• Journal of Navigation and Port Research
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• v.27 no.2
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• pp.163-170
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• 2003

This empirical study takes the activation approach for the competitive power of Korean shipping companies in the Korea-China liner routes. Data for this study were collected from Korea/ China/ 3rd flag shipping companies through the 500 questionnaires. The data of 250 respondents were analyzed statistically to verify the hypotheses and to induce Regression Equation which could predicts the influencing level of the determinants to competitive advantage for Korean shipping companies on Korea-China Liner Shipping Routes. Factor Analysis/ Cronbach's Alpha/ Principal Analysis/ Multiple Regression Analysis were used in order to test the hypotheses for the empirical study.

• The Korean Journal of Food And Nutrition
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• v.14 no.1
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• pp.20-27
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• 2001

The consumption of radish ( Rhaphanus sativus L.) sprout, which is Cruciferae family, is increasing because of its pungent flavor and taste. Its volatile components were analyzed by SDE (simultaneous steam distillation & extraction) method and P&T(purge & cryogenic trapping) method. As a solvent, diethyl ether and diethyl ether : pentane mixture(2:1, v/v) were used in SDE method, and diethyl ether in P&T method. Analyzing by GC and GC-MS, the major component was sulfur compounds (19 species, peak area 76.6%) with diethyl ether, sulfur compounds(15. 44.0%) and hydrocarbons(23, 23.8%) with diethyl ether-pentane mixture in SDE method. Also, hydrocarbons(25, 84.1% ) was major component in P& T method. The major volatile component of fresh radish sprout were n-heptane, methyl pentane and that of boiled radish sprout were 4-methylthio-3-butenyl isothiocyanate, methyl mercaptane, 2,3-dimethyl disulfide. Low molecular volatile components were detected more by P& T method, but types and relative quantities of volatile components were measured less comparing to SDE method.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06c
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• pp.427-431
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• 2007

차세대 지뢰탐지 기술로는 NQR(Nuclear Quadrupole Resonance, 핵4중극자공명), GPR(Ground Penetrating Radar, 지상 침투 레이더)등 이 연구 및 개발 중 이다. 현재 우리나라에서도 이중 GPR을 차세대 지뢰탐지 기술로 연구중에 있다. 그렇지만 지금까지 개발된 GPR 기술을 적용한 지뢰탐지기는 얻어진 2차원 영상에 대해서 육안에 의한 식별만이 가능하여 지뢰 식별이 장시간 소요된다는 단점을 가지고 있다. 이에 본 논문에서는 그러한 문제를 해결하기 위해 주성분 분석법을 적용하여 해결하고, 제안된 시스템이 가능한지 확인하기 위해 유사한 실험 환경을 구성하고, 얻어진 영상을 학습시켜 실제로 얻어진 영상에 대한 분류가 가능한지를 확인하였다.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.41 no.3
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• pp.266-273
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• 1996

To obtain basic information on forage sorghum F$_1$ hybrid breeding a total of 16 lines were selected from 311 introduced sorghum germplasm accessions, assessed and classified by the taxonomic distance and principal component analysis. The lines of which plant height and morphological characters were diverse and the 50% flowering date was similar to each other, were selected for parental lines in sorghum $\times$ sweet sorghum and sorghum $\times$ sudangrass crossing groups. Three varietal groups were classified by the average linkage cluster analysis based on the D$^2$ computed in eleven characters. Group I, II and III included 6 lines of sudangrass, 4 lines of sweet sorghum and 6 lines of grain sorghum, respectively. In the result of principal component analysis for eleven characters, about 82% of total variation could be appreciated by the first four principal components, the first principal component was highly loaded with head compactness and shape, l00-seed weight, plant color and grain covering, the second principal component with flowering date, plant height and awnness.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.2
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• pp.126-132
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• 2015

Technology forecasting is about understanding a status of a specific technology in the future, based on the current data of the technology. It is useful when planning technology management strategies. These days, it is common for countries, companies, and researchers to establish R&D directions and strategies by utilizing experts' opinions. However, this qualitative method of technology forecasting is costly and time consuming since it requires to collect a variety of opinions and analysis from many experts. In order to deal with these limitations, quantitative method of technology forecasting is being studied to secure objective forecast result and help R&D decision making process. This paper suggests a methodology of technology forecasting based on quantitative analysis. The methodology consists of data collection, principal component analysis, and technology forecasting by logistic regression, which is one of the data mining techniques. In this research, patent documents related to autonomous vehicle are collected. Then, the texts from patent documents are extracted by text mining technique to construct an appropriate form for analysis. After principal component analysis, logistic regression is performed by using principal component score. On the basis of this result, it is possible to analyze R&D development situation and technology forecasting.

• Proceedings of the Korean Society of Fisheries Technology Conference
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• 1996.06a
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• pp.224-225
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• 1996

• Analytical Science and Technology
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• v.7 no.3
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• pp.293-299
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• 1994

Fifty ancient Korean coins originated in Choson period have been determined for 11 elements such as Sn, Fe, As, Au, Co, Sb, Ir, Os, Ru and Ni by destructive and non-destructive neutron activation analysis as well as for 3 elements such as Cu, Pb and Zn by atomic absorption spectroscopy. The multivariate data have been analyzed by principal component mapping method. The spread of sample points in the eigenvector polt has been attributed to common origins of some elements.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.6
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• pp.594-600
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• 2017

The new design methodology of prediction model and pattern classification, which is based on the dimension reduction algorithm called principal component analysis, is introduced in this paper. Principal component analysis is one of dimension reduction techniques which are used to reduce the dimension of the input space and extract some good features from the original input variables. The extracted input variables are applied to the prediction model and pattern classifier as the input variables. The introduced prediction model and pattern classifier are based on the very simple regression which is the key point of the paper. The structural simplicity of the prediction model and pattern classifier leads to reducing the over-fitting problem. In order to validate the proposed prediction model and pattern classifier, several machine learning data sets are used.

• Communications of Mathematical Education
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• v.37 no.1
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• pp.65-84
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• 2023

The method of Lagrange multipliers, one of the most fundamental algorithms for solving equality constrained optimization problems, has been widely used in basic mathematics for artificial intelligence (AI), linear algebra, optimization theory, and control theory. This method is an important tool that connects calculus and linear algebra. It is actively used in artificial intelligence algorithms including principal component analysis (PCA). Therefore, it is desired that instructors motivate students who first encounter this method in college calculus. In this paper, we provide an integrated perspective for instructors to teach the method of Lagrange multipliers effectively. First, we provide visualization materials and Python-based code, helping to understand the principle of this method. Second, we give a full explanation on the relation between Lagrange multiplier and eigenvalues of a matrix. Third, we give the proof of the first-order optimality condition, which is a fundamental of the method of Lagrange multipliers, and briefly introduce the generalized version of it in optimization. Finally, we give an example of PCA analysis on a real data. These materials can be utilized in class for teaching of the method of Lagrange multipliers.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.10
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• pp.48-58
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• 1998

We present a method that can recognize face images using independent component analysis that is used mainly for blind sources separation in signal processing. We assumed that a face image can be expressed as the sum of a set of statistically independent feature images, which was obtained by using independent component analysis. Face recognition was peformed by projecting the input image to the feature image space and then by comparing its projection components with those of stored reference images. We carried out face recognition experiments with a database that consists of various varied face images (total 400 varied facial images collected from 10 per person) and compared the performance of our method with that of the eigenface method based on principal component analysis. The presented method gave better results of recognition rate than the eigenface method did, and showed robustness to the random noise added in the input facial images.