• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 1999년도 추계학술대회 논문집 - 한국공작기계학회
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• pp.142-147
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• 1999

This paper presents a new approach to the dynamic control technique for track vehicle system using neural network-fuzzy control method. The proposed control scheme uses a Gaussian function as a unit function in the neural network-fuzzy, and back propagation algorithm to train the fuzzy-neural network controller in the framework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is shown by simulation for trajectory tracking of the speed and azimuth of a track vehicle.

• 대한전기학회논문지
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• 제41권3호
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• pp.274-280
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• 1992

In this paper, an Improved Error Back Propagation Algorithm is introduced, which avoids Network Paralysis, one of the problems of the Error Backpropagation learning rule. For this purpose, we analyzed the reason for Network Paralysis and modified the Activation Function Derivative of the standard Error Backpropagation Algorithm which is regarded as the cause of the phenomenon. The characteristics of the modified Activation Function Derivative is analyzed. The performance of the modified Error Backpropagation Algorithm is shown to be better than that of the standard Error Back Propagation algorithm by various experiments.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1992년도 추계학술대회
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• pp.47-50
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• 1992

This paper describes pattern classification algorithm of ECG using back-propagation neural network. We presents new feature extractor using second order approximating function as the input signals of neural network. We use 9 significant parameters which were extracted by feature extractor. 5 most characterized ECG signal pattern is classified accurately by neural network. We use AHA database to evaluate the performance ol the proposed pattern classification algorithm.

• 한국경영과학회지
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• 제20권3호
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• pp.17-29
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• 1995

Recently, neural network models have been employed as an alternative to regression analysis for point estimation or function fitting in various field. Thus far, however, no theoretical or empirical guides seem to exist for selecting the tool which the most suitable one for a specific function-fitting problem. In this paper, we evaluate performance of three major function-fitting techniques, regression analysis and two neural network models, back-propagation and linear-Hebbian-learning neural networks. The functions to be fitted are simple linear ones of a single independent variable. The factors considered are size of noise both in dependent and independent variables, portion of outliers, and size of the data. Based on comutational results performed in this study, some guidelines are suggested to choose the best technique that can be used for a specific problem concerned.

• 산업공학
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• 제8권1호
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• pp.5-13
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• 1995

Automated vision inspection has become a vital part of computer related industries. Most of the existing inspection systems mainly utilize black and white images. In this paper, we consider an application of automated vision inspection in which cable color has to be recognized in order to detect the quality status of assembled wire harness. A back-propagation neural network is proposed to classify seven different cable colors. To represent a single point in image space, we use the ($L^*,\;a^*,\;b^*$) model which is one of commonly used color-coordinate systems in image processing. After training the neural network with ($L^*,\;a^*,\;b^*$) data obtained from color image, we tested its performance. The results show that the neural network is able to classify cable colors with high performance.

• 한국정밀공학회지
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• 제15권7호
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• pp.26-35
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• 1998

This paper presents a visual inspection algorithm looking for solder joint defects of IC chips on PCBs (Printed Circuit Boards). In this algorithm, seven features are proposed in order to categorize the solder joints into four classes such as normal, insufficient, excess, and no solder, and optimal back-propagation network is determined by error evaluation which depend on the number of neurons in hidden and out-put layers and selection of the features. In the end, a good accuracy of classification performance, an optimal determination of network structure and the effectiveness of chosen seven features are examined by experiment using proposed inspection algorithm.

• 한국정밀공학회지
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• 제13권4호
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• pp.75-86
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• 1996

In this paper, we propose a new fuzzy-neural network control scheme for the speed and azimuth control of a mobile robot. The proposed control scheme uses a gaussian function as a unit function in the fuzzy-neural network, and back propagation algorithm to train the fuzzy-neural network controller in the frame-work of the specialized learning architecture. It is proposed a learning controller consisting of two fuzzy-neural networks based on independent reasoning and a connection net woth fixed weights to simply the fuzzy-neural network. The effectiveness of the proposed controller is illustrated by performing the computer simulation for a circular trajectory tracking of a mobile robot driven by two independent wheels.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1998년도 하계종합학술대회논문집
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• pp.917-920
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• 1998

In this paper, we propose a modified elman neural network structure for nonlinear system identification. The proposed structure is that all of network output feed back into hidden units and output units. Learning algorithm is standard back-propagation algorithm. The simulation showed the effectiveness of using the modified elman neural network structure in the nonlinear system identification.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 1996년도 춘계학술대회 논문집
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• pp.72-78
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• 1996

Based on experimental analysis, the characteristics of pulsating pressure wave propagation is clarified by testing of 4-stroke gasoline engine. The pulsating pressure wave in exhaust system is generated gyulsating gas flow due the working of exhaust valve. The pulsating pressure wave is closely concerned to the loss of engine power according to back pressure and exhaust noise. It is difficult to exactly calculate pulsating pressure wave nonlinear effect. Therefore, in the first step for solving these problems, this paper contains experimental model and analysis method which are applied two-port network analysis. Also, it shows coherence function, frequency response function. back pressure, and gradient of temperature in exhaust system.

• 한국컴퓨터정보학회논문지
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• 제13권5호
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• pp.117-124
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• 2008

편재형 컴퓨팅 환경에서 보다 편리하게 의료 서비스를 받는 것은 인간의 삶의 질을 향상시키는 방법이다. 이를 위해 본 연구에서는 헬스 케어 응용의 일환으로 심장병 환자를 위한 편재형 헬스 케어 시스템을 구현하였다. 이 시스템은 온도, 습도, 조도 등 실내의 인간의 외적 환경과 심전도 센서를 인체에 부착하여 인간의 내적 환경을 감시하는 센서, 이들을 상호 연결시키는 센서 네트워크, 그리고 의료 정보 서버로 구성된다. 단순히 사람이 머무는 공간 혹은 생체정보를 센싱하고 이를 의료진에게 전달하는 것은 비교적 단순한 수준의 헬스 케어 시스템이다. 보다 높은 수준의 의료 서비스를 제공하기 위하여 본 연구에서는 BPNN(back propagation neural network)를 이용하여 감시 대상자의 상황을 인식하는 서비스 모델을 개발하였다. 실험 결과감시 대상자의 활동을 보다 정확하게 인식하여 고수준의 의료 서비스를 제공할 수 있는 헬스 케어 시스템 구현이 가능함을 확인하였다.