文献类型：期刊文献

英文题名：A Novel Type of Feature Extraction Algorithm Based on Multi-Directional Analysis for Visual Optical Electronic Nose

作者：Zhang, Wen-Li[1,2];Liu, Zhao-Yu[1,2];Wang, Heng[3];Liang, Kun[1];Zhao, Zhen-Zhen[4];Wang, Yi[1];Liu, Jian-Qiang[1]

第一作者：Zhang, Wen-Li

通讯作者：Zhang, WL[1];Zhang, WL[2]

机构：[1]Zhengzhou Univ Aeronaut, Coll Intelligent Engn, Zhengzhou 450046, Henan, Peoples R China;[2]Univ Elect Sci & Technol China, Sch Informat & Commun Engn, Sihuan 611731, Peoples R China;[3]Henan Agr Univ, Coll Mech & Elect Engn, Zhengzhou 450002, Henan, Peoples R China;[4]Henan Univ Econ & Law, Coll Comp & Informat Engn, Zhengzhou 450047, Henan, Peoples R China

第一机构：Zhengzhou Univ Aeronaut, Coll Intelligent Engn, Zhengzhou 450046, Henan, Peoples R China

通讯机构：[1]corresponding author), Zhengzhou Univ Aeronaut, Coll Intelligent Engn, Zhengzhou 450046, Henan, Peoples R China;[2]corresponding author), Univ Elect Sci & Technol China, Sch Informat & Commun Engn, Sihuan 611731, Peoples R China.

年份：2021

卷号：16

期号：3

起止页码：374-379

外文期刊名：JOURNAL OF NANOELECTRONICS AND OPTOELECTRONICS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000677665100002)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 61801435, in part by the Henan Provincial Department of Science and Tech-nology Research Project under Grant 202102210315, Grant 192102210109, Grant 212102210517, Grant 212102210559.

语种：英文

外文关键词：Visual Optical E-Nose; Feature Extraction; Directional Filter Bank; Pattern Recognition

摘要：According to the fact that the response images of a visual optical electronic nose (E-nose) have a huge amount of data, various frequency components and complex periodic and directional information, a novel type of visual optical E-nose (VOE-nose) feature extraction algorithm based on multi-directional analysis by directional filter bank (DFB) was proposed in this paper. Firstly, the gas sensing model of the VOE-nose was introduced, and the basic principle of DFB algorithm for feature extraction was described. Second, response images of NO2 in different wavebands were collected by the VOE-nose platform. Third, typical feature extraction and DFB feature extraction algorithms were used to extract features of response data, then the feature dimension reduction and pattern recognition algorithms were used to analyze the features. The mean classification accuracy is more than 95%, which verifies the superiority of the DFB feature extraction algorithm.