• Journal of Advanced Marine Engineering and Technology
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• 제21권1호
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• pp.66-70
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• 1997

In this paper a new learning algorithm for curvature smoothing and improved generalization for multi-layer neural networks is proposed. To enhance the generalization ability a constraint term of hidden neuron activations is added to the conventional output error, which gives the curvature smoothing characteristics to multi-layer neural networks. When the total cost consisted of the output error and hidden error is minimized by gradient-descent methods, the additional descent term gives not only the Hebbian learning but also the synaptic weight decay. Therefore it incorporates error back-propagation, Hebbian, and weight decay, and additional computational requirements to the standard error back-propagation is negligible. From the computer simulation of the time series prediction with Santafe competition data it is shown that the proposed learning algorithm gives much better generalization performance.

• International Journal of Contents
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• 제12권1호
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• pp.49-53
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• 2016

"Hahoe Village" in Andong region is an UNESCO World Heritage Site. It should be protected against various disasters such as fire, flooding, earthquake, etc. Among these disasters, flooding has drastic impact on the lives and properties in a wide area. Since "Hahoe Village" is adjacent to Nakdong River, it is important to monitor the water level near the village. In this paper, we developed a hydrological modelling using multi-layer perceptron (MLP) to predict the water level of Nakdong River near "Hahoe Village". To develop the prediction model, error back-propagation (EBP) algorithm was used to train the MLP with water level data near the village and rainfall data at the upper reaches of the village. After training with data in 2012 and 2013, we verified the prediction performance of MLP with untrained data in 2014.

• 생물환경조절학회지
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• 제1권2호
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• pp.162-168
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• 1992

As the automation of nutrient solution management proceeds in the field of hydroponics, effective supporting systems to manage the nutrient solution by computer become needed. This study was attempt to predict the EC of nutrient solution using the neural networks. The multilayer perceptron consisting of 3 layers with the back propagation learning algorithm was selected for EC prediction, of which nine variables in the input layer were the concentrations of each ion and one variable in the output layer the EC of nutrient solution. The meq unit in ion concentration was selected fir input variable in the input layer. After the 10,000 learning sweeps with 108 sample data, the comparison of predicted and measured ECs for 72 test data showed good agreements with the correlation coefficient of 0.998. In addition, the predicted ECs by neural network showed relatively equal or closer to the measured ones than those by current complicated models.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2008년도 하계학술대회 논문집 Vol.9
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• pp.443-443
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• 2008

차세대 LCD back light 시스템으로 Xe plasma plat lamp를 주목하고 있으나 자외선 여기 효율이 떨어져 램프의 휘도 및 광 효율이 낮은 단점을 가지고 있어 이에 따른 개선이 요구되고 있다. MgO는 AC-PDP에서 방전전압의 저하, 방전의 안정성, 동작마진과 같은 특성에 영향을 미치고 있기에 이번 연구 에서는 졸겔법으로 제작한 MgO를 프린팅 방법으로 flat fluorescent lamp에 적용하였다. 실험시 사용한 flat fluorescent lamp의 경우 ITO 전극과 MgO layer 그리고 형광체층을 가진 두 장의 유리판 사이에 Ne-Xe기체를 채운 단순한 구조로 제작하였다. 실험은 압력과 방전거리에 따른 특성을 살펴보았으며, 그 결과 MgO layer 채용한 경우 방전 전압의 감소 및 static margin의 증가를 알 수 있었다.

• Journal of Electrical Engineering and Technology
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• 제4권1호
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• pp.28-34
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• 2009

In a Gas Insulated Substation (GIS), Very Fast Transients (VFTs) are generated mainly due to switching operations. These transients may cause internal faults, i.e., layer-to-layer faults in a capacitively graded bushing as it is one of the most important terminal equipment for GIS. The healthiness of the bushing is generally verified by measuring its leakage current. However, the change in current magnitude/pattern is only marginal for different types of fault conditions. Leakage current monitoring (LCM) systems generate large amounts of data and computer aided interpretation of defects may be of great assistance when analyzing this data. In view of the above, ANN techniques have been used in this study for identification of these minor faults. A single layer perceptron network, a two layer feed-forward back propagation network and cascade correlation (CC) network models are used to identify interlayer faults in the bushing. The effectiveness of the CC network over perceptron and back propagation networks in identification of a fault has been analysed as part of the paper.

• Journal of Information Processing Systems
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• 제13권4호
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• pp.677-688
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• 2017

In order to solve the undetected probability of multiple targets in ultra-wideband (UWB) through-the-wall radar imaging (TWRI), a time-delay and amplitude modified back projection (BP) algorithm is proposed. The refraction point is found by Fermat's principle in the presence of a wall, and the time-delay is correctly compensated. On this basis, transmission loss of the electromagnetic wave, the absorption loss of the refraction wave, and the diffusion loss of the spherical wave are analyzed in detail. Amplitude compensation is deduced and tested on a model with a single-layer wall. The simulating results by finite difference time domain (FDTD) show that it is effective in increasing the scattering intensity of the targets behind the wall. Compensation for the diffusion loss in the spherical wave also plays a main role. Additionally, the two-layer wall model is simulated. Then, the calculating time and the imaging quality are compared between a single-layer wall model and a two-layer wall model. The results illustrate the performance of the time-delay and amplitude-modified BP algorithm with multiple targets and multiple-layer walls of UWB TWRI.

• 전기학회논문지
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• 제64권10호
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• pp.1469-1478
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• 2015

In this paper, The methodology of Type-2 fuzzy set-based Radial Basis Function Neural Network(T2RBFNN) is proposed for Sewage Treatment Process and the simulator is developed for application to the real-world sewage treatment plant by using the proposed model. The proposed model has robust characteristic than conventional RBFNN. architecture of network consist of three layers such as input layer, hidden layer and output layer of RBFNN, and Type-2 fuzzy set is applied to receptive field in contrast with conventional radial basis function. In addition, the connection weights of the proposed model are defined as linear polynomial function, and then are learned through Back-Propagation(BP). Type reduction is carried out by using Karnik and Mendel(KM) algorithm between hidden layer and output layer. Sewage treatment data obtained from real-world sewage treatment plant is employed to evaluate performance of the proposed model, and their results are analyzed as well as compared with those of conventional RBFNN.

• 신재생에너지
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• 제4권2호
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• pp.74-80
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• 2008

To improve the blue responses of screen-printed single crystalline silicon solar cells, we investigated an emitter etch-back technique to obtain high emitter sheet resistances, where the defective dead layer on the emitter surface was etched and became thinner as the etch-back time increased, resulting in the monotonous increase of short circuit current and open circuit voltage. We found that an optimal etch-back time should be determined to achieve the maximal performance enhancement because of fill factor decrease due to a series resistance increment mainly affected by contact and lateral resistance in this case. To elucidate the reason for the fill factor decrease, we studied the resistance analysis by potential mapping to determine the contact and the lateral series resistance. As a result, we found that the fill factor decrease was attributed to the relatively fast increase of contact resistance due to the dead layer thinning down with the lowest contact resistivity when the emitter was contacted with screen-printed silver electrode.

• 한국신재생에너지학회:학술대회논문집
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• 한국신재생에너지학회 2008년도 춘계학술대회 논문집
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• pp.390-393
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• 2008

To improve the blue responses of screen-printed single crystalline silicon solar cells, we investigated an emitter etch-back technique to obtain high emitter sheet resistances, where the defective dead layer on the emitter surface was etched and became thinner as the etch-back time increased, resulting in the monotonous increase of short circuit current and open circuit voltage. We found that an optimal etch-back time should be determined to achieve the maximal performance enhancement because of fill factor decrease due to a series resistance increment mainly affected by contact and lateral resistance in this case. To elucidate the reason for the fill factor decrease, we studied the resistance analysis by potential mapping to determine the contact and the lateral series resistance. As a result, we found that the fill factor decrease was attributed to the relatively fast increase of contact resistance due to the dead layer thinning down with the lowest contact resistivity when the emitter was contacted with screen-printed silver electrode.

• 정보학연구
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• 제5권2호
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• pp.55-67
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• 2002

본 논문에서는 학습계수를 발생한 오차에 따라서 적응적으로 갱신할 수 있는 학습알고리즘에 은닉 노드의 수를 다양하게 변화시킬 수 있는 적응 역 전파(Back Propagation) 알고리즘을 제안하였다. 제안한 알고리즘은 국소점을 벗어날 수 있는 것으로 기대되고, 수렴환경에 알맞은 은닉 노드의 수를 설정할 수 있다. 모의실험에서는 두 가지의 학습패턴을 가지고 실험하였다. 하나는 X-OR 문제에 대한 학습과 또 다른 하나는 $7{\times}5$ 도트 영문자 폰트에 에 대한 학습이다. 두 모의실험에서 국소 점으로 안주할 확률은 감소하였다. 또한, 영문자 폰트 학습에서의 신경회로망은 기존의 역 전파 알고리즘과 HNAD 알고리즘에 비하여 약 41.56%~58.28%정도 학습효율이 향상됨을 고찰하였다.