• Journal of Korea Technology Innovation Society
• /
• v.12 no.4
• /
• pp.788-818
• /
• 2009

Renewable energy refers to solar energy, biomass energy, hydrogen energy, wind power, fuel cell, coal liquefaction and vaporization, marine energy, waste energy, and liquidity fuel made out of byproduct of geothermal heat, hydrogen and coal; it excludes energy based on coal, oil, nuclear energy and natural gas. Developed countries have recognized the importance of these energies and thus have set the mid to long term plans to develop and commercialize the technology and supported them with drastic political and financial measures. Considering the growing recognition to the field, it is necessary to analysis up-to-now achievement of the government's related projects, in the standards of type of renewable energy, management of sectional goals, and its commercialization. Korean government is chiefly following suit the USA and British policies of developing and distributing renewable energy. However, unlike Japan which is in the lead role in solar rays industry, it still lacks in state-directed support, participation of enterprises and social recognition. The research regarding renewable energy has mainly examinedthe state of supply of each technology and suitability of specific region for applying the technology. The evaluation shows that the research has been focused on supply and demand of renewable as well as general energy and solution for the enhancement of supply capacity in certain area. However, in-depth study for commercialization and the increase of capacity in industry followed by development of the technology is still inadequate. 'Cost-benefit model for each energy source' is used in analysis of technology development of renewable energy and quantitative and macro economical effects of its commercialization in order to foresee following expand in related industries and increase in added value. First, Investment on the renewable energy technology development is in direct proportion both to the product and growth, but product shows slightly higher index under the same amount of R&D investment than growth. It indicates that advance in technology greatly influences the final product, the energy growth. Moreover, while R&D investment on renewable energy product as well as the government funds included in the investment have proportionate influence on the renewable energy growth, private investment in the total amount invested has reciprocal influence. This statistic shows that research and development is mainly driven by government funds rather than private investment. Finally, while R&D investment on renewable energy growth affects proportionately, government funds and private investment shows no direct relations, which indicates that the effects of research and development on renewable energy do not affect government funds or private investment. All of the results signify that although it is important to have government policy in technology development and commercialization, private investment and active participation of enterprises are the key to the success in the industry.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.26 no.1
• /
• pp.103-113
• /
• 2020

Jack-up drilling rigs are widely used in the offshore oil and gas exploration industry. Although originally designed for use in shallow waters, trends in the energy industry have led to a growing demand for their use in deep sea and harsh environmental conditions. To extend the operating range of jack-up units, their design must be based on reliable analysis while eliminating excessive conservatism. In current industrial practice, jack-up drilling rigs are designed using the working(or allowable) stress design (WSD) method. Recently, classifications have been developed for specific regulations based on the load and resistance factor design (LRFD) method, which emphasises the reliability of the methods. This statistical method utilises the concept of limit state design and uses factored loads and resistance factors to account for uncertainly in the loads and computed strength of the leg components in a jack-up drilling rig. The key differences between the LRFD method and the WSD method must be identified to enable appropriate use of the LRFD method for designing jack-up rigs. Therefore, the aim of this study is to compare and quantitatively investigate the differences between actual jack-up lattice leg structures, which are designed by the WSD and LRFD methods, and subject to different environmental load-to-dead-load ratios, thereby delineating the load-to-capacity ratios of rigs designed using theses methods under these different enviromental conditions. The comparative results are significantly advantageous in the leg design of jack-up rigs, and determine that the jack-up rigs designed using the WSD and LRFD methods with UC values differ by approximately 31 % with respect to the API-RP code basis. It can be observed that the LRFD design method is more advantageous to structure optimization compared to the WSD method.

• Proceedings of the KOR-BRONCHOESO Conference
• /
• 1972.03a
• /
• pp.5-6
• /
• 1972

Noise and air pollution, which accompany the development of industry and the increase of population, contribute to the deterioration of urban environment. The air pollution level of Seoul has gradually increased and the city residents are suffering from a high pollution of noise. If no measures were taken against pollution, the amount of emission of pollutant into air would be 36.7 thousand tons per year per square kilometer in 1975, three times more than that of 1970, and it would be the same level as that of United States in 1968. The main sources of air pollution in Seoul are the exhaust has from vehicles and the combustion of bunker-C oil for heating purpose. Thus, it is urgent that an exhaust gas cleaner should be instaled to every car and the fuel substituted by less sulfur-contained-oil to prevent the pollution. Transportation noise (vehicular noise and train noise) is the main component of urban noise problem. The average noise level in downtown area is about 75㏈ with maximum of 85㏈ and the vehicular homing was checked 100㏈ up and down. Therefore, the reduction of the number of bus-stop the strict regulation of homing in downtown area and a better maintenance of car should be an effective measures against noise pollution in urban areas. Within the distance of 200 metres from railroad, the train noise exceeds the limit specified by the pollution control law in Korea. Especially, the level of noise and steam-whistle of train as measured by the ISO evaluation can adversely affect the community activities of residents. To prevent environmental destruction, many developed countries have taken more positive action against worsening pollution and such an action is now urgently required in this country.

• Food Science and Preservation
• /
• v.24 no.3
• /
• pp.394-405
• /
• 2017

This study was conducted to confirm the usefulness of essential oil components in yuzu and kumquat cultivated in Korea for comparison with those in lemon and lime. The volatile flavor compounds in citrus fruits (yuzu, kumquat, lemon and lime) were extracted for 3 h with 100 mL redistilled n-pentane/diethylether (1:1, v/v) mixture, using a simultaneous steam distillation and extraction apparatus (SDE). The volatile flavor compositions of the samples were analyzed by gas chromatography-mass spectrometry (GC-MS). The aroma compounds analyzed were 104 (3,713.02 mg/kg) in yuzu, 87 (621.71 mg/kg) in kumquat 103 (3,024.69 mg/kg) in lemon and 106 (2,209.16 mg/kg) in lime. Limonene was a major volatile flavor compound in four citrus fruits. The peak area of limonene was 35.03% in yuzu, 63.82% in kumquat, 40.35% in lemon, and 25.06% in lime. In addition to limonene, the major volatile flavor compounds were ${\gamma}$-terpinene, linalool, ${\beta}$-myrcene, (E)-${\beta}$-farnesene, ${\alpha}$-pinene and ${\beta}$-pinene in yuzu, and ${\beta}$-myrcene, ${\alpha}$-pinene, (Z)-limonene oxide, (E)-limonene oxide, geranyl acetate and limonen-10-yl acetate in kumquat. Furthermore, ${\gamma}$-terpinene, ${\beta}$-pinene, ${\beta}$-myrcene, geranyl acetate, neryl acetate and (Z)-${\beta}$-bisabolene in lemon and ${\gamma}$-terpinene, ${\beta}$-pinene, (Z)-${\beta}$-bisabolene, neral, geranial and neryl acetate in lime were also detected. As a result, it was confirmed that the composition of volatile flavor compounds in four citrus fruits was different. Also, yuzu and kumquat are judged to be worthy of use alternatives for lemon and lime widely used in the fragrance industry.

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