| アイテムタイプ |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2025-04-18 |
| タイトル |
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タイトル |
RAPTOR-AI: An open-source AI powered radiation protection toolkit for radioisotopes |
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言語 |
en |
| 言語 |
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言語 |
eng |
| 資源タイプ |
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資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
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資源タイプ |
journal article |
| 著者 |
Watabe Hiroshi
Peter K.N. Yu
Dragana Krstic
Dragoslav Nikezic
Kyeong Min Kim
Yamaya Taiga
Kawachi Naoki
Hiroki Tanaka
Zoran Jovanovic
A.K.F. Haque
M. Rafiqul Islam
Gary Tse
Quinncy Lee
Mehrdad Shahmohammadi Beni
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| 抄録 |
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内容記述タイプ |
Abstract |
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内容記述 |
Artificial intelligence (AI) has gained significant attention in various scientific fields due to its ability to process large datasets. In nuclear radiation physics, while AI presents exciting opportunities, it cannot replace physics-based models essential for explaining radiation interactions with matter. To combine the strengths of both, we have developed and open-sourced the Radiation Protection Toolkit for Radioisotopes with Artificial Intelligence (RAPTOR-AI). This toolkit integrates AI with the Particle and Heavy Ion Transport code System (PHITS) Monte Carlo package, enabling rapid radiation protection analysis for radioisotopes and structural shielding. RAPTOR-AI is particularly valuable for emergency scenarios, allowing quick dose dispersion assessments when a facility’s structural map is available, enhancing safety and response efficiency. |
| 書誌情報 |
Applied Radiation and Isotopes
巻 221,
p. 111797,
発行日 2025-07
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| DOI |
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識別子タイプ |
DOI |
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関連識別子 |
10.1016/j.apradiso.2025.111797 |
