• 한국전산구조공학회논문집
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• 제24권1호
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• pp.69-78
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• 2011

본 연구에서는 스마트 TMD를 효과적으로 제어할 수 있는 퍼지제어알고리즘을 개발하기 위하여 다목적 유전자알고리즘을 이용한 최적화기법을 제안하였다. 예제구조물로는 풍하중을 받는 76층 벤치마크건물을 선택하였다. 스마트 TMD를 구성하기 위하여 100kN 용량의 MR 감쇠기를 사용하였고, 스마트 TMD의 진동주기는 예제구조물의 1차모드 고유진동주기에 맞추어 조율되었다. MR 감쇠기의 감쇠력은 예제구조물의 풍응답을 최소화할 수 있도록 퍼지제어기를 통해서 조절된다. 퍼지제어기의 입력변수는 75층의 가속도 응답과 스마트 TMD의 변위응답으로 하였고, 출력변수는 MR 감쇠기로 전달되는 명령전압으로 하였다. 퍼지제어기의 최적화를 위하여 다목적 유전자알고리즘인 NSGA-II 기법이 사용되었고, 이때 75층의 가속도 응답과 스마트 TMD의 변위응답을 목적함수로 사용하였다. 최적화 결과, 구조물의 풍응답과 STMD의 변위응답을 동시에 적절히 제어할 수 있는 다수의 퍼지제어기를 얻을 수 있었다. 수치해석을 통해서 스마트 TMD의 성능이 수동 TMD에 비하여 월등히 뛰어남을 알 수 있었고 경우에 따라서는 샘플 능동 TMD보다 더 우수한 제어성능을 발휘하였다.

• 한국전산구조공학회논문집
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• 제33권6호
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• pp.419-426
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• 2020

본 연구에서는 철골편심가새골조 시스템을 대상으로 다목적최적화기법을 통해 설계를 수행하고 그 결과를 분석하였다. 최적화 설계를 위해 유전 알고리즘의 일종인 NSGA-II를 활용하였다. 여기서, 목적함수는 이율배반적 관계를 갖는 구조물량과 층간변위로 하여 최소화되고, 제약조건에는 구조기준에서 요구하는 내력비, 링크의 회전각 등을 포함하였다. 제약조건은 최적화 알고리즘 내에서 각 항목을 위반할수록 목적함수 값을 크게 증가시키는 벌금함수의 형태를 가지고 있다. 설계기준에서 EBF 시스템의 설계규정은 링크 부재만 항복이 허용되며 나머지 부재는 링크 항복 시 발생되는 부재력을 탄성상태에서 견디도록 의도한 역량설계법에 기초한다. 그러나 최적화를 통해 도출된 결과 중 일부는 구조기준의 설계조항은 만족하지만 특정층 링크에 소성변형이 집중되어 연약층을 형성함으로써 기준에서 의도하는 역량설계의 원칙을 위배하는 결과가 나타났다. 이를 해결하기 위해 모든 링크의 전단 초과강도계수 중 최대값이 최소값의 1.25배를 넘지 않도록 하는 제약식을 추가하였다. 새로운 제약식을 추가한 경우 모든 최적해는 설계기준과 역량설계의 원칙을 준수하는 것으로 나타났다. 모든 설계안에서 보 경간에 대한 링크의 길이비는 전단링크의 범주에 해당하는 10% ~ 14%였다. 전체적으로 설계안들은 링크의 초과강도 계수비가 가장 지배적인 제약으로 작용하였으며, 구조기준의 요구사항 중 층간변위와 내력비 등의 항목에서 허용치에 비해 매우 보수적으로 설계되었다.

• International Journal of Computer Science & Network Security
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• 제21권12호
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• pp.248-256
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• 2021

Now that we're in the big data era, data has taken on a new significance as the storage capacity has exploded from trillion bytes to petabytes at breakneck pace. As the use of cloud computing expands and becomes more commonly accepted, several businesses and institutions are opting to store their requests and data there. Cloud storage's concept of a nearly infinite storage resource pool makes data storage and access scalable and readily available. The majority of them, on the other hand, favour a single cloud because of the simplicity and inexpensive storage costs it offers in the near run. Cloud-based data storage, on the other hand, has concerns such as vendor lock-in, privacy leakage and unavailability. With geographically dispersed cloud storage providers, multicloud storage can alleviate these dangers. One of the key challenges in this storage system is to arrange user data in a cost-effective and high-availability manner. A multicloud storage architecture is given in this study. Next, a multi-objective optimization problem is defined to minimise total costs and maximise data availability at the same time, which can be solved using a technique based on the non-dominated sorting genetic algorithm II (NSGA-II) and obtain a set of non-dominated solutions known as the Pareto-optimal set.. When consumers can't pick from the Pareto-optimal set directly, a method based on Principal Component Analysis (PCA) is presented to find the best answer. To sum it all up, thorough tests based on a variety of real-world cloud storage scenarios have proven that the proposed method performs as expected.

• International Journal of Control, Automation, and Systems
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• 제5권4호
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• pp.444-455
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• 2007

An approach to construct multiple interpretable and precise fuzzy systems based on the Pareto Multi-objective Cooperative Coevolutionary Algorithm (PMOCCA) is proposed in this paper. First, a modified fuzzy clustering algorithm is used to construct antecedents of fuzzy system, and consequents are identified separately to reduce computational burden. Then, the PMOCCA and the interpretability-driven simplification techniques are executed to optimize the initial fuzzy system with three objectives: the precision performance, the number of fuzzy rules and the number of fuzzy sets; thus both the precision and the interpretability of the fuzzy systems are improved. In order to select the best individuals from each species, we generalize the NSGA-II algorithm from one species to multi-species, and propose a new non-dominated sorting technique and collaboration mechanism for cooperative coevolutionary algorithm. Finally, the proposed approach is applied to two benchmark problems, and the results show its validity.

• Earthquakes and Structures
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• 제11권2호
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• pp.347-369
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• 2016

The main objective of this paper is the robust multi-objective optimization design of semi-active tuned mass damper (STMD) system using genetic algorithms and fuzzy logic. For optimal design of this system, it is required that the uncertainties which may exist in the system be taken into account. This consideration is performed through the robust design optimization (RDO) procedure. To evaluate the optimal values of the design parameters, three non-commensurable objective functions namely: normalized values of the maximum displacement, velocity, and acceleration of each story level are considered to minimize simultaneously. For this purpose, a fast and elitist non-dominated sorting genetic algorithm (NSGA-II) approach is used to find a set of Pareto-optimal solutions. The torsional effects due to irregularities of the building and/or unsymmetrical placements of the dampers are taken into account through the 3-D modeling of the building. Finally, the comparison of the results shows that the probabilistic robust STMD system is capable of providing a reduction of about 52%, 42.5%, and 37.24% on the maximum displacement, velocity, and acceleration of the building top story, respectively.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2005년도 춘계학술대회 논문집
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• pp.997-1000
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• 2005

Dynamic job shop environment requires not only more flexible capabilities of a CAPP system but higher utility of the generated process plans. In order to meet the requirements, this paper develops an algorithm that can select machines for the machining operations to be performed by predicting the machine loads. The developed algorithm is based on the multiple objective genetic algorithm that gives rise to a set of optimal solutions (in general, known as Pareto-optimal solutions). The objective shows a combination of the minimization of part movement and the maximization of machine utility balance. The algorithm is characterized by a new and efficient method for nondominated sorting, which can speed up the running time, as well as a method of two stages for genetic operations, which can maintain a diverse set of solutions. The performance of the algorithm is evaluated by comparing with another multiple objective genetic algorithm, called NSGA-II.

• 한국신재생에너지학회:학술대회논문집
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• 한국신재생에너지학회 2011년도 춘계학술대회 초록집
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• pp.58.2-58.2
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• 2011

본 연구에서는 5MW급 수평축 풍력터빈 블레이드에 대해 국내 서남해안의 풍속특성을 고려한 최적설계를 수행 하였다. 최적설계를 수행하기 위해 블레이드 해석은 Blade Element and Momentum Theory를 이용 하였으며, 설계 시 적용된 기저형상은 NREL에서 제안한 5MW급 풍력터빈 블레이드을 선정하였다. 최적설계를 수행하기 전 설계에 사용된 설계변수들이 풍속에 대해 어떠한 경향을 가지고 있는지 알아보기 위해 Parametric Study를 수행 하였으며, 최적설계는 다목적 최적화 유전 알고리즘인 NSGA-II를 이용하여 평균풍속이 낮은 서남해안의 연간에너지 생산량과 설비이용률을 최대화하였다. 최적화 결과들로부터 설계 조건에 맞는 최적해를 도출 할 수 있었으며, 이를 통해 기저형상의 연간에너지 생산량 및 설비이용률을 보다 향상 시킬 수 있었다.

• Structural Engineering and Mechanics
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• 제47권2호
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• pp.227-245
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• 2013

The term "constructability" in regard to cast-in-place concrete construction refers mainly to the ease of reinforcing steel placement. Bar congestion complicates steel placement, hinders concrete placement and as a result leads to improper consolidation of concrete around bars affecting the integrity of the structure. In this paper, a multi-objective approach, based on the non-dominated sorting genetic algorithm (NSGA-II) is developed for optimal design of reinforced concrete cantilever retaining walls, considering minimization of the economic cost and reinforcing bar congestion as the objective functions. The structural model to be optimized involves 35 design variables, which define the geometry, the type of concrete grades, and the reinforcement used. The seismic response of the retaining walls is investigated using the well-known Mononobe-Okabe analysis method to define the dynamic lateral earth pressure. The results obtained from numerical application of the proposed framework demonstrate its capabilities in solving the present multi-objective optimization problem.

• International Journal of Naval Architecture and Ocean Engineering
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• 제9권3호
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• pp.246-255
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• 2017

The proposed design methodology represents a new approach to optimize the propeller-hull system simultaneously. In this paper, two objective functions are considered, the first objective function is Lifetime Fuel Consumption (LFC) and the other one is cost function including thrust, torque, open water and skew efficiencies. The variables of the propeller geometries (Z, EAR, P/D and D) and ship hull parameters (L/B, B/T, T and $C_B$) are considered to be optimized with cavitation, blades stress of propeller. The well-known evolutionary algorithm based on NSGA-II is employed to optimize a multi-objective problem, where the main propeller and hull dimensions are considered as design variables. The results are presented for a series 60 ship with B-series propeller. The results showed that the proposed method is an appropriate and effective approach for simultaneously propeller-hull system design and is able to minimize both of the objective functions significantly.

• Structural Engineering and Mechanics
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• 제50권2호
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• pp.151-161
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• 2014

In this paper, fuel tank of the locomotive ER 24 has been studied. Firstly the behavior of fuel and air during the braking time has been investigated by using a two-phase model. Then, the distribution of pressure on the surface of baffles caused by sloshing has been extracted. Also, the fuel tank has been modeled and analyzed using Finite Element Method (FEM) considering loading conditions suggested by the DIN EN 12663 standard and real boundary conditions. In each loading condition, high stressed areas have been identified. By comparing the distribution of pressure caused by sloshing phenomena and suggested loading conditions, optimization of the tank has been taken into consideration. Moreover, internal baffles have been investigated and by modifying their geometric properties, search of the design space has been done to reach the optimal tank. Then, in order to reduce the mass and manufacturing cost of the fuel tank, Non-dominated Sorting Genetic Algorithm (NSGA-II) and Artificial Neural Networks (ANNs) have been employed. It is shown that compared to the primary design, the optimized fuel tank not only provides the safety conditions, but also reduces mass and manufacturing cost by %39 and %73, respectively.