• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.2
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• pp.458-468
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• 2019

This research attempted to estimate monthly sales of food and beverage at the passenger terminal of Incheon int'l airport from June of 2015 to December 2020. This paper used ARIMA-Intervention model which can estimate the change of the sales amount suggesting the predicted monthly food and beverage sales revenue. The intervention variable was travel-ban policy against south Korea from P.R. China since July 2016 to December 2017 due to THAAD in south Korea. According to ARIMA, it was found normal predicted sales amount showed the slow growth increase rate until 2020 due to the effect of intervened variable. However, the monthly food sales in July and August 2019 was 20.3 and 21.2 billion KRW respectively. Each amount would increase even more in 2020 and the amount would increase to 21.4 and 22.1 billion KRW. The sales amount in 2019 would be 7.7 and 8.1 billion KRW and climb up 7.9 and 8.2 billion KRW in 2020. It was expected LCC passengers tend to spend more money for F&B at airport due to no meal or drink service of LCC or the paid-in meal and beverage service of LCC. The growth of sales of food and beverate will be accompanied with the growth of LCC according to estimated data.

• Journal of Climate Change Research
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• v.6 no.3
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• pp.233-241
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• 2015

In 2014, the United Nations Framework Convention on Climate Change (UNFCCC) agreed to submit the Intended Nationality Determined Contributions (INDCs) at the conference of parties held in Lima, Peru. Then, the South Korean government submitted the INDCs including GHGs reduction target and reduction potential on July, 2015. The goal of this study is to predict GHGs emission and to analyze reduction potential in agricultural sector of Korea. Activity data to estimate GHGs emission was forecast by Korea Agricultural Simulation Model (KASMO) of Korea Rural Economic Institute and estimate methodology was taken by the IPCC and guideline for MRV (Measurement, Reporting and Verification) of national greenhouse gases statistics of Korea. The predicted GHGs emission of agricultural sectors from 2021 to 2030 tended to decrease due to decline in crop production and its gap was less after 2025. Increasing livestock numbers such as sheep, horses, swine, and ducks did not show signigicant impact the total GHGs emission. On a analysis of the reduction potential, GHGs emission was expected to reduce $253Gg\;CO_{2-eq}$. by 2030 with increase of mid-season water drainage area up to 95% of total rice cultivation area. The GHGs reduction potential with intermittent drainage technology applied to 10% of the tatal paddy field area, mid-drainage and no organic matter would be $92Gg\;CO_{2-eq}$. by 2030.

• International Area Studies Review
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• v.12 no.3
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• pp.321-339
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• 2008

The purpose of this study to the analyze characteristics and purchasing activities of consumers by using the Multinomial Logit model, which is a well-known discrete selection model to explain and forecast consumers' selection activities(patterns). The study aims to determine the state of competition between National Brand and Private Band and how some demographic characters and marketing variables influence consumers' brand selections within the facial tissue market. Our analysis process includes reorganization of panel data(individuals' purchasing record at each point) to fit the purpose of our study as well as analysis of probability and influencing factors of consumers' brand selection at each point of purchases. The result showed that consumers at higher age and with higher income hold better probability to purchase National Brand. Likewise, locations also had considerable effect on selecting brand, and Private Brand was preferred in department store and discount stores. On the other hand, consumers loyal to National Brand reported higher probability to purchase if the product prices were higher while Private Brand buyers were more promoted the purchase under price discount.

• Atmosphere
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• v.29 no.3
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• pp.325-342
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• 2019

Precipitation and no-precipitation events under the influence of the Siberian high pressure system in Yeondong region, were analysed and classified as four types [obvious precipitation event (OP) type, obvious no-precipitation event (ON) type, ambiguous precipitation event (AP) type and ambiguous no-precipitation event (AN) type], according to the easiness in determining whether to have precipitation or not in Yeongdong region, to help in improving the forecast skill. Concerning the synoptic pressure pattern, for OP type, the ridge of Siberian high extends from Lake Baikal toward Northeast China, and there is a northerly wind upstream of the northern mountain complex (located near the Korean-Chinese border). On the other hand, for ON type, the ridge of Siberian high extends southeastward from Lake Baikal, and there is a westerly wind upstream of the northern mountain complex. The pressure pattern of AP type was similar to the OP type and that of AN type was also similar to ON type. Thus it was difficult to differentiate AP type and OP type and AN type and ON type based on the synoptic pressure pattern only. The four types were determined by U (wind speed normal to the Taebaek mountains) and Froude number (FN). That is, for OP type, average FN and U at Yeongdong coast are ~2.0 and ${\sim}6m\;s^{-1}$, and those at Yeongseo region are 0.0 and $0.1m\;s^{-1}$, respectively. On the contrary, for ON type, average FN and U at Yeongdong coast are 0.0 and $0.2m\;s^{-1}$, and those at Yeongseo region are ~1.0 and ${\sim}4m\;s^{-1}$, respectively. For AP type, average FN and U at Yeongdong coast are ~1.0 and ${\sim}4m\;s^{-1}$, and those at Yeongseo region are 0.0 and $0.2m\;s^{-1}$, whereas for AN type, average FN and U at Yeongdong coast are 0.1 and $0.6m\;s^{-1}$ and those at Yeongseo region are ~1.0 and ${\sim}3m\;s^{-1}$, respectively. Based on the result, a schematic diagram for each type was suggested.

• The Journal of Bigdata
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• v.3 no.2
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• pp.59-70
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• 2018

In order to provide stable district heat supplying service to the certain limited residential area, it is the most important to forecast the short-term future demand more accurately and produce and supply heat in efficient way. However, it is very difficult to develop a universal heat demand forecasting model that can be applied to general situations because the factors affecting the heat consumption are very diverse and the consumption patterns are changed according to individual consumers and regional characteristics. In particular, considering all of the various variables that can affect heat demand does not help improve performance in terms of accuracy and versatility. Therefore, this study aims to develop a demand forecasting model using deep learning based on only limited information that can be acquired in real time. A demand forecasting model was developed by learning the artificial neural network of the Tensorflow using past data consisting only of the outdoor temperature of the area and date as input variables. The performance of the proposed model was evaluated by comparing the accuracy of demand predicted with the previous regression model. The proposed heat demand forecasting model in this research showed that it is possible to enhance the accuracy using only limited variables which can be secured in real time. For the demand forecasting in a certain region, the proposed model can be customized by adding some features which can reflect the regional characteristics.

• Journal of Convergence for Information Technology
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• v.9 no.6
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• pp.1-6
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• 2019

In this study, the development of a weight estimation model of electronic scale with nonlinear characteristics is presented using polynomial regression analysis. The output voltage of the load cell was measured directly using the reference mass. And a polynomial regression model was obtained using the matrix and curve fitting function of MS Office Excel. The weight was measured in 100g units using a load cell electronic scale measuring up to 5kg and the polynomial regression model was obtained. The error was calculated for simple($1^{st}$), $2^{nd}$ and $3^{rd}$ order polynomial regression. To analyze the suitability of the regression function for each model, the coefficient of determination was presented to indicate the correlation between the estimated mass and the measured data. Using the third order polynomial model proposed here, a very accurate model was obtained with a standard deviation of 10g and the determinant coefficient of 1.0. Based on the theory of multi regression model presented here, it can be used in various statistical researches such as weather forecast, new drug development and economic indicators analysis using logistic regression analysis, which has been widely used in artificial intelligence fields.

• Journal of the Korea Convergence Society
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• v.10 no.4
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• pp.1-6
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• 2019

Neural networks have been used to predict the direction of stock index movement from past data. The conventional research that predicts the upward or downward movement of the stock index predicts a rise or fall even with small changes in the index. It is highly likely that losses will occur when trading ETFs by use of the prediction. In this paper, a neural network model that predicts the movement direction of the daily KOrea composite Stock Price Index (KOSPI) to reduce ETF trading losses and earn more than a certain amount per trading is presented. The proposed model has outputs that represent rising (change rate in index ${\geq}{\alpha}$), falling (change rate ${\leq}-{\alpha}$) and neutral ($-{\alpha}$ change rate < ${\alpha}$). If the forecast is rising, buy the Leveraged Exchange Traded Fund (ETF); if it is falling, buy the inverse ETF. The hit ratio (HR) of PNN1 implemented in this paper is 0.720 and 0.616 in the learning and the evaluation respectively. ETF trading yields a yield of 8.386 to 16.324 %. The proposed models show the better ETF trading success rate and yield than the neural network models predicting KOSPI.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.1
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• pp.85-91
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• 2021

In contrast to standard train tracks, tramlines are often set along public roads, with trams running among pedestrians and other vehicles. In some cities and towns, trams and buses share the same routes and stations. Under the current investment appraisal system, trams are classified into light rail when predicting traffic demand and calculating benefits, but in the case of non-capital areas, it is notable that the origin-destination and transit lines of buses are not provided in the Korea Transport Database distribution data. Due to this problem, it is difficult to reflect proper mode changing behaviors between route buses and trams. This study examines the impact on tramlines of bus routes that are not currently considered in non-capital areas. Following an analysis of the effect of tram projects according to whether bus routes are considered or not, an improvement in methodology is proposed. Through this study, it is expected that the investment appraisal system for the planning of new tramlines will be improved in the future.

• Korean Journal of Construction Engineering and Management
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• v.22 no.2
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• pp.3-11
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• 2021

Myanmar is an undeveloped country with high development value among Asian countries. Therefore, various countries including the U.S. are considering entering the market. In this respect, demand for real estate development project is forecast to grow on increased inflow of foreigners and Myanmar's economic growth. However, Myanmar is a high-risk country in terms of overseas companies, including national risk. In this study, we conducted an in-depth interview with experts (law, finance, technology, and local experts) after analyzing data on Myanmar to extract risk-causing factors. Through this, 106 risk factors were extracted, and the final risk classification system was established by conducting three-time groupings using the affinity diagramming. And the relative importance of each factor was presented using the analytic hierarchy process (AHP) technique. As a result, the country-related risk, the fund-related risk, and the pre-sale-related risk were highly important. The research results are expected to provide risk management standards to companies entering the Myanmar real estate development type project.

• Journal of the Korean Institute of Gas
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• v.24 no.6
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• pp.34-46
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• 2020

In order to supply stable natural gas in the future, it is necessary to forecast the demand in advance and secure the quantity of supply. In this paper, we propose a method of estimating the demand function of industrial natural gas, which is the core of the increase of domestic natural gas demand in the future. The cross-sectional data of 304 domestic industries were used to estimate the demand function of the industrial natural gas, and the effect of industry specific characteristics such as capital investment, manufacturing cost. Finally, the least absolute deviation estimation method which is robust to outliers and does not assume the homogeneity of the error term and the normality, And the results were derived. In addition, the economic value of industrial city gas was estimated using the price elasticity of industrial city gas. Therefore, it can be seen that the continuous expansion and supply of city gas to the industrial sector is beneficial at the national level, and the government needs to promote expansion through the industrial city gas support policy.