• Journal of the Korean Institute of Landscape Architecture
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• v.13 no.1
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• pp.43-66
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• 1985

Korea needs proper and rational public policies on conservation and use of forest land and other natural resources because of the accelerating expansion of national land developments in recent years. Unfortunately, there is no systematic planning system to support the needs. Generally, forest land use planning needs suitability analysis based on efficient land classification system. The goal of this study was to classify a forest land using multivariate satistical methods. A case study was carried out in winter of 1983 on a mountainous area higher than 100m above sea level located at Mt. Kwanak in Anyang -city, Kyung-gi-do (province). The study area was 19.80 km$^2$wide and was divided into 1, 383 Operational Taxonomic Units (OTU's) by a 120m$\times$120m grid. Fourteen descriptors were identified and quantified for each OTU from existing national land data : elevation, slope, aspect, terrain form, geologic material, surface soil permeability, topsoil type, depth of the solum, soil acidity, forest cover type, stand size class, stand age class, stand density class, and simple forest soil capability class. For this study, a FORTRAN IV program was written for input and output map data, and the computer statistics packages, SPSS and BMD, were used to perform the multivariate statistical analysis. Fourteen variables were analyzed to investigate the characteristics of their fire quench distribution and to estimate the correlation coefficients among them. Principal component analysis was executed to find the dimensions of forest land characteristics, and factor scores were used for proper samples of OTU throughout the study area. In order to develop the classes of forest land classification based on 102 surrogates, cluster and discriminant analyses of principal descriptor variable matrix were undertaken. Results obtained through a series of multivariate statistical analyses were as follows ; 1) Principal component analysis was proved to be a useful tool for data selection and identification of principal descriptor variables which represented the characteristics of forest land and facilitated the selection of samples.

• Journal of the military operations research society of Korea
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• v.6 no.2
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• pp.89-127
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• 1980

The multivariate analysis techniques of cluster analysis are examined in this article. The theory and applications of the techniques and computer software concerning these techniques are discussed and sample jobs are included. A hierarchical cluster analysis algorithm, available in the IMSL software package, is applied to a set of data extracted from a group of subjects for the purpose of partitioning a collection of 26 attributes of a weapon system into six clusters of superattributes. A nonhierarchical clustering procedure were applied to a collection of data of tanks considering of twenty-four observations of ten attributes of tanks. The cluster analysis shows that the tanks cluster somewhat naturally by nationality. The principal componant analysis and the discriminant analysis show that tank weight is the single most important discriminator among nationality although they are not shown in this article because of the space restriction. This is a part of thesis for master's degree in operations research.

• Journal of the Korea Society of Computer and Information
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• v.21 no.5
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• pp.167-175
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• 2016

The volatile component analysis of 82 commercialized liquors(44 samples of single malt whisky, 20 samples of blended whisky and 18 samples of brandy) was carried out by gas chromatography after liquid-liquid extraction with dichloromethane. Pattern recognition techniques such as principle component analysis(PCA), cluster analysis(CA), linear discriminant analysis(LDA) and partial least square discriminant analysis(PLSDA) were applied for the discrimination of different liquor categories. Classification rules were validated by considering sensitivity and specificity of each class. Both techniques, LDA and PLSDA, gave 100% sensitivity and specificity for all of the categories. These results suggested that the common characteristics and identities as typification of whiskies and brandys was founded by using multivariate data analysis method.

• Proceedings of the Korean Geotechical Society Conference
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• 2002.03a
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• pp.197-204
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• 2002

In this study, multivariate analysis based on domestic data(958 EA) of road tunnel, and suggest the easy prediction equation of Q-system. We generate applicable Q-value to numerical analysis method with using the equation and investigate the behavior as variable Q-value of rock mass induced excavation with discontinuum numerical analysis method, UDEC. In the result of the experiment, we research the application range of Q-value to apply the continuum model to discontinuous rock mass is below 0.7 and we testify the applicability of continuum model as researched Q-value with continuum numerical analysis method, FLAC.

• Journal of Korean Academy of Nursing Administration
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• v.21 no.2
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• pp.184-192
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• 2015

Purpose: This study was done to assess nursing students' practice of patient safety management (PSM), identify factors affecting PSM and provide basic data to develop education programs to strengthen students' competencies for patient safety. Methods: In this descriptive research the practice of PSM by nursing students was examined and predictive factors were identified. Participants were junior and senior nursing students from 7 universities in 7 cities. Self-report questionnaires were used for data collection. Results: Significant positive correlations were found between knowledge of PSM, perception of the importance of PSM and practice of PSM. In multivariate analysis, women students, participation in patient safety education in school, knowledge of PSM, and practice of PSM predicted high perception of the importance of PSM. In multivariate analysis, senior year and participation in patient safety education in school predicted higher knowledge of PSM. In multivariate analysis, perception of the importance of PSM predicted high practice of PSM. Conclusion: In this study, knowledge was not found to directly affect PSM practice, but was found to affect the perception of the importance of PSM, a significant predictive variable. Thus, the importance of PSM should be strongly emphasized during education.

• Korean Journal of Pharmacognosy
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• v.41 no.4
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• pp.303-307
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• 2010

To overcome the limit of morphological method for classification of herbal drug, a novel method to discriminate its origin using pyrolysis mass spectrometry-multivariate analysis was developed. This method was applied successfully to Scutellaria baicalensis Georgi, one of the most popular herbal drug in oriental countries. The ethylacetate soluble fractions were prepared by sonication from pulverized roots of S. baicalensis which were collected from various regions including Korea and China, and subjected to direct insertion probe (DIP) mass spectrometry to achieve mass spectra of pyrolizates of extracts. The probe temperature was elevated from $30^{\circ}C$ to $320^{\circ}C$ at increasing rate $64^{\circ}C/min$, and the average mass spectrum calculated from total ion chromatography (TIC) was obtained. The relative peak intensities versus m/z were subjected to SAS program, and the training set (9 from Korea origin and 22 from China origin) was clustered two groups as its origin. In the test set, 11 samples among total 13 test sample were successfully classified according to their origin by developed method with accuracy of 85%.

• Journal of the Korean Institute of Gas
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• v.10 no.4 s.33
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• pp.29-33
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• 2006

Estimation of process safety is important in the chemical process design. Prediction for flash points of flammable substances used in chemical processes is the one of the methods for estimating process safety. Flash point is the property used to examine the potential for the fire and explosion hazards of flammable substances. In this paper, multivariate statistical analysis methods(partial least squares(PLS) quadratic partial least squares(QPLS)) using experimental data is suggested for predicting flash points of flammable substances of binary systems. The prediction results are compared with the values calculated by laws of Raoult and Van Laar equation.

• 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.

• Korean Management Science Review
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• v.32 no.2
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• pp.69-78
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• 2015

This paper considers a Bayesian Poisson model for multivariate count data using multiplicative rates. More specifically we compose the parameter for overall arrival rates by the product of two parameters, a common effect and an individual effect. The common effect is composed of autoregressive evolution of the parameter, which allows for analysis on seasonal effects on all multivariate time series. In addition, analysis on individual effects allows the researcher to differentiate the time series by whatevercharacterization of their choice. This type of model allows the researcher to specifically analyze two different forms of effects separately and produce a more robust result. We illustrate a simple MCMC generation combined with a Gibbs sampler step in estimating the posterior joint distribution of all parameters in the model. On the whole, the model presented in this study is an intuitive model which may handle complicated problems, and we highlight the properties and possible applications of the model with an example, analyzing real time series data involving customer arrivals to a large retail store.

• Korean Journal of Construction Engineering and Management
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• v.8 no.5
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• pp.191-200
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• 2007

This paper introduces a tool for predicting potential cost overrun during project execution and for quantifying the uncertainty on the expected project cost, which is occasionally changed by the unknown effects resulted from project's complications and unforeseen environments. The model proposed in this stuff is useful in diagnosing cost performance as a project progresses and in monitoring the changes of the uncertainty as indicators for a warning signal. This model is intended for the use by project managers who forecast the change of the uncertainty and its magnitude. The paper presents a mathematical approach for modifying the costs of incomplete work packages and project cost, and quantifying reduced uncertainties at a consistent confidence level as actual cost information of an ongoing project is obtained. Furthermore, this approach addresses the effects of actual informed data of completed work packages on the re-estimates of incomplete work packages and describes the impacts on the variation of the uncertainty for the expected project cost incorporating Multivariate Probabilistic Analysis (MPA) and Bayes' Theorem. For the illustration purpose, the Introduced model has employed an example construction project. The results are analyzed to demonstrate the use of the model and illustrate its capabilities.