• The Korean Journal of Applied Statistics
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• v.20 no.2
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• pp.387-394
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• 2007

This paper considers several stratification strategies for multivariate and multipurpose survey with several quantitative stratification variables. We propose three methods of stratification based on, respectively, the method of cumulative frequency square root which is the most popular one in univariate stratification, cluster analysis, and factor analysis followed by cluster analysis. We then compare the efficiency of those methods using the Dong-Eup-Myun data of the holding numbers of farming machines, extracted from the 2001 Agricultural Census. It turned out that the method based on cluster analysis with factor analysis would be a relatively satisfactory strategy.

• Journal of the Korean Data and Information Science Society
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• v.10 no.1
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• pp.79-89
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• 1999

A counting rule that allows an element to be linked to more than one enumeration unit is called a multiplicity counting rule. Sample designs that use multiplicity counting rules are called network samples. Defining a network to be a set of observation units with a given linkage pattern, a network may be linked with more than one selection unit, and a single selection unit may be linked with more than one network. This paper considers allocation for multivariate stratified random sampling with multiplicity.

• The Korean Journal of Applied Statistics
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• v.21 no.3
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• pp.377-385
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• 2008

Most of the sample surveys conducted by several statistics preparation agencies are multipurpose surveys inquiring into several distinguishing items through a single sample. In a multipurpose sample design, the stratification tends to be very complex since the stratification variables which are both multivariate and heterogeneous must be considered collectively. In this paper we point out an outlier effect in a multivariate stratification to which the K-means clustering method is applied and propose to consider outliers prior to the stratification step. We also show an empirical stratification effect under consideration of outliers through a case study of sample design for The Rural Living Indicators.

• Survey Research
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• v.9 no.1
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• pp.69-85
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• 2008

Stratification is a feature of the majority of field sample design. This paper considers the multivariate stratification strategy for multipurpose sample survey with several auxiliary variables. In a multipurpose survey, stratification procedure is very complicated because we have to simultaneously consider the efficiencies of stratification for several variables of interest. We propose stratification strategy based on factor analysis and cluster analysis using several stratification variables. To improve the efficiency of stratification, we first select the stratification variables by factor analysis, and then apply the K-means clustering algorithm to the formation of strata. An application of the stratification strategy in the sampling design for the Fisher Production Survey is discussed, and it turns out that the variances of estimators are significantly less than those obtained by simple random sampling.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.9-9
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• 2016

많은 연구들에서 단변량 수문 변량들에 대한 불확실성 분석이 이루어지고 있지만, 다변량에 대한 불확실성에 관한 연구는 아직까지 정확하게 이루어지고 있지 않은 실정이다. 이에 본 연구에서는 갈수기(12월~4월)의 강수, 온도와 남방진동(El Ni?o-Southern Oscillation, ENSO)과 같은 수문기상학적 변량들 사이의 시간에 따른 변동 구조를 조사하고, 식별된 패턴을 이용한 강우와 온도의 예측 향상 가능성을 살펴보았다. 수문기상학적 변수간의 시변성 구조를 이해하기 위해서 각각의 단변량 매개변수와 시간에 따라 변화하는 Copula 매개변수를 동시에 추정할 수 있는 Copula 함수 기반의 새로운 다변량 비정상성 모델을 개발하고자 한다. 강우와 온도의 비정상정 단변량 분포를 생성하기 위해 ENSO 지표 또는 시계열 예측인자와 함께 시변성 모델을 적용할 수 있다. 최종적으로, 확인된 시간 변동적인 구조와 연관된 종관 패턴을 나타내고 논의하고자 한다.

• Journal of Korean Society of Steel Construction
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• v.20 no.5
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• pp.591-599
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• 2008

Weathering steel has been widely used in bridges and cladding materials due to its superior atmospheric corrosion resistance. Actually, weathering steel has often been used in Korea as cladding material. However, the performance of the weathering steel in claddings has not been fully monitored. We conducted a field study on the performance of weathering steels and measured the quantity of corrosion loss on the weathering steel claddings in Korea. Based on the measured corrosion loss and weather (environmental) data, the equation to predict corrosion loss of weathering steels was proposed by using environmental factors in KIHO region in Korea. The proposed equation predicted very well the real corrosion losses of KIHO region.

• Journal of the Korean earth science society
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• v.18 no.6
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• pp.515-521
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• 1997

To investigate the nature of the waste materials in the Nanjido Landfill, we have conducted multivariate statistical analysis of geophysical data set comprised of magnetic, gravity, LandSat TM thermal band and surface depression measurement data. Because these data sets show different responses to the depth, we have transformed the observed total field magnetic data and gravity data to the residual reduced-to-pole(RTP) magnetic anomalies and the three dimensional density anomalies, respectively, and utilized the informations about the upper shallow part of the landfills only in the following process. For the statistical analysis at the points of depression measurement, the magnetic, density and LandSat data values at these points are determined by interpolation process. Since the multivarite statistical analysis technique utilizes a clustering algorithm for classification of data set and we have measured the dissimilarity between objects by using Euclidean distance, standardization was applied prior to distance calculation in order to eliminate any scaling effects due to different measurement unit of each data set. The hierarchial grouping technique was used to construct the dendrogram. The optimum number of statistical groups(clusters), which are classified on the basis of geophysical and geotechnical characteristics, appeared to be six on the resulting dendrogram. The result of this study suggests that the dimension and nature of the multicomponent waste landfills can be identified by application of the multivarite statistical analysis technique to integrated geophysical data sets.

• Journal of The Geomorphological Association of Korea
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• v.19 no.3
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• pp.37-49
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• 2012

The objective of this paper is to propose a methodology for surface sediments classification in tidal flats that can combine ground survey data with high-resolution remote sensing data by multivariate kriging. Unlike conventional methodologies that have classified remote sensing data by using pre-classified sediment components, a new classification methodology presented in this paper first generates sediment component fraction maps and then classifies the sediments on a final stage. For generating sediment component fractions, regression kriging, as one of multivariate kriging algorithms, is applied to integrate ground survey data and remote sensing data. First, trend components of sand, silt, and clay are derived through regression analysis of ground survey data and spectral information from remote sensing data. Then, residuals at sample locations are computed and interpolated to generate residual components in the study area. Finally, the sediment component fractions are computed by adding the residuals to the trend components and are classified on a final stage. A case study at the Baramarae tidal flats with KOMPSAT-2 imagery is carried out to evaluate the classification capability of the proposed classification methodology. Through the case study, the proposed methodology showed the best classification accuracy, compared with the conventional classification methodologies. Especially, much improvement of classification accuracy for fine-grained sediments were also obtained. Therefore, it is expected that the presented classification methodology would be an effective one for surface sediments classification in tidal flats.

• Journal of Korean Tunnelling and Underground Space Association
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• v.2 no.2
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• pp.41-49
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• 2000

Rock Mass Rating has been widely applied to the underground tunnel excavation and many other practical problems in rock engineering. However, Rock Mass Rating is hard, even by the experts of tunnel assessment owing to lack of investigation system. In this study, using multivariate analysis we presented rock mass rating system that is objective and easy to use. The constituents of RMR are decided to RQD, condition of discontinuities, groundwater conditions, intact rock strength, orientation of discontinuities, spacing of discontinuities in important order. In each step, we proposed the best multiple regression model for RMR system.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.421-421
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• 2017

In order to secure the safety of coastal areas from the continuous storm surge in Korea, it is important to predict the wave movement and properties accurately during the storm event. To improve the accuracy of the storm simulation, and to quantify coastal risks from the storm event, the dependencies between wave height, wave period, and storm duration should be analyzed. In this study, therefore, copulas were used to develop multivariate statistical models of sea storms. A case study of the east coast of Korea was conducted, and the dependencies between wave height, wave period, water level, storm duration and storm interarrival time were investigated using Kendall's tau correlation coefficient. As a result of the study, only wave height, wave period, and storm duration appeared to be correlated.