• International Journal of CAD/CAM
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• v.6 no.1
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• pp.139-148
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• 2006

Catmull-Clark subdivision scheme provides a powerful method for building smooth and complex surfaces. But the number of faces in the uniformly refined meshes increases exponentially with respect to subdivision depth. Adaptive tessellation reduces the number of faces needed to yield a smooth approximation to the limit surface and, consequently, makes the rendering process more efficient. In this paper, we present a new adaptive tessellation method for general Catmull-Clark subdivision surfaces. Different from previous control mesh refinement based approaches, which generate approximate meshes that usually do not interpolate the limit surface, the new method is based on direct evaluation of the limit surface to generate an inscribed polyhedron of the limit surface. With explicit evaluation of general Catmull-Clark subdivision surfaces becoming available, the new adaptive tessellation method can precisely measure error for every point of the limit surface. Hence, it has complete control of the accuracy of the tessellation result. Cracks are avoided by using a recursive color marking process to ensure that adjacent patches or subpatches use the same limit surface points in the construction of the shared boundary. The new method performs limit surface evaluation only at points that are needed for the final rendering process. Therefore it is very fast and memory efficient. The new method is presented for the general Catmull-Clark subdivision scheme. But it can be used for any subdivision scheme that has an explicit evaluation method for its limit surface.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1267-1271
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• 2003

This paper proposes an indirect adaptive fuzzy controller for general SISO nonlinear systems. No a priori information on bounding constants of uncertainties including reconstruction errors and optimal fuzzy parameters is needed. The control law and the update laws for fuzzy rule structure and estimates of fuzzy parameters and bounding constants are determined so that the Lyapunov stability of the whole closed loop system is guaranteed. The computer simulation results for an inverted pendulum system show the performance of the proposed robust adaptive fuzzy controller.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10a
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• pp.870-875
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• 1987

The payload variation and modeling error can lye parameterized in such a way that known nonlinear functions are multiplied linearly by parameter errors. An adaptive control algorithm is derived for a perturbed linear system with such parameterization. Hence, in this approach no linear approximation of robot system is needed for the application of an adaptive law. The stability of the adaptive control algorithm is established and also supported by a computer simulation result.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.64.2-64
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• 2002

Contents 1. Introduction $\textbullet$ Contents 2. System description $\textbullet$ Contents 3. Desired feedback control and function approximation $\textbullet$ Contents 4. Robust adaptive controller design $\textbullet$ Contents 5. Simulation study $\textbullet$ Contents 6. Conclusion

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.26-30
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• 1995

In this study, the fuzzy approximator and sliding mode control (SMC) scheme are considered. An adaptive sliding mode control is proposed based on the SMC theory. This proposed control scheme is that a adaptive law is utilized to approximate the unknown function f by fuzzy logic system in designing the sliding mode controller for the nonlinear system. In order to reduce the approximation errors, the differences of nonlinear function and fuzzy approximator, an adaptive law is also intoduced and the stability of proposed control scheme are proven with simple adaptive law and roburst adaptive law. This proposed control scheme is applied to a single link robot arm.

• 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.

• Journal of the Korean Society for Precision Engineering
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• v.23 no.9 s.186
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• pp.92-101
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• 2006

In this paper a adaptive backstepping control scheme is proposed for control of a do motor driving a one-link manipulator. Fuzzy logic systems are used to approximate the unknown nonlinear function including the parametric uncertainty and disturbance throughout the entire electromechanical system. A compensation controller is also proposed to estimate the bound of approximation error. Thus the asymptotic stability of the closed-loop control system can be obtained. Numerical simulations are included to show the effectiveness of the proposed controller.

• The Transactions of the Korea Information Processing Society
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• v.4 no.12
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• pp.3185-3199
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• 1997

This paper suggests techniques to reduce coding time which is a problem in traditional fractal compression and to improve fidelity of reconstructed images by determining fractal coefficient through adaptive selection of block approximation formula. First, to reduce coding time, we construct a linear list of domain blocks of which characteristics is given by their luminance and variance and then we control block searching time according to the first permissible threshold value. Next, when employing three-level block partition, if a range block of minimum partition level cannot find a domain block which has a satisfying approximation error, we choose new approximation coefficients using a non-linear approximation of luminance term. This boosts the fidelity. Our experiment employing the above methods shows enhancement in the coding time more than two times over traditional coding methods and shows improvement in PSNR value by about 1-3dB at the same com- pression rate.

• Journal of Broadcast Engineering
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• v.2 no.2
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• pp.194-205
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• 1997

In video compression standards such as MPEG and H.263. rate control is one of the key components for good coding performance. This paper presents a simple adaptive rate control scheme using a piecewise linear approximation model. While conventional buffer control approach is performed by adjusting the quantization parameter linearly according to the buffer fullness. the proposed approach uses a piecewise linear approximation model derived from logarithmic relation between the quantization parameter and bitrate in data compression. In addition. a forward analyzer performed in the spatial domain is used to improve image quality. Simulation results demonstrate that the proposed method provides better performance than the conventional one and reduces the fluctuation of the PSNR per frame while maintaining the quality of the reconstructed frames at a relatively stable level.

• Journal of Ocean Engineering and Technology
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• v.12 no.3 s.29
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• pp.96-102
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• 1998

This paper presents to create a SAC(Sectional Area Curve) using an ANFIS(Adaptive-Network-based Fuzzy Inference System). First, it defines SACs of parent ships by using a B-spline approximation and a genetic algorithm and accumulates a database about SAC's control points. Second, it learns an ANFIS from parent ship data, which are related with principal dimensions and SAC's control points. This process is to model an ANFIS for SAC inferreice. When an ANFIS modeling is completed, we can determine a SAC through an ANFIS inferring.