• The Transactions of the Korean Institute of Electrical Engineers B
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• v.49 no.7
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• pp.468-474
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• 2000

This paper presents an investigation of the parameter modeling on the basis of Finite Element (FE) analysis in which the variable frequency characteristic in field weakening is considered in Interior Permanent Magnet Synchronous Motors (IPMSM). The parameters of IPMSM have nonlinear characteristics not only in accordance with the load variation but also with the current phase angle of a system fed inverter. From the results of FE analysis, the performances of torque and speed-power are simulated and the validity of the proposed FE analysis is compared with experimental results.

• Proceedings of the KIEE Conference
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• 1998.07a
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• pp.241-243
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• 1998

This paper describes a new method for the faster shape optimization of the electromagnetic devices. In a conventional iterative method of shape design optimization using design sensitivity based on a finite element method, meshes for a new shape of the model are generated and a discretized system equation is solved using the meshes in each iteration. They cause much design time. To save this time, a polynomial approximation of the finite element solution with respect to the geometric design parameters using Taylor expansion is constructed. This approximate state variable expressed explicitly in terms of design parameters is employed in a gradient-based optimization method. The proposed method is applied to the shape design of quadrupole magnet.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.20 no.3
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• pp.979-987
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• 1996

A theoretical study for the laminar round jet diffusion flame impinging on the wall was carried out to predict the characteristics and structure of impinging jet flame and heat transfer to the wall. Finite chemistry via Arrhenius equation was adopted as the combustion model. All the transport properties were considered as the variable depending on the temperature and composition. For the parametric study, the distance from nozzle to perpendicular wall and Reynolds number at nozzle exit were chosen as the major parameters. As the results of the present study, the characteristics of flow field and the distributions of temperature, density and each chemical species were obtained. The heat transfer rate from flame to the wall and the effective heating area were calculated to investigate the influence of the major parameters on the heat transfer characteristics.

• Proceedings of the Safety Management and Science Conference
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• 1999.11a
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• pp.247-257
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• 1999

There are several models for process quality assurance by quality system(ISO 9000), process capability analysis, acceptance control chart and so on. When a high level process capability has been achieved, it takes a long time to monitor the process shift, so it is sometimes necessary to develop a quicker monitoring system. To achieve a quicker quality assurance model for high-reliability process, this paper presents a model for process quality assurance when the fraction nonconforming is very small. We design an acceptance control chart based on variable quality characteristic and time-censored accelerated testing. The distribution of the characteristics is assumed to be normal of lognormal with a location parameter of the distribution that is a linear function of a stress. The design parameters are sample size, control limits and sample proportions allocated to low stress. These parameters are obtained under minimization of the relative variance of the MLE of location parameter subject to APL and RPL constraints.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.581-585
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• 2010

This study introduced a Bayesian based frequency analysis in which the statistical trend seasonal analysis for hydrologic extreme series is incorporated. The proposed model employed Gumbel and GEV extreme distribution to characterize extreme events and a fully coupled bayesian frequency model was finally utilized to estimate design rainfalls in Seoul. Posterior distributions of the model parameters in both trend and seasonal analysis were updated through Markov Chain Monte Carlo Simulation mainly utilizing Gibbs sampler. This study proposed a way to make use of nonstationary frequency model for dynamic risk analysis, and showed an increase of hydrologic risk with time varying probability density functions. In addition, full annual cycle of the design rainfall through seasonal model could be applied to annual control such as dam operation, flood control, irrigation water management, and so on. The proposed study showed advantage in assessing statistical significance of parameters associated with trend analysis through statistical inference utilizing derived posterior distributions.

• Journal of the Korean Data and Information Science Society
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• v.25 no.3
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• pp.623-632
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• 2014

We shall consider the estimation for the parameter and the right tail probability in a general exponential distribution. We also shall consider the estimation of the reliability P(X < Y ) and the skewness trends of the density function of the ratio X=(X+Y) for two independent general exponential variables each having different shape parameters and known scale parameter. We then shall consider the estimation of the failure rate average and the hazard function for a general exponential variable having the density function with the unknown shape and known scale parameters, and for a bivariate density induced by the general exponential density.

• Journal of Electrical Engineering and Technology
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• v.7 no.6
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• pp.948-953
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• 2012

The characteristics of an induction motor vary with the number of parameters and the performance relationship between the parameters also is implicit. In case of the induction motor design, we generally should estimate many objective physical quantities in the optimization procedure. In this article, the multi objective design optimization based on genetic algorithm is applied for the three phase induction motor. The efficiency, starting torque, and material cost are selected for the objectives. The validity of the design results is also clarified by comparison between calculated results and measured ones.

• Industrial Engineering and Management Systems
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• v.1 no.1
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• pp.79-86
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• 2002

This paper proposes a new algorithm for estimating ARMA model parameters. In estimating ARMA model parameters, several methods such as generalized least square method, instrumental variable method have been developed. Among these methods, the utilization of a bootstrap type algorithm is known as one of the effective approach for the estimation, but there are cases that it does not converge. Hence, in this paper, making use of a cross correlation function and utilizing the relation of structural a priori knowledge, a new bootstrap algorithm is developed. By introducing theoretical relations, it became possible to remove terms, which is liable to include much noise. Therefore, this leads to robust parameter estimation. It is shown by numerical examples that using this algorithm, all simulation cases converge while only half cases succeeded with the previous one. As for the calculation time, judging from the fact that we got converged solutions, our proposed method is said to be superior as a whole.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2003.06a
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• pp.169-172
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• 2003

PID control has been well used for several decade. For PID algorithm, some tuning methods are used and with these parameters, control system is designed. But in some cases various kinds of performance are needed, so variable type of performance index must be utilized so that the designed control system meets the conditions. This paper presents some linear combinational type of performance index and with numerical methods, the PID parameters are obtained. Moreover I-PD type controller is used so that this two degree of freedom controller may give more desirable output characteristics. Simulations are done with MATLAB m file and mdl files.

• The Korean Journal of Applied Statistics
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• v.25 no.6
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• pp.1037-1047
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• 2012

The generalized linear mixed model(GLMM) is widely used in fitting categorical responses of clustered data. In the numerical approximation of likelihood function the normality is assumed for the random effects distribution; subsequently, the commercial statistical packages also routinely fit GLMM under this normality assumption. We may also encounter departures from the distributional assumption on the response variable. It would be interesting to investigate the impact on the estimates of parameters under misspecification of distributions; however, there has been limited researche on these topics. We study the sensitivity or robustness of the maximum likelihood estimators(MLEs) of GLMM for counts data when the true underlying distribution is normal, gamma, exponential, and a mixture of two normal distributions. We also consider the effects on the MLEs when we fit Poisson-normal GLMM whereas the outcomes are generated from the negative binomial distribution with overdispersion. Through a small scale Monte Carlo study we check the empirical coverage probabilities of parameters and biases of MLEs of GLMM.