• Journal of the Korean Statistical Society
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• v.25 no.2
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• pp.277-288
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• 1996

The minimum aberration criterion is commonly used for selecting good fractional factorial designs. In this paper we give same necessary conditions for $3^{n-k}$ fractional factorial designs. We obtain minimum aberration $3^{n-k}$ designs for k = 2 and any n. For k > 2, minimum aberration designs have not found yet. As an alternative, we select a design with minimum aberration among minimum-variance designs.

• International Journal of Control, Automation, and Systems
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• v.4 no.3
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• pp.281-292
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• 2006

The design and implementation of Generalized Minimum Variance control laws for nonlinear multivariable systems that can include severe nonlinearities is considered. The quadratic cost index minimised involves dynamically weighted error and nonlinear control signal costing terms. The aim here is to show the controller obtained is simple to design and implement. The features of the control law are explored. The controller obtained includes an internal model of the process and in one form is a nonlinear version of the Smith Predictor.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.18 no.12
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• pp.1217-1223
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• 2008

Vehicle vibration mostly originates from the road excitation and causes discomfort, fatigue and even injury to a driver. Vehicle ride comfort is one of the most important performance indices to achieve a high-quality vehicle design. Since design parameter variations inevitably result in the vehicle ride comfort variance, the variance characteristics should be analyzed in the early design stage of the vehicle. The vehicle ride comfort is often defined by an index which employs a weighted RMS value of the acceleration PSD of a seat position. The design solution is obtained through two steps in this study. An optimization problem to obtain a minimum ride comfort index is solved first. Then another optimization problem to obtain minimum variance of the ride comfort index is solved. For the optimization problems, the equations of motion and the sensitivity equations are derived basing on a 5-DOF vehicle model. The numerical results show that an optimal solution for the minimum ride comfort is not necessarily same as that of the minimum variance of the ride comfort.

• Journal of the Korean Institute of Telematics and Electronics
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• v.25 no.4
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• pp.372-378
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• 1988

The elimination of offsets such as those induced by load disturbance is a principal requirement in the control of industrial processes. In this paper we propose a self-tuning minimum variance control in the two tuypes of k-incremental and integrating form. Since the objective of control design in this paper is a generalized minimum variance control, it can be applied to nonminimum phase system. And we compare the proposed algorithm wiht that of the positional self-tuning control and show that it can also be applied to nonminimum phase system by computer simulation.

• Journal of Institute of Control, Robotics and Systems
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• v.2 no.1
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• pp.14-20
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• 1996

This paper presents a self tuning method of a velocity type PID controller for minimum or non-minimum phase systems with time delays. The velocity type PID control structure is determined in the process of minimizing the variance of the auxilliary output, and self tuning effect is achieved through the recursive least square algorithm at the parameter estimation stage and also through the Robbins-Monro algorithm at the stage of optimizing a design parameter. This method is simple and effective compared with other existing methods[1,2]. Numerical examples are included to illustrate the procedure and to show the performance of the control system.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.629-632
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• 1997

This paper presents the design method of controller which is combined Genetic Algorithms with the Generalized minimum variance self tuning controller. It is shown that the controllers adapts to changes in the system parameters with time delays and noises. The self tuning effect is achieved through the recursive least square algorithm at the parameter estimation stage and also through the Robbins-Monro algorithm at the stage of optimizing a polynomial parameters. The computer simulation results are presented to illustrate the procedure and to show the performance of the control system.

• Journal of Korean Institute of Industrial Engineers
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• v.41 no.6
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• pp.521-529
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• 2015

Statistical process control techniques have been greatly implemented in industries for improving product quality and saving production costs. As a primary tool among these techniques, control charts are widely used to detect the occurrence of assignable causes. In most works on the control charts it considered the problem of monitoring the mean and variance, and the quality characteristic of interest is normally distributed. In some situations monitoring of the minimum and maximum values is more important and the quality characteristic of interest is the Weibull distribution rather than a normal distribution. In this paper, we consider the statistical design of minimum and maximum control charts when the distribution of the quality characteristic of interest is Weibull. The proposed minimum and maximum control charts are applied to the wind data. The results of the application show that the proposed method is more effective than traditional methods.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1055-1056
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• 2011

This paper deals with optimum design criteria for maximum torque density & minimum current density of a single phase line-start permanent-magnet motor (LSPMM) using RSM (Response Surface Methodology) & FEM (Finite Element Method). The focus of this paper is to find a design solution through the comparison of torque density and minimum current density resulting from rotor shape variations. And then, a central composite design (CCD) mixed resolution is introduced, and analysis of variance (ANOVA) is conducted to determine the significance of the fitted regression model.

• International Journal of Quality Innovation
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• v.1 no.1
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• pp.32-37
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• 2000

In an industrial process, the proper objective is to find the optimal operating conditions with minimum process variability around the target. Vining and Myers(1990) suggest to use the separate model for the mean response and the process varian linear predictor ${\tau}_i={\log}\;{\sigma}^2_i$ is unknown and should be estimated. Noting that the variance of $\hat{{\tau}_i}$ is heterogeneous, another appropriate D-optimality criterion $D_3$ based on the method of generalized least squares is proposed in this paper.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.8
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• pp.39-46
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• 1994

In this paper a state reconstruction filter for linear discrete-time stochastic systems with unknown inputs and noises is presented. The suggested filter can estimate the system state vector and the unknown inputs simultaneously As an extension of the filter a fault diagnosis filter for linear discrete-time stochastic systems with unknown inputs and noises is presented for each filters the optimal gain determination methods which minimize the variance of the state reconstruction errorare presented. Finally the usability of the filtersis shown via numerical examples.