Groundwater Hydrograph Patterns in North China Plain during 1982-1986 Interpreted Using Principal Component Analysis

North China Plain ◽

North China ◽

Seasonal Fluctuation ◽

Principal Component ◽

Component Analysis ◽

Analysis Method ◽

Seasonal Fluctuations ◽

Increasing Trend ◽

Taihang Mountain

The groundwater table depths from 1982 to 1986 of 58 unconfined wells in North China Plain(NCP) were analyzed using principal component analysis method. Results showed there were mainly three hydrograph patterns over the area: increasing trend with steady moderate seasonal fluctuations in Taihang Mountain piedmont area; decreasing trend with large seasonal fluctuation magnitudes in central plain of NCP; increasing-decreasing trend with large variance of fluctuation magnitude in piedmont of Yan Mountain piedmont.The distribution of precipitation, irrigating abstraction, and water table depths were the main factors determining the hydrograph patterns and their distribution.

Nonlinear analysis of electroencephalograms of healthy people during driving test based on symplectic principal component analysis method

PsycEXTRA Dataset ◽

10.1037/e573792014-017 ◽

2014 ◽

Author(s):

Min Lei ◽

Guang Meng ◽

Nilanjan Sarkar ◽

Jing Fan ◽

Josh Wade ◽

...

Keyword(s):

Nonlinear Analysis ◽

Principal Component ◽

Component Analysis ◽

Healthy People ◽

Analysis Method ◽

Study on the Effect Factors of Non-Perormance Loan Ratio of Chinese Commerical Banks

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.50-51.728 ◽

2011 ◽

Vol 50-51 ◽

pp. 728-732

Author(s):

Ping Li ◽

Ming Ying Zhuo ◽

Li Chao Feng ◽

Rui Zhang

Keyword(s):

Commercial Banks ◽

Principal Component ◽

Component Analysis ◽

Analysis Method ◽

Analysis Model ◽

Principal Component Analysis Model ◽

Effect Factors ◽

Financial Indicator ◽

Commerical Banks

Non-performance loan ratio is one of the important assessment criteria of the security of credit assets. It is also an important financial indicator to evaluate the general strength of commercial banks. Using principal component analysis method and statistical software SPSS16.0 and based on the non-performance loan ratio and relative data of some commercial banks in China in 2007, this paper provided a principal component analysis model for the non-performance loan ratio of China’s commercial banks. The factors that affect the non-performance loan ratio were refined in this paper. Finally, the characteristics of effect factors of each bank were analyzed and compared in detail.

AHU sensor fault diagnosis using principal component analysis method

Energy and Buildings ◽

10.1016/j.enbuild.2003.10.002 ◽

2004 ◽

Vol 36 (2) ◽

pp. 147-160 ◽

Cited By ~ 120

Author(s):

Shengwei Wang ◽

Fu Xiao

Keyword(s):

Fault Diagnosis ◽

Principal Component ◽

Component Analysis ◽

Analysis Method ◽

Sensor Fault ◽

Application of Principal Component Analysis Method to Determine Prediction Indexes Sensibility Sequence

Procedia Engineering ◽

10.1016/j.proeng.2011.11.2310 ◽

2011 ◽

Vol 26 ◽

pp. 1346-1351

Author(s):

Yang Guo-liang ◽

Wang Can-zhao ◽

Wu Shi-yue ◽

Jia Li-qing ◽

Zhang Sheng-zhu

Keyword(s):

Principal Component ◽

Component Analysis ◽

Analysis Method ◽

Applying metamer sets to investigate data dependency of principal component analysis method in recovery of spectral data

Color Research & Application ◽

10.1002/col.20645 ◽

2010 ◽

Vol 36 (5) ◽

pp. 349-354 ◽

Cited By ~ 2

Author(s):

S. Gorji Kandi ◽

M. Amani Tehran

Keyword(s):

Spectral Data ◽

Principal Component ◽

Component Analysis ◽

Data Dependency ◽

Analysis Method ◽

Chemical Structure-Odor Correlation in a Series of Synthetic n-Nonen-1-ols

Zeitschrift für Naturforschung C ◽

10.1515/znc-1995-11-1204 ◽

1995 ◽

Vol 50 (11-12) ◽

pp. 757-765 ◽

Cited By ~ 4

Author(s):

Yasunobu Sakoda ◽

Kenji Matsui ◽

Tadahiko Kajiwara ◽

Akikazu Hatanaka

Keyword(s):

Double Bond ◽

Spatial Structure ◽

Chemical Structure ◽

Principal Component ◽

Component Analysis ◽

Acetylenic Compound ◽

Analysis Method ◽

Entire Series ◽

Novel Method

In order to elucidate chemical structure-odor correlation in the all isomers of n-nonen-1- ols, an entire series of these alcohols were synthesized stereo-selectively in high purity. For unequivocal syntheses of them, geometrically selective hydrogenation of the respective acetylenic compound was adopted. The synthesized alcohols were converted to their 3,5-dinitrobenzoate derivatives with 3,5-dinitrobenzoyl chloride, and then purified by repeated recrystallization. Chemical structure-odor correlations in all the isomers of n-nonen-1-ols were elucidated by introducing a novel method to evaluate odor characteristics and by treating the obtained data statistically with the principal component analysis method (Cramer et al., 1988). The odor profiles of the tested compounds were attributable largely to the positions of the carbon- double bond. The geometries of compounds had only a little effect. With the principal component analysis, the odor profiles of the series of compounds were successfully integrated into the first and the second principal components. The first component (PC-1) consisted of combined characteristics of fruity, fresh, sweet, herbal and oily-fatty, in which herbal and oily-fatty were conversely correlated each other to the position of double-bond of the tested compounds. Of these, only (6Z)-nonen-1-ol deviated markedly from the correlation, indicative of some special interaction between the spatial structure of this compound and the sensory machinery of human.