支持人工智能的便携式电子鼻子回归预测气体混合物中的有害分子
Jilei Yang1, Xuefeng Hu1, Lihang Feng2,3
1Anhui Province Key Laboratory of Measuring Theory and Precision Instrument, School of Instrument Science and Optoelectronics Engineering, Hefei University of Technology, Hefei 230009, China.
ACS sensors
|June 5, 2024
概括
一个新的聚变网络模型显著提高了生物模拟电子鼻子用于预测气体混合物度的准确性. 这种先进的技术可以在复杂的环境中更好地检测到SO2,NO2和CO等污染物.
科学领域:
- 环境科学 环境科学
- 分析化学 分析化学
- 传感器技术 传感器技术
背景情况:
- 生物仿真电子鼻子 (e-nose) 技术为识别和监测复杂气体分子提供了新的能力.
- 由于混合物的复杂性,传统的回归算法在预测气体度时与e-noses的准确性作斗争.
研究的目的:
- 开发一种先进的聚变网络模型,用于使用便携式电子鼻子增强气体混合物度的回归预测.
- 克服现有模型在准确评估复杂气体组成方面的局限性.
主要方法:
- 引入一个融合网络模型,将基于变压器的多核特征融合 (TMKFF) 模块与1DCNN-LSTM网络集成.
- 使用便携式电子鼻子对拟议模型进行实验验证.
主要成果:
- 与单个CNN和LSTM模型相比,融合网络模型显示出明显优异的回归预测性能.
- 高确定系数 (R2) 值 (93-99%) 表明模型在解释度变化方面的强大能力.
- 低平方根平均误差 (RMSE) 和平均绝对误差 (MAE) 证实了该模型在预测SO2,NO2和CO度方面的准确性,特别是对于低度的SO2.
结论:
- 开发的融合网络模型有效地提高了便携式电子鼻子中气体混合物度预测的准确性.
- 该模型显示了大气污染监测和复杂环境中的分子检测中的实际应用的巨大潜力.
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