-peak ratios. This methodology not only simplified the interpretation of the energy spectra's physical significance but also eliminated the necessity for peak searching and individual peak analyses. Given the requirements of imbalanced multi-classification, we created a detection and identification model using a weighted k-nearest neighbors(KNN) framework. This model recognized that energy spectra of identical radioactive materials exhibit minimal inter-class similarity. Consequently, it considerably boosted the classification accuracy of minority classes, enhancing the classifier's overall efficacy. We also executed a series of comparative experiments. Established methods for radionuclide identification classification, such as standard KNN, support vector machine, Bayesian network, and random tree, were used for comparison purposes. Our proposed algorithm realized an F1 measure of 0.9868 on the time-series detector response data, reflecting a minimum enhancement of 0.3% in comparison with other techniques. The results conclusively show that our algorithm outperforms others when applied to time-series detector response data in urban contexts.

关 键 词：Gamma-ray spectral analysis Nuclide identification Urban environment Temporal energy window Peakratio spectrum analysis Weighted KNN 

分 类 号：TL81[核科学技术—核技术及应用]