• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.518-521
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• 1995

In this paper, a learning control problem is formulated for cooperating multiple-robot manipulators with uncertain system parameters. The commonly held object is also assumed to be unknown and the multiple-robots themselfs experience uncertain operating conditions such as link parameters, viscous friction parameters, suctions, actuator bias, and etc. Under these conditions, the learning controllers designed for learning of uncertain parameters and robot control inputs for multiple-robot systems are shown to drive the multiple-robot manipulators to follow the desired Cartesian trajectory with the desired internal forces to the unknown object.

• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.86-89
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• 1998

Multiple types of information in a multimedia service are delivered though multiple virtual connections on ATM network, while each virtual connection may be controlled independently. A multimedia service requires an associative relationship among multiple information streams to provide required harmonization. There may be required additional traffic descriptors to guarantee the required harmonization among multiple information streams in a multimedia service. For buffering of large bandwidth information stream(e.g., video), extremely large buffer size is necessary, but this approach should not be efficient way to compensate a severely delayed cells/blocks experienced at network. The best way to solve this problem will be minimization of relative-delayed-transfer of cells/blocks to application processes through ATM network control. To minimize a delayed transfer the mapping between relative delay parameter(i.e., associative Group QoS parameters) and per-VC traffic descriptor will be necessary. This paper is present additional functions and parameters to interpret the mapping between relative delay parameters(i.e., associative Group QoS parameters) and per-VC traffic descriptors in ATM API for multimedia application services.

• Journal of the Korean Data and Information Science Society
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• v.9 no.2
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• pp.165-172
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• 1998

This paper considers a linear regression model with censored data where each error term follows a multivariate normal distribution. In this paper we consider the diffuse prior distribution for parameters of the linear regression model. With censored data we derive the full conditional densities for parameters of a multiple regression model in order to obtain the marginal posterior densities of the relevant parameters through the Gibbs Sampler, which was proposed by Geman and Geman(1984) and utilized by Gelfand and Smith(1990) with statistical viewpoint.

• Communications for Statistical Applications and Methods
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• v.26 no.2
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• pp.131-148
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• 2019

Constant stress partially accelerated life tests are studied according to exponentiated Weibull distribution. Grounded on multiple censoring, the maximum likelihood estimators are determined in connection with unknown distribution parameters and accelerated factor. The confidence intervals of the unknown parameters and acceleration factor are constructed for large sample size. However, it is not possible to obtain the Bayes estimates in plain form, so we apply a Markov chain Monte Carlo method to deal with this issue, which permits us to create a credible interval of the associated parameters. Finally, based on constant stress partially accelerated life tests scheme with exponentiated Weibull distribution under multiple censoring, the illustrative example and the simulation results are used to investigate the maximum likelihood, and Bayesian estimates of the unknown parameters.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.14 no.3
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• pp.241-245
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• 2001

In this paper, we present parameter optimization technique for GaAs/AlGaAs multiple quantum well avalanche photodiodes used for image capture mechanism in high-definition system. Even under flawless environment in semiconductor manufacturing process, random variation in process parameters can bring the fluctuation to device performance. The precise modeling for this variation is thus required for accurate prediction of device performance. The precise modeling for this variation is thus required for accurate prediction of device performance. This paper will first use experimental design and neural networks to model the nonlinear relationship between device process parameters and device performance parameters. The derived model was then put into genetic algorithms to acquire optimized device process parameters. From the optimized technique, we can predict device performance before high-volume manufacturign, and also increase production efficiency.

• Communications for Statistical Applications and Methods
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• v.9 no.2
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• pp.521-531
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• 2002

Various diagnostic techniques for identifying influential observations are mostly based on the deletion of a single observation. While such techniques can satisfactorily identify influential observations in many cases, they will not always be successful because of some mask effect. It is necessary, therefore, to develop techniques that examine the potentially influential effects of a subset of observations. The partial regression plots can be used to examine an influential observation for a single parameter in multiple linear regression. However, it is often desirable to detect influential observations for a subset of regression parameters when interest centers on a selected subset of independent variables. Thus, we propose a diagnostic measure which deals with detecting influential observations on a subset of regression parameters. In this paper, we propose a measure M, which can be effectively used for the detection of influential observations on a subset of regression parameters in multiple linear regression. An illustrated example is given to show how we can use the new measure M to identify influential observations on a subset of regression parameters.

• Proceedings of the Safety Management and Science Conference
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• 2000.11a
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• pp.251-254
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• 2000

The critical problem in dealing with multiple characteristics is how to compromise the conflict among the selected levels of the design parameters for each individual characteristic. In this study, First, Methodology using SN ratio optimized by univariate technique is proposed and a parameter design procedure to achieve the optimal compromise among several different response variables is developed. Second, to solve the issue on the optimal design for multiple quality characteristics, this study modelled the expected loss function with cross-product terms among the characteristics and derived range of the coefficients of the terms. The model will be used to determine the global optimal design parameters where there exists the conflict among the characteristics, which shows difference in optimal design parameters for the individual characteristics. Third, this paper propose a decision model to incorporates the values assigned by a group of experts on different factors in weighting decision of characteristic. Using this model, SN ratio of taguchi method for each of subjective factors as well as values of weights are used in this comprehensive method for weighting decision of characteristic.

• The Korean Journal of Applied Statistics
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• v.27 no.6
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• pp.909-922
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• 2014

This paper reviews Bayesian inference with inequality constraints. It focuses on ⅰ) comparison of models with various inequality/equality constraints on parameters, ⅱ) multiple tests on equalities of parameters when parameters are under inequality constraints, ⅲ) multiple test on equalities of score parameters in models for contingency tables with ordinal categorical variables.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.21 no.7
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• pp.43-52
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• 2022

Design of a controller for a high-precision servo control system has been a popular topic while finding optimal parameters for multiple controllers is still a challenging subject. In this paper, we propose a practical scheme to optimize multi-parameters for the robust servo controller design by introducing a new cost function and optimization scheme. The proposed design method provides a simple and practical tool for the systematic servo design to reduce the control error with guaranteeing robust stability of the overall system. The reduction of the position error by 24% along with a faster convergence rate is demonstrated using a typical hard disk drive servo controller with 41 parameters.

• Journal of the Korea Safety Management & Science
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• v.2 no.1
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• pp.161-170
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• 2000

When designing the parameter on the multiple quality characteristics, there has been a study for optimization of problems, but there has been few former study on the possible conflicting phenomena in consideration of the correlations among the characteristics. To solve the issue on the optimal design for multiple quality characteristics, this study modelled the expected loss function with cross-product terms among the characteristics and derived range of the coefficients of the terms. The model will be used to determine the global optimal design parameters where there exists the conflict among the characteristics, which shows difference in optimal design parameters for the individual characteristics.