• Economic and Environmental Geology
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• v.38 no.4 s.173
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• pp.435-450
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• 2005

The purpose of the study is to analyze the hydrogeochemical characteristics by multivariate statistical method, to interpret the hydrogeochemical processes for the new variables calculated from principal components analysis (PCA), and to infer the groundwater flow and circulation mechanism by applying the geostatistical methods for each element and principal component. Chloride and nitrate are the most influencing components for groundwater quality, and the contents of $NO_3$ increased by the input of agricultural activities show the largest variation. The results of PCA, a multivariate statistical method, show that the first three principal components explain $73.9\%$ of the total variance. PC1 indicates the increase of dissolved ions, PC2 is related with the dissolution of carbonate minerals and nitrate contamination, and PC3 shows the effect of cation exchange process and silicate mineral dissolution. From the results of experimental semivariogram, the components of groundwater are divided into two groups: one group includes electrical conductivity (EC), Cl, Na, and $NO_3$, and the other includes $HCO_3,\;SiO_2,$ Ca, and Sr. The results for spatial distribution of groundwater components showed that EC, Cl, and Na increased with approaching the coastal line and nitrate has close relationship with the presence of agricultural land. These components are also correlated with the topographic features reflecting the groundwater recharge effect. The kriging analysis by using principal components shows that PC 1 has the different spatial distribution of Cl, Na, and EC, possibly due to the influence of pH, Ca, Sr, and $HCO_3$ for PC1. It was considered that the linear anomaly zone of PC2 in western area was caused by the dissolution of carbonate mineral. Consequently, the application of multivariate and geostatistical methods for groundwater in the study area is very useful for determining the quantitative analysis of water quality data and the characteristics of spatial distribution.

• Journal of Korean Society on Water Environment
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• v.24 no.6
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• pp.741-749
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• 2008

Many small-scale ponds that serve as ecological habitat, recreation and irrigation are faced to eutrophication problem, which causes aesthetic nuisance and ultimately loss of their functions. Thus accurate evaluation of the trophic state of these ponds is essential to provide rational information to the stakeholders so that they can develop effective management actions. In this study, the trophic state of a small pond (Wonheung) that experiencing water quality degradation due to vicinity land development was assessed using various Trophic State Indexes (TSIs) and statistical analysis including Principal Components Analysis (PCA) based on the field monitoring data obtained from May to December, 2007. The results showed that the pond is under eutrophic state with average total nitrogen (T-N) and total phosphorus (T-P) concentrations of $708.1{\mu}g/L$ and $59.3{\mu}g/L$, respectively. The factor loading plot obtained from PCA showed distinct two influencing factors, PC 1 and PC 2. PC 1 was grouped by T-P, Chlorophyll a (Chl-a), suspended solids (SS), TN/TP ratio, and transparency that all strongly related to the eutrophication state, while PC 2 by temperature, conductivity, dissolved oxygen (DO) and turbidity that explains the seasonal water quality variations. The limiting factor was identified as light rather than phosphorus by both T-N/T-P ratio and TSI indexes analysis. The results and methodology adopted in this study can be used for water quality assessment for other small ponds and lakes.

• Environmental Engineering Research
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• v.19 no.3
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• pp.197-203
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• 2014

This study estimated spatial and seasonal variation of water quality to understand characteristics of Nakdong river basin, Korea. All together 11 parameters (discharge, water temperature, dissolved oxygen, 5-day biochemical oxygen demand, chemical oxygen demand, pH, suspended solids, electrical conductivity, total nitrogen, total phosphorus, and total organic carbon) at 22 different sites for the period of 2003-2011 were analyzed using multivariate statistical techniques (cluster analysis, principal component analysis and factor analysis). Hierarchical cluster analysis grouped whole river basin into three zones, i.e., relatively less polluted (LP), medium polluted (MP) and highly polluted (HP) based on similarity of water quality characteristics. The results of factor analysis/principal component analysis explained up to 83.0%, 81.7% and 82.7% of total variance in water quality data of LP, MP, and HP zones, respectively. The rotated components of PCA obtained from factor analysis indicate that the parameters responsible for water quality variations were mainly related to discharge and total pollution loads (non-point pollution source) in LP, MP and HP areas; organic and nutrient pollution in LP and HP zones; and temperature, DO and TN in LP zone. This study demonstrates the usefulness of multivariate statistical techniques for analysis and interpretation of multi-parameter, multi-location and multi-year data sets.

• Near Infrared Analysis
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• v.1 no.2
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• pp.21-27
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• 2000

Temperature-dependent changes in near-infrared (NIR) spectra have been measured for oleic acid, and nonanoic acid in the pure liquid state. Particular attention has been paid to the 5400-4800 cm$\^$-1/ region where a number of combination bands appear. The NIR spectra of oleic acid show that a band at 5303 cm$\^$-1/ increases with temperature while that at 5270 cm/sup-1/ decreases. It ha been found from their second derivative spectra that these spectral changes take place stepwisely with two break points at 30 and 53$\^{C}$, which correspond to the phase transition temperatures oleic acid reported previously. Principle component analysis (PCA) has been carried out for the NIR spectra of oleic acid in the 5400-4800 cm$\^$-1/ region measured over a temperature range of 15-80$\^{C}$. core plots of the first and second principal components (PCs) show that the NIR spectra are classified into three groups; the spectra measured in the temperature range of 15-30$\^{C}$, those in the range of 31-53$\^{C}$, and those in the range of 54-80$\^{C}$. These temperature ranges correspond to those for quasi-smectic liquid crystal, disordered liquid crystal, and isotropic liquid of oleic acid in the pure liquid state. In other words, PCA provides unambiguous evidence for the phase transitions. similar studies have been carried out for petroselinic acid and nonanoic acid in the pure liquid states, but they do not show any evidence for phase transitions.

• The Journal of the Korea Contents Association
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• v.11 no.11
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• pp.185-193
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• 2011

There is no exact standard of detecting pulmonary tuberculosis(TB) in digital image of simple chest radiography. In this study, I experimented on the principal components analysis(PCA) algorithm in the past and suggested six other parameters as identification of TB lesions. The purpose of this study was to develop and test computer aided diagnosis(detection) method for the detection and measurement of pulmonary abnormalities on digital chest radiography. It showed comparatively low recognition diagnosis rate using PCA method, however, six kinds of texture features parameters algorithm showed similar or higher diagnosis rates of pulmonary disease than that of the clinical radiologists. Proposed algorithms using computer-aided of texture analysis can distinguish between areas of abnormality in the chest digital images, differentiate lesions having pulmonary disease. The method could be useful tool for classifying and measuring chest lesions, it would play a major role in radiologist's diagnosis of disease so as to help in pre-reading diagnosis and prevention of pulmonary tuberculosis.

• Journal of the Korean Solar Energy Society
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• v.39 no.3
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• pp.67-79
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• 2019

The reliable Typical Meteorological Year (TMY) data, sometimes called Test Reference Year (TRY) data, are necessary in the feasibility study of renewable energy installation as well as zero energy building. In Korea, there are available TMY data; TMY from Korea Institute of Energy Research (KIER), TRY from the Korean Solar Energy Society (KSES) and TRY from Passive House Institute Korea (PHIKO). This study aims at examining their characteristics by using Principle Component Analysis (PCA) at six ground observing stations. First step is to investigate the annual averages of meteorological elements from TMY data and their standard deviations. Then, PCA is done to find which principle components are derived from different TMY data. Temperature and solar irradiance are determined as the main principle component of TMY data produced by KIER and KSES at all stations whereas TRY data from PHIKO does not show similar result from those by KIER and KSES.

• Journal of Korea Multimedia Society
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• v.13 no.1
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• pp.47-57
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• 2010

The object detection is essential for identifying objects, location information, and user context-aware in the image. In this paper, we propose a robust object detection system. The System linearly transforms learning data obtained from the background images to Principal components. It organizes the Eigen-background with the selected Principal components which are able to discriminate between foreground and background. The Fuzzy-C-means (FCM) carries out clustering for images with inputs from the Eigen-background information and classifies them into objects and backgrounds. It used various patterns of backgrounds as learning data in order to implement a system applicable even to the changing environments, Our system was able to effectively detect partial movements of a human body, as well as to discriminate between objects and backgrounds removing noises and shadows without anyone frame image for fixed background.

• Journal of Korean Society of Transportation
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• v.31 no.4
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• pp.76-84
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• 2013

The main objective of study was to determine the optimum level of bus service by bus route types for the effective improvement of bus route operating in semi-public transportation management. In pursuing the above, this study proposed to classify by bus route types based on publicity, profitability and potentiality. Using this methods of the classification, 113 bus routes in Busan were classified into bus routes of 8 types. And this study proposed desirable management of bus route operation according to 8 bus route types considering 9 bus operating characteristics such as bus route distance, operating number, state of passing through the CBD and so on. For proposing the desirable management, it was to do a statistical analysis of PCA(Principal Components Analysis) and to abbreviate 9 variables to 3. And it was drawn a conclusion effectively by making comparison between 8 bus rout types and 3 bus operating characteristics.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.664-667
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• 1997

For higher component density per chip, it is necessary to identify and control the semiconductor manufacturing process more stringently. Recently, neural networks have been identified as one of the most promising techniques for modeling and control of complicated processes such as plasma etching process. Since wafer states after each run using identical recipe may differ from each other, conventional neural network models utilizing input factors only cannot represent the actual state of process and equipment. In this paper, in addition to the input factors of the recipe, real-time tool data are utilized for modeling of 64M DRAM s-poly plasma etching process to reflect the actual state of process and equipment. For real-time tool data, we collect optical emission spectroscopy (OES) data. Through principal component analysis (PCA), we extract principal components from entire OES data. And then these principal components are included to input parameters of neural network model. Finally neural network model is trained using feed forward error back propagation (FFEBP) algorithm. As a results, simulation results exhibit good wafer state prediction capability after plasma etching process.

• International Journal of Oral Biology
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• v.43 no.2
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• pp.101-111
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• 2018

Understanding the classification of malocclusion is a crucial issue in Orthodontics. It can also help us to diagnose, treat, and understand malocclusion to establish a standard for definite class of patients. Principal component analysis (PCA) and k-means algorithms have been emerging as data analytic methods for cephalometric measurements, due to their intuitive concepts and application potentials. This study analyzed the macro- and meso-scale classification structure and feature basis vectors of 1020 (415 male, 605 female; mean age, 25 years) orthodontic patients using statistical preprocessing, PCA, random matrix theory (RMT) and k-means algorithms. RMT results show that 7 principal components (PCs) are significant standard in the extraction of features. Using k-means algorithms, 3 and 6 clusters were identified and the axes of PC1~3 were determined to be significant for patient classification. Macro-scale classification denotes skeletal Class I, II, III and PC1 means anteroposterior discrepancy of the maxilla and mandible and mandibular position. PC2 and PC3 means vertical pattern and maxillary position respectively; they played significant roles in the meso-scale classification. In conclusion, the typical patient profile (TPP) of each class showed that the data-based classification corresponds with the clinical classification of orthodontic patients. This data-based study can provide insight into the development of new diagnostic classifications.