• Proceedings of the KIEE Conference
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• 2006.07a
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• pp.83-84
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• 2006

Lightning induced faults accounts for more than 66% at the transmission lines of KEPCO. The lightning causes damages to power system equipments including transmission line, the shut down of electricity and the electro-magnetic interference. Because of this reason, we need the real time lightning information for the optimal operation of power system. And, it is required to obtain and accumulate the lightning current parameters for the insulation design. A lightning detection system, LPATS, has been operated since 1995 in KEPCO. For the improved detection efficiency, we had in stalled the new lightning detection network named as KLDNet in 2005. Also, we had developed the new software for the lightning parameters analysis and real time information service on the WEB. In this paper, we would like to introduce about the new system.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.3
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• pp.207-213
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• 2000

This paper introduces a high-performance speed control system based on artificial neural networks(ANN) to estimate unknown parameters of a DC servo motor. The goal of this research is to keep the rotor speed of the DC servo motor to follow an arbitrary selected trajectory. In detail, the aim is to obtain accurate trajectory control of the speed, specially when the motor and load parameters are unknown. By using an artificial neural network, we can acquire unknown nonlinear dynamics of the motor and the load. A trained neural network identifier combined with a reference model can be used to achieve the trajectory control. The performance of the identification and the control algorithm are evaluated through the simulation and experiment of nonlinear dynamics of the motor and the load using a typical DC servo motor model.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.566-568
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• 1998

In this paper, we suggest a optimal design method of Fuzzy-Neural Networks model for complex and nonlinear systems. FNNs have the stucture of fusion of both fuzzy inference with linguistic variables and Neural Networks. The network structure uses the simpified inference as fuzzy inference system and the BP algorithm as learning procedure. And we use a clustering algorithm to find initial parameters of membership function. The parameters such as membership functions, learning rates and momentum coefficients are easily adjusted using the genetic algorithms. Also, the performance index with weighted value is introduced to achieve a meaningful balance between approximation and generalization abilities of the model. To evaluate the performance index, we use the time series data for gas furnace and the sewage treatment process.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2383-2385
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• 2000

It is proposed a new algorithm for a neural network adaptive tracking control scheme to improve performance in this paper. In supervisory control scheme, the upper and lower bound of the parameters are directly estimated by using RBF neural network without their information, and the weighting parameters of the control input are adjusted on-line by adaptation laws. As a result, the proposed algorithm assured that the output errors go to zero without relation to existing minimum approximation errors and disturbances. The effectiveness of the proposed algorithm is demonstrated through the simulation of one-link rigid robotics manipulator.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.4
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• pp.838-842
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• 2011

This paper proposes an online adaptive neuro-controller for a wind energy conversion system (WECS) that is a highly nonlinear system intrinsically. In real application, to obtain exact system parameters such as power coefficient, many measuring instruments and implementations are required, which is very difficult to perform. This shortcoming can be avoided by introducing neural network in the controller design in this paper. The proposed adaptive neural control scheme using radial-basis function network (RBFN) needs no system parameters to meet control objectives. Combining derivative estimator for wind velocity, the whole closed-loop system is shown to be stable in the sense of Lyapunov.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.10
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• pp.827-836
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• 2001

The PID controller has been widely applied to the most control systems because of its simple structure and east designing. One of the important points to design the PID control system is to tune the approximate control parameters for the given target system. To find the PID parameters using Ziegler Nichols(ZN) method needs a lot of experience and experiments to ensure the optimal performance. In this paper, CMIA(Cell Mediated Immune Algorithm) controller is proposed to drive the autonomous guided vehicle (AGV) more effectively. The proposed controller is based on specific immune responses of the biological immune system which is the cell mediated immunity. To verify the performance of the proposed CMIA controller, some experiments for the control of steering and speed of that AGV are performed. The tracking error of the AGV is mainly investigated for this purpose. As a result, the capability of realization and reliableness are proved by comparing the response characteristics of the proposed CMIA controllers with those of the conventional PID and NNPID(Neural Network PID) controller.

• International Journal of Korean Welding Society
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• v.1 no.2
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• pp.36-44
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• 2001

To get the appropriate welding process variables, mathematical modeling in conjunction with many experiments is necessary to predict the magnitude of weld bead shape. Even though the experimental results are reliable, it has a difficulty in accurately predicting welding process variables for the desired weld bead shape because of nonlinear and complex characteristics of welding processes. The welding condition determined for the desired weld bead shape may cause the weld defect if the welding current/voltage/speed combination is improperly selected. In this study, the $2^{n-1}$ fractional factorial design method and correlation parameter were used to investigate the effect of the welding process variables on the fillet joint shape, and the multiple non-linear regression analysis was used for modeling the gas metal arc welding(GMAW）parameters of the fillet joint. Finally, a fuzzy rule-based method and a neural network method were proposed so that the complexity and non-linearity of arc welding phenomena could be effectively overcome. The performance of the proposed neuro-fuzzy system was evaluated through various experiments. The experimental results showed that the proposed neuro-fuzzy system could effectively check the welding conditions as to whether or not weld defects would occur, and also adjust the welding conditions to avoid these weld defects.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.5B
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• pp.435-447
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• 2003

Current existing network dimensioning method simply relies on long-time measurement data or uses average traffic characteristics of users. In this paper, we propose an efficient network dimensioning method for DiffServ over MPLS networks. First, User's SLA information is distributed from edge nodes to core nodes according to the proposed algorithm. Then at each link, class-based capacity planning is performed. For capacity planning, we proposed sets of network design parameters for DiffServ classes and bandwidth allocation schemes that are most suitable for each class. We have developed a DiffServ over MPLS network design tool using the proposed method. Simulation results show that our proposed method is able to design efficiently the required class link resources.

• International Journal of Advanced Culture Technology
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• v.1 no.2
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• pp.18-23
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• 2013

In this paper, we consider the problem of designing optimal sensing time and the minimization of energy consumption in the Cognitive radio Network. Trade-off between throughput and the sensing time are observed, and the equations are derived for the optimal choice of design variables. In this paper, we also look at the optimization problem involving all the design parameters together. The advantages of the proposed scheme for the spectrum sensing and access process are shown through simulation.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.2
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• pp.87-95
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• 1997

This paper presents an optimal design procedure to realize an BSB neural networks by means of the parametrization of solution space and optimization of parameters using evaluation program. In particular, the performance index based on DOA analysis may make an associative memory implementation reach on the level of practical success.