• Journal of the Korean School Mathematics Society
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• v.9 no.1
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• pp.93-104
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• 2006

The centroid of triangle is physical property but almost mathematics teachers do not teach centroid by the help of experiments an so they have misconception on principle of centroid. In this paper we investigate whether teachers have made an experiment on centroid of triangle, and we check up on the level of understanding on centroid for mathematics teachers. We introduce the method of teaching centroid and study the process of generalization about centroid of triangle for gifted students.

• Kyungpook Mathematical Journal
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• v.47 no.3
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• pp.311-322
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• 2007

This paper deals with space Beltrami system with three characteristic matrices in even dimensions, which can be regarded as a generalization of space Beltrami system with one and two characteristic matrices. It is transformed into a nonhomogeneous $p$-harmonic equation $d^*A(x,df^I)=d^*B(x,Df)$ by using the technique of out differential forms and exterior algebra of matrices. In the process, we only use the uniformly elliptic condition with respect to the characteristic matrices. The Lipschitz type condition, the monotonicity condition and the homogeneous condition of the operator A and the controlled growth condition of the operator B are derived.

• Proceedings of the Korean Society for Quality Management Conference
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• 2004.04a
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• pp.44-47
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• 2004

In this practical case study I studied the launching process of Samgak gimbab(the triangle type of rice), which is one if the best selling products to the young generation convenience store these days. And also explained how to down the cost of samgak gimbab to the level of customer's satisfaction. This case is related to 'S' company's strategy. I studied also the generalization of marketing policy especially product development in th convenience store.

• Journal of Powder Materials
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• v.14 no.6
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• pp.410-414
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• 2007

Carbon contamination from the binder resin is an inherent problem with the metal powder injection molding process. Residual carbon in the W-Cu compacts has a strong impact on the thermal and electric properties. In this study, uncertainty was quantified to evaluate determination of carbon in a W-15%Cu MIM body by the combustition method. For a valid generalization about this evaluation, uncertainty scheme applied even to the repeatability as well as the uncertainty sources of each analyse step and quality appraisal sources. As a result, the concentration of carbon in the W-Cu part were measured as 0.062% with expanded uncertainty of 0.003% at 95% level. This evaluation example may be useful to uncertainty evaluation for other MIM products.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.566-568
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• 1998

In this paper, we suggest a optimal design method of Fuzzy-Neural Networks model for complex and nonlinear systems. FNNs have the stucture of fusion of both fuzzy inference with linguistic variables and Neural Networks. The network structure uses the simpified inference as fuzzy inference system and the BP algorithm as learning procedure. And we use a clustering algorithm to find initial parameters of membership function. The parameters such as membership functions, learning rates and momentum coefficients are easily adjusted using the genetic algorithms. Also, the performance index with weighted value is introduced to achieve a meaningful balance between approximation and generalization abilities of the model. To evaluate the performance index, we use the time series data for gas furnace and the sewage treatment process.

• 한국기계연구소 소보
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• s.19
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• pp.93-115
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• 1989

Cerebellar Model Arithmetic Controller(CMAC) has been introduced as an adaptive control function generator. CMAC computes control functions referring to a distributed memory table storing functional values rather than by solving equations analytically or numerically. CMAC has a unique mapping structure as a coarse coding and supervisory delta-rule learning property. In this paper, learning aspects and a convergence of the CMAC were investigated. The efficient training algorithms were developed to overcome the limitations caused by the conventional maximum error correction training and to eliminate the accumulated learning error caused by a sequential node training. A nonlinear function generator and a motion generator for a two d. o. f. manipulator were simulated. The efficiency of the various learning algorithms was demonstrated through the cpu time used and the convergence of the rms and maximum errors accumulated during a learning process; A generalization property and a learning effect due to the various gains were simulated. A uniform quantizing method was applied to cope with various ranges of input variables efficiently.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.654-656
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• 1998

This paper presents an optimal identification method of nonlinear and complex system that is based on fuzzy-neural network(FNN). The FNN used simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. And we use a HCM Algorithm to find initial parameters of membership function. And then to obtain optimal parameters, we use the genetic algorithm. Genetic algorithm is a random search algorithm which can find the global optimum without converging to local optimum. The parameters such as membership functions, learning rates and momentum coefficients are easily adjusted using the genetic algorithms. Also, the performance index with weighted value is introduced to achieve a meaningful balance between approximation and generalization abilities of the model. To evaluate the performance of the FNN, we use the time series data for 9as furnace and the sewage treatment process.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2000.10a
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• pp.159-165
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• 2000

A new state evaluation approach for structural safety is presented in this study. To reduce the subjectivity of the view and judgement of each expert founded on a limited body of knowledge in cognitive and inferential process of safety assessment, we introduced inductive learning method in AI. Inductive learning derives generalization from experiences. Decision tree induction algorithm analyzes the domain knowledge, produce rules via decision trees and then allow us to determine the classification of an object from case examples. The training set of state evaluation is constructed according to the selected attributes from working reports of RC bridge girders.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1350-1353
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• 1993

A fuzzy control system typically requires“tuning,”or adjuctment of the parameters defining its linguistic variables. Automating this process amounts to applying a second“metacontrol”layer to drive the controller and plant to desired performance levels. Current methods of automated tuning rely on a single crisp numeric functional to evaluate control system performance. A generalization of Box's complex algorithm allows more realistic tuning based on lexicographic aggregation of multiple ordinal scales of performance, such as effectiveness and efficiency. The method is presented and illustrated using a simple inverted pendulum control system.

• Journal of the Korean Society of Safety
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• v.26 no.4
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• pp.80-86
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• 2011

Structural optimization algorithm of steel box girder bridges using improved higher-order approximate reanalysis technique is proposed in this paper. The proposed approximation method is a generalization of the convex approximation method. The order of the approximate reanalysis for each function is analytically adjusted in the optimization process. This self-adjusted capability makes the approximate structural analysis values conservative enough to maintain the optimum design point of the approximate problem. The efficiency of proposed optimazation algorithm, compared with conventional algorithm, is successfully demonstrated in the steel box girder bridges. The efficiency and robustness of proposed algorithm is also demonstrated in practical steel box girder bridges.