• The Korean Journal of Applied Statistics
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• v.25 no.1
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• pp.89-99
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• 2012

This study investigates the Granger-causality between economic growth and demand for electricity in Korea, using two quarterly time-series data (real GDP and electricity consumption) for 1970:Q1 through 2009:Q4. We apply Hsiao's sequential procedure to identify a vector autoregressive model to a decision of the optimal lags in the vector error-correction model because the two time-series data contain unit roots respectively and they are cointegrated. According to the empirical results in this study, we find that Hsiao's approach to the Granger-causality indicates a bidirectional causal relation between economic growth and demand for electricity in Korea. Following the Granger and Engle's approach, we also find the statistical evidence on (1) short-run bidirectional causality between real GDP and electricity consumption, (2) bidirectional strong causality between them, and (3) long-run unidirectional causality running from demand for electricity to economic growth. Our results show an inconsistency with the existing studies on Korea's case; however, the results appear to provide more meaningful policy implications for the Korean economy and its strategy of sustainable growth.

• The Journal of the Korea Contents Association
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• v.12 no.9
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• pp.357-368
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• 2012

The purpose of this study is to empirically examine the impact of enterprise R&D investment on international trade in Korea under the new Normal Era. In order to test whether the time series data of trade variables are stationary or not, we put in operation unit root test and cointegration test. Based on VECM (Vector Error Correction Model), we also apply impulse response functions and variance decomposition to estimate the dynamic effects in the short-run and long-run. The results show that the relationship between enterprise R&D investment and international trade (export and import) exists in the long-run as well as in the short-run. The results of applying impulse response functions and variance decomposition also indicate that the impact of enterprise R&D investment on international trade is positive, and a significant portion of fluctuations in the trade variable is explained by enterprise R&D investment. Therefore, enterprise R&D investment must be continuously increased to improve economic growth with promoting trading competition power in Korea under the new Normal Era.

• Geophysics and Geophysical Exploration
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• v.18 no.2
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• pp.74-84
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• 2015

To yield physically meaningful images through elastic reverse-time migration, the wavefield separation which extracts P- and S-waves from reconstructed vector wavefields by using elastic wave equation is prerequisite. For expanding the application of the elastic reverse-time migration to anisotropic media, not only the anisotropic modelling algorithm but also the anisotropic wavefield separation is essential. The anisotropic wavefield separation which uses pseudo-derivative filters determined according to vertical velocities and anisotropic parameters of elastic media differs from the Helmholtz decomposition which is conventionally used for the isotropic wavefield separation. Since applying these pseudo-derivative filter consumes high computational costs, we have developed the efficient anisotropic wavefield separation algorithm which has capability of parallel computing by using GPUs (Graphic Processing Units). In addition, the highly efficient anisotropic elastic reverse-time migration algorithm using MPI (Message-Passing Interface) and incorporating the developed anisotropic wavefield separation algorithm with GPUs has been developed. To verify the efficiency and the validity of the developed anisotropic elastic reverse-time migration algorithm, a VTI elastic model based on Marmousi-II was built. A synthetic multicomponent seismic data set was created using this VTI elastic model. The computational speed of migration was dramatically enhanced by using GPUs and MPI and the accuracy of image was also improved because of the adoption of the anisotropic wavefield separation.

• Journal of Korea Multimedia Society
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• v.5 no.4
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• pp.458-467
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• 2002

There has been proposed many simplification algorithms for effectively decreasing large-volumed polygonal surface data. These algorithms apply their own cost function for collapse to one of fundamental simplification unit, such as vertex, edge and triangle, and minimize the simplification error occurred in each simplification steps. Most of cost functions adopted in existing works use the error estimation method based on distance optimization. Unfortunately, it is hard to define the local characteristics of surface data using distance factor alone, which is basically scalar component. Therefore, the algorithms cannot preserve the characteristic features in surface areas with high curvature and, consequently, loss the detailed shape of original mesh in high simplification ratio. In this paper, we consider the vector component, such as surface orientation, as one of factors for cost function. The surface orientation is independent upon scalar component, distance value. This means that we can reconsider whether or not to preserve them as the amount of vector component, although they are elements with low scalar values. In addition, we develop a simplification algorithm based on half-edge collapse manner, which use the proposed cost function as the criterion for removing elements. In half-edge collapse, using one of endpoints in the edge represents a new vertex after collapse operation. The approach is memory efficient and effectively applicable to the rendering system requiring real-time transmission of large-volumed surface data.

• Korean Journal of Remote Sensing
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• v.36 no.6_1
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• pp.1367-1377
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• 2020

The purpose of this study is to assess the seismic vulnerability of buildings in Gyeongju city starting with the earthquake that occurred in the city on September 12, 2016, and produce a seismic vulnerability map. 11 influence factors related to geotechnical, physical, and structural indicators were selected to assess the seismic vulnerability, and these were applied as independent variables. For a dependent variable, location data of the buildings that were actually damaged in the 9.12 Gyeongju Earthquake was used. The assessment model was constructed based on random forest (RF) as a mechanic study method and support vector machine (SVM), and the training and test dataset were randomly selected with a ratio of 70:30. For accuracy verification, the receiver operating characteristic (ROC) curve was used to select an optimum model, and the accuracy of each model appeared to be 1.000 for RF and 0.998 for SVM, respectively. In addition, the prediction accuracy was shown as 0.947 and 0.926 for RF and SVM, respectively. The prediction values of the entire buildings in Gyeongju were derived on the basis of the RF model, and these were graded and used to produce the seismic vulnerability map. As a result of reviewing the distribution of building classes as an administrative unit, Hwangnam, Wolseong, Seondo, and Naenam turned out to be highly vulnerable regions, and Yangbuk, Gangdong, Yangnam, and Gampo turned out to be relatively safer regions.

• Journal of Korean Society of Disaster and Security
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• v.13 no.4
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• pp.75-92
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• 2020

In most cases of the water balance analysis, the return flow ratio for each water supply was uniformly determined and applied, so it has been contained a problem that the volume of available water would be incorrectly calculated. Therefore, sewage and wastewater among the return water were focused in this study and the data-driven model was developed to forecast the outflow from the sewage treatment plant. The forecasting results of LSTM (Long Short-Term Memory), GRU (Gated Recurrent Units), and SVR (Support Vector Regression) models, which are mainly used for forecasting the time series data in most fields, were compared with the observed data to determine the optimal model parameters for forecasting outflow. As a result of applying the model, the root mean square error (RMSE) of the GRU model was smaller than those of the LSTM and SVR models, and the Nash-Sutcliffe coefficient (NSE) was higher than those of others. Thus, it was judged that the GRU model could be the optimal model for forecasting the outflow in sewage treatment plants. However, the forecasting outflow tends to be underestimated and overestimated in extreme sections. Therefore, the additional data for extreme events and reducing the minimum time unit of input data were necessary to enhance the accuracy of forecasting. If the water use of the target site was reviewed and the additional parameters that could reflect seasonal effects were considered, more accurate outflow could be forecasted to be ready for climate variability in near future. And it is expected to use as fundamental resources for establishing a reasonable river water management system based on the forecasting results.

• Journal of Technology Innovation
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• v.31 no.2
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• pp.59-82
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• 2023

The purpose of this research is to conduct the empirical analysis of the short- and long-term causal relationship between public R&D investment, corporate R&D investment, and university R&D investment on economic growth in Korea. To this end, based on the time series data from 1976 to 2020, a causality test was conducted through the unit root test, cointegration test, and vector error correction model (VECM). As a result, it was found that there is a long-run equilibrium relationship between economic growth in Korea, public R&D investment, corporate R&D investment, and university R&D investment, in which a causal relationship exists in the long run. Also, while public R&D investment has a short-term effect on economic growth, corporate and university R&D investment does not have a short-term effect on economic growth. In addition, the results shows that there is a bidirectional causal relationship between economic growth and public R&D investment, corporate R&D investment and public R&D investment, and university R&D investment and public R&D investment in the short term. Through this research, it was empirically found that a highly mutual relationship exists between public R&D investment, corporate R&D investment, university R&D investment and economic growth. In order to increase the ripple effect of R&D investment on economic growth in the future, R&D investment between universities and corporations should be mutually promoted, and R&D investment by corporations should have a positive effect on public R&D investment so that public R&D investment can contribute to future economic growth.

• KDI Journal of Economic Policy
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• v.14 no.1
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• pp.121-145
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• 1992

This paper examines characteristics of time series data related to the construction investment(stationarity and time series components such as secular trend, cyclical fluctuation, seasonal variation, and random change) and surveys predictibility, fitness, and explicability of independent variables of various models to build a short-term construction investment forecasting model suitable for current economic circumstances. Unit root test, autocorrelation coefficient and spectral density function analysis show that related time series data do not have unit roots, fluctuate cyclically, and are largely explicated by lagged variables. Moreover it is very important for the short-term construction investment forecasting to grasp time lag relation between construction investment series and leading indicators such as building construction permits and value of construction orders received. In chapter 3, we explicate 7 forecasting models; Univariate time series model (ARIMA and multiplicative linear trend model), multivariate time series model using leading indicators (1st order autoregressive model, vector autoregressive model and error correction model) and multivariate time series model using National Accounts data (simple reduced form model disconnected from simultaneous macroeconomic model and VAR model). These models are examined by 4 statistical tools that are average absolute error, root mean square error, adjusted coefficient of determination, and Durbin-Watson statistic. This analysis proves two facts. First, multivariate models are more suitable than univariate models in the point that forecasting error of multivariate models tend to decrease in contrast to the case of latter. Second, VAR model is superior than any other multivariate models; average absolute prediction error and root mean square error of VAR model are quitely low and adjusted coefficient of determination is higher. This conclusion is reasonable when we consider current construction investment has sustained overheating growth more than secular trend.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.1
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• pp.1-8
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• 2008

We propose how to program the off-line buffing robot path along shoes' outsole shape in the footwear buffing process by a 5-axis microscribe system like robot mechanism. The microscribe system we developed consists of a 5-axis robot link with a turn table, a signal processing unit, PC and an application software program. Itmakes a robot path on the shoes' upper in accordance with the movement of a microscribe with many joints. The developed system calculates the encoder pulse values for the microscribe arm's rotation and transmits the angle pulse values to the PC through a processing unit. Denavit-Hartenberg's(D-H) direct kinematics is used to make the global coordinate from microscribe joint one. Problems with the microscribe's kinematics can be solved efficiently and systematically by D-H representation. With the coordinate values calculated by D-H equation, our system can draw a buffing gauge-line on the upper sole. We obtain shoes' outline points, which are 2 outlines coupled with the points and the normal vector based on the points. By applying the system to the buffing robot in a flexible manufacturing system, it can be used effectively to program the path of a real buffing robot.

• Korean journal of applied entomology
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• v.58 no.4
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• pp.347-354
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• 2019

Temperature-dependent traits of Laodelphax striatellus, rice stripe virus vector, were investigated at 10 constant temperatures (12.5, 15.0, 17.5, 20.0, 22.5, 25.0, 27.5, 30.0, 32.5, and 35.0 ± 1℃) under a fixed photoperiod (14/10-hr light/dark cycle). Unit functions for the oviposition model were estimated and implemented into a population dynamics model using DYMEX. The longevity of L. striatellus adults decreased with increasing temperature (56.0 days at 15.0℃ and 17.7 days at 35.0℃). The highest total fecundity (515.9 eggs/female) was observed at 22.5℃, while the lowest (18.6 eggs/female) was observed at 35.0℃. Adult developmental rates, temperature-dependent fecundity, age-specific mortality rates, and age-specific cumulative oviposition rates were estimated. All unit equations described adult performances of L. striatellus accurately (r² =0.94~0.97). After inoculating adults, the constructed model was tested under pot and field conditions using the rice-plant hopper system. The model output and observed data were similar up to 30 days after inoculation; however, there were large discrepancies between observed and estimated population density after 30 days, especially for 1^st and 2^nd instar nymph densities. Model estimates were one or two nymphal stages faster than was observed. Further refinement of the model created in this study could provide realistic forecasting of this important rice pest.