• 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.

• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.988-992
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• 2006

본 논문은 PC카메라 환경에서 명암왜곡에 강인한 얼굴검출을 위한 컴포넌트 기반 얼굴검출 기법을 제시한다. 영상 내의 얼굴검출을 위해 에지(edge) 분석, 색상 분석, 형판정합(template matching), 신경망(Neural Network), PCA(Principal Component Analysis), LDA(Linear Discriminant Analysis) 등의 기법들이 사용되고 있고, 영상의 왜곡을 보정하기 위해 히스토그램 분석(평활화, 명세화), gamma correction, log transform 등의 영상 보정 방법이 사용되고 있다. 그러나 기존의 얼굴검출 방법과 영상보정 방법은 검출대상 객체의 부분적인 잡음 및 조명의 왜곡에 대처하기가 어려운 단점이 있다. 특히 PC카메라 환경에서 획득된 이미지와 같이 전면과 후면, 상하좌우에서 비추어지는 조명에 의해 검출 대상 객체의 일부분이 왜곡되는 상황이 발생될 경우 기존의 방법으로는 높은 얼굴 검출 성능을 기대할 수 없는 상황이 발생된다. 본 논문에서는 기울어진 얼굴 및 부분적으로 명암 왜곡된 얼굴을 효율적으로 검출할 수 있도록 얼굴의 좌우 대칭성을 고려한 가로방향의 대칭평균화로 얼굴검출을 위한 모델을 생성하여 얼굴검출에 사용한다. 이 방법은 부분적으로 명암왜곡된 얼굴이미지를 기존의 영상 보정기법을 적용한 것 보다 잘 표현하며, 얼굴이 아닌 후보는 비얼굴 이미지의 형상을 가지게 하는 특성이 있다.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.1
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• pp.25-31
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• 2005

In this paper, we present a face detection system using the Support Vector Domain Description (SVDD) in color images. Conventional face detection algorithms require a training procedure using both face and non-face images. In SVDD however we employ only face images for training. We can detect faces in color images from the radius and center pairs of SVDD. We also use Entropic Threshold for extracting the facial feature and sliding window for improved performance while saving processing time. The experimental results indicate the effectiveness and efficiency of the proposed algorithm compared to conventional PCA (Principal Component Analysis)-based methods.

• The Journal of the Acoustical Society of Korea
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• v.23 no.1
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• pp.82-89
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• 2004

This paper describes how to reduce the effect of an occupation threshold by that the transform of mixture components of HMM parameters is controlled in hierarchical tree structure to prevent from over-adaptation. To reduce correlations between data elements and to remove elements with less variance, we employ PCA (principal component analysis) and ICA (independent component analysis) that would give as good a representation as possible, and decline the effect of over-adaptation. When we set lower occupation threshold and increase the number of transformation function, ordinary WLLR adaptation algorithm represents lower recognition rate than SI models, whereas the proposed MLLR adaptation algorithm represents the improvement of over 2% for the word recognition rate as compared to performance of SI models.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.7
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• pp.1439-1446
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• 2011

This paper describes the algorithm that lowers the dimension, maintains the object recognition and significantly reduces the eigenspace configuration time by combining the higher correlation feature information and Principle Component Analysis. Since the suggested method doesn't require a lot of computation than the method using existing geometric information or stereo image, the fact that it is very suitable for building the real-time system has been proved through the experiment. In addition, since the existing point to point method which is a simple distance calculation has many errors, in this paper to improve recognition rate the recognition error could be reduced by using several successive input images as a unit of recognition with K-Nearest Neighbor which is the improved Class to Class method.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2006.06a
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• pp.505-506
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• 2006

PD(partial discharge) occurred from variable PD sources in air may be the cause of breakdown in high voltage equipment which affect huge outage in power system. Identification and localization of PD sources is very important for engineer to cope with huge accident beforhand. PD phenomena can be detected by acoustic emission sensor or electromagnetic sensor like antenna. This paper has investigated the identification method using PCA(principal component analysis) for the PD signals from variable PD sources, for which the electric field distribution and PD inception voltages were simulated by using commercial FEM program. PD signals was detected by ultra wideband antenna. Their own features were extracted as the frequency coefficients transformed with FFT(fast fourier transform) and used to obtain independent pincipal components of each PD signals.

• Journal of Multimedia Information System
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• v.8 no.1
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• pp.1-10
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• 2021

Heart Rate is a crucial physiological parameter that provides basic information about the state of the human body in the cardiovascular system, as well as in medical diagnostics and fitness assessments. At present day, it has been demonstrated that facial video-based photoplethysmographic signal captured using a low-cost RGB camera is possible to retrieve remote heart rate. Traditional heart rate measurement is mostly obtained by direct contact with the human body, therefore, it can result inconvenient for long-term measurement due to the discomfort that it causes to the subject. In this paper, we propose a non-contact-based remote heart rate measuring approach of the subject which depends on the color intensity variation of the subject's facial skin. The proposed method is applied in two regions of the subject's face, forehead and cheeks. For this, three different algorithms are used to measure the heart rate. i.e., Fast Fourier Transform (FFT), Independent Component Analysis (ICA) and Principal Component Analysis (PCA). The average accuracy for the three algorithms utilizing the proposed method was 89.25% in both regions. It is also noteworthy that the FastICA algorithm showed a higher average accuracy of more than 92% in both regions. The proposed method obtained 1.94% higher average accuracy than the traditional method based on average color value.

• Journal of the Korean Association of Geographic Information Studies
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• v.12 no.3
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• pp.13-20
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• 2009

Techniques for change detection using satellite images enable efficient detection of natural and artificial changes in use of land through multi-phase images. As for change detection, different results are made based on methods of calibration of satellite images, types of input data, and techniques in change analysis. Thus, an analytic technique that is appropriate to objectives of a study shall be applied as results are different based on diverse conditions even when an identical satellite and an identical image are used for change detection. In this study, Normalized Difference Vegetation Index (NDVI) and Principal Component Analysis (PCA) were conducted after geometric calibration of satellite images which went through absolute and relative radiometric calibrations and change detection analysis was conducted using Image Difference (ID) and Image Rationing (IR). As a result, ID-NDVI showed excellent accuracy in change detection related to vegetation. ID-PCA showed 90% of accuracy in all areas. IR-NDVI had 90% of accuracy while it was 70% and below as for paddies and dry fields${\rightarrow}$grassland. IR-PCA had excellent change detection over all areas.

• Analytical Science and Technology
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• v.34 no.3
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• pp.134-141
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• 2021

In this study, the fatty acid (FA) composition of commercial sesame oils (n = 62) was investigated using gas chromatography with flame ionization detector (GC-FID). Multivariate statistical techniques, including principal component analysis (PCA) and linear discriminant analysis (LDA), were applied to the chromatographic data of the FAs to discriminate the geographical origin of sesame oils. A statistically significant difference was observed in the content of C16:0, C18:0, C18:1, and C18:2 between domestic and imported sesame oils. A satisfactory recovery rate of 82.8-100.2 % was achieved for C16:0, C18:0, C18:1, C18:2, and C18:3. The correlation of C16:0, C18:1, and C18:2 in domestic sesame oils showed opposite trends compared to imported oils. The PCA plot demonstrated that sesame oils were clustered in distinct groups according to their origin. LDA was used to predict sesame oil samples in one of the two groups. C16:0 (Wilks λ = 0.361) and C18:1 (Wilks λ = 0.637) demonstrated the highest discriminant power for classifying the origin of the samples. The correct prediction rates were 88.9 % and 100 % for the domestic and imported samples, respectively. Further, 60 of the 62 sesame oil samples (96.8 %) were correctly classified, indicating that this approach can be used as a valuable tool to predict and classify the geographical origin of sesame oils.

• Korean Journal of Plant Resources
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• v.19 no.1
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• pp.105-111
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• 2006

The objectives of this study, an analysis of fruit and leaf morphological characteristics among the five Zizyphus cultivars could be used for the investigation of cultivars classification and could provide information to make out the UPOV TG(Test Guidelines). ANOVA tests showed that there were statistically significant differences in all fruit and leaf morphological characteristics among the five Zizyphus cultivars at 1% level. But, for kernel characteristics, differences were statistically non-significant among the cultivars. Approximately, the Wolchul and Boeun cultivars showed larger and smaller values in overall characteristics and cultivars, respectively. The results of principal component analysis(PCA) for the fruit and leaf morphological characteristics showed that the first for principal components(PC's) explained about 65.3% of the total variation. The first PC was correlated with those characteristics that were mainly related to the terminal leaf length(TLL), leaf length(LL), fruit length(FL), terminal leaf width(TLW), and leaf petiole length(LPL). The second and third PC was mainly correlated with the terminal leaf morphological index(TLMI). Therefore, these characteristics were important to analysis of the fruit and leaf morphological characteristics and classification among the five Zizyphus cultivars. Cluster analysis using UPGMA method based on principal components showed that five Zizyphus cultivars could be clustered into two groups. Group I comprises Mudung, Wolchul, and Bokjo and Geumsung cultivars, Group II is Boeun cultivar. These results well similar to that of principal component analysis.