• Wind and Structures
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• v.18 no.5
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• pp.497-510
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• 2014

Seismic and wind load performances of buildings are commonly improved by using bracing systems. In practice, standard bracing systems, such as X, Y, V, and K types are used. To determine the appropriate bracing type, the designer uses trial & error method among the standard bracings to obtain better results. However, using topology optimization yields more efficient bracing systems or new bracing can be developed depending on building and loading types. Determination of optimum bracing type for minimum deformation on a building under the effect of wind load is given in this study. A new bracing system is developed by using topology optimization. Element removal method is used to determine and remove the comparatively inefficient materials. Optimized bracing is compared with proposed bracing types available in the related literature. Maximum deformation value of building is used as performance indicator to compare effectiveness of different bracings to resist wind loads. The proposed bracing, yielded 99%, deformation reduction compared to the unbraced building.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.33 no.11
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• pp.1225-1232
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• 2009

This study suggests a structural design process for the upper control arm installed at a vehicle. Static strength and durability are the most important responses in the structural design of a control arm. This study considers the static strength in the optimization process. The inertia relief method for FE analysis is utilized to simulate the static loading conditions. According to the classification of structural optimization, the structural design of a control arm is included in the category of shape optimization. In this study, the metamodel technique using the kriging method is adopted to obtain the minimum weight satisfying the strength constraint. Then, the final design is suggested by considering the durability criteria. The durability assessment is obtained by the index of fatigue durability called the SWT (Smith-Watson-Topper) index. The final optimum shape has been proposed by trial and error method.

• International Journal of Precision Engineering and Manufacturing
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• v.3 no.1
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• pp.13-19
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• 2002

Although the design of magnetic bearing needs a systematic optimization due to several design variables, constraints, geometric limitations, nonlinearities, and so on, the present designs for magnetic bearings have been conducted in the linear region of the characteristics for magnetic materials by trial and error considering design constraints. This article, therefore, provides the possibility of a genetic algorithm(GA) based optimization with two dimensional nonlinear finite element magnetic field analysis for the design of a radial heteropolar magnetic bearing. The magnetic bearing design by GA based optimization makes good agreements with that by a commercial optimization software DOT using the sensitivity analysis.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.30 no.2 s.245
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• pp.128-134
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• 2006

Topology optimization is an effective scheme to obtain the initial design concept: however, it is hard to apply in case of non-linear or multi-objective problems. In this study, a modified topology optimization method is proposed to generate a structure of a swing arm type actuator satisfying maximum compliance as well. as maximum stiffness using the multi-objective optimization. approach. The multi-objective function is defined to maximize the compliance in the direction of focusing of the actuator and the second eigen-frequency of the structure. The design of experiments are performed and the response surface functions are formulated to construct the multi-objective function. The weighting factors between conflicting functions are determined by the back-error propagation neural network and the solution of multi-objective function is acquired using the genetic algorithm.

• Journal of Broadcast Engineering
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• v.17 no.4
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• pp.634-639
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• 2012

In this paper, we propose nonlinear optimization method for feature matching from multiple view image. Typical solution of feature matching is by solving linear equation. However this solution has large error due to nonlinearity of image formation model. If typical nonlinear optimization method is used, complexity grows exponentially over the number of features. To make complexity lower, we use sparse Levenberg-Marquardt nonlinear optimization for matching of features over multiple view image.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.8
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• pp.53-58
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• 1999

Although the design of magnetic bearing needs a systematic optimization du e to several design variables, constraints, geometric limitations, nonlinearities, and so on, the present for magnetic bearings have been conducted in the linear region of the characteristics for magnetic by trial and error considering design constraints. This article, therefore, provides the possibility of a genetic algorithm(GA) based optimization with two dimensional-nonlinear finite element magnetic field analysis for design of a radial heteropolar magnetic bearing. The magnetic bearing design by GA based optimization makes good agreements with that by a commercial optimization software DOT using the sensitivity analysis.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.7
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• pp.1404-1409
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• 2011

The fundamental research for the mobile robot navigation using the numerical optimization method is presented. We define the mobile robot navigation problem as an unconstrained optimization problem to minimize the cost function with the pose error between the goal position and the position of a mobile robot. Using the nonlinear least squares optimization method, the optimal speeds of the left and right wheels can be found as the solution of the optimization problem. Especially, the rotational speed of wheels of a mobile robot can be directly related to the overall speed of a mobile robot using the Jacobian derived from the kinematic model. It will be very useful for applying to the mobile robot navigation. The performance was evaluated using the simulation.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.184-184
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• 2017

A nation-wide water-energy-food (WEF) nexus simulation model has been developed by the authors and successfully applied to South Korea to predict the sustainability of those three resources in the next 30 years. The model was also capable of simulating future scenarios of resources allocation based on priority rules aiming to maximize resources sustainability. However, the process was still relying on several assumptions and trial-and-error approach, which sometimes resulted in non-optimal solutions of resources allocation. In this study, an optimization module was introduced to enhance the model in generating optimal resources management rules. The objective of the optimization was to maximize the reliability index of resources by determining the resources' allocation and/or priority rules for each demand type that accordingly reflect the resources management policies. Implementation of the optimization module would result in balanced allocation and management of limited resources and assist the stakeholders in deciding resources' management plans, either by fulfilling the domestic production or by global trading.

• Journal of Aerospace System Engineering
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• v.15 no.2
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• pp.1-9
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• 2021

In this paper, a thickness compensation function is introduced to consider the shear deformation and warping effect resulting from increased thickness in the composite multi-cell wing box. The thickness compensation function is used to perform the structure optimization of the multi-cell. It is determined by minimizing the error of an analytical formula using solid mechanics and the Ritz method. It is used to define a structural performance prediction expression due to the increase in thickness. The parameter is defined by the number of spars and analyzed by the critical buckling load and the limited failure index as a response. Constraints in structural optimization are composed of displacements, torsional angles, the critical buckling load, and the failure index. The objective function is the mass, and its optimization is performed using a genetic algorithm.

• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.312-320
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• 2021

Increase in computational cost and exhaustive search can lead to more complexity and computational energy. Thus, there is need for effective and efficient scheme to reduce the complexity to achieve optimal energy utilization. This will improve the energy efficiency and enhance the proficiency in terms of the resources needed to achieve convergence. This paper primarily focuses on the development of hybrid swarm intelligence scheme for reducing the computational complexity in binary optimization. In order to reduce the complexity, both artificial bee colony (ABC) and particle swarm optimization (PSO) have been employed to effectively minimize the exhaustive search and increase convergence. First, a new approach using ABC and PSO has been proposed and developed to solve the binary optimization problem. Second, the scout for good quality food sources is accomplished through the deployment of PSO in order to optimally search and explore the best source. Extensive experimental simulations conducted have demonstrate that the proposed scheme outperforms the ABC approaches for reducing complexity and energy consumption in terms of convergence, search and error minimization performance measures.