• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.36 no.10
• /
• pp.979-987
• /
• 2008

In aerospace structural design, the position of lightening hole is often required to be optimized from the initial design in order to avoid an excessive stress concentration. To remodel the updated configuration in optimization procedure, re-meshing procedure is conventionally adopted. However, this approach is time-consuming, and has limitations especially in handling hexahedral or quadrilateral meshes, which are preferred because of their good numerical performances. To attenuate these disadvantages, new optimization scheme is proposed by combining the MLS(Moving Least Squares) based overset method and the genetic algorithm in this work. To test the validity of the proposed optimization scheme, optimizations of positions of lightening holes in 2D structures have been carried out.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.29 no.3
• /
• pp.253-260
• /
• 2016

In this paper, the optimal seismic design method for inducing the beam-hinge collapse mechanism of steel moment frames is presented. This uses the non-dominated sorting genetic algorithm II(NSGA-II) as an optimal algorithm. The constraint condition for preventing the occurrence of plastic hinges at columns is used to induce the beam-hinge collapse mechanism. This method uses two objective functions to minimize the structural weight and maximize the dissipated energy. The proposed method is verified by the application to nine story steel moment frame example. The minimum column-to-beam strength ratio to induce the beam-hinge collapse mechanism are investigated based on the simulation results. To identify the influence of panel zone on the minimum column-to-beam strength ratio, three analytic modeling methods(nonlinear centerline model without rigid end offsets, nonlinear centerline model with rigid end offsets, nonlinear model with panel zones) are used.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.40 no.10
• /
• pp.2054-2061
• /
• 2015

Biologically inspired modeling techniques have received considerable attention for their robustness, scalability, and adaptability with simple local interactions and limited information. Among these modeling techniques, Gene Regulatory Networks (GRNs) play a central role in understanding natural evolution and the development of biological organisms from cells. In this paper, we apply GRN principles to the WSN system and propose a new GRN model for decentralized node scheduling design to achieve energy balancing while meeting delay requirements. Through this scheme, each sensor node schedules its state autonomously in response to gene expression and protein concentration, which are controlled by the proposed GRN-inspired node scheduling model. Simulation results indicate that the proposed scheme achieves superior performance with energy balancing as well as desirable delay compared with other well-known schemes.

• The KIPS Transactions:PartB
• /
• v.9B no.3
• /
• pp.277-286
• /
• 2002

Genetic programming (GP) has been combined with quantum mechanical perturbation theory to make a new algorithm to construct mathematical models and perform predictions for chaotic time series from real world. Procedural similarities between time series modeling and perturbation theory to solve quantum mechanical wave equations are discussed, and the exemplary GP approach for implementing them is proposed. The approach is based on multiple populations and uses orthogonal functions for GP function set. GP is applied to original time series to get the first mathematical model. Numerical values of the model are subtracted from the original time series data to form a residual time series which is again subject to GP modeling procedure. The process is repeated until predetermined terminating conditions are met. The algorithm has been successfully applied to construct highly effective mathematical models for many real world chaotic time series. Comparisons with other methodologies and topics for further study are also introduced.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.5 no.5
• /
• pp.977-984
• /
• 2001

In this paper, Genetic-Fuzzy Algorithm for Inverted Pendulum is presented. This Algorithms is combine Fuzzy logic with the Genetic Algorithm. The Fuzzy Logic Controller is only designed to two inputs and one output. After Fuzzy control rules are determined, Genetic Algorithm is applied to tune the membership functions of these rules. To measure of performance of the designed Genetic-Fuzzy controller, Computer simulation is applied to Inverted Pendulum system. In the simulation, In the case of f[0.3, 0.3] Fuzzy controller is measured that maximum undershoot is $-5.0 \times 10^{-2}[rad]$, maximum undershoot is $3.92\times10^{-2}[rad]$ individually however, Designed algorithm is zero. The Steady state time is approximated that Fuzzy controller is 2.12[sec] and designed algorithm is 1.32[sec]. The result of simulation, Resigned algorithm is showed it's efficient and effectiveness for Inverted Pendulum system.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
• /
• 2004.11a
• /
• pp.159-162
• /
• 2004

플라즈마 공정 모델 개발에 역전파 신경망이 가장 많이 응용되고 있으나, 관여하는 다수의 학습인자로 인해 그 최적화가 매우 어렵다. Radial basis function network (RBFN)은 관여하는 학습인자의 수가 적어 그 최적화가 상대적으로 용이하지만, 두인자의 다양한 조합에 의해 RBFN의 예측성능이 상당히 영향을 받을 수 있다. 본 연구에서는 학습인자 상호간의 작용을 유전자 알고리즘 (genetic algorithm-GA)을 이용하여 최적화하는 기법을 소개한다. 제안하는 알고리즘을 광도파로 제작을 위해 수행한 실리카 식각공정 데이터에 적용하여 평가하였다. 평가에 이용된 식각 응답은, 실리카 식각률, aluminum (Al) 식각률, Al 선택비, 그리고 실리카 프로파일 각도이다. 최적화한 모델은 종래의 모델과 비교하였으며, 그 향상도는 실리카 식각률, Al 식각률, Al 선택비, 그리고 실리카 프로파일 각도에 대해서 각 기 0.8%, 32.4%, 20.3%, 1.3% 등이었다. Al 식각률과 선택비에 대해서 예측성능은 상당이 향상되었다.

• Journal of the Korean Geotechnical Society
• /
• v.18 no.6
• /
• pp.117-127
• /
• 2002

A deterministic approach of slope stability, which is generally corresponding to the model of a simple non-linear function for slopes, is problematic in that it does not account the versatile characteristics of ground layers in an effective way. To resolve this problem, this study proposes a new way of analyzing slope stability, so-called “genetic algorithm method, ” so as to reflect some particular conditions pertaining to the grounds under concern. Similarities and differences in slope stability that may exist between homogeneous and multiple ground layers are examined in a competitive manner, Overall, though similarities deemed a little bit salient, the algorithm method turned out to be very applicable to estimating the validity of slope stability. Furthermore, an additional effort to consider long-standing sequential and dynamic changes in both the amount of rainfall and the underground water level is made in order to improve the results.

• Journal of the Korean Institute of Telematics and Electronics C
• /
• v.36C no.9
• /
• pp.66-75
• /
• 1999

Image segmentation is the process where an image is segmented into regions that are set of homogeneous pixels. The result has a ciritical effect on accuracy of image understanding. In this paper, an Markov random field (MRF) image segmentation is proposed using genetic algorithm(GA). We model an image using MRF which is resistant to noise and blurring. While MRF based methods are robust to degradation, these require accurate parameter estimation. So GA is used as a segmentation algorithm which is effective at dealing with combinatorial problems. The efficiency of the proposed method is shown by experimental results with real images and application to automatic vehicle extraction system.

• 한국데이터정보과학회:학술대회논문집
• /
• 2003.05a
• /
• pp.41-47
• /
• 2003

한우 6번 염색체 유전자 지도에서 한우의 질을 높이기 위한 QTL(quantitative trait loci)분석을 실시하여 선별된 Loci 값을 Permutation Test를 이용하여 계산하였다. 한편, 경제적으로 주요한 한우의 특성부위(질적부위와 육량등)에 따른, 우수 경제형질 DNA marker를 K-평균 군집법을 실시 파악하였다. 이들 QTL과 K-평균법에 의해 한우의 염색체 6번, ILST035의 주요 경제 형질별 DNA marker들을 선별하여, Bootstrap BCa방법을 이용하여 각 DNA marker들의 신뢰구간을 구했다.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
• /
• 2007.11a
• /
• pp.462-463
• /
• 2007

신경망을 이용하여 반구형 유도결합형 플라즈마 장비에 대한 전자밀도의 예측모델을 개발하였다. 신경망으로는 Radial Basis function Network를 이용하였고, 신경망의 예측성능은 유전자 알고리즘을 이용하여 최적화하였다. 체계적인 모델링을 위해 $2^4$ 전 인자 (Full Factorial) 실험계획법을 이용하였다. 개발된 모델을 이용하여 공정변수에 따른 전자밀도의 영향을 고찰하였다. 전자밀도는 팁 위치(즉 챔버 높이)에 가장 많은 영향을 받았으며, 소스전력과 압력의 변화에 따른 전자밀도의 변화는 작았다. 팁 위치는 소스전력 변화에 영향을 받지 않았지만, 압력변화는 팁 위치에 따라 복잡하게 전자밀도에 영향을 미쳤다.