• Journal of Korean Society of Environmental Engineers
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• v.32 no.1
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• pp.47-52
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• 2010

Physical, chemical and biological properties of soils and cadmium(Cd) content of Artemisia princeps var. orientalis collected from 10 metalliferous mines were analysed. Cd contents of unplanted soils and rhizosphere soils were not significantly different(p < 0.05), and mean values were 5.92 and 5.91 mg/kg, respectively. In addition, Cd content of rhizosphere soils were correlated with Cd content of Artemisia princeps (p < 0.05, ${R^2}_{shoot}$ = 0.3120, ${R^2}_{root}$ = 0.4177). Minimum data set(MDS) of soil quality parameters for statistical assessment of Cd uptake was established by principal component analysis, and it was identified as organic matter(OM), dehydrogenase activity(DHA), pH, exchangeable Mg. According to multiple regression analysis using the MDS, coefficients of determination ($R^2$) for Cd uptake of shoot and root of Artemisia princeps were found to be 0.3418 and 0.5121, respectively. This suggests that statistical soil quality assessment using the MDS seems a useful tool to interpret heavy metal uptake of plant.

• Journal of Bio-Environment Control
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• v.19 no.3
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• pp.165-170
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• 2010

This experiment was conducted to select the affected main factors on growth and fruit characteristics of 'Fuyu' persimmon (Diospyros kaki Thunb.) in 25 factors of air temperature factors in Naju. Mean air temperature, cumulative temperature and days for March and April of 25 factors were the highest annual variation. Number of the first and second principal components extracted from 25 air temperature factors were 14 and 3 factors related with mean temperature for annul and April, and cumulative contribution of these was 52.2%. Also the affected years by the first principal components were 1990, 1980 and 1986. Annual standard deviation on leafing, flowering and maturing date were 4.0~6.7 days range, and flowering date and days from leafing to flowering had the highest coefficient of variation. Annual variation of days from flowering to maturing date was affected by greatly mean air temperature and days of cumulative temperature in October, days from March 1 to leafing date was affected by cumulative temperature for growing period, days from leafing to flowering date was affected by mean air temperature in April. Annual variation of fruit weight was affected by mean air temperature for March and October.

• Journal of Wetlands Research
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• v.22 no.2
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• pp.106-112
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• 2020

Wetlands play an important function and role in hydrological, environmental, and ecological, aspects of the watershed. Water level in wetlands is essential for various analysis such as for the determination of wetland function and its effects on the environment. Since several wetlands are ungauged, research on wetland water level prediction are uncommon. Therefore, this study developed a water level prediction model using multiple regression analysis, principal component regression analysis, artificial neural network, and DNN to predict wetland water level. Geumjeong-Mountain Wetland located in Yangsan-city, Gyeongsangnam-do province was selected as the target area, and the water level measurement data from April 2017 to July 2018 was used as the dependent variable. On the other hand, hydrological and meteorological data were used as independent variables in the study. As a result of evaluating the predictive power, the water level prediction model using DNN was selected as the final model as it showed an RMSE value of 6.359 and an NRMSE value of 18.91%. This research study is believed to be useful especially as a basic data for the development of wetland maintenance and management techniques using the water level of the existing unmeasured points.

• Journal of Digital Convergence
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• v.14 no.9
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• pp.579-588
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• 2016

This study is to identify the relationship between cultural physical education class of enjoyment factor, class satisfaction, and exercise continuation behavior. For this purpose, 275 out of 300 copies of survey aimed at students in four universities located in Gyeong-gi province were used. PAWS 18.0 program was used for analysis to present descriptive statistics. Principal component analysis and rotation of Max Berry were used among exploratory factor analysis to check factor extraction and internal consistency. Cronbach's alpha was used for checking reliability. Multiple regression analysis was used to clarify the relationship between enjoyment factor, class satisfaction, and exercise continuation behavior of cultural physical education. These are the following conclusions. First, instructional behavior, grade in physical education, environment, and exercise ability among the enjoyment factors in cultural physical education class have a significant effect on persistence of exercise. Second, instructional behavior and grade in physical education are the factors of class satisfaction and the have a significant effect on persistence of exercise.

• Journal of the Korea Convergence Society
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• v.7 no.4
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• pp.1-7
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• 2016

Structural MRI(sMRI) imaging is used to extract morphometric features after Grey Matter (GM), White Matter (WM) for several univariate and multivariate method, and Cerebro-spinal Fluid (CSF) segmentation. A new approach is applied for the diagnosis of very mild to mild AD. We propose the classification method of Alzheimer disease patients from normal controls by combining morphometric features and Gaussian Mixture Models parameters along with MMSE (Mini Mental State Examination) score. The combined features are fed into Multi-kernel SVM classifier after getting rid of curse of dimensionality using principal component analysis. The experimenral results of the proposed diagnosis method yield up to 96% stratification accuracy with Multi-kernel SVM along with high sensitivity and specificity above 90%.

• Journal of the Korean Vacuum Society
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• v.15 no.4
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• pp.338-346
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• 2006

In semiconductor process, it is so hard to predict an exact failure point of the vacuum pump due to its harsh operation conditions and nonlinear properties, which may causes many problems, such as production of inferior goods or waste of unnecessary materials. Therefore it is very urgent and serious problem to develop diagnostic models which can monitor the operation conditions appropriately and recognize the failure point exactly, indicating when to replace the vacuum pump. In this study, many influencing factors are totally considered and eventually the monitoring model using multivariate statistical methods is suggested. The pivotal algorithms are Multiway Principal Component Analysis(MPCA), Dynamic Time Warping Algorithm(DTW Algorithm), etc.

• Journal of Environmental Impact Assessment
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• v.27 no.4
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• pp.353-366
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• 2018

The purpose of this study was assessment the quality of water by using the statistical analysis technique of the Water flow and water quality from January 2012 to December 2016 at the unit basin for total pollutant load management system (TPLMS) in the Imjin River. Water flow and water quality were monitored at an average of 8 day intervals, 11 parameters were used for correlation analysis, principal component analysis (PCA), factor analysis (FA), and cluster analysis (CA). The Hierarchical CA was classified into three according to the change of space, such as natural rivers, urban rivers, point with large influence of point pollution source, it was found that the type of contamination source the similarity of water quality affected the classification of cluster. Using one-way analysis of variance (ANOVA) and post-hoc Analysis, there were statistically significant differences between mean values among the clusters. Correlation analysis showed the correlation coefficient between $COD_{Mn}$ and TOC was 0.951 (p<0.01) and the correlation was statistically significantly higher. According to the result PCA and FA, 3 principal components can explaining 72% of the total variations in water quality characteristics and main factor was EC, $BOD_5$, $COD_{Mn}$, TN, TP and TOC indirect indicators of organic matter and nutrients were influenced. This study presented the regression equation obtained by applying the factor scores to the multiple linear regression analysis and concluded that the management Indirect indicators of organic matter and nutrients is important for water quality management in the Imjin River basin.

• Journal of Korean Society of Transportation
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• v.27 no.2
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• pp.117-131
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• 2009

When most drivers take to the freeway, they don't necessarily pay attention to the geometric design. They expect proper design by depending on their own senses and recognition. When they evaluate the features of traveling on the freeway, they can think differently than engineers. The design needs to predict the exact speed of the driver to satisfy the driver's expectation, safety, pleasure and so on. This study categorized the factors influencing the speed of six freeways considering geometric and operational features to make a prediction model of speed. The model used multiple regression with these factors and produced statically appropriate results. This study utilized the principle component analysis and the quantification II analysis based on the image data of the satisfaction of the traveling environment collected through individual interviews. As a result, this study found the factors of satisfaction in a traveling environment. It made a satisfaction model of the traveling environment on freeways considering the change of driver's actual recognition and societal recognition using structural equations and the quantification II theory. Through the model made in this study, This model can present not only qualitative factors like satisfaction of traveling environment on freeways, but also the quantitative elements like speed. What is important is the evaluation of features of traveling on freeways reflected in the recognition and traffic environment felt by drivers.

• Journal of Wetlands Research
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• v.21 no.4
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• pp.281-289
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• 2019

In this study, the typhoon damage forecasting model was developed for southern inland district. The typhoon damage in the inland district is caused by heavy rain and strong winds, variables are many and varied, but the damage data of the inland district are not enough to develop the model. The hydrological data related to the typhoon damage were hour maximum rainfall amount which is accumulated 3 hour interval, the total rainfall amount, the 1-5 day anticipated rainfall amount, the maximum wind speed and the typhoon center pressure at latitude 33° near the Jeju island. The Multivariate Analysis such as cluster Analysis considering the lack of damage data and principal component analysis removing multi-collinearity of rainfall data are adopted for the damage forecasting model. As a result of applying the developed model, typhoon damage estimated and observed values were up to 2.2 times. this is caused it is difficult to estimate the damage caused by strong winds and it is assumed that the local rainfall characteristics are not considered properly measured by 69 ASOS.

• Journal of KIISE
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• v.41 no.9
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• pp.666-673
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• 2014

This paper proposes a scalable face recognition method for unconstrained face databases, and shows a simple experimental result. Existing face recognition research usually has focused on improving the recognition rate in a constrained environment where illumination, face alignment, facial expression, and background is controlled. Therefore, it cannot be applied in unconstrained face databases. The proposed system is face feature extraction algorithm for unconstrained face recognition. First of all, we extract the area that represent the important features(landmarks) in the face, like the eyes, nose, and mouth. Each landmark is represented by a high-dimensional LBP(Local Binary Pattern) histogram feature vector. The multi-scale LBP histogram vector corresponding to a single landmark, becomes a low-dimensional face feature vector through the feature reduction process, PCA(Principal Component Analysis) and LDA(Linear Discriminant Analysis). We use the Rank acquisition method and Precision at k(p@k) performance verification method for verifying the face recognition performance of the low-dimensional face feature by the proposed algorithm. To generate the experimental results of face recognition we used the FERET, LFW and PubFig83 database. The face recognition system using the proposed algorithm showed a better classification performance over the existing methods.