• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.6
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• pp.864-870
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• 2003

In recent engineering, the designer has become more and more dependent on the computer simulations such as FEM(Finite Element Method) and BEM(Boundary Element Method). In order to optimize such implicit models more efficiently and reliably, the meta -modeling technique has been developed for solving such a complex problems combined with the DACE(Design and Analysis of Computer Experiments). It is widely used for exploring the engineer's design space and for building approximation models in order to facilitate an effective solution of multi-objective and multi-disciplinary optimization problems. Optimization of a train suspension is performed according to the minimization of forty -six responses that represent ten ride comforts, twelve derailment quotients, twelve unloading ratios, and twelve stabilities by using the Kriging model of a train suspension. After each Kriging model is constructed, multi -objective optimal solutions are achieved by using a nonlinear programming method called SQP(Sequential Quadratic Programming).

• Journal of the Korean Society for Aviation and Aeronautics
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• v.13 no.3
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• pp.22-33
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• 2005

Response Surface Method (RSM) constructs approximate response surfaces using sample data from experiments or simulations and finds optimum levels of process variables within the fitted response surfaces of the interest region. It will be necessary to get the most suitable response surface for the accuracy of the optimization. The application of RSM plan experimental designs. The RSM is used in the sequential optimization process. The first goal of this study is to improve the plan of central composite designs of experiments with various locations of axial points. The second is to increase the optimal efficiency applying a modified method to update interest regions.

• 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 information and communication convergence engineering
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• v.12 no.4
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• pp.215-220
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• 2014

We consider quantization optimized for distributed estimation, where a set of sensors at different sites collect measurements on the parameter of interest, quantize them, and transmit the quantized data to a fusion node, which then estimates the parameter. Here, we propose an iterative quantizer design algorithm with a weighted distance rule that allows us to reduce a system-wide metric such as the estimation error by constructing quantization partitions with their optimal weights. We show that the search for the weights, the most expensive computational step in the algorithm, can be conducted in a sequential manner without deviating from convergence, leading to a significant reduction in design complexity. Our experments demonstrate that the proposed algorithm achieves improved performance over traditional quantizer designs. The benefit of the proposed technique is further illustrated by the experiments providing similar estimation performance with much lower complexity as compared to the recently published novel algorithms.

• Korean Management Science Review
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• v.31 no.3
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• pp.113-130
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• 2014

Cooperative coevolutionary algorithm (CCEA) has proven to be a very powerful means of solving optimization problems through problem decomposition. CCEA implies the use of several populations, each population having the aim of finding a partial solution for a component of the considered problem. Populations evolve separately and they interact only when individuals are evaluated. Interactions are made to obtain complete solutions by combining partial solutions, or collaborators, from each of the populations. In this respect, we can think of various interaction modes. The goal of this research is to develop a CCEA for a supply chain network design (SCND) problem and identify which interaction mode gives the best performance for this problem. We present general design principle of CCEA for the SCND problem, which require several co-evolving populations. We classify these populations into two groups and classify the collaborator selection scheme into two types, the random-based one and the best fitness-based one. By combining both two groups of population and two types of collaborator selection schemes, we consider four possible interaction modes. We also consider two modes of updating populations, the sequential mode and the parallel mode. Therefore, by combining both four possible interaction modes and two modes of updating populations, we investigate seven possible solution algorithms. Experiments for each of these solution algorithms are conducted on a few test problems. The results show that the mode of the best fitness-based collaborator applied to both groups of populations combined with the sequential update mode outperforms the other modes for all the test problems.

• Journal of Distribution Science
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• v.20 no.3
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• pp.33-43
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• 2022

Purpose: More and more marketers use social media platforms to create and spread information called Marketer Generated Content (MGC) to inform consumers of products. MGC often embeds product purchase links, thus directing consumers to online distribution channels for online purchases. This study examined the effect of social media MGC on consumers' willingness to buy online in the anchor of consumers' perspective to answer the question of "how social media generated content support corporate online distribution". Research design, data, and methodology: According to the means-end-chain theory, we introduce perceived value and continuous following intention as chain mediators to explain the mechanism of MGC influence on consumers' online purchase intention and consider product type to discuss boundary conditions. Two experiments were designed to test hypothesizes. Results and Conclusion: First, emotional MGC (vs. informational MGC) has lower (higher) perceived utility (hedonic) value. Second, perceived value has a significant mediate role in the effect of MGC on continuous following intention. Third, perceived value and continuous following intention significantly and sequentially mediated the effect of MGC on online purchase intention. Through the sequential mediations of perceived utility value and continuous following intention, Informational MGC of search products significantly increase online purchase intentions. Another parallel sequential mediation, including perceived hedonic, emotional MGC of experience products, partially enhanced online purchase intentions. Finally, this study gives implications for how corporates can use social media MGC to promote product sales online.

• Journal of KSNVE
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• v.7 no.6
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• pp.967-974
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• 1997

Distributed piezoeletric sensor and actuator have been designed for efficient vibration control of a cantilevered beam. Both PZT and PVDF have been used in this study, the former as an actuator and the latter as a sensor for the integrated structure. We have optimized the position and the size of the PZT actuator and the electrode shape of the PVDF sensor. Finite element method is used to model the structure and the optimized actuators, we have designed the active electrode width of the PVDF sensor along the span of the beam. Actuator design is based on the criterion of minimizing the system energy in the control modes under a given initial condition. Model control forces for the residual (uncontrolled) modes have been minimized during the sensor design to minimize the observation spill-over. Genetic algorithm and sequential quadratic programming technique have been utilized as an optimization scheme. Discrete LQG control law has been applied to the integrated structure for real time vibration control. Performance of the sensor, the actuator, and the integrated smart structure has been demonstrated by experiments.

• Journal of Korean Society for Quality Management
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• v.46 no.1
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• pp.39-74
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• 2018

Purpose: For more than 30 years, robust parameter design (RPD), which attempts to minimize the process bias (i.e., deviation between the mean and the target) and its variability simultaneously, has received consistent attention from researchers in academia and industry. Based on Taguchi's philosophy, a number of RPD methodologies have been developed to improve the quality of products and processes. The primary purpose of this paper is to review and discuss existing RPD methodologies in terms of the three sequential RPD procedures of experimental design, parameter estimation, and optimization. Methods: This literature study composes three review aspects including experimental design, estimation modeling, and optimization methods. Results: To analyze the benefits and weaknesses of conventional RPD methods and investigate the requirements of future research, we first analyze a variety of experimental formats associated with input control and noise factors, output responses and replication, and estimation approaches. Secondly, existing estimation methods are categorized according to their implementation of least-squares, maximum likelihood estimation, generalized linear models, Bayesian techniques, or the response surface methodology. Thirdly, optimization models for single and multiple responses problems are analyzed within their historical and functional framework. Conclusion: This study identifies the current RPD foundations and unresolved problems, including ample discussion of further directions of study.

• Proceedings of the KIEE Conference
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• 2004.07d
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• pp.2242-2244
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• 2004

This paper deals with the design of a reduced-order stabilizing controller for the linear system. The coupled lineal matrix inequality (LMI) problem subject to a rank condition is solved by a sequential semidefinite programming (SDP) approach. The nonconvex rank constraint is incorporated into a strictly linear penalty function, and the computation of the gradient and Hessian function for the Newton method is not required. The penalty factor and related term are updated iteratively. Therefore the overall procedure leads to a successive LMI relaxation method. Extensive numerical experiments illustrate the proposed algorithm.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.46 no.10
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• pp.798-805
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• 2018

This study introduces an adaptive design of experiment (DoE) approach for the hypersonic shock-tunnel testing. A series of experiments are conducted to model the pitch moment coefficient of a cone as the function of the angle of attack and the pitch rate. An algorithm to construct the trajectory of the test model from the images obtained by the high-speed camera is developed to effectively analyze multiple time series experimental data. An adaptive DoE procedure to determine the experimental point based on the analysis results of the past experiments using the algorithm is proposed.