Research on classification of bacteria based on kinetics model for gas sensor

YUAN Wei; SHI Rui

doi:10.13873/j.1000-9787(2017)11-0018-03

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Research on classification of bacteria based on kinetics model for gas sensor

更新时间：2025-09-11

- Research on classification of bacteria based on kinetics model for gas sensor
- Vol. 36, Issue 11, Pages: 18-20+24(2017)
- 作者机构：
  
  重庆大学计算机学院
- 作者简介：
- 基金信息：
- DOI：10.13873/j.1000-9787(2017)11-0018-03
  CLC： R318;TP212
- Published：2017
- 稿件说明：
移动端阅览
[1]袁伟,石锐.基于气体传感器动力学模型的细菌分类研究[J].传感器与微系统,2017,36(11):18-20+24.

YUAN Wei, SHI Rui. Research on classification of bacteria based on kinetics model for gas sensor[J]. 2017, 36(11): 18-20+24.
[1]袁伟,石锐.基于气体传感器动力学模型的细菌分类研究[J].传感器与微系统,2017,36(11):18-20+24. DOI： 10.13873/j.1000-9787(2017)11-0018-03.

YUAN Wei, SHI Rui. Research on classification of bacteria based on kinetics model for gas sensor[J]. 2017, 36(11): 18-20+24. DOI： 10.13873/j.1000-9787(2017)11-0018-03.

摘要

对于气体传感器获得的样本数据

常规的处理方法是基于样本的表象来提取特征进行分类

具有固有的局限性。基于热电子发射理论和等温吸附理论

对气体传感器的电压响应值与温度、样本浓度等参数建立数学方程

将方程简化

构造了简易的响应动力学模型。通过主成分分析

降低样本维数。将模型向降维后的样本数据拟合

可得到模型的一组系数

作为样本的特征值。将特征值集合运用模式识别方法进行训练

测试分类性能。实验结果显示:分类预测的准确率较高。

Abstract

For sample data acquired by gas sensor

ordinary way is to extract features based on surface pattern of samples

so as to classify

so it has inherent limitation. Based on thermionic emission theory and adsorption theory

a mathematic equation can be built to demonstrate the connection of voltage response values of gas sensor with temperature

sample concentration

etc. A simple model of gas sensor’s response kinetics can be derived by simplify the equation before. Through the method of principal component analysis（ PCA）

the dimensions of samples is reduced. A group of parameters

which will be treated as features of samples eventually

can be settled when fitting the model to the samples. A prediction model will be trained and benchmarked by using the feature sets. The result shows that the kinetics model has a satisfied prediction accuracy in classification

this is for the reason that the model is set up based on reflection of nature of sensor response.

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