• Journal of Korean Society for Quality Management
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• v.24 no.2
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• pp.44-53
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• 1996

This paper deals with the problem of selecting optimal wear limit and initial process mean in a wear-out process. Consider a material removal processing machinery where 1)there are deffective items by linear shift in the mean of the diameter of to be processed with varing process variance and 2)there can be any failure in the machine tools or to be processed. In the previous studies, the one is analyzed by 'Model of Producing Goods' in quality control area and the other, any failure, is analyzed by 'Model of Producing Services' in reliability area. We propose a new integrated maintenance model, considering the percent defective and the failure rate. A numerical example for the model is given.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.17 no.31
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• pp.91-98
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• 1994

Most of machines are physically or chemically degenerated by continuous usage. There- fore, a preventive maintenance is necessary. Producing defects are caused by process shift in mean and variance which are due to three types of degeneration. We develope the function of process variance from the experimental data and determine the optimal tool wear limit and the initial setting position of tool by considering the percent defective cost and the preventive maintenance cost.

• Structural Engineering and Mechanics
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• v.32 no.1
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• pp.21-36
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• 2009

An importance sampling method is presented for computing the first passage probability of elasto-plastic structures under stochastic excitations. The importance sampling distribution corresponds to shifting the mean of the excitation to an 'adapted' stochastic process whose future is determined based on information only up to the present. A stochastic control approach is adopted for designing the adapted process. The optimal control law is determined by a control potential, which satisfies the Bellman's equation, a nonlinear partial differential equation on the response state-space. Numerical results for a single-degree-of freedom elasto-plastic structure shows that the proposed method leads to significant improvement in variance reduction over importance sampling using design points reported recently.

• Journal of the Korean Data and Information Science Society
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• v.11 no.2
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• pp.217-223
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• 2000

In this paper, we develop optimal decision interval exponentially weighted moving average(EWMA) scheme for the process mean and the reciprocal measure of dispersion of the inverse Gaussian distribution.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.31 no.1 s.256
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• pp.113-120
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• 2007

Design optimization of layered plates bonding process is conducted by considering uncertainties in a manufacturing process, in order to reduce the crack failure arising due to the residual stress at the surface of the adherent which is caused by different thermal expansion coefficients. Robust optimization is peformed to minimize the mean as well as its variance of the residual stress, while constraining the distortion as well as the instantaneous maximum stress under the allowable reliability limits. In this optimization, the dimension reduction (DR) method is employed to quantify the reliability such as mean and variance of the layered plate bonding. It is expected that the DR method benefits the optimization from the perspectives of efficiency, accuracy, and simplicity. The obtained robust optimal solution is verified by the Monte Carlo simulation.

• Korean Journal of Mathematics
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• v.25 no.1
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• pp.45-60
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• 2017

This paper focuses on estimation of an non-integral quadratic function (NIQF) and integral quadratic function (IQF) of a random signal in dynamic system described by a linear stochastic differential equation. The quadratic form of an unobservable signal indicates useful information of a signal for control. The optimal (in mean square sense) and suboptimal estimates of NIQF and IQF represent a function of the Kalman estimate and its error covariance. The proposed estimation algorithms have a closed-form estimation procedure. The obtained estimates are studied in detail, including derivation of the exact formulas and differential equations for mean square errors. The results we demonstrate on practical example of a power of signal, and comparison analysis between optimal and suboptimal estimators is presented.

• Journal of Korean Society for Quality Management
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• v.23 no.3
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• pp.126-139
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• 1995

We describe a formal approach to the construction of optimal classification rule for the two-group normal classification with equal population mean problem. Based on the utility function of Bernardo, we suggest a balanced design for the classification and construct the optimal rule under the balanced design condition. The rule is characterized by a constrained minimization of total risk of misclassification, the constraint of which is constructed by the process of equation between expected utilities of the two group conditional densities. The efficacy of the suggested rule is examined through numerical studies. This indicates that, in case little is known about the relative population sizes, dramatic gains in accuracy of classification result can be achieved.

• Journal of Biomedical Engineering Research
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• v.9 no.1
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• pp.101-108
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• 1988

In this paper the power spectrum of background EEG is estimated by the LMS PHD based on least mean square. At the power spectrum estimatiom, the stocastic process of background EEG is assumed to consist of the nonharmonic sinusoid and the white noise. In the LMS PHD the model parameters are obtained by the least mean square at optimal order which is obtained from the fact that the eigenvalue's fluctuation of autocorrelation matrix of the normal back-ground EEG is smaller at some order than at other order when the power spectrum of background EEG is esitmated by PHD. The optimal order of this model is the 6-th order when the eigenvalue's fluctuation of autocorrelation matrix of background EEG is considered. The estimation results are with compared the results from the Maximum Entropy Spectral Estimation and Pisarenko Harmonic Decomposition. From the comparison results. The LMS PHD is possible to estimate the power spectrum of background EEG.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.10
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• pp.2001-2013
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• 1994

In this paper, the method of processing a blurred noisy image has been researched. The conventional method of processing signal has faluts which are slow convergence speed and long time-consuming process at the singular point and or in the ill condition. There is the process, the Gauss Seidel's method to remove these faults, but it takes too much time because it processed singnal repeatedly. For overcoming the faults, this paper shows a image restoration method which takes shorter than the Gauss-Seidel's by comparing the Gauss Seidel's with proposed alogorithm and accelerating convergence speed at the singular point and/or in the ill condition. In this paper, the conventional process method(Gauss-Seidel) and proposed optimal algorithm were used to get a standard image($256{\times}56{\times}bits$). and then the results are simulated and compared each other in order to examine the variance of MSE(Mean Square Error) by the acceleration parameter in the proposed image restoration. The result of the signal process and the process time was measured at all change of acceleration parameter in order to verify the effectveness of the proposed algorithm.

• Journal of Welding and Joining
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• v.26 no.2
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• pp.85-91
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• 2008

In general, the quality of a welding process tends to vary with depending on the work environment or external disturbances. Hence, in order to achieve the desirable quality of welding, we should have the optimal welding condition that is not significantly affected by these changes in the environment or external disturbances. In this study, we used a dual response surface method in consideration of both the mean output variables and the standard deviation in order to optimize the V-groove arc welding process. The input variables for GMA welding process with the dual response surface are welding voltage, welding current and welding speed. The output variables are the welding quality function using the shape factor of bead geometry. First, we performed welding experiment on the interested area according to the central composite design. From the results obtained, we derived the regression model on the mean and standard deviation between the input and output variables of the welding process and then obtained the dual response surface. Finally, using the grid search method, we obtained the input variables that minimize the object function which led to the optimal V-groove arc welding process.