• Journal of Electrical Engineering and Technology
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• v.7 no.3
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• pp.330-335
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• 2012

The shape optimization of a Thomson coil actuator used in an arc eliminator is done for fast response by adopting topology modification method. The displacement of the plate in a fixed calculation time is taken as the objective function. The objective function and contribution factor are calculated by using an adaptive equivalent circuit method which has been proved accurate and efficient. Both shape optimization and performance analysis are accomplished based on the segmentation of plate. Through the refinement of the sensitive segments a precise optimal plate shape can be obtained. The effectiveness of the proposed method is proved by the comparison of results before and after the shape optimization.

• Journal of Electrical Engineering and Technology
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• v.7 no.1
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• pp.58-63
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• 2012

The shape optimization of a Thomson coil actuator used in an arc eliminator is done for fast response by adopting topology modification method. The displacement of the plate in a fixed calculation time is taken as the objective function. The objective function and contribution factor are calculated by using an adaptive equivalent circuit method which has been proved accurate and efficient. Both shape optimization and performance analysis are accomplished based on the segmentation of plate. Through the refinement of the sensitive segments a precise optimal plate shape can be obtained. The effectiveness of the proposed method is proved by the comparison of results before and after the shape optimization.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.20 no.3
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• pp.322-328
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• 2011

This paper presents inverse kinematics and workspace analysis of a planar three degree-of-freedom (DOF) parallel manipulator. Furthermore, optimization problem of the manipulator is presented. The manipulator adopts PRR (Prismatic-Revolute-Revolute) mechanism and the prismatic actuators are fixed to the base. This leads to a reduction of the inertia of the moving links and hence enables it to move with high speed. The actuators are linear electric motors. First, the mechanism based on the geometry of the manipulator is introduced. Second, a workspace analysis is performed. Finally, design optimization is carried out to have large workspace. The proposed approach can be applied to the design optimization of various three DOF parallel manipulators in order to maximize their workspace. The performance of mechanism is improved and satisfies the requirements of workspace to align micro-components.

• Korean Journal of Computational Design and Engineering
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• v.14 no.4
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• pp.281-289
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• 2009

Topology optimization has been widely used for the optimal structure design for weight reduction and high performance. Since the result of three-dimensional topology optimization is represented by the discrete material distribution in finite elements, it is hard to interpret from a design point of view. In this paper, the method for interpreting three-dimensional topology optimization resuIt into a series of cross-sectional curve representation is proposed and interfaced with the existing CAD system for the practical use. The concept of node density and virtual grid is introduced to transform element density values into grid density and material boundaries in each cross section are identified based on the element volume rate to satisfy the amount of material specified in the original design intent. Design exampIes show that three-dimensional topology result can be converted into a form of curve CAD model and the seamless interface with CAD software can be achieved.

• International Journal of Automotive Technology
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• v.4 no.3
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• pp.135-140
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• 2003

In the deterministic optimization of a structural system, objective function, design constraints and design variables, are treated in a nonstatistical fashion. However, such deterministic engineering optimization tends to promote the structural system with lest reliability redundancy than obtained with conventional design procedures using the factor of safety. Consequently, deterministic optimized structures will usually have higher failure probabilities than unoptimized structures. Therefore, a balance must be developed between the satisfactions of the design requirements and the objectives of reducing manufacturing cost. This paper proposes the reliability-based design optimization (RBDO) technique, which enables the optimum design that considers confidence level for the vibration characteristics of structural system. Response surface method (RSM) is utilized to approximate the performance functions describing the system characteristics in the RBDO procedure. The proposed optimization technique is applied to the pillar section design considering natural frequencies of a vehicle structure.

• 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.

• Journal of Korean Association for Spatial Structures
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• v.23 no.1
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• pp.15-23
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• 2023

In this work, we deal with the feasibility of structural topology optimization for beam designs using retrofits that optimally allocates the reinforcement to the web under the condition that designers set bolt regions for H-beams of different dimensions. Mean compliance or minimal strain energy is considered for the optimization. Volume fraction is given to the design space to assign appropriate steel material quantities. The purpose of this study is to evaluate optimal shapes of stiffeners with the maximum rigidity that improves the axial and shear performance of the H-beam and to satisfy a given safety design standard of H-beam and stiffeners in case arbitrary load effect and resistances. Finally, the effectiveness of stiffness-based topology optimization on stiffeners is verified with several practical applicable examples.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.2
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• pp.127-136
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• 2005

In this paper, a new architecture and comprehensive design methodology of genetically optimized Rule-based Fuzzy Polynomial Neural Networks(gRFPNN) are introduced and a series of numeric experiments are carried out. The architecture of the resulting gRFPNN results from asynergistic usage of the hybrid system generated by combining rule-based Fuzzy Neural Networks(FNN) with polynomial neural networks (PNN). FNN contributes to the formation of the premise part of the overall rule-based structure of the gRFPNN. The consequence part of the gRFPNN is designed using PNNs. At the premise part of the gRFPNN, FNN exploits fuzzy set based approach designed by using space partitioning in terms of individual variables and comes in two fuzzy inference forms: simplified and linear. As the consequence part of the gRFPNN, the development of the genetically optimized PNN dwells on two general optimization mechanism: the structural optimization is realized via GAs whereas in case of the parametric optimization we proceed with a standard least square method-based learning. To evaluate the performance of the gRFPNN, the models are experimented with the use of several representative numerical examples. A comparative analysis shows that the proposed gRFPNN are models with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• Journal of the Korea Society of Computer and Information
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• v.26 no.7
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• pp.1-7
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• 2021

In this paper, we propose a feature selection technique for multi-label classification. Many existing feature selection techniques have selected features by calculating the relation between features and labels such as a mutual information scale. However, since the mutual information measure requires a joint probability, it is difficult to calculate the joint probability from an actual premise feature set. Therefore, it has the disadvantage that only a few features can be calculated and only local optimization is possible. Away from this regional optimization problem, we propose a feature selection technique that constructs a low-rank space in the entire given feature space and selects features with sparsity. To this end, we designed a regression-based objective function using Nuclear norm, and proposed an algorithm of gradient descent method to solve the optimization problem of this objective function. Based on the results of multi-label classification experiments on four data and three multi-label classification performance, the proposed methodology showed better performance than the existing feature selection technique. In addition, it was showed by experimental results that the performance change is insensitive even to the parameter value change of the proposed objective function.

• Journal of the Korean Society for Precision Engineering
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• v.11 no.4
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• pp.138-147
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• 1994

Performance management of computer networks is intended to improve a given network performance in order for more efficient information exchange between subsystems of an integrated large-scale system. Improtance of performance management is growing as many function of the large- scale system depend on the quality of communication services provided by the network. The role of performance management is to manipulate the adjustable protocol parameters on line so that the network can adapt itself to a dynamic environment. This can be divided into two subtasks : performance evaluation to find how changes in protocol parameters affect the network performance and decision making to detemine the magnitude and direction of parameter adjustment. This paper is the second part of the two papers focusing on conceptual design, development, and evaluation of performance management for token bus networks. This paper specifically deals with the task of decision making which utilizes the principles of stochastic optimization and learning automata. The developed algorithm can adjuxt four timer settings of a token bus protocol based on the result of performance evaluation. The overall performance management has been evaluated for its efficacy on a network testbed.