• Korean Journal of Breeding Science
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• v.43 no.6
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• pp.554-558
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• 2011

A single cross hybrid, 'Eolrukchal 1', is a bicolor waxy corn (Zea mays L.) developed by the maize breeding team at the National Institute of Crop Science (NICS), RDA in 2007. This hybrid, which has a high eating quality, was produced by crossing two inbred lines, KBW23 and KW33. KBW23 was a seed parent and KW33 was a the pollen parent of 'Eolrukchal 1'. Ear length and diameter of 'Eolrukchal 1' is 18.4 cm and 4.5 cm, respectively. The ratio of kernel set length/ear length is 89%, similar with that of a check hybrid, 'Chalok 1'. It is resistant to Exserohilum turcicum (Northern corn leaf blight) and its lodging resistance is higher than that of 'Chalok 1'. The yield of 'Eolrukchal 1' in fresh ear weight was 9.80 ton/ha and 14% higher than that of 'Chalok 1' in regional yield trials (RYT) from 2005 to 2007. A seed production of this hybrid has been well due to good match during crossing between the seed and the pollen parents. It is adaptable to the whole country except Jeju-do.

• Korean Journal of Breeding Science
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• v.40 no.4
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• pp.533-536
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• 2008

Hopum is a new japonica rice cultivar developed from the cross between Milyang165 and F1 crossing Milyang165 and Iksan438 at Department of Rice and Winter Cereal Crop, NICS, RDA, in 2006. This cultivar has a short grain shape and about 141 days growth duration from direct seeding to harvesting in the southern plain including Chungcheong province. This cultivar has short culm and spikelet number per panicle is similar to that of Nampyeongbyeo, while filled grain rate is lower than standard variety. This cultivar has medium size of brown rice and shows moderate resistance to leaf blast, to bacterial blight pathogens of $K_1$, $K_2$ and $K_3$ and stripe virus disease but susceptible to major virus diseases and insect pests. The milled kernel of Hopum is translucent with non-glutinous endosperm. Protein and amylose content of Hopum is about 6.5% and 18.7%, respectively. This cultivar has better palatability of cooked rice than Chucheongbyeo harvested in Gyeongki province. Its milling recovery (76.8%) and percentage of perfect-shaped milled rice (94.7%) were higher than Nampyeongbyeo. The milled rice yield of Hopum was 5.83 MT/ha (15% higher than Juan) under wet-direct seeding, 5.66 MT/ha (8% higher than Juan) under dry-direct seeding, and 6.00 MT/ha (8% higher than Nampyeong) under ordinary transplanting cultivation. "Hopum" would be adaptable for ordinary transplanting and direct seeding in the southern plain including Chungcheong province.

• Korean Journal of Remote Sensing
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• v.34 no.6_4
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• pp.1479-1488
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• 2018

On September 3rd 2017, strong artificial seismic signals from North Korea were detected in KMA (Korea Meteorological Administration) seismic network. The location of the epicenter was estimated to be Punggye-ri nuclear test site and it was the most powerful to date. The event was not studied well due to accessibility and geodetic measurements. Therefore, we used remote sensing data to analyze surface changes around Mt. Mantap area. First of all, we tried to detect surface deformation using InSAR method with Advanced Land Observation Satellite-2 (ALOS-2). Even though ALOS-2 data used L-band long wavelength, it was not working well for this particular case because of decorrelation on interferogram. The main reason would be large deformation near the Mt. Mantap area. To overcome this limitation of decorrelation, we applied offset tracking method to measure deformation. However, this method is affected by window kernel size. So we applied various window sizes from 32 to 224 in 16 steps. We could retrieve 2D surface deformation of about 3 m in maximum in the west side of Mt. Mantap. Second, we used Pleiadas-A/B high resolution satellite optical images which were acquired before and after the 6th nuclear test. We detected widespread surface damage around the top of Mt. Mantap such as landslide and suspected collapse area. This phenomenon may be caused by a very strong underground nuclear explosion test. High-resolution satellite images could be used to analyze non-accessible area.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1723-1735
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• 2022

Landslides are one of the most prevalent natural disasters, threating both humans and property. Also landslides can cause damage at the national level, so effective prediction and prevention are essential. Research to produce a landslide susceptibility map with high accuracy is steadily being conducted, and various models have been applied to landslide susceptibility analysis. Pixel-based machine learning models such as frequency ratio models, logistic regression models, ensembles models, and Artificial Neural Networks have been mainly applied. Recent studies have shown that the kernel-based convolutional neural network (CNN) technique is effective and that the spatial characteristics of input data have a significant effect on the accuracy of landslide susceptibility mapping. For this reason, the purpose of this study is to analyze landslide vulnerability using a pixel-based deep neural network model and a patch-based convolutional neural network model. The research area was set up in Gangwon-do, including Inje, Gangneung, and Pyeongchang, where landslides occurred frequently and damaged. Landslide-related factors include slope, curvature, stream power index (SPI), topographic wetness index (TWI), topographic position index (TPI), timber diameter, timber age, lithology, land use, soil depth, soil parent material, lineament density, fault density, normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) were used. Landslide-related factors were built into a spatial database through data preprocessing, and landslide susceptibility map was predicted using deep neural network (DNN) and CNN models. The model and landslide susceptibility map were verified through average precision (AP) and root mean square errors (RMSE), and as a result of the verification, the patch-based CNN model showed 3.4% improved performance compared to the pixel-based DNN model. The results of this study can be used to predict landslides and are expected to serve as a scientific basis for establishing land use policies and landslide management policies.

• 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.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.36 no.1
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• pp.41-51
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• 1991

This study was conducted to determine the effect of 20 years long term fertilizations on the physical and chemical properties of paddy soil and the growth, yield, yield components and grain development of rice. Non-fertilized, PK, NK, NP, NPK, NPK + compost, NPK+straw and NPK+lime have been applied since 1968 after surface paddy soil was removed. NPK+compost and NPK+straw applications increased the content of organic matter, available P and CEC, and lime increased soil acidity and SiO$_2$ content. While chemical contents in non-fertilized treatment were low as compared with other treatments. Soil porosity was higher in NPK+straw (51.4%) and NPK+lime(53.1%) than in NPK application (49.8%). Soil hardness was highest in the NPK application and was lowest in the NPK + lime. Continuous application of straw with NPK markedly increased the content of aggregate with over 1mm(19.6%) as compared with NPK application (7.1%). Plant height, tiller number, root number, leaf area index and total dry weight were higher in the applications of compost, straw and lime with NPK than in any other treatments. Brown rice yield in non-fertilized, PK and NP applications was decreased 45, 55, 15 and 5% of that in NPK application, respectively, while application of compost, straw and lime with NPK increased the yield by 11, 14 and 4%, respectively, during 20 years. The number of differentiated rachis branchs in the application of compost, straw and lime was 17 to 21 and that in the other application was 13 to 15, whereas the degenerated rachis branchs was low in the application of compost, straw and lime with NPK. The applications having higher level of perfect rice grain such as non-fertilized, NPK+compost, NPK+straw and NPK+lime had high grain weight and had low level of white core rice, white belly rice. The white core and belly rice was highest in the NP application and notched belly rice kernel was markedly increased in NK and NP applications. The period of grain filling was 30 DAH at NP and NPK applications, 35 DAH at NK and NPK+lime, 40DAH at NPK+compost and NPK+ straw, and 45DAH at non-fertilized, respectively.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.67 no.4
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• pp.265-273
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• 2022

Waxy corn (Zea mays L.), which contains homozygous mutant alleles for the waxy1 (wx1) gene, is widely consumed as a snack food in Asia. This study evaluated sixteen agronomic characteristics of inbred Korean waxy corn lines to aid development of high-quality waxy corn cultivars. The plant materials studied were 177 inbred waxy corn lines developed by the National Institute of Crop Science, Rural Development Administration, Republic of Korea. For the tested lines, days to tasseling and silking averaged 77.69±2.22 days (with a range of 56-97 days), and 81.12±7.56 days (66-99 days), respectively. Plant length ranged from 88 to 237 cm (averaged 164.88±22.67 cm), ear length averaged 11.75±2.52 cm (5.0-18.5 cm), and ear width averaged 2.94±0.68 cm (1.4-4.5 cm). The number of rows on each ear of corn averaged 12.22±2.22 (7-32 rows) and the kernel number averaged 24.30±4.22 (9-37 kernels) per row. The crude protein content was 12.05±1.53% (8.90-21.80%) and total starch content was 69.27±5.74% (49.5-83.9%). Principal component analysis revealed that ear width, grain length, ear length, days to tasseling, days to silking, percentage of ear setting height, and total starch are features that allow distinction between the 177 waxy inbred corn lines. Hierarchical cluster analysis identified twelve waxy inbred lines that produce tall plants and have a short silking period. These lines may improve yield among quickly growing corn varieties.

• 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.26 no.1
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• pp.119-133
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• 2020

Fama asserted that in an efficient market, we can't make a trading rule that consistently outperforms the average stock market returns. This study aims to suggest a machine learning algorithm to improve the trading performance of an intraday short volatility strategy applying asymmetric volatility spillover effect, and analyze its trading performance improvement. Generally stock market volatility has a negative relation with stock market return and the Korean stock market volatility is influenced by the US stock market volatility. This volatility spillover effect is asymmetric. The asymmetric volatility spillover effect refers to the phenomenon that the US stock market volatility up and down differently influence the next day's volatility of the Korean stock market. We collected the S&P 500 index, VIX, KOSPI 200 index, and V-KOSPI 200 from 2008 to 2018. We found the negative relation between the S&P 500 and VIX, and the KOSPI 200 and V-KOSPI 200. We also documented the strong volatility spillover effect from the VIX to the V-KOSPI 200. Interestingly, the asymmetric volatility spillover was also found. Whereas the VIX up is fully reflected in the opening volatility of the V-KOSPI 200, the VIX down influences partially in the opening volatility and its influence lasts to the Korean market close. If the stock market is efficient, there is no reason why there exists the asymmetric volatility spillover effect. It is a counter example of the efficient market hypothesis. To utilize this type of anomalous volatility spillover pattern, we analyzed the intraday volatility selling strategy. This strategy sells short the Korean volatility market in the morning after the US stock market volatility closes down and takes no position in the volatility market after the VIX closes up. It produced profit every year between 2008 and 2018 and the percent profitable is 68%. The trading performance showed the higher average annual return of 129% relative to the benchmark average annual return of 33%. The maximum draw down, MDD, is -41%, which is lower than that of benchmark -101%. The Sharpe ratio 0.32 of SVS strategy is much greater than the Sharpe ratio 0.08 of the Benchmark strategy. The Sharpe ratio simultaneously considers return and risk and is calculated as return divided by risk. Therefore, high Sharpe ratio means high performance when comparing different strategies with different risk and return structure. Real world trading gives rise to the trading costs including brokerage cost and slippage cost. When the trading cost is considered, the performance difference between 76% and -10% average annual returns becomes clear. To improve the performance of the suggested volatility trading strategy, we used the well-known SVM algorithm. Input variables include the VIX close to close return at day t-1, the VIX open to close return at day t-1, the VK open return at day t, and output is the up and down classification of the VK open to close return at day t. The training period is from 2008 to 2014 and the testing period is from 2015 to 2018. The kernel functions are linear function, radial basis function, and polynomial function. We suggested the modified-short volatility strategy that sells the VK in the morning when the SVM output is Down and takes no position when the SVM output is Up. The trading performance was remarkably improved. The 5-year testing period trading results of the m-SVS strategy showed very high profit and low risk relative to the benchmark SVS strategy. The annual return of the m-SVS strategy is 123% and it is higher than that of SVS strategy. The risk factor, MDD, was also significantly improved from -41% to -29%.

• 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.