• Journal of the Korea Computer Graphics Society
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• v.17 no.3
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• pp.43-50
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• 2011

We present a trade-off technique for fast but qualitative planar shape deformation using a layered mesh. We construct a layered mesh that is embedding a planar input shape; the upper-layer is denoted as a control mesh and the other lower-layer as a shape mesh that is defined by mean value coordinates relative to the control mesh. First, we try to preserve some shape properties including user constraints for the control mesh by means of a known existing nonlinear least square optimization technique, which produces deformed positions of the control mesh vertices. Then, we compute the deformed positions of the shape mesh vertices indirectly from the deformed control mesh by means of simple coordinates computation. The control mesh consists of a small number of vertices while the shape layer contains relatively a large number of vertices in order to embed the input shape as tightly as possible. Since the time-consuming optimization technique is applied only to the control mesh, the overall execution is extremely fast; however, the quality of deformation is sacrificed due to the sacrificed quality of the control mesh and its relativity to the shape mesh. In order to change the deformation behavior and consequently to compensate the quality sacrifice, we present a method to control the deformation stiffness by incorporating the orientation into the user constraints. According to our experiments, the proposed technique produces a planar shape deformation fast enough for real-time applications on limited embedded systems such as cell phones and tablet PCs.

• Journal of Radiation Protection and Research
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• v.7 no.1
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• pp.23-33
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• 1982

The characteristic thermoluminescence responses of Teflon thermoluminescent dosimeters to radiations have been studied by the variation of radiation qualities as well as the high dose radiations. The change in the sensitivity of TLDs for different radiation qualities were studied through not only the photon energy dependence but also the change of supralinearity on the photon energy dependence, by exposing $^{60}Co$ gamma rays, the effective X-rays of 44keV, 69keV, 108keV, and thermal neutron of 0.04 eV. The results were as the following: The TL response of $T-CaSO_4$: Dy as a function of absorbed dose was linear up to about 5 Gy, and the response beyond 5Gy was supralinear for $^{60}Co$ gamma rays. The supralinearity of T-LiF-7 became noticeably apparent more than that of $T-CaSO_4$:Dy and also the lower the LET of radiation became the higher the supralinear effects were. No supralinearity appeared for the thermal neutron irradiations equivalent to 10Gy of $^{60}Co$ gamma rays. The relative sensitivities (Rs), which depended on the doses of $^{60}Co$ gamma rays to the TLDs of T-LiF-7 and T-$CaSO_4$:Dy could be, respectively, approximated to the following empirical formula fitted by the least square method: $$R_{LiF}=1.021-0.04581\;logD+0.402(logD)^2-0.405(logD)^3,\;\;5{\times}10^3{\geq}D{\geq}1(Gy)$$ $$R_{CaSO_4}=0.976-0.3241\;logD+0.262(logD)^2-0.298(logD)^3,\;5{\times}10^3{\geq}D{\geq}1(Gy)$$.

• 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.