• Proceedings of the Korean Society of Computer Information Conference
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• 2017.01a
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• pp.275-277
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• 2017

사물인터넷(Internet of things)의 발전과 함께 스마트 팩토리에 대한 관심이 증대되고 있다. 제조의 전 과정에서 발생하는 데이터를 실시간으로 수집하고 관리를 자동화하는 것이 스마트 팩토리의 목적이다. 그러나 공장에서는 현재까지도 많이 사용되는 아날로그 게이지를 관리하는 일은 사람의 노동력을 필요로 한다. 또한 아날로그 게이지는 쓰임새에 따라 모양과 형태가 매우 다양하다. 본 논문에서는 아날로그 게이지의 형태에 따라 분류하는 방법에 대해 제안한다. 제안하는 방법은 학습하기 위해 필요한 게이지 영상 데이터를 수집하고 나서 각 분류에 속하는 이미지 데이터를 CNN(Convolutional Neural Network) 딥러닝 기법으로 학습시킨 후, 각 분류에 해당하는 특징 정보를 추출하고 아날로그 게이지의 형태를 인식하는 방법을 제안한다.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.4
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• pp.112-117
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• 2003

This paper deals with the problem of synthesis of the 3-dimensional Grasp Planning. In previous studies the genetic algorithm has been used to find optimal grasping points, but it had a limitation such as the determination time of grasping points was so long. To overcome this limitation we proposed a new algorithm which employs the Neural Network. In the Neural network we chose input parameters based on the shape of the object and output parameters resulted from optimization with the GA method. In this study the GRNN method is employed, it has been trained by the result value of optimization method and it has been tested by known object. The algorithm is verified by computer simulation.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.10a
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• pp.365-373
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• 1999

Recently, numerous studies have demonstrated that artificial intelligence such as neural networks can be an alternative methodology for classification problems to which traditional statistical methods have long been applied. In building neural network model, the selection of independent and dependent variables should be approached with great care and should be treated as a model construction process. Irrespective of the efficiency of a learning procedure in terms of convergence, generalization and stability, the ultimate performance of the estimator will depend on the relevance of the selected input variables and the quality of the data used. Approaches developed in statistical methods such as correlation analysis and stepwise selection method are often very useful. These methods, however, may not be the optimal ones for the development of neural network models. In this paper, we propose a genetic algorithms approach to find an optimal or near optimal input variables for neural network modeling. The proposed approach is demonstrated by applications to bankruptcy prediction modeling. Our experimental results show that this approach increases overall classification accuracy rate significantly.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.2075-2076
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• 2006

In this paper, we propose a new architecture of Genetic Algorithms(GAs)-based Backpropagation(BP). The conventional BP does not guarantee that the BP generated through learning has the optimal network architecture. But the proposed GA-based BP enable the architecture to be a structurally more optimized network, and to be much more flexible and preferable neural network than the conventional BP. The experimental results in BP neural network optimization show that this algorithm can effectively avoid BP network converging to local optimum. It is found by comparison that the improved genetic algorithm can almost avoid the trap of local optimum and effectively improve the convergent speed.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.11a
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• pp.272-275
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• 2004

플라즈마 식각공정은 소자제조를 위한 미세 패턴닝 제작에 이용되고 있다. 공정 메커니즘의 정성적 해석, 최적화, 그리고 제어를 위해서는 컴퓨터 예측모델의 개발이 요구된다. 역전파 신경망 (backpropagation neural network-BPNN) 모델을 개발하는 데에는 다수의 학습인자가 관여하고 있으며, 가장 그 최적화가 어려운 학습인자는 초기웨이트이다. 모델개발시, 초기웨이트는 random 값으로 설정이 되며, 이로 인해 초기웨이트의 최적화가 어렵다. 본 연구에서는 유전자 알고리즘 (genetic algorithm-GA)을 이용하여 BPNN의 초기웨이트를 최적화하였으며, 이를 식각공정 모델링에 적용하여 평가하였다. 실리카 식각공정 데이터는 $2^3$ 인자 실험계획법을 이용하여 수집하였으며, GA에 관여하는 두 확률인자의 영향을 42 인자 실험계획법을 이용하여 최적화 하였다. 종래의 모델에 비해, 최적화된 모델은 실리카 식각률, Al 식각률, Al 선택비, 그리고 프로파일 응답에 대해서 각 기 24%, 13%,, 16%, 그리고 17%의 향상률을 보였다. 이는 제안된 최적화 기법이 플라즈마 모델의 예측성능을 증진하는데 효과적으로 응용될 수 있음을 의미한다.

• Journal of the Korean Geotechnical Society
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• v.21 no.9
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• pp.25-34
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• 2005

This paper proposes a selection methodology composed of neural network (NN) and genetic algorithm (GA) to design optimal NN structure. We combine the characteristics of GA and NN to reduce the computational complexity of artificial intelligence applications and increase the precision of NN' prediction in the design of NN structure. Genetic selection approach of design parameters of NN is introduced to obtain optimal NN structure. Analyzed results for geotechnical problems are given to evaluate the performance of the proposed hybrid methodology.

• Advances in Computational Design
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• v.1 no.3
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• pp.235-251
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• 2016

We have performed a design optimization of a stiffened panel with curvilinear stiffeners using an artificial neural network (ANN) residual kriging based surrogate modeling approach. The ANN residual kriging based surrogate modeling involves two steps. In the first step, we approximate the objective function using ANN. In the next step we use kriging to model the residue. We optimize the panel in an iterative way. Each iteration involves two steps-shape optimization and size optimization. For both shape and size optimization, we use ANN residual kriging based surrogate model. At each optimization step, we do an initial sampling and fit an ANN residual kriging model for the objective function. Then we keep updating this surrogate model using an adaptive sampling algorithm until the minimum value of the objective function converges. The comparison of the design obtained using our optimization scheme with that obtained using a traditional genetic algorithm (GA) based optimization scheme shows satisfactory agreement. However, with this surrogate model based approach we reach optimum design with less computation effort as compared to the GA based approach which does not use any surrogate model.

• Proceedings of the KIEE Conference
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• 2007.10a
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• pp.175-176
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• 2007

Radial Basis Function Network (RBFN)을 이용하여 플라즈마 전위의 예측 모델을 개발하였다. RBFN의 예측성능은 Genetic Algorithm (GA)를 이용하여 최적화 하였다. 체계적인 모델링을 위해 통계적인 실험계획법이 적용되었으며, 실험은 반구형 유도 결합형 플라즈마 장비를 이용하여 수행이 되었다. $Cl_2$ 플라즈마에서의 데이터 측정에는 Langmuir probe가 이용되었다. 최적화된 GA-RBFN 모델을 일반 RBFN 모델과 비교하였으며, 15%정도 모델의 예측성능을 향상시켰다.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1169-1172
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• 1996

A time-optimal control law for quick, strongly nonlinear systems has been developed and demonstrated. This procedure involves the utilization of neural networks as state feedback controllers that learn the time-optimal control actions by means of an iterative minimization of both the final time and the final state error for the known and unknown systems with constrained inputs and/or states. The nature of neural networks as a parallel processor would circumvent the problem of "curse of dimensionality". The control law has been demonstrated for a velocity input type motor identified by a genetic algorithm called GENOCOP.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2007.06a
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• pp.797-798
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• 2007