开发一个神经网络,用于在互数字化的电极传感器基于电子鼻系统中检测目标气体
Kadir Kaya1, Mehmet Ali Ebeoğlu1
1Department of Electrical-Electronics Engineering, Dumlupınar University, Kutahya 43100, Turkey.
Sensors (Basel, Switzerland)
|August 29, 2024
概括
一个神经网络有效地检测气体,如NO2,乙,乙醇和,使用电子鼻子 (e-nose) 系统与互数字电极传感器. 该系统显示了提高传感器灵敏度的气体检测能力.
科学领域:
- * 材料科学与工程 * 材料科学与工程
- * 电气工程 电气工程
- * 化学传感技术的使用
背景情况:
- *电子鼻子 (e-nose) 系统对于气体检测至关重要.
- *数字间电极 (IDE) 传感器为气体传感提供了一个平台.
- *生物阻抗光谱 (BIS) 提供了一种测量传感器响应的方法.
研究的目的:
- * 开发一个神经网络模型来检测特定的气体.
- * 使用基于IDE传感器的电子鼻子系统与BIS接口.
- * 评估系统在气体分类中的性能.
主要方法:
- * 构建了一个使用IDE传感器和BIS接口的e-nose系统.
- * 传感器响应使用侧面电压进行测量,并以数字处理.
- * 卡尔曼过和主要组件分析 (PCA) 用于数据预处理,然后训练一个多层神经网络与反向传播和Xavier初始化.
主要成果:
- * 电子鼻子系统表现出对 (24.86 Ω变化) 和NO2 (0.7825 Ω/ppm) 的敏感性.
- *神经网络在分类NO2,,乙醇和的测试准确率达到了87.16%.
- *模型在239.54毫秒内被快速训练,这表明处理效率高.
结论:
- *开发的神经网络与IDE传感器e-nose集成,对气体检测非常有效.
- *数据预处理技术,如卡尔曼过和PCA增强检测能力.
- * 增加的传感器灵敏度与改善的神经网络检测性能有积极的相关性.
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