• 한국정보전자통신기술학회논문지
• /
• 제3권4호
• /
• pp.23-27
• /
• 2010

본 연구에서는 감성 평가 시스템 가장 적합한 파라미터를 찾기 위하여 3가지 뇌파 파라미터를 이용하여 감정 분류 실험을 하였다. 뇌파 파라미터는 선형예측기계수(linear predictor coefficients)와 FFT 스펙트럼 및 AR 스펙트럼의 밴드별 상호상관계수(cross-correlation coefficients)를 이용하였으며, 감정은 relaxation, joy, sadness, irritation으로 설정하였다. 뇌파 데이터는 대학의 연극동아리 학생 4명을 대상으로 수집하였으며, 전극 위치는 Fp1, Fp2, F3, F4, T3, T4, P3, P4, O1, O2를 사용하였다. 수집된 뇌파 데이터는 전처리를 거친 후 특징 파라미터를 추출하고 패턴 분류기로 사용된 신경회로망(neural network)에 입력하여 감정 분류를 하였다. 감정 분류실험 결과 선형예측기계수를 이용하는 것이 다른 2가지 보다 좋은 성능을 나타내었다.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2005년도 제36회 하계학술대회 논문집 A
• /
• pp.810-812
• /
• 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.

• 제어로봇시스템학회논문지
• /
• 제13권4호
• /
• pp.296-303
• /
• 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.

• 한국경영과학회:학술대회논문집
• /
• 대한산업공학회/한국경영과학회 2006년도 춘계공동학술대회 논문집
• /
• pp.1350-1356
• /
• 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.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2002년도 하계학술대회 논문집 D
• /
• pp.2788-2790
• /
• 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.

• 한국지능시스템학회:학술대회논문집
• /
• 한국퍼지및지능시스템학회 1998년도 춘계학술대회 학술발표 논문집
• /
• pp.71-74
• /
• 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.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 1995년도 하계학술대회 논문집 B
• /
• pp.798-800
• /
• 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.

• 대한전기학회논문지:전력기술부문A
• /
• 제48권4호
• /
• pp.434-443
• /
• 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.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 1996년도 추계학술대회 논문집 학회본부
• /
• pp.62-64
• /
• 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.

• 한국정보전자통신기술학회논문지
• /
• 제2권2호
• /
• pp.23-28
• /
• 2009

본 연구에서는 뇌파를 AR모델로 모델링하여 선형예측계수를 특징 파라미터로 이용할 때와 뇌파의 주파수 대역별 상호상관계수를 이용할 때의 감정상태 분류 성능을 비교해 보고자 하였다. 이를 위하여 분노, 슬픔, 기쁨, 안정의 4가지 감정상태에 따른 뇌파를 4개 채널로부터 수집하여 선형예측계수와 ${\theta}$, ${\alpha}$, ${\beta}$ 대역의 주파수 영역에서의 상호상관계수를 추출하여 이들을 특징 파라미터로 한 감정상태 분류 실험을 수행함으로써 두 방법의 감정상태 분류 성능을 비교하였고, 패턴 분류기로는 신경회로망을 이용하였다. 감정 분류 실험 결과 뇌파의 특징 파라미터로서 선형예측계수를 이용한 결과가 상호상관계수를 이용할 때보다 성능이 월등히 좋은 것을 알 수 있었다.