• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.11
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• pp.755-760
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• 2016

The demand for independent generators using renewable energy has been increasing. Among those independent generators, small wind turbine generators have been actively developed. Permanent magnets are generally used for small wind turbine generators to realize a simple structure and small volume. On the other hand, cogging torque is included due to the structure of the permanent magnet synchronous machine, which can be the source of noise and vibration. The cogging torque can be varied by the shape of the permanent magnet and core, and it can be reduced using the appropriate design techniques. This paper proposes a design technique that can reduce the cogging torque by changing the shape of the permanent magnets for SPMSM (Surface Permanent Magnet Synchronous Motor), which is used widely for small wind turbine generators. Evolution Strategy, which is one of non-deterministic optimization techniques, was adopted to find the optimal shape of the permanent magnets that can reduce the cogging torque. The angle and outer diameter of permanent magnet were set as the design variable. A 300W class wind turbine generator, whose pole/slot combination was 8 poles/18 slots, was designed with the proposed design technique. The properties of the generator, including the cogging torque and output voltage, were calculated. The calculation results showed that the cogging torque of the optimized model was reduced compared to that of the initial model. The design technique proposed by this paper can be an effective measure to reduce the cogging torque.

• Proceedings of the KIEE Conference
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• 2002.07c
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• pp.1943-1945
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• 2002

본 논문에서는 마이크로스트립 방향성 결합기의 지향성을 향상시키기 위한 최적설계를 수행하였다. 제안된 설계는 기존의 지향성을 향상시키기 위한 다른 시도들과는 달리 하우징 구조를 설계함으로써 비교적 제작하기가 수월한 장점이 있다. 최적화를 위해서 결정론적 알고리즘과 결합된 (1+1)진화 알고리즘을 적용하였으며, 최적화결과 보다 향상된 방향성 결합기의 특성을 얻을 수 있었다. Ansoft-HFSS를 이용하여 해석결과의 타당성을 검증하였다.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.343-345
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• 2004

We propose a new category of fuzzy set-based fuzzy inference systems based on data granulation related to fuzzy space division for each variables. Data granules are viewed as linked collections of objects(data, in particular) drawn together by the criteria of proximity, similarity, or functionality. Granulation of data with the aid of Hard C-Means(HCM) clustering algorithm help determine the initial parameters of fuzzy model such as the initial apexes of the membership functions and the initial values of polyminial functions being used in the premise and consequence part of the fuzzy rules. And the initial parameters are tuned effectively with the aid of the genetic algorithms(GAs) and the least square method. Numerical example is included to evaluate the performance of the proposed model.

• Journal of The Korean Society of Agricultural Engineers
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• v.49 no.6
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• pp.37-46
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• 2007

In designing the structure, the stress path is the basic data. But the stress path is not standardized to analysis the structure. So the one-dimensional frame element structure model with the triangle irregular network is used to solve the problem. And the refined evolutionary structural optimization(RESO) used in structural topology optimization is applied to this study. Through this process, the search method of the stress path is advanced and the burden of the calculation. is reduced.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.441-447
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• 1999

본 논문은 복잡 적응 시스템의 분석 및 모델링을 위해, 인공생명의 기본 패러다임인 셀룰라 오토마타를 선택하여, 무정형의 구조를 가지며 투명한 자료 전파 특성을 갖는 셀룰라 신경 회로망의 설계하고 개발하는데 중점을 두었다. 우선, 신경 회로망의 불규칙한 구조를 발생학적으로 다루어 무정형의 은닉층을 생성하고, 다윈의 진화론을 적용하여 구조적 진화 및 선택을 통해 최적화된 신경 회로망을 설계하였다. 주변 셀의 상태를 감지하여 자신의 상태를 수정해나가는 방식의 셀룰라 오토마타의 투명한 신호 전파 모델로 자료 및 오차의 역전파에 적용하도록 고안하였고, 라마르크의 용불용설을 활용한 오차의역전파 학습 알고리즘을 유도하였다. 이러한 복잡 적응계의 학습 과정을 유도하여 시뮬레이션에서 그 타당성을 입증하였다. 시뮬레이션에서는 신경 회로망의 XOR 문제와 다중 입력 다중 출력 함수에 대한 근사화 문제를 풀었다.

• Journal of Korean Association for Spatial Structures
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• v.3 no.3 s.9
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• pp.67-74
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• 2003

The purpose of this study is to improve convergence speed of topology optimization procedure using the existing ESO method and to deal with topology decision of the truss structures according to a boundary condition, such as cantilever type. At the existing ESO topology optimization procedure for the truss structures, the adjustment of member sizes according to target stress has been executed by increasing or reducing a very small value from each member size. In this case, it takes too much iteration till convergence. Accordingly, it is practically hard to obtain optimum topology for a large scale structures. For that reason, it is necessary to improve convergence speed of ESO method more effectively. During the topology decision procedure, member sizes are adjusted by calculating approximate solution for member sizes corresponding to the target stress at every step, the new member sizes are adjusted by such method are applied in FEA procedure of next step.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.10
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• pp.6969-6976
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• 2015

As new technologies are advancing and development paradigms are changing, software development process and development methods are evolving progressively. As a result, because the number of developing and managing processes and methodologies are increasing as a project in companies, effective management methods are needed. Especially, because companies should apply optimized methodology according to project's size and characteristics, customization technique of methodology is required urgently. In this paper, we propose a meta-model based methodology framework which can integrate and manage methodologies being developed progressively. Applying proposed methodology framework, a company is able to manage as well as develop optimized methodology easily as a project. Especially, because a proposed methodology framework is developed by meta-model, adding or extending new methodology elements can be realized simply as well as method elements are reused easily in case of customization of methodology as a project.

• Journal of the Korean Geotechnical Society
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• v.35 no.4
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• pp.27-35
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• 2019

This study confirmed the applicability to the field of geotechnical engineering for relatively complicated space and many target design variables in back analysis. The Sharan's equation and the Blum's method were used for the tunnel field and the retaining wall as a model for the multi-variate problem of geotechnical engineering. Optimization methods are generally divided into a deterministic method and a stochastic method. In this study, Simulated Annealing Method (SA) was selected as a deterministic method and Differential Evolution Algorithm (DEA) and Particle Swarm Optimization Method (PSO) were selected as stochastic methods. The three selected optimization methods were compared by applying a multi-variate model. The problem of deterministic method has been confirmed in the multi-variate back analysis of geotechnical engineering, and the superiority of DEA can be confirmed. DEA showed an average error rate of 3.12% for Sharan's solution and 2.23% for Blum's problem. The iteration number of DEA was confirmed to be smaller than the other two optimization methods. SA was confirmed to be 117.39~167.13 times higher than DEA and PSO was confirmed to be 2.43~6.91 times higher than DEA. Applying a DEA to the multi-variate back analysis of geotechnical problems can be expected to improve computational speed and accuracy.

• Journal of KIISE:Software and Applications
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• v.35 no.8
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• pp.501-511
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• 2008

Probe design is one of the essential tasks in successful DNA microarray experiments. The requirements for probes vary as the purpose or type of microarray experiments. In general, most previous works use the simple filtering approach with the fixed threshold value for each requirement. Here, we formulate the probe design as a multiobjective optimization problem with the two objectives and solve it using ${\epsilon}$-multiobjective evolutionary algorithm. The suggested approach was applied in designing probes for 19 types of Human Papillomavirus and 52 genes in Arabidopsis Calmodulin multigene family and successfully produced more target specific probes compared to well known probe design tools such as OligoArray and OligoWiz.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.279-281
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• 2006

In this paper, we study an approach to design a self-tuning Fuzzy-PI controller in HVDC(High Voltage Direct Current) system. In the rectifier of conversional HVDC system, turning on, turning off, triggering and protections of thyristors have lots of problems that can make the dynamic instability and cannot damp the dynamic disturbance efficiently. The above problems are solved by adapting Fuzzy-PI controller for the fire angle control of rectifier.[7] The performance of the Fuzzy-PI controller is sensitive to the variety of scaling factors. The design procedure dwells on the use of evolutionary computing(Genetic Algorithms, GAs). Then we can obtain the optimal scaling factors of the Fuzzy-PI controller by Genetic Algorithms. In order to improve Fuzzy-PI controller, we adopt FIS to tune the scaling factors of the Fuzzy-PI controller on line. A comparative study has been performed between Fuzzy-PI and self-tuning Fuzzy-PI controller, to prove the superiority of the proposed scheme.