• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.6
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• pp.616-628
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• 2000

In this paper, we present the modeling of the ozone prediction system using Neuro-Fuzzy approaches. The mechanism of ozone concentration is highly complex, nonlinear, and nonstationary, the modeling of ozone prediction system has many problems and the results of prediction is not a good performance so far. The Dynamic Polynomial Neural Network(DPNN) which employs a typical algorithm of GMDH(Group Method of Data Handling) is a useful method for data analysis, identification of nonlinear complex system, and prediction of a dynamical system. The structure of the final model is compact and the computation speed to produce an output is faster than other modeling methods. In addition to DPNN, this paper also includes a Fuzzy Logic Method for modeling of ozone prediction system. The results of each modeling method and the performance of ozone prediction are presented. The proposed method shows that the prediction to the ozone concentration based upon Neuro-Fuzzy approaches gives us a good performance for ozone prediction in high and low ozone concentration with the ability of superior data approximation and self organization.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.24 no.9
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• pp.26-35
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• 2010

The maximum output torque developed by the machine is dependent on the allowable current rating and maximum voltage that the inverter can supply to the machine. Therefore, to use the inverter capacity fully, it is desirable to use the control scheme considering the voltage and current limit condition, which can yield the maximum torque per ampere over the entire speed range. This controller is controlled speed and current using hybrid PI(HBPI) controller and estimation of speed using ANN. Also, this paper is proposed maximum torque control of induction motor using slip angular speed and current condition at widely speed range. The performance of the proposed induction motor drive with maximum torque control using HBPI controller is verified by analysis results at dynamic operation conditions.

• Journal of Electrical Engineering and information Science
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• v.2 no.6
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• pp.111-115
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• 1997

ZnO varistors are widely used as surge arresters in power system based on their excellent nonlinearity. The property of power loss of ZnO varistors is related to the thermal stability and their life-spans of ZnO surge arresters. The power losses of ZnO varistors under different temperatures and applied voltages were measured, and the properties of power losses were analyzed. The Artificial Neural Network (ANN) was used to simulate the power losses properties of ZnO varistors which is an adaptive nonlinear dynamic system, and the results calculated by ANN simulating model were in good agreement with the tested ones.

• Journal of Advanced Marine Engineering and Technology
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• v.31 no.5
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• pp.531-542
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• 2007

Simultaneous measurement technique for temperature and velocity fields near a heated solid body has been constructed. The measurement system consists of a 3-late CCD color camera, a color image grabber, a 1ighting system, a host computer and a software for the whole quantification process. Thermo Chromic Liquid Crystals (TCLC) was used as temperature sensors. A neural network was used to get a calibration curve between the temperature and the color change of the TCLC in order to enhance the dynamic range of temperature measurement. The velocity field measurement was attained by the use of the fray-level images taken for the flow field, and by introducing the cross-correlation technique. The temperature and the velocity fields of the forced and the natural convective flows neat the surface of a cartridge heater were measured simultaneously with the constructed measurement system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.195-198
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• 2002

고농도오존이 발생되는 원인과 환경적 요인의 상호관계를 모델링하기 위해 신경회로 망과 같은 지능제어 기법들이 많이 적용되어 왔다 분석과 모델링을 위해 유전자 알고리즘과 같은 최적화 방법을 적용하기도 하지만, 고농도 오존이 발생되는 메커니즘이 매우 복잡하고, 비선형적이며, 패턴파악이 어렵기 때문에 고농도 오존의 예측 모델링에는 여전히 문제점이 있다 따라서 본 논문에서는 신뢰수준과 신뢰구간을 이용하여 초농도 오존을 예측할 수 있는 모델링 방법을 서술하였다 예측값의 신뢰수준의 평가는 예측에 대한 실측값을 구하여 신뢰구간내의 데이터의 개수를 파악함으로써 신뢰성을 평가할 수 있다. 또한 이 테스트는 우리가 가지고 있지 않은 데이터에 대한 유효성을 평가하는데 적용될 수 있다 그리고 본 논문에서는 GMDH(Group Method of data handling)의 전형적인 알고리즘에 바탕을 두고 있는 DPNN(Dynamic Polynomial Neural Network)를 이용하여 예측 모델을 구성하였다. DPNN은 데이터 해석이 용이하고 비선형적인 동적 시스템 예측에 유용하게 적용될 수 있는 장점을 가지고 있다.

• Journal of Electrical Engineering and Technology
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• v.3 no.2
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• pp.143-151
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• 2008

This paper presents the operation of Fuel Cell Distributed Generation(FCDG) systems in distribution systems. Hence, modeling, controller design, and simulation study of a Solid Oxide Fuel Cell(SOFC) distributed generation(DG) system are investigated. The physical model of the fuel cell stack and dynamic models of power conditioning units are described. Then, suitable control architecture based on fuzzy logic and the neural network for the overall system is presented in order to activate power control and power quality improvement. A MATLAB/Simulink simulation model is developed for the SOFC DG system by combining the individual component models and the controllers designed for the power conditioning units. Simulation results are given to show the overall system performance including active power control and voltage regulation capability of the distribution system.

• Journal of information and communication convergence engineering
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• v.9 no.4
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• pp.369-374
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• 2011

Generally the neural network and the Fuzzy compensative algorithms are applied to forecast the time series for power demand with the characteristics of a nonlinear dynamic system, but, relatively, they have a few prediction errors. They also make long term forecasts difficult because of sensitivity to the initial conditions. In this paper, we evaluate the chaotic characteristic of electrical power demand with qualitative and quantitative analysis methods and perform a forecast simulation of electrical power demand in regular sequence, attractor reconstruction and a time series forecast for multi dimension using Lyapunov Exponent (L.E.) quantitatively. We compare simulated results with previous methods and verify that the present method is more practical and effective than the previous methods. We also obtain the hourly predictability of time series for power demand using the L.E. and evaluate its accuracy.

• Proceedings of the KIEE Conference
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• 2004.05a
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• pp.144-146
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• 2004

It is known that Global Positioning System(GPS) is the most efficient navigation system because it provides precise position information on the all areas of Earth regardless of metrology. Until now, the size of GPS receivers has become smaller and the performance of receivers has become higher. So receivers provide the position information of not only static system but also dynamic system. Usually, users make similar movement trajectory according to their life pattern and it is possible to build up efficient database by collecting only the repeated users' position. Because position information calculated by the receiver is erroneous about 10-30m within 5% error tolerance, the position information is oscillated even on the same area. In this paper, we propose the system that can estimate whether users are out of trajectory or in dangerous situation by soft-computing method.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1993.10a
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• pp.5-18
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• 1993

This paper reports the recent trends in active vibration control in Japan, especially, based on papers selected in the Proceedings of First International Conference on Motion and Vibration Control (1st MOVIC) held at Yokohama, Japan on Sept.7-11, 1992. Firstly, it classifiers vibration control methods and vibration controllers, especially active dynamic absorbers which are widely used in mechanical and civil engineering. Secondly, it covers basic problems in the control of vibration of flexible structures such as formulating a reduced-order model required for designing vibration controller, proper arranging of sensors and actuators, and preventing of spillover instability. Finally, the practical use of control theories such as LQ control theory, $H^{\infty}$ control theory, neural network theory, and other topics are discussed..

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2006.03a
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• pp.613-619
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• 2006

This paper introduces a new structural health monitoring using a nano sensor. The sensor is made of nano smart composite material based on carbon nanotubes. The nano sensor is fabricated as a thin and narrow polymer film sensor that is bonded or deposited onto a structure. The electrochemical impedance and dynamic strain response of the neuron change due to deterioration of the structure where the sensor is located. A network of the long nano sensorcan form a structural neural system to provide large area coverage and an assurance of the operational health of a structure without the need for actuators and complex wave propagation analyses that are used with other methods.