使用基于学习的模型,对beta发射源的快速核素识别的可行性研究.
1Nuclear Engineering, Ulsan National Institute of Science and Technology, Ulsan, 44919, Republic of Korea.
概括
这项研究展示了一种快速,人工智能驱动的方法,用于使用β频谱数据识别β发射放射性核酸. 这种方法通过在紧急情况和核设施中快速识别核来提高辐射安全.
科学领域:
- 核物理 核物理 核物理
- 放射学科学 放射学科学
- 人工智能的人工智能
背景情况:
- 快速识别贝塔放射性放射性核素对于在紧急情况下和核设施中的辐射安全至关重要.
- 目前用于β核素识别的方法通常很慢,由于连续的β光谱,需要化学预处理或复杂的设备.
- 需要快速识别环境监测 (例如,放射性) 和立即响应空气污染警报.
研究的目的:
- 通过机器学习模型和在空气中测量的β频谱数据,研究快速识别β发射放射性核素的可行性.
- 开发和验证基于人工智能的方法,以快速识别β核素.
- 为了提高贝塔核素识别的速度和效率,以改善辐射保护策略.
主要方法:
- 使用β盘源和合适的检测系统建立了一个实验系统.
- 采用了两种基于学习的模型,支持矢量机 (SVM) 和时间序列分类与变压器 (TSCT).
- 这些模型被训练分类15个核化合物组合,从60Co,90Sr/90Y,137Cs和152Eu.
主要成果:
- SVM和TSCT模型实现了高分类准确度,分别为100%和98.0%.
- 在受过训练的两个模型可以在受控的实验室条件下在几秒钟内识别核化合物组合.
- 拟议的基于人工智能的方法显著提高了β核素识别的速度.
结论:
- 这项研究证实了使用基于学习的模型来从空气测量的β光谱数据中快速识别β核素的可行性.
- 与传统方法相比,开发的AI方法在核素识别速度上有了显著的改进.
- 建议在可变的场条件下进行进一步验证,以将这种方法应用于现实世界的辐射保护场景.
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