• Structural Engineering and Mechanics
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• 제45권2호
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• pp.183-199
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• 2013

Current methods of analysis of trusses depend on matrix formulations based on equilibrium equations which are in fact derived from energy principles, and compatibility conditions. Recently it has been shown that the minimum energy principle, by itself, in its pure and unmodified form, can well be exploited to analyze structures when coupled with an optimization algorithm, specifically with a meta-heuristic algorithm. The resulting technique that can be called Total Potential Optimization using Meta-heuristic Algorithms (TPO/MA) has already been applied to analyses of linear and nonlinear plane trusses successfully as coupled with simulated annealing and local search algorithms. In this study the technique is applied to both 2-dimensional and 3-dimensional trusses emphasizing robustness, reliability and accuracy. The trials have shown that the technique is robust in two senses: all runs result in answers, and all answers are acceptable as to the reliability and accuracy within the prescribed limits. It has also been shown that Harmony Search presents itself as an appropriate algorithm for the purpose.

• 한국환경복원기술학회지
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• 제20권6호
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• pp.133-149
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• 2017

As cities face increasing problems such as aging, environmental pollution and growth limits, we have been trying to incorporate sustainability into urban planning and related policies. However, it is very difficult to generate a 'sustainable spatial plans' because there are trade-offs among environmental, society, and economic values. This is a kind of non-linear problem, and has limitations to be solved by existing qualitative expert knowledge. Many researches from abroad have used the meta heuristic optimization algorithms such as Genetic Algorithms(GAs), Simulated Annealing(SA), Ant Colony Optimization(ACO) and so on to synthesize competing values in spaces. GAs is the most frequently applied theory and have been known to produce 'good-enough plans' in a reasonable time. Therefore we collected the research on 'spatial optimization model based GAs' and analyzed in terms of 'study area', 'optimization objective', 'fitness function', and 'effectiveness/efficiency'. We expect the results of this study can suggest that 'what problems the spatial optimization model can be applied to' and 'linkage possibility with existing planning methodology'.

• 제어로봇시스템학회논문지
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• 제4권2호
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• pp.256-263
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• 1998

Among process synthesis problems, the heat exchanger network (HEN) has been subjected to the most concentrated effort because this kind of problems was well defined for solving it and 20-30% energy savings could be realized in the present chemical processes. In this paper, we use an evolutionary approach for HEN synthesis because this approach can overcome the local optimum and combine some heuristic rules. The basic evolutionary approach is composed of three parts, that is, initialization step, growth step and mutation step, as in the simulated annealing and genetic algorithm. This algorithm uses the ecological rule that a better cell will live and worse cell should decompose after repeated generations. With this basic concept, a new procedure is developed and a more efficient method is proposed to generate initial solutions. Its effectiveness is shown using test examples.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권9호
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• pp.4197-4219
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• 2017

Cloud computing is a new service to provide dynamic, scalable virtual resource services via the Internet. Cloud market is available to multiple cloud computing resource providers and users communicate with each other and participate in market transactions. However, since cloud computing is facing with more and more security issues, how to complete the allocation process effectively and securely become a problem urgently to be solved. In this paper, we firstly analyze the cloud resource allocation problem and propose a mathematic model based on combinatorial double auction. Secondly, we introduce a trust evaluation mechanism into our model and combine genetic algorithm with simulated annealing algorithm to increase the efficiency and security of cloud service. Finally, by doing the overall simulation, we prove that our model is highly effective in the allocation of cloud resources.

• 센서학회지
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• 제6권2호
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• pp.145-154
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• 1997

회로 시뮬레이션과 국부적 탐색을 갖는 시뮬레이티드 아닐링을 사용한 새로운 탐색 전략에 기초하여 고성능 연산 증폭기의 설계 자동화를 위한 기법을 제안하였다. 임의의 연산 증폭기 구조와 성능 규격에 대해서, 이산 설계 변수들을 갖는 비용 함수의 이산 최적화를 통해 연산 증폭기의 설계가 이루어진다. 설계 시간의 단축을 위해서 전용 회로 시뮬레이터와 몇 가지 휴리스틱을 사용하였다. 스마트 센서와 10 비트 25 MS/s 파이프라인 A/D 변환기에 사용 가능한 저전력 고속 전차동 CMOS 연산 증폭기의 설계를 통해서, 제안된 기법을 사용하여 개발된 설계 도구는 적은 설계 지식과 설계 노력을 가지고 고성능 연산 증폭기를 설계하는데 사용될 수 있음을 보였다.

• Nuclear Engineering and Technology
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• 제54권10호
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• pp.3864-3877
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• 2022

We propose a new approach called PESA (Prioritized replay Evolutionary and Swarm Algorithms) combining prioritized replay of reinforcement learning with hybrid evolutionary algorithms. PESA hybridizes different evolutionary and swarm algorithms such as particle swarm optimization, evolution strategies, simulated annealing, and differential evolution, with a modular approach to account for other algorithms. PESA hybridizes three algorithms by storing their solutions in a shared replay memory, then applying prioritized replay to redistribute data between the integral algorithms in frequent form based on their fitness and priority values, which significantly enhances sample diversity and algorithm exploration. Additionally, greedy replay is used implicitly to improve PESA exploitation close to the end of evolution. PESA features in balancing exploration and exploitation during search and the parallel computing result in an agnostic excellent performance over a wide range of experiments and problems presented in this work. PESA also shows very good scalability with number of processors in solving an expensive problem of optimizing nuclear fuel in nuclear power plants. PESA's competitive performance and modularity over all experiments allow it to join the family of evolutionary algorithms as a new hybrid algorithm; unleashing the power of parallel computing for expensive optimization.

• 한국멀티미디어학회논문지
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• 제16권4호
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• pp.472-482
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• 2013

본 논문에서는 실내 위치 인식 및 네트워크 성능 향상을 고려한 무선 랜 토폴로지의 구성 방안을 제안하였다. 먼저 위치 인식 및 네트워크 성능 향상을 고려하여 최적화된 무선 랜 토폴로지를 생성하는데 사용되는 4개의 목적 함수들을 설계하였다. 그리고 주어진 목적 함수로부터 근사 최적해를 생성하는 시뮬레이티드 어닐링과 타부 탐색 및 유전자 알고리즘 기반 메타 휴리스틱 알고리즘을 구현하였다. 마지막으로, 목적 함수와 메타 휴리스틱 알고리즘을 사용하여 제안한 무선 랜 토폴로지의 구성 방안에 대한 성능 분석을 수행하였다.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2007년도 추계종합학술대회
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• pp.407-412
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• 2007

본 논문에서는 비선형 블라인드 채널등화기의 구현을 위하여 가우시안 가중치(gaussian weights)를 이용한 개선된 퍼지 클러스터(Modified Fuzzy C-Means with Gaussian Weights: MFCM_GW) 알고리즘을 제안한다. 제안된 알고리즘은 기존 FCM 알고리즘의 유클리디언 거리(Euclidean distance) 값 대신 Bayesian Likelihood 목적함수(fitness function)와 가우시안 가중치가 적용된 멤버쉽 매트릭스(partition matrix)를 이용하여, 비선형 채널의 출력으로 수신된 데이터들로부터 최적의 채널 출력 상태 값(optimal channel output states)들을 직접 추정한다. 이렇게 추정된 채널 출력 상태 값들로 비선형 채널의 이상적 채널 상태(desired channel states) 벡터들을 구성하고, 이를 Radial Basis Function(RBF) 등화기의 중심(center)으로 활용함으로써 송신된 데이터 심볼을 찾아낸다. 실험에서는 무작위 이진 신호에 가우시안 잡음이 추가된 데이터를 사용하여 기존의 Simplex Genetic Algorithm(GA), 하이브리드 형태의 GASA(GA merged with simulated annealing (SA)), 그리고 과거에 발표되었던 MFCM 등과 그 성능을 비교 분석하였으며, 가우시안 가중치가 적용된 MFCM_GW를 이용한 채널등화기가 상대적으로 정확도와 속도 면에서 우수함을 보였다.

• 한국세라믹학회지
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• 제39권9호
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• pp.884-889
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• 2002

나노 결정질 $ZrO_2$ 박막을 제조하여 박막의 표면에서 인산칼슘을 유도하는 능력을 편가하기 위하여, 지르코늄 나프테네이트를 출발물질로 사용하고 화학적 용액법을 이용하여, $ZrO_2/Si$ 구조를 제작하였다. 코팅용액을 (100)Si 기판 위에 스핀코팅한 후, 500$^{\circ}C$에서 10분간 전열처리와 800$^{\circ}C$에서 30분간 최종열처리를 행하였고, 모든 열처리는 공기분위기에서 실시하였다. X-ray diffraction analysis를 이용하여 열처리된 박막의 결정화도를 조사하였고, 표면의 미세구조와 표면 거칠기를 field emission-scanning electron microscope와 atomic force microscope를 이용하여 관찰하였다. 열처리 후의 박막은 표면에 미세한 $ZrO_2$ 나노 결정이 생성되어 있었으며, 박막의 계면은 매우 균질 하였다. 유사생채용액에 1일 및 5일간 침적된 샘플의 표면위에 형성된 인산칼슘을 energy dispersive X-ray spectrometer를 이용하여 관찰하였고, fourier transform infrared spectroscopy를 이용하여 인산칼슘에 카본이 치환되어 있음을 확인하였다.

• International Journal of Precision Engineering and Manufacturing
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• 제2권2호
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• pp.65-72
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• 2001

The design of multi-stage gear drives is a time-consuming process, since on includes more complicated problems, which are not considered in the design of single-stage gear drives. The designer has th determine the number of reduction stages and the gear ratios of each reduction state. In addition, the design problems include not only the dimensional design but also the configuration design of gear drive elements. There is no definite rule and principle for these types of design problems. Thus the design practices largely depend on the sense and the experiences of the designer , and consequently result in undesirable design solution. We propose a new generalized design algorithm to support the designer at the preliminary design phase of multi-stage gear drives. The proposed design algorithm automates the design process by integrating the dimensional design and the configuration design process. The algorithm consists of four steps. In the first step, a designer determines the number of reduction stage. In the second step. gear ratios se chosen by using the random search method. In the third step, the values of basic design parameter are chosen by using the generate and test method. Then, the values of other dimension, such ad pitch diameter, outer diameter, and face width, are calculated for the configuration design in the final step. The strength and durability of a gear is guaranteed by the bending strength and the pitting resistance rating practices by using the AGMA rating formulas. In the final step, the configuration design is carried out b using the simulated annealing algorithm. The positions of gears and shafts are determined to minimize the geometrical volume(size) of a gearbox, while satisfying spatial constraints between them. These steps are carried out iteratively until a desirable solution is acquired. The propose design algorithm has been applied to the preliminary design of four-stage gear drives in order to validate the availability. The design solution have shown considerably good results in both aspects of the dimensional and the configuration design.