• Journal of Korean Society of Transportation
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• v.11 no.2
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• pp.27-44
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• 1993

The purpose of this study is to classify the regional structure of Pusan based on socio-economic phenomena in 1979 and 1991, and to analyze correlations between the regional structure change and traffic demend. To formalize the land-use by each zone, the Principal Component Analysis were performed by using 15 socio-sconomic variables. As a result of the analysis, five land-use factors(i.e., official, residential, commercial, manufactural, and other functions) in 1979 and four factors(i. e., residential, 3rd industrial, manufactural, and other functions) in 1991 were extracted as main regional structure components. It was proved that there is strong correlations between the regional structure change and traffic demand by using Quantification Theory II and also by testing correlation coefficient.

• Proceedings of the Korea Water Resources Association Conference
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• 2004.05b
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• pp.875-877
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• 2004

We analyze the changes of the Palmer Drought Severity Index (PDSI) over Korea to assess the regional climate change associated with global warming. For the regional-scale analysis, we used the MM5 simulation in 27 km horizontal resolution for the period of 1971-2100, which is driven by ECHAM4/HOPE-G under the greenhouse gas omission scenario. The downscaled climate variables capture improved regional features consistent with the observation. Based on the simulation, we investigated the temporal and spatial distributions of PDSI over Korea. The area-averaged PDSI is expected to decrease in global warming. Considering the horizontal distribution of climate change, the negative peak values of PDSI anomalies appear in the southern part of Korea.

• International Journal of Highway Engineering
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• v.10 no.4
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• pp.139-150
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• 2008

In this study, It is focused on development of the forecasting model about trumpet InterChange(IC) ramp accident because of the frequency of accident in ramp more than highway basic section and trend the increasing accident in ramp. The independent variables was selected through statistical analysis(correlation analysis, multi-collinearity etc) by ramp types(direct, semi-direct and loop). The independent variables and accident rate is non-linear relationship. So it made new variables by transformation of the independent variables. The forecasting models according to exit-ramp type (direct, semi-direct and loop) are built with statistical multi-variable regression using all possible regression method. And the forecasts of the models showed high accuracy statistically. It is expected that the developed models could be employed to design trumpet IC ramp more cost-efficiently and safely and to analyze the causes of traffic accidents happened on the IC ramp.

• Structural Monitoring and Maintenance
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• v.5 no.3
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• pp.363-377
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• 2018

The proper functioning of critical points on transport infrastructure is decisive for the entire network. Tunnels and bridges certainly belong to the critical points of the surface transport network, both road and rail. Risk management should be a holistic and dynamic process throughout the entire life cycle. However, the level of risk is usually determined only during the design stage mainly due to the fact that it is a time-consuming and costly process. This paper presents a simplified quantitative risk analysis method that can be used any time during the decades of a tunnel's lifetime and can estimate the changing risks on a continuous basis and thus uncover hidden safety threats. The presented method is a decision support system for tunnel managers designed to preserve or even increase tunnel safety. The CAPITA method is a deterministic scenario-oriented risk analysis approach for assessment of mortality risks in road tunnels in case of the most dangerous situation - a fire. It is implemented through an advanced risk analysis CAPITA SW. Both, the method as well as the resulting software were developed by the authors' team. Unlike existing analyzes requiring specialized microsimulation tools for traffic flow, smoke propagation and evacuation modeling, the CAPITA contains comprehensive database with the results of thousands of simulations performed in advance for various combinations of variables. This approach significantly simplifies the overall complexity and thus enhances the usability of the resulting risk analysis. Additionally, it provides the decision makers with holistic view by providing not only on the expected risk but also on the risk's sensitivity to different variables. This allows the tunnel manager or another decision maker to estimate the primary change of risk whenever traffic conditions in the tunnel change and to see the dependencies to particular input variables.

• The Korean Journal of Applied Statistics
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• v.16 no.1
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• pp.87-100
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• 2003

Most of statistical data are generated by a set of dynamic, stochastic, and simultaneous relations. An important question is how to specify statistical models so that they are consistent with the dynamic feature of those data. A general hypothesis is that the lagged effect of a change in an explanatory variable is not felt all at once at a single point in time, but The impact is distributed over a number of future points in time. In other words, current control variables are determined by a function that can be reduced to a distributed lag function of past observations. It is possible to explain the relationship between variables in different points of time and to estimate the long-run impacts of a change in a variable on another if time lag series of explanatory variables are incorporated in the model specification. In this study, distributed lag structure is applied to the domestic stock market model to capture the dynamic response of the market by exogenous shocks. The Domestic market is found more responsive to the changes in foreign market factors both in the short and the long run.

• Tribology and Lubricants
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• v.11 no.2
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• pp.44-55
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• 1995

Frictional characteristics with the change of dynamic parameters, such as stiffness, inertia and damping, in the friction measurement at dry sliding surfaces were experimentally and theoretically investigated throughout the study. Dynamic frictional force and the variation in the normal load were mainly measured at the various conditions of system dynamic parameters with which stiffness in the normal direction, loading mechanisms and test materials were varied. For the normal load, mechanisms using both a dead weight and a pneumatic cylinder were applied, which resulted in change of the inertia and damping of the test rig. Test materials were steel, rosin and PTFE, which have different types of intrinsic frictional characteristics. Test results showed that frictional characteristics under different dynamic parameters could be different even though the operating variables were the same and also they could result in the variation in the normal load, which could consequently affect the wear mechanism.

• Journal of the Korean Data and Information Science Society
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• v.26 no.6
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• pp.1409-1416
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• 2015

The change of temperature influences on the various aspect, especially human health, plant and animal's growth, economics, industry, and culture of the country. In this article, the autoregressive error (ARE) model has been considered for analyzing the monthly temperature data at the Suwon monitoring site in Korea. In the ARE model, five meteorological variables, four greenhouse gas variables and five pollution variables are used as the explanatory variables for the temperature data set. The five meteorological variables are wind speed, rainfall, radiation, amount of cloud, and relative humidity. The four greenhouse gas variables are carbon dioxide ($CO_2$), methane ($CH_4$), nitrous oxide ($N_2O$), and chlorofluorocarbon ($CFC_{11}$). And the five air pollution explanatory variables are particulate matter ($PM_{10}$), sulfur dioxide ($SO_2$), nitrogen dioxide ($NO_2$), ozone ($O_3$), and carbon monoxide (CO). Among five meteorological variables, radiation, amount of cloud, and wind speed are more influence on the temperature. The radiation influences during spring, summer and fall, whereas wind speed influences for the winter time. Also, among four greenhouse gas variables and five pollution variables, chlorofluorocarbon, methane, and ozone are more influence on the temperature. The monthly ARE model explained about 43-69% for describing the temperature.

• Journal of Korean Society on Water Environment
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• v.39 no.1
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• pp.46-60
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• 2023

Climate change causes fluctuations in water quality in the aquatic environment, which can cause changes in water circulation patterns and severe adverse effects on aquatic ecosystems in the future. Therefore, research is needed to predict and respond to water quality changes caused by climate change in advance. In this study, we tried to predict the dissolved oxygen (DO), chlorophyll-a, and turbidity of the Paldang reservoir for about two weeks using long short-term memory (LSTM) and gated recurrent units (GRU), which are deep learning algorithms based on recurrent neural networks. The model was built based on real-time water quality data and meteorological data. The observation period was set from July to September in the summer of 2021 (Period 1) and from March to May in the spring of 2022 (Period 2). We tried to select an algorithm with optimal predictive power for each water quality parameter. In addition, to improve the predictive power of the model, an important variable extraction technique using random forest was used to select only the important variables as input variables. In both Periods 1 and 2, the predictive power after extracting important variables was further improved. Except for DO in Period 2, GRU was selected as the best model in all water quality parameters. This methodology can be useful for preventive water quality management by identifying the variability of water quality in advance and predicting water quality in a short period.

• Journal of The Korean Digital Architecture Interior Association
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• v.12 no.1
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• pp.89-98
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• 2012

While the housing transaction price of Seoul tends to be stagnant or declining in line with the housing market recession since 2007, the jeonse price keeps continual increase. Such flow of jeonse price change has a serious influence on ordinary person's housing stability seriously. Therefore, it is very meaningful in terms of social policy to analyze the trend of recent jeonse price change. This study aims to have an empirical analysis of structural characteristics of the trend of recent jeonse price change. After the review of various previous studies, this study selected housing jeonse price index, non-sold house quantity, jeonse vs. transaction price rate, and housing construction performance as analytical variables, and employed monthly time series resources from January 2007 to April 2011. As a result, when the housing supply reduced, the potential quantity for jeonse market reduced that occurred unbalance of supply and demand in jeonse market. In turn, it caused the increase of jeonse price. And, in case of jeonse vs. transaction price rate change, the rate increased which means the increase of required rate of return of invested demand. As such, the increase of market risk degenerates the investment sentiment which caused the reduction of quantity for jeonse market as a submarket.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.1B
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• pp.71-80
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• 2010

This study aimed to figure out the effect of urbanization on meteorological variables (air temperature, wind speed, relative humidity, solar radiation and precipitation) and reference evapotranspiration (RET). The research area of 6 urban areas and 6 rural areas near each urban area was selected. The monthly average daily data were collected from 12 ground stations operated by Korea Meteorological Administration (KMA) and the changes in climate variables were analyzed. Results of annual analysis have shown that the reference evapotranspiration (RET) tends to increase in urban areas while decreasing in rural areas. In particular, due to rising RET in urban areas and decreasing RET in rural areas, we can infer that the urbanization has affected to the RET. Results of monthly analysis showed that the urbanization has effects on the RET of July compared to other months (January, April and October). The yearly and monthly effects of urbanization on RET were closely related to solar radiation, relative humidity and change in temperature, and related to wind speed.