• Journal of Korean Society of Industrial and Systems Engineering
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• v.32 no.3
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• pp.99-109
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• 2009

Negative binomial yield model for semiconductor manufacturing consists of two parameters which are the average number of defects per die and the clustering parameter. Estimating the clustering parameter is quite complex because the parameter has not clear closed form. In this paper, a Bayesian approach using Markov Chain Monte Carlo is proposed to estimate the clustering parameter. To find an appropriate estimation method for the clustering parameter, two typical estimators, the method of moments estimator and the maximum likelihood estimator, and the proposed Bayesian estimator are compared with respect to the mean absolute deviation between the real yield and the estimated yield. Experimental results show that both the proposed Bayesian estimator and the maximum likelihood estimator have excellent performance and the choice of method depends on the purpose of use.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.22 no.2
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• pp.298-303
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• 2013

Magnetic abrasive polishing is one of the most promising finishing methods applicable to complex surfaces. Nevertheless this process has a low efficiency when applied to very hardened materials. For this reason, EP-MAP hybrid process was developed. EP-MAP process is expected to machine complex and hardened materials. In this research, deburring process using EP-MAP hybrid process was proposed. EP-MAP deburring process is applied to micro channel, thereby it can obtain both deburring process and polishing process. EP-MAP deburring process on the micro channel was performed. Through design of experiment method, error of height in this process according to process parameter is analyzed. When the level 1 parameter A(magnetic flux density) and level 2 parameter B(electric potential), C(working gap) and level 3 parameter D(feed rate) are applied in the deburring process using EP-MAP hybrid process, it provides optimum result of EP-MAP hybrid deburring process.

• Journal of the Korean Vacuum Society
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• v.5 no.2
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• pp.99-106
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• 1996

Two types of regularization method (singular system and HMP approaches) for generating depth-concentration profiles from angle-resolved XPS data were evaluated. Both approaches showed qualitatively similar results although they employed different numerical algorithms. The application of the regularization method to simulated data demonhstrates its excellent utility for the complex depth profile system . It includes the stable restoration of depth-concentration profiles from the data with considerable random error and the self choice of smoothing parameter that is imperative for the successful application of the regularization method. The self choice of smoothing parameter is based on generalized cross-validation method which lets the data themselves choose the optimal value of the parameter.

• Proceedings of the KIPE Conference
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• 1999.07a
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• pp.58-61
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• 1999

This paper describes the program of optimally choosing parameter in designing inductor, which applied by fuzzy theory, and verifies the reliability of program to use in design of power supply of electronic machine and information communication. It is available to find optimal value of complex and various parameter, such as core, winding, winding number, and air-gap, etc., needed on designing inductor. We expects to minimize time and cost of inductor design.

• Journal of KSNVE
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• v.9 no.1
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• pp.60-69
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• 1999

In the first paper of this research$^{(1)}$. a new time series method. directional ARMAX (dARMAX) model-based approach. was proposed for rotordynamics identification. The dARMAX processes complex-valued signals, utilizing the complex modal testing theory which enables the separation of the backward and forward modes in the two-sided frequency domain and makes effective modal parameter identification possible. to account for the dynamic characteristics inherent in rotating machinery. In this second part. an evaluation of its performance characteristics based on both simulated and experimental data is presented. Numerical simulations are carried out to show that the method. a complex time series method. successfully implements the complex modal testing in the time domain. and it is superior in nature to the conventional ARMAX and the frequency-domain methods in the estimation of the modal parameters for isotropic and weakly anisotropic rotor systems. Experiments are carried out to demonstrate the applicability and the effectiveness of the dARMAX model-based approach, following the proposed fitting strategy. for the rotordynamics identification.

• The Korean Journal of Applied Statistics
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• v.24 no.1
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• pp.177-183
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• 2011

Hidden Markov random eld(HMRF) is one of the most common model for image segmentation which is an important preprocessing in many imaging devices. The HMRF has unknown hyper-parameters on Markov random field to be estimated in segmenting testing images. However, in practice, due to computational complexity, it is often assumed to be a fixed constant. In this paper, we numerically show that the segmentation results very depending on the fixed hyper-parameter, and, if the parameter is misspecified, they further depend on the choice of the class-labelling algorithm. In contrast, the HMRF with estimated hyper-parameter provides consistent segmentation results regardless of the choice of class labelling and the estimation method. Thus, we recommend practitioners estimate the hyper-parameter even though it is computationally complex.

• Journal of Aerospace System Engineering
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• v.7 no.4
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• pp.27-35
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• 2013

Identifying the stability of rotor systems is prerequisite for clear determination of the parameter identification and safety, through which operating conditions may be rationally ascertained. For this purpose, the complex modal analysis of periodically time-varying system has been introduced by transforming the relation between periodic eigen-vectors and the corresponding adjoint vectors into the latent value problem. Stability investigation associated with modal features for rotor systems is performed using numerical simulation based upon the analysis model.

• The Journal of the Acoustical Society of Korea
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• v.17 no.1E
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• pp.36-43
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• 1998

A fuzzy logic filter is constructed from a set of fuzzy IF-THEN rules which change adaptively to minimize some criterion function as new information becomes available. This paper generalizes the fuzzy logic filter and it's adaptive filtering algorithm to include complex parameters and complex signals. Using the complex Stone-Weierstrass theorem, we prove that linear combinations of the fuzzy basis functions are capable of uniformly approximating and complex continuous function on a compact set to arbitrary accuracy. Based on the fuzzy basis function representations, a complex orthogonal least-squares (COLS) learning algorithm is developed for designing fuzzy systems based on given input-output pairs. Also, we propose an adaptive algorithm based on LMS which adjust simultaneously filter parameters and the parameter of the membership function which characterize the fuzzy concepts in the IF-THEN rules. The modeling of a nonlinear communications channel based on a complex fuzzy is used to demonstrate the effectiveness of these algorithm.

• Journal of Korea Water Resources Association
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• v.52 no.10
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• pp.681-695
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• 2019

A Transient Storage Model (TSM) is one of the most widely used model accounting for complex solute transport in natural river to understanding natural river properties with four TSM key parameters. The TSM parameters are estimated via inverse modeling. Parameter estimation of the TSM is carried out by solving optimization problem about finding best fitted simulation curve with measured curve obtained from tracer test. Several studies have reported uncertainty in parameter estimation from non-convexity of the problem. In this study, we assessed best combination of optimization method and objective function for TSM parameter estimation using Cheong-mi Creek tracer test data. In order to find best optimization setting guaranteeing convergence and speed, Evolutionary Algorithm (EA) based global optimization methods, such as CCE of SCE-UA and MCCE of SP-UCI, and error based objective functions were compared, using Shuffled Complex-Self Adaptive Hybrid EvoLution (SC-SAHEL). Overall results showed that multi-EA SC-SAHEL with Percent Mean Squared Error (PMSE) objective function is the best optimization setting which is fastest and stable method in convergence.

• Journal of Power System Engineering
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• v.10 no.4
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• pp.147-152
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• 2006

The current type amplifier is the device that is used for an actuator as the motor's torque controller. However, it is too difficult to select the parameter value that has the desired output because the current type amplifier's transfer function is too complex. This study concern about the design of the current type amplifier with the desired output. From the modeled transfer function of the current type amplifier, the optimal parameter values of the transfer function can be selected in order to have the desired output using the Real Coded Genetic Algorithm(RCGA). The real circuit is made with the selected parameter value. The step response of the real circuit is in good agreement with the desired step response.