基于多变量的纸张相似性聚类分析程序与界面的设计
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摘要
基于PCA和K-means聚类的算法,采用LabVIEW编写了程序界面交互友好的操作软件系统,用于研究纸张在不同变量上的相似程度。程序设计主要赋予了简便操作,人机友好,数据可视化的特点。基于不同的多变量,采用PCA结合K-means聚类方法对纸张进行了相似程度的分析研究,结果表明:程序界面具有清晰简洁、交互友好、可操作性强的特点,可对纸张进行聚类分析,且可信度高。本程序可用于不同品质等级纸张在不同多变量上差异的识别和相似品质纸张的聚类,有效地进行纸张的各种差异化的分析。
Based on the PCA and K-means clustering algorithm, LabVIEW was used to write a program-friendly interactive operating software system for studying the similarity of paper with different variables. The program design mainly gives the characteristics of easy operation, human-machine friendly and data visualization. Based on different multivariates, PCA combined with K-means clustering method was used to analyze the similarity degree of paper. The results show that the program interface is clear and concise, interactive and operability, and can be used to clustering analyze paper and has high credibility. This program can be used for the recognition of differences in different multi-variable papers of different quality grades and the clustering of similar quality papers, effectively analyzing various kinds of papers.
Based on the PCA and K-means clustering algorithm, LabVIEW was used to write a program-friendly interactive operating software system for studying the similarity of paper with different variables. The program design mainly gives the characteristics of easy operation, human-machine friendly and data visualization. Based on different multivariates, PCA combined with K-means clustering method was used to analyze the similarity degree of paper. The results show that the program interface is clear and concise, interactive and operability, and can be used to clustering analyze paper and has high credibility. This program can be used for the recognition of differences in different multi-variable papers of different quality grades and the clustering of similar quality papers, effectively analyzing various kinds of papers.
引文
[1] 刘文,肖贵华,李政,等.特种纸技术发展现状[J].中国造纸,2018,37(7):59-64.DOI:CNKI:SUN:ZGZZ.0.2018-07-015
[2] S.希拉里,朱尧辰.多变量分析[J].国外科技新书评介,2011,(9):12-13.
[3] 段茵,陈恺煊,刘昕,等.基于BP神经网络的纸张缺陷检测与识别研究[J].西安理工大学学报,2018,34(2):235-239.DOI:10.19322/j.cnki.issn.1006-4710.2018.02.018
[4] 王龄松,马璞璠,叶凤英,等.造纸废水处理过程微小故障检测方法研究[J].中国造纸,2017,36(8):20-25.DOI:10.11980/j.issn.0254-508X.2017.08.004
[5] 宋留,杨冲,张辉,等.造纸废水处理过程的高斯过程回归软测量建模[J].中国环境科学,2018,38(7):2564-2571.DOI:CNKI:SUN:ZGHJ.0.2018-07-026
[6] 赵艳子,汤伟.基于机器学习方法的磨浆过程打浆度软测量及其实现[J].化工自动化及仪表,2011,38(9):1087-1089.DOI:10.3969/j.issn.1000-3932.2011.09.014
[7] 何智恒,柴欣生,陈春霞,等.生活用纸纤维原生状态识别程序的界面设计和算法改进[J].造纸科学与技术,2018,37(4):67-72+90.DOI:CNKI:SUN:GDZZ.0.2018-04-015
[8] 杨善林,李永森,胡笑旋,等.K-MEANS算法中的K值优化问题研究[J].系统工程理论与实践,2006(2):97-101.DOI:10.3321/j.issn:1000-6788.2006.02.013
[9] 张鹏.基于主成分分析的综合评价研究[D].南京理工大学,2004.
[10] 张建萍,刘希玉.基于聚类分析的K-means算法研究及应用[J].计算机应用研究,2007(5):166-168.DOI:10.3969/j.issn.1001-3695.2007.05.051
[11] 光俊叶,刘明霞,张道强.有效距离在聚类算法中的应用[J].计算机科学与探索,2017,11(3):406-413.DOI:10.3778/j.issn.1673-9418.1603046
[2] S.希拉里,朱尧辰.多变量分析[J].国外科技新书评介,2011,(9):12-13.
[3] 段茵,陈恺煊,刘昕,等.基于BP神经网络的纸张缺陷检测与识别研究[J].西安理工大学学报,2018,34(2):235-239.DOI:10.19322/j.cnki.issn.1006-4710.2018.02.018
[4] 王龄松,马璞璠,叶凤英,等.造纸废水处理过程微小故障检测方法研究[J].中国造纸,2017,36(8):20-25.DOI:10.11980/j.issn.0254-508X.2017.08.004
[5] 宋留,杨冲,张辉,等.造纸废水处理过程的高斯过程回归软测量建模[J].中国环境科学,2018,38(7):2564-2571.DOI:CNKI:SUN:ZGHJ.0.2018-07-026
[6] 赵艳子,汤伟.基于机器学习方法的磨浆过程打浆度软测量及其实现[J].化工自动化及仪表,2011,38(9):1087-1089.DOI:10.3969/j.issn.1000-3932.2011.09.014
[7] 何智恒,柴欣生,陈春霞,等.生活用纸纤维原生状态识别程序的界面设计和算法改进[J].造纸科学与技术,2018,37(4):67-72+90.DOI:CNKI:SUN:GDZZ.0.2018-04-015
[8] 杨善林,李永森,胡笑旋,等.K-MEANS算法中的K值优化问题研究[J].系统工程理论与实践,2006(2):97-101.DOI:10.3321/j.issn:1000-6788.2006.02.013
[9] 张鹏.基于主成分分析的综合评价研究[D].南京理工大学,2004.
[10] 张建萍,刘希玉.基于聚类分析的K-means算法研究及应用[J].计算机应用研究,2007(5):166-168.DOI:10.3969/j.issn.1001-3695.2007.05.051
[11] 光俊叶,刘明霞,张道强.有效距离在聚类算法中的应用[J].计算机科学与探索,2017,11(3):406-413.DOI:10.3778/j.issn.1673-9418.1603046