• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.43 no.7
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• pp.601-608
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• 2015

The static aeroelastic analysis and optimization of flexible wings are conducted for steady state conditions while both aerodynamic and structural parameters can be used as optimization variables. The system of multidisciplinary design optimization as a robust methodology to couple commercial codes for a static aeroelastic optimization purpose to yield a convenient adaptation to engineering applications is developed. Aspect ratio, taper ratio, sweepback angle are chosen as optimization variables and the skin thickness of the wing. The real-coded adaptive range multi-objective genetic algorithm code, which represents the global multi-objective optimization algorithm, was used to control the optimization process. The support vector regression(SVR) is applied for optimization, in order to reduce the time of computation. For this multi-objective design optimization problem, numerical results show that several useful Pareto optimal designs exist for the flexible wing.

• Proceedings of the KIEE Conference
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• 2008.04c
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• pp.122-124
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• 2008

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. In order to maximize the efficiency in such applications, this paper proposes the Adaptive-FNIS(Fuzzy Neural Network Inference System). The controllable electrical loss which consists of the copper loss and the iron loss can be minimized by the optimal d-axis current $i_d$. This paper considers the parameter variation about the motor operation. The operating characteristics controlled by efficiency optimization control are examined in detail.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.1505-1506
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• 2008

In this paper, a robust adaptive backstepping control is developed for efficiency optimization of induction motors with uncertainties. The proposed control scheme consists of efficiency flux control(EFC) using a sliding mode adaptive flux observer and robust speed control(RSC) using a function approximation for mechanical uncertainties. In EFC, it is important to find the flux reference to minimize power losses of induction motors. Therefore, we proposed the optimal flux reference using the electrical power loss function. The sliding mode flux observer is designed to estimate rotor fluxes and variation of inverse rotor time constant. In RSC, the unknown function approximation technique employs nonlinear disturbance observer(NDO) using fuzzy neural networks(FNNs). The proposed controller guarantees both speed tracking and flux tracking. Simulation results are presented to illustrate the effectiveness of the approaches proposed.

• Advances in Computational Design
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• v.1 no.3
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• pp.275-296
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• 2016

This article presents an adaptive directional differential evolution (ADDE) algorithm and its application in solving discrete sizing truss optimization problems. The algorithm is featured by a new self-adaptation approach and a simple directional strategy. In the adaptation approach, the mutation operator is adjusted in accordance with the change of population diversity, which can well balance between global exploration and local exploitation as well as locate the promising solutions. The directional strategy is based on the order relation between two difference solutions chosen for mutation and can bias the search direction for increasing the possibility of finding improved solutions. In addition, a new scaling factor is introduced as a vector of uniform random variables to maintain the diversity without crossover operation. Numerical results show that the optimal solutions of ADDE are as good as or better than those from some modern metaheuristics in the literature, while ADDE often uses fewer structural analyses.

• Journal of Electrical Engineering and Technology
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• v.9 no.5
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• pp.1544-1550
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• 2014

In this paper, an adaptive optimization strategy utilizing Kriging model and genetic algorithm is proposed for the optimal design of electromagnetic devices. The ordinary Kriging assisted by the spherical covariance model is used to construct surrogate models. In order to improve the computational efficiency, the adaptive uniform sampling strategy is applied to generate sampling points in design space. Through several iterations and gradual refinement process, the global optimal point can be found by genetic algorithm. The proposed algorithm is validated by application to the optimal design of a switched reluctance motor, where the stator pole face and shape of pole shoe attached to the lateral face of the rotor pole are optimized to reduce the torque ripple.

• Journal of Power Electronics
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• v.18 no.5
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• pp.1470-1478
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• 2018

Recently, wireless power transfer (WPT) via coupled magnetic resonances has attracted a lot of attention owing to its long operation distance and high efficiency. However, the WPT systems is over-coupling and a frequency splitting phenomenon occurs when resonators are placed closely, which leads to a decrease in the transfer power. To solve this problem, an adaptive frequency tracking control (AFTC) was used based on a closed-loop control scheme. An improved particle swarm optimization (PSO) algorithm was proposed with the AFTC to track the maximum power point in real time. In addition, simulations were carried out. Finally, a WPT system with the AFTC was demonstrated to experimentally validate the improved PSO algorithm and its tracking performance in terms of optimal frequency.

• The Journal of the Korea Contents Association
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• v.6 no.4
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• pp.11-18
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• 2006

A new algorithm for removing salt and pepper impulse noise in image using impulse noise detector and total variation optimization is presented. The proposed two types of noise detectors which are based on the adaptive median filter, can detect impulse noise with high accuracy while reducing the probability of detecting image details as impulses. And the detectors maintain its performance independent of noise density. For removing impulses, total variation optimization is applied only to those detected noise candidate to reduces unnecessary computation. The proposed approach successfully remove impulse noise while preserving image details.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.4
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• pp.485-490
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• 2003

RP(Rapid Prototyping) has been used in the various industrial applications. This paper presents the optimization techniques fur fabricated 3D model design using RP machine for the medical field. Once the original brain model data are obtained from 2D slices of MRI/CT machine, the data can be modeled as an optimal ellipse. The objective of this study includes optimization of fabrication time and surface roughness using the adaptive slicing method. It can reduce fabrication time without losing surface roughness quality by accumulating the slices with variable thickness. According to the parameter tuning and synthesis of its effect, more suitable parameter values can be obtained by enhanced 3D brain model fabrication. Therefore, accurate 3D brain model fabricated by RP machine can enable a surgeon to perform pre-operation. to make a decision for the operation sequence and to perceive the 3D positions in prototype, before delicate operation of actual surgery.

• Journal of the Korea institute for structural maintenance and inspection
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• v.12 no.5
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• pp.125-132
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• 2008

Structural optimization is widely adopted in the design of structures with the development of computer aided design and computer technique recently. By applying the structural optimization in the last decades, designers have gained the design scheme of structures more feasibly and easily. In this paper, an optimal design of one 30-story high rise steel structure is performed considering material non-linearity. Based on finite element analysis and adaptive simulated annealing algorithm, the optimal weight of structure is derived under constraints of allowable yield stress, shear stress and serviceability.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.5
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• pp.451-461
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• 2002

Differential evolution(DE) has been preyed to be an efficient method for optimizing real-valued multi-modal objective functions. DE's main assets are its conceptual simplicity and ease of use. However, the convergence properties are deeply dependent on the control parameters of DE. This paper proposes an adaptive differential evolution(ADE) method which combines with a variant of DE and an adaptive mechanism of the control parameters. ADE contributes to the robustness and the easy use of the DE without deteriorating the convergence. 12 optimization problems is considered to test ADE. As an application of ADE the paper presents a supervised clustering method for predicting events, what is called, an evolutionary event clustering(EEC). EEC is tested for 4 cases used widely for the validation of data modeling.