• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.07b
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• pp.1129-1133
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• 2004

반도체공정 최적화에 소요되는 시간과 경비를 줄이기 위해 신경망 모델이 개발되고 있다. 주로 역전파 신경망을 이용하여 모델이 개발되고 있으며, 본 연구에서는 Radial Basis Function Network (RBFN)을 이용하여 플라즈마 식각공정 모델을 개발한다. 실험데이터는 유도결합형 플라즈마를 이용한 Silicon Carbide 박막의 식각공정으로부터 수집되었다. 모델개발을 위해 $2^4$ 전인자 (full factorial) 실험계획법이 적용되었으며, 모델에 이용된 식각응답은 식각률과 atomic force microscopy로 측정한 식각표면 거칠기이다. 모델검증을 위해 추가적으로 16번의 실험을 수행하였다. RBFN의 예측성능은 세 학습인자, 즉 뉴런수, width, 초기 웨이트 분포 (initial weight distribution-IWD) 크기에 의해 결정된다. 본 연구에서는 각 학습인자의 영향을 최적화하였으며, IWD의 불규칙성을 고려하여 주어진 학습인자에 대해서 100개의 모델을 발생하고, 이중 최소의 IWD를 갖는 모델을 선택하였다. 최적화한 식각률과 표면거칠기 모델의 RMSE는 각기 26 nm/min과 0.103 nm이었다. 통계적인 회귀모델과 비교하여, 식각률과 표면거칠기 모델은 각기 52%와 24%의 향상된 예측정확도를 보였다. 이로써 RBFN이 플라즈마 공정을 효과적으로 모델링 할 수 있음을 확인하였다.

• The KIPS Transactions:PartD
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• v.15D no.1
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• pp.99-106
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• 2008

Recently developed compilers perform some optimizations in order to speed up the execution time of source program. These optimizations require the reordering of the sequence of program statements. This reordering does not give any problems in a single-threaded program. However, the reordering gives some significant errors in a multi-threaded program. State-of-the-art model checkers such as JavaPathfinder do not consider the reordering resulted in the optimization step in a compiler since they just consider a single memory model. In this paper, we develop a new verification tool to verify Java source program based on Java Memory Model. And our tool is capable of handling the reordering in verifying Java programs. As a result, our tool finds an error in the test program which is not revealed with the traditional model checker JavaPathFinder.

• 한국신재생에너지학회:학술대회논문집
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• 2007.06a
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• pp.404-408
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• 2007

현재 국내에서 운용중인 풍력발전시스템은 국내 풍력자원에 대한 정확한 정보의 부재와 국내 풍황에 맞지 않는 국외 모델을 그대로 운용하는 등의 몇 가지 문제를 드러내었다. 본 연구의 목적은 국내 연안의 해상에서 한국형 해상풍력터빈을 설치하기 위한 잠재적 최적위치와 풍황자료 산출 최적화 알고리즘을 구현하는 것이다. 최적화 알고리즘은 얕은 수심 분포와 연안에서의 거리를 제약조건으로 하고 최대 에너지밀도를 가진 지점을 구하는 것으로 정식화하였다. 풍황자료 산출을 위해서 국내 연안의 해상 풍황자료를 포함하는 기상풍황자료를 통계적 모델로 분석하여 바람지도를 작성하였다. 이 바람지도를 이용하여 지질 통계학 분야의 관측기법인 크리깅 모델을 구성하고, 전역최적화기법인 유전자알고리즘을 이용하여 제약조건을 만족하는 최대에너지밀도값과 그 위치를 도출하였다. 수치최적화 결과 우리나라 풍력 자원의 대략적인 잠재량과 현황파악이 가능하였고, 해상풍력발전단지가 조성 가능한 개략적인 위치를 예측할 수 있었다.

• Journal of the Earthquake Engineering Society of Korea
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• v.10 no.6 s.52
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• pp.19-28
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• 2006

Most conventional model updating methods must use mathematical objective function with experimental modal matrices and analytical system matrices or must use information about the gradient or higher derivatives of modal properties with respect to each updating parameter. Therefore, most conventional methods are not appropriate for complex structural system such as bridge structures due to stability problem in inverse analysis with ill-conditions. Sometimes, moreover, the updated model may have no physical meaning. In this paper, a new FE model updating method based on a hybrid optimization technique using genetic algorithm (GA) and Holder-Mead simplex method (NMS) is proposed. The performance of hybrid optimization technique on the nonlinear problem is demonstrated by the Goldstein-Price function with three local minima and one global minimum. The influence of the objective function is evaluated by the case study of a simulated 10-dof spring-mass model. Through simulated case studies, finally, the objective function is proposed to update mass as well as stiffness at the same time. And so, the proposed hybrid optimization technique is proved to be an efficient method for FE model updating.

• Composites Research
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• v.16 no.4
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• pp.36-43
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• 2003

In this research, the optimization of composite structures was performed using metamodels. The optimization of composite structures requires a lot of time when optimizing the result of the time-consuming analysis. Thus, metamodels are used to replace the time-consuming analysis with simple models. RSM, kriging and neural networks are widely used metamodels. RSM and kriging were used in this study. The ultimate failure load analysis of the composite structure was approximated by metamodels. The optimizations of the composite plate were performed to maximize ultimate failure load using genetic algorithm and metamodels.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.846-849
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• 2020

하이퍼파라미터(초매개변수) 최적화란 모델의 학습에 앞서 미리 설정해야 하는 값인 하이퍼파라미터의 최적값을 탐색하는 문제이다. 이때의 최적값은 학습을 끝낸 모델의 성능을 가능한 최대치로 높이게 하는 값이다. 한편, 최근 모바일 장치를 이용한 포지셔닝 데이터의 대량 수집이 가능해지면서 이를 활용하여 위치 기반 서비스(Location-Based Service)를 위한 데이터 분석 및 예측에 관한 연구가 활발히 이루어졌다. 그중 이동 경로를 이미지로 패턴화하여 국소 지역 내에서 다음 위치를 예측하는 CNN 모델에 대해서 하이퍼파라미터 튜닝을 진행하였다. 결과적으로 베이지안 최적화(Bayesian Optimization)를 통해 모델의 성능을 평균 3.7%, 최대 9.5%까지 개선할 수 있음을 확인하였다.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.2
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• pp.105-110
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• 2015

This study proposes the more improved mathematical model than conventional that for the 3-DOF Helicopter System in Quanser Inc., and checks the validity about the proposed model by performance comparison between the controller based on the conventional model and that based on the proposed model. Research process is next : First, analyze the dynamics for the 3-DOF helicopter system and establish the linear mathematical model. Second, check the eliminated nonlinear-elements in linearization process for establishing the linear mathematical model. And establish the improved mathematical model including the parameters corresponding to the eliminated nonlinear-elements. At that time, it is used for modeling that Particle Swarm Optimization algorithm the meta-heuristic global optimization method. Finally, design the controller based on the proposed model, and verify the validity of the proposed model by comparison about the experimental results between the designed controller and the controller based on the conventional model.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.2
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• pp.299-304
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• 2023

Among artificial intelligence techniques, deep learning is a model that has been used in many places and has proven its effectiveness. However, deep learning models are not used effectively in everywhere. In this paper, we will show the limitations of deep learning models through comparison of regression analysis and deep learning models, and present a guide for effective use of deep learning models. In addition, among various techniques used for optimization of deep learning models, data normalization and data shuffling techniques, which are widely used, are compared and evaluated based on actual data to provide guidelines for increasing the accuracy and value of deep learning models.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.1
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• pp.42-54
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• 2009

Contrast to the 6-DOF nonlinear dynamic modeling of nonlinear tracking problem, 3-DOF point-mass modeling of flight mechanics is efficient and adequate for applying the trajectory optimization problem. There exist limitations to apply an optimal trajectory from point-mass modeling as a reference trajectory directly to conduct the nonlinear tracking control, In this paper, new matching trajectory optimization scheme is proposed to compensate those differences of mismatching. To verify performance of proposed method, full ascent three-dimensional flight trajectories are obtained by reflecting the real constraints of flight conditions and airship performance with and without jet stream condition. Then, they are compared with the optimal trajectories obtained from conventional method.

• Journal of the Korean association of regional geographers
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• v.16 no.6
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• pp.673-688
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• 2010

The purpose of this study is to develop an optimization model for reconstructing local administrative districts using spatial analysis and modeling. For this, literature related to reorganization of local administrative systems was critically reviewed and criteria for redistricting were extracted. An optimization model for reconstructing administrative districts was formulated based on these criteria. The model considered three criteria; homogeneity within a reconstructed district, equity among reconstructed districts, and spatial arrangement. Homogeneity for relieving spatial mismatch between administrative and living(economic) boundaries is measured by spatial interaction within a district. Equity among districts is evaluated using population, area, and financial independence. Finally, spatial arrangement is measured by compactness and contiguity. The developed optimization model was implemented using Automated Zoning Procedure(AZP) in GIS environment and applied to a problem aggregating Si-Gun administrative units into broader districts. Application results demonstrate that the model can provide optimal districts according to alternative objective functions.