• The Korean Journal of Applied Statistics
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• v.30 no.5
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• pp.747-757
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• 2017

We investigate multivariate volatilities based on high frequency time series. The PCA (principal component analysis) method is employed to achieve a dimension reduction in multivariate volatility. Multivariate realized volatilities (RV) with various frequencies are calculated from high frequency data and "optimum" frequency is suggested using PCA. Specifically, RVs with various frequencies are compared with existing daily volatilities such as Cholesky, EWMA and BEKK after dimension reduction via PCA. An analysis of high frequency stock prices of KOSPI, Samsung Electronics and Hyundai motor company is illustrated.

• Journal of Korean Society on Water Environment
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• v.23 no.1
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• pp.161-168
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• 2007

The main aim of this work is focus on the Geum river water quality evaluation of pollution data obtained by monitoring measurement during the period 2001-2005. The complex data matrix 19 (entire monitoring stations)*13 (parameters), 60 (month)*13 (parameters) and 20 (season)*13 (parameters) were treated with different multivariate techniques such as factor analysis/principal component analysis (FA/PCA). FA/PCA identified two factor (19*13) classified pollutant Loading factor (BOD, COD, pH, Cond, T-N, T-P, $NH_3$-N, $NO_3$-N, $PO_4$-P, Chl-a), seasonal factor (water temp, SS) and three Factor (60*13, 20*13) classified pollutant Loading factor (BOD, COD, Cond, T-N, T-P, $NH_3$-N, $NO_3$-N, $PO_4$-P), seasonal factor (water temp, SS) and metabolic factor (Chl-a, pH). Loadings of pollutant factor is potent influence main factor in the Geum river which is explained by loadings of pollutant factor at whole sampling stations (71.16%), month (52.75%) and season (56.57%) of main water quality stations. Result of this study is that pollutant loading factor is affected at Gongju 1, 2, Buyeo 1, 2, Gangkyeong, Yeongi stations by entire stations and entire month (Gongju 1, Cheongwon stations), April, May, July and August (buyeo 1) by month. Also the pollutant Loading factor is season gives an influence in winter (Gongju 1, buyeo 1) from main sampling stations, but Cheongwon characteristic is non-seasonal influenced. This study presents necessity and usefulness of multivariate statistic techniques for evaluation and interpretation of large complex data set with a view to get better information data effective management of water sources.

• Journal of the Korean Society of Safety
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• v.37 no.1
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• pp.20-29
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• 2022

To stem the ever-prevalent occurrence of industrial accidents in the construction industry, which is emerging as a social problem, efforts must be invested by various stakeholders. Specifically, among stakeholders, the orderer is at the top of a project's decision-making structure. Therefore, the orderer's awareness of safety and health directly affects the process of securing the safety of the overall construction site. In this light, the present study aims to identify differences in the perceptions of each stakeholder regarding the obligatory safety and health measures for clients that have recently been introduced. In addition, it suggests specific implementation plans in the Korean context. The data used for analysis were collected through a survey targeting stakeholders such as orderers, safety managers, and site managers, and the collected data were quantitatively reviewed by using multivariate analysis methods such as analysis of variance. As a result of the analysis, the introduction of safety and health obligations for the owner was found to be necessary, and the designation and operation of safety and health experts as an action plan was deemed reasonable. The authors expect that the results of this study can be used as basic data for revising the related regulations in Korea. Moreover, as a further study, a review of the effectiveness after improving regulations would contribute strongly to the domain.

• Computers and Concrete
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• v.20 no.1
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• pp.23-29
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• 2017

In this study engineered steel fibres used as reinforcement for concrete were characterized by number of key mechanical and spatial parameters, which are easy to measure and quantify. Such commonly used parameters as length, diameter, fibre intrinsic efficiency ratio (FIER), hook geometry, tensile strength and ductility were considered. Effective classification of various fibres was demonstrated using simple multivariate computations involving principal component analysis (PCA). Contrary to univariate data mining approach, the proposed analysis can be efficiently adapted for fast, robust and direct classification of engineered steel fibres. The results have revealed that in case of particular spatial/geometrical conditions of steel fibres investigated the FIER parameter can be efficiently replaced by a simple aspect ratio. There is also a need of finding new parameters describing properties of steel fibre more precisely.

• Journal of Information Technology Applications and Management
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• v.31 no.1
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• pp.27-43
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• 2024

This study investigated technological and managerial barriers in technology startups through a survey of 151 companies, yielding 118 responses (78.1% response rate). Factor and multivariate analyses identified two distinct barriers: technological and managerial. Reliability analysis validated the measurement tool. Using MANCOVA, 12 hypotheses were tested, incorporating six independent variables. Results revealed significant disparities in technological and managerial barriers based on establishment type, commercialization goals, growth stage, and commercialization stage, with 5 hypotheses supported. This study highlights the crucial role of these variables in understanding barriers within technology-based startups.

• Journal of Korea Water Resources Association
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• v.51 no.1
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• pp.49-60
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• 2018

The multivariate regional frequency analysis has many advantages such as an adaption of regional parameters and consideration of a correlated structure of the data. The multivariate regional frequency analysis can provide the broader and more detailed information for the hydrological variables. The multivariate regional frequency analysis has not been attempted to model hydrological variables in South Korea yet. Therefore, it is required to investigate the applicability of the multivariate regional frequency analysis in the modeling of the hydrological variables. The current study investigated the applicability of the homogeneous region delineation and their characteristics in bivariate regional frequency analysis of annual maximum rainfall depth-duration data. The K-medoid method was employed as a clustering method. The discordancy and heterogeneous measures were used to assess the appropriateness of the delineation results. According to the results of the clustering analysis, the employed stations could be grouped into five regions. All stations at three of the five regions led to acceptable values of discordancy measures than the threshold. The stations where have short record length led to the large discordancy measures. All grouped regions were identified as a homogeneous region based on heterogeneous measure estimates. It was observed that there are strong cross-correlations among the stations in the same region.

• 한국데이터정보과학회:학술대회논문집
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• 2003.10a
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• pp.57-72
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• 2003

This article provides integrated neural network models for the interest rate forecasting using change-point detection. The model is composed of three phases. The first phase is to detect successive structural changes in interest rate dataset. The second phase is to forecast change-point group with data mining classifiers. The final phase is to forecast the interest rate with BPN. Based on this structure, we propose three integrated neural network models in terms of data mining classifier: (1) multivariate discriminant analysis (MDA)-supported neural network model, (2) case based reasoning (CBR)-supported neural network model and (3) backpropagation neural networks (BPN)-supported neural network model. Subsequently, we compare these models with a neural network model alone and, in addition, determine which of three classifiers (MDA, CBR and BPN) can perform better. For interest rate forecasting, this study then examines the predictability of integrated neural network models to represent the structural change.

• Journal of the Korean Data and Information Science Society
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• v.18 no.1
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• pp.73-80
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• 2007

Cointegration(together with VARMA(vector ARMA)) has been proven to be useful for analyzing multivariate non-stationary data in the field of financial time series. It provides a linear combination (which turns out to be stationary series) of non-stationary component series. This linear combination equation is referred to as long term equilibrium between the component series. We consider two sets of Korean bivariate financial time series and then illustrate cointegration analysis. Specifically estimated VAR(vector AR) and VECM(vector error correction model) are obtained and CV(cointegrating vector) is found for each data sets.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.135-135
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• 2021

In water resources management, rainfall prediction with high accuracy is still one of controversial issues particularly in countries facing heavy rainfall during wet seasons in the monsoon climate. The aim of this study is to develop an artificial neural network (ANN) for predicting future six months of rainfall data (from April to September 2020) from daily meteorological data (from 1971 to 2019) such as rainfall, temperature, wind speed, and humidity at Seoul, Korea. After normalizing these data, they were trained by using a multilayer perceptron (MLP) as a class of the feedforward ANN with 15,000 neurons. The results show that the proposed method can analyze the relation between meteorological datasets properly and predict rainfall data for future six months in 2020, with an overall accuracy over almost 70% and a root mean square error of 0.0098. This study demonstrates the possibility and potential of MLP's applications to predict future daily rainfall patterns, essential for managing flood risks and protecting water resources.

• Journal of Korean Society of Environmental Engineers
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• v.37 no.3
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• pp.137-144
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• 2015

The purpose of this study is to develop an warning system to detect real-time water quality abnormality using a multivariate statistical approach. In this study, we applied principal component analysis among multivariate data analyses which was used for the correlation between water quality parameters considering the real-time algorithm to determine abnormality in water quality. We applied our approach to real field data and showed the utilization of algorithm for the real-time monitoring to find water quality abnormality. In addition, our approach with Korea Meterological Adminstration database identified heavy rain data due to climate change is one of the most important factors to explain water quality abnormality.