• Electrical & Electronic Materials
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• v.8 no.4
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• pp.509-517
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• 1995

• Proceedings of the Acoustical Society of Korea Conference
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• 1991.06a
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• pp.73-77
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• 1991

As the basic analysis to solve the stop consonants in phoneme based speech recognition using Back Propagation learning algorithm, changes in hidden units, training set and iteration. Also we propose an efficient processing method of separation between consonants and vowels.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.27-30
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• 2000

본 논문에서는 선형 이진 신경회로망(Linear Binary Neural Network)을 이용하여 이진 영상으로부터 골격(skeleton)을 추출하는 병렬 구조를 제안하였다. 기존의 골격 추출 알고리즘으로부터 이진함수를 추출하고 이를 MSP Term Grouping Algorithm을 이용하여 학습시켰다. 결과에서는 기존의 역전파(Back-propagation) 학습알고리즘을 사용한 신경회로망보다 더 쉽게 하드웨어로 구현할 수 있음을 보여준다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.113-117
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• 1998

We propose a way to show the inherent learning complexity for the multilayer perceptron. We display the solution space and the error surfaces on the input space of a single neuron with two inputs. The evolution of its weights will follow one of the two error surfaces. We observe that when we use the back-propagation(BP) learning algorithm (1), the wight cam not jump to the lower error surface due to the implicit continuity constraint on the changes of weight. The self-relaxation approach is to explicity find out the best combination of all neurons' two error surfaces. The time complexity of training a multilayer perceptron by self-relaxationis exponential to the number of neurons.

• International Journal of Aeronautical and Space Sciences
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• v.17 no.4
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• pp.491-500
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• 2016

This paper presents a highly efficient aeroelastic optimization method based on a surrogate model; the model is verified by considering the case of a high-aspect-ratio composite wing. Optimization frameworks using the Kriging model and genetic algorithm (GA), the Kriging model and improved particle swarm optimization (IPSO), and the back propagation neural network model (BP) and IPSO are presented. The feasibility of the method is verified, as the model can improve the optimization efficiency while also satisfying the engineering requirements. Moreover, the effects of the number of design variables and number of constraints on the optimization efficiency and objective function are analysed in detail. The accuracy of two surrogate models in aeroelastic optimization is also compared. The Kriging model is constructed more conveniently, and its predictive accuracy of the aeroelastic responses also satisfies the engineering requirements. According to the case of a high-aspect-ratio composite wing, the GA is better at global optimization.

• Journal of Industrial Technology
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• v.28 no.B
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• pp.143-148
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• 2008

A real-time face detection is to find human faces robustly under the cluttered background free from the effect of occlusion by other objects or various lightening conditions. We propose a face detection system for real-time applications using wavelet decomposition method based on Gabor features. Firstly, skin candidate regions are extracted from the given image by skin color filtering and projection method. Then Gabor-feature based template matching is performed to choose face cadidate from the skin candidate regions. The chosen face candidate region is transformed into 2-level wavelet decomposition images, from which feature vectors are extracted for classification. Based on the extracted feature vectors, the face candidate region is finally classified into either face or nonface class by the Levenberg-Marguardt back-propagation neural network.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 1998.10a
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• pp.137-141
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• 1998

Todays the efficient management of container has not been realized in container terminal, because of the excessive quantity of container transported and manual system. For the efficient and automated management of container in terminal, the automated container identifier recognition system in terminal is a significant problem. However, the identifier recognition rate is decreased owing to the difficulty of image preprocessing caused the refraction of container surface, the change of weather and the damaged identifier characters. Therefore, this paper proposes more accurate system for container identifier recognition as suggestion of Line-Scan Proper Region Detect for stronger preprocessing against external noisy element and Moment Back-Propagation Neural Network to recognize identifier.

• Journal of Ocean Engineering and Technology
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• v.10 no.1
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• pp.65-74
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• 1996

Up to now, the existing crack growth modelling has used a mathematical approximation but an assumed function have a great influence on this method. Especially, crack growth behavior that shows very strong nonlinearity needed complicated function which has difficulty in setting parameter of it. The main characteristics of neural network modelling to engineering field are simple calculations and absence of assumed function. In this paper, after discussing learning and generalization of neural networks, we performed crack growth modelling on the basis of above learning algorithms. J'-da/dt relation predicted by neural networks shows that test condition with unlearned data is simulated well within estimated mean error(5%).

• Journal of Electrical Engineering and Technology
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• v.8 no.3
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• pp.559-564
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• 2013

In this paper a new hybrid forecast method composed of wavelet transform and neural network is proposed to forecast the wind speed more accurately. In the field of wind energy research, accurate forecast of wind speed is a challenging task. This will influence the power system scheduling and the dynamic control of wind turbine. The wind data used here is measured at 15 minute time intervals. The performance is evaluated based on the metrics, namely, mean square error, mean absolute error, sum squared error of the proposed model and compared with the back propagation model. Simulation studies are carried out and it is reported that the proposed model outperforms the compared model based on the metrics used and conclusions were drawn appropriately.

• Proceedings of the KIPE Conference
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• 1998.07a
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• pp.384-387
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• 1998

This paper presents a newly developed speed sensorless drive using RLS(Recursive Least Squares) based on Neural Network Training Algorithm. The proposed algorithm based on the RLS has just the time-varying learning rate, while the well-known back-propagation (or generalized delta rule) algorithm based on gradient descent has a constant learning rate. The number of iterations required by the new algorithm to converge is less than that of the back-propagation algorithm. The RLS based on NN is used to adjust the motor speed so that the neural model output follows the desired trajectory. This mechanism forces the estimated speed to follow precisely the actual motor speed. In this paper, a flux estimation strategy using filter concept is discussed. The theoretical analysis and experimental results to verify the effectiveness of the proposed analysis and the proposed control strategy are described.