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嵌入式基线补偿模型与无线传感器网络的设计和验证,用于监测总挥发性有机化合物
Wangze Ni1,2, Tao Wang3, Yu Wu4
1National Key Laboratory of Advanced Micro and Nano Manufacture Technology, Shanghai Jiao Tong University, Shanghai 200240, China.
ACS sensors
|October 31, 2025
概括
一个新的轻量级人工神经网络模型纠正金属氧化物半导体气体传感器的基线漂移,以准确实时监测总挥发性有机化合物 (TVOC) 的空气质量. 这提高了传感器的一致性,并使实际的室内空气质量评估成为可能.
科学领域:
- 环境科学 环境科学
- 传感器技术 传感器技术
- 人工智能的人工智能
背景情况:
- 金属氧化物半导体气体传感器对于实时空气质量监测至关重要.
- 挑战包括传感器不一致性,基线漂移和复杂的算法,限制广泛应用.
- 准确监测总挥发性有机化合物 (TVOC) 对室内环境至关重要.
研究的目的:
- 开发和实施基于轻量级人工神经网络 (ANN) 的模型,用于TVOC传感中的基线补偿.
- 将该模型集成到最小化的空气质量监测系统中,以便实时应用.
- 在便携式气体传感器系统中解决传感器不一致性和基线漂移问题.
主要方法:
- 一个轻量级的ANN模型被设计用于基线漂移校正.
- 该模型在多个VOC数据集上进行了训练和验证,包括使用1D卷积神经网络进行评估.
- 补偿模型部署在微控制器上,在基于ZigBee的传感器网络中进行自主实时校正.
主要成果:
- 基于ANN的补偿方法在特定数据集的TVOC度预测中提高了31.4%的R平方得分.
- 在节点上部署自主纠正实验内基线漂移和跨传感器协调的初始基线.
- 传感器节点之间的最大初始基线变化在正常化后减少了四倍.
结论:
- 拟议的轻量级ANN模型有效地弥补了TVOC传感中的基线漂移.
- 集成系统可实现精确的,实时的室内空气质量评估,提高传感器网络性能.
- 这项工作有助于实施紧,高效的传感器网络,用于实际监测空气质量.
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