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논문 기본 정보

자료유형
학술저널
저자정보
S.M. Galib (Missouri University of Science and Technology) P.K. Bhowmik (Missouri University of Science and Technology) A.V. Avachat (Missouri University of Science and Technology) H.K. Lee (University of New Mexico)
저널정보
한국원자력학회 Nuclear Engineering and Technology Nuclear Engineering and Technology 제53권 제12호
발행연도
2021.12
수록면
4,072 - 4,079 (8page)
DOI
https://doi.org/10.1016/j.net.2021.06.020

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This article presents a study on the state-of-the-art methods for automated radioactive materialdetection and identification, using gamma-ray spectra and modern machine learning methods. Therecent developments inspired this in deep learning algorithms, and the proposed method providedbetter performance than the current state-of-the-art models. Machine learning models such as: fullyconnected, 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 hybridmodel is developed by combining the fully-connected and convolutional neural network, which showsthe 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%e12%than the state-of-the-art model at various conditions. The experimental results show that fusion ofclassical neural networks and modern deep learning architecture is a suitable choice for interpretinggamma spectra data where real-time and remote detection is necessary

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