• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.162-164
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• 2004

This paper proposes a new classification system combining the adaptive feature weighting algorithm using the genetic algorithm and the modified KNN rule. GA is employed to choose the middle value of weights and weights of features for high performance of the system. The modified KNN rule is proposed to estimate the class of test pattern using adaptive feature space. Experiments with the unconstrained handwritten digit database of Concordia University in Canada are conducted to show the performance of the proposed method.

• Journal of Digital Convergence
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• v.12 no.4
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• pp.277-283
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• 2014

Fuzzy modeling is generally using the given data and the fuzzy rules are established by the input variables and the space division by selecting the input variable and dividing the input space for each input variables. The premise part of the fuzzy rule is presented by selection of the input variables, the number of space division and membership functions and in this paper the consequent part of the fuzzy rule is identified by polynomial functions in the form of linear inference and modified quadratic. Parameter identification in the premise part devides input space Min-Max method using the minimum and maximum values of input data set and C-Means clustering algorithm forming input data into the hard clusters. The identification of the consequence parameters, namely polynomial coefficients, of each rule are carried out by the standard least square method. In this paper, membership function of the premise part is dividing input space by using trapezoid-type membership function and by using gas furnace process which is widely used in nonlinear process we evaluate the performance.

• Journal of applied mathematics & informatics
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• v.7 no.2
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• pp.629-640
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• 2000

this paper present an efficient line-drawing algorithm that reduces the amount of space required, Because of its efficiency , this line-drawing algorithm is faster than the Bresenham algorithm or the recursive bisection method. this efficiency was achieved through a new data structure; namely , the modified segment tree (MST). Using the modified segment tree and the distribution rule suggested in this paper, we dra lines without generating the recursive calls used in [3] and without creating the binary operation used in [4]. we also show that line accuracy improves in proportion to the display resolution . In practice, we can significantly improve the algorithm's performance with respect to time and space, This improvement offer an increase in speed, specially with lines at or near horizontal, diagonal. or vertical ; that is, this algorithm requires the time complexity of (n) and the space complexity O(2k+1), where n is the number of pixels and k is a level of the modified segment tree.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.5
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• pp.481-491
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• 2014

In this paper, we discussed a problem for improving the throughput of a crankshaft manufacturing line in an automotive factory in which the budget for purchasing new machines and installing additional buffers is limited. We also considered the constraint of available space for both of machine and buffer. Although this problem seems like a kind of buffer allocation problem, it is different from buffer allocation problem because additional machines are also considered. Thus, it is not easy to calculate the throughput by mathematical model, and therefore simulation model was developed using $ARENA^{(R)}$ for estimating throughput. To determine the investment plan, a modified Arrow Assignment Rule under some constraints was suggested and it was applied to the real case.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2007.05a
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• pp.272-275
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• 2007

This paper is concerned with modified integration algorithm on the strain-space for rate and temperature dependent elasto-plastic constitutive relations in order to obtain more accurate results in numerical implementation. The proposed algorithm is integrated analytically using integration by part and chain rule and then is applied to the 2-stage Lobatto IIIA with second-order accuracy. It has advantage that is able to consider the convective stress rates on the yield surface of the strain-space. Also this paper is carried out the iteration procedure using the Newton-Raphson method to enforce consistency at the end of the step. And the performance of the proposed algorithm for rate and temperature dependent constitutive relation is illustrated by means of analysis of adiabatic shear bands.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.40 no.7
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• pp.564-573
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• 2012

In this work, 1 MW class horizontal axis wind turbine blade configuration is properly sized and analyzed using the newly proposed aerodynamic design procedure and the in-house code developed by authors, and its design results are verified through comparison with experimental results of previously developed wind turbine blade. The structural design of the wind turbine blade is carried out using a composite materials and the netting and rule of mixture deign methods. The structural safety of the designed blade structure is investigated through the various load cases, stress, deformation, buckling and vibration analyses using the commercial FEM code, MSC.NASTRAN. Finally the required fatigue life is investigated using the modified Spera's experimental equation.

• Journal of Internet Computing and Services
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• v.13 no.5
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• pp.1-8
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• 2012

In this paper, we propose a new clustering combination based on numerical data driven for rule generation mechanism. In large and complicated space, a clustering method can obtain limited performance results. To overcome the single clustering method problem, hybrid combined methods can solve problem to divided simple cluster estimation. Fundamental structure of the proposed method is combined by mountain clustering and modified Chen clustering to extract detail cluster information in complicated data distribution of non-parametric space. It has automatic rule generation ability with advanced density based operation when intelligent systems including neural networks and fuzzy inference systems can be generated by clustering results. Also, results of the mechanism will be served to information of decision support system to infer the useful knowledge. It can extend to healthcare and medical decision support system to help experts or specialists. We show and explain the usefulness of the proposed method using simulation and results.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.48 no.9
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• pp.663-670
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• 2020

A study on mechanical property characterization and modeling technique was carried out to approximate the behaviour of structures with 2.5D C/SiC material. Several tensile tests were performed to analyze the behaviour characteristics of the 2.5D C/SiC material and elastic property was characterized by applying a mathematical homogenization and a modified rule of mixture. SiC matrix representing the elasto-plastic behavior approximates as a bilinear function. Then the equivalent yield strength and equivalent plastic stiffness were calculated by minimizing errors in experiment and approximation. RVE(Representative Volume Element)was defined from the fiber and matrix configuration of 2.5D C/SiC and a process of calculating the effective stiffness matrix by applying the modified rule of mixture to RVE was implemented in the ABAQUS User-defined subroutine. Finite element analysis was performed by applying the mechanical properties of fiber and matrix calculated based on the proposed process, and the results were in good agreement with the experimental results.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.42 no.4 s.304
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• pp.43-50
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• 2005

This paper proposes a new pattern recognition system combining the new adaptive feature weighting based on the genetic algorithm and the modified KNN(K Nearest-Neighbor) rules. The new feature weighting proposed herein avoids the overfitting and finds the Proper feature weighting value by determining the middle value of weights using GA. New GA operators are introduced to obtain the high performance of the system. Moreover, a class dependent feature weighting strategy is employed. Whilst the classical methods use the same feature space for all classes, the Proposed method uses a different feature space for each class. The KNN rule is modified to estimate the class of test pattern using adaptive feature space. Experiments were performed with the unconstrained handwritten numeral database of Concordia University in Canada to show the performance of the proposed method.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.7
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• pp.378-388
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• 2000

In this paper, we propose Fuzzy Polynomial Neural Networks(FPNN) based on Polynomial Neural Networks(PNN) and Fuzzy Neural Networks(FNN) for model identification of complex and nonlinear systems. The proposed FPNN is generated from the mutually combined structure of both FNN and PNN. The one and the other are considered as the premise part and consequence part of FPNN structure respectively. As the consequence part of FPNN, PNN is based on Group Method of Data Handling(GMDH) method and its structure is similar to Neural Networks. But the structure of PNN is not fixed like in conventional Neural Networks and self-organizing networks that can be generated. FPNN is available effectively for multi-input variables and high-order polynomial according to the combination of FNN with PNN. Accordingly it is possible to consider the nonlinearity characteristics of process and to get better output performance with superb predictive ability. As the premise part of FPNN, FNN uses both the simplified fuzzy inference as fuzzy inference method and error back-propagation algorithm as learning rule. The parameters such as parameters of membership functions, learning rates and momentum coefficients are adjusted using genetic algorithms. And we use two kinds of FNN structure according to the division method of fuzzy space of input variables. One is basic FNN structure and uses fuzzy input space divided by each separated input variable, the other is modified FNN structure and uses fuzzy input space divided by mutually combined input variables. In order to evaluate the performance of proposed models, we use the nonlinear function and traffic route choice process. The results show that the proposed FPNN can produce the model with higher accuracy and more robustness than any other method presented previously. And also performance index related to the approximation and prediction capabilities of model is evaluated and discussed.