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

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Abstract

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 gamma-ray 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.

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Data availability

The data that support the findings of this study are openly available in Science Data Bank at https://doi.org/10.57760/sciencedb.10892 and http://cstr.cn/31253.11.sciencedb.10892.

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Acknowledgements

Thanks for the https://doi.org/10.13139/ORNLNCCS/1597414 dataset which is provided by the Oak Ridge National Laboratory.

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Authors and Affiliations

  1. Department of Automation, University of Science and Technology of China, Hefei, 230026, China

    Hao-Lin Liu & Hai-Bo Ji

  2. School of Information Engineering, Southwest University of Science and Technology, Mianyang, 621010, China

    Hao-Lin Liu, Cao-Lin Zhang & Xing-Hua Feng

  3. Fundamental Science on Nuclear Wastes and Environment Safety Laboratory, Southwest University of Science and Technology, Mianyang, 621010, China

    Jiang-Mei Zhang

  4. School of Automation and Information Engineering, Sichuan University of Science and Engineering, Zigong, 643000, China

    Jing Lu

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Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Hai-Bo Ji, Jiang-Mei Zhang, Cao-Lin Zhang, Jing Lu, and Xing-Hua Feng. The first draft of the manuscript was written by Hao-Lin Liu, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Hao-Lin Liu.

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The authors declare that they have no competing interests.

Additional information

This work was supported by the National Defense Fundamental Research Projects (Nos. JCKY2020404C004 and JCKY2022404C005) and Sichuan Science and Technology Program (No. 22NSFSC0044).

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Liu, HL., Ji, HB., Zhang, JM. et al. Novel algorithm for detection and identification of radioactive materials in an urban environment. NUCL SCI TECH 34, 154 (2023). https://doi.org/10.1007/s41365-023-01304-1

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