Target discrimination, concentration prediction, and status judgment of electronic nose system based on large-scale measurement and multi-task deep learning

电子鼻 计算机科学 人工智能 卷积神经网络 一般化 任务(项目管理) 模式识别(心理学) 人工神经网络 深度学习 机器学习 比例(比率) 试验数据 特征(语言学) 块(置换群论) 工程类 数学分析 哲学 物理 量子力学 语言学 程序设计语言 系统工程 数学 几何学
作者
Tao Wang,Hexin Zhang,Yu Wu,Wenkai Jiang,Xinwei Chen,Min Zeng,Jianhua Yang,Yanjie Su,Nantao Hu,Zhi Yang
出处
期刊:Sensors and Actuators B-chemical [Elsevier]
卷期号:351: 130915-130915 被引量:60
标识
DOI:10.1016/j.snb.2021.130915
摘要

Pattern recognition is the core component of the electronic nose (E-nose). Traditional machine learning algorithms highly rely on the feature data selected manually for model training and testing. A complete experiment must be performed before the data can be further processed. To realize the automatic extraction of response features and simplify the model’s training and application process, a multi-task convolutional neural network (MTL-CNN) with a dual-block knowledge-sharing structure is designed to train a model for the E-nose system. This model can simultaneously perform three different classification tasks, for the purposes of target discrimination, concentration prediction, and state judgment. Only a few consecutive seconds of response data are needed to be input into the trained model to obtain various information about the E-nose. With the utilization of an unmanned gas-sensing test system, large-scale measurements of the E-nose can be carried out automatically. A baseline tracking algorithm (BTA) is designed based on the relative changes of short-term data, reducing the impact of long-term shifts. Over thousands of gas response processes and more than 10 million sensing data have participated in the training of the deep learning model. The 5-fold cross-validation method shows that the fully trained model has an outstanding generalization performance. After the baseline is tracked automatically, the accuracy of three tasks towards 12 kinds of volatile organic compounds (VOCs) is about 95% (type recognition: 95.2%, concentration prediction: 92.1%, status judgment: 97.3%) using only 4 s of sensing data during the response status of the E-nose. Our work shows the distinct advantages of combining “big data” and “deep learning” in the gas-sensing field and further proves that the employment of MTL-CNN can significantly improve the training and application efficiency of the E-nose.
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