• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.3 no.4
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• pp.23-27
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• 2010

This paper performs emotion classification test to find out the best parameter of electroencyphalogram(EEG) signal. Linear predictor coefficients, band cross-correlation coefficients of fast Fourier transform(FFT) and autoregressive model spectra are used as the parameters of 10-channel EEG signal. A multi-layer neural network is used as the pattern classifier. Four emotions for relaxation, joy, sadness, irritation are induced by four university students of an acting circle. Electrode positions are Fp1, Fp2, F3, F4, T3, T4, P3, P4, O1, O2. As a result, the Linear predictor coefficients showed the best performance.

• Proceedings of the KIEE Conference
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• 2005.07a
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• pp.810-812
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• 2005

In this paper, the short-term load forecasting of pole-transformer is performed by artificial neural networks. Input parameters of the Nosed algorithm are peak loads of pole-transformer of previous days and their temperatures. The proposed algorithm is tested for ore of the pole-transformers in seoul, korea. Test results show that the proposed algorithm improves the accuracy of the load forecasting of pole-transformer compared with the conventional algorithm.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.4
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• pp.296-303
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• 2007

This paper proposes a new width control system composed of an ANWC(Automatic Neural network based Width Control) and a fuzzy-PID controller in hot strip finishing mills which aims at obtaining the desirable width. The ANWC is designed using a neural network based width prediction model to minimize a width variation between the measured width and its target value. Input variables for the neural network model are chosen by using the hypothesis testing. The fuzzy-PlD control system is also designed to obtain the fast looper response and the high width control precision in the finishing mill. It is shown through the field test of the Pohang no. 1 hot strip mill of POSCO that the performance of the width margin is considerably improved by the proposed control schemes.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.05a
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• pp.1350-1356
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• 2006

The old technique of sandblasting which has been used for paint or scale removing, deburring and glass decorating has recently been developed into a powder blasting technique for brittle materials, capable of producing micro structures larger than $100{\mu}m$. In this paper, The surface characteristics of powder blasted glass surface were tested under different blasting parameter. Finally, we proposed a predictive model for powder blasting process using a neural network. A detailed analysis of the simulation results has been carried out and compared with experimental results.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2788-2790
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• 2002

In this paper, we designed neural network predictive two degree of freedom PID controller to control sway of crane Crane's trolley arrive minimum oscillation of transfer body and establishment position in minimum time. When various establishment location and surrounding disturbance were approved based on mathematical modeling of crane, controller designed to become effective control location error and oscillation angle of two control variables that simultaneously can predictive control. We wish to develop automatic travel control system through anti-sway skill of crane.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.03a
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• pp.71-74
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• 1998

In recent years, advances in construction techniques and materials have given rese to flexible light-weight structures like high-rise buildings and long-span bridges. Because these structures extremely susceptible to environmental loads, such as earthquakes and strong winds, these random loadings usually produce large deflection and acceleration on these structures. Vibration control system of structures are becoming an integral part of the structural system of the next generation of tall building. The proposed control system is applied to single degree of structure with mass damping and compared with conventional PID and neural network PID control system.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.798-800
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• 1995

The neural network predictive controller(NNPC) is proposed for the attempt to mimic the function of brain that forecasts the future. It consists of two loops, one is for the prediction of output(Neural Network Predictor) and the other one is for control the plant(Neural Network Controller). The output of NNC makes the control input of plant, which is followed by the variation of both plant error and prediction error. The NNP forecasts the future output based upon the current control input and the estimated control output. The method is applied to the control of temperature in boiler systems. The proposed NNPC is compared with the other conventional control methods such as PID controller, neural network controller with specialized learning architecture, and one-step-ahead controller. The computer simulation and experimental results show that the proposed method has better performances than the other methods.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.4
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• pp.434-443
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• 1999

In this paper, neural network predictive PID (NNPPID) control system is proposed to reduce the vibration of building structure. NNPPID control system is made up predictor, controller, and self-tuner to yield the parameters of controller. The neural networks predictor forecasts the future output based on present input and output of building structure. The controller is PID type whose parameters are yielded by neural networks self-tuning algorithm. Computer simulations show displacements of single and multi-story structure applied to NNPPID system about disturbance loads-wind forces and earthquakes.

• Proceedings of the KIEE Conference
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• 1996.11a
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• pp.62-64
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• 1996

Neural network as a controller of a nonlinear system and a system identifier has been studied during the past few years. A well trained neural network identifier can be used as a system predictor. We proposed the method to design multi-step ahead predictor and multi-step predictive controller using neural network. We used the input and out put data of B system to train the NNP and used the forecasted approximat system output from NNP as B input of NNC. In this paper we used two-step ahead predictive controller to test B heating controll system and compared with PI controller.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.2 no.2
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• pp.23-28
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• 2009

This study describes an emotion classification method using two different feature parameters of four-channel EEG signals. One of the parameters is linear prediction coefficients based on AR modelling. Another one is cross-correlation coefficients on frequencies of ${\theta}$, ${\alpha}$, ${\beta}$ bands of FFT spectra. Using the linear predictor coefficients and the cross-correlation coefficients of frequencies, the emotion classification test for four emotions, such as anger, sad, joy, and relaxation is performed with an artificial neural network. The results of the two parameters showed that the linear prediction coefficients have produced the better results for emotion classification than the cross-correlation coefficients of FFT spectra.