• Advances in Computational Design
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• v.1 no.2
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• pp.119-138
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• 2016

In this article, hybridization of enhanced colliding bodies optimization (ECBO) with upper bound strategy (UBS) that is called UECBO is proposed for optimum design of truss structures with frequency constraints. The distinct feature of the proposed algorithm is that it requires less computational time while preserving the good accuracy of the ECBO. Four truss structures with frequency limitations selected from the literature are studied to verify the viability of the algorithm. This type of problems is highly non-linear and non-convex. The numerical results show the successful performance of the UECBO algorithm in comparison to the CBO, ECBO and some other metaheuristic optimization methods.

• Proceedings of the Korea Technology Innovation Society Conference
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• 2017.11a
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• pp.1343-1358
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• 2017

4차 산업혁명이 예견하는 변화가 머지않아 보편적으로 확산되고 우리의 삶을 유의미하게 변화시킬 것이라는 점은 부정하기 어렵다. 따라서 일부 선도적인 개인이나 기업의 노력에만 의존하는 것은 충분하지 않고. 국가 차원의 정책적 제도적 대응이 필요하다. 특히 UBS(2016)의 4차 산업혁명 준비수준 평가에서 세계 25위라는 기대 이하의 결과를 차지한 한국은 더욱 정확한 방향과 전략적인 수단으로 미래의 변화를 준비해야 한다. 따라서 정부와 기업의 선제적이고 적극적인 대응책 마련이 필요한 상황이다. 무엇보다 과감한 선제적 규제 개혁과 유인제도 도입으로 한국 경제 시스템 유연성을 강화하여 민간 부분의 역량을 최대한 발휘할 수 있는 시장여건 조성에 힘써야 한다. 이 논문에서는 4차 산업혁명 관련 산업을 효율적으로 견인할 수 있는 유인수단 중 기업투자관련 세제혜택, 입법방안 등의 내용을 중심으로 논의하고자 한다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.4
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• pp.889-891
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• 2017

Visible light communication technology, which is a communication using LED lighting, is defined by IEEE 802.15.7 WG and active research is under way. Visible light communication is advantageous not only to avoid interference with existing RF communication but also to provide location based service through accurate positioning by utilizing LOS (Line of Sight) characteristic. Therefore, it is very easy and efficient to locate and track the user's location. In this paper, we implemented a visible light communication transceiver using USB interface for easy application to portable devices. It supports the mobility of mobile devices through internet protocol and showed BER performance of less than $10^{-3}dBm$ at over 1m, which is the height of lighting and smart device during walking.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.127-146
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• 2017

Services using artificial intelligence have begun to emerge in daily life. Artificial intelligence is applied to products in consumer electronics and communications such as artificial intelligence refrigerators and speakers. In the financial sector, using Kensho's artificial intelligence technology, the process of the stock trading system in Goldman Sachs was improved. For example, two stock traders could handle the work of 600 stock traders and the analytical work for 15 people for 4weeks could be processed in 5 minutes. Especially, big data analysis through machine learning among artificial intelligence fields is actively applied throughout the financial industry. The stock market analysis and investment modeling through machine learning theory are also actively studied. The limits of linearity problem existing in financial time series studies are overcome by using machine learning theory such as artificial intelligence prediction model. The study of quantitative financial data based on the past stock market-related numerical data is widely performed using artificial intelligence to forecast future movements of stock price or indices. Various other studies have been conducted to predict the future direction of the market or the stock price of companies by learning based on a large amount of text data such as various news and comments related to the stock market. Investing on commodity asset, one of alternative assets, is usually used for enhancing the stability and safety of traditional stock and bond asset portfolio. There are relatively few researches on the investment model about commodity asset than mainstream assets like equity and bond. Recently machine learning techniques are widely applied on financial world, especially on stock and bond investment model and it makes better trading model on this field and makes the change on the whole financial area. In this study we made investment model using Support Vector Machine among the machine learning models. There are some researches on commodity asset focusing on the price prediction of the specific commodity but it is hard to find the researches about investment model of commodity as asset allocation using machine learning model. We propose a method of forecasting four major commodity indices, portfolio made of commodity futures, and individual commodity futures, using SVM model. The four major commodity indices are Goldman Sachs Commodity Index(GSCI), Dow Jones UBS Commodity Index(DJUI), Thomson Reuters/Core Commodity CRB Index(TRCI), and Rogers International Commodity Index(RI). We selected each two individual futures among three sectors as energy, agriculture, and metals that are actively traded on CME market and have enough liquidity. They are Crude Oil, Natural Gas, Corn, Wheat, Gold and Silver Futures. We made the equally weighted portfolio with six commodity futures for comparing with other commodity indices. We set the 19 macroeconomic indicators including stock market indices, exports & imports trade data, labor market data, and composite leading indicators as the input data of the model because commodity asset is very closely related with the macroeconomic activities. They are 14 US economic indicators, two Chinese economic indicators and two Korean economic indicators. Data period is from January 1990 to May 2017. We set the former 195 monthly data as training data and the latter 125 monthly data as test data. In this study, we verified that the performance of the equally weighted commodity futures portfolio rebalanced by the SVM model is better than that of other commodity indices. The prediction accuracy of the model for the commodity indices does not exceed 50% regardless of the SVM kernel function. On the other hand, the prediction accuracy of equally weighted commodity futures portfolio is 53%. The prediction accuracy of the individual commodity futures model is better than that of commodity indices model especially in agriculture and metal sectors. The individual commodity futures portfolio excluding the energy sector has outperformed the three sectors covered by individual commodity futures portfolio. In order to verify the validity of the model, it is judged that the analysis results should be similar despite variations in data period. So we also examined the odd numbered year data as training data and the even numbered year data as test data and we confirmed that the analysis results are similar. As a result, when we allocate commodity assets to traditional portfolio composed of stock, bond, and cash, we can get more effective investment performance not by investing commodity indices but by investing commodity futures. Especially we can get better performance by rebalanced commodity futures portfolio designed by SVM model.