核安全的运行:一个远程α/β检测系统,集成CNN和射电发光,用于应急响应
Zeqian Wu1, Mei Xu1, Qingbo Wang2
1Institute of NBC Defence, Changping District, Beijing 102205, China.
Journal of hazardous materials
|March 12, 2026
概括
本研究引入了一种使用卷积神经网络 (CNN) 的新远程方法,用于在核紧急情况下区分α/β放射性材料,提高安全性和检测性.
科学领域:
- 核物理 核物理 核物理
- 辐射检测 辐射检测 辐射检测
- 人工智能的人工智能
背景情况:
- 在核突发事件期间,区分α/β放射性材料对于公共卫生和环境安全至关重要.
- 传统的检测方法面临信号干扰和短检测范围等局限性,阻碍了快速准确的识别.
研究的目的:
- 开发一种使用卷积神经网络 (CNN) 的创新远程α/β歧视技术.
- 在核事故场景中提高放射性物质检测的效率和可靠性.
主要方法:
- 开发了一个多层CNN模型,接受2Dα/β图像地图作为输入.
- 粒子运输模拟产生了10万个样本用于模型训练和测试.
- 该模型在8万个数据集上进行了训练,并在2万个数据集上进行了测试.
主要成果:
- 在区分α/β放射性物质方面,CNN模型实现了99.1%的准确率.
- 位置坐标预测显示,72.4%的样本的相对误差小于10%.
- 实验验证证证实了该模型的有效性和可靠性.
结论:
- 拟议的基于CNN的遥感技术为放射性物质安全监测提供了重大进展.
- 这项技术提高了检测效率,并有可能减轻与核事故相关的危险.
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