一个集成的CBLA-Net与分数离散波段变换和基于频率的CARS以XRF预测重金属元素
Wanqi Yang1, Fusheng Li1, Qinglun Zhang1
1School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, 611731, PR China; Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, 313001, PR China.
Analytica chimica acta
|August 25, 2024
概括
这项研究引入了一个新的深度学习框架,用于使用X射线光 (XRF) 光谱数据在土壤中准确估计重金属. 该方法提高了信号质量,并为精确的元素度预测选择了关键特征.
科学领域:
- 环境科学 环境科学
- 分析化学 分析化学
- 数据科学数据科学数据科学
背景情况:
- X射线光 (XRF) 是元素分析的一个有前途的技术.
- 射频频谱数据通常包含噪声和环境信息,使准确的量化变得复杂.
- 现有的模型难以预测元素的低度.
研究的目的:
- 开发一个全面的框架,以快速,非破坏性,准确地预测土壤中的元素度.
- 使用XRF数据提高重金属估计的准确性.
- 为了应对量化光谱强度和元素度的挑战.
主要方法:
- 使用分数离散波量变换 (FDWT) 来提高信号质量,实现最佳的无声化.
- 通过基于频率的竞争性适应性重权取样 (FCARS) 进行特征选择,以提取有信息的光谱变量.
- 一个新的深度学习网络,ConvBiLSTM-Attention (CBLA-Net),用于精确的元素度估计.
主要成果:
- 该CBLA-Net实现了V,Cr,Mn,Zn,Cd和Pb的高精度,其中R2值高达0.9952.
- 与其他先进的算法相比,在元素度预测方面表现出卓越的性能.
- 有效地提取当地特征,捕获全球信息,并利用注意力机制进行集中分析.
结论:
- 拟议的深度学习框架显著提高了土壤元素含量预测的准确性.
- 这种新的方法可以更好地评估重金属度.
- 提供了使用XRF进行准确的土壤元素分析的新方法.
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