在湿度下使用神经网络辅助模式识别分析对CO和NO2混合物的分类和度估计
Jin-Young Kim1, Somalapura Prakasha Bharath2, Ali Mirzaei3
1Department of Materials Science and Engineering, Inha University, Incheon 22212, Republic of Korea.
Journal of hazardous materials
|July 28, 2023
概括
这项研究使用机器学习来改进金属氧化物气体传感器,即使在湿度下,在识别二氧化 (NO2) 和一氧化碳 (CO) 中也能达到100%的准确性. 这种方法绕过了漫长的数据处理,以获得更快的结果.
科学领域:
- 材料科学 材料科学 材料科学
- 化学传感器 化学传感器
- 人工智能的人工智能
背景情况:
- 金属氧化物传感器面临交叉敏感性问题,限制了它们的特异性.
- 准确的气体检测对于环境监测和工业安全至关重要.
- 机器学习为复杂的传感器数据分析提供了潜在的解决方案.
研究的目的:
- 利用一系列的金属氧化物传感器,开发出一套强大的气体传感系统.
- 使用机器学习技术来克服传感器交叉敏感性.
- 在识别特定气体及其混合物方面实现高精度.
主要方法:
- 使用In2O3,Au-ZnO,Au-SnO2和Pt-SnO2.2制造一个传感器阵列.
- 传感器同时暴露于不同度的二氧化 (NO2) 和一氧化碳 (CO).
- 主要组件分析 (PCA) 和深度神经网络 (DNN) 的应用用于数据分析.
- 使用卷积神经网络 (CNN) 通过将传感器数据视为图像来处理.
主要成果:
- 在交叉验证和测试期间,在气体分类中实现了100%的准确性.
- 在NO2,CO和它们的混合物之间进行了有效的歧视.
- 在不同的湿度水平 (40%和90%RH) 中验证了性能.
结论:
- 拟议的机器学习方法有效地解决了金属氧化物传感器的交叉敏感性.
- 深度神经网络和CNN提供准确可靠的气体识别.
- 这种方法消除了需要耗时的手动特征提取的需求,使得分析速度更快.
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