• Journal of Fisheries and Marine Sciences Education
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• v.29 no.3
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• pp.785-798
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• 2017

This study was conducted to evaluated the surface(10 variables) and bottom(10 variables) water quality, and sediment(3 variables) in the cage fish farm off Baegyado in Gamak Bay using a multivariate analysis from January 2013 to November 2014. Generally, the environmental data did not show a certain tendency by months during two years investigated. The pairwise simple correlation matrices among variables were also shown. The first four principal components of the surface water in 2013 explain 93% of the total sample variance; the first principal component($z_1$) showed the freshwater inflow and/or precipitation, $z_2$, $z_3$ and $z_4$ related to freshwater inflow and/or precipitation, organic matters and eutrophy, respectively; the first four principal components of the bottom water in 2013 explain 93% of the total sample variance; the $z_1$, $z_2$ and $z_4$ related to freshwater inflow and/or precipitation, and $z_3$ water temperature. In 2014, at the surface water the first three principal components explain 87%; the $z_1$, $z_2$ and $z_3$ related to water temperature, eutrophy and freshwater inflow and/or precipitation, respectively; at the bottom water the first three principal components explain 93%; $z_1$, $z_2$ and $z_3$ related to water temperature, freshwater inflow and/or precipitation and eutrophy. Half of the principal components related to freshwater inflow and/or precipitation.

• Communications for Statistical Applications and Methods
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• v.10 no.3
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• pp.1057-1068
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• 2003

Results of using principal component analysis prior to cluster analysis are compared with results from applying agglomerative clustering algorithm alone. The retrieval ability of the agglomerative clustering algorithm is improved by using principal components prior to cluster analysis in some situations. On the other hand, the loss in retrieval ability for the agglomerative clustering algorithms decreases, as the number of informative variables increases, where the informative variables are the variables that have distinct information(or, necessary information) compared to other variables.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.150-154
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• 2008

In this research, stress components and principal stresses of O-ring under internal pressure and under uniform squeeze rate were obtained from the stress freezing method of photoelastic experiment and photoelastic experimental Hybrid method for 3-dimensional problems. The obtaining processes of those were introduced. It was certified that the processes of those are effective for the 3-dimensional stress analysis of structures. Stress freezing method, the obtaining processes of those and photoelastic experimental hybrid method were effectively applied to the stress analysis of O-ring made from rubber that under uniform deformation and internal pressure. Stress components and principal stress of Oring under uniform squeeze rate and under internal pressure were analyzed.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.54 no.1
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• pp.24-28
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• 2009

This study was conducted to evaluate the growth characters and yield components of 18 collected sesame cultivars to get basic information on the variation for the sesame breeding using principal component analysis. All characters except days to flowering, days to maturity and 1,000 seed weight showed significantly different. Seed weight per 10 are showed higher coefficient of variance. Capsule bearing stem length and liter weight showed positive correlation with seed yield per 10 are. The principal components analysis grouped the estimated sesame cultivars into four main components which accounted for 83.7% of the total variation at the eigenvalue and its contribution to total variation obtained from principal component analysis. The first principal component ($Z_1$) was applicable to increase plant height, capsule bearing stem length and 1,000-seed weight. The second principal component ($Z_2$) negatively correlated with days to flowering and maturity by which it was applicable to shorten flowering and maturity date of sesame. At the scatter diagram, Yangbaek, Ansan, M1, M2, M4, M7 and M9 were classified as same group, but M10, Yanghuk, Kanghuk, M5, M6, M12 and M13 were classified as different group. This results would be helpful for sesame breeder to understand genetic relationship of some agronomic characters and select promising cross lines for the development of new sesame variety.

• Journal of environmental and Sanitary engineering
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• v.9 no.2
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• pp.8-23
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• 1994

During the period from Mar., 1991 to Feb., 1992 66 tSP samples were collected by Hi volume air sampler at 1 sampling site in Seoul and the amount of concentration of 21 components(SO$_{4}$$^{2-}$, NO$_{3}$$^{-}$, NH$_{4}$$^{+}$, Cl$^{-}$, Al, Ba, Ca, Cd, Cr, Cu, Fe, It Mg, Mn, Na, Ni, Pt Si, Ti, Zn, Zr ) were measured. And monthly and seasonal variation were surveyed and the principal component analysis( PCA ) were carried out with respect to these amount of pollutants, minimum of visibility and radiation on a horizontal surface. The total amount of soluble ion in water was high in order o(SO$_{4}$$^{2-}$> NO$_{3}$$^{-}$> N%'>Cl$^{-}$ and metal ion was high in order of Na> Ca>Si> Fe> Al> K> Mg> Zn> Pb> Cu>Ti> Mn > Ba> Cr> Zr> Ni> Cd. There was Seasonal variation in concentration for SO$_{4}$$^{2-}$, NH$_{4}$$^{+}$, Cl$^{-}$, Na, Al, Ca, Bt Mg, Fe and Si. It was assumed that the components of the highest concentration on April were depend on yellow sand and the frequency of wind velocity and direction. As the results of PCA, the amount of pollution components was able to characterized with two principal components(Z$_{1}$, Z$_{2}$ ). The first principal components Z$_{1}$ was considered to be a factor indicating the pollutants originated from natural generation and The second principal components Z$_{2}$ was considered to be a factor indicating the pollutants originated from human work. The monthly concentration of pollutants in ISP, minimum of visibility and radiation on a horizontal surface was possible to evaluate by the use of these two principal components Z$_{1}$ and Z$_{2}$ .

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.05a
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• pp.120-126
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• 2007

A principal components analysis of the entire median HRIRs in the CIPIC HRTF database reveals that the individual HRIRs can be adequately reconstructed by a linear combination of several orthonormal basis functions. The basis functions cover the inter-individual and inter-elevation variations in median HRIRs. There are elevation-dependent tendencies in the weights of basis functions, and the basis functions can be ordered according to the magnitude of standard deviation of the weights at each elevation. We propose a HRIR customization method via tuning of the weights of 3 dominant basis functions corresponding to the 3 largest standard deviations at each elevation. Subjective listening test results show that both front-back reversal and vertical perception can be improved with the customized HRIRs.

• Bulletin of the Korean Chemical Society
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• v.28 no.4
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• pp.557-560
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• 2007

The metabolic discrimination of safflowers from various geographical origins was performed using 1H nuclear magnetic resonance (NMR) spectroscopy followed by principal components analysis. With a combination of these techniques, safflower samples from different origins could be discriminated using the first two principal components (PC) of the 1H NMR spectra of the 50% methanol fractions. PC1 and PC2 accounted cumulatively for 91.3% of the variation in all variables. The major peaks in the 1H NMR spectra that contributed to the discrimination were assigned to fatty acid (terminal CH3), lactic acid, acetic acid, choline derivatives, glycine, and safflower yellow derivatives. In this study, we suggest that various types of safflower can be discriminated using PCA and 1H NMR spectra.

• International Journal of Contents
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• v.9 no.3
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• pp.1-9
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• 2013

In this paper, we propose a fast and accurate system for detecting pedestrians from a static image. Histogram of Oriented Gradients (HOG) is a well-known feature for pedestrian detection systems but extracting HOG is expensive due to its high dimensional vector. It will cause long processing time and large memory consumption in case of making a pedestrian detection system on high resolution image or video. In order to deal with this problem, we use Principal Components Analysis (PCA) technique to reduce the dimensionality of HOG. The output of PCA will be input for a linear SVM classifier for learning and testing. The experiment results showed that our proposed method reduces processing time but still maintains the similar detection rate. We got twenty five times faster than original HOG feature.

• Bulletin of the Korean Chemical Society
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• v.18 no.7
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• pp.695-702
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• 1997

The objective of this work is to determine the sugar profiles in commercial fruit juices, and to obtain chemometric characteristics. Sugar compositions of fruit juices were determined by HPLC-RID and GC-FID via methoxymation and trimethylsilylation with BSTFA. The appearance of multiple peaks in GC analysis for carbohydrates was disadvantageous as described in earlier literatures. Fructose, glucose, and sucrose were major carbohydrates in most fruit juices. Glucose/fructose ratios obtained by GC were lower than those by HPLC. Orange juices are similar to pineapple juices in the sugar profiles. However, grape juices are characterized by its lower or no detectable sucrose content. In addition, it was also found that unsweeten juices contained considerable level of sucrose. Chemometric technique such as principal components analysis was applied to provide an overview of the distinguishability of fruit juices based on HPLC or GC data. Principal components plot showed that different fruit juices grouped into distinct cluster. Principal components analysis was very useful in fruit juices industry for many aspects such as pattern recognition, detection of adulterants, and quality evaluation.

• The Korean Journal of Applied Statistics
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• v.21 no.4
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• pp.571-580
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• 2008

The logistic regression analysis is widely used in the area of customer relationship management and credit risk management. It is well known that the maximum likelihood estimation is not appropriate when multicollinearity exists among the regressors. Thus we propose the logistic principal components regression to deal with the multicollinearity problem. In particular, new method is suggested to select proper principal components. The selection method is based on the condition index instead of the eigenvalue. When a condition index is larger than the upper limit of cutoff value, principal component corresponding to the index is removed from the estimation. And hypothesis test is sequentially employed to eliminate the principal component when a condition index is between the upper limit and the lower limit. The limits are obtained by a linear model which is constructed on the basis of the conjoint analysis. The proposed method is evaluated by means of the variance of the estimates and the correct classification rate. The results indicate that the proposed method is superior to the existing method in terms of efficiency and goodness of fit.