E-Nose: Time–Frequency Attention Convolutional Neural Network for Gas Classification and Concentration Prediction

卷积神经网络 电子鼻 人工智能 人工神经网络 模式识别(心理学) 计算机科学 环境科学 机器学习 语音识别
作者
Minglv Jiang,Na Li,Mingyong Li,Zhou Wang,Yuan Tian,Kaiyan Peng,Haoran Sheng,Haoyu Li,Qiang Li
出处
期刊:Sensors [Multidisciplinary Digital Publishing Institute]
卷期号:24 (13): 4126-4126 被引量:1
标识
DOI:10.3390/s24134126
摘要

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.
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