• KOREAN JOURNAL OF CROP SCIENCE
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• v.35 no.5
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• pp.413-423
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• 1990

Corn proteins have been known as nutritionally poor, being deficient in the essential amino acids. lysine and tryptophan. Improving the quality of protein in the corn grain would be a great benefit to the farmer. This study was conducted to evaluate the variation of the protein content and the protein constitution of the maize germplasms in the Crop Experiment Station in 1989. The average protein content of 101 germplasms was 11.5% with range from 8.0% to 17.3%. Elite hybrid field corns and table corns possessed 9.1-13.9% protein for the dried whole kernel. Major amino acids were glutamic acid and leucine. Lysine and methionine were limited. Varietal differences were observed in the amino acid composition. Qpm, a modified opaque-2 mutant had 1.4-1.7 times higher lysine content than Suwon 19, a dent corn and Suwon SS-21, a sweet corn. Suwon SS-21 had high threonine content. Maize seed protein gave three fractions. an alchol-soluble fraction (zein), an alkali-soluble fraction (glutelin), and a salt-soluble fraction (globulin) by the Osborne method. The zein fraction accounted respectively for 50.7% and 41.7% of the total protein is Suwon 19 and Suwon SS-21. The nonzein fractions increased in percentage of total protein in Qpm kernels. The amino acid composition of zein fraction from three types maize endoperms of dent, sweet and opaque-2 was essentially identical. Zein contained the high contents of glutamic acid and leucine but low content of lysine. The glutelin fractions of three types maize endosperms were mainly similar in overall amino acid composition. The lysine content of glutelin was higher than that of zein. The amino acid composition of globulin fraction was some different from those of zein and glutelin In Qpm it had higher levels of histidine and lysine than both of zein and glutelin. The increased lysine content in Qpm was resulted from changing the proportions of proteins which contained different levels of lysine.

• Journal of Practical Agriculture & Fisheries Research
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• v.20 no.1
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• pp.5-18
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• 2018

The field trial was performed to evaluate the rice growth and yield in direct seeding cultivation with iron-coated rice seeds. The required time for seed emergence was for 9~11days in the tested direct seeding methods. That was 1~2days earlier in direct seeding with pregerminated seeds than that of direct seeding with iron-coated seeds. The seedling establishment was highest in water seeding with iron-coated seeds but there was not significant difference in terms of statistical analysis. The rice plant height was taller in water seeding with broadcasting method than that of wet hill-seeding methods and in direct seeding with iron-coated seeds than that of direct seeding with pregerminated seeds. The tiller number in the rice plant was the highest in machine transplanting at 30days after direct seeding(June 17) and in water seeding with iron-coated seeds at 45days after seeding(DAS) and 60DAS. The tiller number of 75 and 90DAS in the tested rice cultivation methods being with 352~405/m² was not significantly different in terms of statistical analysis. The heading time was not different in rice direct seeding methods but 2 day earlier in direct seeding with iron-coated seeds than that of direct seeding with pregerminated seeds. The culm length was the highest in water seeding with iron-coated seeds and the panicle length was the longest in wet hill-seeding with pregerminated seeds. The panicle number per m² was highest in water seeding with iron-coated seeds but not significant difference among the tested rice cultivation methods. The water seeding with iron-coated seeds resulted in the highest spikelet number per m² and the heaviest grain weight of brown rice. Percentage of ripened kernel was the highest in wet hill-seeding with iron-coated seeds. But there were not significant among the tested rice cultivation methods. The milled rice yield in direct seeding methods was 3~21% higher than that in machine transplanting. Water seeding with iron-coated seeds recorded the highest milled rice yield being with 6.86t/ha.The occurrence of sheath blight was high according to machine transplanting>wet hill-seeding>water seeding. Weed occurrence was the highest in water seeding with pregerminated seeds. Weedy rice occurred not in machine transplanting but occured 0.6~0.7% in direct seeding methods with pregerminated seeds and 0.1% in direct seeding with iron-coated seeds.

• 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.

• Korean Journal of Breeding Science
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• v.43 no.3
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• pp.165-171
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• 2011

A new rice variety 'Jinbo' is a japonica rice (Oryza sativa L.) with good eating quality, lodging tolerance, and resistance to rice stripe virus (RSV) and bacterial blight disease (BB). It was developed by the rice breeding team of Yeongdeog Substation, National Institute of Crop Science (NICS), RDA in 2009. This variety was derived from a cross between 'Yeongdeog26' with good grain quality and wind tolerance and 'Koshihikari' with good eating quality in 1998 summer season. A promising line, YR21324-56-1-1, selected by pedigree breeding method, was designated as the name of 'Yeongdeog45' in 2005. After the local adaptability test was carried out at nine locations from 2006 to 2008, 'Yeongdeog45' was released as the name of 'Jinbo' in 2009. 'Jinbo' has short culm length as 74 cm and medium maturating growth duration. This variety is resistant to $K_1$, $K_2$, and $K_3$ races of bacterial blight and stripe virus and moderately resistant to leaf blast disease with durable resistance, and also it has tolerance to unfavorable environments such as cold and dried wind. 'Jinbo' has translucent and clear milled rice kernel without white core and white belly rice, and good eating quality as a result of panel test. The yield potential of 'Jinbo' in milled rice is about 5.65 MT/ha at ordinary fertilizer level in local adaptability test. This cultivar would be adaptable to middle plain, mid-west costal area, east-south coastal area, and south mid-mountainous area.

• Korean Journal of Breeding Science
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• v.42 no.6
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• pp.638-644
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• 2010

A new rice variety 'Haeoreumi' is a japonica rice (Oryza sativa L.) with lodging tolerance, resistance to rice stripe virus (RSV) and bacterial leaf blight (BLB), and high grain quality. It was developed by the rice breeding team of Yeongdeog Substation, National Institute of Crop Science (NICS), RDA in 2008. This variety was derived from a cross between 'Milyang165' with good grain quality and lodging resistance, and 'Haepyeongbyeo' with wind tolerance in winter season of 2000/2001. A promising line, YR22375-B-B-1, selected by pedigree breeding method, was designated as the name of 'Yeongdeog46' in 2005. 'Yeongdeog46' was released as the name of 'Haeoreumi' in 2008 after the local adaptability test that was carried out at nine locations from 2006 to 2008. 'Haeoreumi' has 74 cm short culm length as and medium maturating growth duration. This variety showed resistance to $K_1,\;K_2$, and $K_3$ races of bacterial blight, and stripe virus and moderate resistant to leaf blast disease with durable resistance, and also has tolerance to unfavorable environment such as cold, dry and cold salty wind. 'Haeoreumi' has translucent and clear milled rice kernel without white core and white belly rice, and good eating quality as a result of panel test. The yield potential of 'Haeoreumi' in milled rice is about 5.58MT/ha at ordinary fertilizer level of local adaptability test. This cultivar would be adaptable to Middle plain, mid-west costal area, and east-south coastal area.