• Journal of Korean Society of Forest Science
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• v.62 no.1
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• pp.24-42
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• 1983

To develop Ilex cornuta which grow naturally in the southwest seaside district as new ornamental tree, the author chose I. cornuta growing in the four natural communities and those cultivated in Kwangju city as a sample, and investigated its ecology, morphology and characteristics. The results obtained was summarized as follows; 1) The natural distribution of I. cornuta marks $35^{\circ}$43'N and $126^{\circ}$44'E in the southwestern part of Korea and $33^{\circ}$20'N and $126^{\circ}$15'E in Jejoo island. This area has the following necessary conditions for Ilex cornuta: the annual average temperature is above $12^{\circ}C$, the coldness index below $-12.7^{\circ}C$, annual average relative humidity 75-80%, and the number of snow-covering days is 20-25 days, situated within 20km of from coastline and within, 100m above sea level and mainly at the foot of the mountain facing the southeast. 2) The vegetation in I. cornuta community can be divided that upper layer is composed of Pinus thunbergii and P. densiflora, middle layer of Eurya japonica var. montana, Ilex cornuta and Vaccinium bracteatum, and the ground vegetation is composed of Carex lanceolata and Arundinella hirta var. ciliare. The community has high species diversity which indicates it is at the stage of development. Although I. cornuta is a species of the southern type of temperate zone where coniferous tree or broad leaved, evergreen trees grow together, it occasionally grows in the subtropical zone. 3) Parent rock is gneiss or rhyolite etc., and soil is acidic (about pH 4.5-5.0) and the content of available phosphorus is low. 4) At maturity, the height growth averaged $10.48{\pm}0.23cm$ a year and the diameter growth 0.43 cm a year, and the annual ring was not clear. Mean leaf-number was 11.34. There are a significant positive correlation between twig-elongation and leaf-number. 5) One-year-old seedling grows up to 10.66 cm (max. 18.2 cm, min. 4.0 cm) in shoot-height, with its leaf number 12.1 (max. 18, min), its basal diameter 2.24 mm (max. 4.0 mm, min. 1.0 mm) and shows rhythmical growth in high temperature period. There were significant positive correlations between stalk-height and leaf-number, between stalk-height and basal-diameter, and between number and basal diameter. 6) The flowering time ranged from the end of April to the beginning of May, and the flower has tetra-merouscorella and corymb of yellowish green. It has a bisexual flower and dioecism with a sexual ratio 1:1. 7) The fruit, after fertilization, grows 0.87 cm long (0.61-1.31 cm) and 0.8 cm wide (0.62-1.05 cm) by the beginning of May. Fruits begin to turn red and continue to ripen until the end of October or the beginning of November and remain unfading until the end of following May. With the partial change in color of dark-brown at the beginning of the June fruits begin to fall, bur some remain even after three years. 8) The seed acquision ratio is 24.7% by weight, and the number of grains per fruit averages 3.9 and the seed weight per liter is 114.2 gram, while the average weight of 1,000 seeds is 24.56 grams. 9) Seeds after complete removal of sarcocarp, were buried under ground in a fixed temperature and humidity and they began to develop root in October, a year later and germinated in the next April. Under sunlight or drought, however, the dormant state may be continued.

• Progress in Medical Physics
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• v.22 no.1
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• pp.42-51
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• 2011

Nuclear medicine images (SPECT, PET) were widely used tool for assessment of myocardial viability and perfusion. However it had difficult to define accurate myocardial infarct region. The purpose of this study was to investigate methodological approach for automatic measurement of rat myocardial infarct size using polar map with adaptive threshold. Rat myocardial infarction model was induced by ligation of the left circumflex artery. PET images were obtained after intravenous injection of 37 MBq $^{18}F$-FDG. After 60 min uptake, each animal was scanned for 20 min with ECG gating. PET data were reconstructed using ordered subset expectation maximization (OSEM) 2D. To automatically make the myocardial contour and generate polar map, we used QGS software (Cedars-Sinai Medical Center). The reference infarct size was defined by infarction area percentage of the total left myocardium using TTC staining. We used three threshold methods (predefined threshold, Otsu and Multi Gaussian mixture model; MGMM). Predefined threshold method was commonly used in other studies. We applied threshold value form 10% to 90% in step of 10%. Otsu algorithm calculated threshold with the maximum between class variance. MGMM method estimated the distribution of image intensity using multiple Gaussian mixture models (MGMM2, ${\cdots}$ MGMM5) and calculated adaptive threshold. The infarct size in polar map was calculated as the percentage of lower threshold area in polar map from the total polar map area. The measured infarct size using different threshold methods was evaluated by comparison with reference infarct size. The mean difference between with polar map defect size by predefined thresholds (20%, 30%, and 40%) and reference infarct size were $7.04{\pm}3.44%$, $3.87{\pm}2.09%$ and $2.15{\pm}2.07%$, respectively. Otsu verse reference infarct size was $3.56{\pm}4.16%$. MGMM methods verse reference infarct size was $2.29{\pm}1.94%$. The predefined threshold (30%) showed the smallest mean difference with reference infarct size. However, MGMM was more accurate than predefined threshold in under 10% reference infarct size case (MGMM: 0.006%, predefined threshold: 0.59%). In this study, we was to evaluate myocardial infarct size in polar map using multiple Gaussian mixture model. MGMM method was provide adaptive threshold in each subject and will be a useful for automatic measurement of infarct size.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.

• Korean Journal of Weed Science
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• v.2 no.2
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• pp.75-113
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• 1982

Studies were carried out 1) to define the shape and size of sampling quadrat and its number of observations for weed experiments, 2) to characterize the growth and community of major summer weeds under upland condition and 3) to investigate the factors influencing competition between weeds and soybeans under weed-free and weedy conditions in early and late season cultures. No significant difference was noted among different shapes of quadrat (regular, rectangular, band, and circular) in the sampling efficiency of weeds. The results also suggested that the minimum size of quadrat was 0.25$m^2$ and the minimum number of replication was 2 times per plot. The major dominant weeds were about 10 species in the experimental field and the total number of weeds was in the range of 70 - 1,600 plants per $m^2$. Among the weeds Digitaria sanguinalis and Portulaca oleracea were the most dominant species. Growth amount and reproduction capability were also measured by weed species. Five different weed communities were identified in the field. The degree of dispersion by weed species and association among weeds were investigated. Intra-(within soybeans) and inter-specific (between soybeans and weeds) competition were studied in early and late season cultures of soybeans. The average yield of soybeans per plant was significantly decreased in both season cultures due to intra-specific competition as the planting density of soybeans increased, On the other hand, the average yield of soybeans per l0a was proportionally increased to the increase of planting density and the rate of its increase was more significant under weedy than weed-free condition. Most of the agronomic characteristics of soybeans were affected by weeds and its degree was greater in sparse planting than in dense planting and in early season than in late-season culture. Digitaria sanguinalis was the most competitive to soybeans in early season and both of Digitaria sanguinalis and Portulaca oleracea affected primarily the growth of soybeans in late season with about the same competitiveness. The occurrence of weeds was significantly decreased in early season and slightly decreased in late-season by dense planting of soybeans. The total growth amount of weeds was also considerably decreased by increase of soybean planting density both in early- and late-season cultures. The occurrence of Digitaria sanguinalis which was the most dominant in both seasons, and its growth amount was significantly decreased as the planting density of soybean was increased. On the other hand, the occurrence of Portulaca oleracea which was only dominant in late-season culture did not show significant response to the planting density of soybeans.