MTL_TX:一个多任务变压器模型,用于改进辐射时间序列估计
Hongfang Zhang1, Adam Stavola1,2, Hal Ferguson1
1Department of Electrical & Computer Engineering, Old Dominion University, Norfolk, VA 23529, USA.
Sensors (Basel, Switzerland)
|March 14, 2026
概括
一个新的多任务变压器 (MTL_TX) 模型准确地估计了加速器设施的辐射剂量. 这种先进的AI通过使用历史数据预测辐射水平来提高人员和公众的安全性.
科学领域:
- 核工程 核工程是指核工程.
- 人工智能的人工智能
- 辐射安全 辐射安全
背景情况:
- 控制辐射剂量对于托马斯·杰斐逊国家加速器设施 (JLab) 等设施的安全至关重要.
- 在运行过程中,JLab利用多个传感器监测光束电流,能量,泄漏和辐射.
研究的目的:
- 为加速器设施开发一个准确的辐射剂量估计模型.
- 通过预测辐射水平来提高人员和公众的安全.
主要方法:
- 开发了一种多任务变压器 (MTL_TX) 模型,该模型包含分层特征嵌入 (HFE) 和多层分解注意力 (MDA).
- 利用多任务学习 (MTL) 框架,利用多个传感器之间的相关性进行个人估计.
主要成果:
- 根据2018年的数据,MTL_TX实现了0.1464的MSE,0.2353的RMSE和0.8584的R2.
- 在2016-2019年未见的数据上显示出强烈的泛化,MSE为0.1407,RMSE为0.2263和R2为0.8831.
结论:
- 与最先进的方法相比,MTL_TX模型显著提高了辐射剂量估计的准确性.
- 开发的模型为加速器设施和周围地区提供了增强的安全解决方案.
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