• Journal of Advanced Marine Engineering and Technology
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• v.16 no.4
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• pp.50-59
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• 1992

The paper presents a digital speed control approach of induction motor systems by using a digital servo control method and a well-known second order differential equation as model. The basic concept of using the modeling equation stated in the above is induced from the control theory stand point such that we can describe usually the motor system connected by inverter, generator and load etc, just as a mechanical system to be controlled. The concept does not demand us the complicated vector-based modeling equation adopted in the traditional methods for the speed control of induction motor. Futhermore, the proposed speed control system can be treated as a single input and single output system. The effectiveness of the servo control system obtained by the above-mentioned design concept is illustrated by the experimental results in the presence of both step reference changes and load variations. It is observed from the experimental results that the steady state-error of the experimental set up becomes zero after some regulation time and the induction motor system is robust in spite of reference signal changes and load variations.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.4
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• pp.235-243
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• 2000

The probabilistic data association filter(PDAF) is known to provide better tracking performance than the standard Kalman filter(KF) in a cluttered environment. In this paper, the stability of the PDAF of Fortmann et al[7], in the presence of uncertainties with regard to the origin of measurement, is investigated. The modified Riccati equation derived by approximating two random terms with their expectations is used to prove the stability of the PDAF. A new Lyapunov function based approach, which is different from the quantitative evaluation of Li and Bar-Shalom[7], is pursued. With the assumption that the system and observation noises are bounded, specific tracking error bounds are established.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.28 no.1
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• pp.27-38
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• 1992

The paper presents a digital speed control approach of induction motor systems by using a digital redesign method and adopting a well known 2nd order model as the system model equation. The basic concept using the modeling equation is induced from the control theory stand point such that we can describe usually the motor system connected by inverter, generator and load etc. just as a mechanical system to be controlled. The concept does not demand us the complicated vector-based modeling equation adopted in the traditional methods for the speed control of induction motor. The effectiveness of the servo control system composed by the above mentioned design concept is illustrated by the experimental results in the presence of step reference change and generator load variation. It is observed from the experimental results that the steady state error of the experimental set up becomes zero after some regulation time and the induction motor system is robust in spite of reference signal change and load variation of generator.

• Journal of Communications and Networks
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• v.11 no.5
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• pp.473-479
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• 2009

Conventional frequency domain equalization (FDE) schemes were originally devised for quasi-static channels. Thus, such equalization schemes could suffer from significant performance degradation in fast-fading channels. This paper proposes a frequency domain equalization algorithm to mitigate the effect of fast time-varying fading. First, a mathematical expression is derived to quantify the total interference resulting from the time variation of the channel. Then, the proposed approach attempts to eliminate the effect of time-variations of the channel. This cancellation allows efficient use of the classical FDE structures in fast time-varying fading environments, although they are built upon the quasi-static channel model. Simulation results of bit-error-rate performance are provided to demonstrate the effectiveness of the proposed algorithm.

• KIEE International Transaction on Systems and Control
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• v.2D no.2
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• pp.108-114
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• 2002

This paper describes the design of a robust adaptive controller for a nonlinear dynamical system with unknown nonlinearities. These unknown nonlinearities are approximated by multilayered neural networks (MNNs) whose parameters are adjusted on-line, according to some adaptive laws far controlling the output of the nonlinear system, to track a given trajectory. The main contribution of this paper is a method for considering input constraint with a rigorous stability proof. The Lyapunov synthesis approach is used to develop a state-feedback adaptive control algorithm based on the adaptive MNN model. An overall control system guarantees that the tracking error converges at about zero and that all signals involved are uniformly bounded even in the presence of input saturation. Theoretical results are illustrated through a simulation example.

• Proceedings of the KIEE Conference
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• 1999.07a
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• pp.253-255
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• 1999

In this paper, a shape optimization algorithm of superconducting magnet using finite element method is presented. Since the superconductor loses its superconductivity over the critical magnetic field and critical current density, this material property should be taken into account in the design process. Trial and error approach of repeating the change of the design variables costs much time and it sometimes does not guarantee an optimal design. This paper presents a systematic and efficient design algorithm for the superconducting magnet. We employ the sensitivity analysis based on finite element formulation. As for optimization algorithm, the inequality constraint for the superconducting state is removed by modifying the objective function and the nonlinear equality constraint of constant volume is satisfied by the gradient projection method. This design algorithm is applied to an optimal design problem of a solenoid air-cored superconducting magnet that has a design objective of the maximum energy storage.

• Journal of Adhesion and Interface
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• v.3 no.1
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• pp.43-51
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• 2002

Advanced adhesive technologies and demanding applications of adhesive joints can no longer be developed successfully by the traditional "trial and error" approach. Appropriate technical solutions require reference to a reliable basis of well-established scientific knowledge about the elementary mechanisms of adhesion (i.e. the 'fundamental adhesion') as they are responsible for the capability of the compound w transmit mechanical force between the adhesive and the substrate surface (i.e. the 'practical adhesion'). Adhesion mechanisms also influence the formation of polymer structure in the adhesive and the resulting macromolecular dynamics in the interphase that is formed in the adhesive near to the substrate. These manifold molecular factors rule the macroscopic behaviour of an adhesive bond line in terms of mechanical and other physical properties as well as in terms of durability. This paper reviews the level of refinement that understanding of fundamental adhesion has achieved up to now.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.1041-1044
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• 2000

The enhancement to the back-propagation algorithm presented in this paper has resulted from the need to extract sparsely connected networks from networks employing product terms. The enhancement works in conjunction with the back-propagation weight update process, so that the actions of weight zeroing and weight stimulation enhance each other. It is shown that the error measure, can also be interpreted as rate of weight change (as opposed to ${\Delta}W_{ij}$), and consequently used to determine when weights have reached a stable state. Weights judged to be stable are then compared to a zero weight threshold. Should they fall below this threshold, then the weight in question is zeroed. Simulation of such a system is shown to return improved learning rates and reduce network connection requirements, with respect to the optimal network solution, trained using the normal back-propagation algorithm for Multi-Layer Perceptron (MLP), Higher Order Neural Network (HONN) and Sigma-Pi networks.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.10
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• pp.82-88
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• 2003

Many visual servoing algorithms have been recently developed by the robot vision researchers. They do not, however, consider the stability of servoing system. The camera calibration is the most important factor to the control stability and performance of position based visual servoing. In this article we describe the ECL(End Point Closed Loop) servoing can make no steady state error for the control of 6-DOF robot of which accuracy is dependent on the camera calibration and kinematics. And we propose a dynamic calibration algorithm, which can improve stability and performance of ECL visual servoing. To verify the potential of our approach, we run assembly experiments and present our finding.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.11
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• pp.2139-2146
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• 2011

In this paper, a robust adaptive controller is proposed for trajectory tracking of robot manipulators with the unknown friction coefficient and bounded disturbance. A new adaptive control law is developed based on sliding mode and derived from the Lyapunov stability analysis. The introduction of a boundary layer solves the problem of chattering. The proposed adaptive controller is globally asymptotically stable and guarantees zero steady state error for joint positions. The estimated friction coefficients can also approach the actual coefficients asymptotically. A simulation example is provided to demonstrate the performance of the proposed algorithm.