电子鼻
鉴定(生物学)
维数之咒
线性判别分析
计算机科学
过程(计算)
模式识别(心理学)
人工智能
灵敏度(控制系统)
算法
工程类
电子工程
植物
生物
操作系统
作者
Jianxin Yin,Yongli Zhao,Zhi Peng,Fushuai Ba,Peng Peng,Xiaolong Liu,Rong Qian,Youmin Guo,Yafei Zhang
出处
期刊:Sensors
[Multidisciplinary Digital Publishing Institute]
日期:2023-03-09
卷期号:23 (6): 2975-2975
被引量:9
摘要
The inherent cross-sensitivity of semiconductor gas sensors makes them extremely challenging to accurately detect mixed gases. In order to solve this problem, this paper designed an electronic nose (E-nose) with seven gas sensors and proposed a rapid method for identifying CH4, CO, and their mixtures. Most reported methods for E-nose were based on analyzing the entire response process and employing complex algorithms, such as neural network, which result in long time-consuming processes for gas detection and identification. To overcome these shortcomings, this paper firstly proposes a way to shorten the gas detection time by analyzing only the start stage of the E-nose response instead of the entire response process. Subsequently, two polynomial fitting methods for extracting gas features are designed according to the characteristics of the E-nose response curves. Finally, in order to shorten the time consumption of calculation and reduce the complexity of the identification model, linear discriminant analysis (LDA) is introduced to reduce the dimensionality of the extracted feature datasets, and an XGBoost-based gas identification model is trained using the LDA optimized feature datasets. The experimental results show that the proposed method can shorten the gas detection time, obtain sufficient gas features, and achieve nearly 100% identification accuracy for CH4, CO, and their mixed gases.
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