• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2009년도 IWAIT
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• pp.623-627
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• 2009

Recently, we have many kinds of picture format and display, and resizing (scaling) of picture becomes important. In this processing, quality of picture depends on re-sizing method. For this, some methods to improve the PSNR have been proposed. However, subjective picture quality is more important. Especially, degradation caused by re-sizing, such as jaggy (aliasing) and ringing, should be reduced. To solve them, we have proposed the method using directional adaptive interpolation. To improve the performance of this method, we consider the shape analysis this time. In the proposed method, directional adaptive processing is applied for pure edge only. In the texture area and flat area, 8 tap re-sampling filter is used. As the results of processing, the reductions of jaggy and incorrect interpolated pixels are recognized. The subjective picture quality of proposed method is significantly better than 8-tap re-sampling which gives good PSNR.

• 한국지능시스템학회논문지
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• 제14권1호
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• pp.118-124
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• 2004

In this paper, we present a indirect adaptive control method using a wavelet neural network (WNN) for the control of chaotic nonlinear systems without precise mathematical models. The proposed indirect adaptive control method includes the off-line identification and on-line control procedure for chaotic nonlinear systems. In the off-line identification procedure, the WNN based identification model identifies the chaotic nonlinear system by using the serial-parallel identification structure and is trained by the gradient-descent method. And, in the on-line control procedure, a WNN controller is designed by using the off-line identification model and is trained by the error back-propagation algorithm. Finally, the effectiveness and feasibility of the proposed control method is demonstrated with applications to the chaotic nonlinear systems.

• Journal of Mechanical Science and Technology
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• 제20권10호
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• pp.1730-1740
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• 2006

A nodeless variables finite element method for analysis of two-dimensional, steady-state viscous incompressible flow is presented. The finite element equations are derived from the governing Navier-Stokes differential equations and a corresponding computer program is developed. The proposed method is evaluated by solving the examples of the lubricant flow in journal bearing and the flow in the lid-driven cavity. An adaptive meshing technique is incorporated to improve the solution accuracy and, at the same time, to reduce the analysis computational time. The efficiency of the combined adaptive meshing technique and the nodeless variables finite element method is illustrated by using the example of the flow past two fences in a channel.

• Structural Engineering and Mechanics
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• 제10권6호
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• pp.635-650
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• 2000

In this paper, the adaptive nodal generation procedure based on the estimated local and global error in the element-free Galerkin (EFG) method is proposed. To investigate the possibility of h-type adaptivity of EFG method, a simple nodal refinement scheme is used. By adding new node along the background cell that is used in numerical integration, both of the local and global errors can be controlled adaptively. These errors are estimated by calculating the difference between the values of the projected stresses and original EFG stresses. The ultimate goal of this study is to develop the reliable nodal generator based on the local and global errors that is estimated posteriori. To evaluate the performance of proposed adaptive procedure, the convergence behavior is investigated for several examples.

• Structural Engineering and Mechanics
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• 제5권2호
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• pp.115-126
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• 1997

Importance sampling methods have been developed with the aim of reducing the computational costs inherent in Monte Carlo methods. This study proposes a new algorithm called the adaptive kernel method which combines and modifies some of the concepts from adaptive sampling and the simple kernel method to evaluate the structural reliability of time variant problems. The essence of the resulting algorithm is to select an appropriate starting point from which the importance sampling density can be generated efficiently. Numerical results show that the method is unbiased and substantially increases the efficiency over other methods.

• Journal of Information Processing Systems
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• 제19권3호
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• pp.370-376
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• 2023

Aiming at the problems of low image enhancement accuracy, long enhancement time and poor image quality in the traditional robot sequence motion image enhancement methods, an adaptive enhancement method for robot sequence motion image is proposed. The feature representation of the image was obtained by Karhunen-Loeve (K-L) transformation, and the nonlinear relationship between the robot joint angle and the image feature was established. The trajectory planning was carried out in the robot joint space to generate the robot sequence motion image, and an adaptive homomorphic filter was constructed to process the noise of the robot sequence motion image. According to the noise processing results, the brightness of robot sequence motion image was enhanced by using the multi-scale Retinex algorithm. The simulation results showed that the proposed method had higher accuracy and consumed shorter time for enhancement of robot sequence motion images. The simulation results showed that the image enhancement accuracy of the proposed method could reach 100%. The proposed method has important research significance and economic value in intelligent monitoring, automatic driving, and military fields.

• International Journal of Control, Automation, and Systems
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• 제4권2호
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• pp.139-145
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• 2006

A multi-variable Golden-Section adaptive controller is proposed for the tracking control of robotic manipulators with unknown dynamics. With a small sample time, the unknown dynamics of the robotic manipulator are denoted equivalently by a characteristic model of a 2-order multivariable time-varying difference equation. The coefficients of the characteristic model change slowly with time and some of their valuable characteristic relationships emerge. Based on the characteristic model, an adaptive algorithm with a simple form for the control of robotic manipulators is presented, which combines the multi-variable Golden-Section adaptive control law with the weighted least squares estimation method. Moreover, a compensation neural network law is incorporated into the designed controller to reduce the influence of the coefficients estimation error on the control performance. The results of the simulations indicate that the developed control scheme is effective in robotic manipulator control.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2003년도 추계학술대회
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• pp.219-226
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• 2003

In this paper, it is presented a new scheme of adaptive-neuro control system to implement real-time control of robot manipulator. Unlike the well-established theory fir the adaptive control of linear systems, there exists relatively little general theory fir the adaptive control of nonlinear systems. Adaptive control technique is essential fir providing a stable and robust performance fir application of robot control. The proposed neuro control algorithm is one of teaming a model based error back-propagation scheme using Lyapunov stability analysis method. Through simulation, the proposed adaptive-neuro control scheme is proved to be a efficient control technique f3r real-time control of robot system using DSPs.

• 대한전자공학회논문지
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• 제27권3호
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• pp.145-155
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• 1990

適應 循環 필터에서 시스템 傳達 函數의 極點이 單位圓 밖으로 나감으로써 시스템이 불안정해지는 것을 방지하기 위해 適應 補償器를 채용한 適應 循環 필터 알고리듬을 제안하였고 제안한 알고리듬이 超安定性을 만족한다는 것을 證明하였다. 제안한 알고리듬 適應 雜音 除去器에 응용하여 LS방법의 適應 循環 필터를 이용한 適應 雜音 除去器, 適應 非循環 필터를 이용한 適應 雜音 除去器와의 성능을 比較하여 제안한 알고리듬의 妥當性을 立證하였다.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2004년도 추계학술대회 논문집
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• pp.193-200
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• 2004

In this paper, it is presented a new scheme of adaptive-neuro control system to implement real-time control of robot manipulator. Unlike the well-established theory for the adaptive control of linear systems, 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 robot control. The proposed neuro control algorithm is one of learning a model based error back-propagation scheme using Lyapunov stability analysis method. Through simulation, the proposed adaptive-neuro control scheme is proved to be a efficient control technique for real-time control of robot system using DSPs.