• Journal of Aerospace System Engineering
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• v.11 no.4
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• pp.36-43
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• 2017

Blade design optimization of QTP-UAV prop-rotor was conducted using ModelCenter(R). Performance efficiency of the blade in hover and forward flight were adopted as the multi-objective function. Required power and pitch link force applied to constraint in each flight mode and limited lower than the value of the baseline blade. Design variables of root chord length of the blade, taper ratio, twist slope, twist angle at 0.5R of the blade, anhedral angle, parabolic coefficient of a tip shape and location of airfoil were used to generate the blade planform. CAMRAD-II, the comprehensive analysis program of rotorcraft, was used for performance analysis of prop-rotor blade in design process. Performance of the optimized blade improved 1.6% of figure of merit in hover and 13.6% of propulsive efficiency in forward flight. Pitch link force also reduced approximately 30% less than that of the baseline blade.

• Proceedings of the IEEK Conference
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• 2004.06b
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• pp.459-462
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• 2004

In this paper, we present a reducing power consumption of a scheduling for module selection under the time constraint. A a reducing power consumption of a scheduling for module selection under the time constraint execute scheduling and allocation for considering the switching activity. The focus scheduling of this phase adopt Force-Directed Scheduling for low power to existed Force-Directed Scheduling. and it constructs the module selection RT library by in account consideration the mutual correlation of parameters in which the power and the area and delay. when it is, in this paper we formulate the module selection method as a multi-objective optimization and propose a branch and bound approach to explore the large design space of module selection. Therefore, the optimal module selection method proposed to consider power, area, delay parameter at the same time. The comparison experiment analyzed a point of difference between the existed FDS algorithm and a new FDS_RPC algorithm.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.04a
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• pp.208-213
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• 2001

Objective of this paper is to develop a new approach involving the use of an Artificial Neural Network(ANN) and multiple regression methods in the prediction of process parameters on bead height for GMA welding process. Using a series of robotic are welding, multi-pass butt welds carried out in order to verify the performance of the neural network estimator and multiple regression methods. To verify the developed system, the design parameters of the neural network estimator are selected from an estimation error analysis. The experimental results show that the proposed models can predict the bead height with reasonable accuracy and guarantee the uniform weld quality.

• Journal of Power Electronics
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• v.19 no.5
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• pp.1289-1302
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• 2019

In this paper, an optimal allocation method of a hybrid active power filter in an active distribution network is designed based on the differential evolution algorithm to resolve the harmonic generation problem when a distributed generation system is connected to the grid. A distributed generation system model in the calculation of power flow is established. An improved back/forward sweep algorithm and a decoupling algorithm are proposed for fundamental power flow and harmonic power flow. On this basis, a multi-objective optimization allocation model of the location and capacity of a hybrid filter in an active distribution network is built, and an optimal allocation scheme of the hybrid active power filter based on the differential evolution algorithm is proposed. To verify the effect of the harmonic suppression of the designed scheme, simulation analysis in an IEEE-33 nodes model and an experimental analysis on a test platform of a microgrid are adopted.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2008.03a
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• pp.283-292
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• 2008

The foremost criterion in the design of a Satellite Launch Vehicle(SLV) is its performance capability to boost the designated payload to the desired mission orbit; it starts from focusing on the SLV configuration to achieve the velocity requirements($}\Delta}V$) for the mission. In this paper we review an analytical approach which is suitable enough for preliminary conceptual design and is used previously to optimize stage configurations for Two Stage to Orbit SLV for Low Earth Orbit(LEO) Missions; we have extended this approach to Three Stage to Orbit SLV and compared different propellant options for the mission. The objective is to minimize the Gross Lift off Weight(GLOW). The primary performance figures of merit were the total inert weight of the SLV and the payload weight that the SLV could lift into LEO, given candidate propulsion systems. The optimization is achieved by configuring the $}\Delta}V$ between stages. A comparison of configurations of single-stage and multi-stage SLVs is made for different propellants. Based upon the optimized stage configurations a comparative performance analysis is made between Liquid and Solid fueled SLV. A 3 degree of freedom trajectory-analysis program is modeled in SIMULINK and used to conduct the performance analysis. Furthermore, a cost analysis is performed on our stage optimized SLVs. The cost estimation relationships(CER) used give us a comparison of development and fabrication costs for the Liquid vs. Solid fueled SLV in man years. The pros and cons of the production, operation ability, performance, responsiveness, logistics, price, shelf life, storage etc of both Solid and Liquid fueled SLVs are discussed. The statistics and data are used from existing or historical(real) SLV stages.

• Journal of the Korea Society for Simulation
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• v.18 no.4
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• pp.137-148
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• 2009

The purpose of this study is to implement variable selection algorithm which helps construct a reliable linear regression model. If we use all candidate variables to construct a linear regression model, the significance of the model will be decreased and it will cause 'Curse of Dimensionality'. And if the number of data is less than the number of variables (dimension), we cannot construct the regression model. Due to these problems, we consider the variable selection problem as a combinatorial optimization problem, and apply GA (Genetic Algorithm) to the problem. Typical measures of estimating statistical significance are $R^2$, F-value of regression model, t-value of regression coefficients, and standard error of estimates. We design GA to solve multi-objective functions, because statistical significance of model is not to be estimated by a single measure. We perform experiments using simulation data, designed to consider various kinds of situations. As a result, it shows better performance than LARS (Least Angle Regression) which is an algorithm to solve variable selection problems. We modify algorithm to solve portfolio selection problem which construct portfolio by selecting stocks. We conclude that the algorithm is able to solve real problems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.11
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• pp.5282-5300
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• 2017

Multi-hop cellular networks (MCNs) reduce the transmit power and improve the system performance. Recently, several research studies have been conducted on MCNs. The mobile relay selection scheme is a rising issue in the design of MCNs that achieves these advantages. The conventional opportunistic relaying (OR) is performed on the single factor for maximum signal-to-interference-plus-noise ratio (SINR). In this paper, a comprehensive OR scheme based on Bi-Target is proposed to improve the system throughput and reduce the relay handover by constraining the amount of required bandwidth and SINR. Moreover, the proposed algorithm captures the variability and the mobility that makes it more suitable for dynamic real scenarios. Numerical and simulation results show the superiority of the proposed algorithm in both enhancing the overall performance and reducing the handover.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.6
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• pp.527-536
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• 2009

A cascade apparatus was designed with only one blade. Its passage is a 160% width of the cascade pitch. This kind of apparatus can give more accurate experimental result than those applying multi-blades even though the apparatus is small. However, this causes difficulties to make the periodic condition along the pitchwise direction. In this study, sidewalls were designed to satisfy the periodic condition based on the flow structure using a gradient based optimization and a genetic algorism. The objective function was adopted the surface Mach number obtained on the cascade and fourteen design variables were selected for controlling sidewall shapes. The designed sidewalls using the genetic algorism shows better result.

• Steel and Composite Structures
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• v.51 no.5
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• pp.473-485
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• 2024

This paper proposes a composite form of fuzzy adaptive control plan based on a robust observer. The fuzzy 2D control gains are regulated by the parameters in the LMIs. Then, control and learning performance indices with weight matrices are constructed as the cost functions, which allows the regulation of the trade-off between the two performance by setting appropriate weight matrices. The design of 2D control gains is equivalent to the LMIs-constrained multi-objective optimization problem under dual performance indices. By using this proposed smart tracking design via fuzzy nonlinear criterion, the data link can be further extended. To evaluate the performance of the controller, the proposed controller was compared with other control technologies. This ensures the execution of the control program used to track position and trajectory in the presence of great model uncertainty and external disturbances. The performance of monitoring and control is verified by quantitative analysis. The goals of this paper are towards access to adequate, safe and affordable housing and basic services, promotion of inclusive and sustainable urbanization and participation, implementation of sustainable and disaster-resilient buildings, sustainable human settlement planning and manage. Therefore, the goal is believed to achieved in the near future by the ongoing development of AI and control theory.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.5 no.1
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• pp.1-12
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• 1985

This study is conserned for the optimum design of reinforced concrete slab-beam-column structures with multi-storys and multi-bays by Direct Method. Flexural and shear strength, sectional size, and steel ratio etc., were considered as the design-constraints and the cost function was taken as to objective function. They became high degree nonlinear problems. Using SLP as an analytical method of nonlinear optimal problems, an optimal algorithm was developed in this study and the algorithm was applied to the optimization of reinforced concrete structure system of 5 storys. The result converged to a optimal solution with 3 to 5 iterations, and proved that economical design could be possible when compared with conventional designs.