• Journal of Electrical Engineering and Technology
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• v.8 no.2
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• pp.385-392
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• 2013

An uncalibrated visual servo control method for tracking a target is presented. We define the robot-positioning problem as an unconstrained optimization problem to minimize the image error between the target feature and the robot end-effector feature. We propose a method to find the residual term for more precise modeling using the secant approximation method. The composite image Jacobian is estimated by the proper method for eye-to-hand configuration without knowledge of the kinematic structure, imaging geometry and intrinsic parameter of camera. This method is independent of the motion of a target feature. The algorithm for regulation of the joint velocity for safety and stability is presented using the cost function. Adaptive regulation for visibility constraints is proposed using the adaptive parameter.

• Steel and Composite Structures
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• v.7 no.4
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• pp.263-278
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• 2007

A finite element implementation of the modified compression field theory (MCFT) using a tangential formulation is presented in this work. Previous work reported on implementations of MCFT has concentrated mainly on secant formulations. This work describes details of the implementation of a modular algorithmic structure of a reinforced concrete constitutive model in nonlinear finite element schemes that use a Jacobian matrix in the solution of the nonlinear system of algebraic equations. The implementation was verified and validated using experimental and analytical data reported in the literature. The developed algorithm, which converges accurately and quickly, can be easily implemented in any finite element code.

• Proceedings of the Korea Multimedia Society Conference
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• 2002.05c
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• pp.336-341
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• 2002

본 연구에서는 엔트로피 최적화를 위한 목적함수의 근을 구하기 위해 단순히 함수 값만을 이용하여 계산을 근사화한 할선법에 기초한 고정점 알고리즘의 독립성분분석 기법을 제안하였다. 이렇게 하면 기존의 뉴우턴법에 기초한 고정점 알고리즘에서 요구되는 복잡한 도함수의 계산과정을 간략화 할 수 있어 더 우수한 학습성능의 독립성분분석이 가능하다. 제안된 학습알고리즘의 독립성분분석 기법을 512$\times$512의 픽셀을 가지는 10개의 영상을 대상으로 임의의 혼합행렬에 따라 발생되는 혼합영상들을 실험하였다. 실험결과, 기존의 뉴우턴법에 기초한 고정점 알고리즘의 분석기법보다 빠른 학습속도와 개선된 분리성능이 있음을 확인하였다. 특히 기존의 알고리즘에서 임의로 설정되는 초기값에 덜 의존하는 학습성능이 있음도 확인할 수 있었다.

• The Transactions of the Korean Institute of Power Electronics
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• v.10 no.2
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• pp.124-129
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• 2005

This paper presents an advanced method for an initial pole position estimation of a Permanent Magnet Linear Synchronous Motor(PMLSM) that has an accurate incremental encoder for servo applications but does not have Hall sensors as a magnetic pole sensor. By appropriately using the secant method as a numerical method the proposed algorithm finds either of two zero force positions and then the correct d-axis by applying a q-axis test current. It only requires the tuned current controller and the relative position information md so it can be simply applicable to a rotary PMSM. The experimental results show the validity of the proposed method, which has an excellent performance with respect to an accurate pole position estimation under the minimal moving distance(average of about 85㎛) during the estimation process.

• Journal of the Earthquake Engineering Society of Korea
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• v.11 no.2 s.54
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• pp.105-113
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• 2007

In the current research, an algorithm of performance-based seismic retrofit design of reinforced concrete columns using FRP jacket has been proposed. For exact prediction of the nonlinear flexural analysis or FRP composite RC members, multiaxial constitutive laws of concrete and composite materials have been presented. For seismic retrofit design, an algorithm of direct displacement-based design method (DDM) proposed by Chopra and Goel (2001) has been newly applied to determine the design thickness of FRP jacket in seismic retrofit of reinforced concrete columns. To compare with the displacement coefficient method (DCM), the DDM gives an accurate prediction of the target displacement in highly nonlinear region, since the DCM uses the elastic stiffness before reaching the yield load as the effective stiffness but the DDM uses the secant stiffness.

• Computers and Concrete
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• v.13 no.1
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• pp.49-70
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• 2014

Steel fiber-reinforced concrete (SFRC) is known as one of the efficient modern composites that can greatly enhance the material performance of cracked concrete in tension. Such improved tensile resistance mechanism at crack interfaces in SFRC members can be heavily influenced by methodologies of treatments of crack direction. While most existing studies have focused on developing the numerical analysis model with the rotating-angle theory, there are only few studies on finite element analysis models with the fixed-angle model approach. According to many existing experimental studies, the direction of principal stress rotated after the formation of initial fixed-cracks, but it was also observed that new cracks with completely different angles relative to the initial crack direction very rarely occurred. Therefore, this study introduced the direct tension force transfer model (DTFTM), in which tensile resistance of the fibers at the crack interface can be easily estimated, to the nonlinear finite element analysis algorithm with the fixed-angle theory, and the proposed model was also verified by comparing the analysis results to the SFRC shear panel test results. The secant modulus method adopted in this study for iterative calculations in nonlinear finite element analysis showed highly stable and fast convergence capability when it was applied to the fixed-angle theory. The deviation angle between the principal stress direction and the fixed-crack direction significantly increased as the tensile stresses in the steel fibers at crack interfaces increased, which implies that the deviation angle is very important in the estimation of the shear behavior of SFRC members.

• Computational Structural Engineering
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• v.9 no.1
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• pp.151-159
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• 1996

A finite element formulation based on the CFT(Compression Field Theory) concept such as the effect of compression softening in cracked concrete, and macroscopic and rotating crack models etc. was presented for the nonlinear behaviour of structural concrete. In this category, tangential or secant material stiffnesses for cracked concrete were also defined and discussed in view of the iterative solution schemes for nonlinear equations. Considering the computational efficiency and the ability of modelling the post-ultimate behaviour as major concerns, the incremental displacement solution algorithm involving initial material stiffnesses and the relaxation procedure for fast convergence was adopted and formulated in a type of 8-noded quadrilateral isoparametric elements. The analysis program NASCOM(Nonlinear Analysis of structrual Concrete by FEM : Monotonic Loading) developed baed on the CFT constitutive relationships and the incremetal solution strategy described enables the predictions of strength and deformation capacities in a full range. crack patterns and their corresponding widths, and yield extents of reinforcement. As the verfication purpose of NASCOM, the prediction of Cervenka's panel test results including the load resistance and the deformation history was made. A limited number of predictions indicate a good correlation in a general sense.

• Journal of Korea Water Resources Association
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• v.34 no.4
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• pp.303-316
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• 2001

In this study, Radial Basis Function(RBF) Neural Networks Model, a kind of Hybrid Neural Networks was applied to hydrological forecasting in a small watershed. RBF Neural Networks Model has four kinds of parameters in it and consists of unsupervised and supervised training patterns. And Gaussian Kernel Function(GKF) was used among many kinds of Radial Basis Functions(RBFs). K-Means clustering algorithm was applied to optimize centers and widths which ate the parameters of GKF. The parameters of RBF Neural Networks Model such as centers, widths weights and biases were determined by the training procedures of RBF Neural Networks Model. And, with these parameters the validation procedures of RBF Neural Networks Model were carried out. RBF Neural Networks Model was applied to Wi-Stream basin which is one of the IHP Representative basins in South Korea. 10 rainfall events were selected for training and validation of RBF Neural Networks Model. The results of RBF Neural Networks Model were compared with those of Elman Neural Networks(ENN) Model. ENN Model is composed of One Step Secant BackPropagation(OSSBP) and Resilient BackPropagation(RBP) algorithms. RBF Neural Networks shows better results than ENN Model. RBF Neural Networks Model spent less time for the training of model and can be easily used by the hydrologists with little background knowledge of RBF Neural Networks Model.