• 대한수학회지
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• 제34권4호
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• pp.895-909
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• 1997

We state the necessary conditions on optimizing the parameters which the damping differential operators contain in abstract linear damped second order evolution equation on the Gelfand five fold.

• 대한전기학회논문지
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• 제35권12호
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• pp.567-572
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• 1986

In this paper, a new type of step motor with two degree of mechanical freedom, which is named rotary-Linear Step Motor(RLSM), is presented. Its rotor axis can perform linear and rotary motions either separately or simultaneously. This paper discribes the design of RLSM using finite element method in which the magnetic saturation effect of the iron core is taken into account. The design parameters such as torques, forces and inductances are obtained from the computed magnetic vector potentials. A new type of Rotary-Linear Step Motor was constructed. The calculated parameters agree well with measurements.

• Journal of the Korean Statistical Society
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• 제28권3호
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• pp.325-336
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• 1999

Collinearity among independent variables can have severe effects on the precision of response estimation for some region of interest in the experiments with mixture. A method of finding optimal linear restriction on regression parameter in linear model for mixture experiments in the sense of minimizing integrated mean squared error is studied. We use the formulation of optimal restrictions on regression parameters for estimating responses proposed by Park(1981) by transforming mixture components to mathematically independent variables.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1990년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 26-27 Oct. 1990
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• pp.1424-1429
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• 1990

This paper presents an indirect adaptive control scheme for discrete linear systems whose parameters are not necessrily slowly varying. It is assumed that system parameters are modelled as linear combinations of known bounded functions with unknown constant coefficients. Unknown coefficients are estimated using a recursive least squares algorithm with a dead zone and a forgetting factor. A control law which makes the estimated model exponentially stable is constructed. With this control law and a state observer, all based on the parameter estimates, it is shown that the resulting closed-loop system is globally stable and robust to bounded external disturbances and small unmodelled plant uncertainties.

• 한국정밀공학회지
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• 제17권3호
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• pp.129-135
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• 2000

Air-dynamic bearings are increasingly used in supporting small high-speed rotating bodies. This study investigates the effects of design parameters on the axial stiffness of spiral-grooved air bearings of various curvatures. Design parameters are fundamental clearance, groove depth, and bearing number. The pressure distribution at the clearance between the stator and rotor of the bearing is obtained by solving the Reynolds equation, and the supporting load and the axial linear stiffness are calculated from the pressure distribution. It is found that a larger curvature increases the axial linear stiffness more and that there exist an optimal groove depth for the linear stiffness of the air bearing. It is also found that the linear stiffness has a linear relationship with the bearing number.

• Journal of the Korean Data and Information Science Society
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• 제24권2호
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• pp.391-399
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• 2013

Partially linear regression is capable of providing more complete description of the linear and nonlinear relationships among random variables. In support vector regression (SVR) the hyper-parameters are known to affect the performance of regression. In this paper we propose an iterative reweighted least squares (IRWLS) procedure to solve the quadratic problem of partially linear support vector regression with a modified loss function, which enables us to use the generalized approximate cross validation function to select the hyper-parameters. Experimental results are then presented which illustrate the performance of the partially linear SVR using IRWLS procedure.

• 제어로봇시스템학회논문지
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• 제8권5호
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• pp.351-356
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• 2002

This papaer deals with a model reduction problem for linear systems with time varying parameters. For this problem, a controllability Grammian and an observability Grammian are introduced and computed by solving linear matrix inequalities. Using the controllability/observability Grammian, a balanced state space realization for linear parameter varying systems is obtained. From the balanced state space realization, a reduced model can be obtained by truncating not only states but also time varying parameters and an upper bound of the model reduction error is derived as well.

• Journal of Animal Science and Technology
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• 제48권2호
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• pp.161-168
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• 2006

Genetic parameters for linear type and composite traits were estimated by using Bayesian inference via Gibbs sampling with a multiple threshold animal model in Holstein cows. Fifteen linear type traits and 5 composite traits were included to estimate genetic variance and covariance components in the model. In this study, 30,204 records were obtained in the cows from 305 sires. Heritability estimates for linear type traits had the estimates as high as 0.28~0.64. Heritability estimates for composite traits were also high, when the traits were assumed to be categorical traits. Final score was more correlated with the composite traits than with the linear type traits.

• 한국지진공학회:학술대회논문집
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• 한국지진공학회 2006년도 학술발표회 논문집
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• pp.519-526
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• 2006

This paper compares the seismic response control performance of linear and non-linear models fer tuned liquid damper (TLD). The existing linear and nonlinear TLD models were used for the numerical analysis of single degree of freedom (SDOF) and multi degree of freedom (MDOF) systems with TLD. The nonlinear model considers the variation of the frequency and damping of the TLD with varying excitation amplitude while the linear one has the invariant parameters. Numerical analysis results from SDOF systems indicate that the nonlinear model shows about 5% better control performance than linear one when the mass ratio is 2% and the optimal parameters for reducing peak responses are dependent on the characteristics of the excitation earthquake loads.

• 전기학회논문지
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• 제57권6호
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• pp.915-923
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• 2008

This paper focuses on optimal tuning of nonlinear parameters of a dual-input power system stabilizer(dual-input PSS), which can improve the system damping performance immediately following a large disturbance. Until recently, various PSS models have developed to bring stability and reliability to power systems, and some of these models are used in industry applications. However, due to non-smooth nonlinearities from the interaction between linear parameters(gains and time constants of linear controllers) and nonlinear parameters(saturation output limits), the output limit parameters cannot be determined by the conventional tuning methods based on linear analysis. Only ad hoc tuning procedures('trial and error' approach) have been used. Therefore, the steepest descent method is applied to implement the optimal tuning of the nonlinear parameters of the dual-input PSS. The gradient required in this optimization technique can be computed from trajectory sensitivities in hybrid system modeling with the differential-algebraic-impulsive-switched(DAIS) structure. The optimal output limits of the dual-input PSS are evaluated by time-domain simulation in both a single machine infinite bus(SMIB) system and a multi-machine power system in comparison with those of a single-input PSS.