一维卷积神经网络基于液相介质中介电介质光谱丰度的分析
Jiangyang Yu1,2, Zhenyu Jia1,2, Xuan Han1,2
1College of Water Conservancy and Construction Engineering, Northwest Agricultural and Forestry University, Xianyang, 712100, Yangling, People's Republic of China.
Scientific reports
|March 3, 2025
概括
这项研究使用一维卷积神经网络来分析混合介电光谱,准确预测水和乙醇等混合物的成分比率. 这种机器学习方法增强了用于水化应用的介电光谱分析.
科学领域:
- 材料科学 材料科学 材料科学
- 分析化学 分析化学
- 机器学习 机器学习
背景情况:
- 分析混合介电光谱是一项挑战.
- 介电光谱对于材料的表征至关重要.
研究的目的:
- 开发一种用于分析混合介电谱中的组件信息的方法.
- 应用机器学习来准确预测混合物成分.
主要方法:
- 对水,乙醇和异氧乙醇混合物的介电光谱进行实验测量.
- 单一频谱分析 (SSA) 用于数据拒绝.
- 使用线性混合模型训练一维卷积神经网络 (1D-CNN).
主要成果:
- 高精度 (R2值为0.999和0.9887) 在预测二元和三元混合物的成分比率.
- 验证了 1D-CNN 不混合算法的可靠性.
- 使用SSA对介电光谱进行有效的数据处理.
结论:
- 1D-CNN模型提供了一种有效的方法来分析和预测混合介质的介电性质.
- 这种介电谱学和机器学习的整合为水环境治理和水化分析提供了新的工具.
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