Earticle

초록

영어

Background: Identification of radioisotopes for plastic scintillation detectors is challenging because their spectra have poor energy resolutions and lack photo peaks. To overcome this weakness, many researchers have conducted radioisotope identification studies using machine learning algorithms; however, the effect of data normalization on radioisotope identification has not been addressed yet. Furthermore, studies on machine learning-based radioisotope identifiers for plastic scintillation detectors are limited. Materials and Methods: In this study, machine learning-based radioisotope identifiers were implemented, and their performances according to data normalization methods were compared. Eight classes of radioisotopes consisting of combinations of 22Na, 60Co, and 137Cs, and the background, were defined. The training set was generated by the random sampling technique based on probabilistic density functions acquired by experiments and simulations, and test set was acquired by experiments. Support vector machine (SVM), artificial neural network (ANN), and convolutional neural network (CNN) were implemented as radioisotope identifiers with six data normalization methods, and trained using the generated training set. Results and Discussion: The implemented identifiers were evaluated by test sets acquired by experiments with and without gain shifts to confirm the robustness of the identifiers against the gain shift effect. Among the three machine learning-based radioisotope identifiers, prediction accuracy followed the order SVM >ANN>CNN, while the training time followed the order SVM>ANN>CNN. Conclusion: The prediction accuracy for the combined test sets was highest with the SVM. The CNN exhibited a minimum variation in prediction accuracy for each class, even though it had the lowest prediction accuracy for the combined test sets among three identifiers. The SVM exhibited the highest prediction accuracy for the combined test sets, and its training time was the shortest among three identifiers.

목차

ABSTRACT
Introduction
Materials and Methods
1. Machine Learning Approaches
2. Data Set Generation
3. Model Implementation
Results and Discussion
1. Identification Results
2. Gain Shift Sensitivity
3. Gain Shift Sensitivity Combined Results on Test Sets with and without Gain Shift Effect
4. Discussions
Conclusion
Conflict of Interest
Acknowledgements
Author Contribution
References

키워드

저자

• Byoungil Jeon [ Artificial Intelligence Application & Strategy Team, Korea Atomic Energy Research Institute, Daejeon, Korea ]
• Jongyul Kim [ Neutron Science Division, Korea Atomic Energy Research Institute, Daejeon, Korea ]
• Yonggyun Yu [ Artificial Intelligence Application & Strategy Team, Korea Atomic Energy Research Institute, Daejeon, Korea ]
• Myungkook Moon [ Neutron Science Division, Korea Atomic Energy Research Institute, Daejeon, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  방사선방어학회지 [Journal of Radiation Protection and Research]

• 간기

  계간

• pISSN

  2508-1888

• 수록기간

  1976~2026

• 등재여부

  KCI 등재,SCOPUS

• 십진분류

  KDC 559 DDC 629