• Journal of Korean Institute of Industrial Engineers
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• v.16 no.2
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• pp.129-134
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• 1990

Exponential smoothing methods have enjoyed a long history of successful applications and have been used in forecasting for many years. However, it has been long known that one of the deficiencies of the method is an inability to respond quickly to interventions to interruptions, or to large changes in level of the underlying process. An exponential smoothing method adaptive to repeated random level changes is proposed using a change-detection statistic derived from a simple dynamic linear model. The results are compared with Trigg and Leach's and the exponential smoothing methods.

• Journal of Korean Institute of Industrial Engineers
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• v.12 no.1
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• pp.95-105
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• 1986

This paper presents an adaptive algorithm to generate a near-optimal closed-loop solution for a non-zero sum differential game by periodically updating the solutions of the two-point boundary-value problem. Applications to competitive advertising problem show that the adaptive algorithm can be used as an efficient tool to solve the differential game problem in which one player may take advantage of the other's non-optimal play.

• Communications for Statistical Applications and Methods
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• v.21 no.4
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• pp.349-361
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• 2014

This paper compares of lasso type estimators in various high-dimensional data situations with sparse parameters. Lasso, adaptive lasso, fused lasso and elastic net as lasso type estimators and ridge estimator are compared via simulation in linear models with correlated and uncorrelated covariates and binary regression models with correlated covariates and discrete covariates. Each method is shown to have advantages with different penalty conditions according to sparsity patterns of regression parameters. We applied the lasso type methods to Arabidopsis microarray gene expression data to find the strongly significant genes to distinguish two groups.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.360-363
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• 1993

Fuzzy Logic Control immitating human decision making process is a novel control strategy based on expert's experience and knowledge and many process designers are developing its applications. But it is difficult to obtain a set of rules from human operator. And there is a limitation on adjusting to environmental changes. In this paper, we proposed adaptive fuzzy algorithm to overcome these difficulties using weights added to the rules. To verify the validity of this control strategy, we have implemented this algorithm for a DC servo motor with PD-type fuzzy controller.

• 한국전산유체공학회:학술대회논문집
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• 2006.10a
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• pp.24-25
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• 2006

An unstructured mesh method has been developed for the simulation of steady and time-accurate flows around helicopter rotors. A dynamic and quasi-unsteady solution-adaptive mesh refinement technique was adopted for the enhancement of the solution accuracy in the local region of interest involving highly vortical flows. Applications were made to the 2-D blade-vortex interaction aerodynamics and the 3-D rotor blades in hover. The interaction between the rotor and the airframe in forward flight was investigated by introducing an overset mesh technique.

• Proceedings of the IEEK Conference
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• 2003.11b
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• pp.99-102
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• 2003

In this paper we propose an adaptive learning method for HMM classifiers by using splitting and merging techniques to overcome the problem of the conventional teaming, where one HMM classifier per class has been trained, individually. The experimental results demonstrate a possibility that the proposed mechanism could be applied for applications of having multiple clusters in a class.

• Communications for Statistical Applications and Methods
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• v.9 no.2
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• pp.325-335
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• 2002

In this paper we introduce some adaptive M-estimators using selector statistics to estimate the center of symmetric and continuous underlying distributions. This selector statistics is based on the idea of Hogg(1983) and Hogg et. al. (1988) who used averages of some order statistics to discriminate underlying distributions. In this paper, we use the functions of sample quantiles as selector statistics and determine the suitable quantile points based on maximizing the distance index to discriminate distributions under consideration. In Monte Carlo study, this robust estimation method works pretty good in wide range of underlying distributions.

• Proceedings of the KIEE Conference
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• 1997.07a
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• pp.44-46
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• 1997

The intrinsic simplicity, ruggedness, and simple power electronic drive requirement of a switched reluctance motor (SRM) make it a viable use for many commercial adjustable speed applications. However, higher torque ripple is one of the few disadvantages of the SRM drives. This paper describes the robust control scheme that permits the phase torque flatted by adaptive reference model.

• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.955-957
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• 1998

This paper presents an adaptive reclosing scheme to improve the reliability in power distribution systems. For an originated faults, this scheme can determine the number of reclosing attempts, so that minimizes the affect of electric facility and customers' load. To verify the effectiveness of the proposed scheme. numerical simulation which calculates a various indices to consider the reliability and the effect of electric facility, is carried out with actual field data. Results show that the proposed scheme can be applicable to field operation.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.1
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• pp.26-33
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• 1992

This paper presents lattice structure of bilinear filter and the conversion equations from lattice parameters to direct-form parameters. Billnear models are attractive for adaptive filtering applications because they can approximate a large class of nonlinear systems adequately, and usually with considerable parsimony in the number of coefficients required. The lattice filter formulation transforms the nonlinear filtering problem into an equivalent multichannel linear filtering problem and then uses multichannel lattice filtering algorithms to solve the nonlinear filtering problem. The lattice filters perform a Gram-Schmidt orthogonalization of the input data and have very good easily extended to more general nonlinear output feedback structures.