• Proceedings of the KIEE Conference
• /
• 1998.07g
• /
• pp.2363-2366
• /
• 1998

The intent of this paper is to describe a neural network structure called dynamic neural processor(DNP), and examine how it can be used in developing a learning scheme for computing robot inverse kinematic transformations. The architecture and learning algorithm of the proposed dynamic neural network structure, the DNP, are described. Computer simulations are provided to demonstrate the effectiveness of the proposed learning using the DNP.

• Proceedings of the KIEE Conference
• /
• 1998.07c
• /
• pp.1100-1102
• /
• 1998

This paper presents the Two-Phase Neural Network(TPNN) to slove the Optimal Economic Environmental Dispatch problem of thermal generating units in electric power system. The TPNN, Compared with other Neural Networks, is very accurate and it takes smaller computer time for a optimization problem to converge. In this work, in order to provide useful information to the system operator, we are used the total environmental weight and relative weighting of individual insults(e.g., $SO_2$, $NO_X$ and $CO_2$) also, presented the simulation results of the dispatch changes according to the weights. The Two-Phase Neural Network is tested on a 11-unit 3-pollutant system to prove of effectiveness and applicability.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 2005.05a
• /
• pp.91-96
• /
• 2005

In this study, researchers developed the estimative algorithm for artificial defects in semiconductor packages and performed it by pattern recognition technology. For this purpose, the estimative algorithm was included that researchers made software with MATLAB. The software consists of some procedures including ultrasonic image acquisition, equalization filtering, Self-Organizing Map and Backpropagation Neural Network. Self-Organizing Map and Backpropagation Neural Network are belong to methods of Neural Networks. And the pattern recognition technology has applied to classify three kinds of detective patterns in semiconductor packages: Crack, Delamination and Normal. According to the results, we were confirmed that estimative algorithm was provided the recognition rates of $75.7\%$ (for Crack) and $83_4\%$ (for Delamination) and $87.2\%$ (for Normal).

• Transactions of the Korean Society of Machine Tool Engineers
• /
• v.10 no.6
• /
• pp.95-101
• /
• 2001

This work is concerned with construction of the intelligence stress predictor far compression strength evaluation using neural network-ultrasonic waves. The contact pressure in jointed plates was measured by using ultrasonic technique. Neural network is used to evaluate and predict contact pressure from the results of the calibration curves. The organized neural system was leaned with the accuracy of 99%, as a result of learning the ultrasonic echo ratio to the contact pressure measurement between SM45C and STS410 materials. And it could be evaluated and predicted with the accuracy of 90% in the evaluation of ultrasonic echo ratio difference in the same surface roughness and contact pressure, and 85% in the prediction of virtual ultrasonic echo ratio. Thus the proposed stress predictor is very useful for the evaluation and prediction of the contact pressure between SM45C and STS410 materials.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.29B no.10
• /
• pp.37-45
• /
• 1992

For a precise manipulator control in the presence of environmental uncertainties, it has long been recognized that the robot should be controlled in a task-referenced space. In this respect, an effective visual servo control system for robot manipulators based on neural networks is proposed. In the proposed control system, a Backpropagation neural network is used first to learn the mapping relationship between the robot's joint space and the video image space. However, in the real control loop, this network is not used in itself, but its first and second derivatives are used to generate servo commands for the robot. Second, and Adaline neural network is used to identify the approximately linear dynamics of the robot and also to generate the proper joint torque commands. Computer simulation has been performed demonstrating the proposed method's superior performance. Futrhermore, the proposed scheme can be effectively utilized in a robot skill acquisition system where the robot can be taught by watching a human behavioral task.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1993.06a
• /
• pp.972-974
• /
• 1993

This paper presents a new method to automatically design fuzzy logic controller(FLC). The main problems of designing FLC are how to optimally and automatically select the control rules and the parameters of membership function (MF). Cell state space algorithms (CSS), differential competitive learning (DCL) and multialyer neural network are combined in this paper to solve the problems. When the dynamical model of a control process is known. CSS can be used to generate a group of optimal input output pairs(X, Y) used by a controller. The(X, Y) then can be used to determine the FLC rules by DCL and to determine the optimal parameters of MF by DCL and to determine the optimal parameters of MF by multilayer neural network trained by BP algorithm.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1993.06a
• /
• pp.1177-1180
• /
• 1993

In this paper, a fuzzy-neural interpolating network is proposed to efficiently approximate a nonlinear function. Specifically, basis functions are first constructed by Fuzzy Membership Function based Neural Networks (FMFNN). And the fuzzy similarity, which is defined as the degree of matching between actual output value and the output of each basis function, is employed to determine initial weighting of the proposed network. Then the weightings are updated in such a way that square of the error is minimized. To show the capability of function approximation of the proposed fuzzy-neural interpolating network, a numerical example is illustrated.

• The Transactions of the Korean Institute of Electrical Engineers
• /
• v.41 no.5
• /
• pp.533-540
• /
• 1992

The Self Creating and Organizing (SCO) is a new architecture and one of the unsupervized learning algorithm for the artificial neural network. SCO begins with only one output node which has a sufficiently wide response range, and the response ranges of all the nodes decrease automatically whether adapting the weights of existing node or creating a new node. It is compared to the Kohonen's Self Organizing Feature Map (SOFM). The results show that SCONN has lots of advantages over other competitive learning architecture.

• Journal of Electrical Engineering and Technology
• /
• v.9 no.4
• /
• pp.1375-1384
• /
• 2014

Flashover of power transmission line insulators is a major threat to the reliable operation of power system. This paper deals with the flashover prediction of polymeric insulators used in power transmission line applications using the novel condition monitoring technique developed by PD signal time-frequency map and neural network technique. Laboratory experiments on polymeric insulators were carried out as per IEC 60507 under AC voltage, at different humidity and contamination levels using NaCl as a contaminant. Partial discharge signals were acquired using advanced ultra wide band detection system. Salient features from the Time-Frequency map and PRPD pattern at different pollution levels were extracted. The flashover prediction of polymeric insulators was automated using artificial neural network (ANN) with back propagation algorithm (BPA). From the results, it can be speculated that PD signal feature extraction along with back propagation classification is a well suited technique to predict flashover of polymeric insulators.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.17 no.1
• /
• pp.74-79
• /
• 2003

In this paper, the PID controller is constructed with a neural network and wavelet function. And the wavelet neural PID controller is adapted by choosing the values of the dilation and translation parameter of the wavelet function. Weights are adjusted by the inverse propagation algolithm. Applying this method to the position control system, its usefulness is verified from the results of experiment.