• Proceedings of the KIPE Conference
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• 1998.10a
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• pp.748-753
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• 1998

Machine parameters need to measure in advance for vector control and theoretical analysis. Therefore, we present the machine parameter measurement scheme, considering stator iron loss, by the single-phase test at standstill condition. Using this method, the d-q axis inductances and the equivalent iron loss resistance in the voltage equation considering stator iron loss are easily measured. This method is simple and does not need to use complex theory and expensive equipment.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.8
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• pp.792-804
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• 1990

This paper describes an efficient optimization algorithm by calculating sensitivity function for power system stabilization. In power system, the dynamic performance of exciter, governor etc. following a disturbance can be presented by a nonlinear differential equation. Since a nonlinear equation can be linearized for small disturbances, the state equation is expressed by a system matrix with system parameters. The objective function for power system operation will be related to the system parameter and the initial state at the optimal control condition for control or stabilization. The object function sensitivity to the system parameter can be considered to be effective in selecting the optimal parameter of the system.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.177-178
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• 2008

An LMI-based method for robust $L_2-L_{\infty}$ filter design is proposed for poly topic uncertain time-delay systems. By using the Projection Lemma and a suitable linearizing transformation, a strict LMI condition for $L_2-L_{\infty}$ filter design is obtained, which does not involve any iterations for design-parameter search, any couplings between the Lyapunov and system matrices, nor any system-dependent filter parameterization. Therefore, the proposed condition enables one to easily adopt, with help of efficient numerical solvers, a parameter-dependent Lyapunov function approach for reducing conservatism, and to design both robust and parameter-dependent filters for uncertain and parameter-dependent time-delay systems, respectively.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.6 s.237
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• pp.815-821
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• 2005

Biaxial low cycle fatigue test was carried out to predict fatigue life under combined axial-torsional-loading condition which is that of in-phase and out-of-phase for CF8M cast stainless steels. Fatemi-Socie(FS) parameter which is based on critical plane approach is not only one of methods but also the best method that can predict fatigue life under biaxial loading condition. But the result showed that, biaxial fatigue life prediction by using FS parameter with several different parameters for the CF8M cast stainless steels is not conservative but best results. So in this present research, we proposed new fatigue life prediction parameter considering effective shear stress instead of FS parameter which considers the maximum normal stress acting on maximum shear strain and its effectiveness was verified.

• Bulletin of the Korean Mathematical Society
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• v.59 no.4
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• pp.979-991
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• 2022

A special class of exponential dispersion models is the class of Tweedie distributions. This class is very significant in statistical modeling as it includes a number of familiar distributions such as Gaussian, Gamma and compound Poisson. A Tweedie distribution has a power parameter p, a mean m and a dispersion parameter 𝜙. The value of the power parameter lies in identifying the corresponding distribution of the Tweedie family. The basic objective of this research work resides in investigating the existence of the implicit estimator of the power parameter of the Tweedie distribution. A necessary and sufficient condition on the mean parameter m, suggesting that the implicit estimator of the power parameter p exists, was established and we provided some asymptotic properties of this estimator.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1843-1846
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• 2004

This paper considers a robust observer-based $H_{\infty}$ controller design method for descriptor systems with parameter uncertainties using just one LMI condition. The sufficient condition for the existence of controller and the controller design method are presented by a perfect LMI condition in terms of all variables using singular value decomposition, Schur complement, and change of variables. Therefore, one of the main advantages is that a robust observer-based $H_{\infty}$ controller is found by solving one LMI condition compared with existing results. Numerical example is given to illustrate the effectiveness of the proposed controller design method.

• Proceedings of the KIPE Conference
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• 2013.11a
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• pp.69-70
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• 2013

By using differential force between left and right wheel, lateral motion can be controlled known as Direct Yaw-moment Control (DYC). In previous researches, DYC control is proposed to increase the stability of the vehicle, but maneuverability has not been discussed sufficiently. The car handling condition which is called the index parameter of maneuverability is dependent on the vehicle velocity and steering angle. To achieve the desired vehicle's cornering path, the car handling condition must be considered sufficiently. In this paper, the novel DYC method is proposed which gives the car handling condition regardless of the longitudinal speed. The proposed controller is based on the PI controller to feedback the curvature parameter. The controlled system shows the advantages of DYC regarding to the reference trajectory by the dual motor system. With respect to the uncontrolled model, the effectiveness of the proposed method is validated by numerical examples.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.7 no.4
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• pp.42-49
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• 1998

This wear debris can be harvested from the lubricants of operating machinery and its morphology is directly related to the damage to the interacting surface from which the particles originated. The morphological identification of wear debris can therefore provide very early detection of a fault and can also often facilitate a diagnosis. The purpose of this study is to attempt the developement of intelligent system for moving condition diagnosis of the machine driving system. The four shape parameter(50% volumetric diameter, aspect, roundness and reflectivity) of war debris are used as inputs to the neural network and learned the moving condition of five values(material3, applied load 1, sliding distance 1). It is shown that identification results depend on the ranges of these shape parameter learned. The three kinds of the wear debris had a different pattern characteristics and recognized the moving condition and materials very well by neural network.

• Journal of Power System Engineering
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• v.11 no.3
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• pp.3-8
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• 2007

The emergency AC power supply system of the nuclear power plant is designed to supply the power to the nuclear reactor at the emergency operating condition. The safety function of the diesel generator at the nuclear power plant is to supply AC electric power to the plant safety system whenever the preferred AC power supply is unavailable. The reliable operation of onsite emergency diesel generator should be ensured by a conditioning monitoring system designed to maintain and monitor and forecast the reliability level of diesel generator. To do this kind of diesel generator condition monitoring we reviewed several operating factors and history of the wolsong unit 3 diesel generator and selected the proper conditioning monitoring operating factors.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 1997.10a
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• pp.135-144
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• 1997

This wear debris can be harvested from the lubricants of operating machinery and its morphology is directly related to the damage to the interacting surfaces from which the particles originated. The morphologies of the wear particles are therefore directly indica- rive of wear processes occuring in machinery and their severity. The neural network was applied to identify wear debris generated from the lubricated moving system. The four parameter(50% volumetric diameter, aspect, roundness and reflectivity) of wear debris are used as inputs to the network and learned the friction condition of five values(material 3, applied load 1, sliding distance 1). It is shown that identification results depend on the ranges of these shape parameter learned. The three kinds of the wear debris had a different pattern characteristic and recognized the friction condition and materials very well by neural network. We dicuss how the network determines difference in wear debris feature, and this approach can be applied to condition diagnosis of the lubricated moving system.