• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.215-218
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• 1997

A dynamic back-propagation neural network is addressed for adaptive neural control system to approximate non-linear control system rather than static networks. It has the capability to represent the approximation of nonlinear system without mathematical analysis and to carry out the on-line learning algorithm for real time application. The simulated results show fast tracking capability and adaptive response by using dynamic back-propagation neurons.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.48.3-48
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• 2002

1) Linear state feedback control design problem for plant with unknown deterministic disturbance is considered and a method to realize state feedback by using adaptive observer which estimates the unknown disturbance simultaneously is proposed. 2) From the viewpoint of practical application, we propose an extended adaptive observer with direct plant path from input to output, which is necessary to use the acceleration type sensors as plant output. 3) Theoretical result is confirmed by numerical simulation of 1-DOF vibration control system.

• Proceedings of the KIEE Conference
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• 2002.11c
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• pp.79-82
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• 2002

In this paper, we propose an adaptive repetitive control algorithm for the system the task of which is repetitive. The feedforward controller in the repetitive control system is modified by using the system parameter identifier in order to improve the convergence characteristics. The proposed algorithm is applied to the tracking control of a linear BLDC motor to which a periodic reference input is applied. It is illustrated by simulation results that the proposed adaptive repetitive control method yields better control performance than existing repetitive control even when modeling errors exist.

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.268-273
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• 1998

This study applies an on-line system, which employes an adaptive reconstruction technique to monitor and forecast ocean surface changes. The system adaptively generates an appropriate synthetic time series with recovering missing measurements for sequential images. The reconstruction method incorporates temporal variation according to physical properties of targets and anisotropic spatial optical properties into image processing techniques. This adaptive approach allows successive refinement of the structure of objects that are barely detectable in the observed series. The system sequentially collects the estimated results from the adaptive reconstruction and then statistically analyzes them to monitor and forecast the change in surface characteristics.

• Structural Engineering and Mechanics
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• v.6 no.1
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• pp.115-124
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• 1998

A novel boundary stress resolution method is suggested in this paper, which is based upon the displacements of finite element analysis and of high precision with stress boundary condition strictly satisfied. The method is used to modify the Zienkiewicz-Zhu ($Z^2$) a posteriori error estimator and for the h-version adaptive finite element analysis of crack problems. Successful results are obtained.

• Asia pacific journal of information systems
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• v.34 no.1
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• pp.248-291
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• 2024

A few years before, MOOCs appeared and developed at a rapid pace. Various MOOC methods, theories, strategies, elements, and techniques were used to improve distance education. In their development, their weaknesses like dropout, low participation, low completion rate, low engagement, and others have emerged that are addressed by recent studies including that of gamified and adaptive gamified MOOCs. This article presents the most important theories, strategies, and elements used in gamification and their usefulness and contribution to MOOCs, with the ultimate goal of proving rich information to researchers, application designers, and practitioners of gamified and adaptive gamified MOOCs.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.87.3-87
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• 2002

1. Introduction 2. Overview of Collected Data 3. Adaptive Digital Filter 4. Experiment and Results 5. Conclusion

• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06a
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• pp.873-878
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• 1994

Application of the filtered-x LMS adaptive filter to active noise cancellation requires to estimate the transfer characteristics between the output and the error signal of the adaptive canceller. We analyze the effects of estimation accuracy on the convergence behavior of the canceller when the input noise is modeled as a sinusoid.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.6 no.1
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• pp.7-17
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• 1997

During the past decade, there were many well-established theories for the adaptive control of linear systems, but there exists relatively little general theory for the adaptive control of nonlinear systems. Adaptive control technique is essential for providing a stable and robust performance for application of industrial robot control. Neural network computing methods provide one approach to the development of adaptive and learning behavior in robotic system for manufacturing. Computational neural networks have been demonstrated which exhibit capabilities for supervised learning, matching, and generalization for problems on an experimental scale. Supervised learning could improve the efficiency of training and development of robotic systems. In this paper, a new scheme of adaptive-neuro control system to implement real-time control of robot manipulator using digital signal processors is proposed. Digital signal processors, DSPs, are micro-processors that are developed particularly for fast numerical computations involving sums and products of variables. The proposed neuro control algorithm is one of learning a model based error back-propagation scheme using Lyapunov stability analysis method. The proposed adaptive-neuro control scheme is illustrated to be an efficient control scheme for implementation of real-time control for SCARA robot with four-axes by experiment.

• International Journal of Control, Automation, and Systems
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• v.1 no.1
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• pp.19-27
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• 2003

It is well known that one can easily design a high-gain adaptive output feedback control for a class of nonlinear systems which satisfy a certain condition called output feedback exponential passivity (OFEP). The designed high-gain adaptive controller has simple structure and high robustness with regard to bounded disturbances and unknown order of the controlled system. However, from the viewpoint of practical application, it is important to consider a robust control scheme for controlled systems for which some of the assumptions of output feedback stabilization are not valid. In this paper, we design a robust high-gain adaptive output feedback control for the OFEP nonlinear systems with uncertain nonlinearities and/or disturbances. The effectiveness of the proposed method is shown by numerical simulations.