基于变压器的深度学习架构,用于多变量放射性源的术语反转
Yangfan Zhao1, Deyi Chen2, Yuxuan Wang2
1Institute of Nuclear Fuel Cycle and Materials, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China; CTBT Beijing National Data Centre and Beijing Radionuclide Laboratory, Beijing, 100085, China.
Journal of environmental radioactivity
|October 13, 2025
概括
这项研究引入了一个深度学习模型,用于准确的放射性源期估计,这对于应对核紧急情况至关重要. 基于变压器的方法有效地确定释放率,高度和位置,提高安全评估.
科学领域:
- 核工程 核工程是指核工程.
- 环境科学 环境科学
- 人工智能的人工智能
背景情况:
- 放射性源期估计对于核应急响应和后果评估至关重要,特别是在福岛之后.
- 准确确定释放速度,高度和位置对于有效的核事故管理是必要的.
研究的目的:
- 开发和评估基于变压器的深度学习架构,用于多变量放射性源的术语估计.
- 评估模型在各种场景中的性能,包括时间变化的释放参数.
主要方法:
- 利用CALMET-LAPMOD合模型,通过Kincaid追踪器实验验证,生成全面的数据集.
- 开发了一个变压器深度学习模型,使用贝叶斯优化来进行自适应式超参数调整.
- 为五种场景构建数据集:单个参数 (速率,高度,位置) 和合变化.
主要成果:
- 在源热逆转中实现了高精度,R2>0.96用于释放速率和高度.
- 在95%的置信度水平下,平均位置预测误差为1.19公里.
- 在合情景中,R2对于释放速度和位置保持在>0.92,而高度的R2为0.72.
结论:
- 基于变压器的深度学习模型在放射性源的术语估计方面表现出色.
- 特性剥离分析强调了高度监测点对于优化网络布局的重要性.
- 这些发现为改善核应急准备和后果评估提供了宝贵的见解.
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