This paper selects 998 articles as its data sources from four Chinese core journals in the field of Library and Information Science from 2003 to 2007.Some pertinent aspects of reference citations particularly from web...This paper selects 998 articles as its data sources from four Chinese core journals in the field of Library and Information Science from 2003 to 2007.Some pertinent aspects of reference citations particularly from web resources are selected for a focused analysis and discussion.This includes primarily such items as the number of web citations,web citations per each article,the distribution of domain names of web citations and also certain aspects about the institutional and/or geographical affiliations of the author.The evolving situation of utilizing online networked academic information resources in China is the central thematic discussion of this study.The writing of this paper is augmented by the explicatory presentation of 3 graphic figures,6 tables and 18 references.展开更多
To extract features of fabric defects effectively and reduce dimension of feature space,a feature extraction method of fabric defects based on complex contourlet transform （CCT） and principal component analysis （PC...To extract features of fabric defects effectively and reduce dimension of feature space,a feature extraction method of fabric defects based on complex contourlet transform （CCT） and principal component analysis （PCA） is proposed.Firstly,training samples of fabric defect images are decomposed by CCT.Secondly,PCA is applied in the obtained low-frequency component and part of highfrequency components to get a lower dimensional feature space.Finally,components of testing samples obtained by CCT are projected onto the feature space where different types of fabric defects are distinguished by the minimum Euclidean distance method.A large number of experimental results show that,compared with PCA,the method combining wavdet low-frequency component with PCA （WLPCA）,the method combining contourlet transform with PCA （CPCA）,and the method combining wavelet low-frequency and highfrequency components with PCA （WPCA）,the proposed method can extract features of common fabric defect types effectively.The recognition rate is greatly improved while the dimension is reduced.展开更多
基金supported by National Social Science Fund of China(Grant No.08CTQ015)
文摘This paper selects 998 articles as its data sources from four Chinese core journals in the field of Library and Information Science from 2003 to 2007.Some pertinent aspects of reference citations particularly from web resources are selected for a focused analysis and discussion.This includes primarily such items as the number of web citations,web citations per each article,the distribution of domain names of web citations and also certain aspects about the institutional and/or geographical affiliations of the author.The evolving situation of utilizing online networked academic information resources in China is the central thematic discussion of this study.The writing of this paper is augmented by the explicatory presentation of 3 graphic figures,6 tables and 18 references.
基金National Natural Science Foundation of China(No.60872065)the Key Laboratory of Textile Science&Technology,Ministry of Education,China(No.P1111)+1 种基金the Key Laboratory of Advanced Textile Materials and Manufacturing Technology,Ministry of Education,China(No.2010001)the Priority Academic Program Development of Jiangsu Higher Education Institution,China
文摘To extract features of fabric defects effectively and reduce dimension of feature space,a feature extraction method of fabric defects based on complex contourlet transform （CCT） and principal component analysis （PCA） is proposed.Firstly,training samples of fabric defect images are decomposed by CCT.Secondly,PCA is applied in the obtained low-frequency component and part of highfrequency components to get a lower dimensional feature space.Finally,components of testing samples obtained by CCT are projected onto the feature space where different types of fabric defects are distinguished by the minimum Euclidean distance method.A large number of experimental results show that,compared with PCA,the method combining wavdet low-frequency component with PCA （WLPCA）,the method combining contourlet transform with PCA （CPCA）,and the method combining wavelet low-frequency and highfrequency components with PCA （WPCA）,the proposed method can extract features of common fabric defect types effectively.The recognition rate is greatly improved while the dimension is reduced.
