• Journal of the Korean Society of Food Science and Nutrition
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• v.43 no.7
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• pp.1017-1024
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• 2014

This study was carried out to develop mulberry wines fermented with traditional microorganisms (Saccharomyces cerevisiae B-8). S. cerevisiae B-8 is a traditional fermentation microorganism isolated from domestically grown Rubus occidentalis. Each S. cerevisiae B-8 and Fermivin was inoculated into mulberry up to $1{\times}10^9$ CFU/kg, followed by incubation at $25^{\circ}C$ for 10 days. Mulberry fermented with S. cerevisiae B-8 (MBB) had a high alcohol content (16.47%), and the fermentation rate of MBB was faster than that of mulberry fermented with Fermivin (MBF). The total polyphenol and flavonoid contents of MBB were higher than those of MBF. DPPH radical scavenging activity of MBB was as high as that of MBF. ABTS radical scavenging activity of MBF was higher than those of MBB and mulberry juice (MBJ). In addition, reducing power of MBB was much higher than other samples. Flavor constituents of the two fermented wines were analyzed by gas chromatography and mass spectrometry. Twenty-three compounds from the sample were separated and identified as fifteen esters, six alcohols, an aldehyde, and an acetate. Particularly, tetradecanoic acid, ethyl ester of orris and violet flavor were ten times more abundant in MBB than in MBF. Several ester components were two times more abundant in MBB than in MBF. In conclusion, current findings indicate that MBB might have better antioxidant activities with flavor, which contributes to improved wine production with high quality and function.

• Journal of the Korean Society of Marine Environment & Safety
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• v.13 no.1 s.28
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• pp.9-20
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• 2007

PAHs(Polycyclic Aromatic Hydrocarbons) are widespread contaminants in the marine environment. They are of mainly anthropogenic origin from urban runoff, oil spill and combustion of fossil fuels. Some PAHs are potentially carcinogenic and mutagenic to aquatic organism The contamination of PAHs in the coastal environments has not been well known yet in Korea. This study was carried out to survey the contamination of PAHs in sediments around Gwangyang bay. The Yeosu petrochemical industrial complex, POSCO(Pohang steel company) and Gwangyang container harbor are located around the bay. PAHs in sediment samples were extracted in soxhlet extractor and were identified and quantified by GC-MS(Gas Chromatography-Mass Spectrometry) TOC(Total Organic carbon) and textural parameters in sediment samples were also analyzed 13 species of PAHs were detected at all of the surface sediments. Total PAHs concentrations in the surface sediments ranged from 171.40 to $1013.54{\mu}g/kg$ dry wt.. In most of the surface sediments, Naphthalene was the highest in the range of 14.08 to $691.39{\mu}g/kg$ dry wt. and Anthracene was the lowest in the range of 0.49 to $22.66{\mu}g/kg$ dry wt.. The correlation coefficients between individual PAHs and Total PAHs in the surface sediments were relatively higher in the low molecular compounds such as Naphthalene and Phenanthrene. In the relationship of the P/A(Phenanthrene/Anthracene) ratio and F/P(Fluoranthene/Pyrene) ratio, P/A ratio was generally above 10 and F/P ratio was shown to be above 1 in all sediment samples. These data indicate that PAHs in sediments around Gwangyang bay seem to be of both pyrolytic and petrogenic origin. Total PAHs in the surface sediments were correlated with TOC and textural parameters. The values of PAHs in the surface and core sediments were lower than the biological effect guidelines.

• Analytical Science and Technology
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• v.11 no.1
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• pp.62-72
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• 1998

This study was performed to characterize the trace organic pollutants in the industrial wastewater and to establish the database of the trace organic pollutants. The four manufacturing industries, which are refined petroleum, industrial chemicals, rubber & plastics and fabricated metals, were surveyed. The wastewater and discharging water of these 30 factories are analyzed to characterize the trace organic pollutants. In industrial chemicals, the kinds of products and organic pollutants are very various. Therefore to select the characteristic organic pollutants in this categories are also very difficult. In industrial chemicals, the gas chromatograpic peak patterns of wastewater are represented the various type according to their products, therefore the typical patterns of the characteristic organic pollutants could not be obtained because the kinds of manufactured goods and organic pollutants are very various. In refined petroleum, the effluent is discharged in the distillatory process of atmosphere pressure and contained the saturated hydrocarbons, phenol compounds, benzene compounds and naphtalene compounds. The saturated hydrocarbons peaks from $C_{15}$ to $C_{35}$ are represented the typical oil patterns by the uniform intervals therefore the peak can be easily distinguished. In rubber & plastics, the wastewater is discharged in the washing process which contains the additives. The problem of wastewater is not serious because the manufacturing process is not produced the effluent or the produced cooling water is recycled in that process.

• Journal of Korea Technology Innovation Society
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• v.12 no.4
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• pp.788-818
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• 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 Applied Science and Technology
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• v.34 no.4
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• pp.962-973
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• 2017

Concerns about an air pollution are gradually increasing at home and abroad. The automotive and fuel researchers are trying to reduce emissions and greenhouse gases of vehicles through a research on new engine designs and innovative after-treatment systems using clean fuels (eco-alternative fuel) and fuel quality improvements. In this paper, we stduy the emission characteristics of greenhouse gases on seven vehicles using gasoline, diesel, and LPG by legal test mode in domestic and abroad.(Urban mode, Highway mode, rapidly acceleration and deceleration, using air conditioner, low temperature condition) Regardless of fuels, most of the greenhouse gases tend to show the worst results in cold FTP-75 mode. In the case of A vehicles (2.0 MPI) and B vehicles (2.4 GDI) using a gasoline fuel, the factors that increase greenhouse gases are in order of a rapidly acceleration and deceleration, using air conditioner, low temperature condition. But G vehicles(LPLi) have different emission characteristics from another vehicles. In the case of A vehicles (2.0 w/o DPF) and B vehicles (2.2 with DPF) using a diesel fuel, the factors that increase greenhouse gases are in order of a rapidly acceleration and deceleration, using air conditioner, low temperature condition. However, the factor of F vehicles are in order of low temperature condition, using air conditioner, rapidly acceleration and deceleration. In conclusion, it will be an effective method to apply different technologies of emission reduction for each fuel.

• Food Science and Preservation
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• v.24 no.3
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• pp.394-405
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• 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 the Society of Disaster Information
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• v.16 no.4
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• pp.701-711
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• 2020

Purpose: In this study, the risk of flammability of a liquid mixture was experimentally confirmed because the purpose of this study was to confirm the increase or decrease of the flammability risk in a mixture of two substances (combustible+combustible) and to present the risk of the mixture. Method: Flash point test method and result processing were tested based on KS M 2010-2008, a tag sealing test method used as a flash point test method for crude oil and petroleum products. The manufacturer of the equipment used in this experiment was Japan's TANAKA. The flash point was measured with a test equipment that satisfies the test standards of KS M 2010 with equipment produced by the company, and LP gas was used as the ignition source and water as the cooling water. In addition, when measuring the flash point, the temperature of the cooling water was tested using cooling water of about 2℃. Results: First of all, in the case of flammable + combustible mixtures, there was little change in flash point if the flash point difference between the two substances was not large, and if the flash point difference between the two substances was low, the flash point tended to increase as the number of substances with high flash point increased. However, in the case of toluene and methanol, the flash point of the mixture was lower than that of the material with a lower flash point. Also, in the case of a paint thinner, it was not easy to predict the flash point of the material because it was composed of a mixture, but as a result of experimental measurement, it was measured between -24℃ and 7℃. Conclusion: The results of this study are to determine the risk of mixtures through experimental studies on flammable mixtures for the purpose of securing the effectiveness of the details of the criteria for determining dangerous goods in the existing dangerous goods safety management method and securing the reliability and reproducibility of the determination of dangerous goods Criteria have been presented, and reference data on experimental criteria for flammable liquids that are regulated in firefighting sites can be provided. In addition, if this study accumulates know-how on differences in test methods, it is expected that it can be used as a basis for research on risk assessment of dangerous goods and as a basis for research on dangerous goods determination.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1651-1669
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• 2023

Sea ice currently covers approximately 7% of the world's ocean area, primarily concentrated in polar and high-altitude regions, subject to seasonal and annual variations. It is very important to analyze the area and type classification of sea ice through time series monitoring because sea ice is formed in various types on a large spatial scale, and oil and gas exploration and other marine activities are rapidly increasing. Currently, research on the type and area of sea ice is being conducted based on high-resolution satellite images and field measurement data, but there is a limit to sea ice monitoring by acquiring field measurement data. High-resolution optical satellite images can visually detect and identify types of sea ice in a wide range and can compensate for gaps in sea ice monitoring using Geostationary Ocean Color Imager-II (GOCI-II), an ocean satellite with short time resolution. This study tried to find out the possibility of utilizing sea ice monitoring by training a rule-based machine learning model based on learning data produced using high-resolution optical satellite images and performing detection on GOCI-II images. Learning materials were extracted from Liaodong Bay in the Bohai Sea from 2021 to 2022, and a Random Forest (RF) model using GOCI-II was constructed to compare qualitative and quantitative with sea ice areas obtained from existing normalized difference snow index (NDSI) based and high-resolution satellite images. Unlike NDSI index-based results, which underestimated the sea ice area, this study detected relatively detailed sea ice areas and confirmed that sea ice can be classified by type, enabling sea ice monitoring. If the accuracy of the detection model is improved through the construction of continuous learning materials and influencing factors on sea ice formation in the future, it is expected that it can be used in the field of sea ice monitoring in high-altitude ocean areas.

• 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.

• Journal of the Korean Applied Science and Technology
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• v.13 no.1
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• pp.21-29
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• 1996

Levels of total, neutral and polar lipids from the seeds of eight species of the Cucurbitaceae f Cucurbita moschata, Lufa cylindrica, Citrullus vulgari, Cucumis melo var. makuwa, Cucumis satvus, Lag leucantha. Trichosanthes kirilowii and Momordica charantia, were determinded, and their fatty compositions were also analyzed by gas-liquid chromatography. The results were summarized as foll. Lipid contents of the seeds range from 21.9 to 50.7%, which contained 98% up of neutral lipi the fatty acid compositon of ottal lipids from the seeds of Cucurbita moschata, Lufa cylindrica, Ci vulgari, Cucumis melo var. makuwa, Cucumis sativus and Lagenaria leucantha, linoleic acid is the mos dominant component(56.8${\sim}$84.0%) followed by oleic acid(5.7${\sim}$22.2%) and palmitic acid(6.1${\sim}$1) with a trace amount of ${\alpha}-linolenic$ acid(below 0.6%). On the contrary, the seed oils of Tricho kirilowii and Momordica charantia are characterized by presence of considerable amounts of con trienoic acid such as punicic acid($_{9c.11t.13c-}C_{18:3}$) and ${\alpha}-eleostearic$ acid($_{9c.11t.13c-}C_{18:3}$). For example total lipids of T. kirilowii seeds were mainly composed of linoleic acid(40.5%) and punicic acid(3) in the fatty acid composition, while those of M. charantia seeds predominantly comprised ${\alpha}-eleos$ acid as a main component(66.9%), accompanied by oleic acid(11.7%) and linoleic acid(10.4%). oil ${\beta}-eleostearic$ acid($_{9t.11t.13c-}C_{18:3}$) was checked as a trace. Fatty acid profiles of neutral lipids close resemblance to those of total lipids in all the seed oils, but are different from those of polar In particular, conjugate trienoic acids including punicic acid and ${\alpha}-eleostearic$ acid which are oc as the most abundant component in both neutral lipids of T. kirilowii and M. charantia seed oils, ar ent in a extremely small amount in both polar lipids. The fatty acid distribution in the polar lipid the samples except for T. kirilowii and M. charantia seed oils, showed a tendency of consid increased level of saturated fatty acids(25.0${\sim}$29.4%) compared with that in the neutral lipids(9.9%). The results obtained in this experiment suggest us that the seed oils of the Cucurbitaceae