• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.6
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• pp.315-322
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• 2005

In this article, 1 would like to explore the estimation method of time-varying voltage sags in large industrial systems considering the short circuit contributions of rotating machines. For the power distribution system of KEPCO(Korea Electric Power Corporation), the magnitude of initial symmetrical short circuit current is generally not changed. However, in industrial systems which contain a number of rotating machines, the magnitude of voltage sag is generally changed from the initial to the clearing time of a fault due to the decreasing contribution of rotating machines for a fault current. The time-varying characteristics of voltage sags can be calculated using a short circuit analysis that is considered the time-varying fault currents. For this, the prediction formulations of time-varying voltage sags are proposed using a foreign standard. The proposed method contains the consideration of generator and motor effects. For the test of proposed formulations, a simple system of industrial consumer is used for the comparison conventional and proposed estimation method of voltage sag characteristics.

• Transactions of the Korean Society of Mechanical Engineers
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• v.15 no.2
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• pp.611-617
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• 1991

Effective stiffness of short fiber composite with a three-dimensional random orientation of fibers is derived theoretically and compared with available experimental data. The laminate analogy and transformed laminate analogy are used for modulus prediction of 2-D and 3-D random composites, respectively. The effective stiffness of random oriented fiber composite can be expressed in terms of longitudinal and transverse stiffnesses of unidirectional composites. The result of transformed laminate analogy is more accurate than other approaches such as, Christensen-Waals equational and Lavengood-Goettler equation, etc. Also the effective properties of random oriented fiber composite can be expressed in terms of fiber and matrix properties such as elastic modulus, shear modulus and Poisson's ratio.

• IE interfaces
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• v.23 no.3
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• pp.229-238
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• 2010

Virtual metrology (VM), a critical component of semiconductor manufacturing, is an efficient way of assessing the quality of wafers not actually measured. This is done based on a model between equipment sensor data (obtained for all wafers) and the quality characteristics of wafers actually measured. This paper considers principal component regression (PCR), partial least squares regression (PLSR), kernel PCR (KPCR), and kernel PLSR (KPLSR) as VM models. For each regression model, two cases are considered. One utilizes all explanatory variables in developing a model, and the other selects significant variables using the genetic algorithm (GA). The prediction performances of 8 regression models are compared for the short- and long-term etch process data. It is found among others that the GA-KPLSR model performs best for both types of data. Especially, its prediction ability is within the requirement for the short-term data implying that it can be used to implement VM for real etch processes.

• Structural Engineering and Mechanics
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• v.57 no.2
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• pp.265-280
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• 2016

A NLFE model was proposed to investigate the mechanical behavior of short columns, cast using plain or fibrous lightweight aggregate concrete (LWAC), and subjected to elevated temperatures of up to $700^{\circ}C$. The model was validated, before its predictions were extended to study the effect of other variables, not studied experimentally. The three-dimensional NLFE model was developed using ANSYS software and involved rational simulation of thermal mechanical behavior of plain and fibrous LWAC as well as longitudinal and lateral steel reinforcement. The prediction from the NLFE model of columns' mechanical behavior, as represented by the stress-strain diagram and its characteristics, compared well with the experimental results. The predictions of the proposed models, considering wide range of lateral reinforcement ratios, confirmed the behaviors observed experimentally and stipulated the importance of steel confinement in preserving post-heating mechanical properties of plain and fibrous LWAC columns, being subjected to high temperature.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.2
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• pp.108-112
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• 2012

A sustainable production of electricity is essential for low carbon green growth in South Korea. The generation of wind power as renewable energy has been rapidly growing around the world. Undoubtedly wind energy is unlimited in potential. However, due to its own intermittency and volatility, there are difficulties in the effective harvesting of wind energy and the integration of wind power into the current electric power grid. To cope with this, many works have been done for wind speed and power forecasting. It is reported that, compared with physical persistent models, statistical techniques and computational methods are more useful for short-term forecasting of wind power. Among them, support vector regression (SVR) has much attention in the literature. This paper proposes an SVR based wind speed forecasting. To improve the forecasting accuracy, a fuzzy clustering is adopted in the process of SVR modeling. An illustrative example is also given by using real-world wind farm dataset. According to the experimental results, it is shown that the proposed method provides better forecasts of wind power.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.55 no.7
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• pp.306-312
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• 2006

Support Vector Machine(SVM), of which the foundations have been developed by Vapnik (1995), is gaining popularity thanks to many attractive features and promising empirical performance. In this paper, we propose a new short-term load forecasting technique based on SVM. We discuss the input vector selection of SVM for load forecasting and analyze the prediction performance for various SVM parameters such as kernel function, cost coefficient C, and $\varepsilon$ (the width of 8 $\varepsilon-tube$). The computer simulation shows that the prediction performance of the proposed method is superior to that of the conventional neural networks.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.32 no.6
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• pp.191-198
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• 1983

This paper describes the aplication of different model which can be used for short-term load prediction. The model is based on Bohlin's approach to first develop a load profile model representing the nominal load component and the Box-Jenkins approach is used to predict residuals. An on-line algorithm using Kalman Filter and Time Series is implemented for and hour-ahead prediction. In the Kalman Filter system equation and measurement equation were fixed and parameters of Time Series were varied week after week. A set of data for Korea Electric Power Corporation from April to June 1981 was used for the evaluation of the model. As the result of this simulation 1.2% rms error was acquired.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.45 no.4
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• pp.602-611
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• 1996

In this paper, radial basis function networks with two hidden layers, which employ the K-means clustering method and the hierarchical training, are proposed for improving the short-term predictability of chaotic time series. Furthermore the recursive training method of radial basis function network using the recursive modified Gram-Schmidt algorithm is proposed for the purpose. In addition, the radial basis function networks trained by the proposed training methods are compared with the X.D. He A Lapedes's model and the radial basis function network by nonrecursive training method. Through this comparison, an improved radial basis function network for predicting chaotic time series is presented. (author). 17 refs., 8 figs., 3 tabs.

• Environmental Engineering Research
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• v.10 no.2
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• pp.62-70
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• 2005

Pollution loading of Combined Sewer Overflows (CSOs) is frequently over the capacity of a wastewater treatment plant (WWTP) receiving the water. The objectives of this study are to investigate water quality of CSOs in Anmyun-ueup, Tean province and to apply Storm Water Management Model to predict flow rate and water quality of the CSOs. The capacity of a local WWTP was also estimated according to rainfall duration and intensity. Eleven water quality parameters were analyzed to characterize overflows. SWMM model was applied to predict the flow rate and pollutant load of CSOs during rain event. Overall, profile of the flow and pollutant load predicted by the model well followed the observed data. Based on model prediction and observed data, CSOs frequently occurs in the study area, even with light precipitation or short rainfall duration. Model analysis also indicated that the local WWTP’s capacity was short to cover the CSOs.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.33 no.10
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• pp.1119-1124
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• 2009

This paper deals with the variability analysis of short term creep rupture test data based on the previous creep rupture tests and the possibility of the creep life prediction. From creep tests performed by constant uniaxial stresses at 600, 650 and $700^{\circ}C$ elevated temperature, in order to investigate the variability of short-term creep rupture data, the creep curves were analyzed for normalized creep strain divided by initial strain. There are some variability in the creep rupture data. And, the difference between general creep curves and normalized creep curves were obtained. The effects of the creep rupture time (RT) and steady state creep rate (SSCR) on the Weibull distribution parameters were investigated. There were good relation between normal Weibull parameters and normalized Weibull parameters. Finally, the predicted creep life were compared with the Monkman-Grant model.