• Korean Journal of Soil Science and Fertilizer
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• v.43 no.5
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• pp.748-754
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• 2010

The contents of soil organic matter (SOM) and $Al_o+1/2Fe_o$ in soils are important criteria for the classification of new Andisols in Soil Taxonomy system. There are many soil types in Jeju Island with various soil forming environments. This paper was conducted to estimate the contents of soil organic matter and the content of ammonium oxalate extracted Al and Fe ($Al_o+1/2Fe_o$) using various environmental variables and to make soil property maps using a statistical analyses. The soil samples were collected from 321 locations and analyzed to measure the contents of SOM and $Al_o+1/2Fe_o$. It was analyzed the relationships among them and various environmental variables such as temperature, precipitation, net primary product, radiation, evapotranspiration, altitude, soil forming energy, topographic wetness index, elevation, difference surrounded area, and distances from the shore and the peak. We can exclude multi-collinearity among environmental variables with principal component analysis and reduce all the variables to 3 principal components. The contents of SOM and $Al_o+1/2Fe_o$ were estimated by multiple regression models and maps of them were made using the models.

• Journal of Navigation and Port Research
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• v.43 no.6
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• pp.335-343
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• 2019

As the regulations of ship exhaust gas have been strengthened recently, many measures are under consideration to reduce fuel consumption. Among them, research has been performed actively to develop a machine-learning model that predicts fuel consumption by using data collected from ships. However, many studies have not considered the methodology of the main parameter selection for the model or the processing of the collected data sufficiently, and the reckless use of data may cause problems such as multicollinearity between variables. In this study, we propose a method to predict the fuel consumption of the ship by using the principal component analysis to solve these problems. The principal component analysis was performed on the operational data of the 13K TEU container ship and the fuel consumption prediction model was implemented by regression analysis with extracted components. As the R-squared value of the model for the test data was 82.99%, this model would be expected to support the decision-making of operators in the voyage planning and contribute to the monitoring of energy-efficient operation of ships during voyages.

• Journal of KIISE:Software and Applications
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• v.31 no.6
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• pp.808-816
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• 2004

This paper proposes the method of implementation of practical online face verification system based on multiple feature combination and a similarity space. The main issue in face verification is to deal with the variability in appearance. It seems difficult to solve this issue by using a single feature. Therefore, combination of mutually complementary features is necessary to cope with various changes in appearance. From this point of view, we describe the feature extraction approaches based on multiple principal component analysis and edge distribution. These features are projected on a new intra-person/extra-person similarity space that consists of several simple similarity measures, and are finally evaluated by a support vector machine. From the experiments on a realistic and large database, an equal error rate of 0.029 is achieved, which is a sufficiently practical level for many real- world applications.

• The KIPS Transactions:PartB
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• v.14B no.3 s.113
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• pp.191-198
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• 2007

In this paper, we present a marker detection and recognition method from camera image for a disabled person to interact with a server system which can control appliance of surrounding environment. It converts the camera image to a binary image by using multi-threshold and extracts contours of objects in the binary image. After that, it approximates the contours to a list of line segments. It finds rectangular markers by using geometrical features which are extracted from the approximated line segments. It normalizes the shape of extracted markers into exact squares by using the warping technique. It extracts feature vectors from marker image by using principal component analysis and then recognizes the marker. The proposed marker recognition system is robust for light change by using multi-threshold. Also, it is robust for angular variation of camera by using warping technique and principal component analysis. Experimental results show that the proposed method achieves 100% recognition rate at maximum for 21 markers and execution speed of 12 frames/sec.

• 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.

• The Korean Journal of Applied Statistics
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• v.36 no.4
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• pp.279-294
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• 2023

It is well known to generate synthetic data sets by the sequential regression multiple imputation (SRMI) method. The R-package synthpop are widely used for generating synthetic data by the SRMI approaches. In this paper, I suggest generating synthetic data based on the probabilistic principal component analysis (PPCA) method. Two simple data sets are used for a simulation study to compare the SRMI and PPCA approaches. Simulation results demonstrate that pairwise coefficients in synthetic data sets by PPCA can be closer to original ones than by SRMI. Furthermore, for the various data types that PPCA applications are well established, such as time series data, the PPCA approach can be extended to generate synthetic data sets.

• The KIPS Transactions:PartD
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• v.9D no.1
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• pp.75-80
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• 2002

Putnam develops SLIM (Software LIfecycle Management) model based upon the assumption that the manpower utilization during software project development is followed by a Rayleigh distribution. To obtain the manpower distribution, we have to be estimate the total development effort and difficulty ratio parameter. We need a way to accurately estimate these parameters early in the requirements and specification phase before investment decisions have to be made. Statistical tests show that system attributes are highly correlation (redundant) so that Putnam discards one and get a parameter estimator from the other attributes. But, different statistical method has different system attributes and presents different performance. To select the principle system attributes, this paper uses the principle component analysis (PCA) instead of Putnam's method. The PCA's results improve a 9.85 percent performance more than the Putnam's result. Also, this model seems to be simple and easily realize.

• The Korean Journal of Applied Statistics
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• v.7 no.1
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• pp.69-74
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• 1994

Kim (1992) derived influence functions of rows and columns on the eigenvalues obtained in correspondence analysis (CA) of two-way contingency tables. As in principal component analysis, the eigenvalues are of great importance in CA. The goodness of a two dimensional correspondence plot is determined by the ratio of the sum of the two largest eigenvalues to the sum of all the eigenvalues. By investigating those rows and columns with high influence, a correspondence plot may be improved. In this paper, we extend the influence functions of CA to multiple correspondence analysis (MCA), which is a CA of multi-way contigency tables. An explicit formula of the influence function is given.

• Journal of agriculture & life science
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• v.46 no.2
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• pp.1-8
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• 2012

This study is a precedent study for deriving transfer function model between growing stock and forest management policies. Its goal is to solve the multicollinearity between forest works inducing growing stock changes through principal component analysis using annual time series data from 1997 to 2008. As the results, the total explanatory power showed 91.4% on the summarized 3 principal components. They were renamed 'good forest management' 'pest & insets management' 'forest fires' for conceptualization on the derived each component.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.120-120
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• 2017

인천지역의 상수도공급은 팔당댐을 취수원으로 하여 도수, 송수관을 거쳐 인천지역 내 정수장을 통하여 각 급수지역까지 일원화된 관로시스템으로 공급되고 있다. 관망에서의 적절한 수압관리, 노후관로 교체사업 등은 급수관망 내 관로 사고위험을 줄일 수 있고, 누수량을 저감하여 무수율의 감소로 이어질 수 있다. 상수관망 내 누수에 영향을 주는 물리적, 운영적 요소를 파악하고, 이를 이용하여 누수해결을 위한 방법론을 제시하는 것은 매우 중요하다. 본 연구에서는 인천시 배수관망 데이터를 활용하여 통계분석 및 인공신경망을 통하여 무수율에 영향을 미치는 인자를 선별하고, 무수율과의 연관성을 분석하고자 하였다. 이를 위해 대상지역에 대한 시설현황 및 운영자료를 취득하고, 무수율 분석에 활용하였다. 인천시의 소블럭을 대상으로 관로노후도, 배수관연장, 평균관경, 급수전당 공급량, 누수발생 횟수, 용도지역, 관망구성 형태 등을 고려하여 무수율과의 관계분석을 위한 통계분석을 수행하였다. 특히 급수에 필요한 최소에너지와 관망에서 공급되는 에너지를 비교하기 위하여 관망해석 프로그램인 EPANET을 이용하여 관망내 절점에서의 수압과 수요량이 적용된 최소공급에너지를 활용하였고, 이를 통하여 블록 내 과잉공급에너지와 무수율의 영향성을 비교하였다. 최종적으로 산출된 주요인자에 대한 주성분분석, 분산분석, 다중회귀분석 등의 통계분석과 인공신경망에 의해 학습된 알고리즘을 통하여 산정된 무수율을 실측 무수율과 비교, 분석하였다. 인공신경망에 의해 산정된 무수율과 실측 무수율의 정확도를 평가하기 위하여 MAE, MSE, PBIAS 등의 정확도 평가와 산점도 분석을 수행하고, 상관계수를 도출하여 가장 정확한 방법을 결정하였다. 분석 결과 통계분석에 의한 다중회귀식으로 산출된 무수율 보다 인공신경망에 의한 무수율이 실측값에 더욱 근접한 것으로 나타났으며 이용된 뉴런의 수의 따라 산출결과가 상이하기 때문에 최적 뉴런의 수를 산정해야 할 필요가 있음을 확인하였다. 특히 사용된 상수관망 주요인자 중 주성분분석을 통하여 선정된 각 성분을 인공신경망에 적용시 더욱 정확한 무수율 예측이 가능한 것으로 나타났다.