• Proceedings of the Computational Structural Engineering Institute Conference
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• 2007.04a
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• pp.481-486
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• 2007

The Zienkiewicz-Zhu(Z/Z) error estimate is slightly modified for the hierarchical p-refinement, and is then applied to L-shaped plates subjected to bending to demonstrate its effectiveness. An adaptive procedure in finite element analysis is presented by p-refinement of meshes in conjunction with a posteriori error estimator that is based on the superconvergent patch recovery(SPR) technique. The modified Z/Z error estimate p-refinement is different from the conventional approach because the high order shape functions based on integrals of Legendre polynomials are used to interpolate displacements within an element, on the other hand, the same order of basis function based on Pascal's triangle tree is also used to interpolate recovered stresses. The least-square method is used to fit a polynomial to the stresses computed at the sampling points. The strategy of finding a nearly optimal distribution of polynomial degrees on a fixed finite element mesh is discussed such that a particular element has to be refined automatically to obtain an acceptable level of accuracy by increasing p-levels non-uniformly or selectively. It is noted that the error decreases rapidly with an increase in the number of degrees of freedom and the sequences of p-distributions obtained by the proposed error indicator closely follow the optimal trajectory.

• ETRI Journal
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• v.33 no.4
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• pp.487-496
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• 2011

Korea Multi-Purpose Satellite-5 (KOMPSAT-5) is the first satellite in Korea that provides 1 m resolution synthetic aperture radar (SAR) images. Precise orbit determination (POD) using a dual-frequency IGOR receiver data is performed to conduct high-resolution SAR images. We suggest orbit determination strategies based on a differential GPS technique. Double-differenced phase observations are sampled every 30 seconds. A dynamic model approach using an estimation of general empirical acceleration every 6 minutes through a batch least-squares estimator is applied. The orbit accuracy is validated using real data from GRACE and KOMPSAT-2 as well as simulated KOMPSAT-5 data. The POD results using GRACE satellite are adjusted through satellite laser ranging data and compared with publicly available reference orbit data. Operational orbit determination satisfies 5 m root sum square (RSS) in one sigma, and POD meets the orbit accuracy requirements of less than 20 cm and 0.003 cm/s RSS in position and velocity, respectively.

• Proceedings of the KIEE Conference
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• 1993.07b
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• pp.784-786
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• 1993

Recently, in the industrial applications, the sensorless system is developed, but the sensorless system is required to have robustness for the measurement noise and disturbance. In this paper, for the sensorless system, the method of designing a robust sliding mode observer taking account of the ability of disturbance and noise attenuation is presented. Also, the strategy for the estimation of rotor flux using the sliding mode observer, which is robust to the measurement noise, is described. Robustness are achieved by assigning the pole of the the system during the sliding motion in such a way as to minimize the effects of the disturbances on the rotor flux estimation error. Finally, using worst case desist and LQC(least square error design), the sliding mode absolver is verified by computer simulations.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.4
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• pp.263-276
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• 2021

This study examines the effect of international capital flows on corporate capital structures in Vietnam by analyzing panel data from all non-financial listed firms from 2005 to 2014 using pooled ordinary least square (OLS) with a variance estimator. The analysis includes a comparison of the signs and significance of the variable coefficients from the perking order and static trade-off theories to the empirical results to determine the optimum approach to the corporate capital structure given Vietnam's high-inflation environment. The results indicate that international capital flows have a positive relation to the debt ratio in the long term, and the relationship is more robust for 2005-2009 than for 2010-2014. Corporate capital structures adjusted to changes in the business environment in different sub-periods (2005-2009 and 2010-2014). When the economic environment became more favorable, the pecking order theory's predictive power increased, and that of trade-off theory lessened. Manufacturing and non-manufacturing firms required different capital structure decisions to fuel their operations and grow under foreign competition. The analysis demonstrates that firms should intensify their use of long-term debt relative to the availability of capital, which is an implication not only for firms in particular but also for industrial innovation overall.

• International Journal of Korean Welding Society
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• v.1 no.2
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• pp.12-17
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• 2001

With the advance of the robotic welding process, procedure optimization that selects the welding procedure and predicts bead width that will be deposited is increased. A major concern involving procedure optimization should define a welding procedure that can be shown to be the best with respect to some standard and chosen combination of process parameters, which give an acceptable balance between production rate and the scope of defects for a given situation. This paper presents a new algorithm to establish a mathematical model f3r predicting bead width through a neural network and multiple regression methods, to understand relationships between process parameters and bead width, and to predict process parameters on bead width for GMA welding process. Using a series of robotic arc welding, additional multi-pass butt welds were carried out in order to verify the performance of the neural network estimator and multiple regression methods as well as to select the most suitable model. The results show that not only the proposed models can predict the bead width with reasonable accuracy and guarantee the uniform weld quality, but also a neural network model could be better than the empirical models.

• Journal of the Korean Data and Information Science Society
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• v.21 no.6
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• pp.1171-1180
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• 2010

It is well known that the asymptotic convergence rates of nonparametric regression estimator gets worse as the dimension of covariates gets larger. One possible way to overcome this problem is reducing the dimension of covariates by using single index models. Two coefficient estimation methods in single index models are introduced. One is semiparametric least square estimation method, which tries to find approximate solution by using iterative computation. The other one is weighted average derivative estimation method, which is non-iterative method. Both of these methods offer the parametric convergence rate to normal distribution. However, practical comparison of these two methods has not been done yet. In this article, we compare these methods by examining the variances of estimators in various models.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.3
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• pp.466-472
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• 2008

The paper concerns Fuzzy C-Means clustering based fuzzy neural networks (FCM-FNN) and the optimization of the network is carried out by means of hierarchal fair competition-based parallel genetic algorithm (HFCPGA). FCM-FNN is the extended architecture of Radial Basis Function Neural Network (RBFNN). FCM algorithm is used to determine centers and widths of RBFs. In the proposed network, the membership functions of the premise part of fuzzy rules do not assume any explicit functional forms such as Gaussian, ellipsoidal, triangular, etc., so its resulting fitness values directly rely on the computation of the relevant distance between data points by means of FCM. Also, as the consequent part of fuzzy rules extracted by the FCM-FNN model, the order of four types of polynomials can be considered such as constant, linear, quadratic and modified quadratic. Since the performance of FCM-FNN is affected by some parameters of FCM-FNN such as a specific subset of input variables, fuzzification coefficient of FCM, the number of rules and the order of polynomials of consequent part of fuzzy rule, we need the structural as well as parametric optimization of the network. In this study, the HFCPGA which is a kind of multipopulation-based parallel genetic algorithms(PGA) is exploited to carry out the structural optimization of FCM-FNN. Moreover the HFCPGA is taken into consideration to avoid a premature convergence related to the optimization problems. The proposed model is demonstrated with the use of two representative numerical examples.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.6
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• pp.673-679
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• 2012

Vehicle rollover is a serious kind of accident, particularly for sport utility vehicles, and its occurrence can be minimized by utilizing active rollover prevention systems. The performance of these protection systems is very sensitive to vehicle inertial parameters such as the vehicle's mass and center of mass. These parameters vary with the number of passengers and in different load situations. In this paper, a unified method for vehicle mass estimation is proposed that takes into account the available driving conditions. Three estimation algorithms are developed based on longitudinal, lateral, and vertical vehicle motion, respectively. Then, the three algorithms are combined to extract information on the vehicle's mass during arbitrary vehicle maneuvering. The performance of the proposed vehicle mass estimation method is demonstrated through real-time experiments.

• 한국태양에너지학회:학술대회논문집
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• 2012.03a
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• pp.419-424
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• 2012

This paper discusses the state-of-the-art techniques in real-time state estimation for the Smart Microgrids. The most popular method used in traditional power system state estimation is a Weighted Least Square(WLS) algorithm which is based on Maximum Likelihood(ML) estimation under the assumption of static system state being a set of deterministic variables. In this paper, we present a survey of dynamic state estimation techniques for Smart Microgrids based on Belief Propagation (BP) when the system state is a set of stochastic variables. The measurements are often too sparse to fulfill the system observability in the distribution network of microgrids. The BP algorithm calculates posterior distributions of the state variables for real-time sparse measurements. Smart Microgrids are modeled as a factor graph suitable for characterizing the linear correlations among the state variables. The state estimator performs the BP algorithm on the factor graph based the stochastic model. The factor graph model can integrate new models for solar and wind correlation. It provides the Smart Microgrids with a way of integrating the distributed renewable energy generation. Our study on Smart Microgrid state estimation can be extended to the estimation of unbalanced three phase distribution systems as well as the optimal placement of smart meters.

• The Korean Journal of Applied Statistics
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• v.23 no.5
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• pp.967-975
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• 2010

Since the least squares estimation is not appropriate when multicollinearity exists among the regressors of the linear regression model, the principal components regression is used to deal with the multicollinearity problem. This article suggests a new procedure for the selection of suitable principal components. The procedure is based on the condition index instead of the eigenvalue. The principal components corresponding to the indices are removed from the model if any condition indices are larger than the upper limit of the cutoff value. On the other hand, the corresponding principal components are included if any condition indices are smaller than the lower limit. The forward inclusion method is employed to select proper principal components if any condition indices are between the upper limit and the lower limit. The limits are obtained from the linear model which is constructed on the basis of the conjoint analysis. The procedure is evaluated by Monte Carlo simulation in terms of the mean square error of estimator. The simulation results indicate that the proposed procedure is superior to the existing methods.