• The Transactions of the Korean Institute of Electrical Engineers B
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• v.48 no.4
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• pp.174-181
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• 1999

To obtain more accurate result in the finite element analysis for electric machinery, it is important to have a mesh being of good quality. This paper describes a new technique of mesh generation for the finite element method. When the list of points defining the region of analysis is given, an appropriate distribution of interior points is generated first. Secondly the points are connected to from the trianlges. Finally the connectivity data are used to reposition the interior points using Laplacian smoothing and mesh relaxation technique. In this paper, a mesh searching technique of Lawson which modifies the start mesh is proposed in addition to the above three steps. This algorithm is simple and produces the meshes being of good quality with high speed in comparison with the existing one.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.275-276
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• 2007

In this paper, we propose an efficient illumination preprocessing algorithm for face recognition. One of the best known illumination preprocessing method, based on anisotropic smoothing, enhances the edge information, but instead deteriorates the contrast of the original image. Our proposed method reduces the deterioration of the contrast while enhancing the edge information, and thus the preprocessed image does not lose features like Gabor features of the original images much.. The effectiveness of the proposed illumination preprocessing method is verified through experiments of face recognition.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.9
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• pp.1695-1699
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• 2009

This paper analyzed the characteristics of the demand of electric power in Jeju by year, day. For this analysis, this research used the correlation between the changes in the temperature and the demand of electric power in summer, and cleaned the data of the characteristics of the temperatures, using the coefficient of correlation as the standard. And it proposed the algorithm of forecasting the short-term electric power demand in Jeju, Therefore, in the case of summer, the data by each cleaned temperature section were used. Based on the data, this paper forecasted the short-term electric power demand in the exponential smoothing method. Through the forecast of the electric power demand, this paper verified the excellence of the proposed technique by comparing with the monthly report of Jeju power system operation result made by Korea Power Exchange-Jeju.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2003.06a
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• pp.141-144
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• 2003

This paper proposes a design method of a CDBC(Continuous-time Deadbeat Controller)system that takes into account the response between the sampling instant and using second-order smoothing elements. The continuous deadbeat controller is composed of a serial integral compensator and a local feedback compensator introduced into the state feedback loop. A DC servo motor is chosen for implementing CDBC algorithm. Especially according to the variable input and disturbance, corresponding CDBC design method is suggested. A Matlab Simulink is used for simulation with the Motor parameter. By computer simulations, control inputs and system outputs are shown to have desirable property such as smoothness.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.13 no.6
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• pp.438-444
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• 2003

A method using beamforming algorithm has been developed to measure oblique incidence reflection coefficients of sound absorption materials. MUSIC(multiple signal classification) method detects the angles of incidence and reflection. By separating the incident and reflected waves using beamforming method, the reflection coefficient is calculated. Spatial smoothing technique Is also used to reduce the coherence between the incident and reflected waves. Numerical and experiment results are performed to verify the accuracy of proposed method.

• Communications for Statistical Applications and Methods
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• v.26 no.1
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• pp.69-78
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• 2019

A structured model with both single-index and varying coefficients is a powerful tool in modeling high dimensional data. It has been widely used because the single-index can overcome the curse of dimensionality and varying coefficients can allow nonlinear interaction effects in the model. For high dimensional index vectors, variable selection becomes an important question in the model building process. In this paper, we propose an efficient estimation and a variable selection method based on a smoothing spline approach in a partially linear single-index-coefficient regression model. We also propose an efficient algorithm for simultaneously estimating the coefficient functions in a data-adaptive lower-dimensional approximation space and selecting significant variables in the index with the adaptive LASSO penalty. The empirical performance of the proposed method is illustrated with simulated and real data examples.

• Journal of information and communication convergence engineering
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• v.19 no.4
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• pp.221-227
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• 2021

An early warning system was developed to help identify stock status as early as possible. For performance to improve, there needs to be a feature to predict the amount of stock that must be provided and a feature to estimate when to buy goods. This research was conducted to improve the inventory early warning system and optimize the Reminder Block's performance in minimum stock settings. The models used in this study are the single exponential smoothing (SES) method for prediction and the economic order quantity (EOQ) model for determining the quantity. The research was conducted by analyzing the Reminder Block in the early warning system, identifying data needs, and implementing the SES and EOQ mathematical models into the Reminder Block. This research proposes a new Reminder Block that has been added to the SES and EOQ models. It is hoped that this study will help in obtaining accurate information about the time and quantity of repurchases for efficient inventory management.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.41 no.5
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• pp.9-18
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• 2004

This paper presents a vehicle detecting algorithm using microwave system signals. The Proposed algerian decides the breakpoint of signals using the likelihood criteria. The decided signals are segmented and simplified. The proposed searching algorithm uses the Euclid distance from the weighted signal data. We tested the proposed algorithm to compare with the segmentation which is a method using smoothing and edge detection. We confirm that the proposed algorithm is very useful for detecting vehicles by field test.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 1992.03a
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• pp.159-176
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• 1992

Limit analysis has been rendered versatile in many problems such as structural problems and metal forming problems. In metal forming analysis, a slip-line method and an upper bound method approach to limit solutions is considered as the most challenging areas. In the present work, a general algorithm for limit solutions of plastic flow is developed with the use of finite element limit analysis. The algorithm deals with a generalized Holder inequality, a duality theorem, and a combined smoothing and successive approximation in addition to a general procedure for finite element analysis. The algorithm is robust such that from any initial trial solution, the first iteration falls into a convex set which contains the exact solution(s) of the problem. The idea of the algorithm for limit solution is extended from rigid/perfectly-plastic materials to work-hardening materials by the nature of the limit formulation, which is also robust with numerically stable convergence and highly efficient computing time.

• Journal of Communications and Networks
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• v.12 no.4
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• pp.317-329
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• 2010

In recent years, many sparse estimation methods, also known as compressed sensing, have been developed. However, most of these methods presume that the measurement matrix is completely known. We develop a new blind maximum likelihood method-the expectation-sparse-maximization (ESpaM) algorithm-for models where the measurement matrix is the product of one unknown and one known matrix. This method is a variant of the expectation-maximization algorithm to deal with the resulting problem that the maximization step is no longer unique. The ESpaM algorithm is justified theoretically. We present as well numerical results for two concrete examples of blind channel identification in digital communications, a doubly-selective channel model and linear time invariant sparse channel model.