• Structural Engineering and Mechanics
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• v.22 no.4
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• pp.401-418
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• 2006

Solutions to the problems of structural parameter estimation from modal response using leastsquares minimization of force or displacement residuals are generally sensitive to noise in the response measurements. The sensitivity of the parameter estimates is governed by the physical characteristics of the structure and certain features of the noisy measurements. It has been shown that the regularization method can be used to reduce effects of the measurement noise on the estimation error through adding a regularization function to the parameter estimation objective function. In this paper, we adopt the regularization function as the Euclidean norm of the difference between the values of the currently estimated parameters and the a priori parameter estimates. The effect of the regularization function on the outcome of parameter estimation is determined by a regularization factor. Based on a singular value decomposition of the sensitivity matrix of the structural response, it is shown that the optimal regularization factor is obtained by using the maximum singular value of the sensitivity matrix. This selection exhibits the condition where the effect of the a priori estimates on the solutions to the parameter estimation problem is minimal. The performance of the proposed algorithm is investigated in comparison with certain algorithms selected from the literature by using a numerical example.

• Communications of the Korean Mathematical Society
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• v.11 no.4
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• pp.1147-1157
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• 1996

Nonlinear ill-posed problems arise in many application including parameter estimation and inverse scattering. We introduce a least squares regularization method to solve nonlinear ill-posed problems with constraints robustly and efficiently. The regularization method uses Trust-Region approach to handle the constraints on variables. The Generalized Cross Validation is used to choose the regularization parameter in computational tests. Numerical results are given to exhibit faster convergence of the method over other methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.8
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• pp.1310-1318
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• 2002

In this study, we developed a 3D facial modeler and animator that will not use the existing method by 3D scanner or camera. Without expensive image-input equipments, we can easily create 3D models only using front and side images. The system is available to animate 3D facial models as we connect to animation server on the WWW which is independent from specific platforms and softwares. It was implemented using Java 3D API. The facial modeler detects MPEG-4 FDP(Facial Definition Parameter) feature points from 2D input images, creates 3D facial model modifying generic facial model with the points. The animator animates and renders the 3D facial model according to MPEG-4 FAP(Facial Animation Parameter). This system can be used for generating an avatar on WWW.

• Journal of Mechanical Science and Technology
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• v.17 no.4
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• pp.469-476
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• 2003

Despite of considerable research results or uniaxial tension creep available for superalloys, few studies have been made on high temperature creep using the Initial Stram Method (ISM) In this paper, the real-time prediction of high temperature creep strength and creep lift for the nickel-based superalloy Udimet 720 (high-temperature and high-pressure gas turbine engine materials) was performed on round-bar type specimens under pure static load at the temperatures of 538$^{\circ}C$. 649$^{\circ}C$, and 704$^{\circ}C$. The predictive equation derived from the ISM in creep tests showed better reliability than those from LMP (Larson-Miller Parameter) and LMP-lSM (Larson Miller Parameter-Initial Strain Method) specially for long time creep prediction (10$^3$∼10$\^$5/h).

• Korean Journal of Computational Design and Engineering
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• v.3 no.4
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• pp.243-250
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• 1998

In parametric surface interpolation, the choice of the parameter values to the set of scattered points makes a great deal of difference in the resulting surface. A new method is developed and tested for the parametrization in NURBS surface global interpolation. This method uses the parameter value at the maximal value of relevant rational basis function, to assign the parameter values to the arbitrary set of design data. This method gives us several important advantages in geometric modeling, the freedom of the selection of knot values, the feasible transformation of the data set to the matrix, the possibility of affinite transformation between the design data and generated surface, etc.

• Journal of Korean Society on Water Environment
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• v.40 no.4
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• pp.179-194
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• 2024

Biophysicochemical processes in water environments and treatment systems have been great concerns of engineers and scientists for controlling the fate and transport of contaminants. These processes are practically formulated as mathematical models written in coupled differential equations. However, because these process-based mathematical models consist of a large number of model parameters, they are complicated in analytical or numerical computation. Users need to perform substantial trials and errors to achieve the best-fit simulation to measurements, relying on arbitrary selection of fitting parameters. Therefore, this study adopted a Bayesian calibration method to estimate best-fit model parameters in a systematic way and evaluated the applicability of the calibration method to biophysicochemical processes of water environments and treatment systems. The Bayesian calibration method was applied to the microbial growth-decay kinetics and flocculation kinetics, of which experimental data were obtained with batch kinetic experiments. The Bayesian calibration method was proven to be a reasonable, effective way for best-fit parameter estimation, demonstrating not only high-quality fitness, but also sensitivity of each parameter and correlation between different parameters. This state-of-the-art method will eventually help scientists and engineers to use complex process-based mathematical models consisting of various biophysicochemical processes.

• Transactions on Control, Automation and Systems Engineering
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• v.4 no.4
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• pp.289-295
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• 2002

In this paper, a linear matrix inequality(LMI)-based robust control method, which combines model predictive control(MPC) with the feedback linearization(FL), is presented for constrained nonlinear systems with parameter uncertainty. The design procedures consist of the following 3 steps: Polytopic description of nonlinear system with a parameter uncertainty via FL, Mapping of actual input constraint by FL into constraint on new input of linearized system, Optimization of the constrained MPC problem based on LMI. To verify the performance and usefulness of the control method proposed in this paper, some simulations with application to a flexible single link manipulator are performed.

• Journal of applied mathematics & informatics
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• v.29 no.3_4
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• pp.813-829
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• 2011

In this article, we present a numerical scheme for solving singularly perturbed (i.e. highest -order derivative term multiplied by small parameter) Burgers-Huxley equation with appropriate initial and boundary conditions. Most of the traditional methods fail to capture the effect of layer behavior when small parameter tends to zero. The presence of perturbation parameter and nonlinearity in the problem leads to severe difficulties in the solution approximation. To overcome such difficulties the present numerical scheme is constructed. In construction of the numerical scheme, the first step is the dicretization of the time variable using forward difference formula with constant step length. Then, the resulting non linear singularly perturbed semidiscrete problem is linearized using quasi-linearization process. Finally, differential quadrature method is used for space discretization. The error estimate and convergence of the numerical scheme is discussed. A set of numerical experiment is carried out in support of the developed scheme.

• Wind and Structures
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• v.31 no.4
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• pp.311-319
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• 2020

India is a developing nation and heavily spends on the development of wind power plants to meet the national energy demand. The objective of this paper is to investigate wind power potential of Ennore site using wind data collected over a period of two years by three parameter Weibull distribution. The Weibull parameters are estimated using maximum likelihood, least square method and moment method and the accuracy is determined using R² and root mean square error values. The site specific capacity factor is calculated by the mathematical model developed by three parameter Weibull distribution at different hub heights above the ground level. At last, the wind energy economic analysis is carried out using capacity factor at 30 m, 40 m and 50 m height for different wind turbine models. The analysis showed that the site has potential to install utility wind turbines to generate energy at the lowest cost per kilowatt-hour at height of 50 m. This research provides information of wind characteristics of potential sites and helps in selecting suitable wind turbine.

• Journal of Power Electronics
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• v.11 no.4
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• pp.418-423
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• 2011

This paper presents a simulation model and a parameter identification scheme of an induction motor drive for electric vehicle. The induction motor in automotive applications should operate in very high efficiency and achieve the maximum-torque-per-ampere (MTPA) feature even with saturated magnetic flux under very high torque. The indirect vector control which is typically adopted in traction drive system requires precise information of motor parameters, particularly rotor time constants. This work models an induction motor considering magnetic saturation and proposes an empirical identification method using the current controller in the synchronous reference frame. The proposed method is applied to a 22kW-rated induction motor for electric vehicle.