• Progress in Medical Physics
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• v.26 no.4
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• pp.286-293
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• 2015

The purpose of this study was to evaluate the set up accuracy using stereotactic body frame and frameless immobilizer for lung stereotactic body radiation therapy (SBRT). For total 40 lung cancer patients treated by SBRT, 20 patients using stereotactic body frame and other 20 patients using frameless immobilizer were separately enrolled in each group. The setup errors of each group depending on the immobilization methods were compared and analyzed. All patients received the dose of 48~60 Gy for 4 or 5 fractions. Before each treatment, a patient was first localized to the treatment isocenter using room lasers, and further aligned with a series of image guidance procedures; orthogonal kV radiographs, cone-beam CT, orthogonal fluoroscopy. The couch shifts during these procedures were recorded and analyzed for systematic and random errors of each group. Student t-test was performed to evaluate significant difference depending on the immobilization methods. The setup reproducibility was further analyzed using F-test with the random errors excluding the systematic setup errors. In addition, the ITV-PTV margin for each group was calculated. The setup errors for SBF were $0.05{\pm}0.25cm$ in vertical direction, $0.20{\pm}0.38cm$ in longitudinal direction, and $0.02{\pm}0.30cm$ in lateral direction, respectively. However the setup errors for frameless immobilizer showed a significant increase of $-0.24{\pm}0.25cm$ in vertical direction while similar results of $0.06{\pm}0.34cm$, $-0.02{\pm}0.25cm$ in longitudinal and lateral directions. ITV-PTV margins for SBF were 0.67 cm (vertical), 0.99 cm (longitudinal), and 0.83 cm (lateral), respectively. On the other hand, ITV-PTV margins for Frameless immobilizer were 0.75 cm (vertical), 0.96 cm (longitudinal), and 0.72 cm (lateral), indicating less than 1 mm difference for all directions. In conclusion, stereotactic body frame improves reproducibility of patient setup, resulted in 0.1~0.2 cm in both vertical and longitudinal directions. However the improvements are not substantial in clinic considering the effort and time consumption required for SBF setup.

• The Journal of the Petrological Society of Korea
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• v.9 no.4
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• pp.238-253
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• 2000

In the eastern part of the Euiseong Basin acidic~intermediate volcanic rocks widely distribute on the Cretaceous sedimentary basement. Coeval granitic rocks and dyke rocks intruded into the volcanic rocks. Volcanic stratigraphy of study area are andesite lava, dacitic lapilli tuff, dacitic flow-banded lava, rhyolitic bedded tuff, rhyolitic massive tuff, dacitic massive lava, rhyolitlc welded tuff occur from the lower to the upper strata. $SiO_2$ content of the volcanic rocks range from 51 to 74 wt.%. With the increase of $SiO_2$, the contents of $TiO_2$, $Al_2$$O_3$, MgO, FeOT MnO, CaO, $P_2$$O_{5}$ decrease but those of $K_2$O increase. The contents of $Na_2$O show dispersive variation. This trend is quite sim-ilar to the major oxide variation in the volcanic rocks from the Yucheon sub-basin. The geochemical natures indicate that the volcanic rocks in the study area are discriminated to the island-arc type high K to medium K calc-alkaline rocks. The compositional variation of the volcanic rocks can be explained by the plagioclase fractionation of the volcanic magmas originated from similar source materials. The volcanic stratigraphy seems to have formed by at least two eruptive sequences of andesitic to rhyolitic and dacitic to rhyolitic magmas which underwent crystallization differentiation.

• 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.