• Journal of the Korean Geotechnical Society
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• v.25 no.9
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• pp.93-100
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• 2009

A core procedure of the direct search method used in this study is optimizing a difference between objective function and real displacement and correcting unknown variables. Because the research procedure comes from back-analyzing of the unknown variable of each layer, back-analyzing results need an additional optimization to minimize interferential effects of unknown variables. Therefore, the direct search method Is used to obtain optimized solutions without a partial differentiation of an objective function. The object of this research is developing the back analysis technique for multi-unknown variables by modeling the soil including underground structure Into upper and lower layer. In order to minimize interferent errors, repeated back analysis is performed and applicability on the real tunnel is examined. Consequently, the multi-layer analysis model is more precise in describing the real behavior of underground structure. It shows the validity of back analysis far multi-layer model which is the understructure placed on multi-layer boundaries.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.36 no.2
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• pp.105-114
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• 2008

In this study, the sizing and performance analysis program is developed for the canard rotor wing(CRW) aircraft which operates in dual modes (fixed wing mode and rotary wing mode). The developed program is verified for both fixed wing and rotary wing modes using the existing aircraft data and the design optimization formulation is made to perform the reconnaissance mission. For the canard rotor wing aircraft optimization , multi-objective function is constructed to consider both the fixed wing mode and rotary wing mode the weighting factor. For six design cases with different weighting factors and different design constraints, the optimization is performed and improved rotor design results are derived.

• Journal of the Earthquake Engineering Society of Korea
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• v.12 no.6
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• pp.13-23
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• 2008

In this study, a finite element (FE) model updating method based on the hybrid genetic algorithm (HGA) is proposed to improve the grillage FE model for plate girder bridges. HGA consists of a genetic algorithm (GA) and direct search method (DS) based on a modification of Nelder & Mead's simplex optimization method (NMS). Fitness functions based on natural frequencies, mode shapes, and static deflections making use of the measurements and analytical results are also presented to apply in the proposed method. In addition, a multi-objective function has been formulated as a linear combination of fitness functions in order to simultaneously improve both stiffness and mass. The applicability of the proposed method to girder bridge structures has been verified through a numerical example on a two-span continuous grillage FE model, as well as through an experimental test on a simply supported plate girder skew bridge. In addition, the effect of measuring error is considered as random noise, and its effect is investigated by numerical simulation. Through numerical and experimental verification, it has been proven that the proposed method is feasible and effective for FE model updating on plate girder bridges.

• Proceedings of the KIEE Conference
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• 2004.04a
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• pp.46-48
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• 2004

본 논문에서는 유한 요소법과 등가회로법을 이용하여 윤곽 진동형 압전 변압기의 특성을 해석하였으며 이를 통해 얻어진 결과를 기반으로 매칭 임피던스와 전기 및 기계적 결합 계수를 계산하였다. 전력 소자로서 사용되는 압전 변압기는 부하 등 특정 목적에 부합하는 정확한 설계가 필요하기 때문에 다중 목적 함수를 갖는 진화 전략을 이용하여 형상 최적화를 수행하였다.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 1997.10a
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• pp.153-157
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• 1997

The investigation deals with the process design in cold rolling mill of electric plant. In this study, multiple objective optimization is conducted by a genetic algorithm, where the fitness values are evaluated on the basis of one - dimensional model of flat rolling. The approach is applied to the determination of the process conditions which are optimal with regard to minimization of roll power and maximization of productivity.

• Proceedings of the KIEE Conference
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• 2003.07b
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• pp.891-893
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• 2003

This paper Presents the optimum design of Brushless DC Motor using multi-objective function Neural Network to maximize efficiency and Ratio of Torque Per unit weight.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.1
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• pp.97-105
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• 2015

Multiresponse optimization (MRO) seeks to find the setting of input variables, which optimizes the multiple responses simultaneously. The approach of weighted mean squared error (WMSE) minimization for MRO imposes a different weight on the squared bias and variance, which are the two components of the mean squared error (MSE). To date, a weighted sum-based method has been proposed for WMSE minimization. On the other hand, this method has a limitation in that it cannot find the most preferred solution located in a nonconvex region in objective function space. This paper proposes a Tchebycheff metric-based method to overcome the limitations of the weighted sum-based method.

• Journal of Korean Society of Transportation
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• v.20 no.5
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• pp.81-98
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• 2002

This paper documents the development of a bi-objective(minimizing delays and Queue lengths) cycle-free signal timing length model using Genetic Algorithm. The model was embodied using MATLAB. the language of technical computing. A special feature of this model is its ability to concurrently manage delays and queue lengths of turning movement concurrently. The model produces a cycle-free signal timing(cycles and green times) for each intersection on the cycle basis. Appropriate offsets could be also accomplished by applying cycle-free based signal timings for respective intersections. The model was applied to an example network which consists of four intersections. The result shows that the model produces superior signal timings to the existing signal timing model in terms of managing delays and queue lengths of turning movements.

• Proceedings of the Korean Information Science Society Conference
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• 2000.10c
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• pp.659-661
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• 2000

폰 노이만 모델의 지역성과 데이터플로우 모델의 병렬성을 결합하여 등장한 모델이 다중 스레드 모델이다. 다중스레드 모델의 목적은 통신시간과 계산 시간을 겹침으로써 프로세서의 활용도를 높이고자 하는 것이다. 기존의 대부분의 다중 스레드 모델의 스레드 스케쥴링 기법은 FIFO 혹은 FILO 방식을 사용하고 있다. 본 논문에서는 프로세서의 활용도를 높이고 프로세서의 휴지 시간을 줄이기 위해서 원격 함수 호출 혹은 원격 메모리 참조 기능의 스레드(이후로는 원격 스레드라 부름)와 계산 기능의 스레드가 동시에 활성화되었을 때 원격 스레드들을 먼저 수행하는 것이 프로세서의 지연 시간을 줄이고 병렬성을 높이는 데 효과적임을 제안한다. 이것을 구현하기 위해서 프레임 내부의 지속 벡터(CV)를 CCV(call continuation vector)와 LCV(local continuation vector) 둘로 구분하였다. 스레드가 활성화될 때 CCV에는 원격 스레드들을, LCV에는 계산 스레드들을 저장한 후, CCV에 저장된 스레드들을 먼저 수행하고 LCV를 나중에 수행한다.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.4
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• pp.343-350
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• 2000

In this paper, we propose the Multi-FPNN(Fuzzy Polynomial Neural Networks) model based on FNN and PNN(Polyomial Neural Networks) for optimal system identifacation. Here FNN structure is designed using fuzzy input space divided by each separated input variable, and urilized both in order to get better output performace. Each node of PNN structure based on GMDH(Group Method of Data handing) method uses two types of high-order polynomials such as linearane and quadratic, and the input of that node uses three kinds of multi-variable inputs such as linear and quadratic, and the input of that node and Genetic Algorithms(GAs) to identify both the structure and the prepocessing of parameters of a Multi-FPNN model. Here, HCM clustering method, which is carried out for data preproessing of process system, is utilized to determine the structure method, which is carried out for data preprocessing of process system, is utilized to determance index with a weighting factor is used to according to the divisions of input-output space. A aggregate performance inddex with a wegihting factor is used to achieve a sound balance between approximation and generalization abilities of the model. According to the selection and adjustment of a weighting factor of this aggregate abjective function which it is acailable and effective to design to design and optimal Multi-FPNN model. The study is illustrated with the aid of two representative numerical examples and the aggregate performance index related to the approximation and generalization abilities of the model is evaluated and discussed.