• Journal of Mechanical Science and Technology
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• v.16 no.5
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• pp.609-618
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• 2002

In practical designs, most of the multidisciplinary problems have a large-size and complicate design system. Since multidisciplinary problems have hundreds of analyses and thousands of variables, the grouping of analyses and the order of the analyses in the group affect the speed of the total design cycle. Therefore, it is very important to reorder and regroup the original design processes in order to minimize the total computational cost by decomposing large multidisciplinary problems into several multidisciplinary analysis subsystems (MDASS) and by processing them in parallel. In this study, a new decomposition method is proposed for parallel processing of multidisciplinary design optimization, such as collaborative optimization (CO) and individual discipline feasible (IDF) method. Numerical results for two example problems are presented to show the feasibility of the proposed method.

• Journal of Korean Association for Spatial Structures
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• v.21 no.1
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• pp.61-70
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• 2021

This paper describes a novel zero-stress member selecting method for sizing optimization of truss structures. When a sizing optimization method with static constraints is implemented, the member stresses are affected sensitively with changing the variables. However, because some truss members are unaffected by specific loading cases, zero-stress states are experienced by the elements. The zero-stress members could affect the computational cost and time of sizing optimization processes. Feature selection approaches can be then used to eliminate the zero-stress member from the whole variables prior to the process of optimization. Several numerical truss examples are tested using the proposed methods.

• Journal of the Korea Institute of Building Construction
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• v.12 no.6
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• pp.664-673
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• 2012

Determining the layout of temporary facilities that support construction activities at a site is an important planning activity, as layout can significantly affect cost, quality of work, safety, and other aspects of the project. The construction site layout problem involves difficult combinatorial optimization. Recently, various artificial intelligence(AI)-based algorithms have been applied to solving many complex optimization problems, including neural networks(NN), genetic algorithms(GA), and swarm intelligence(SI) which relates to the collective behavior of social systems such as honey bees and birds. This study proposes a site facility layout optimization algorithm based on self-organizing maps(SOM). Computational experiments are carried out to justify the efficiency of the proposed method and compare it with particle swarm optimization(PSO). The results show that the proposed algorithm can be efficiently employed to solve the problem of site layout.

• Structural Engineering and Mechanics
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• v.70 no.6
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• pp.649-659
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• 2019

Vibration-based structural damage detection through optimization algorithms and minimization of objective function has recently become an interesting research topic. Application of various objective functions as well as optimization algorithms may affect damage diagnosis quality. This paper proposes a new damage identification method using Moth-Flame Optimization (MFO). MFO is a nature-inspired algorithm based on moth's ability to navigate in dark. Objective function consists of a term with modal assurance criterion flexibility and natural frequency. To show the performance of the said method, two numerical examples including truss and shear frame have been studied. Furthermore, Los Alamos National Laboratory test structure was used for validation purposes. Finite element model for both experimental and numerical examples was created by MATLAB software to extract modal properties of the structure. Mode shapes and natural frequencies were contaminated with noise in above mentioned numerical examples. In the meantime, one of the classical optimization algorithms called particle swarm optimization was compared with MFO. In short, results obtained from numerical and experimental examples showed that the presented method is efficient in damage identification.

• Transactions of the Korean Society of Automotive Engineers
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• v.12 no.2
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• pp.123-130
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• 2004

The automobile suspension system is composed of parts that affect performances of a vehicle such as ride quality, handling characteristics, straight performance and steering effort, etc. Moreover, by using the finite element analysis the cost for the initial design step can be decreased. In the design of a suspension system, usually system vibration and structural rigidity must be considered simultaneously to satisfy dynamic and static requirements simultaneously. In this paper, we consider the weight reduction and the increase of the first eigen-frequency of a suspension part, the upper control arm, especially using topology optimization and size optimization. Firstly, we obtain the initial design to maximize the first eigen-frequency using topology optimization. Then, we apply the multi-objective parameter optimization method to satisfy both the weight reduction and the increase of the first eigen-frequency. The design variables are varying during the optimization process for the multi-objective. Therefore, we can obtain the deterministic values of the design variables not only to satisfy the terms of variation limits but also to optimize the two design objectives at the same time. Finally, we have executed reliability based optimal design on the upper control arm using the Monte-Carlo method with importance sampling method for the optimal design result with 98％ reliability.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2000.04b
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• pp.449-457
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• 2000

The paper describes the study of global approximate optimization utilizing soft computing techniques such as genetic algorithms (GA's), neural networks (NN's), and fuzzy inference systems(FIS). GA's provide the increasing probability of locating a global optimum over the entire design space associated with multimodality and nonlinearity. NN's can be used as a tool for function approximations, a rapid reanalysis model for subsequent use in design optimization. FIS facilitates to handle the quantitative design information under the case where the training data samples are not sufficiently provided or uncertain information is included in design modeling. Properties of soft computing techniques affect the quality of global approximate model. Evolutionary fuzzy modeling (EFM) and adaptive neuro-fuzzy inference system (ANFIS) are briefly introduced for structural optimization problem in this context. The paper presents the success of EFM depends on how optimally the fuzzy membership parameters are selected and how fuzzy rules are generated.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.10
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• pp.137-146
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• 2004

This is the second of two companion papers concerned with the analysis and design of a pneumatic vibration isolation system. The properties of the system are clarified by observation of the transmissibility surface calculated by the models and algorithm developed in the first paper of this research. It Is shown that the nonlinear model proposed in this research is more closer to experimental results than the linear model that have been used in previous studies. The design optimization of the major design variables that affect the performance of the system is achieved by using the condition for attenuation, disturbance rejection and maximum damping in resonance peak. The design space search method is adopted for the optimization of the orifice area. The models, transmissibility calculation algorithms and design optimization techniques developed in this research are shown to be greatly helpful to the optimal design of the pneumatic vibration isolation system by experiment.

• Journal of Mechanical Science and Technology
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• v.18 no.10
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• pp.1722-1729
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• 2004

This paper presents a design methodology for determining configurations of slider air bearings considering the randomness of the air-bearing surface (ABS) geometry by using the iSIGHT. A reliability-based design optimization (RBDO) problem is formulated to minimize the variations in the mean values of the flying heights from a target value while satisfying the desired probabilistic constraints keeping the pitch and roll angles within a suitable range. The reliability analysis is employed to estimate how the fabrication tolerances of individual slider parameters affect the final flying attitude tolerances. The proposed approach first solves the deterministic optimization problem. Then, beginning with this solution, the RBDO is continued with the reliability constraints affected by the random variables. Reliability constraints overriding the constraints of the deterministic optimization attempt to drive the design to a reliability solution with minimum increase in the objective. The simulation results of the RBDO are listed in comparison with the values of the initial design and the results of the deterministic optimization, respectively. To show the effectiveness of the proposed approach, the reliability analyses are simply carried out by using the mean value first-order second-moment (MVFO) method. The Monte Carlo simulation of the RBDO's results is also performed to estimate the efficiency of the proposed approach. Those results are demonstrated to satisfy all the desired probabilistic constraints, where the target reliability level for constraints is defined as 0.8.

• Journal of the Korean Society of Clothing and Textiles
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• v.38 no.4
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• pp.598-609
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• 2014

The increased use of online shopping by older consumers means that online retailers need to consider older consumers when designing websites. We investigated the specific characteristics of commercial websites (i.e., perceptual fluency) through an online experiment. Guided by perceptual fluency and affect optimization literature, hypotheses highlighting older consumers' responses to websites were proposed and tested. Results confirmed that older consumers (in their 50s) are more generous in evaluating online retailers' websites than younger consumers (in their 20s) and that responses to websites are dependent on perceptual fluency. The findings are consistent with previous research and provide additional support for theories that deal with an online apparel shopping context. Practical implications and limitations are discussed.

• Journal of Families and Better Life
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• v.21 no.2
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• pp.131-144
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• 2003

Selective Optimization with Compensation (SOC), a concept defined by Baltes and Baltes, is known to predict successful aging. This study was conducted to find out which factors affect Korean elderly people SOC The data for this study were obtained from a survey conducted between March and May 2001, on a sample of middle-class male and female participants over 60 years old. Two hundred and fifty four completed questionnaires were used for final analyses. Descriptive statistics, t-test, ANOVA, Duncan test, Pearson correlations, multiple regressions, multiple response frequencies and sequential threshold methods were used to analyze the data. In order to measure successful aging, the Selective Optimization with Compensation Scale developed by Baltes, Baltes, Freud, and Lang (1996) was used. The SOC scale consists of four subscales, Elective Selection, Loss-based Selection, Optimization, and Compensation. The major findings are summarized in the following. First, the level of SOC by various socio-demographic variables was examined. It tuned out that health status is the most important variable in predicting SOC. Also important was satisfaction with family life. Second, significant correlations were found between SOC and duration of the marriage (negative), practicing a religion, health, and economic stability (all positive). Third, religion and health status affected SOC, but health was a stronger predictor Those who practiced a religion and were healthy had a higher score in SOC as a whole. Fourth, the participants were divided into three groups by their SOC score, and their idea.; of successful aging were compared. The top- and middle-score groups considered satisfaction with family life to be more important, whereas the bottom-score group regarded the social status as more important.