• Journal of the Korean Society of Environmental Restoration Technology
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• v.10 no.3
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• pp.14-24
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• 2007

The purpose of this study was to investigate how the landscape characteristics and the physical factors of landscape would affect the preference for the Gapcheon in Daejeon Metropolitan City. The Gapcheon was divided in three sections of the outskirts, Expopark areas, and residential complexes. After selecting seven landscape points where the sections could be expressed best, photographs were taken both in the upstream and downstream direction. The questionnaire used to evaluate the river's landscape included 20 items of adverbs that described the form of the river and one item to rate the overall preference. By analyzing the 14 pictures taken, the occupancy rates of the landscape elements in terms of the sky, river, vegetation of the river, mountain, and artificial structures. Image factor analysis was conducted for each of the sections in order to analyze the landscape characteristics of the Gapcheon, and then regression analysis was conducted in order to analyze the relationships among the physical factors influencing the preference of the landscapes. The results were as follows : Factors that compose the visual characters of urban river were classified be the aesthetic factor, the emotional factor and the situation factor. These 3 factors showed a 65.8% total variance. The river landscape with the biggest preference was the one from the Daedeok Grand Bridge as the occupancy area of the mountain, sky, and river was large and distributed evenly and the vegetation of the river was in a good harmony with the surroundings. After carrying out regression analysis to examine the relationships between the visual preference of Gapcheon and the physical factors of landscape(the sky, river, vegetation of the river, mountain, and artificial structure), the following regressions model was made : PRE=5.906+0.017(river)-0.053(artificial structure)-0.060(vegetation of the river) (R-square=0.48).

• Tunnel and Underground Space
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• v.29 no.5
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• pp.356-366
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• 2019

Various TBM performance prediction models have been developed and most of them were considered penetration rate only. Despite the fact that some models have suggested equations and charts for estimating the utilization factor, but there are a few studies to estimate the TBM utilization factor. Utilization factor is affected by the type of TBM machine, operation, maintenance of machine, geological conditions, contractor experience and other factors. In this study, more than 100 case studies are analyzed to determine the relationship between the utilization factor and RMR, geological conditions, TBM types, tunnel length, and TBM diameter. Simple and multiple linear regression analysis are performed to develop predictive models for the utilization factor. The predictive model with explanatory variables of geological conditions, TBM types, tunnel length, and TBM diameter does not give a good correlation. The predictive models with explanatory variable of RMR give higher values of the coefficient of determination.

• Journal of the Korean Data and Information Science Society
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• v.17 no.2
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• pp.279-289
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• 2006

This paper concerns with the effects of the specialized projected for the local W university on education which was planned and conducted in October 2004. From the empirical study using the correlation analysis, regression analysis, and structured equation model, we found some results that educational service satisfaction was highly correlated with general instruction factor and hard ware factor less correlated. Also we investigated that university educational service satisfaction was deeply correlated with word of mouth.

• Knowledge Management Research
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• v.9 no.1
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• pp.1-21
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• 2008

The present study investigatesthe reliability and validity of the innovation leadership scale. Originally the seven factors with 25 items were developed through literature review. With a sample of 177 employees in a large company, the results of factor analyses showed that the five-factor model with 14 items had a better fit to the data than the seven-factor model. These five factors were innovativeness pursue, problem solving, vision presentation, risk-taking, and showing initiative. All of these factors were significantly related to various criteria such as identification with the group, attachment to the group, organizational commitment, and supervisor satisfaction, confirming criterion-related validity of the scale. Results of multiple regression analyses showedthat risk taking and showing initiative were more important predictors in explaining criteria. Finally, implications and limitations were discussed. The findings suggest that the key factors of innovation leadership were initiative and risk-taking.

• The Journal of the Korea Contents Association
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• v.9 no.8
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• pp.147-157
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• 2009

Recently, there has been continued research on UCP from the development effort estimation method to a software development project applying object oriented development methodology. Current research proposes a linear model estimating the developmenteffort by multiplying a constant to AUCP which applies technical and environmental factors. However, the fact that a non-linear regression model is more appropriate as the software size increases, the development period increases exponentially. In addition, in the UCP calculation process the occurrence of FP errors due to the application of TCF and EF, it is unrealistic to estimate the size with AUCP. This paper presents the issue of current research based on UCP without considering problems of the research, for example, TCF and EF and expresses the models (linear, logarithmic, polynomial, power and exponential type) estimating the development effort directly from UUCP. Consequently, the exponential model within non-linear models exhibit more accurate results than the current linear model. Therefore, after calculating the UUCP of the developing software system, using the proposed model to estimate the development effort, it is possible to estimate the direct cost required in development.

• Journal of the Korean Institute of Landscape Architecture
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• v.34 no.6 s.119
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• pp.78-86
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• 2007

Research into the use of recreation forest accommodations, the main facilities in these forests, and user satisfaction with them has been carried out for recreation forests in the suburbs of Daegusi and Gyongsangbukdo. This study aimed at providing background material to support the increasing demand to improve the facilities of recreation forests and to educate recreation forest staff about how to provide better service. User satisfaction with recreation forests as determined through regression analysis was affected by the following factors in this order: indoor recreation, the indoor space, outdoor recreation, time satisfaction factor, and activity opportunities. The level of satisfaction is affected by the force of factors above. The order of effective offerings of accommodations is presented according to the results.

• Journal of Navigation and Port Research
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• v.33 no.2
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• pp.153-159
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• 2009

The awareness of the high-value industry for container terminal leads competitiveness of container terminals to keep high fiercely. In regards to competitive factors of container terminal, the most important point among several factors is seemed to be the speed of container loading and unloading on quayside. In container terminals in Korea, the productivity shows big difference even though its condition is similar to each terminal. The objective of this paper is to find the critical factors of container terminal productivity, which is dependant upon the capability, quantity of quay crane, transfer vehicle, and so on. For this purpose, we have researched related literatures, and collected data about container terminals in South Korea. Furthermore, we tested sensitive analysis to evaluate the extent of productivity by changing independent variable. And then we established the regression model to evaluate which factor has had the biggest impact on productivity. The results of this paper can give terminal operators guideline to improve productivity.

• The Korean Journal of Financial Management
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• v.15 no.2
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• pp.401-419
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• 1998

This paper examines the international arbitrage pricing model (IAPM) in regional equity markets setting. Factor analyses are used to estimate the international common risk factors. And the cross-sectional regression analyses are used to test the validity of regional IAPMs and Chow tests are used to evaluate the integration of regional equity markets. The results of factor analyses show that the number of common factors in each regional group is seven. The cross-sectional regression results lead us not to reject that the IAPMs are regionally valid but Chow test results lead us to reject that regional equity markets are integrated.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.04a
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• pp.601-604
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• 2000

In this paper, we compare the classification performance of both data fusion and clustering algorithms (Data Bagging, Variable Selection Bagging, Parameter Combining, Clustering) to logistic regression in consideration of various characteristics of input data. Four factors used to simulate the logistic model are (1) correlation among input variables (2) variance of observation (3) training data size and (4) input-output function. Since the relationship between input & output is not typically known, we use Taguchi design to improve the practicality of our study results by letting it as a noise factor. Experimental study results indicate the following: Clustering based logistic regression turns out to provide the highest classification accuracy when input variables are weakly correlated and the variance of data is high. When there is high correlation among input variables, variable bagging performs better than logistic regression. When there is strong correlation among input variables and high variance between observations, bagging appears to be marginally better than logistic regression but was not significant.

• Journal of Environmental Science International
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• v.16 no.11
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• pp.1301-1312
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• 2007

The objective of this study is to propose a critical storm duration forecasting model on storm runoff in small river basin. The critical storm duration data of 582 sub-basin which introduced disaster impact assessment report on the National Emergency Management Agency during the period from 2004 to 2007 were collected, analyzed and studied. The stepwise multiple regression method are used to establish critical storm duration forecasting models(Linear and exponential type). The results of multiple regression analysis discriminated the linear type more than exponential type. The results of multiple linear regression analysis between the critical storm duration and 5 basin characteristics parameters such as basin area, main stream length, average slope of main stream, shape factor and CN showed more than 0.75 of correlation in terms of the multi correlation coefficient.