• Wind and Structures
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• v.18 no.3
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• pp.253-265
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• 2014

The platform and floating structure of spar type offshore wind turbine systems should be designed in order for the 6-DOF motions to be minimized, considering diverse loading environments such as the ocean wave, wind, and current conditions. The objective of this study is to optimally design the platform and substructure of a 3MW spar type wind turbine system with the maximum postural stability in 6-DOF motions as well as the minimum material cost. Therefore, design variables of the platform and substructure were first determined and then optimized by a hydrodynamic analysis. For the hydrodynamic analysis, the body weight of the system was considered, and the ocean wave conditions were quantified to the wave forces using the Morison's equation. Moreover, the minimal number of computation analysis models was generated by the Design of Experiments (DOE), and the design variables of the platform and substructure were finally optimized by using a genetic algorithm with a neural network approximation.

• Structural Engineering and Mechanics
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• v.67 no.4
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• pp.359-368
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• 2018

In this paper, a method is presented to identify the physical and modal parameters of multistory shear building based on substructural technique using block pulse generalized operational matrix and genetic algorithm. The substructure approach divides a complete structure into several substructures in order to significantly reduce the number of unknown parameters for each substructure so that identification processes can be independently conducted on each substructure. Block pulse functions are set of orthogonal functions that have been used in recent years as useful tools in signal characterization. Assuming that the input-outputs data of the system are known, their original BP coefficients can be calculated using numerical method. By using generalized BP operational matrices, substructural dynamic vibration equations can be converted into algebraic equations and based on BP coefficient for each story can be estimated. A cost function can be defined for each story based on original and estimated BP coefficients and physical parameters such as mass, stiffness and damping can be obtained by minimizing cost functions with genetic algorithm. Then, the modal parameters can be computed based on physical parameters. This method does not require that all floors are equipped with sensor simultaneously. To prove the validity, numerical simulation of a shear building excited by two different normally distributed random signals is presented. To evaluate the noise effect, measurement random white noise is added to the noise-free structural responses. The results reveal the proposed method can be beneficial in structural identification with less computational expenses and high accuracy.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.35 no.3
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• pp.175-182
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• 2022

In many engineering problems, the dynamic substructuring can be useful to analyze complex structures which made with many substructures, such as aircrafts and automotive vehicles. It was originally intended as a method to simplify the engineering problem. The powerful advantage to this is that computational efficiency dramatically increases with eliminating unnecessary degrees-of-freedom of the system and the system targets are concurrently satisfied. Craig-Bampton method has been widely used for the linear system reduction. Recently, multi-level optimization (such as target cascading), which propagates the system-level targets to the subsystem-level targets, has been widely utilized. To this concept, the genetic algorithm which one of the global optimization technique has been utilized to the substructure optimization. The number of internal modes for each substructure can be obtained by the genetic algorithm. Simultaneously, the reduced system meets the top-level targets. In this paper, various numerical examples are tested to verify this concept.

• Journal of Plant Biology
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• v.38 no.4
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• pp.329-336
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• 1995

Raphanus sativus L. var. hortensis f. raphanistroides (wild radish: Brassicaceae), a herbaceous perennial, occurs only on beaches in East Asia. Genetic diversity and population structure of seven Korean populations were investigated using starch gel electrophoresis. Although the Korean populatins are small, isolated with patchy distribution, the population maintain a moderate level of genetic diversity; the mean percentage fo polymorphic loci was 51.4%, mean number of alleles per locus was 1.84, and mean expected heterozygosity was 0.116. A combination of animal-outcrossing breeding system, wide geographical distribution, restricted ecological distribution, and a propensity for high fecundity may in part be explanatory factors contributing the moderate level of genetic diversity within populations. An overall excess of homozygotes relative to Hardy-Weinberg expetations (mean FISa=0.116) indicates that consanguineous mating occur within wild radish populations, leading to a family structure within a circumscribed area. Although population of wild radish experience a limited gene flow, only 5% of the total genetic variation found in Korean wild radish populations examined is due to differences among populations (mean GST=0.052). This value is considerably lower than the mean values of species with similar life history and ecological characteristics. However, significant differences were found in allele frequencies between populations for all polymorphic loci (P<0.01). It is supposed that directional selection toward genetic uniformity (similar gene frequencies) in a relatively homogenous habitat is thought to be operated among Korean wild radish populations.

• Computers and Concrete
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• v.23 no.5
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• pp.311-320
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• 2019

The substructuring technique is one of the efficient methods for reducing computational effort and memory usage in the finite element method, especially in large-scale structures. Proper mesh partitioning plays a key role in the efficiency of the technique. In this study, new algorithms are proposed for mesh partitioning based on an element search technique. The computational cost function is optimized by aligning each element of the structure to a proper substructure. The genetic algorithm is employed to minimize the boundary nodes of the substructures. Since the boundary nodes have a vital performance on the mesh partitioning, different strategies are proposed for the few number of substructures and higher number ones. The mesh partitioning is optimized considering both computational and memory requirements. The efficiency and robustness of the proposed algorithms is demonstrated in numerous examples for different size of substructures.

• Applied Microscopy
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• v.47 no.1
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• pp.8-12
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• 2017

The ultrastructures of the secretory acinar granules of submandibular and parotid salivary gland were examined in the Korean striped field mouse, Apodemus agraius. The acini of the submandibular salivary gland had serous and mucous acinar cells filled with numerous secretory granules. The serous acinar granules had uniformly fine dense contents and were round typed with a definite boundary between the granules. The mucous acinar granules were relatively coarse, with moderate density, and clustered together as a result of the indistinct boundaries between the granules. The acini of the parotid salivary glands contained only serous cells filled with numerous round-typed serous acinar granules. Serous acinar granules had uniformed dense matrix and definite boundaries. The ultrastructures without substructure in a matrix of serous and mucous acinar granules in the submandibular and parotid salivary glands of A. agraius were similar to those of species of Rodentia but different from those of Soricidae in Korea with a characteristic substructure in a matrix. This ultrastructure and charateristics in secretory acinar granules provide fundamental data for molecular comparisions of genetic relationships and are one of the key methods for classifying A. agraius.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 1997.03a
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• pp.287-290
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• 1997

각 스탠드 유한요소해석 결과인 기계적-열적 변수들을 이용하여 재결정계산을 한다. 이때 온도변화를 정확히 반영하기 위해 재결정부피율 계산과 결정성장 계산에 additivity rule을 도입하였다. 또한 여러단계의 압연공정 각각에서의 재결정거동을 계속 추적하기 위해 또한 이때의 재결정의 영향을 재료유동에 반영하기 위해 substructure의 개념을 도입하였다. 이러한 과정을 거쳐 7패스후의 최종 두께방향으로의 결정크기분포를 얻을 수 있다. 본 연구 에서는 이때의 최종결정크기 분포를 균일화 시킬 수 있는 공정을 유전 알고리즘을 이용하여 찾아보았다.

• Earthquakes and Structures
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• v.19 no.5
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• pp.347-359
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• 2020

The Mega-Sub Controlled Structure System (MSCSS), an innovative vibration passive control system for building structures, is improved by adding lead rubber bearings (LRBs) on top of the substructure. For the new system, a genetic algorithm is used to optimize the dynamic parameters and distributions of dampers and LRBs. The program uses various seismic performance indicators as optimization objectives, and corresponding results are compared. It is found that the optimization procedure for maximizing the energy dissipation ratio yields the best solutions, and optimized models have consistent seismic performances under different earthquakes. Seismic performances of optimized MSCSS models with and without LRBs, as well as the traditional Mega-Sub Structure model, are evaluated and compared under El Centro wave, Taft wave and 20 other artificial waves. In both elastic and plastic analysis, the model with LRBs shows significantly smaller story drift and horizontal acceleration than those of the other two models, and fewer plastic hinges are developed during severe earthquakes. Energy analysis also shows that LRBs installed in proper locations increase the deformation and energy dissipation of dampers, thereby significantly reduce the kinetic, potential, and hysteretic energy in the structure. However, LRBs do not have to be mounted on all the additional columns. It is also demonstrated that LRBs at unfavorable locations can decrease the energy dissipation for dampers. After LRBs are installed, the optimal damping coefficient and the optimal damping exponent of dampers are reduced to produce the best damping effect.

• International Journal of Naval Architecture and Ocean Engineering
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• v.7 no.4
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• pp.750-769
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• 2015

The geometry of engineering systems affects their performances. For this reason, the shape of engineering systems needs to be optimized in the initial design stage. However, engineering system design problems consist of multi-objective optimization and the performance analysis using commercial code or numerical analysis is generally time-consuming. To solve these problems, many engineers perform the optimization using the approximation model (response surface). The Response Surface Method (RSM) is generally used to predict the system performance in engineering research field, but RSM presents some prediction errors for highly nonlinear systems. The major objective of this research is to establish an optimal design method for multi-objective problems and confirm its applicability. The proposed process is composed of three parts: definition of geometry, generation of response surface, and optimization process. To reduce the time for performance analysis and minimize the prediction errors, the approximation model is generated using the Backpropagation Artificial Neural Network (BPANN) which is considered as Neuro-Response Surface Method (NRSM). The optimization is done for the generated response surface by non-dominated sorting genetic algorithm-II (NSGA-II). Through case studies of marine system and ship structure (substructure of floating offshore wind turbine considering hydrodynamics performances and bulk carrier bottom stiffened panels considering structure performance), we have confirmed the applicability of the proposed method for multi-objective side constraint optimization problems.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.15-24
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• 2024

One of the most significant issues in combinatorial optimization is the classical NP-complete conundrum known as the 0/1 Knapsack Problem. This study delves deeply into the investigation of practical solutions, emphasizing two classic algorithmic paradigms, brute force, and dynamic programming, along with the metaheuristic and nature-inspired family algorithm known as the Genetic Algorithm (GA). The research begins with a thorough analysis of the dynamic programming technique, utilizing its ability to handle overlapping subproblems and an ideal substructure. We evaluate the benefits of dynamic programming in the context of the 0/1 Knapsack Problem by carefully dissecting its nuances in contrast to GA. Simultaneously, the study examines the brute force algorithm, a simple yet comprehensive method compared to Branch & Bound. This strategy entails investigating every potential combination, offering a starting point for comparison with more advanced techniques. The paper explores the computational complexity of the brute force approach, highlighting its limitations and usefulness in resolving the 0/1 Knapsack Problem in contrast to the set above of algorithms.