Spatially weighted principal component analysis (PCA) method for water quality analysis

2013 ◽  
Vol 40 (3) ◽  
pp. 315-324 ◽  
Author(s):  
Ozan Arslan
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Quality Analysis ◽  
Water Quality Analysis ◽  
Weighted Principal Component ◽  
Pca Method

Water Quality Evaluation for Tributaries of Danjiangkou Reservoir with Principal Component Analysis

2014 ◽  
Vol 955-959 ◽  
pp. 3586-3594 ◽  
Author(s):  
Xiao Kang Xin ◽  
Wei Yin ◽  
Hai Yan Jia
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Water Quality Evaluation ◽  
Mean Values ◽  
Danjiangkou Reservoir ◽  
Research Task ◽  
Pca Method ◽  
Water Quality Status

To reflect the water quality status of Danjiangkou reservoir tributaries, identify the main pollution factors, and compare pollution degree between tributaries. Principal component analysis (PCA) method is used to assess and explicate research task with annual mean values for 11 main water quality indicators of 16 priority tributaries measured in 2013. The results show that: The main pollution factors of Danjiangkou reservoir are oxygen-consuming pollutants and heavy metal, and the former is the dominant one. Shending, Sihe, Jianghe, Jianhe and Danjiang are heavily-polluted tributaries while Duhe, Jiangjun, Taogou, Hanjiang and Taohe are lightly-polluted tributaries.

Optimization of Process Parameters for Submerged Arc Welding by Weighted Principal Component Analysis Based Taguchi Method

2012 ◽  
Vol 622-623 ◽  
pp. 45-50 ◽  
Author(s):  
Joydeep Roy ◽  
Bishop D. Barma ◽  
J. Deb Barma ◽  
S.C. Saha
Keyword(s):  
Principal Component Analysis ◽  
Taguchi Method ◽  
Arc Welding ◽  
Principal Component ◽  
Traverse Speed ◽  
Component Analysis ◽  
Submerged Arc Welding ◽  
Bead Geometry ◽  
Weighted Principal Component ◽  
Submerged Arc

In submerged arc welding (SAW), weld quality is greatly affected by the weld parameters such as welding current, traverse speed, arc voltage and stickout since they are closely related to weld joint. The joint quality can be defined in terms of properties such as weld bead geometry and mechanical properties. There are several control parameters which directly or indirectly affect the response parameters. In the present study, an attempt has been made to search an optimal parametric combination, capable of producing desired high quality joint in submerged arc weldment by Taguchi method coupled with weighted principal component analysis. In the present investigation three process variables viz. Wire feed rate (Wf), stick out (So) and traverse speed (Tr) have been considered and the response parameters are hardness, tensile strength (Ts), toughness (IS).

The Design of Index about Corporate Governance Based on PCA Method

2014 ◽  
Vol 926-930 ◽  
pp. 4085-4088
Author(s):  
Chuan Jun Li
Keyword(s):  
Principal Component Analysis ◽  
Corporate Governance ◽  
Principal Components ◽  
Principal Component ◽  
Component Analysis ◽  
Contribution Rate ◽  
Variance Contribution ◽  
Pca Method

This article uses the PCA method (Principal component analysis) to evaluate the level of corporate governance. PCA is used to analyze the correlation among 10 original indicators, and extract some principal components so that most of the information of the original indicators is extracted. The formulation of the index of corporate governance can be got by calculating the weight based on the variance contribution rate of the principal component, which can comprehensively evaluate corporate governance.

scholarly journals Patch-Based Principal Component Analysis for Face Recognition

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Tai-Xiang Jiang ◽  
Ting-Zhu Huang ◽  
Xi-Le Zhao ◽  
Tian-Hui Ma
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Nearest Neighbor ◽  
Spatial Information ◽  
Principal Component ◽  
Component Analysis ◽  
Two Dimensional ◽  
Face Images ◽  
Pca Method ◽  
Local Spatial Information

We have proposed a patch-based principal component analysis (PCA) method to deal with face recognition. Many PCA-based methods for face recognition utilize the correlation between pixels, columns, or rows. But the local spatial information is not utilized or not fully utilized in these methods. We believe that patches are more meaningful basic units for face recognition than pixels, columns, or rows, since faces are discerned by patches containing eyes and noses. To calculate the correlation between patches, face images are divided into patches and then these patches are converted to column vectors which would be combined into a new “image matrix.” By replacing the images with the new “image matrix” in the two-dimensional PCA framework, we directly calculate the correlation of the divided patches by computing the total scatter. By optimizing the total scatter of the projected samples, we obtain the projection matrix for feature extraction. Finally, we use the nearest neighbor classifier. Extensive experiments on the ORL and FERET face database are reported to illustrate the performance of the patch-based PCA. Our method promotes the accuracy compared to one-dimensional PCA, two-dimensional PCA, and two-directional two-dimensional PCA.

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