• International Area Studies Review
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• v.16 no.3
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• pp.239-266
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• 2012

The purpose of this paper is to suggest desirable improvement for KRX derivatives market plagued with many problems in spite of its world level of quantitative growth. In order to try to find desirable improvement for KRX derivatives market which has many problems like that, I suggest various ways of improvement for regulatory system in the future in terms of behavioral regulation for investor protection. First of all, in order to relieve speculative tendency of trading, KOSPI200 option market with ATM-oriented option trading needs to be induced from the market in which OTM-oriented option is now trading. So discount or exemption of brokerage fee for ATM trading and the introduction of market-maker for ATM type can be considered. For the protection of individual investors, we suggest feasible plans such as differential regulation between professional and individual investors, consolidation of basic deposit management, and enlargement of opportunities for risk management education & simulation trading.

• The Journal of Bigdata
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• v.5 no.2
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• pp.231-240
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• 2020

With the development in the IT industry and the growth in the game industry, user's game data is recorded in seconds according to various plays and options, and a vast amount of game data can be analyzed based on Bigdata. Combined with business, Bigdata is used to discover new values for profit creation in various fields, but it is utilized in the game industry in insufficient ways. In this study, considering the characteristics of the subdivided lines, we constructed a win-loss prediction model for each line using the game data of League of Legends, and derived the importance of variables. This study can contribute to planning of strategies for general game users to get information about team members in advance and increase the win rate by using the record search sites.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.6
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• pp.51-58
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• 2020

The blind people have difficulty living an independent clothing life. The furniture and home appliance are adding AI or IoT with the recent growth of the smart appliance market. To support the independent clothing life of the blind, this paper suggests a smart wardrobe with closet control function, voice recognition function and clothes information recognition using CNN algorithm. The number of layers of the model was changed and Maxpooling was adjusted to create the model to increase accuracy in the process of recognizing clothes. Early Stopping Callback option is applied to ensure learning accuracy when creating a model. We added Dropout to prevent overfitting. The final model created by this process can be found to have 80 percent accuracy in clothing recognition.

• Journal of Life Science
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• v.28 no.3
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• pp.275-283
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• 2018

Human fibroblast growth factor (FGF) has the potential to be a commercially important therapeutic or cosmeceutical agent due to its ability to generate tissue and heal wounds. Granting permeability into skin tissues increases the therapeutic effects of FGF. Thus, several researchers have attempted the fusion of FGF conjugates with protein transduction domains (PTDs) to investigate the transduction ability and therapeutic effects of FGF. Less is known, however, about whether the location of PTD fused to the N- or C-terminus of FGF proteins has a significant impact on the folding and stability in Escherichia coli, and eventually, on transduction. Here, we report cloning of human basic fibroblast growth factor (FGF2) as a control and FGF2 with PTD fused to the N- or C-terminal ends of FGF proteins by an overlap extension PCR. We performed expression, verified expression properties of recombinant FGF2 without or with PTD fused to the N-terminus and the C-terminus, and investigated transduction ability into tissue by treating the dorsal skin of mice subjects. As a result, FGF2 and FGF2-PTD (fused to C-terminus) fusion protein were expressed as soluble forms suitable for straight-forward purification, unlike insoluble PTD-FGF2 (fused to N-terminus), but only FGF2-PTD fusion protein could transduce into the dorsal skin tissue of the mice subjects. Our results suggest that FGF2 with PTD fused to the C-terminus is more efficient than other options in terms of expression, purification, and delivery into skin tissue, as it does not require labor-intensive, costly, and time-consuming methods.

• Journal of Environmental Policy
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• v.8 no.4
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• pp.95-124
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• 2009

This study aims to introduce greenhouse gas emission trading in Korea as a highly cost-effective mechanism for controlling emissions. The Basic Act on Low-Carbon Green Growth will cover methods of emissions allocation, national inventory, and trading systems (i.e. emissions trading platforms, national registry,and clearing and settlement platforms). The Korean emission scheme will be based on the Korean Climate Change Act proposed by the National Assembly and Government with a cap-and-trade scheme. The national allowances will be allocated by the hybrid system. To establish the national inventory, TRADEMARKS (Telemetering System) and emissions factors are effective for greenhouse gas emissions measurement. It will likewise be effective for the national registry to be implemented via a Korean Integrated Registry, the emissions trading platform via the KRX (Korean Exchange), and the clearing and settlement platform via the KSD (Korean Securities Depository). In other words, the KRX will manage product development and marketing for Korean Carbon Financial Instruments (including commodities, futures, and options contracts) listed and admitted to trading on the KRX. All emissions trades will be standardized and cleared by the KSD.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.187-201
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• 2011

KOSPI200 index is the Korean stock price index consisting of actively traded 200 stocks in the Korean stock market. Its base value of 100 was set on January 3, 1990. The Korea Exchange (KRX) developed derivatives markets on the KOSPI200 index. KOSPI200 index futures market, introduced in 1996, has become one of the most actively traded indexes markets in the world. Traders can make profit by entering a long position on the KOSPI200 index futures contract if the KOSPI200 index will rise in the future. Likewise, they can make profit by entering a short position if the KOSPI200 index will decline in the future. Basically, KOSPI200 index futures trading is a short-term zero-sum game and therefore most futures traders are using technical indicators. Advanced traders make stable profits by using system trading technique, also known as algorithm trading. Algorithm trading uses computer programs for receiving real-time stock market data, analyzing stock price movements with various technical indicators and automatically entering trading orders such as timing, price or quantity of the order without any human intervention. Recent studies have shown the usefulness of artificial intelligent systems in forecasting stock prices or investment risk. KOSPI200 index data is numerical time-series data which is a sequence of data points measured at successive uniform time intervals such as minute, day, week or month. KOSPI200 index futures traders use technical analysis to find out some patterns on the time-series chart. Although there are many technical indicators, their results indicate the market states among bull, bear and flat. Most strategies based on technical analysis are divided into trend following strategy and non-trend following strategy. Both strategies decide the market states based on the patterns of the KOSPI200 index time-series data. This goes well with Markov model (MM). Everybody knows that the next price is upper or lower than the last price or similar to the last price, and knows that the next price is influenced by the last price. However, nobody knows the exact status of the next price whether it goes up or down or flat. So, hidden Markov model (HMM) is better fitted than MM. HMM is divided into discrete HMM (DHMM) and continuous HMM (CHMM). The only difference between DHMM and CHMM is in their representation of state probabilities. DHMM uses discrete probability density function and CHMM uses continuous probability density function such as Gaussian Mixture Model. KOSPI200 index values are real number and these follow a continuous probability density function, so CHMM is proper than DHMM for the KOSPI200 index. In this paper, we present an artificial intelligent trading system based on CHMM for the KOSPI200 index futures system traders. Traders have experienced on technical trading for the KOSPI200 index futures market ever since the introduction of the KOSPI200 index futures market. They have applied many strategies to make profit in trading the KOSPI200 index futures. Some strategies are based on technical indicators such as moving averages or stochastics, and others are based on candlestick patterns such as three outside up, three outside down, harami or doji star. We show a trading system of moving average cross strategy based on CHMM, and we compare it to a traditional algorithmic trading system. We set the parameter values of moving averages at common values used by market practitioners. Empirical results are presented to compare the simulation performance with the traditional algorithmic trading system using long-term daily KOSPI200 index data of more than 20 years. Our suggested trading system shows higher trading performance than naive system trading.