• 대한기계학회논문집A
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• 제24권6호
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• pp.1408-1418
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• 2000

Gradient-based optimization methods are inefficient in applications which require expensive function evaluations, and useless in applications where objective and/or constraint functions are 'noisy' due to modeling and cumulative numerical inaccuracy since gradient evaluation results cannot be reliable. Moreover, it is difficult to be integrated with commercial analysis software, and they cannot be employed when only experimental analysis results are available. In this research an optimization program based on a response surface method has been developed to overcome the aforementioned difficulties. Various methods for design of experiments and new proposed approximation models are implemented in the program. The effectiveness of the optimization program is tested on several test problems and results are discussed.

• Structural Engineering and Mechanics
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• 제6권1호
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• pp.31-40
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• 1998

The research and applications of numerical methods of design optimization on structural dynamic behaviors are presented in this paper. The emphasis is focused on the dynamic design optimization of aerospace structures, particularly those composed of composite laminate and sandwich plates. The methods of design modeling, sensitivity analysis on structural dynamic responses, and the optimization solution approaches are presented. The numerical examples of sensitivity analysis and dynamic structural design optimization are given to demonstrate the effectiveness of the numerical methods.

• 대한기계학회논문집A
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• 제29권9호
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• pp.1243-1252
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• 2005

A current trend of design methodologies is to make engineers objectify or automate the decision-making process. Numerical optimization is an example of such technologies. However, in numerical optimization, the uncertainties are uncontrollable to efficiently objectify or automate the process. To better manage these uncertainties, the Taguchi method, reliability-based optimization and robust optimization are being used. To obtain the target performance with the maximum robustness is the main functional requirement of a mechanical system. In this research, a design procedure for global robust optimization is developed based on the kriging and global optimization approaches. The DACE modeling, known as the one of Kriging interpolation, is introduced to obtain the surrogate approximation model of the function. Robustness is determined by the DACE model to reduce real function calculations. The simulated annealing algorithm of global optimization methods is adopted to determine the global robust design of a surrogated model. As the postprocess, the first order second-moment approximation method is applied to refine the robust optimum. The mathematical problems and the MEMS design problem are investigated to show the validity of the proposed method.

• 한국자동차공학회논문집
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• 제10권3호
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• pp.209-217
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• 2002

This gaper discusses a multidisciplinary design optimization of the engine mounting system to improve the ride quality of a vehicle and to remove the possibility of the resonance between the powertrain system and vehicle systems. The driveline model attempts to support engine mount development by providing sufficient detail for design modification assessment in a modeling environment. Design variables used in this study are the locations, the angles and the stiffness of an engine mount system. The goal of the optimization is both decoupling the roll mode ova powertrain and minimizing the vibration transmitted to the vehicle including the powertrain, simultaneously. By applying forced vibration analysis for vehicle systems and mode decouple analysis for the engine mount system, it is shown that improved optimization result is obtained.

• 한국시뮬레이션학회논문지
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• 제4권2호
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• pp.69-78
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• 1995

Simulation is generally used for the performance analysis and optimization of manufacturing systems. Therefore in this paper using the simulation techniques we obtain the information about the efficiency improvement and the optimization. Because simulation optimization method is subjected to the applied field and environment the general simulation optimization method does not exist. So we do not take the fixed optimization procedure but suggest the alternative one which is modified for applied field. This procedure supplies the optimized simulation information and helps improve the productivity of Geared-Motor assembly line. In order to optimize the manufacturing system we use two simulation languages, FACTOR/AIM and SLAMSYSTEM. The former gives the abundant output information. The latter gives the flexibility in simulation modeling.

• 제어로봇시스템학회논문지
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• 제17권8호
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• pp.768-776
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• 2011

This paper considers a data fitting problem for rational function models using the LM (Levenberg-Marquardt) optimization method. Rational function models have various merits on representing a wide range of shapes and modeling complicated structures by polynomials of low degrees in both the numerator and denominator. However, rational functions are nonlinear in the parameter vector, thereby requiring nonlinear optimization methods to solve the fitting problem. In this paper, we propose a data fitting method for rational function models based on the LM algorithm which is renowned as an effective nonlinear optimization technique. Simulations show that the fitting results are robust against the measurement noises and uncertainties. The effectiveness of the proposed method is further demonstrated by the real application to a 3D depth camera calibration problem.

• Structural Engineering and Mechanics
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• 제52권4호
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• pp.739-753
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• 2014

There have been many packages that can be employed to analyze plane frames. However, because most structural analysis packages suffer from closeness of system, it is very difficult to integrate it with an optimization package. To overcome the difficulty, we proposed a possible alternative, DAMDO, which integrate Design, Analysis, Modeling, Definition, and Optimization phases into an integrative environment. The DAMDO methodology employs neural networks to integrate structural analysis package and optimization package so as not to need directly to integrate these two packages. The key problem of the DAMDO approach is how to generate a set of reasonable random designs in the first phase. According to the characteristics of optimized plane frames, we proposed the ratio variable approach to generate them. The empirical results show that the ratio variable approach can greatly improve the accuracy of the neural networks, and the plane frame optimization problems can be solved by the DAMDO methodology.

• International Journal of Aeronautical and Space Sciences
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• 제14권3호
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• pp.222-228
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• 2013

A jetfighter shape modeling method based on design features is researched, to improve the efficiency of shape modeling in the stage of conceptual aircraft design. The aircraft's general design features and shape parameters, including geometric and position parameters, are described. The coordinate systems of the entire aircraft and its components are defined. As a sample of local shape, a method of inlet intake modeling is introduced. The whole process of the modeling method is proposed. Three examples of different jetfighters are listed, to describe the achievement of basic layout, which includes four main elements. The Fusion of Components can be achieved by regulating the details of the sections of the fuselage. Sample Cases of typical layouts are shown to verify the effectiveness of the proposed method, which provides the basis for further analysis and optimization.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.421-426
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• 2005

Fuzzy logic, neural network, fuzzy-neural network play an important as the key technology of linguistic modeling for intelligent control and decision making in complex systems. The fuzzy-neural network (FNN) learning represents one of the most effective algorithms to build such linguistic models. This paper proposes particle swarm optimization algorithm based optimal learning fuzzy-neural network (PSOA-FNN). The proposed learning scheme is the fuzzy-neural network structure which can handle linguistic knowledge as tuning membership function of fuzzy logic by particle swarm optimization algorithm. The learning algorithm of the PSOA-FNN is composed of two phases. The first phase is to find the initial membership functions of the fuzzy neural network model. In the second phase, particle swarm optimization algorithm is used for tuning of membership functions of the proposed model.

• 한국멀티미디어학회논문지
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• 제11권12호
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• pp.1716-1721
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• 2008

Currently, Wireless Networks have some nice characteristics such as multi-hop, multi-channel, multi-radio, etc but these kinds of resources are not fully used. The most difficulty to solve this issue is to solve mixed integer optimization. This paper proposes a method to solve a class of mixed integer optimization for wireless networks by using AMPL modeling language with CPLEX solver. The result of method is scheduling and congestion control in multi-channel multi-radio wireless networks.