• Proceedings of the KSR Conference
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• 2008.11b
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• pp.61-65
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• 2008

Urban flooding with surcharges in sewer system was investigated because of unexpected torrential storm events these days, causing significant amounts of human and economic damages. Although there are limitations in forecasting and preventing natural disasters, integrated urban flooding management system using the SWMM engine and Web technology will be an effective tool in securing safety in operating Bi-modal transportation system. In addition, the integrated urban flooding management system can be linked with general and transportation-related disaster management system in the future. In this study, With simulated values by the SWMM, which is a core engine of the Bi-modal disaster management system, flash flooding area estimation module was developed. Thus, the SWMM system codes were modified and new module was developed and integrated with the existing SWMM interface using the Delphi programming language. The flash flooding area estimation module is fully integrated with the SWMM interface, thus the area is estimated on-the-fly inside the system.

• STI Policy Review
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• v.9 no.1
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• pp.1-29
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• 2018

With the constant growth of R&D investment, it has been increasingly necessary to evaluate the effectiveness of R&D performance and there is a high emphasis on ensuring the accountability and effectiveness of R&D programs. The evaluation of performance of a firm is especially necessary in times of economic downturn to justify R&D investment. However, there is a marked shortage of clear guidelines as to where and how particular metrics are used to measure the output and outcome of R&D activity in firms. Many firms have difficulties in selecting appropriate indicators for their R&D and financial performances. To fill this gap, this article discusses and presents the findings from the literature in such a way that they become useful for researchers or managers who are in charge of measuring the R&D and business performances arising from innovation activities. Finally, based on the findings about metrics of R&D performance, this article proposes the hypothetical framework to investigate the relationship between technology forecasting, strategic technology planning, and business performance. The framework of this article will assist policy makers, universities, research institutes/national laboratories, and companies to enhance their decision making process in technology development.

• Journal of Industrial Convergence
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• v.15 no.1
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• pp.43-52
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• 2017

Robot refers to machines that recognize the external environment and assess the given situations in order to operate autonomously by imitating the manner in which humans behave. Although Korea still lacks global competitiveness, Korea, as the $4^{th}$ ranked robot manufacturing country in the world, is currently expanding the domains of robots from application in manufacturing to application in service provision. Accordingly, this study aims to analyze the factors for the success in entry into the cooperation robot market among various robotic markets in accordance with the literary research method in consideration for the importance of robot industry that could determine the future national competitiveness. The result of the analysis of the factors for the success in entry into the cooperation robot market, shows that factors including analysis of the trends in manufacturing robot market, strategy for benchmarking of the leading cooperation robot companies, activation of small and medium enterprise-centered cooperation robotic industry, excavation of demands for cooperation robots with focus on automobile, semiconductor and IT industries, utilization of the opportunities provided by government's robotic industry policies and standardization of cooperation robot components, etc. determine whether one will succeed in the market or not. Furthermore, it is believed that fortification of competitiveness of the manufacturing sector through the powerful policy support for the robotic industry at government level and policies on cultivation of new growth engine through specialization of the robotic areas closely related to daily life must be implemented concurrently because it is forecasted that competitiveness in robotics technology will become the criterion for national competitiveness in the future.

• Korea Trade Review
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• v.44 no.2
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• pp.127-141
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• 2019

In this study, the relationship between Baltic Dry Index(BDI) and maritime trade volume in the dry cargo market was verified using the vector autoregressive (VAR) model. Data was analyzed from 1992 to 2018 for iron ore, steam coal, coking coal, grain, and minor bulks of maritime trade volume and BDI. Granger causality analysis showed that the BDI affects the trade volume of coking coal and minor bulks but the trade volume of iron ore, steam coal and grain do not correlate with the BDI freight index. Impulse response analysis showed that the shock of BDI had the greatest impact on coking coal at the two years lag and the impact was negligible at the ten years lag. In addition, the shock of BDI on minor cargoes was strongest at the three years lag, and were negligible at the ten years lag. This study examined the relationship between maritime trade volume and BDI in the dry bulk shipping market in which uncertainty is high. As a result of this study, there is an economic aspect of sustainability that has helped the risk management of shipping companies. In addition, it is significant from an academic point of view that the long-term relationship between the two time series was analyzed through the causality test between variables. However, it is necessary to develop a forecasting model that will help decision makers in maritime markets using more sophisticated methods such as the Bayesian VAR model.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.1
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• pp.51-58
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• 2022

Various studies have been conducted from the past to the present because stock price forecasts provide stability in the national economy and huge profits to investors. Recently, there have been many studies that suggest stock price prediction models using various input data such as macroeconomic indicators and emotional analysis. However, since each study was conducted individually, it is difficult to objectively compare each method, and studies on their impact on stock price prediction are still insufficient. In this paper, the effect of input data currently mainly used on the stock price is evaluated through the predicted value of the deep learning model and the error rate of the actual stock price. In addition, unlike most papers in emotional analysis, emotional analysis using the news body was conducted, and a method of supplementing the results of each emotional analysis is proposed through three emotional analysis models. Through experiments predicting Microsoft's revised closing price, the results of emotional analysis were found to be the most important factor in stock price prediction. Especially, when all of input data is used, error rate of ensembled sentiment analysis model is reduced by 58% compared to the baseline.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.31 no.1
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• pp.92-98
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• 2023

This paper used the golf course visitors' data in Jeju region to forecast the growth of inbound air traveler to Jeju. This is because the golf course visitors were proven to bring the highest economic and production inducement effect to the Jeju region. Based on such a data, this paper forecast the short-term growth rate of inbound air traveler using ARIMA to the Jeju until December 2025. According to ARIMA (0,1,0) (0,1,1) model, it was analyzed that the monthly number of golf course visitors to Jeju has been increasing steadily even since COVID-19 pandemic and the number is expected to grow until the end of 2025. Applying the same parameters of ARIMA (0,1,0) (0,1,1) to inbound air travel data, it was found the growth rate of inbound air travelers would be higher than the growth rate of 2019 shortly without moderate variation even though the monthly number of inbound travelers to Jeju had been dropped during COVID-19 pandemic.

• Korea Trade Review
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• v.47 no.3
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• pp.211-232
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• 2022

Due to the impact of the public health event COVID-19 epidemic, the Chinese futures market showed "Black Swan". This has brought the unpredictable into the economic environment with many commodities falling by the daily limit, while gold performed well and closed in the sunshine(Yan-Li and Rui Qian-Wang, 2020). Volatility is integral part of financial market. As an emerging market and a special precious metal, it is important to forecast return of gold futures price. This study selected data of the SHFE gold futures returns and conducted an empirical analysis based on the generalised autoregressive conditional heteroskedasticity (GARCH)-type model. Comparing the statistics of AIC, SC and H-QC, ARMA (12,9) model was selected as the best model. But serial correlation in the squared returns suggests conditional heteroskedasticity. Next part we established the autoregressive moving average ARMA-GARCH-type model to analysis whether Volatility Clustering and the leverage effect exist in the Chinese gold futures market. we consider three different distributions of innovation to explain fat-tailed features of financial returns. Additionally, the error degree and prediction results of different models were evaluated in terms of mean squared error (MSE), mean absolute error (MAE), Theil inequality coefficient(TIC) and root mean-squared error (RMSE). The results show that the ARMA(12,9)-TGARCH(2,2) model under Student's t-distribution outperforms other models when predicting the Chinese gold futures return series.

• New & Renewable Energy
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• v.19 no.4
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• pp.98-107
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• 2023

A study was conducted to analyze data from 1981 to 2020 for understanding the impact of climate on solar energy generation. A significant increase of 104.6 kWhm^-2 was observed in the annual cumulative solar radiation over this period. Notably, the distribution of solar radiation shifted, with the solar radiation in Busan rising from the seventh place in 1981 to the second place in 2020 in South Korea. This study also examined the correlation between long-term temperature trends and solar radiation. Areas with the highest solar radiation in 2020, such as Busan, Gwangju, Daegu, and Jinju, exhibited strong positive correlations, suggesting that increased solar radiation contributed to higher temperatures. Conversely, regions like Seosan and Mokpo showed lower temperature increases due to factors such as reduced cloud cover. To evaluate the impact on solar energy production, simulations were conducted using climate data from both years. The results revealed that relying solely on historical data for solar energy predictions could lead to overestimations in some areas, including Seosan or Jinju, and underestimations in others such as Busan. Hence, considering long-term climate variability is vital for accurate solar energy forecasting and ensuring the economic feasibility of solar projects.

• KDI Journal of Economic Policy
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• v.31 no.1
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• pp.169-206
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• 2009

This study stems from a question, "How should we understand the pattern of the Korean economy after the 1990s?" Among various analytic methods applicable, this study chooses a Structural Vector Autoregression (SVAR) with long-run restrictions, identifies diverse impacts that gave rise to the current status of the Korean economy, and differentiates relative contributions of those impacts. To that end, SVAR is applied to four economic models; Blanchard and Quah (1989)'s 2-variable model, its 3-variable extensions, and the two other New Keynesian type linear models modified from Stock and Watson (2002). Especially, the latter two models are devised to reflect the recent transitions in the determination of foreign exchange rate (from a fixed rate regime to a flexible rate one) as well as the monetary policy rule (from aggregate targeting to inflation targeting). When organizing the assumed results in the form of impulse response and forecasting error variance decomposition, two common denominators are found as follows. First, changes in the rate of economic growth are mainly attributable to the impact on productivity, and such trend has grown strong since the 2000s, which indicates that Korea's economic growth since the 2000s has been closely associated with its potential growth rate. Second, the magnitude or consistency of impact responses tends to have subsided since the 2000s. Given Korea's high dependence on trade, it is possible that low interest rates, low inflation, steady growth, and the economic emergence of China as a world player have helped secure capital and demand for export and import, which therefore might reduced the impact of each sector on overall economic status. Despite the fact that a diverse mixture of models and impacts has been used for analysis, always two common findings are observed in the result. Therefore, it can be concluded that the decreased rate of economic growth of Korea since 2000 appears to be on the same track as the decrease in Korea's potential growth rate. The contents of this paper are constructed as follows: The second section observes the recent trend of the economic development of Korea and related Korean articles, which might help in clearly defining the scope and analytic methodology of this study. The third section provides an analysis model to be used in this study, which is Structural VAR as mentioned above. Variables used, estimation equations, and identification conditions of impacts are explained. The fourth section reports estimation results derived by the previously introduced model, and the fifth section concludes.

• Journal of Navigation and Port Research
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• v.46 no.4
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• pp.375-384
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• 2022

The shipping industry is cyclically impacted by complex variables such as various economic indicators, social events, and supply and demand. The purpose of this study was to analyze the operating profit of 13 Korean liner companies over 30 years, including the financial crisis of the late 1990s, the global financial crisis of the late 2000s, and the COVID-19 global pandemic. This study was conducted to also identify factors that impacted the profit ratio of Korea's liner shipping companies according to economic conditions. It was divided into ocean-going and short-sea shipping, reflecting the characteristics of liner shipping companies, and was analyzed by hierarchical multiple regression analysis. The time series data are based on the Korean International Financial Reporting Standards (K-IFRS) and comprise seaborne trade volume, fleet evolution, and macroeconomic indicators. The outliers representing the economic downturn due to social events were separately analyzed. As a result of the analysis, the China Container Freight Index (CCFI) positively impacted ocean-going as well as short-sea liner shipping companies. However, the Korean container shipping volume only impacted ocean-going liners positively. Additionally, world and Korea's GDP, world seaborne trade volume, and fuel price are factored in the operating profit of short sea liner shipping. Also, the GDP growth rate of China, exchange rate, and interest rate did not significantly impact both groups. Notably, the operating profitability of Korea's liner shipping shows an exceptionally high rate during the recessions of 1998 and 2020. It is paradoxical, and not correlated with the classical economic indicators. Unlike other studies, this paper focused on the operating profit before financial expenses, considering the complexity as well as difficulty in forecasting the shipping cycle, and rendered conclusions using relatively long-term empirical analysis, including three economic shocks.