Nuclide Identification Algorithm for the Large-Size Plastic Detectors Based on Artificial Neural Network
This paper proposes a multi-label classification model using an artificial neural network to identify both an individual and a mixture of radionuclides in gamma spectra obtained from a 250×250×50 mm3 EJ-200 plastic scintillation detector. This model is evaluated under the scenario applied to pedes...
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2023
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| Truy cập trực tuyến: | https://scholar.dlu.edu.vn/handle/123456789/2067 https://ieeexplore.ieee.org/abstract/document/9772480 |
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oai:scholar.dlu.edu.vn:123456789-20672023-12-13T04:22:30Z Nuclide Identification Algorithm for the Large-Size Plastic Detectors Based on Artificial Neural Network Cao Van Hiep, Dinh Tien Hung, Nguyen Ngoc Anh, Nguyen Ninh Giang, Pham Dinh Khang, Nguyen Xuan Hai, Kien-Cuong NGUYEN, Trinh Van Ninh Phan, Văn Chuân Nuclide identification algorithms artificial neural network multi-label classification plastic scintillation detectors. This paper proposes a multi-label classification model using an artificial neural network to identify both an individual and a mixture of radionuclides in gamma spectra obtained from a 250×250×50 mm3 EJ-200 plastic scintillation detector. This model is evaluated under the scenario applied to pedestrian radiation portal monitors to judge how well it works in practice. The simulated and measured gamma of 241Am, 133Ba, 137Cs, 60Co, 152Eu, 131I radioactive sources and background, are used to generate the training dataset. Measurement data with varying source-to-detector distances, shielding thicknesses, and incidence angles are also taken into account. The experimental results show that the mean value of the accuracy can be achieved at about 98.8% and 94.9% for single and multi-isotope identification, respectively. In addition, the model can well precisely recognize radionuclides in the gamma spectrum whose gain shift is up to 10%. The dependence of the True Positive rate on the count quality factors of individual radionuclides, which was defined as the ratio between the net count rate and its associated uncertainty, is examined. The detection sensitivities, defined as the minimum count quality factor to obtain a True Positive rate of 95%, for 241Am, 133Ba, 137Cs, 60Co, 152Eu, 131I are 8.90, 11.86, 8.96, 8.21, 12.54, and 11.89, respectively. With such encouraging results, the proposed model should be a useful technique for radionuclide recognition. 69 6 Khoa Vật lý và Kỹ thuật hạt nhân 9 Phan Văn Chuân - 2023-04-26T12:35:23Z 2023-04-26T12:35:23Z 2022-07 Journal article Bài báo đăng trên tạp chí thuộc ISI, bao gồm book chapter https://scholar.dlu.edu.vn/handle/123456789/2067 10.1109/TNS.2022.3173371 https://ieeexplore.ieee.org/abstract/document/9772480 en IEEE Transactions on Nuclear Science |
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Thư viện Trường Đại học Đà Lạt |
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Thư viện số |
| language |
English |
| topic |
Nuclide identification algorithms artificial neural network multi-label classification plastic scintillation detectors. |
| spellingShingle |
Nuclide identification algorithms artificial neural network multi-label classification plastic scintillation detectors. Cao Van Hiep, Dinh Tien Hung, Nguyen Ngoc Anh, Nguyen Ninh Giang, Pham Dinh Khang, Nguyen Xuan Hai, Kien-Cuong NGUYEN, Trinh Van Ninh Phan, Văn Chuân Nuclide Identification Algorithm for the Large-Size Plastic Detectors Based on Artificial Neural Network |
| description |
This paper proposes a multi-label classification model using an artificial neural network to identify both an
individual and a mixture of radionuclides in gamma spectra obtained from a 250×250×50 mm3 EJ-200 plastic scintillation detector. This model is evaluated under the scenario applied to pedestrian radiation portal monitors to judge how well it works in practice. The simulated and measured gamma of 241Am, 133Ba, 137Cs, 60Co, 152Eu, 131I radioactive sources and background, are used to generate the training dataset. Measurement data with varying source-to-detector distances, shielding thicknesses, and incidence angles are also taken into account. The experimental results show that the mean value of the accuracy can be achieved at about 98.8% and 94.9% for single and multi-isotope identification, respectively. In addition, the model can well precisely recognize radionuclides in the gamma spectrum whose gain shift is up to 10%. The dependence of the True Positive rate on the count quality factors of individual radionuclides, which was defined as the ratio between the net count rate and its associated uncertainty, is examined. The detection sensitivities, defined as the minimum count quality factor to obtain a True Positive rate of 95%, for 241Am, 133Ba, 137Cs, 60Co, 152Eu, 131I are 8.90, 11.86, 8.96, 8.21, 12.54, and 11.89, respectively. With such encouraging results, the proposed model should be a useful technique for radionuclide recognition. |
| format |
Journal article |
| author |
Cao Van Hiep, Dinh Tien Hung, Nguyen Ngoc Anh, Nguyen Ninh Giang, Pham Dinh Khang, Nguyen Xuan Hai, Kien-Cuong NGUYEN, Trinh Van Ninh Phan, Văn Chuân |
| author_facet |
Cao Van Hiep, Dinh Tien Hung, Nguyen Ngoc Anh, Nguyen Ninh Giang, Pham Dinh Khang, Nguyen Xuan Hai, Kien-Cuong NGUYEN, Trinh Van Ninh Phan, Văn Chuân |
| author_sort |
Cao Van Hiep, Dinh Tien Hung, Nguyen Ngoc Anh, Nguyen Ninh Giang, Pham Dinh Khang, Nguyen Xuan Hai, Kien-Cuong NGUYEN, Trinh Van Ninh |
| title |
Nuclide Identification Algorithm for the Large-Size Plastic Detectors Based on Artificial Neural Network |
| title_short |
Nuclide Identification Algorithm for the Large-Size Plastic Detectors Based on Artificial Neural Network |
| title_full |
Nuclide Identification Algorithm for the Large-Size Plastic Detectors Based on Artificial Neural Network |
| title_fullStr |
Nuclide Identification Algorithm for the Large-Size Plastic Detectors Based on Artificial Neural Network |
| title_full_unstemmed |
Nuclide Identification Algorithm for the Large-Size Plastic Detectors Based on Artificial Neural Network |
| title_sort |
nuclide identification algorithm for the large-size plastic detectors based on artificial neural network |
| publishDate |
2023 |
| url |
https://scholar.dlu.edu.vn/handle/123456789/2067 https://ieeexplore.ieee.org/abstract/document/9772480 |
| _version_ |
1785973032092696576 |