• Economic and Environmental Geology
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• v.40 no.2 s.183
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• pp.153-170
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• 2007

Loading time and loading environment of the Dokdo seamounts were studied from flexure model and VGP(Virtual Geomagnetic Pole) determined by gravity and magnetic data. In spite of their similarity in size. a large difference about 50 mGal between gravity anomaly peaks of Dokdo and the Isabu Tablemount suggests different compensation degrees. Flexural modeling results show that the flexural rigidity(effective elastic thickness) of lithosphere for Dokdo is stronger(thicker) than that for the Isabu Tablemount. Also, it implies that the age of lithosphere at the time of loading of the Isabu Tablemount may be younger than that of Dokdo. Magnetic anomalies occur complicated over the Dokdo seamounts. Paleomagnetism was studied from VGP estimated by the least square and the seminorm magnetization methods with 1500 m upward continued magnetic anomalies. Age dating of Dokdo from previous study, flexural modeling, VGP, and geomagnetic polarity time scale suggest that after the cease of spreading in the Ulleung Basin, the Isabu Tablemount was formed first in normal polarity interval and followed by Dokdo. Also, they indicate that the fist large eruption of Dokdo was in normal polarity interval and the second large eruption in reversed polarity interval. The Simheungtaek Tablemount was formed in normal polarity interval between the formations of the Isabu Tablemount and Dokdo. These loading times for the Dokdo seamounts show a good coherence with the compressive stress period after the end of the opening of the East Sea. The Dokdo seamounts probably was caused by volcanism associated with the compressive stress.

• Korean Journal of Crystallography
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• v.6 no.2
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• pp.125-133
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• 1995

The crystal structure of dehydrated, partially Co(II)-exchanged zeolite X, stoichiometry Co2+Na+-X (Co41+Na10Si100Al92O384) per unit cell, has been determined from three-dimensional X-ray diffraction data gathered by counter methods. The structure was solved and refined in the cubic space group Fd3:α=24.544(1)Å at 21(1)℃. The crystal was prepared by ion exchange in a flowing stream using a solution 0.025 M each in Co(NO3)2 and Co(O2CCH3)2. The crystal was then dehydrated at 380℃ and 2×10-6 Torr for two days. The structure was refined to the final error indices, R1=0.059 and R2=0.046 with 211 reflections for which I > 3σ(I). Co2+ ions and Na+ ions are located at the four different crystallographic sites. Co2+ ions are located at two different sites of high occupancies. Sixteen Co2+ ions are located at the center of the double six-ring (site I; Co-O = 2.21(1)Å, O-Co-O = 90.0(4)°) and twenty-five Co2+ ions are located at site II in the supercage. Twenty-five Co2+ ions are recessed 0.09Å into the supercage from its three oxygen plane (Co-O = 2.05(1)Å, O-Co-O = 119.8(7)°). Na+ ions are located at two different sites of occupandies. Seven Na+ ions are located at site II in the supercage (Na-O = 2.29(1)Å, O-Na-O = 102(1)°). Three Na+ ions are statistically distribyted over site III, a 48-fold equipoint in the supercages on twofold axes (Na-O = 2.59(10)Å, O-Na-O = 69.0(3)°). Seven Na+ ions are recessed 1.02Å into the supercage from the three oxygen plane. It appears that Co2+ ions prefer sites I and II in order, and that Na+ ions occupy the remaining sites, II and III.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.31 no.3
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• pp.115-128
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• 2019

In this study, a probabilistic model for the estimation of yearly workable wave condition period for offshore operations is developed. In doing so, we first hindcast the significant wave heights and peak periods off the Ulsan every hour from 2003.1.1 to 2017.12.31 based on the meteorological data by JMA (Japan Meterological Agency) and NOAA (National Oceanic and Atmospheric Administration), and SWAN. Then, we proceed to derive the long term significant wave height distribution from the simulated time series using a least square method. It was shown that the agreements are more remarkable in the distribution in line with the Modified Glukhovskiy Distribution than in the three parameters Weibull distribution which has been preferred in the literature. In an effort to develop a more comprehensive probabilistic model for the estimation of yearly workable wave condition period for offshore operations, wave height distribution over the 15 years with individual waves occurring within the unit simulation period (1 hour) being fully taken into account is also derived based on the Borgman Convolution Integral. It is shown that the coefficients of the Modified Glukhovskiy distribution are $A_p=15.92$, $H_p=4.374m$, ${\kappa}_p=1.824$, and the yearly workable wave condition period for offshore work is estimated to be 319 days when a threshold wave height for offshore work is $H_S=1.5m$. In search of a way to validate the probabilistic model derived in this study, we also carry out the wave by wave analysis of the entire time series of numerically simulated significant wave heights over the 15 years to collect every duration periods of waves the height of which are surpassing the threshold height which has been reported to be $H_S=1.5m$ in the field practice in South Korea. It turns out that the average duration period is 45.5 days from 2003 to 2017, which is very close to 46 days from the probabilistic model derived in this study.

• Journal of the Korean Chemical Society
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• v.40 no.6
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• pp.427-435
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• 1996

The structures of fully dehydrated $Ca^{2+}$- and $Cs^+$-exchanged zeolite X, $Ca_{35}Cs_{22}Si_{100}Al_{92}O_{384}$($Ca_{35}Cs_{22}$-X; a=25.071(1) $\AA)$ and $Ca_{29}Cs_{34}Si_{100}Al_{92}O_{384}$($Ca_{29}Cs_{34}$-X; a=24.949(1) $\AA)$, have been determined by single-crystal X-ray diffraction methods in the cubic space group Fd3 at $21(1)^{\circ}C.$ Their structures were refined to the final error indices $R_1$=0.051 and $R_2$=0.044 with 322 reflections for $Ca_{35}Cs_{22}$-X, and $R_1$=0.058 and $R_2$=0.055 with 260 reflections for $Ca_{29}Cs_{34}$-X; $I>3\sigma(I).$ In both structures, $Ca^{2+}$ and $Cs^+$ ions are located at five different crystallographic sites. In dehydrated $Ca_{35}Cs_{22}$-X, sixteen $Ca^{2+}$ ions fill site I, at the centers of the double 6-rings(Ca-O=2.41(1) $\AA$ and $O-Ca-O=93.4(3)^{\circ}).$ Another nineteen $Ca^{2+}$ ions occupy site II (Ca-O=2.29(1) $\AA$, O-Ca-O=118.7(4)') and ten $Cs^+$ ions occupy site II opposite single six-rings in the supercage; each is $1.95\AA$ from the plane of three oxygens (Cs-O=2.99(1) and $O-Cs-O=82.3(3)^{\circ}).$ About three $Cs^+$ ions are found at site II', 2.27 $\AA$ into sodalite cavity from their three-oxygen plane (Cs-O=3.23(1) $\AA$ and $O-Cs-O=75.2(3)^{\circ}).$ The remaining nine $Cs^+$ ions are statistically distributed over site Ⅲ, a 48-fold equipoint in the supercages on twofold axes (Cs-O=3.25(1) $\AA$ and Cs-O=3.49(1) $\AA).$ In dehydrated $Ca_{29}Cs_{34}$-X, sixteen $Ca^{2+}$ ions fill site I(Ca-O=2.38(1) $\AA$ and $O-Ca-O=94.1(4)^{\circ})$ and thirteen $Ca^{2+}$ ions occupy site II (Ca-O=2.32(2) $\AA$, $O-Ca-O=119.7(6)^{\circ}).$ Another twelve $Cs^+$ ions occupy site II; each is $1.93\AA$ from the plane of three oxygens (Cs-O=3.02(1) and $O-Cs-O=83.1(4)^{\circ})$ and seven $Cs^+$ ions occupy site II'; each is $2.22\AA$ into sodalite cavity from their three-oxygen plane (Cs-O=3.21(2) and $O-Cs-O=77.2(4)^{\circ}).$ The remaining sixteen $Cs^+$ ions are found at III site in the supercage (Cs-O=3.11(1) $\AA$ and Cs-O=3.46(2) $\AA).$ It appears that $Ca^{2+}$ ions prefer sites I and II in that order, and that $Cs^+$ ions occupy the remaining sites, except that they are too large to be stable at site I.

• 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.