• The Transactions of the Korea Information Processing Society
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• v.3 no.1
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• pp.55-62
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• 1996

MRNS network is a general algebraic structure of Hypercube network which has recently drawn considerable attention to supercomputing and message-passing communication. In this paper, we investigate the routing of a message in an n- dimensional MRNS network that is a key to the performance of this network. On the n-dimensional MRNS network we would like to transmit packets from a source node to a destination node simultaneously along a fixed number of paths, where the superscript packet will traverse along the superscript path. In order for all packets to arrive at the destination node quickly and securely, the ith path must be node-disjoint from all other paths. By investigating the conditions of node-disjoint paths, we will employ the special matrices called as the Hamiltonian Circuit Latin Square(HCLS) described in 〔1〕to construct a set of node-disjoint paths and suggest a linear-time parallel routing algorithm for the MRNS network.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.2
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• pp.335-352
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• 2023

When solving multi-objective optimization problems, the blindness of the evolution direction of the population gradually emerges with the increase in the number of objectives, and there are also problems of convergence and diversity that are difficult to balance. The many- objective optimization problem makes some classic multi-objective optimization algorithms face challenges due to the huge objective space. The sine cosine algorithm is a new type of natural simulation optimization algorithm, which uses the sine and cosine mathematical model to solve the optimization problem. In this paper, a knee-driven many-objective sine-cosine algorithm (MaSCA-KD) is proposed. First, the Latin hypercube population initialization strategy is used to generate the initial population, in order to ensure that the population is evenly distributed in the decision space. Secondly, special points in the population, such as nadir point and knee points, are adopted to increase selection pressure and guide population evolution. In the process of environmental selection, the diversity of the population is promoted through diversity criteria. Through the above strategies, the balance of population convergence and diversity is achieved. Experimental research on the WFG series of benchmark problems shows that the MaSCA-KD algorithm has a certain degree of competitiveness compared with the existing algorithms. The algorithm has good performance and can be used as an alternative tool for many-objective optimization problems.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.19 no.6
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• pp.55-60
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• 2020

The injection molding technique is a processing method widely used for the production of plastic parts. In this study, the gate position, gate size, packing time, and melt temperature were optimized to minimize both the stress and deformation that occur during the injection molding process of medical suction device components. We used a central composite design and Latin hypercube sampling to acquire the data and adopted the response surface method as an approximation method. The efficiency of the optimization of the injection molding problem was determined by comparing the results of a genetic algorithm, sequential quadratic programming, and a non-dominant classification genetic algorithm.

• International Journal of Fluid Machinery and Systems
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• v.2 no.1
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• pp.1-12
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• 2009

Surrogate modeling is applied to a compressor blade shape optimization to modify its stacking line and thickness to enhance adiabatic efficiency and total pressure ratio. Six design variables are defined by parametric curves and three objectives; efficiency, total pressure and a combined objective of efficiency and total pressure are considered to enhance the performance of compressor blade. Latin hypercube sampling of design of experiments is used to generate 55 designs within design space constituted by the lower and upper limits of variables. Optimum designs are found by formulating a PRESS (predicted error sum of squares) based averaging (PBA) surrogate model with the help of a gradient based optimization algorithm. The optimum designs using the current variables show that, to optimize the performance of turbomachinery blade, the adiabatic efficiency objective is improved substantially while total pressure ratio objective is increased a very small amount. The multi-objective optimization shows that the efficiency can be increased with the less compensation of total pressure reduction or both objectives can be increased simultaneously.

• Proceedings of the KIEE Conference
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• 2008.04c
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• pp.65-67
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• 2008

An adaptive response surface method with Latin Hypercube sampling strategy is employed to optimize a magnet pole shape of large scale BLDC motor to minimize the cogging torque. The proposed algorithm consists of the multi-objective Pareto optimization and ($1+{\lambda}$) evolution strategy to find the global optimal points with relatively fewer sampling data. In the adaptive RSM, an adaptive sampling point insertion method is developed utilizing the design sensitivities computed by using finite element method to set a reasonable response surface with a relatively small number of sampling points. The developed algorithm is applied to the shape optimization of PM poles for 6MW BLDC motor.

• The KSFM Journal of Fluid Machinery
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• v.16 no.2
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• pp.35-41
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• 2013

Inlet part of a printed circuit heat exchanger has been optimized by using three-dimensional Reynolds-Averaged Navier-Stokes analysis and surrogate modeling techniques. Kriging model has been used as the surrogate model. The objective function for the optimization has been defined as a linear combination of uniformity of mass flow rate and the pressure loss with a weighting factor. For the optimization, the angle of the inlet plenum wall, radius of curvature of the inlet plenum wall, and width of the inlet pipes have been selected as design variables. Twenty six design points are obtained by Latin Hypercube Sampling in design space. Through the optimization, considerable improvement in the objective function has been obtained in comparison with the reference design of PCHE.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.640_641
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• 2009

본 논문에서는 6MW급 BLDC Motor의 슬롯형상을 변화시켜 코깅토그와 공극에서의 자속밀도, 저장 에너지를 해석하고, 슬롯형상의 중앙에 설계변수를 주어 공극에 흐르는 자속의 변화를 계산하여 분석 하였다. 폭길이는 최소화에 기준을 두었으며 Parameter는 LHS를 이용한 샘플링 포인트 기법을 사용하여 최적의 조건에 가장 알맞은 최적점를 찾아내어 특성해석을 하였다. 최종적으로 고용량에서의 슬롯형상변화에 따라 코깅토크를 최소화하는 최적화 설계를 위하여 이번 특성해석 연구를 하였다.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.646_647
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• 2009

소형 다상 외전형 BLDC 전동기의 코깅토크를 최소화하기 위해 코깅토크의 발생원인 중 하나인 영구자석의 형상에 샘플링 포인트 기법인 LHS를 이용한 최적화 알고리즘을 적용하여 코깅토크를 최소화하는 최적 설계를 수행 하였다. 총 3번의 반복계산을 하여 코깅토크가 최소화 되는 최적점을 계산한 결과,최대 코깅토크가 0.20 [N.m]에서 0.15 [N.m]로, 초기 형상의 약 75%로 감소되었다

• Journal of the Korean Society for Aviation and Aeronautics
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• v.18 no.4
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• pp.67-72
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• 2010

In this study, performance analysis and design optimization is carried out for a multi-rate spring brake system, which is used in a cable ride to stop the arriving passengers in safe and comfortable manner. Mathematical model for the spring is developed toward the objective of minimizing the impact at the arrival while satisfying the constraint of limited distance at the stop. Matlab code is utilized to examine parameters affecting the performance of the brake system. The results are validated by a commercial software RecurDyn. Kriging meta model is used to reduce the computational cost of the analysis. Optimization is conducted by RecurDyn, from which the design parameters are determined that minimizes the impact at the stop.

• Structural Engineering and Mechanics
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• v.34 no.4
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• pp.417-447
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• 2010

The uncertainty often observed in experimental strengths of masonry constituents makes critical the selection of the appropriate inputs in finite element analysis of complex masonry buildings, as well as requires modelling the building ultimate load as a random variable. On the other hand, the utilization of expensive Monte Carlo simulations to estimate collapse load probability distributions may become computationally impractical when a single analysis of a complex building requires hours of computer calculations. To reduce the computational cost of Monte Carlo simulations, direct computer calculations can be replaced with inexpensive Response Surface (RS) models. This work investigates the use of RS models in Monte Carlo analysis of complex masonry buildings with random input parameters. The accuracy of the estimated RS models, as well as the good estimations of the collapse load cumulative distributions obtained via polynomial RS models, show how the proposed approach could be a useful tool in problems of technical interest.