使用人工神经网络和群优化检测马辐射光谱中的异常
1Department of Radiation Engineering, National Center for Radiation Research and Technology (NCRRT), Egyptian Atomic Energy Authority, Cairo, Egypt.
Journal of environmental radioactivity
|August 26, 2025
概括
这项研究引入了一种新的机器学习方法来检测环境监测中的放射性异常. 它改善了对弱辐射信号的检测, 这对于辐射保护和核安全至关重要.
科学领域:
- 核物理
- 环境科学
- 计算机科学
背景情况:
- 准确检测异常放射源对于辐射保护和核安全至关重要.
- 从自然背景噪声中分辨出弱异常辐射信号,特别是在源与背景的低比率下,是一个重大挑战.
研究的目的:
- 开发一种新的机器学习方法来检测马射线光谱中的异常.
- 提高辐射监测系统的灵敏度和准确性,特别是对于环境和安全应用.
主要方法:
- 一种机器学习方法,将神经网络建模与生物灵感优化 (群优化) 结合起来.
- 辐射光谱被划分为互补的子光谱,神经网络建立背景相关性.
- 通过测量值和神经网络预测之间的显著偏差来识别异常;殖民地优化选择最佳的光谱分区.
主要成果:
- 与现有的基准技术相比,拟议的方法显示出更高的性能.
- 该系统在低源与背景比率下检测异常具有特别优势.
- 来自分布式辐射探测器的实证数据,包括137Cs和57Co源,验证了该方法的有效性.
结论:
- 这种新技术通过提高对弱异常信号的敏感度来提高辐射监测.
- 通过使用标准的辐射探测器网络,
- 提高辐射异常的早期检测对于环境监测和安全至关重要.
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