• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.5
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• pp.671-683
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• 2018

The flood damage caused by heavy rains in urban watershed is increasing, and, as evidenced by many previous studies, urban flooding usually exceeds the water capacity of drainage networks. The flood on the area which considerably urbanized and densely populated cause serious social and economic damage. To solve this problem, deterministic and probabilistic studies have been conducted for the prediction flooding in urban areas. However, it is insufficient to obtain lead times and to derive the prediction results for the flood volume in a short period of time. In this study, IDNN, TDNN and NARX were compared for real-time flood prediction based on urban runoff analysis to present the optimal real-time urban flood prediction technique. As a result of the flood prediction with rainfall event of 2010 and 2011 in Gangnam area, the Nash efficiency coefficient of the input delay artificial neural network, the time delay neural network and nonlinear autoregressive network with exogenous inputs are 0.86, 0.92, 0.99 and 0.53, 0.41, 0.98 respectively. Comparing with the result of the error analysis on the predicted result, it is revealed that the use of nonlinear autoregressive network with exogenous inputs must be appropriate for the establishment of urban flood response system in the future.

• Journal of Environmental Policy
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• v.9 no.1
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• pp.57-81
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• 2010

Since the Republic of Korea is highly dependent on fossil fuels despite high oil prices, it urgently needs to renew its economic and social system to cut carbon emissions and achieve green growth. Therefore, reforming or eliminating subsidies related to the use of fossil fuels is a timely and oppropriate policy recommendation for Korea. It would be a win-win deal for Korean society as it would not only reduce the use of environmentally harmful fossil fuels but also enhance economic efficiency. In particular, cross-subsidies for industrial, agricultural and night thermal-storage power services make up more than 80 percent of all subsidies provided to the entire electric power industry sector of Korea. Of these cross-subsidies, this paper analyzes the electricity subsidy for industries, which takes up the largest share (about KRW 1.6583 trillion yearly), among the environmentally harmful subsidies in the electric power sector. Thus, the paper focuses on the analysis of ripple effect anticipated when this is reformed. To examine the effects of this subsidy reform, price elasticities were estimated using the ARDL (autoregressive distributed lag) model and quarterly data from 1990 to 2007. The main results of this study show that 1) annual energy demand for electric power in the industrial sector would drop by 12,475,930MWh and 2) $CO_2$ emissions would plummet by 2,644,897 tons per year if the subsidy were reformed. We can deduct from this that the abolition of environmentally harmful subsidies in the electric power sector in the Republic of Korea would considerably contribute to $CO_2$ emissions abatement in the country.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.12
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• pp.2511-2517
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• 2009

Recently, a FMCW radar is used for the various purposes in the short range detection and tracking of targets. The main advantages of a FMCWradar are the comparative simplicity of implementation and the low peak power transmission characterizing the very low probability of signal interception. Since it uses the frequency modulated continuous wave for transmission and demodulation, the received beat frequency represents the range and Doppler information of targets. Detection and extraction of useful information from targets are performed in this beat frequency domain. Therefore, the resolution and accuracy in the estimation of a beat spectrum are very important. However, using the conventional FFT estimation method, the high resolution spectrum estimation with a low sidelobe level is not possible if the acquisition time is very short in receiving target echoes. This kind of problems deteriorates the detection performance of adjacent targets having the large magnitude differences in return echoes and also degrades the reliability of the extracted information. Therefore, in this paper, the model parameter estimation methods such as autoregressive and eigenvector spectrum estimation are applied to mitigate these problems. Also, simulation results are compared and analyzed for further improvement.

• Journal of Convergence for Information Technology
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• v.9 no.12
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• pp.62-70
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• 2019

The study used cross-lagged path modeling to examine the longitudinal associations between self-esteem and community spirit among adolescents. This is a longitudinal study designed to examine the developmental changes of adolescents' self-esteem and community spirit in Korea. This study used the data collected by the Korean Children and Youth Panel Survey (KCYPS) from 2014 through 2016. Participants were surveyed from the 5^th grade to the 7^th, which belonged to the 1^st elementary school cohort panel of the KCYPS. To be used as the data of this study, children should have all information at 3-time points: the 5^th grade; 6^th grade; 7^th grade. The collected data were analyzed with PSAW 18.0 and AMOS statistical program. The participants in this study were 903 males (51.6%) and 847 females (48.4%). The level of self-esteem was 3.29±.51, 3.19±.55, and 3.15±.57 point at 5^th grade, 6^th grade, and 7^th grade each. The level of community spirit was 3.12±.52, 3.09±.59, 3.15±.55 point respectively. Community spirit from elementary school to middle school is consistently predicted by previous self-esteem. Likewise self-esteem has a significant predictive effect on subsequent community spirit.

• Journal of Digital Convergence
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• v.17 no.9
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• pp.135-145
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• 2019

Prolonged recession has caused the youth unemployment rate in Korea to remain at a high level of approximately 10% for years. Recently, the number of unemployed Koreans in their 30s and 40s has shown an upward trend. To expand the government's employment promotion and unemployment benefits from youth-centered policies to diverse age groups, including people in their 30s and 40s, prediction models for different age groups are required. Thus, we aimed to develop unemployment prediction models for specific age groups (30s and 40s) using available unemployment rates provided by Statistics Korea and Google search queries related to them. We first estimated multiple linear regressions (Model 1) using seasonal autoregressive integrated moving average approach with relevant unemployment rates. Then, we introduced Google search queries to obtain improved models (Model 2). For both groups, consequently, Model 2 additionally using web queries outperformed Model 1 during training and predictive periods. This result indicates that a web search query is still significant to improve the unemployment predictive models for Koreans. For practical application, this study needs to be furthered but will contribute to obtaining age-wise unemployment predictions.

• 한국노년학
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• v.41 no.3
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• pp.421-444
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• 2021

The purpose of this research is to examine the reciprocal relationships between depression and marital satisfaction among older couples. For longitudinal dyadic analysis, this study sets up a research model based on the Actor-Partner Interdependence Model, the Common Fate Model, and Autoregressive Cross-Lagged Model. Data came from four annual waves from the 10th year (2015) to the 13th year (2018) of the Korean Welfare Panel Survey and the final sample comprised a total of 1,383 married couples over 60 years of age in 2015. Structural Equation Modeling identified the reciprocal relationship between depression and marital satisfaction among older couples, with higher marital satisfaction of older couples leading to lower depression of husbands' and wives', and with higher depression of husbands' and wives' inducing lower marital satisfaction of the couples. Thus, this study suggested that longitudinal interplay between depression and marital satisfaction can lead to a vicious cycle. Based on these findings, the need to intervene at both the individual level and the couple level was discussed in order to reduce depression and improve marital satisfaction.

• Journal of Intelligence and Information Systems
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• v.16 no.2
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• pp.19-32
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• 2010

Volatility plays a central role in both academic and practical applications, especially in pricing financial derivative products and trading volatility strategies. This study presents a novel mechanism based on generalized autoregressive conditional heteroskedasticity (GARCH) models that is able to enhance the performance of intelligent volatility trading systems by predicting Korean stock market volatility more accurately. In particular, we embedded the concept of the volatility asymmetry documented widely in the literature into our model. The newly developed Korean stock market volatility index of KOSPI 200, VKOSPI, is used as a volatility proxy. It is the price of a linear portfolio of the KOSPI 200 index options and measures the effect of the expectations of dealers and option traders on stock market volatility for 30 calendar days. The KOSPI 200 index options market started in 1997 and has become the most actively traded market in the world. Its trading volume is more than 10 million contracts a day and records the highest of all the stock index option markets. Therefore, analyzing the VKOSPI has great importance in understanding volatility inherent in option prices and can afford some trading ideas for futures and option dealers. Use of the VKOSPI as volatility proxy avoids statistical estimation problems associated with other measures of volatility since the VKOSPI is model-free expected volatility of market participants calculated directly from the transacted option prices. This study estimates the symmetric and asymmetric GARCH models for the KOSPI 200 index from January 2003 to December 2006 by the maximum likelihood procedure. Asymmetric GARCH models include GJR-GARCH model of Glosten, Jagannathan and Runke, exponential GARCH model of Nelson and power autoregressive conditional heteroskedasticity (ARCH) of Ding, Granger and Engle. Symmetric GARCH model indicates basic GARCH (1, 1). Tomorrow's forecasted value and change direction of stock market volatility are obtained by recursive GARCH specifications from January 2007 to December 2009 and are compared with the VKOSPI. Empirical results indicate that negative unanticipated returns increase volatility more than positive return shocks of equal magnitude decrease volatility, indicating the existence of volatility asymmetry in the Korean stock market. The point value and change direction of tomorrow VKOSPI are estimated and forecasted by GARCH models. Volatility trading system is developed using the forecasted change direction of the VKOSPI, that is, if tomorrow VKOSPI is expected to rise, a long straddle or strangle position is established. A short straddle or strangle position is taken if VKOSPI is expected to fall tomorrow. Total profit is calculated as the cumulative sum of the VKOSPI percentage change. If forecasted direction is correct, the absolute value of the VKOSPI percentage changes is added to trading profit. It is subtracted from the trading profit if forecasted direction is not correct. For the in-sample period, the power ARCH model best fits in a statistical metric, Mean Squared Prediction Error (MSPE), and the exponential GARCH model shows the highest Mean Correct Prediction (MCP). The power ARCH model best fits also for the out-of-sample period and provides the highest probability for the VKOSPI change direction tomorrow. Generally, the power ARCH model shows the best fit for the VKOSPI. All the GARCH models provide trading profits for volatility trading system and the exponential GARCH model shows the best performance, annual profit of 197.56%, during the in-sample period. The GARCH models present trading profits during the out-of-sample period except for the exponential GARCH model. During the out-of-sample period, the power ARCH model shows the largest annual trading profit of 38%. The volatility clustering and asymmetry found in this research are the reflection of volatility non-linearity. This further suggests that combining the asymmetric GARCH models and artificial neural networks can significantly enhance the performance of the suggested volatility trading system, since artificial neural networks have been shown to effectively model nonlinear relationships.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.6
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• pp.117-125
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• 2020

This paper aims to investigate FDI attractive factors, which are important to formulate policies to attract Korean direct investment into Vietnam. Based on the literature review and the results of interview with 27 Korean investors in Vietnam, we determined the model of variables attracting Korea's FDI into Vietnam. It is used to assess the impact of attractive factors belonging to three groups of variables to support investment decision; they are macroeconomics variables (including market size factor, labor cost factor, and market openness factor), policies variables (including monetary policy factor and tax rate gap factor), and microeconomics variables (geographic advantage factor representative by location). This research also utilized a relatively new quantitative research method based on the Autoregressive Distributed Lag model (ARDL) with the time data chain from 1995 to 2017 of Korean FDI into Vietnam. It analyzes long-term relationships between dependent variables and independent variables. The result of this study indicates that there are three positive factors (low wages, trade openness and government policy) explaining the FDI flows in the long term. The result also shows that incentive tax policy has had a positive impact on Korean FDI, which has satisfied the aim of seeking efficiency of Korean investors.

• Journal of KIISE
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• v.44 no.9
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• pp.954-965
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• 2017

A stable power supply is very important for the maintenance and operation of the power infrastructure. Accurate power consumption prediction is therefore needed. In particular, a university campus is an institution with one of the highest power consumptions and tends to have a wide variation of electrical load depending on time and environment. For this reason, a model that can accurately predict power consumption is required for the effective operation of the power system. The disadvantage of the existing time series prediction technique is that the prediction performance is greatly degraded because the width of the prediction interval increases as the difference between the learning time and the prediction time increases. In this paper, we first classify power data with similar time series patterns considering the date, day of the week, holiday, and semester. Next, each ARIMA model is constructed based on the classified data set and a daily power consumption forecasting method of the university campus is proposed through the time series cross-validation of the predicted time. In order to evaluate the accuracy of the prediction, we confirmed the validity of the proposed method by applying performance indicators.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.1A
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• pp.30-37
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• 2006

In this paper, we propose a QoS-aware efficient bandwidth re-provisioning scheme in a next generation wireless packet transport network. At the transport network layer, it classifies the traffic of the radio network layer into a real time class and a non-real time class. Using an auto-regressive time-series model and a given packet loss probability, our scheme predicts the needed bandwidth of the non-real time class at every re-provisioning interval. Our scheme increases the system capacity by releasing the unutilized bandwidth of the non-real time traffic class for the real-time traffic class while insuring a controllable upper bound on the packet loss probability of a non-real time traffic class. Through empirical evaluations using the real Internet traffic traces, our scheme is validated that it can increase the bandwidth efficiency while guaranteeing the quality of service requirements of the non-real time traffic class.