• Proceedings of the KIEE Conference
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• 1991.07a
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• pp.166-170
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• 1991

An advanced on-resistance model of VDMOS devices in the low voltage regime is proposed and verified by 2-D device simulations. The model considers the lateral gaussian doping profiles in the channel region and exact current spreading angles in the epitaxial layer for both linear and cellular geometries by employing the conformal mapping. It is found out that the on-resistance of low voltage VDMOS may be overestimated considerably if it is analyzed by the conventional method. The 2-D device simulation results show that the proposed model is valid for all ranges of cell spacings and breakdown voltages.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.14 no.3
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• pp.57-64
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• 2015

In this paper, a model reference adaptive control (MRAC) scheme is applied for the precise and robust motion control of a pneumatic system with load variation. The reference model for MRAC is designed systematically using linear quadratic Gaussian control with loop transfer recovery (LQG/LTR). The sigmoid function of inverse velocity is used to compensate for the nonlinear friction force between the sliding parts. The experimental results show that MRAC effectively overcame the limit of the PID controller when there was unknown disturbance, including abrupt load variation and model uncertainty in the pneumatic control system.

• Journal of Advanced Navigation Technology
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• v.15 no.4
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• pp.556-561
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• 2011

It is generally known that particle filters can produce consistent target tracking performance in comparison to the Kalman filter for non-linear and non-Gaussian systems. In this paper, I propose a Rao-Blackwellized multiple model particle filter(RBMMPF) to enhance computational efficiency of the particle filters as well as to reduce sensitivity of modeling. Despite that the Rao-Blackwellized particle filter needs less particles than general particle filter, it has a similar tracking performance with a less computational load. Comparison results for performance is listed for the using single sensor information RBMMPF and using multisensor data fusion RBMMPF.

• Korean Journal of Remote Sensing
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• v.25 no.6
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• pp.547-557
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• 2009

LIDAR (LIght Detection And Ranging) is an active remote sensing technology which provides 3D coordinates of the Earth's surface by performing range measurements from the sensor. Early small footprint LIDAR systems recorded multiple discrete returns from the back-scattered energy. Recent advances in LIDAR hardware now make it possible to record full digital waveforms of the returned energy. LIDAR waveform decomposition involves separating the return waveform into a mixture of components which are then used to characterize the original data. The most common statistical mixture model used for this process is the Gaussian mixture. Waveform decomposition plays an important role in LIDAR waveform processing, since the resulting components are expected to represent reflection surfaces within waveform footprints. Hence the decomposition results ultimately affect the interpretation of LIDAR waveform data. Computational requirements in the waveform decomposition process result from two factors; (1) estimation of the number of components in a mixture and the resulting parameter estimates, which are inter-related and cannot be solved separately, and (2) parameter optimization does not have a closed form solution, and thus needs to be solved iteratively. The current state-of-the-art airborne LIDAR system acquires more than 50,000 waveforms per second, so decomposing the enormous number of waveforms is challenging using traditional single processor architecture. To tackle this issue, four parallel LIDAR waveform decomposition algorithms with different work load balancing schemes - (1) no weighting, (2) a decomposition results-based linear weighting, (3) a decomposition results-based squared weighting, and (4) a decomposition time-based linear weighting - were developed and tested with varying number of processors (8-256). The results were compared in terms of efficiency. Overall, the decomposition time-based linear weighting work load balancing approach yielded the best performance among four approaches.

• The Korean Journal of Applied Statistics
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• v.34 no.5
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• pp.697-710
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• 2021

Relative error prediction is preferred over ordinary prediction methods when relative/percentile errors are regarded as important, especially in econometrics, software engineering and government official statistics. The relative error prediction techniques have been developed in linear/nonlinear regression, nonparametric regression using kernel regression smoother, and stationary time series models. However, random effect models have not been used in relative error prediction. The purpose of this article is to extend relative error prediction to some of generalized linear mixed model (GLMM) with panel data, which is the random effect models based on gamma, lognormal, or inverse gaussian distribution. For better understanding, the real auto insurance data is used to predict the claim size, and the best predictor and the best relative error predictor are comparatively illustrated.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.7-12
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• 2003

In this paper, S(stochastic)-eigenvalue concept and its S-eigenvector for linear continuous-time systems with probabilistic uncertainties are proposed. The proposed concept is concerned with the perturbation of eigenvalues due to the probabilistic variable parameters in the dynamic model of a plant. S-eigenstructure assignment scheme via the Sylvester equation approach based on the S-eigenvalue concept is also proposed. The proposed design scheme is applied to the longitudinal dynamics of open-loop-unstable aircraft with possible uncertainties in aerodynamic and thrust effects as well as separate dynamic pressure.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.22 no.8
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• pp.729-735
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• 2012

A LQG/LTR(linear quadratic Gaussian/loop transfer recovery) controller with an integrator is designed to control the electro-hydraulic positioning system. Without considering the nonlinearity in the dead-zone, computer simulations are performed and show good performances and tracking abilities with the feedback controller based on the linear system model. However, the performance of the closed loop hydraulic positioning system shows big steady-state error in real system because of the dead-zone. In this paper, the feedback controller with a nonlinear compensator is introduced to overcome the dead-zone phenomenon in hydraulic systems. The inverse dead-zone as a nonlinear compensator is used to cancel out the dead-zone phenomenon. Experimental tests are performed to verify the performance of the controller.

• Journal of Mechanical Science and Technology
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• v.18 no.6
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• pp.933-945
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• 2004

In this paper, S (stochastic)-eigenvalue concept and its S-eigenvector for linear continuous-time systems with probabilistic uncertainties is proposed. The proposed concept is concerned with the perturbation of eigenvalues due to the probabilistic variable parameters in the dynamic model of a plant. S-eigenstructure assignment scheme via the Sylvester equation approach based on the S-eigenvalue concept is also proposed. The proposed design schemes are illustrated by numerical examples, and applied to the longitudinal dynamics of open-loop-unstable aircraft with possible uncertainties in aerodynamic and thrust effects as well as separate dynamic pressure. These results explicitly characterize how S-eigenvalues in the complex plane may impose stability on S-eigenstructure assignment.

• Journal of Ocean Engineering and Technology
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• v.21 no.5
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• pp.1-8
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• 2007

A long wave induced by a Gaussian-shape submarine landslide is simulated by a 2D fully nonlinear numerical wave tank (NWT). The NWT is based on the boundary element method and the mixed Eulerian/Lagrangian approach. Using the NWT, physical characteristics of land-slide tsunami, including wave generation, propagation, particle kinematics, hydrodynamic pressure, run-up and depression, are simulated for the early stage of long wave generation and propagation. Various sliding mass heights are applied to the developed model for a systematic sensitivity analysis. In particular, the fully nonlinear NWT results are compared with linear results (exact body-boundary conditions with linear free-surface conditions) to identify the nonlinear effects in the respective cases.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2012.04a
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• pp.614-619
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• 2012

A LQG/LTR(Linear Quadratic Gaussian/Loop Transfer Recovery) controller with an integrator is designed to control the electro-hydraulic positioning system. Without considering the nonlinearity in the dead-zone, computer simulations are performed and show good performances and tracking abilities with the feedback controller based on the linear system model. However, the performance of the closed loop hydraulic positioning system shows big steady-state error in real system because of the dead-zone. In this paper, the feedback controller with a nonlinear compensator is introduced to overcome the dead-zone phenomenon in hydraulic systems. The inverse dead-zone as a nonlinear compensator is used to cancel out the dead-zone phenomenon. Experimental tests are performed to verify the performance of the controller.