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聚氯乙烯耐有机溶剂性能的图形分类
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作者 易忠胜 《广西科学》 CAS 2001年第1期30-33,共4页
用溶解度参数理论对非晶态聚氯乙烯 73种有机溶剂计算的δA或δB和δd,δps,δhs-δhp,进行主成分分解 ,以第 1主成分对第 2主成分作图 ,从图中很明显地看到耐蚀与不耐蚀的容剂分成两个区域。与模式识别方式确定是否耐蚀的结果相近 ,较... 用溶解度参数理论对非晶态聚氯乙烯 73种有机溶剂计算的δA或δB和δd,δps,δhs-δhp,进行主成分分解 ,以第 1主成分对第 2主成分作图 ,从图中很明显地看到耐蚀与不耐蚀的容剂分成两个区域。与模式识别方式确定是否耐蚀的结果相近 ,较好地符合实验事实 ,且直观 。 展开更多
关键词 聚氯乙烯 耐有机溶剂性 溶解度参数 图形 主成分分析分分析
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Identification of Heterogeneity of Social and Economic Environment of Land Uses in China 被引量:12
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作者 邓祥征 黄维 +1 位作者 杜继福 韩健智 《Agricultural Science & Technology》 CAS 2010年第1期167-170,共4页
The robust principal component analysis (RPCA) is a technique of multivariate statistics to assess the social and economic environment quality. This paper aims to explore a RPCA algorithm to analyze the spatial hete... The robust principal component analysis (RPCA) is a technique of multivariate statistics to assess the social and economic environment quality. This paper aims to explore a RPCA algorithm to analyze the spatial heterogeneity of social and economic environment of land uses (SEELU). RPCA supplies one of the most efficient methods to derive the most important components or factors affecting the regional difference of the social and economic environment. According to the spatial distributions of the levels of SEELU,the total land resources of China were divided into eight zones numbered by Ⅰ to Ⅷ which spatially referred to the eight levels of SEELU. 展开更多
关键词 Principal component analysis Robust principal component analysis Land uses Social and economic environment Social and economic environment of land uses
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Application of PCA and HCA to the Structure-Activity Relationship Study of Fluoroquinolones 被引量:2
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作者 李小红 张现周 +2 位作者 程新路 杨向东 朱遵略 《Chinese Journal of Chemical Physics》 SCIE CAS CSCD 北大核心 2006年第2期143-148,共6页
Density functional theory (DFT) was used to calculate molecular descriptors (properties) for 12 fluoro-quinolone with anti-S.pneumoniae activity. Principal component analysis (PCA) and hierarchical cluster analy... Density functional theory (DFT) was used to calculate molecular descriptors (properties) for 12 fluoro-quinolone with anti-S.pneumoniae activity. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were employed to reduce dimensionality and investigate in which variables should be more effective for classifying fluoroquinolones according to their degree of an-S.pneumoniae activity. The PCA results showed that the variables ELUMO, Q3, Q5, QA, logP, MR, VOL and △EHL of these compounds were responsible for the anti-S.pneumoniae activity. The HCA results were similar to those obtained with PCA.The methodologies of PCA and HCA provide a reliable rule for classifying new fluoroquinolones with antiS.pneumoniae activity. By using the chemometric results, 6 synthetic compounds were analyzed through the PCA and HCA and two of them are proposed as active molecules with anti-S.pneumoniae, which is consistent with the results of clinic experiments. 展开更多
关键词 Structure-activity relationship Density functional theory Principal component analysis Hierarchical cluster analysis
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Kernel principal component analysis network for image classification 被引量:5
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作者 吴丹 伍家松 +3 位作者 曾瑞 姜龙玉 Lotfi Senhadji 舒华忠 《Journal of Southeast University(English Edition)》 EI CAS 2015年第4期469-473,共5页
In order to classify nonlinear features with a linear classifier and improve the classification accuracy, a deep learning network named kernel principal component analysis network( KPCANet) is proposed. First, the d... In order to classify nonlinear features with a linear classifier and improve the classification accuracy, a deep learning network named kernel principal component analysis network( KPCANet) is proposed. First, the data is mapped into a higher-dimensional space with kernel principal component analysis to make the data linearly separable. Then a two-layer KPCANet is built to obtain the principal components of the image. Finally, the principal components are classified with a linear classifier. Experimental results showthat the proposed KPCANet is effective in face recognition, object recognition and handwritten digit recognition. It also outperforms principal component analysis network( PCANet) generally. Besides, KPCANet is invariant to illumination and stable to occlusion and slight deformation. 展开更多
关键词 deep learning kernel principal component analysis net(KPCANet) principal component analysis net(PCANet) face recognition object recognition handwritten digit recognition
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Comparative Analysis of Morphologic Traits of 50 Large-flowered Chrysanthemum Varieties
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作者 沈珍 毛燕 +2 位作者 吴德智 袁伟 杨旭 《Agricultural Science & Technology》 CAS 2016年第2期317-322,共6页
With 50 large-flowered Chrysanthemum varieties from germplasm nursery of Wunaoshan Forest Farm in Macheng City as research objects, 64 morphological traits were investigated by field experiments adopting randomized bl... With 50 large-flowered Chrysanthemum varieties from germplasm nursery of Wunaoshan Forest Farm in Macheng City as research objects, 64 morphological traits were investigated by field experiments adopting randomized block design. The morphological differences were observed by uniformity analysis, variability analysis, principal component analysis and cluster analysis. The result showed that the vari- able coefficients of 59 traits were greater than 15%; the contribution rate of first seven principal components reached 81.45%; and it was found by clustering analy- sis that the 50 germplasm resources could be divided into four clusters with obvious morphological differences, and plant type could be used as an index for classifica- tion. 展开更多
关键词 Large-flowered Chrysanthemum Morphologic traits Variability analysis Principal component analysis Clustering analysis
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Numerical study of resting-state fMRI based on kernel ICA
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作者 朱冬娟 王训恒 阮宗才 《Journal of Southeast University(English Edition)》 EI CAS 2010年第1期78-81,共4页
In order to facilitate the extraction of the default mode network(DMN), reduce the data complexity of the functional magnetic resonance imaging (fMRI)and overcome the restriction of the linearity of the mixing pro... In order to facilitate the extraction of the default mode network(DMN), reduce the data complexity of the functional magnetic resonance imaging (fMRI)and overcome the restriction of the linearity of the mixing process encountered with the independent component analysis(ICA), a framework of dimensionality reduction and nonlinear transformation is proposed. First, the principal component analysis(PCA)is applied to reduce the time dimension 153 594×128 of the fMRI data to 153 594×5 for simplifying complexity computation and obtaining 95% of the information. Secondly, a new kernel-based nonlinear ICA method referred as the kernel ICA(KICA)based on the Gaussian kernel is introduced to analyze the resting-state fMRI data and extract the DMN. Experimental results show that the KICA provides a better performance for the resting-state fMRI data analysis compared with the classical ICA. Furthermore, the DMN is accurately extracted and the noise is reduced. 展开更多
关键词 kernel independent component analysis principal component analysis functional magnetic resonance imaging(fMRI) RESTING-STATE
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Morphological analysis of the Chinese Cipangopaludina species(Gastropoda; Caenogastropoda: Viviparidae) 被引量:1
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作者 Hong-Fa LU Li-Na DU +2 位作者 Zhi-Qiang LI Xiao-Yong CHEN Jun-Xing YANG 《Zoological Research》 CAS CSCD 北大核心 2014年第6期510-527,共18页
Viviparidae are widely distributed around the globe, but there are considerable gaps in the taxonomic record. To date, 18 species of the viviparid genus Cipangopaludina have been recorded in China, but there is substa... Viviparidae are widely distributed around the globe, but there are considerable gaps in the taxonomic record. To date, 18 species of the viviparid genus Cipangopaludina have been recorded in China, but there is substantial disagreement on the validity of this taxonomy. In this study, we described the shell and internal traits of these species to better discuss the validity of related species. We found that C. ampulliformis is synonym of C. lecythis, and C. wingatei is synonym of C. chinensis, while C. ampullacea and C. fluminalis are subspecies of C. lecythis and C. chinensis, respectively. C. dianchiensis should be paled in the genus Margarya, while C. menglaensis and C. yunnanensis belong to genus Mekongia. Totally, this leaves 11 species and 2 subspecies recorded in China. Based on whether these specimens' spiral whorl depth was longer than aperture depth, these species or subspecies can be further divided into two groups, viz. chinensis group and cathayensis group, which can be determined from one another via the ratio of spiral depth and aperture depth, vas deferens and number of secondary branches of vas deferens. Additionally, Principal Component Analysis indicated that body whorl depth, shell width, aperture width and aperture length were main variables during species of Cipangopaludina. A key to all valid Chinese Cipangopaludina species were given. 展开更多
关键词 Cipangopaludina GASTROPODA Bellamyinae ANATOMY TAXONOMY CHINESE
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Adaptive partitioning PCA model for improving fault detection and isolation 被引量:6
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作者 刘康玲 金鑫 +1 位作者 费正顺 梁军 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2015年第6期981-991,共11页
In chemical process, a large number of measured and manipulated variables are highly correlated. Principal component analysis(PCA) is widely applied as a dimension reduction technique for capturing strong correlation ... In chemical process, a large number of measured and manipulated variables are highly correlated. Principal component analysis(PCA) is widely applied as a dimension reduction technique for capturing strong correlation underlying in the process measurements. However, it is difficult for PCA based fault detection results to be interpreted physically and to provide support for isolation. Some approaches incorporating process knowledge are developed, but the information is always shortage and deficient in practice. Therefore, this work proposes an adaptive partitioning PCA algorithm entirely based on operation data. The process feature space is partitioned into several sub-feature spaces. Constructed sub-block models can not only reflect the local behavior of process change, namely to grasp the intrinsic local information underlying the process changes, but also improve the fault detection and isolation through the combination of local fault detection results and reduction of smearing effect.The method is demonstrated in TE process, and the results show that the new method is much better in fault detection and isolation compared to conventional PCA method. 展开更多
关键词 Adaptive partitioning Fault detection Fault isolation Principal component analysis
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Processing Human Colonic Pressure Signals by Using Overdetermined ICA
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作者 田社平 潘城 颜国正 《Journal of Measurement Science and Instrumentation》 CAS 2010年第4期401-405,共5页
Independent component analysis (ICA) is a widely used method for blind source separation (BSS). The mature ICA model has a restriction that the number of the sources must equal to that of the sensors used to colle... Independent component analysis (ICA) is a widely used method for blind source separation (BSS). The mature ICA model has a restriction that the number of the sources must equal to that of the sensors used to collect data, which is hard to meet in most practical cases. In this paper, an overdetermined ICA method is proposed and successfully used in the analysis of human colonic pressure signals. Using principal component analysis (PCA), the method estimates the number of the sources firstly and reduces the dimensions of the observed signals to the same with that of the sources; and then, Fast- ICA is used to estimate all the sources. From 26 groups of colonic pressure recordings, several colonic motor patterns are extracted, which riot only prove the effectiveness of this method, but also greatly facilitate further medical researches. 展开更多
关键词 medical signal processing overdetermined ICA PCA colonic motor pattern
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Comparison between PCA and KPCA methods in comprehensive evaluation of robotic kinematic dexterity 被引量:1
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作者 孙志娟 Zhao Jing Li Liming 《High Technology Letters》 EI CAS 2014年第2期154-160,共7页
Due to the correlation and diversity of robotic kinematic dexterity indexes, the principal component analysis (PCA) and kernel principal component analysis (KPCA) based on linear dimension reduction and nonlinear ... Due to the correlation and diversity of robotic kinematic dexterity indexes, the principal component analysis (PCA) and kernel principal component analysis (KPCA) based on linear dimension reduction and nonlinear dimension reduction principle could be respectively introduced into comprehensive kinematic dexterity performance evaluation of space 3R robot of different tasks. By comparing different dimension reduction effects, the KPCA method could deal more effectively with the nonlinear relationship among different single kinematic dexterity indexes, and its calculation result is more reasonable for containing more comprehensive information. KPCA' s calculation provides scientific basis for optimum order of robotic tasks, and furthermore a new optimization method for robotic task selection is proposed based on various performance indexes. 展开更多
关键词 ROBOT kinematic dexterity comprehensive performance evaluation task optimizing selection principal component analysis (PCA) kernel principal component analysis (KPCA)
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