• Journal of Korean Society of Forest Science
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• v.21 no.1
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• pp.1-34
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• 1974

The author intended to investigate external and internal changes in the cone structure, changes in water content, sugar, fat and protein during the period of seed maturation which bears a proper germinability. The experimental results can be summarized as in the following. 1. Male flowers 1) Pollen-mother cells occur as a mass from late in April to early in May, and form pollen tetrads through meiosis early and middle of May. Pollen with simple nucleus reach maturity late in May. 2) Stamen number of a male flower is almost same as the scale number of cone and is 69-102 stamens. One stamen includes 5800-7300 pollen. 3) The shape is round and elliptical, both of a pollen has air-sac with $80-91{\mu}$ in length, and has cuticlar exine and cellulose intine. 4) Pollen germinate in 68 hours at $25^{\circ}C$ with distilled water of pH 6.0, 2% sugar and 0.8% agar. 2. Female flowers 1) Ovuliferous scales grow rapidly in late April, and differentiation of ovules begins early in May. Embryo-sac-mother cells produce pollen tetrads through meiosis in the middle of May, and flower in late May. 2) The pollinated female flowers show repeated divisions of embryo-sac nucleus, and a great number of free nuclei form a mass for overwintering. Morphogenesis of isolation in the mass structure takes place from the middle of March, and that forms albuminous bodies of aivealus in early May. 3. Formation of pollinators and embryos. 1) Archegonia produce archegonial initial cells in the middle and late April, and pollinators are produced in the late April and late in early May. 2) After pollination, Oespore nuclei are seen to divide in the late May forming a layer of suspensor from the diaphragm in early June and in the middle of June. Thus this happens to show 4 pro-embryos. The organ of embryos begins to differentiate 1 pro-embryo and reachs perfect maturation in late August. 4. The growth of cones 1) In the year of flowering, strobiles grow during the period from the middle of June to the middle of July, and do not grow after the middle of August. Strobiles grow 1.6 times more in length 3.3 times short in diameter and about 22 times more weight than those of female flower in the year of flowering. 2) The cones at the adult stage grow 7 times longer in diameter, 12-15 times shorter diameter than those of strobiles after flowering. 3) Cone has 96-133 scales with the ratio of scale to be 69-80% and the length of cone is 11-13cm. Diameter is 5-8cm with 160-190g weight, and the seed number of it is 90-150 having empty seed ratio of 8-15%. 5. Formation of seed-coats 1) The layers of outer seed-coat become most for the width of $703{\mu}$ in the middle of July. At the adult stage of seed, it becomes $550-580{\mu}$ in size by decreasing moisture content. Then a horny and the cortical tissue of outer coats become differentiated. 2) The outer seed-coat of mature seeds forms epidermal cells of 3-4 layers and the stone cells of 16-21 layers. The interior part of it becomes parenchyma layer of 1 or 2 rows. 3) Inner seed-coat is formed 2 months earlier than the outer seed-coat in the middle of May, having the most width of inner seed-coat $667{\mu}$. At the adult stage it loses to $80-90{\mu}$. 6. Change in moisture content After pollination moisture content becomes gradually increased at the top in the early June and becomes markedly decreased in the middle of August. At the adult stage it shows 43~48% in cone, 23~25% in the outer seed-coat, 32~37% in the inner seed-coat, 23~26% in the inner seed-coat and endosperm and embryo, 21~24% in the embryo and endosperm, 36~40% in the embryos. 7. The content compositions of seed 1) Fat contents become gradually increased after the early May, at the adult stage it occupies 65~85% more fat than walnut and palm. Embryo includes 78.8% fat, and 57.0% fat in endosperm. 2) Sugar content after pollination becomes greatly increased as in the case of reducing sugar, while non-reducing sugar becomes increased in the early June. 3) Crude protein content becomes gradually increased after the early May, and at the adult stage it becomes 48.8%. Endosperm is made up with more protein than embryo. 8. The test of germination The collected optimum period of Pinus koraiensis seeds at an adequate maturity was collected in the early September, and used for the germination test of reduction-method and embryo culture. Seeds were taken at the interval of 7 days from the middle of July to the middle of September for the germination test at germination apparatus.

• 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.