PDF(Pubmed)
Abstract：
In the electronic nose (E-nose) systems, gas type recognition and accurate concentration prediction are some of the most challenging issues. This study introduced an innovative pattern recognition method of time-frequency attention convolutional neural network (TFA-CNN). A time-frequency attention block was designed in the network, aiming to excavate and effectively integrate the temporal and frequency domain information in the E-nose signals to enhance the performance of gas classification and concentration prediction tasks. Additionally, a novel data augmentation strategy was developed, manipulating the feature channels and time dimensions to reduce the interference of sensor drift and redundant information, thereby enhancing the model\'s robustness and adaptability. Utilizing two types of metal-oxide-semiconductor gas sensors, this research conducted qualitative and quantitative analysis on five target gases. The evaluation results showed that the classification accuracy could reach 100%, and the coefficient of the determination (R2) score of the regression task was up to 0.99. The Pearson correlation coefficient (r) was 0.99, and the mean absolute error (MAE) was 1.54 ppm. The experimental test results were almost consistent with the system predictions, and the MAE was 1.39 ppm. This study provides a method of network learning that combines time-frequency domain information, exhibiting high performance in gas classification and concentration prediction within the E-nose system.
摘要：
在电子鼻(E-nose)系统中,气体类型识别和准确的浓度预测是一些最具挑战性的问题。本研究提出了一种基于时频注意力卷积神经网络(TFA-CNN)的模式识别方法。在网络中设计了时频注意块，目的在于挖掘并有效整合电子鼻信号中的时域和频域信息,提高瓦斯分类和浓度预测任务的性能。此外,开发了一种新的数据增强策略，操纵特征通道和时间维度，以减少传感器漂移和冗余信息的干扰，从而增强模型的鲁棒性和适应性。利用两种类型的金属氧化物半导体气体传感器，本研究对5种目标气体进行了定性和定量分析。评价结果表明,分类准确率可以达到100%,回归任务的判定系数(R2)最高可达0.99。Pearson相关系数(r)为0.99,平均绝对误差(MAE)为1.54ppm。实验测试结果与系统预测结果几乎一致，MAE为1.39ppm。本研究提供了一种结合时频域信息的网络学习方法,在电子鼻系统内的气体分类和浓度预测方面表现出高性能。
