• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.1691-1692
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• 2008

In this paper, the adaptive neural network technique is proposed to control the speed of wind power generation system. For maximizing generated power effectively, adaptive neural algorithm based on SRWMM(Self Recurrent Wavelet Neural Network) is derived to on-line adjust the excitation winding voltage of the generator. Through computer simulations, it is shown that the proposed method can achieve smooth and asymptotic rotor speed tracking.

• Journal of Advanced Navigation Technology
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• v.22 no.5
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• pp.429-435
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• 2018

In radar systems, FFT (fast Fourier transform) operation is necessary to obtain the range and velocity of target, and the design of an FFT processor which operates at high speed is required for real-time implementation. The perfect shuffle network is suitable for high-speed FFT processor. In particular, twice perfect shuffle network based on radix-4 is preferred for very high-speed FFT processor. Moreover, radar systems that requires various velocity resolution should support scalable FFT points. In this paper, we propose a 8~1024-point scalable FFT processor based on twice perfect shuffle network algorithm and present hardware design and implementation results. The proposed FFT processor was designed using hardware description language (HDL) and synthesized to gate-level circuits using $0.65{\mu}m$ CMOS process. It is confirmed that the proposed processor includes logic gates of 3,293K.

• Smart Structures and Systems
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• v.14 no.2
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• pp.145-158
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• 2014

The objective of this study is to improve the survivability and reliability of the FBG sensor network in the structural health monitoring (SHM) system. Therefore, a model reconstruction soft computing recognition algorithm based on support vector regression (SVR) is proposed to achieve the high reliability of the FBG sensor network, and the grid search algorithm is used to optimize the parameters of SVR model. Furthermore, in order to demonstrate the effectiveness of the proposed model reconstruction algorithm, a SHM system based on an eight-point fiber Bragg grating (FBG) sensor network is designed to monitor the foreign-object low velocity impact of a CFRP composite plate. Simultaneously, some sensors data are neglected to simulate different kinds of FBG sensor network failure modes, the predicting results are compared with non-reconstruction for the same failure mode. The comparative results indicate that the performance of the model reconstruction recognition algorithm based on SVR has more excellence than that of non-reconstruction, and the model reconstruction algorithm almost keeps the consistent predicting accuracy when no sensor, one sensor and two sensors are invalid in the FBG sensor network, thus the reliability is improved when there are FBG sensors are invalid in the structural health monitoring system.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.18 no.9
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• pp.698-706
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• 2006

Experiment and numerical calculation have been peformed to investigate mixed convection heat transfer between inclined parallel plates. Particle image velocimetry (PIV) with thermo-sensitive liquid crystal (TLC) tracers is used for visualizing and analysis. This method allows simultaneous measurement of velocity and temperature fields at a given instant of time. Quantitative data of the temperature and velocity are obtained by applying the color-image processing to a visualized image, and neural network is applied to the color-to-temperature calibration. The governing equations are discretized using the finite volume method. The results are presented for the Reynolds number ranges from 0.004 to 0.062, the angle of inclination, ${\Theta}$, from 0 to 45 degree and Prandtl number of the high viscosity fluid is 909. The results show velocity, temperature and mean Nusselt numbers distributions. It is found that the periodic flow of mixed convection between inclined parallel plates is shown at $0^{\circ}{\leq}{\Theta}<30^{\circ}$, Re<0.062, and the flow pattern can be classified into three patterns which depend on Reynolds number and the angle of inclination. The minimum Nusselt numbers occur at Re=0.05 regardless of the angle of inclination.

• Annals of Clinical Neurophysiology
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• v.1 no.2
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• pp.173-181
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• 1999

Eye movements serve vision by placing the image of an object on the fovea of each retina, and by preventing slippage of images on the retina. The brain employs two modes of ocular motor control, fast eye movements (saccades) and smooth eye movements. Saccades bring the fovea to a target, and smooth eye movements prevent retinal image slip. Smooth eye movements comprise smooth pursuit, the optokinetic reflex, the vestibulo-ocular reflex (VOR), vergence, and fixation. Saccades achieve rapid refixation of targets that fall on the extrafoveal retina by moving the eyes at peak velocities that can exceed $700^{\circ}/s$. Various brain lesions can affect saccadic latency, velocity, or accuracy. Smooth pursuit maintains fixation of a slowly moving target. The pursuit system responds to slippage of an image near the fovea in order to accelerate the eyes to a velocity that matches that of the target. When smooth eye movements velocity fails to match target velocity, catch-up saccades are used to compensate for limited smooth pursuit velocities. The VOR subserves vision by generating conjugate eye movements that are equal and opposite to head movements. If the VOR gain (the ratio of eye velocity to head velocity) is too high or too low, the target image is off the fovea, and head motion causes oscillopsia, an illusory to-and-fro movement of the environment.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.524-527
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• 1995

In most cases, a crane is controlled by an open-loop technique. That is, the controller tries to follow a given velocity profile that is designed to minimize the swing of rope and the transfer time. But such a system is not capable of handling various disturbances such as changing rope length and wind effect. In order to overcome this kind of difficulty, this research focuses on the design of a feedback controller using intelligent techniques such as fuzzy logic and neural network. These intelligent techniques has been emplyoyed in order to represent human knowledge and to imitate human learning. The deveped controllers have been evaluated via computer simulation

• Journal of Power System Engineering
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• v.20 no.6
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• pp.51-57
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• 2016

Experiment is carried out to investigate the mixed convective flow in three-dimensional horizontal rectangular channels filled with high viscous fluid. The particle image velocimetry(PIV) with thermo-sensitive liquid crystal tracers is used for visualizing and analysis. Quantitative data of temperature and velocity are obtained by applying the color-image processing to a visualized image, and neural network is applied to the color-to-temperature calibration. In this study, the fluid used is silicon oil(Pr=909), the aspect ratio(channel width to heigh) is 4 and Reynolds number is $2{\times}10^{-2}$. From the present study, we can visualize the quantitative temperature and velocity of mixed convective flow in three-dimensional horizontal rectangular channels simultaneously.

• Journal of information and communication convergence engineering
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• v.2 no.3
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• pp.177-181
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• 2004

In this paper, we suggest an Iris recognition system with an excellent recognition rate and confidence as an alternative biometric recognition technique that solves the limit in an existing individual discrimination. For its implementation, we extracted coefficients feature values with the wavelet transformation mainly used in the signal processing, and we used neural network to see a recognition rate. However, Scale Conjugate Gradient of nonlinear optimum method mainly used in neural network is not suitable to solve the optimum problem for its slow velocity of convergence. So we intended to enhance the recognition rate by using Levenberg-Marquardt Back-propagation which supplements existing Scale Conjugate Gradient for an implementation of the iris recognition system. We improved convergence velocity, efficiency, and stability by changing properly the size according to both convergence rate of solution and variation rate of variable vector with the implementation of an applied algorithm.

• Structural Engineering and Mechanics
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• v.78 no.2
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• pp.145-161
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• 2021

Dynamic compaction of Aluminum powder using gas detonation forming technique was investigated. The experiments were carried out on four different conditions of total pre-detonation pressure. The effects of the initial powder mass and grain particle size on the green density and strength of compacted specimens were investigated. The relationships between the mentioned powder design parameters and the final features of specimens were characterized using Response Surface Methodology (RSM). Artificial Neural Network (ANN) models using the Group Method of Data Handling (GMDH) algorithm were also developed to predict the green density and green strength of compacted specimens. Furthermore, the desirability function was employed for multi-objective optimization purposes. The obtained optimal solutions were verified with three new experiments and ANN models. The obtained experimental results corresponding to the best optimal setting with the desirability of 1 are 2714 kg·m-3 and 21.5 MPa for the green density and green strength, respectively, which are very close to the predicted values.

• Journal of the Korean Society of Industry Convergence
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• v.7 no.4
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• pp.391-397
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• 2004

This paper presented results of experimental and numerical work for natural convection in a square enclosure by using PIV technique. 2D PIV technique and liquid crystal are employed for velocity and temperature measurement in water. The numerical method used this work is a CFD corde, STAR-CD. The experimental work are compared with these of numerical results.