| 基于深度学习的高温环境下QCM湿度传感器温度补偿模型 |
| A temperature compensation model for QCM humidity sensor in high temperature environment based on deep learning |
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| 中文摘要: |
| 为减小高温低湿环境下温度漂移对传感器测量结果的影响，以基频4 MHz的石英晶体为基片，使用滴注法将氧化石墨烯（Graphene Oxide，GO）沉积在基片上，研制出一种基于石英晶体微天平（Quartz Crystal Microbalance，QCM）的湿度传感器。AT切石英晶片以及氧化石墨烯材料在高温环境中的温度漂移现象显著，导致传感器的频率输出发生漂移，因此使用深度学习方法对温度漂移进行修正，在不同的绝对湿度条件下，测试了反向传播（Back Propagation，BP）神经网络修正模型对QCM湿度传感器的适应特性。实验结果表明，通过深度学习方法得到的修正模型能够有效提高QCM湿度传感器的灵敏度、稳定性以及响应速度，对于研究温湿度耦合条件下的QCM湿度传感器的频率修正技术具有重要意义。 |
| 英文摘要: |
| To reduce the impact of temperature drift on sensor measurement results in high temperature and low humidity environments, a humidity sensor based on quartz crystal microbalance (QCM) was developed by using a quartz crystal with a fundamental frequency of 4 MHz as a substrate and depositing graphene oxide (GO) on the substrate using a drop?on?demand method. The temperature drift phenomenon of AT?cut quartz crystal wafers and graphene oxide materials in high temperature environments is significant, resulting in frequency output drift of the sensor. Therefore, a deep?learning method was used to correct the temperature drift. The adaptability of the back propagation (BP) neural network correction model to the QCM humidity sensor was tested under different absolute humidity conditions. The experimental results show that the correction model obtained through deep learning can effectively improve the sensitivity, stability, and response speed of the QCM humidity sensor. It is of great significance for studying the frequency correction technology of QCM humidity sensors under temperature and humidity coupling conditions. |
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| 中文关键词: 石英晶体微天平 深度学习 频率修正 湿度检测 |
| 英文关键词:quartz crystal microbalance (QCM) deep learning frequency correction humidity detection |
| 基金项目: |
| DOI：10.11823/j.issn.1674-5795.2023.05.04 |
| 引用本文：冯俊一, 崔健敏, 温连鹏, 王国华, 聂晶.基于深度学习的高温环境下QCM湿度传感器温度补偿模型[J].计测技术,2023,(5):. |
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