• Journal of Korea Water Resources Association
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• v.42 no.2
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• pp.149-160
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• 2009

The objective of this study is to suggest parameter regionalization scheme which is integrated two multivariate statistical methods: principal components analysis(PCA) and hierarchical cluster analysis(HCA). This technique is to apply semi-distributed rainfall-runoff model on ungauged catchments. 7 catchment characteristics (area, mean altitude, mean slope, ratio of forest, water content at saturation, field capacity and wilting point) are estimated for 109 mid-sized sub-basins. The first two components from PCA results account for 82.11% of the total variance in the dataset. Component 1 is related to the location of the catchments relevant to the altitude and Component 2 is connected with the area of these. 103 ungauged catchments are clustered using HCA as the following 6 groups: Goesan 23, Andong 6, Imha 5, Hapcheon 21, Yongdam 4, Seomjin 44. SWAT model is used to simulate runoff and the parameters of the model on the 6 gauged basins are estimated. The model parameters were regionalized for Soyang, Chungju and Daecheong dam basins which are assumed as ungauged ones. The model efficiency coefficients of the simulated inflows for these three dams were at least 0.8. These results also mean that goodness of fit is high to the observed inflows. This research will contribute to estimate and analyze hydrologic components on the ungauged catchments.

• The Journal of the Korea Contents Association
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• v.12 no.4
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• pp.358-366
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• 2012

Cirrhosis is a consequence of chronic liver disease characterized by replacement of liver tissue by fibrosis, scar tissue and regenerative nodules leading to loss of liver function. Liver Cirrhosis is most commonly caused by alcoholism, hepatitis B and C, and fatty liver disease, but has many other possible causes. Some cases are idiopathic disease from unknown cause. Abdomen of liver Computed tomography(CT) is one of the primary imaging procedures for evaluating liver disease such as liver cirrhosis, Alcoholic liver disease(ALD), cancer, and interval changes because it is economical and easy to use. The purpose of this study is to detect technique for computer-aided diagnosis(CAD) to identify liver cirrhosis in abdomen CT. We experimented on the principal components analysis(PCA) algorithm in the other method and suggested texture information analysis(TIA). Forty clinical cases involving a total of 634 CT sectional images were used in this study. Liver cirrhosis was detected by PCA method(detection rate of 35%), and by TIA methods(detection rate of 100%-AGI, TM, MU, EN). Our present results show that our method can be regarded as a technique for CAD systems to detect liver cirrhosis in CT liver images.

• Journal of the Institute of Convergence Signal Processing
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• v.19 no.4
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• pp.174-179
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• 2018

Currently, various security technologies such as fingerprint recognition and face recognition are being developed. However, although many technologies have been developed, the field of incorporating technologies is quite limited. In particular, it is easy to adapt modern security technologies into existing digital systems, but it is difficult to introduce new digital technologies in systems using analog systems. However, if the system can be widely used, it is worth replacing the analog system with the digital system. Therefore, the selected topic is the electronic attendance system. In this paper, a camera is installed to a door to perform a Haar-like feature training for face detecting and real-time face recognition with a Eigenface in principal component analysis(PCA) based face recognition using raspberry pi. The collected data was transmitted to the smartphone using wireless communication, and the application for the viewer who can receive and manage the information on the smartphone was completed.

• Natural Product Sciences
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• v.24 no.3
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• pp.164-170
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• 2018

Rapid geographical classification of Bupleuri Radix is important in quality control. In this study, near infrared spectroscopy (NIRS) combined with supervised pattern recognition was attempted to classify Bupleuri Radix according to geographical origins. Three supervised pattern recognitions methods, partial least square discriminant analysis (PLS-DA), quadratic discriminant analysis (QDA) and radial basis function support vector machine (RBF-SVM), were performed to establish the classification models. The QDA and RBF-SVM models were performed based on principal component analysis (PCA). The number of principal components (PCs) was optimized by cross-validation in the model. The results showed that the performance of the QDA model is the optimum among the three models. The optimized QDA model was obtained when 7 PCs were used; the classification rates of the QDA model in the training and test sets are 97.8% and 95.2% respectively. The overall results showed that NIRS combined with supervised pattern recognition could be applied to classify Bupleuri Radix according to geographical origin.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.68-68
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• 2017

The status of corn genetic diversity in the uplands of Sarangani in Southern Philippines was investigated using 12 morphological traits subjected to multivariate statistical analyses. Information about traditional farming, post-harvest and storage practices were also elicited especially in relation to losses of traditional varieties, a phenomenon known as genetic erosion. While a handful of farmers still plant traditional corn varieties in the remotest areas, a significant number had already shifted to genetically modified corn. Furthermore, principal component analysis (PCA) reduced the 12 morphological traits into 5 principal components and identified ear length and ear weight to be major contributors to variation. Cluster Analysis, on the other hand, formed two distinct groups but failed to give information about intra-cluster variability among the 32 collected corn accessions. These results warrant that more informative morphological traits and that molecular markers will be used to obtain a better picture of genetic diversity in Sarangani upland corn. Molecular analysis is also needed to establish genetic identities of these cultivars and to detect gene introgression from GM varieties into the gene pool of farmers' corn varieties. These analyses are imperative for the conservation of traditional corn varieties before they disappear in the Sarangani uplands because of shifting priorities of upland farmers.

• Weed & Turfgrass Science
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• v.3 no.4
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• pp.269-275
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• 2014

A survey was conducted to identify the occurrence of problematic weed species on the Dioscorea oppostifolia fields in South Korea. Total 43 sites of the 8 different regions in S. Korea were investigated from May to October, 2014. In yam fields, the identified weeds were distributed in 11 families and 44 species. The exotic plants were identified as 3 families, 10 genera, 10 species. The vegetation of Dioscorea oppostifolia fields was classified into communities of 7 groups by methods of the Zurich-Montpellier school of phytosociology (Xanthium canadense Community, Bidens frondosa Community, Echinochloa oryzoides Community, Eclipta prostrata Community, Portulaca oleracea Community, Centipeda minima Community, Rorippa islandica Community). The weeds occurred in Dioscorea oppostifolia fields were divided into three groups in principal component plot analysis (PCA). Without weed control, yields loss in yam production was reached up to 82% as compared to weed controlled fields.

• Journal of Biosystems Engineering
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• v.40 no.2
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• pp.153-158
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• 2015

Purpose: The research presented in this paper applied the chemometric analysis to the near-infrared spectral data from line-scanned hyperspectral images of beef and fish meals in animal feeds. The chemometric statistical models were developed to distinguish beef meals from fish ones. Methods: The meal samples of 40 fish meals and 15 beef meals were line-scanned to obtain hyperspectral images. The spectral data were retrieved from each of 3600 pixels in the Region of Interest (ROI) of every sample image. The wavebands spanning 969 nm to 1551 nm (across 176 spectral bands) were selected for chemometric analysis. The partial least squares regression (PLSR) and the principal component analysis (PCA) methods of the chemometric analysis were applied to the model development. The purpose of the models was to correctly classify as many beef pixels as possible while misclassified fish pixels in an acceptable amount. Results: The results showed that the success classification rates were 97.9% for beef samples and 99.4% for fish samples by the PLSR model, and 85.1% for beef samples and 88.2% for fish samples by the PCA model. Conclusion: The chemometric analysis-based PLSR and PCA models for the hyperspectral image analysis could differentiate beef meals from fish ones in animal feeds.

• Journal of the Korean Society of Safety
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• v.34 no.5
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• pp.46-54
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• 2019

Even though the fire performance-based design concept has been introduced for various structures and buildings, which have their own specific fire performance level, the uncertainties of input parameters always exist and, then, could reduce significantly the reliability of the fire modeling. Sensitivity analysis was performed with three limited input parameters, HRRPUA, type of combustible materials, and mesh size, which are significantly important for fire modeling. The output variables are limited to the maximum HRR, the time reaching the reference temperature($60^{\circ}C$), and that to reach limited visible distance(5 m). In addition, correlation coefficient analysis was attempted to analyze qualitatively and quantitatively the degree of relation between input and output variables above. Finally, the relationship among the three variables is also analyzed by the principal component analysis (PCA) to systematically analyze the input data bias. Sensitivity analysis showed that the type of combustible materials is more sensitive to maximum HRR than the ignition source and mesh size. However, the heat release parameter of the ignition source(HRR) is shown to be much more sensitive than the combustible material types and mesh size to both time to reach the reference temperature and that to reach the critical visible distance. Since the derived results can not exclude the possibility that there is a dependency on the fire model applied in this study, it is necessary to generalize and standardize the results of this study for the fire models such as various buildings and structures.

• Korean Journal of Plant Resources
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• v.19 no.2
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• pp.300-307
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• 2006

To clarify the morphological and geographical differentiation among the polyploid complexes of L. lancifolium collections in Korea, the mo게hological variation of 173 accessions were analyzed by ANOVA (one-way analysis of variance) and PCA (principal component analysis) on the basis of 38 morphological characters. 173m accessions were grouped into 78 diploids and 95 triploids by ploid levels and the triploids separated into 75 inland triploids (all around the Korea) and 20 island triploids (Backryung-do and Sochung-do, westemmost and northernmost islands of Korea) by geographic distribution and morphology. Island triploids showed significant morphological differences with inland triploids in ANOVA by many floral and leaf characters. In PCAs, diploids were separated from inland triploids by having longer plant height, smaller flower characters, higher pollen fertility and more stomata. The first four principal components accounted for 44.1% of the total variation. Plots of the island and inland groups for the first and second principal components separated each other with slight overlapping. Although the ploid forms are different between diploid and island triploid, island triploids were more closely overlapped with diploids by principal component 1 and 2 than inland triploids. This reflects that the whole external morphology of island triploids are similar to that of diploids. This, the phenotypic differentiation between inland and island triploids seems to be partly related to their geographical origins.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.3
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• pp.1390-1403
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• 2016

Facial expression recognition (FER) plays a very significant role in computer vision, pattern recognition, and image processing applications such as human computer interaction as it provides sufficient information about emotions of people. For video-based facial expression recognition, depth cameras can be better candidates over RGB cameras as a person's face cannot be easily recognized from distance-based depth videos hence depth cameras also resolve some privacy issues that can arise using RGB faces. A good FER system is very much reliant on the extraction of robust features as well as recognition engine. In this work, an efficient novel approach is proposed to recognize some facial expressions from time-sequential depth videos. First of all, efficient Local Binary Pattern (LBP) features are obtained from the time-sequential depth faces that are further classified by Generalized Discriminant Analysis (GDA) to make the features more robust and finally, the LBP-GDA features are fed into Hidden Markov Models (HMMs) to train and recognize different facial expressions successfully. The depth information-based proposed facial expression recognition approach is compared to the conventional approaches such as Principal Component Analysis (PCA), Independent Component Analysis (ICA), and Linear Discriminant Analysis (LDA) where the proposed one outperforms others by obtaining better recognition rates.