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

• 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 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.

• Horticultural Science & Technology
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• v.29 no.5
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• pp.447-455
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• 2011

To investigate the effects of the improvement of postharvest quality on fresh tomato, antimicrobial microperforated (AMP) films were prepared and their antimicrobial abilities were observed. AMP films were made by coating different types of natural antimicrobial agents such as cinnamon, clove, and clary sage essential oils into microperforated (MP) films. Cinnamon essential oil of 10% (v/v) has proven to be very effective as inhibitor of the mold growth on tomato, compared to the clove and clary sage essential oils. Quality changes of fresh tomatoes packed using the natural AMP films (AMP10 and AMP30) and MP films (MP10 and MP30) during storage were evaluated. Total microbial growth, weight loss, firmness, lycopene content, and decay rate as the major quality parameters were monitored over 9 days at $15^{\circ}C$. The oxygen transmission rates and mechanical properties between the natural AMP and MP films were also compared. There was no significant difference in change of oxygen transmission rate, tensile strength and elongation between the AMP and MP films. For storage studies, the freshness of tomato packaged in AMP30 film was higher than that in OPP film (the control), MP10, MP30, and AMP10 films. Especially, AMP30 film exhibited high efficiency compared to the control for tomato decay during storage periods. Based on the results, the microperforation and antimicrobial properties of the packaged films may significantly affect the maintenance of an optimum gas composition within the package atmosphere for increasing the storage life and quality of produce. They were also effective on the inhibition of microbial growth by controlled release of antimicrobial agent at an appropriate rate from the package into the tomato. Natural antimicrobial agent coating microperforated films could use potential functional package as a method of extending the freshness of postharvest tomato for storage.

• 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

• Economic and Environmental Geology
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• v.5 no.4
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• pp.187-209
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• 1972

Drilling position is one of the most important factors affecting on the blasting effects. There has been many reports on several blasting factors of burn-cut by Messrs. Brown and Cook, but in this study the author tried to compare drilling positions of burn-cut to pyramid-cut, and also to correlate burn-cut effects of drilling patterns, not being dealt by Prof. Ito in his theory, which emphasized on dynamical stress analysis between explosion and free face. According to former theories, there break out additional tensile stress reflected at the free face supplemented to primary compressive stress on the blasting with one-free-face. But with these experimented new drilling patterns of burn-cut, more free faces and nearer distance of each drilling holes make blasting effects greater than any other methods. To promote the above explosive effect rationary, it has to be considered two important categories under-mentioned. First, unloaded hole in the key holes should be drilled in wider diameter possibly so that it breaks out greater stress relief. Second, key holes possibly should have closer distances each other to result clean blasting. These two important factors derived from experiments with, theories of that the larger the dia of the unloaded hole, it can be allowed wider secondary free faces and closes distances of each holes make more developed stress relief, between loaded and unloaded holes. It was suggested that most ideal distance between holes is about 4 clearance in U. S. A., but the author, according to the experiments, it results that the less distance allow, the more effective blasting with increased broken rock volume and longer drifted length can be accomplished. Developed large hole burn-cut method aimed to increase drifting length technically under the above considerations, and progressive success resulted to achieve maximum 7 blasting cycles per day with 3.1m drifting length per cycle. This achievement originated high-speed-drifting works, and it was also proven that application of Metallic AN-FO on large hole burn-cut method overcomes resistance of one-free-face. AN-FO which was favored with low price and safety handling is the mixture of the fertilizer or industrial Ammonium-Nitrate and fuel oil, and it is also experienced that it shows insensible property before the initiation, but once it is initiated by the booster, it has equal explosive power of Ammonium Nitrate Explosives (ANE). There was many reports about AN-FO. On AN-FO mixing ratio, according to these experiments, prowdered AN-FO, 93.5 : 6.5 and prilled AN-FO 94 : 6, are the best ratios. Detonation, shock, and friction sensities are all more insensitive than any other explosives. Residual gas is not toxic, too. On initation and propagation of the detonation test, prilled AN-FO is more effective than powered AN-FO. AN-FO has the best explosion power at 7 days elapsed after it has mixed. While AN-FO was used at open pit in past years prior to other conditions, the author developed new improved explosives, Metallic AN-FO and Underwater explosive, based on the experiments of these fundmental characteristics by study on its usage utilizing AN-FO. Metallic AN-FO is the mixture of AN-FO and Al, Fe-Si powder, and Underwater explosive is made from usual explosive and AN-FO. The explanations about them are described in the other paper. In this study, it is confirmed that the blasting effects of utilizing AN-FO explosives are very good.