• Journal of Navigation and Port Research
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• v.30 no.1 s.107
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• pp.35-43
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• 2006

This study suggests strategies to prevent traffic accidents by utilizing impact factors per each cluster and the typical patterns of 81 cities based on the statistical analysis of the data concerning the TCI which was developed from the partnership of the Traffic Safety Authority and the Green Traffic Movement Corporation in 2002 and 2003. The Principal Component Analysis and Cluster Analysis on impact factors and TCI result in 4 components and 4 clusters. Also as the results of Stepwise Multiple Regression Analysis examining the relationship between impact factors and TCI, R2 values of these models show high to all clusters. According to the results, we suggest strategies to prevent traffic accidents per cluster concretely and it is necessary to analyze how effective the invested facilities are in reducing traffic accidents in the future.

• Journal of the Korean Chemical Society
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• v.31 no.6
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• pp.567-575
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• 1987

Fourty five ancient Korean glass pieces have been determined for 19 elements such as Ag, As, Br, Ce, Co, Cr, Eu, Fe, Hf, K, La, Lu, Na, Ru, Sb, Sc, Sm, Th and Zn, and for one such as Pb by instrumental neutron activation analysis and by atomic absorption spectrometry, respectively. The multivariate data have been analyzed for the relation among elemental contents through the variance-covariance matrix. The data have been further analyzed by a principal component mapping method. As the results training set of 5 class have been chosen, based on the spread of sample points in an eigen vector plot and archaeological data. The 5 training set consisting of 36 species and a test set consisting of 9 species bave finally been analyzed for the assignment to certain classes or outliers through the statistical isolinear multiple component analysis (SIMCA). The results have showed the whole species for 5 training set and 3 species in the test set are assigned appropriately and these are in accord with the results by principal component mapping.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.9C
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• pp.748-755
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• 2010

In this paper we propose an improved face recognition method using Gabor tensor. Gabor transform is known to be able to represent characteristic feature in face and reduced environmental influence. It may contribute to improve face recognition ratio. We attempted to combine three-dimensional tensor from Gabor transform with MPCA(Multilinear PCA) and LDA. MPCA with tensor which use various features is more effective than traditional one or two dimensional PCA. It is known to be robust to the change of face expression or light. Proposed method is simulated by MATALB9 using ORL and Yale face database. Test result shows that recognition ratio is improved maximum 9~27% compared with exisisting face recognition method.

• Analytical Science and Technology
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• v.24 no.6
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• pp.519-526
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• 2011

Chemometrics using near-infrared (NIR) and Raman spectroscopy have found significant uses in a variety quantitative and qualitative analyses of pharmaceutical products in complex matrixes. Most of the pharmaceutical can be measured directly with little or no sample preparation using these spectroscopic methods. During pharmaceutical manufacturing process, analytical techniques with no or less sample preparation are very critical to confirm the quality. This study showed NIR and Raman spectroscopy with principal component analysis (PCA) was very effective for the blending processing control. It is of utmost importance to evaluate critical parameters related to quality of products during pharmaceutical processing. The blending is confirmed by off-line determination of active pharmaceutical ingredient (API) by a conventional method such as high performance liquid chromatography (HPLC) and UV spectroscopy. These analytical methods are time-consuming and ineffective for real time control. This study showed the possibility for the determination of blend uniformity end-point of CR tablets with the use of both NIR and Raman spectroscopy. The samples were acquired from six positions during blending processing with U-type blender from 0 to 30 min. Using both collected NIR and Raman spectral data, principal component analysis (PCA) was used to follow the uniformity of blending and finally determine the end-point. The variation of homogeneity of six samples during blending was clearly found and blend uniformity end-point was successfully confirmed in the domains of principal component (PC) scores.

• Journal of the Korean Geographical Society
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• v.33 no.1
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• pp.17-40
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• 1998

The purpose of this study is to classify climatic environment according to its characteristics in Korea using GIS. The necessary condition of climatic division is that it is able to indicate climatic phenomena systematically and it has scientific persuasive power. Precipitaiton, rainfall days, temperature and weather entropy which are consist of Korean climatic elements are of advantage to indicate climatic phenomena systematically. GIS(Geographic Information System)has scientific persuasive power. This paper shows the time-spatial variations of each climatic elements, using GIS to precipitation, rainfall days, Temperature and weather entropy in Korea. And writers tried to know these regional characteristics and to divide the detailed climatic environment objectively and systematically. The main result of this study is that the regional division of climatic environment in Korea can be classified into 8 types, in details, 26 or 48 types.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06b
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• pp.414-416
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• 2012

고차 주성분 방법으로는 텐서 분석이 있었다. Electroencephalography(EEG) 데이터나 Social Network 데이터에 텐서 분석이 적용되어 주요한 성분들을 찾는 연구들이 있었다. 그러나 텐서 분석은 직관적으로 이해하기에 어려움이 있으며 중요한 노드를 찾는데에는 다소 어려움이 있다. 본 논문에서는 고차 하이퍼에지로 이차원 행렬을 만들고 주성분분석법을 이용하여 중요한 노드를 찾는 새로운 방법론을 제시한다. 데이터로는 Multimodal Memory Game(MMG) 수행시 촬영한 EEG 데이터를 사용하였다. MMG는 TV 드라마 기반의 기억인출게임이다. 베타파의 Power Spectrum Density(PSD)는 각 위치의 채널들의 활성도를 나타내는 지표이다. 우리는 Random Sampling을 바탕으로 PSD 상위 50%의 채널들간의 전이행렬을 구하였다. 그 후 고유치와 고유벡터를 구하였다. 가장 큰 고유치의 고유벡터는 주성분을 나타내며 고유벡터의 각 원소들은 중요도를 나타내는 centrality 이다. 세 명의 피험자에 대한 centrality 상위 30개의 중요한 채널들을 구하였고 세명에 공통적으로 포함되는 채널을 확인하였다.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.10a
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• pp.241-242
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• 2012

• Journal of the Korean Geophysical Society
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• v.5 no.4
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• pp.321-328
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• 2002

Difficulties encountered in downhole S-wave (shear wave) surveys include the precise determination of shear wave travel times and determination of geophone orientation relative to the direction of polarization caused by the seismic source. In this study an S-wave enhancing and a principal component analysis method were adopted as a tool for determination of S-wave arrivals and the direction of polarization from downhole S-wave survey data. An S-wave enhancing method can almost double the amplitudes of S-waves, and the angle between direction of polarization and a geophone axis can be obtained by a principal component analysis. Once the angle is obtained data recorded by two horizontal geophones are transformed to principal axes, yielding so called scores. The scores gathered along depth are all in-phase, consequently, the accuracy of S-wave arrival picking could be remarkably improved. Applying this processing method to the field data reveals that the test site consists of a layered ground earth structure.

• The Korean Journal of Applied Statistics
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• v.36 no.6
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• pp.501-514
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• 2023

When analyzing high dimensional data such as text data, if we input all the variables as explanatory variables, statistical learning procedures may suffer from over-fitting problems. Furthermore, computational efficiency can deteriorate with a large number of variables. Dimensionality reduction techniques such as feature selection or feature extraction are useful for dealing with these problems. The sparse principal component analysis (SPCA) is one of the regularized least squares methods which employs an elastic net-type objective function. The SPCA can be used to remove insignificant principal components and identify important variables from noisy observations. In this study, we propose a dimension reduction procedure for text data based on the SPCA. Applying the proposed procedure to real data, we find that the reduced feature set maintains sufficient information in text data while the size of the feature set is reduced by removing redundant variables. As a result, the proposed procedure can improve classification accuracy and computational efficiency, especially for some classifiers such as the k-nearest neighbors algorithm.

• Korean Journal of Environmental Agriculture
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• v.13 no.1
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• pp.90-97
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• 1994

This experiment was conducted to improve the analytical method of tricyclazole- and IBP-combined dust. When the tricyclazole and IBP active ingadients were analyzed by the official analytical method, their recovery rates were 89.5 and 100%, respectively. A reason of the lower recovery rate in tricyclazole was found to be due to strong binding to the minor inorganic compoments, $Al_2O_3$, $Fe_2O_3$, CaO and MgO, of talc and kaoline. However, addition of 0.2% dimethylamine to extraction solvent for tricyclazole- and IBP-combined dust effectively raised the recovery rate of tricyclazole by providing higher basicity than tricyclazole. We have suggest an improved analytical method which is applicable to effective and simultaneous analysis of the active ingradients of tricyclazole- and IBP-combined dust.