• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.2
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• pp.46-56
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• 1999

A predictive controller is designed based upon stochastic methods for compensation time-delay effect on a system which has inherent time-delay caused by the spatial separation between controllers and actuators. The predictive controller estimates current outputs through linear prediction methods and probability functions utilizing previous outputs, and minimizes the malicious phenomena caused by the time-delay in precision control systems. To demonstrate effectiveness of this control methodology, we applied this algorithm for the control of a tele-operated DC servomotor. The experimental results show that this predictive controller is superior to the PID controller in terms of convergence-characteristics, and show that this controller expands the maximum allowable time-delay for a system maintaining the stability. Since the proposed predictor does not require models of plants - it requires only stochastic information for outputs --, it is a general scheme which can be applied for the control of systems which are difficult to model or the compensator of PID control.

• Environmental and Resource Economics Review
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• v.20 no.1
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• pp.119-153
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• 2011

This study reviews various aspects of model formulating processes of dichotomous choice responses of the contingent valuation method (CVM), which has been increasingly used in the preliminary feasibility test of Korea public investment projects. The theoretical review emphasizes the consistency between WTP estimation process and WTP measurement process. The empirical analysis suggests that two common parametric models for dichotmous choice responses (RUM and RWTP) and two commonly used probability distributions of random components (probit and logit) resulted in all most the same empirical WTP distributions, as long as the WTP functions are specified to be a linear function of the bid amounts. However, the efficiency gain of DB response compared to SB response were supported on the ground that the two CV responses are derived from the same WTP distribution. Moreover for the exponential WTP function which guarantees the non-negative WTP measures, sample mean WTP were quite different from median WTP if the scale parameter of WTP function turned out to be large.

• Structural Engineering and Mechanics
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• v.56 no.6
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• pp.959-982
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• 2015

In this study, a non-stationary random earthquake Clough-Penzien model is used to describe earthquake ground motion. Using stochastic direct integration in combination with an equivalent linear method, a solution is established to describe the non-stationary response of lead-rubber bearing (LRB) system to a stochastic earthquake. Two parameters are used to develop an optimization method for bearing design: the post-yielding stiffness and the normalized yield strength of the isolation bearing. Using the minimization of the maximum energy response level of the upper structure subjected to an earthquake as an objective function, and with the constraints that the bearing failure probability is no more than 5% and the second shape factor of the bearing is less than 5, a calculation method for the two optimal design parameters is presented. In this optimization process, the radial basis function (RBF) response surface was applied, instead of the implicit objective function and constraints, and a sequential quadratic programming (SQP) algorithm was used to solve the optimization problems. By considering the uncertainties of the structural parameters and seismic ground motion input parameters for the optimization of the bearing design, convex set models (such as the interval model and ellipsoidal model) are used to describe the uncertainty parameters. Subsequently, the optimal bearing design parameters were expanded at their median values into first-order Taylor series expansions, and then, the Lagrange multipliers method was used to determine the upper and lower boundaries of the parameters. Moreover, using a calculation example, the impacts of site soil parameters, such as input peak ground acceleration, bearing diameter and rubber shore hardness on the optimization parameters, are investigated.

• Journal of The Korean Astronomical Society
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• v.52 no.4
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• pp.133-144
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• 2019

We develop forecast models of daily probabilities of major flares (M- and X-class) based on empirical relationships between photospheric magnetic parameters and daily flaring rates from May 2010 to April 2018. In this study, we consider ten magnetic parameters characterizing size, distribution, and non-potentiality of vector magnetic fields from Solar Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI) and Geostationary Operational Environmental Satellites (GOES) X-ray flare data. The magnetic parameters are classified into three types: the total unsigned parameters, the total signed parameters, and the mean parameters. We divide the data into two sets chronologically: 70% for training and 30% for testing. The empirical relationships between the parameters and flaring rates are used to predict flare occurrence probabilities for a given magnetic parameter value. Major results of this study are as follows. First, major flare occurrence rates are well correlated with ten parameters having correlation coefficients above 0.85. Second, logarithmic values of flaring rates are well approximated by linear equations. Third, using total unsigned and signed parameters achieved better performance for predicting flares than the mean parameters in terms of verification measures of probabilistic and converted binary forecasts. We conclude that the total quantity of non-potentiality of magnetic fields is crucial for flare forecasting among the magnetic parameters considered in this study. When this model is applied for operational use, it can be used using the data of 21:00 TAI with a slight underestimation of 2-6.3%.

• Journal of the Semiconductor & Display Technology
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• v.22 no.1
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• pp.124-128
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• 2023

Semiconductor engineers have long sought to enhance the energy efficiency of mobile semiconductors by reducing their voltage. During the final stages of the semiconductor manufacturing process, the screening and evaluation of voltage is crucial. However, determining the optimal test start voltage presents a significant challenge as it can increase testing time. In the semiconductor manufacturing process, a wealth of test element group information is collected. If this information can be controlled to predict the test voltage, it could lead to a reduction in testing time and increase the probability of identifying the optimal voltage. To achieve this, this paper is exploring machine learning techniques, such as linear regression and ensemble models, that can leverage large amounts of information for voltage prediction. The outcomes of these machine learning methods not only demonstrate high consistency but can also be used for feature engineering to enhance accuracy in future processes.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.6
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• pp.679-686
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• 2020

In this thesis, an enhanced method for the feature extraction of vocal source signals and score combination using an MCE-Based weight estimation of the score of multiple feature vectors are proposed for the performance improvement of speaker recognition systems. The proposed feature vector is composed of perceptual linear predictive cepstral coefficients, skewness, and kurtosis extracted with lowpass filtered glottal flow signals to eliminate the flat spectrum region, which is a meaningless information section. The proposed feature was used to improve the conventional speaker recognition system utilizing the mel-frequency cepstral coefficients and the perceptual linear predictive cepstral coefficients extracted with the speech signals and Gaussian mixture models. In addition, to increase the reliability of the estimated scores, instead of estimating the weight using the probability distribution of the convectional score, the scores evaluated by the conventional vocal tract, and the proposed feature are fused by the MCE-Based score combination method to find the optimal speaker. The experimental results showed that the proposed feature vectors contained valid information to recognize the speaker. In addition, when speaker recognition is performed by combining the MCE-based multiple feature parameter scores, the recognition system outperformed the conventional one, particularly in low Gaussian mixture cases.

• Journal of the Korean Society of Food Science and Nutrition
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• v.38 no.2
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• pp.235-243
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• 2009

This study was performed to find the optimum ratio of ingredients in the Chinese radish jam. The experiment was designed according to the RSM (response surface methodology), which included 18 experimental points with 4 replicates for three independent variables (sugar $45{\sim}70%$, pectin $0.5{\sim}2.0.%$, citric acid $0.2{\sim}0.5%$). The compositional and functional properties of the prepared products were measured, and these values were applied to the mathematical models. By use of F-test, sweetness, color values (L, a, b), and firmness were expressed by a linear model, while the sensory characteristics (color, smell, taste, texture and overall acceptance) were by a quadratic model. In the numeric optimization, the optimal ingredient amounts were 53.7% sugar, 1.0% pectin, and 0.3% citric acid. And in the graphical optimization, 53.9% sugar, 1.0% pectin, and 0.3% citric acid; these data were equivalent to 0.6985 desirability, indicating that the values were almost equivalent to the numerical optimization points. The above results demonstrate the feasibility of Chinese radish jam, and therefore, the commercialization of a Chinese radish jam marketed as a functional food is deemed possible.

• Journal of the Earthquake Engineering Society of Korea
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• v.12 no.5
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• pp.47-56
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• 2008

This study was performed to evaluate the effect of Response modification factors (R-factor) in 3-, 9- and 20- story steel Moment Resisting Frame (MRF) buildings. Each structure was designed using a R-factor of 8, as tabulated in the 2000 International Building Code provision (IBC 2000) and Korea Building Code (KBC) 2008. In order to evaluate the maximum and minimum performance expected for such structures, an upper bound and lower bound design were adopted for each model. Next, each analytical model was designed using different R-factors (8, 9, 10, 11, 12) and four different structural periods with the original fundamental period. For a detailed case study, a total of 150 analytical models were subjected to 20 ground motions representing a hazard level with a 2% probability of being exceeded in 50 years. In order to evaluate the performance of the structures, static push-over and non-linear time history analysis (NTHA) were performed, and displacement ductility demand was investigated to consider the ductility capacity of the structures. The results show that the dynamic behaviors for the 3- and 9-story buildings are relatively stable and conservative, while the 20-story buildings show a large displacement ductility demand due to dynamic instability factors. (e.g. P-delta effect and high mode effect)

• Journal of the Korean Society of Food Science and Nutrition
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• v.37 no.4
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• pp.497-504
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• 2008

This study was conducted for the optimization of ingredients in salad dressing using Bokbunja (Rubus coreanum Miquel) juice. The experiment was designed according to the D-optimal design of mixture design, which included 14 experimental points with 4 replicates for three independent variables (Bokbunja juice $15.70\sim47.10%$, oil $23.50\sim39.20%$, vinegar $3.90\sim19.60%$). The compositional and functional properties of the prepared products were measured, and these values were applied to the mathematical models. A canonical form and trace plot showed the influence of each variable on the quality attribute of final mixture product. By the use of F-test, viscosity, color values (L, a, and b), emulsion stability and sensory characteristics (color) were expressed by a linear model, while the color values (L) and sensory characteristics (smell, taste, and overall acceptance) were by a quadratic model. The optimum formulations by numerical and graphical method were analogous: Bokbunja juice, oil and vinegar of 36.02%, 26.48%, and 12.00% by numerical method, respectively; those of 36.00%, 26.44%, and 12.06% by graphical method, respectively.

• Journal of Intelligence and Information Systems
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• v.16 no.2
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• pp.19-32
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• 2010

Volatility plays a central role in both academic and practical applications, especially in pricing financial derivative products and trading volatility strategies. This study presents a novel mechanism based on generalized autoregressive conditional heteroskedasticity (GARCH) models that is able to enhance the performance of intelligent volatility trading systems by predicting Korean stock market volatility more accurately. In particular, we embedded the concept of the volatility asymmetry documented widely in the literature into our model. The newly developed Korean stock market volatility index of KOSPI 200, VKOSPI, is used as a volatility proxy. It is the price of a linear portfolio of the KOSPI 200 index options and measures the effect of the expectations of dealers and option traders on stock market volatility for 30 calendar days. The KOSPI 200 index options market started in 1997 and has become the most actively traded market in the world. Its trading volume is more than 10 million contracts a day and records the highest of all the stock index option markets. Therefore, analyzing the VKOSPI has great importance in understanding volatility inherent in option prices and can afford some trading ideas for futures and option dealers. Use of the VKOSPI as volatility proxy avoids statistical estimation problems associated with other measures of volatility since the VKOSPI is model-free expected volatility of market participants calculated directly from the transacted option prices. This study estimates the symmetric and asymmetric GARCH models for the KOSPI 200 index from January 2003 to December 2006 by the maximum likelihood procedure. Asymmetric GARCH models include GJR-GARCH model of Glosten, Jagannathan and Runke, exponential GARCH model of Nelson and power autoregressive conditional heteroskedasticity (ARCH) of Ding, Granger and Engle. Symmetric GARCH model indicates basic GARCH (1, 1). Tomorrow's forecasted value and change direction of stock market volatility are obtained by recursive GARCH specifications from January 2007 to December 2009 and are compared with the VKOSPI. Empirical results indicate that negative unanticipated returns increase volatility more than positive return shocks of equal magnitude decrease volatility, indicating the existence of volatility asymmetry in the Korean stock market. The point value and change direction of tomorrow VKOSPI are estimated and forecasted by GARCH models. Volatility trading system is developed using the forecasted change direction of the VKOSPI, that is, if tomorrow VKOSPI is expected to rise, a long straddle or strangle position is established. A short straddle or strangle position is taken if VKOSPI is expected to fall tomorrow. Total profit is calculated as the cumulative sum of the VKOSPI percentage change. If forecasted direction is correct, the absolute value of the VKOSPI percentage changes is added to trading profit. It is subtracted from the trading profit if forecasted direction is not correct. For the in-sample period, the power ARCH model best fits in a statistical metric, Mean Squared Prediction Error (MSPE), and the exponential GARCH model shows the highest Mean Correct Prediction (MCP). The power ARCH model best fits also for the out-of-sample period and provides the highest probability for the VKOSPI change direction tomorrow. Generally, the power ARCH model shows the best fit for the VKOSPI. All the GARCH models provide trading profits for volatility trading system and the exponential GARCH model shows the best performance, annual profit of 197.56%, during the in-sample period. The GARCH models present trading profits during the out-of-sample period except for the exponential GARCH model. During the out-of-sample period, the power ARCH model shows the largest annual trading profit of 38%. The volatility clustering and asymmetry found in this research are the reflection of volatility non-linearity. This further suggests that combining the asymmetric GARCH models and artificial neural networks can significantly enhance the performance of the suggested volatility trading system, since artificial neural networks have been shown to effectively model nonlinear relationships.