• Proceeding of EDISON Challenge
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• 2015.03a
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• pp.223-228
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• 2015

In this paper, the study of the optimum design for a geometry of the handle bar to obtain a high stiffness and light weight is investigated, using EDISON simulation program. High stiffness and weight lightening are considered as the major performance indicators of the component of the bicycle. Four design factors and three levels of the design factors are selected for the structural optimization and experiments are designed using the orthogonal array of L9 by Taguchi method. We calculated SN ratio of larger-the-better and smaller-the-better characteristics from FEA results and analysed the effects of design factors on characteristics. We choosed the optimum level of design factors based on deflection and safety factor. Comparing the results of FE analysis with converted value of predicted SN ration, we made sure for reliability of Taguchi method and FE method for structural optimization.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.6
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• pp.677-682
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• 2011

In this study, a simulation model developed for a hydraulic breaker is verified through comparison of simulation results to the experimental results. For performance optimization, the design parameters are selected based on the physical parameters of the equipment for ease of modification. Also a factorial experiment and regression analysis were conducted to observe the effect of each parameter on the performance of the hydraulic breaker. As a result, a method for optimizing the performance of a hydraulic breaker is proposed.

• Journal of Ocean Engineering and Technology
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• v.20 no.3 s.70
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• pp.96-102
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• 2006

The butterfly valve has been used to control the switch and flux of fluid. While research about the characteristics of butterfly valve fluid have been done, study of the optimum design, considering structural safety, must keep pace with it. Thus, a method is proposed for an optimum butterfly valve. Initially, the stability of the butterfly valve, using FEM and CFD, is evaluated, and a variable is selected using the initial analysis results. Also, the shape optimization design is accomplished using the DACE model. In terms of research results, the experiment satisfied the objective and limitation functions.

• Proceedings of the KSME Conference
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• 2007.05a
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• pp.1350-1354
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• 2007

As the importance of low power design is emphasized, power consumption became one of the standards that represent the performance of the system. The purpose of this study is to decide design variable that minimize power consumption for the oxygen concentrator in two bed-one compressor 8 step PSA process that has above 90% purity at 3lpm by using given constants and selected parameters. Setting selected parameters as cycle time and equalization time, optimization for PSA process in the oxygen concentrator is progressed. For this, we need to know the features and basic principals of PSA process and to deduce objective function of performance analysis. Validations for objective function and lots of experiments are needed too. By using the characteristic curve of the compressor and the pressure curve of the process for 1 cycle, objective function was set. After theoretical 2 dimensional optimized paths was obtained. And then, by experiment, theoretical optimized path was verified.

• Journal of Multimedia Information System
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• v.1 no.1
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• pp.87-93
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• 2014

The aim of microscopic image restoration is to recover the image by applying the inverse process of degradation, and the results facilitate automated and improved analysis of the image. In this work, we consider the problem of image restoration as a minimization problem of convex cost function, which consists of a least-squares fitting term and regularization terms with non-negative constraints. The finite step method is proposed to solve this constrained convex optimization problem. We demonstrate the convergence of this method. Efficiency and restoration capability of the proposed method were tested and illustrated through numerical experiments.

• Proceedings of the KSME Conference
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• 2004.04a
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• pp.1035-1040
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• 2004

In the proto design stage of a new car, the performance of an occupant protection system is often evaluated by CAE instead of the real test. CAE predicts and recommends the appropriate design values hence reducing the number of the real tests. However, the existing researches using CAE in predicting the performances do not consider the uncertainties of parameters, in which inconsistency between the actual test results and CAE exists. In this research, the optimization procedure of a protection system such as airbag and load limiter is suggested for the frontal collision. The DACE modeling known as Kriging interpolation is introduced to obtain the meta model of the system followed by the tabu search method to determine a global optimum. Finally, the distribution of a suggested design is determined through the Monte-Carlo Simulation.

• Journal of Information Processing Systems
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• v.18 no.1
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• pp.146-158
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• 2022

With the success of the digital economy and the rapid development of its technology, network security has received increasing attention. Intrusion detection technology has always been a focus and hotspot of research. A hybrid model that combines particle swarm optimization (PSO) and kernel extreme learning machine (KELM) is presented in this work. Continuous-valued PSO and binary PSO (BPSO) are adopted together to determine the parameter combination and the feature subset. A fitness function based on the detection rate and the number of selected features is proposed. The results show that the method can simultaneously determine the parameter values and select features. Furthermore, competitive or better accuracy can be obtained using approximately one quarter of the raw input features. Experiments proved that our method is slightly better than the genetic algorithm-based KELM model.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.5
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• pp.429-434
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• 2013

This paper proposes a CPSO (Cooperative Particle Swarm Optimization)-based MPC (Model Predictive Control) scheme to deal with formation control problem of multiple nonholonomic mobile robots. In a distributed MPC framework, each robot needs to optimize control input sequence over a finite prediction horizon considering control inputs of the other robots where their cost functions are coupled by the state variables of the neighboring robots. In order to optimize the control input sequence, a CPSO algorithm is adopted and modified to fit into the formation control problem. Experiments are performed on a group of nonholonomic mobile robots to demonstrate the effectiveness of the proposed CPSO-based MPC for multi-robot formation.

• 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.

• Journal of the Korean Magnetic Resonance Society
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• v.13 no.1
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• pp.15-26
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• 2009

The NMR detection of methyl groups is of keen interest because they provide the long-range distance information required to establish global folds of high molecular weight proteins. Using longitudinal $^1H$ relaxation optimization, we achieve a gain in sensitivity of approximately 1.6-fold in the methyl-TROSY and its NOESY experiments for the 38 kDa protein mitogen activated protein kinase p38 in its fully protonated and $^{13}C$ and $^{15}N$ labeled state.