• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.568-579
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• 2018

Virtual power plants can be regarded as systems that have entered the network after restructure of power systems. In fact, these plants are a set of consumers capable of consuming and generating power. In response to widespread implementation of plug-in hybrid electric vehicles, further investigation of energy management in this type of power plants seems to be of great value. In effect, these vehicles are able to receive and inject power from/into the network. Hence, study of the effects of these vehicles on management of virtual power plants seems to be illuminative. In this paper, management of power consumption/generation in virtual power plants has been investigated in the presence of hybrid electric vehicles. The objective function of virtual power plants problem management is to minimize the overall costs including not only the costs of energy production in power generation units, fuels, and degradation of batteries of vehicles, but also the costs of purchasing electricity from the network. Furthermore, the constraints on the operational of plants, loads and hybrid vehicles, level of penalty for greenhouse gas emissions ($CO_2$ and $NO_x$) produced by power plants and vehicles, and demand response to the immediate price of market have all been attended to in the present study. GAMS/Cplex software system and sample power system have been employed to pursue computer implementation and simulation.

• Journal of the Society of Naval Architects of Korea
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• v.55 no.5
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• pp.415-420
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• 2018

The development of sail gas has increased the production of ethane as well as natural gas. The decline in the market price for ethane has led to a change in the petroleum-based ethylene production process into an ethane-based ethylene production process and an increase in the ethane/ethylene trade volume. Large-scale ethane/ethylene carrier have been needed due to an increase in long-distance trade from the US, and cargo type change have leaded to consider a liquefaction process to re-liquefy Boil-Off gas generated during the voyage. In this paper, the liquefaction system of Liquefied Ethane Gas carrier was evaluated with Low-GWP (Low-Global Warming Potential) refrigerant and process parameters, Boil-Off Gas pressure and expansion valve outlet pressure, were optimized. Low-GWP refrigerants were propane (R290), propylene(R1270), carbon dioxide(R744) was considered at two type of liquefaction process such as Linde and cascade cycle. The results show that the optimal pressure point depends on the individual refrigerant and the highest liquefaction efficiency of carbon dioxide (R744) - propane (R290) refrigerant.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.1
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• pp.158-166
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• 2019

In pursuing carbon emission reduction efforts, companies have focused for the most part on reducing emissions due to the more efficient equipment and facilities. However they overlook a significant source of carbon emissions, one that is driven by operational policies. Currently companies are looking for solutions to reduce carbon emissions associated with their operations. Operational adjustments, such as modifications in order quantities could an effective way in reducing carbon emissions in the supply chain. Also, Cap-and-Trade mechanism is generally accepted as on of the most effective market-based mechanism to reduce carbon emissions. In this paper, we investigate a supply chain with single manufacturer and multiple retailers multi-product inventory model under the cap-and-trade system incorporating the carbon emissions caused by transportation and warehousing activities. Also, we provide an iterative solution algorithm and derive the common order interval and the number of intervals for each product. We show by numerical example that the inventory model incorporating cap & trade mechanism can reduce total cost and carbon emissions compared to the classical inventory model. Using the numerical examples, we also investigates different carbon price on the performance of the inventory model.

• Journal of Information Processing Systems
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• v.18 no.5
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• pp.637-649
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• 2022

Investors aim to increase their profitability by investing in the stock market. An adroit strategy for minimizing related risk lies through diversifying portfolio operationalization. In this paper, we propose a six-step stocks portfolio selection model. This model is based on data mining clustering techniques that reflect the ensuing impact of the political, economic, legal, and corporate governance in Mongolia. As a dataset, we have selected stock exchange trading price, financial statements, and operational reports of top-20 highly capitalized stocks that were traded at the Mongolian Stock Exchange from 2013 to 2017. In order to cluster the stock returns and risks, we have used k-means clustering techniques. We have combined both k-means clustering with Markowitz's portfolio theory to create an optimal and efficient portfolio. We constructed an efficient frontier, creating 15 portfolios, and computed the weight of stocks in each portfolio. From these portfolio options, the investor is given a choice to choose any one option.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.728-729
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• 2015

As environmental issues have been increased globally, eco-friendliness in the construction area, which accounts for more than 30% of total GHG gas emission has being urged. In response, the Korean government has implemented G-SEED(Green Standard for Energy and Environmental Design) certification from 2002. However, total number of certified apartment buildings is only around 1% of total number of approved apartment buildings. As a basic research to find out reasons of low rate of the certification, this paper analyzes consumers' decision-making process in G-SEED certified apartment building market comparing to non G-SEED certified one and draw System Dynamics modeling based on causal relationship. As a result, consumers' demand for the certified one is increased by 'Perceived Relative Utility' which is resulted from comparison process with non-certified one. The 'Perceived Relative Utility' is ascended upward steadily by 'Relative Perceived Price' considered as relatively short-term effect and 'Favorable Image of Certified Housing' referred to long-term effect.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.2
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• pp.279-292
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• 2018

Unlike the global art market which experienced rapid recovery from the impacts of the Global Financial Crisis in 2008, the Korean art market has not yet fully recovered. The gallery-oriented distribution system, vulnerable primary art market functions, and the market structure centered on a small number of collectors make it difficult for young and medium artists to enter the market and, as a result, deepen the economic polarization of artists. In addition, the high price of art works limits market participation by restricting the general public. This study began with the idea that the interest of the public in the art market as well as their participation in the market are urgent. To this end, we noted that public awareness of art transactions can be a starting point for improving the constitution of the fragile art market, focusing on the 'Artist-centered Art Fair' rather than existing art fairs. To examine the contribution of such an art fair to the popularization of the art market, we analyzed the case of the 'Visual Artist Market (VAM)' project of the Korea Arts Management Service. Results found that the 'Artist-centered Art Fair' focuses on providing opportunities for market entry to young and medium artists rather than on the interests of distributors, and promotes the popularization of the art market by promoting low-priced works to the general public. Also, the 'Artist-centered Art Fair' seems to play a primary role in the public sector to foster solid groups of artists as well as to establish healty distribution networks of Korean Art market. However, in the long run, it is necessary to promote sustainable development of the 'Artist-centered Art Fair' through indirect support, such as the provision of a publicity platform or consumer finance support, rather than direct support.

• Journal of Technology Innovation
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• v.22 no.3
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• pp.101-133
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• 2014

Climate change and low-carbon consumer movement is demanding proper response around the world while rising oil price increases consumers' needs for green car. As a preliminary study to establish an industrial platform for green car and bring out corporate strategies, this article aims to propose an academic research framework by using various methodologies including conceptual/mathematical modeling, system dynamics, and ABM from different angles. First, an analysis framework for the industrial platform was introduced to analyze green car cases, required elements were proposed, and econometrics was applied to build a basic model related to green platform (two-sided market). Also, to analyze from a dynamic perspective, a system dynamics model was applied to green car environment to build a system dynamics analysis model that is applicable to particular green car industry analysis. Lastly, an agent based model was used to study the way to activate the hybrid car market in Korea from individual consumers' perspective. Based on the result, vehicle policies that are either being enforced or planned to be enforced in the Korean HEV market can be analyzed.

• Spatial Information Research
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• v.21 no.6
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• pp.69-80
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• 2013

Our country has made every efforts to develop Real Estate Transaction culture with emphasis on Licensed Realtors by introducing Real Estate Transaction Law in 1983. Also, MOLIT(Ministry of Land, Infrastructure and Transport) designated several organizations including KAR(Korea Association of Realtors) as Real Estate Transaction Information Network Licensees for data credibility enhancement and transaction transparency. Nevertheless, the level of law abiding spirit and transaction culture are still similar to those of the old 'Bokdeokbang' era. The under-developed transaction behaviors prevent the social capital of people's credibility on Licensed Realtors from advancing, and results in the outcomes of unnecessary social cost. That is, very low credibility on the data on Sales Items in the market and the fear of speculative real estate price uprise and market distortions are continuing on. In this context, the purpose of this study is to propose the model of GIS-based Modernized Real Estate Transaction System and its execution policies to support credible Real Estate Information to the general public for efficient transactions in the market. Accordingly, the study aims at contributing to the modernization of Real Estate Transactions, fostering competitiveness of Realtors in the Real Estate Market.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.59-71
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• 2014

System trading is becoming more popular among Korean traders recently. System traders use automatic order systems based on the system generated buy and sell signals. These signals are generated from the predetermined entry and exit rules that were coded by system traders. Most researches on system trading have focused on designing profitable entry and exit rules using technical indicators. However, market conditions, strategy characteristics, and money management also have influences on the profitability of the system trading. Unexpected price deviations from the predetermined trading rules can incur large losses to system traders. Therefore, most professional traders use strategy portfolios rather than only one strategy. Building a good strategy portfolio is important because trading performance depends on strategy portfolios. Despite of the importance of designing strategy portfolio, rule of thumb methods have been used to select trading strategies. In this study, we propose a SVM-based strategy portfolio management system. SVM were introduced by Vapnik and is known to be effective for data mining area. It can build good portfolios within a very short period of time. Since SVM minimizes structural risks, it is best suitable for the futures trading market in which prices do not move exactly the same as the past. Our system trading strategies include moving-average cross system, MACD cross system, trend-following system, buy dips and sell rallies system, DMI system, Keltner channel system, Bollinger Bands system, and Fibonacci system. These strategies are well known and frequently being used by many professional traders. We program these strategies for generating automated system signals for entry and exit. We propose SVM-based strategies selection system and portfolio construction and order routing system. Strategies selection system is a portfolio training system. It generates training data and makes SVM model using optimal portfolio. We make $m{\times}n$ data matrix by dividing KOSPI 200 index futures data with a same period. Optimal strategy portfolio is derived from analyzing each strategy performance. SVM model is generated based on this data and optimal strategy portfolio. We use 80% of the data for training and the remaining 20% is used for testing the strategy. For training, we select two strategies which show the highest profit in the next day. Selection method 1 selects two strategies and method 2 selects maximum two strategies which show profit more than 0.1 point. We use one-against-all method which has fast processing time. We analyse the daily data of KOSPI 200 index futures contracts from January 1990 to November 2011. Price change rates for 50 days are used as SVM input data. The training period is from January 1990 to March 2007 and the test period is from March 2007 to November 2011. We suggest three benchmark strategies portfolio. BM1 holds two contracts of KOSPI 200 index futures for testing period. BM2 is constructed as two strategies which show the largest cumulative profit during 30 days before testing starts. BM3 has two strategies which show best profits during testing period. Trading cost include brokerage commission cost and slippage cost. The proposed strategy portfolio management system shows profit more than double of the benchmark portfolios. BM1 shows 103.44 point profit, BM2 shows 488.61 point profit, and BM3 shows 502.41 point profit after deducting trading cost. The best benchmark is the portfolio of the two best profit strategies during the test period. The proposed system 1 shows 706.22 point profit and proposed system 2 shows 768.95 point profit after deducting trading cost. The equity curves for the entire period show stable pattern. With higher profit, this suggests a good trading direction for system traders. We can make more stable and more profitable portfolios if we add money management module to the system.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.161-177
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• 2015

With the rapid evolution of technology, the size, number, and the type of databases has increased concomitantly, so data mining approaches face many challenging applications from databases. One such application is discovery of fraud patterns from agricultural product wholesale transaction instances. The agricultural product wholesale market in Korea is huge, and vast numbers of transactions have been made every day. The demand for agricultural products continues to grow, and the use of electronic auction systems raises the efficiency of operations of wholesale market. Certainly, the number of unusual transactions is also assumed to be increased in proportion to the trading amount, where an unusual transaction is often the first sign of fraud. However, it is very difficult to identify and detect these transactions and the corresponding fraud occurred in agricultural product wholesale market because the types of fraud are more intelligent than ever before. The fraud can be detected by verifying the overall transaction records manually, but it requires significant amount of human resources, and ultimately is not a practical approach. Frauds also can be revealed by victim's report or complaint. But there are usually no victims in the agricultural product wholesale frauds because they are committed by collusion of an auction company and an intermediary wholesaler. Nevertheless, it is required to monitor transaction records continuously and to make an effort to prevent any fraud, because the fraud not only disturbs the fair trade order of the market but also reduces the credibility of the market rapidly. Applying data mining to such an environment is very useful since it can discover unknown fraud patterns or features from a large volume of transaction data properly. The objective of this research is to empirically investigate the factors necessary to detect fraud transactions in an agricultural product wholesale market by developing a data mining based fraud detection model. One of major frauds is the phantom transaction, which is a colluding transaction by the seller(auction company or forwarder) and buyer(intermediary wholesaler) to commit the fraud transaction. They pretend to fulfill the transaction by recording false data in the online transaction processing system without actually selling products, and the seller receives money from the buyer. This leads to the overstatement of sales performance and illegal money transfers, which reduces the credibility of market. This paper reviews the environment of wholesale market such as types of transactions, roles of participants of the market, and various types and characteristics of frauds, and introduces the whole process of developing the phantom transaction detection model. The process consists of the following 4 modules: (1) Data cleaning and standardization (2) Statistical data analysis such as distribution and correlation analysis, (3) Construction of classification model using decision-tree induction approach, (4) Verification of the model in terms of hit ratio. We collected real data from 6 associations of agricultural producers in metropolitan markets. Final model with a decision-tree induction approach revealed that monthly average trading price of item offered by forwarders is a key variable in detecting the phantom transaction. The verification procedure also confirmed the suitability of the results. However, even though the performance of the results of this research is satisfactory, sensitive issues are still remained for improving classification accuracy and conciseness of rules. One such issue is the robustness of data mining model. Data mining is very much data-oriented, so data mining models tend to be very sensitive to changes of data or situations. Thus, it is evident that this non-robustness of data mining model requires continuous remodeling as data or situation changes. We hope that this paper suggest valuable guideline to organizations and companies that consider introducing or constructing a fraud detection model in the future.