• Proceedings of the KIEE Conference
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• 2005.07b
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• pp.939-941
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• 2005

This research presents a 3D multi-objective approach regarding both magnetic and thermal characteristics associated with design of C-core actuator. The adjoint variable topology sensitivity equations are derived using the continuum method for three dimension. The sensitivity is verified using the Finite Difference Method(FDM). Convection interpolation function is proposed for density method of topologies such that convection term can be taken into consideration for practical design in the process of the optimization.

• Structural Engineering and Mechanics
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• v.62 no.4
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• pp.465-475
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• 2017

Photovoltaic (PV) panels are used in high-rise buildings to convert solar energy to electricity. Due to the considerable energy consumption of high-rise buildings, applying PV technology is of great significance to energy saving. In the application of PV panels, one of the most important construction issues is the connection of the PV panel with the main structures. One major difficulty of the connection design is that the PV panel connection consists of two separate components with coupling and indeterminate dimension. In this paper, the gap element is employed in these two separated but coupled components, i.e., hook and catch. Topology optimization is applied to optimize and design the cross-section of the PV panel connection. Pareto optimization is conducted to operate the optimization subject to multiple load scenarios. The initial design for the topology optimization is determined by the common design specified by the Technical Code for Glass Curtain Wall Engineering (JGJ 102-2003). Gravity and wind load scenarios are considered for the optimization and numerical analysis. Post analysis is conducted for the optimal design obtained by the topology optimization due to the manufactory requirements. Generally, compared with the conventional design, the optimized connector reduces material use with improved structural characteristics.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.1
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• pp.158-164
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• 2002

This paper proposes the optimization of ultrasonic signal recognition in ferrite carbon steel weld zone using neural networks. For these purposes, the ultrasonic signals for defects as porosity, incomplete penetration and slag inclusion in the weld zone are acquired in the type of time series data. And then their applications evaluated feature extraction based on the time-frequency-attractor domain(peak to peak, rise time, rise slope, fall time, fall slope, pulse duration, power spectrum, and bandwidth) and attractor characteristics (fractal dimension and attractor quadrant) etc. The proposed neural networks system in this study can enhances performance of ultrasonic signal recognition.

• Steel and Composite Structures
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• v.19 no.1
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• pp.75-92
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• 2015

In this paper, optimization of cylindrical shells under external pressure to minimize its weight has been studied. Buckling equations are based on standard of ABS underwater vehicles. Dimension and type of circumferential stiffeners, and its distance from each other are assumed as variables of optimization problem. Considering the extent of these variables, genetic algorithms have been used for optimization. To study the effect of hydrostatic pressure on the shell and its fabrication according to the existing standards, geometrical and construction as well as stress and buckling constraints have been used in optimization algorithm and also penalty functions are applied to eliminate weak model. Finally, the best model which has the minimum weight considering the applied pressure has been presented.

• Journal of applied mathematics & informatics
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• v.23 no.1_2
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• pp.269-279
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• 2007

This paper describes a direct search method for a class of linearly constrained optimization problem. Through research we find it can be treated as an unconstrained optimization problem. And with the decrease of dimension of the variables need to be computed in the algorithms, the implementation of convergence to KKT points will be simplified to some extent. Convergence is shown under mild conditions which allow successive frames to be rotated, translated, and scaled relative to one another.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.10
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• pp.2211-2218
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• 2002

In this study, an optimal design study has been made to determine dimensions of die and multi stress rings for extrusion process. For this purpose, a thermo-rigid-viscoplastic finite element program, CAMPform, was used fur forming analysis of extrusion process and a developed elastic finite element program fur elastic stress analysis of the die set including stress rings. And an optimization program, DOT, was employed for the optimization analysis. From this investigation, it was found out that the amount of shrink fitting incurred by the order of assembly of the die set should be taken into account for optimization when the multi stress rings are used in practice. In addition, it is construed that the proposed design method can be beneficial fur improving the tool life of cold extrusion die set.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.10
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• pp.1574-1582
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• 2004

Binary manipulators have recently received much attention due to hyper-redundancy, light weight, good controllability and high reliability. The precise positioning of the manipulator end-effecter requires the use of many modules, which results in a high-dimensional workspace. When the workspace dimension is large, existing inverse kinematics methods such as the Ebert-Uphoff algorithm may require impractically large memory size in determining the binary positions of all actuators. To overcome this limitation, we propose a new inverse kinematics algorithm: the inverse kinematics problem is formulated as an optimization problem using real-valued design variables, The key procedure in this approach is to transform the integer-variable optimization problem to a real-variable optimization problem and to push the real-valued design variables as closely as possible to the permissible binary values. Since the actual optimization is performed in real-valued design variables, the design sensitivity becomes readily available, and the optimization method becomes extremely efficient. Because the proposed formulation is quite general, other design considerations such as operation power minimization can be easily considered.

• Smart Structures and Systems
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• v.25 no.3
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• pp.345-368
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• 2020

The present work proposes a self-controlled multi-stage optimization method for damage identification of structures utilizing standard particle swarm optimization (PSO) algorithm. Damage identification problem is formulated as an inverse optimization problem where damage severity in each element of the structure is considered as optimization variables. An efficient objective function is formed using the first few frequencies and mode shapes of the structure. This objective function is minimized by a self-controlled multi-stage strategy to identify and quantify the damage extent of the structural members. In the first stage, standard PSO is utilized to get an initial solution to the problem. Subsequently, the algorithm identifies the most damage-prone elements of the structure using an adaptable threshold value of damage severity. These identified elements are included in the search space of the standard PSO at the next stage. Thus, the algorithm reduces the dimension of the search space and subsequently increases the accuracy of damage prediction with a considerable reduction in computational cost. The efficiency of the proposed method is investigated and compared with available results through three numerical examples considering both with and without noise. The obtained results demonstrate the accuracy of the present method can accurately estimate the location and severity of multi-damage cases in the structural systems with less computational cost.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2013.05a
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• pp.1269-1270
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• 2013

• Steel and Composite Structures
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• v.18 no.2
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• pp.289-303
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• 2015

This study presents a strategy so-called Subspace Search Mechanism (SSM) for reducing the computational time for convergence of population based metaheusristic algorithms. The selected metaheuristic for this study is the Cuckoo Search algorithm (CS) dealing with size optimization of trusses. The complexity of structural optimization problems can be partially due to the presence of high-dimensional design variables. SSM approach aims to reduce dimension of the problem. Design variables are categorized to predefined groups (subspaces). SSM focuses on the multiple use of the metaheuristic at hand for each subspace. Optimizer updates the design variables for each subspace independently. Updating rules require candidate designs evaluation. Each candidate design is the assemblage of responsible set of design variables that define the subspace of interest. SSM is incorporated to the Cuckoo Search algorithm for size optimizing of three small, moderate and large space trusses. Optimization results indicate that SSM enables the CS to work with less number of population (42%), as a result reducing the time of convergence, in exchange for some accuracy (1.5%). It is shown that the loss of accuracy can be lessened with increasing the order of complexity. This suggests its applicability to other algorithms and other complex finite element-based engineering design problems.