• Transactions of the Korean Society of Automotive Engineers
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• v.4 no.4
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• pp.9-17
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• 1996

This paper develops a sequential updating method of the Euler parameter generalized coordinates for the machine kinematics and dynamics, The Newton's method is slightly modified so as to utilize the Jacobian matrix with respect to the virtual rotation instead of this with repect to the Euler parameters. An intermediate variable is introduced and the modified Newton's method solves for the variable first. Relational equation of the intermediate variable is then solved for the Euler parameters. The solution process is carried out efficiently by symoblic inversion of the relational equation of the intermediate variable and the iteration equation of the Euler parameter normalization constraint. The proposed method is applied to a kinematic and dynamic analysis with the Generalized Coordinate Partitioning method. Covergence analysis is performed to guarantee the local convergence of the proposed method. To demonstrate the validity and practicalism of the proposed method, kinematic analysis of a motion base system and dynamic analysis of a vehicle are carried out.

• Journal of the Korea Society of Computer and Information
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• v.15 no.4
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• pp.57-63
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• 2010

The damping parameter of Levenberg-Marquardt algorithm switches between error backpropagation and Gauss-Newton learning and affects learning speed. Fixing the damping parameter induces some oscillation of error and decreases learning speed. Therefore, we propose the way of a variable damping parameter with referring to the alternation of error. The proposed method makes the damping parameter increase if error rate is large and makes it decrease if error rate is small. This method so plays the role of momentum that it can improve learning speed. We tested both iris recognition and wine recognition for this paper. We found out that this method improved learning speed in 67% cases on iris recognition and in 78% cases on wine recognition. It was also showed that the oscillation of error by the proposed way was less than those of other algorithms.

• Journal of the Korean Data and Information Science Society
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• v.8 no.2
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• pp.271-276
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• 1997

A dynamic discount approach is proposed for the estimation of the Poisson parameter and the forecasting of the Poisson random variable, where the parameter of the Poisson distribution varies over time intervals. The recursive estimation procedure of the Poisson parameter is provided. Also the forecasted distribution of the Poisson random variable in the next time interval based on the information gathered until the current time interval is provided.

• Journal of the Korean Statistical Society
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• v.29 no.4
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• pp.489-499
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• 2000

We propose a new instrumental variable estimator of the complex parameter of a class of univariate complex-valued diffusion processes defined by the possibly non-stationary and/or nonlinear stochastic differential equations. On the basis of the exact finite sample distribution of the pivotal quantity, we construct the exact confidence intervals and the exact tests for the parameter. Monte-Carlo simulation suggests that the new estimator seems to provide a viable alternative to the maximum likelihood estimator (MLE) for nonlinear and/or non-stationary processes.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.35.1-35
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• 2002

$\textbullet$ Sliding mode control (SMC) with nonlinear interpolation in variable boundary layer (VBL) is proposed $\textbullet$ A sigmoid function is used for nonlinear interpolation in VBL. $\textbullet$ The Parameter of the sigmoid function is tuned by fuzzy controller $\textbullet$ The choice of parameter for the sigmoid function is guided by FC. $\textbullet$ The parameter is continuously updated as boundary layer thickness varies. $\textbullet$ The proposed method hasbetter tracking performance than the conventional linear interpolation $\textbullet$ To demonstrate its performance the proposed control algorithm is applied to a nonlinear system.

• Communications for Statistical Applications and Methods
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• v.1 no.1
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• pp.87-93
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• 1994

Consider the problem of estimating the parameter of the model in which an observable random variable is represented by a unknown scalar parameter plus another random variable and the parameter, sample, and decision spaces consist of all integers. We first characterize the class of all admissible estimators and then characterize the class of all finitely admissible estimators. Finally, we show that two classes are identical.

• Journal of Advanced Marine Engineering and Technology
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• v.28 no.1
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• pp.136-144
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• 2004

In general a hydraulic system which uses a single rod hydraulic as an actuator is modeled as a nonlinear system and reveals uncertain Parameter characteristics such as the density variation of hydraulic oil and is subject to load variations and severe disturbances during operation. A variable design-parameter fuzzy PID controller is adopted to solve these undesirable internal and external problems and its effectiveness is verified through computer simulations for control performance and real time control possibility.

• Journal of Korean Institute of Industrial Engineers
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• v.27 no.1
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• pp.47-53
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• 2001

In this paper, we compare the classification performances of both ensemble and clustering algorithms (Data Bagging, Variable Selection Bagging, Parameter Combining, Clustering) to logistic regression in consideration of various characteristics of input data. Four factors used to simulate the logistic model are (1) correlation among input variables (2) variance of observation (3) training data size and (4) input-output function. In view of the unknown relationship between input and output function, we use a Taguchi design to improve the practicality of our study results by letting it as a noise factor. Experimental study results indicate the following: When the level of the variance is medium, Bagging & Parameter Combining performs worse than Logistic Regression, Variable Selection Bagging and Clustering. However, classification performances of Logistic Regression, Variable Selection Bagging, Bagging and Clustering are not significantly different when the variance of input data is either small or large. When there is strong correlation in input variables, Variable Selection Bagging outperforms both Logistic Regression and Parameter combining. In general, Parameter Combining algorithm appears to be the worst at our disappointment.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.18 no.3
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• pp.257-268
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• 2014

The objective of the present study is to investigate thermal diffusion and radiation effects on unsteady MHD flow past a linearly accelerated vertical porous plate with variable temperature and also with variable mass diffusion in presence of heat source or sink under the influence of applied transverse magnetic field. The fluid considered here is a gray, absorbing/emitting radiation but a non-scattering medium. At time t > 0, the plate is linearly accelerated with a velocity $u=u_0t$ in its own plane. And at the same time, plate temperature and concentration levels near the plate raised linearly with time t. The dimensionless governing equations involved in the present analysis are solved using the closed analytical method. The velocity, temperature, concentration, skin-friction, the rate or heat transfer and the rate of mass transfer are studied through graphs in terms of different physical parameters like magnetic field parameter (M), radiation parameter (R), Schmidt parameter (Sc), Soret number (So), Heat source parameter (S), Prandtl number (Pr), thermal Grashof number (Gr), mass Grashof number (Gm) and time (t).

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2009.10a
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• pp.647-653
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• 2009

Taguchi parameter design is an approach to reducing performance variation of quality characteristic value in products and processes. Taguchi has used SN (Signal to Noise) ratio to achieve the appropriate set of operating conditions where variability around target is low in the Taguchi parameter design. This paper describes the prediction technique of vehicle performance using parameter analysis to reduce man hour and test development period as well as to achieve stable NVH performance. Design engineer could efficiently decide the design variable using parameter analysis database in early design stage. These improvements can reduce the time needed to develop better vehicles.