一种新颖的气味检测识别系统数据预处理方法
a novel data pre-processing method for odour detection and identication system
一种新颖的气味检测识别系统数据预处理方法
wentian zhanga, taoping liua, lin yea, maiken uelandb, shari l. forbesb, steven w. sua,
afaculty of engineering and information technology, university of technology sydney, sydney, nsw 2007, australia.
bschool of mathematical and physical sciences, university of technology sydney, sydney, nsw 2007, australia
abstract
this paper presents a novel electronic nose (e-nose) data pre-processing method, based on a recently developed non-parametric kernel-based modelling (kbm) approach. the proposed method is tested by an automated odour detection and classication system, named nos.e, developed by the nos.e team in university of technology sydney. experimental results show that when extracting the derivative-related features from signals collected by the nos.e, the proposed non-parametric kbm odour data pre-processing method achieves more reliable and stable pre-processing results comparing with other pre-processing methods such as wavelet package correlation lter (wpcf), mean lter (mf), polynomial curve tting (pcf) and locally weighted regression (lwr). based on these derivative-related features, the nos.e can achieve a 96.23% accuracy of classication with the popular support vector machine (svm) classier.
本文提出了一种新的基于非参数核模型(kbm)的电子鼻数据预处理方法。该方法由悉尼理工大学团队开发的名为“nos.e”的自动气味检测和分类系统进行测试。实验结果表明，与小波包相关滤波器(wpcf)、均值滤波器(mf)、小波包相关滤波器(po)等预处理方法相比，该非参数kbm气味数据预处理方法在提取信号的导数相关特征时，获得了更加可靠、稳定的预处理结果。林氏曲线拟合(pcf)和局部加权回归(lwr)。基于这些与导数相关的特征，利用目前流行的支持向量机(svm)分类工具，能使分类精度达到96.23%。
keywords: electronic nose (e-nose), instrumentation, data pre-processing, non-parametric kernel-based modelling method
关键词：电子鼻，仪表，数据预处理，非参数核模型(kbm)
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一种新颖的气味检测识别系统数据预处理方法
wentian zhanga, taoping liua, lin yea, maiken uelandb, shari l. forbesb, steven w. sua,
afaculty of engineering and information technology, university of technology sydney, sydney, nsw 2007, australia.
bschool of mathematical and physical sciences, university of technology sydney, sydney, nsw 2007, australia
abstract
this paper presents a novel electronic nose (e-nose) data pre-processing method, based on a recently developed non-parametric kernel-based modelling (kbm) approach. the proposed method is tested by an automated odour detection and classication system, named nos.e, developed by the nos.e team in university of technology sydney. experimental results show that when extracting the derivative-related features from signals collected by the nos.e, the proposed non-parametric kbm odour data pre-processing method achieves more reliable and stable pre-processing results comparing with other pre-processing methods such as wavelet package correlation lter (wpcf), mean lter (mf), polynomial curve tting (pcf) and locally weighted regression (lwr). based on these derivative-related features, the nos.e can achieve a 96.23% accuracy of classication with the popular support vector machine (svm) classier.
本文提出了一种新的基于非参数核模型(kbm)的电子鼻数据预处理方法。该方法由悉尼理工大学团队开发的名为“nos.e”的自动气味检测和分类系统进行测试。实验结果表明，与小波包相关滤波器(wpcf)、均值滤波器(mf)、小波包相关滤波器(po)等预处理方法相比，该非参数kbm气味数据预处理方法在提取信号的导数相关特征时，获得了更加可靠、稳定的预处理结果。林氏曲线拟合(pcf)和局部加权回归(lwr)。基于这些与导数相关的特征，利用目前流行的支持向量机(svm)分类工具，能使分类精度达到96.23%。
keywords: electronic nose (e-nose), instrumentation, data pre-processing, non-parametric kernel-based modelling method
关键词：电子鼻，仪表，数据预处理，非参数核模型(kbm)
D671X气动橡胶密封对夹蝶阀之产品特点及执行器特性
相容性对蠕动泵软管重要吗？
宠物除臭剂该如何选择?
分析式铁谱仪的操作过程
千叶豆腐消泡机都有哪些作用呢？
一种新颖的气味检测识别系统数据预处理方法
水利机械厂家的技术资料详解
DZF-6210真空干燥箱注意事项
环境推荐：el检测仪-一款易于安装的太阳能光伏检测设备
矿用风门控制用电控装置主要组成及各部件的作业参数
上海兰仪超声波细胞粉碎机采用*技术制造
气缸常见问题的原因建议收藏
新型吸料机用途
冻干机冻干物料时的塌陷温度
精密机械加工过程精准度变大是为什么呢？
激光测厚仪进行定期的自动校准很有必要
二级生化污水处理设备工艺原理
小型钻攻机的使用价值在哪里？
NVI-1305R料位开关KOBOLD德国科宝技术参数
瓦克VINNAPAS®威耐实® H系列可再分散乳胶粉助力提升防水砂浆综合性能