• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.5
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• pp.93-99
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• 2019

In this paper, we propose a smart meter that uses GRU model, which is one of artificial neural networks, for the efficient energy management. We collected power consumption data that train GRU model through the proposed smart meter. The implemented smart meter has automatic power measurement and real-time observation function and load control function through power consumption prediction. We determined a reference value to control the load by using Root Mean Squared Error (RMS), which is one of performance evaluation indexes, with 20% margin. We confirmed that the smart meter with automatic load control increases the efficiency of energy management.

• Journal of Korea Trade
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• v.26 no.5
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• pp.56-75
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• 2022

Purpose - This study examines the influencing factors of China's cross-border e-commerce exports in the context of the current situation and trends of China's cross-border e-commerce development. Through an improved trade gravity model, it provides more in-depth research and constructive opinions on the development of cross-border e-commerce in China. In this paper, factors such as consumption gap, volume of trade frictions, number of tourists, Internet usage and trade openness are added to the formula of the traditional trade gravity model in the improved trade gravity model to examine the influencing factors on China's cross-border e-commerce exports. Design/methodology - According to the empirical analysis, China's cross-border e-commerce exports to ten countries are used as dependent variables, and consumption gap, trade friction volume, trade distance, trade openness and number of Internet users are taken as independent variables. Regression analysis is conducted through a modified gravity model to test whether the hypotheses hold. Findings - The analysis shows that the hypothesis that China's cross-border e-commerce exports are influenced by trade openness, trade distance, consumption gap between trade parties, and the number of Internet users in the importing country is supported by these four hypotheses, but not all independent variables have an impact on them. Specifically, the number of travelers, trade frictions do not have an impact on China's cross-border e-commerce. That is to say, trade friction between China and the United States and political issues such as China-India and China-Japan territorial disputes that emerged before do not affect the development of cross-border e-commerce in China. Originality/value - The analysis shows that the factors influencing China's cross-border e-commerce exports are the trade openness of the importing country, the trade distance, the number of Internet users in the importing country, and the consumption gap between the two sides of the trade. The trade openness and the number of Internet users positively contribute to China's cross-border e-commerce, while the consumption gap and trade distance are negatively related to them. And the analysis found that the Sino-US trade war and the Sino-Indian territorial disputes and other trade frictions to China's cross-border e-commerce exports did not have a substantial impact.

• Proceedings of the CALSEC Conference
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• 2003.09a
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• pp.261-267
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• 2003

This study deals with controversial issues surrounding the today′s cyber-taxation and recommends feasible consumption tax system architecture titled Global Electronic Tax Invoice System (GETI). The GETI is an electronic consumption tax architecture to provide "all-in-one" tax and e-payment services through a trusted third party (TTP). GETI is designed to streamline the overall cyber-taxation process and provide simplified and transparent tax invoice services through an authorized np. To ensure information security, GETI incorporates public Key infrastructure (PKI) based digital certificates and other data encryption schemes when calculating, reporting, paying, and auditing tax in the electronic commerce environment. GETI is based on the OECD cyber-taxation agreement that was reached in January 2001, which established the taxation model for B2B and B2C electronic commerce transactions. For the value added tax systems, tax invoice is indispensable to commerce activities, since they provide documentations to prove the validity of commercial transactions. As paper-based tax invoice systems are gradually phased out and are replaced with electronic tax invoice systems, there is an increasing need to develop a reliable, efficient, transparent, and secured cyber-taxation architecture. To design such architecture, several desirable system attributes were considered -- reliability, efficiency, transparency, and security. GETI was developed with these system attributes in mind.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.585-588
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• 2019

Predictions of Energy Consumption for Industries gain an important place in energy management and control system, as there are dynamic and seasonal changes in the demand and supply of energy. This paper presents and discusses the predictive models for energy consumption of the steel industry. Data used includes lagging and leading current reactive power, lagging and leading current power factor, carbon dioxide (tCO2) emission and load type. In the test set, four statistical models are trained and evaluated: (a) Linear regression (LR), (b) Support Vector Machine with radial kernel (SVM RBF), (c) Gradient Boosting Machine (GBM), (d) random forest (RF). Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) are used to measure the prediction efficiency of regression designs. When using all the predictors, the best model RF can provide RMSE value 7.33 in the test set.

• The Korean Journal of Applied Statistics
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• v.32 no.2
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• pp.319-330
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• 2019

Forecasting the electricity consumption through analyzing the past electricity consumption a advantageous for energy planing and policy. Machine learning is widely used as a method to predict electricity consumption. Among them, ensemble learning is a method to avoid the overfitting of models and reduce variance to improve prediction accuracy. However, ensemble learning applied to daily data shows the disadvantages of predicting a center value without showing a peak due to the characteristics of ensemble learning. In this study, we overcome the shortcomings of ensemble learning by considering the temperature trend. We compare nine models and propose a model using random forest with the linear trend of temperature.

• Journal of Korea Trade
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• v.27 no.1
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• pp.119-138
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• 2023

Purpose - This research is to empirically explore the differences in apparel consumption among male and female teenagers and college students in Korea and China. By conducting a survey to understand customers' needs and behaviors, fashion businesses will be able to improve their customer satisfaction and avoid redundancy, inventory, and the waste of resources, effort and money. Design/methodology - The research design considers the consumption patterns of male and female high school and college students in Korea and China. To analyze the data, the study employs decision trees, a type of machine learning algorithm. A decision tree model was developed to examine the relationship between the explanatory and response variables, which can be either quantitative or qualitative in nature. Findings - The main findings of this study indicate that there are differences in shopping behavior among different customer segments. The results show that men have a simpler shopping behavior compared to women. Additionally, cultural factors and the difference in fashion needs between students and non-students have a significant impact on the shopping choices of Chinese and Korean individuals. Originality/value - Existing studies often assume that the shopping behavior of high school and university students is similar and that there are no significant differences in clothing purchases between men and women across countries. The results provide valuable insights into the unique shopping behavior of different customer segments, and can inform fashion businesses in their efforts to meet the needs of their customers.

• The Korean Journal of Franchise Management
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• v.14 no.4
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• pp.17-33
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• 2023

Purpose: This study divides choice attributes that can help strengthen the competitiveness of traditional markets into product, price, personnel, and physical evidence. This study also examines which choice attributes affect customer value perception, satisfaction, and loyalty. Research design, data, and methodology: The data were collected from 542 traditional customers aged 20 or older who frequently visit traditional markets across the country and analyzed using the Smart PLS 4.0 program. The survey was conducted with the help of an online survey company for a total of 14 days from April 7, 2023 to April 20, 2023. Result: First, product, price, and employee quality have a positive impact on utilitarian and hedonic value, but physical evidence does not. Second, product, price, and employee quality have a positive impact on hedonic and hedonic value. Second, utilitarian value has a positive impact on satisfaction and revisit intention. Third, hedonic value has a positive impact on satisfaction, but does not on revisit intention. Lastly, satisfaction has a positive impact on revisit intention. Conclusions: Based on the S-O-R model and the theory of consumption value, this study proposed and examined an integrated framework in which satisfaction leads to revisit intention through selection attributes acting on perceived value.

• Journal of the Korean Solar Energy Society
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• v.23 no.1
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• pp.57-67
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• 2003

This study aims to presents Evaluation Method of Green Remodeling that analyze the value of environment through expense, using the method of life cycle cost and life cycle assessment simultaneously. The results of this study are summarized as follows. Evaluation Model developed in this study can convert economical value of environment into cost by integrating. In addition, the model can apply as a useful tool to estimation of economical design alternative as well as quantification of environmental loads and costs. Evaluation Model presented In this study observe energy consumption and the environmental load emission with qualification, it can forecast effect of environmental cost that cost estimation is expected to be added to energy cost rate by being possible. Synthetically, when Estimation Model and computer program that developed in this study is applies to the construction industry; reasonable management of environmental load is convenient at each step of Green Remodeling. In addition, at preliminary design phase, practical use may be possible by reasonable yardstick about various alternatives and improvement of design alternatives likewise by grasping environmental effect.

• The Journal of Fisheries Business Administration
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• v.46 no.3
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• pp.119-127
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• 2015

This study is to analyse the causality and volatility spillover between farming fish species in consumption replacement relation using flatfish(oliver flounder) and rockfish's wholesale market price data from September 2006 to July 2015. For the analysis, VAR(5) model and bivariate asymmetric GARCH-BEKK model are employed. The empirical results of this study are summarized as follows: First, the price volatility of flatfish and rockfish is very large without the trend during the sample period. Second, the correlation coefficient between flatfish and rockfish wholesale markets has positive 0.1059 value. Third, causality relation is unidirectional from rockfish market to flatfish market. Fourth, conditional volatility spillover effect is unidirectional from rockfish market to flatfish market, but asymmetric volatility effect is bidirectional between flatfish and rockfish markets that implies the bad news arising from flatfish wholesale market impact on rockfish market's volatility and the bad news arising from rockfish wholesale market impact on flatfish market's volaltilty. Consequently, based on the thus results, the volatility spillover effect interacts and is bidirectional between flatfish and rockfish wholesale markets.

• Korean Journal of Agricultural Science
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• v.48 no.2
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• pp.219-230
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• 2021

In Korea, the industry and marketing of livestock has grown because of increases in consumers' income and changes in food consumption trends. Livestock production and consumption increased tenfold from 1970 to 2018, and this rise will continue. However, the quality of marketing information for Korean livestock has remained low. The Korea Institute for Animal Products Quality Evaluation (KAPE) operates programs that provide marketing information on livestock, but the social benefits of these programs have not been objectively evaluated. The purpose of this study was to estimate the social benefit of the programs offering marketing information on Korean livestock. Survey and analysis using an economic model (double-bounded dichotomous choice contingent valuation model), revealed a few findings. First, the users of the marketing information programs offered by KAPE recognized the value of these programs and demonstrated their willingness to pay for this marketing information. Second, the social values of the programs offering marketing information on livestock were estimated as 1.1 billion won (marketing information on main livestock) or 5.3 billion won (price information on poultry), and these social values were 2 or 6 times greater than the cost to operate the programs for offering information. Finally, the program that provides marketing information on domestic livestock provides sufficient social benefits, so KAPE should expand these programs.