Nuclear Engineering and Technology

  • 제53권12호
  • /
  • Pages.4072-4079
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  • 2021
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  • 1738-5733(pISSN)
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  • 2234-358X(eISSN)
한국원자력학회 (Korean Nuclear Society)
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A comparative study of machine learning methods for automated identification of radioisotopes using NaI gamma-ray spectra

  • Galib, S.M. (Department of Nuclear Engineering and Radiation Science, Missouri University of Science and Technology) ;
  • Bhowmik, P.K. (Department of Nuclear Engineering and Radiation Science, Missouri University of Science and Technology) ;
  • Avachat, A.V. (Department of Nuclear Engineering and Radiation Science, Missouri University of Science and Technology) ;
  • Lee, H.K. (Department of Nuclear Engineering, University of New Mexico)
  • 투고 : 2021.03.27
  • 심사 : 2021.06.12
  • 발행 : 2021.12.25
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초록

This article presents a study on the state-of-the-art methods for automated radioactive material detection and identification, using gamma-ray spectra and modern machine learning methods. The recent developments inspired this in deep learning algorithms, and the proposed method provided better performance than the current state-of-the-art models. Machine learning models such as: fully connected, recurrent, convolutional, and gradient boosted decision trees, are applied under a wide variety of testing conditions, and their advantage and disadvantage are discussed. Furthermore, a hybrid model is developed by combining the fully-connected and convolutional neural network, which shows the best performance among the different machine learning models. These improvements are represented by the model's test performance metric (i.e., F1 score) of 93.33% with an improvement of 2%-12% than the state-of-the-art model at various conditions. The experimental results show that fusion of classical neural networks and modern deep learning architecture is a suitable choice for interpreting gamma spectra data where real-time and remote detection is necessary.

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과제정보

Thanks to detecting radiological threats in urban areas - challenge. https://www.topcoder.com/challenges/30085346 for sharing the necessary data for this study.

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