• 한국전산구조공학회논문집
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• 제21권4호
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• pp.317-324
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• 2008

대형 구조물에 대한 최적설계를 고려할 때 구조해석에 많은 시간과 노력이 소비된다. 한대의 개인용 컴퓨터에 의한 대형 구조물의 구조해석은 대용량의 기억장치와 많은 계산시간이 요구되므로 반복적 해석이 필요한 대형 구조물의 설계에 효율적으로 이용되기 어렵다. 따라서 본 논문에서는 이러한 문제의 대안으로 인터넷이 연결된 다수의 개인용 컴퓨터들로 고성능 병렬연산시스템을 구성하여 구조해석을 분산 처리하여 계산시간을 절감하였다. 아울러 반응표면의 근사를 위해 요구되는 구조해석을 상용 구조해석 어플리케이션으로 해결할 수 있다면 상용성이 확보되어 일반 구조물에 대하여도 반응표면법을 이용한 최적설계를 수행할 수 있을 것이다.

• Wind and Structures
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• 제14권6호
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• pp.559-582
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• 2011

This paper presents an integrated wind-induced dynamic analysis and computer-based design optimization technique for minimizing the structural cost of general tall buildings subject to static and dynamic serviceability design criteria. Once the wind-induced dynamic response of a tall building structure is accurately determined and the optimal serviceability design problem is explicitly formulated, a rigorously derived Optimality Criteria (OC) method is to be developed to achieve the optimal distribution of element stiffness of the structural system satisfying the wind-induced drift and acceleration design constraints. The effectiveness and practicality of the optimal design technique are illustrated by a full-scale 60-story building with complex 3D mode shapes. Both peak resultant acceleration criteria and frequency dependent modal acceleration criteria are considered and their influences on the optimization results are highlighted. Results have shown that the use of various acceleration criteria has different implications in the habitability evaluations and subsequently different optimal design solutions. The computer based optimization technique provides a powerful tool for the lateral drift and occupant comfort design of tall building structures.

• Journal of Electrical Engineering and Technology
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• 제12권2호
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• pp.911-917
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• 2017

There are generally three folds when developing neural network classifiers. They are as follows: 1) discriminant function; 2) lots of parameters in the design of classifier; and 3) high dimensional training data. Along with this viewpoint, we propose space search optimized polynomial neural network classifier (PNNC) with the aid of data preprocessing technique and simultaneous tuning strategy, which is a balance optimization strategy used in the design of PNNC when running space search optimization. Unlike the conventional probabilistic neural network classifier, the proposed neural network classifier adopts two type of polynomials for developing discriminant functions. The overall optimization of PNNC is realized with the aid of so-called structure optimization and parameter optimization with the use of simultaneous tuning strategy. Space search optimization algorithm is considered as a optimize vehicle to help the implement both structure and parameter optimization in the construction of PNNC. Furthermore, principal component analysis and linear discriminate analysis are selected as the data preprocessing techniques for PNNC. Experimental results show that the proposed neural network classifier obtains better performance in comparison with some other well-known classifiers in terms of accuracy classification rate.

• Smart Structures and Systems
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• 제22권4호
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• pp.469-479
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• 2018

The present research study utilizes a multi-objective optimization method for Pareto optimization of an eight-degree of freedom full vehicle vibration model, adopting a non-dominated sorting genetic algorithm II (NSGA-II). In this research, a full set of ride comfort as well as ride safety parameters are considered as objective functions. These objective functions are divided in to two groups (ride comfort group and ride safety group) where the ones in one group are in conflict with those in the other. Also, in this research, a special optimizing technique and combinational method consisting of weighted sum method and Pareto optimization are applied to transform Pareto double-objective optimization to Pareto full-objective optimization which can simultaneously minimize all objectives. Using this technique, the full set of ride parameters of three dimensional vehicle model are minimizing simultaneously. In derived Pareto front, unique trade-off design points can selected which are non-dominated solutions of optimizing the weighted sum comfort parameters versus weighted sum safety parameters. The comparison of the obtained results with those reported in the literature, demonstrates the distinction and comprehensiveness of the results arrived in the present study.

• Structural Engineering and Mechanics
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• 제47권4호
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• pp.513-530
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• 2013

In this paper, an improved version of particle swarm optimization based optimum design algorithm (IPSO) is presented for the steel grillage systems. The optimum design problem is formulated considering the provisions of American Institute of Steel Construction concerning Load and Resistance Factor Design. The optimum design algorithm selects the appropriate W-sections for the beams of the grillage system such that the design constraints are satisfied and the grillage weight is the minimum. When an improved version of the technique is extended to be implemented, the related results and convergence performance prove to be better than the simple particle swarm optimization algorithm and some other metaheuristic optimization techniques. The efficiency of different inertia weight parameters of the proposed algorithm is also numerically investigated considering a number of numerical grillage system examples.

• 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.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2001년도 춘계학술대회 논문집
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• pp.895-898
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• 2001

This paper presents an optimization cutting speed(OCS) program developed to improve the machining precision and tool life in high speed machining using ball end milling. This program optimized the cutting speed that is changing at any time in free surface machining of an automobile part like a connecting load die. The technique of optimization cutting speed makes the CAD/CAM-generated NC code go through a reverse post process, conducts cutting simulation, and obtain the effective tool diameter of the ball end milling. Then it changes the spindle revolution to within the range of critical cutting speed fit for the material of the workpieces depending upon the effective tool diameter. In this study, the machining precision and tool life were compared for the two connecting load dies processed using the general cutting method and the proposed optimization cutting speed technique, respectively.

• 한국공작기계학회논문집
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• 제13권6호
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• pp.107-113
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• 2004

This paper presents the pillar section optimization technique considering the reliability of the vehicle body structure consisted of complicated thin-walled panels. The response surface method is utilized to obtain the response surface models that describe the approximate performance functions representing the system characteristics on the section properties of the pillar and on the mass and the natural frequencies of the vehicle B.I.W. The reliability-based design optimization on the pillar sections Is performed and compared with the conventional deterministic optimization. The FORM is applied for the reliability analysis of the vehicle body structure. The developed optimization system is applied to the pillar section design considering the fundamental natural frequencies of passenger car body structure. By applying the proposed RBDO technique, it can be possible to optimize the pillar sections considering the reliability that engineers require.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.298-298
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• 2000

This paper presents the optimization technique to analyze the effect of the design parameters of rapid prototyping system for human brain model fabrication. The optimization method considers the functional relationships among the design parameters such as thickness gap, shrink rate, and laser speed that govern the operation of fabrication system. This paper applies a discrete optimization technique as the optimization method to determine the dominant parameter values. Additional study includes manner of complement surface image of ellipse which approximates the brain model using the adaptive slicing and the offset contour. According to the parameters tuning and interaction of effect, more suitable parameter values can be obtained by enhanced 3D brain model fabrication.

• 한국유체기계학회 논문집
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• 제12권4호
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• pp.44-53
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• 2009

Numerical design optimization of a fan-shaped hole for film-cooling has been carried out to improve film-cooling effectiveness by combining a three-dimensional Reynolds-averaged Navier-Stokes analysis with the radial basis neural network method, a well known surrogate modeling technique for optimization. The injection angle of hole, lateral expansion angle of hole and ratio of length-to-diameter of the hole are chosen as design variables and spatially averaged film-cooling effectiveness is considered as an objective function which is to be maximized. Twenty training points are obtained by Latin Hypercube sampling for three design variables. Sequential quadratic programming is used to search for the optimal point from the constructed surrogate. The film-cooling effectiveness has been successfully improved by the optimization with increased value of all design variables as compared to the reference geometry.