• Journal of KSNVE
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• v.7 no.2
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• pp.223-229
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• 1997

Presented is a method to estimate system parameters of a system with polynomial non-linerities from the measured higher order frequency response functions. Higher order FRFs can be measured on some restricted regions by sinusoidally exciting a non-linear system with various input amplitudes and measuring the response component at the excitation frequency. These higher order FRFs can be expressed in terms of system parameter, and the system parameters can be estimated from the measured FRFs. Since the expressions for higher order FRFs are complicated, system parameters can be estimated from them using an optimization technique. The present method has been applied to a simulated single degree of freedom system with non-linear stiffness and damping, and has estimated accurate system parameters.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.7
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• pp.644-649
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• 2006

In this paper, a methodology for estimating the model parameters of a discrete-time system and tuning a digital PID controller based on the estimated model and a genetic algorithm is presented. To deal with optimization problems regarding parameter estimation and controller tuning, pseudo-parallel genetic algorithms(PPGAs) are used. The parameters of a discrete-time system are estimated using both the model adjustment technique and a PPGA. The digital PID controller is described by the pulse transfer function and then its three gains are tuned based on both the model reference technique and another PPGA. A set of experimental works on two processes are carried out to illustrate the performance of the proposed method.

• Journal of Environmental Impact Assessment
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• v.15 no.6
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• pp.435-441
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• 2006

For the calibration of rainfall-runoff model, automatic calibration methods are used instead of manual calibration to obtain the reliable modeling results. When mathematical programming techniques such as linear programming and nonlinear programming are applied, there is a possibility to arrive at the local optimum. To solve this problem, genetic algorithm is introduced in this study. It is very simple and easy to understand but also applicable to any complicated mathematical problem, and it can find out the global optimum solution effectively. The objective of this study is to develope a parameter optimization program that integrate a genetic algorithm and a rainfall-runoff model. The program can calibrate the various parameters related to the runoff process automatically. As a rainfall-runoff model, SWMM is applied. The automatic calibration program developed in this study is applied to the Jangcheon watershed flowing into the Youngrang Lake that is in the eutrophic state. Runoff surveys were carried out for two storm events on the Jangcheon watershed. The peak flow and runoff volume estimated by the calibrated model with the survey data shows good agreement with the observed values.

• Journal of KIISE:Software and Applications
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• v.37 no.9
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• pp.694-705
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• 2010

The mutation operation is the main operation in the evolutionary programming which has been widely used for the optimization of real valued function. In general, the mutation operation utilizes both a probability distribution and its parameter to change values of variables, and the parameter itself is subject to its own mutation operation which requires other parameters. However, since the optimal values of the parameters entirely depend on a given problem, it is rather hard to find an optimal combination of values of parameters when there are many parameters in a problem. To solve this shortcoming at least partly, if not entirely, in this paper, we propose a new mutation operation in which the parameter for the variable mutation is theoretically estimated from the self-adaptive perspective. Since the proposed algorithm estimates the scale parameter of the Cauchy probability distribution for the mutation operation, it has an advantage in that it does not require another mutation operation for the scale parameter. The proposed algorithm was tested against the benchmarking problems. It turned out that, although the relative superiority of the proposed algorithm from the optimal value perspective depended on benchmarking problems, the proposed algorithm outperformed for all benchmarking problems from the perspective of the computational time.

• Proceedings of the Korean Geotechical Society Conference
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• 1994.09a
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• pp.191-198
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• 1994

This paper deals with a feedback system for the estimation of geotechnical parameters in underground construction works. The Ordinary Least Square (OLS) Optimization Method is utilized and combined with Finite Element Program so that optimum values of ground properties can be estimated. The preperties that can be estimated are Young's and Brown's failure criteria is proposed.

• 한국방재학회:학술대회논문집
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• 2008.02a
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• pp.723-726
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• 2008

It is not always easy to estimate the parameters in hydrologic models due to insufficient hydrologic data when hydraulic structures are designed or water resources plan are established, uncertainty analysis, therefore, are inevitably needed to examine reliability for the estimated results. With regard to this point, this study applies a Bayesian Markov Chain Monte Carlo scheme to the NWS-PC rainfall-runoff model that has been widely used, and a case study is performed in Soyang Dam watershed in Korea. The NWS-PC model is calibrated against observed daily runoff, and thirteen parameters in the model are optimized as well as posterior distributions associated with each parameter are derived. The Bayesian Markov Chain Monte Carlo shows a improved result in terms of statistical performance measures and graphical examination. The patterns of runoff can be influenced by various factors and the Bayesian approaches are capable of translating the uncertainties into parameter uncertainties. One could provide against an expected runoff event by utilizing information driven by Bayesian methods. Therefore, the rainfall-runoff analysis coupled with the uncertainty analysis can give us an insight in evaluating flood risk and dam size in a reasonable way.

• 한국신재생에너지학회:학술대회논문집
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• 2009.06a
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• pp.512-515
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• 2009

This research describes on airfoil shape design, crucial to core technique and algorithm optimization for the wind turbine blade development. We grasped the parameter to define the airfoil shape in the wind turbine blade and aircraft, and the important performance characteristic of the airfoil. The airfoil shape function is selected by studying which is suitable for wind turbine blade airfoil development. The selected method is verified by to compare the generated airfoil shape with base airfoil. The new airfoils were created by the selecting shape function based on the well-known airfoil for wind turbine blades. In addition, we performed aerodynamic analysis about the generated airfoils by XFOIL and estimated the point of difference in the airfoil shape parameter using the aerodynamic performance results which is compared with basic airfoil. This result data applies to the fundamental research for a wind turbine blade optimization design and accomplished the aerodynamic analysis manual.

• Journal of the military operations research society of Korea
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• v.28 no.1
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• pp.47-66
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• 2002

Instead of using signal-to-noise ratio, we attempt to optimize both the mean and variance responses using dual response optimization technique. The alternative experimental strategy analyzes a robust parameter design problem to obtain the best settings that give a target condition on the mean while minimizing its variance. The mean and variance are treated as the two responses of interest to be optimized. Unlike to the crossed array and combined array approaches, our experimental setup requires replicated runs for each control factor's treatment under noise sampling. When the postulated response models are true, they enable the coefficients to be estimated and the desired performance measure to be analyzed more efficiently. The procedure and illustrative example are given for the dual response optimization techniques of nonlinear goal programming.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2005.06a
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• pp.314-320
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• 2005

In this paper, a methodology for estimating the parameters of a discrete-time system and designing a digital PID controller based on the estimated model and a genetic algorithm is presented. To deal with optimization problems occurring regarding parameter estimation and controller design, a pseudo parallel genetic algorithm (PPGA) is used. The parameters of a discrete-time system are estimated using both the model technique and a PPGA. The digital PID controller is described by the pulse transfer function and its parameters are tuned based on both the model reference technique and another PPGA. A set of experimental works on two processes are carried out to illustrate the performance of the proposed method.

• Journal of Electrical Engineering and Technology
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• v.1 no.3
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• pp.302-307
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• 2006

The purpose of this paper is to develop an improved three-phase transformer model for ATP and parameter estimation methods that can efficiently utilize the limited available information such as factory test reports. In this paper, improved topologically-correct duality-based models are developed for three-phase autotransformers having shell-form cores. The problem in the implementation of detailed models is the lack of complete and reliable data. Therefore, parameter estimation methods are developed to determine the parameters of a given model in cases where available information is incomplete. The transformer nameplate data is required and relative physical dimensions of the core are estimated. The models include a separate representation of each segment of the core, including hysteresis of the core, ${\lambda}-i$ saturation characteristic and core loss.