• Proceedings of the KIEE Conference
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• 2004.05b
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• pp.109-112
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• 2004

In this study, the applicability of SOM(Self Organizing Map) algorithm to partial discharge pattern recognition have been investigated. For the purpose, using acquired data from the artificial defects in GIS, SOM algorithm which has some advantages such as data accumulation ability and the degradation trend trace ability was compared with conventionally used BP(Back Propagation) algorithm. As a result, basically BP algorithm was found out to be better than SOM algorithm. Therefore, it is needed to apply SOM algorithm in combination with BP algorithm in order to improve on-site applicability using the advantages of SOM. Also, for the pattern recognition by use of PRPDA(Phase Resolved Partial Discharge Analysis) it is required the normalization of the PRPDA graph. However, in case of the normalization both BP and SOM algorithm have shown worse results, so that it is required further study to solve the problem.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2007.05a
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• pp.351-356
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• 2007

Factory location selection is very important to the success of operation of the whole supply chain, but few effective solutions exist to deliver a good result, motivated by this, this paper tries to introduce a new factory location selection methodology by employing the artificial neural networks technology. First, we reviewed previous research related to factory location selection problems, and then developed a (neural network-based factory selection model) NNFSM which adopted back-propagation neural network theory, next, we developed computer program using C++ to demonstrate our proposed model. then we did case study by choosing a Korean steelmaking company P to show how our proposed model works,. Finnaly, we concluded by highlighting the key contributions of this paper and pointing out the limitations and future research directions of this paper. Compared to other traditional factory location selection methods, our proposed model is time-saving; more efficient.and can produce a much better result.

• Proceedings of the KIEE Conference
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• 1997.11a
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• pp.61-65
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• 1997

In control of complex system, like robot manipulators, BP neural network have several drawbacks. To overcome this problems, the modified BP neural networks have proposed To find neural network of proper structure for robot manipulator, in this paper, actual experiments using ADSP-21020 for SCARA robot were implemented and have shown the possibility of real-time control and industrial application, without neural chip.

• Structural Monitoring and Maintenance
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• v.10 no.2
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• pp.175-190
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• 2023

This article aims to quantitatively predict the snowmelt in extreme cold regions, considering a combination of grayscale and neural models. The traditional non-equidistant GM(1,1) prediction model is optimized by adjusting the time-distance weight matrix, optimizing the background value of the differential equation and optimizing the initial value of the model, and using the BP neural network for the first. The adjusted ice forecast model has an accuracy of 0.984 and posterior variance and the average forecast error value is 1.46%. Compared with the GM(1,1) and BP network models, the accuracy of the prediction results has been significantly improved, and the quantitative prediction of the ice sheet is more accurate. The monitoring and maintenance of the structure by quantitative prediction model by gray models was clearly demonstrated in the model.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10b
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• pp.1845-1847
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• 1991

In general, it is not easy to find the linearizing coordinate transformation map for a class of systems which are state equivalent to linear systems, because it is required to solve a set of partial differential equations. It is possible to construct an arbitrary nonlinear function with a backpropagation(BP) net. Utilizing this property of BP neural net, we construct a desired linearizing coordinate transformation map. That is, we implement a unknown coordinate transformation map through the training of neural weights. We have shown an example which supports this idea.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.11
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• pp.83-88
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• 2002

In order to predict the moldability of a injection molded part, a simulation of filling is needed. Short shot is one of the most frequent troubles encountered during injection molding process. The adjustment of process conditions is the most economic way to troubleshoot the problematic short shot in cost and time since the mold doesn't need to be modified at all. But it is difficult to adjust the process conditions appropriately in no times since it requires an empirical knowledge of injection molding. In this paper, the intelligent CAE system synergistically combines fuzzy-neural network (FNN) for heuristic knowledge with CAE programs for analytical knowledge. To evaluate the intelligent algorithms, a cellular phone flip has been chosen as a finite element model and filling analyses have been performed with a commercial CAE software. As the results, the intelligent CAE system drastically reduces the troubleshooting time of short shot in comparison with the experts' conventional methodology which is similar to the golden section search algorithm.

• Wind and Structures
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• v.30 no.3
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• pp.289-298
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• 2020

Gusts generated by downburst have caused a great variety of structural damages in many regions around the world. It is of great significance to accurately evaluate the downburst-induced wind load on high-rise building for the wind resistance design. The main objective of this paper is to propose a computational modeling approach which can satisfactorily predict the mean and fluctuating wind pressure coefficients induced by downburst on high-rise building surfaces. In this study, using an impinging jet to simulate downburst-like wind, and simultaneous pressure measurements are obtained on a high-rise building model at different radial locations. The model test data are used as the database for developing back propagation neural network (BPNN) models. Comparisons between the BPNN prediction results and those from impinging jet test demonstrate that the BPNN-based method can satisfactorily and efficiently predict the downburst-induced wind pressure coefficients on single and overall surfaces of high-rise building at various radial locations.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.3
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• pp.107-113
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• 2001

This paper deals with speed control of DC servo motor using a PID controller with a gain tuning based on a Back-Propagation(BP) Learning Algorithm. Conventionally a PID controller has been used in the industrial control. But a PID controller should produce suitable parameters for each system. Also, variables of the PID controller should be changed according to environments, disturbances and loads. In this paper described by a experiment that contained a method using a PID controller with a gain tuning based on a Back-Propagation(BP) Learning Algorithm, we developed speed characteristics of a DC servo motor on variable loads. The parameters of the controller are determined by neural network performed on on-line system after training the neural network on off-line system.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.6 no.4
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• pp.243-248
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• 2013

In this thesis, we have designed the indirect adaptive controller using Dynamic Neural Units(DNU) for unknown nonlinear systems. Proposed indirect adaptive controller using Dynamic Neural Unit based upon the topology of a reverberating circuit in a neuronal pool of the central nervous system. In this thesis, we present a genetic DNU-control scheme for unknown nonlinear systems. Our method is different from those using supervised learning algorithms, such as the backpropagation (BP) algorithm, that needs training information in each step. The contributions of this thesis are the new approach to constructing neural network architecture and its training.

• International Journal of Aeronautical and Space Sciences
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• v.16 no.4
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• pp.510-519
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• 2015

Concerning the issue of high-dimensions, hybrid uncertainties of randomness and intervals including implicit and highly nonlinear limit state function, reliability analysis based on the hybrid uncertainty reliability mode combining with back propagation neural network (HU-BP neural network) is proposed in this paper. Random variables and interval variables are as input layer of the neural network, after the training and approximation of the neural network, the response variables are obtained through the output layer. Reliability index is calculated by solving the optimization model of the most probable point (MPP) searching in the limit state band. Two numerical cases are used to demonstrate the method proposed in this paper, and finally the method is employed to solving an engineering problem of the aerospace friction plate. For this high nonlinear, small failure probability problem with interval variables, this method could achieve a good analysis result.