• Structural Engineering and Mechanics
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• 제47권4호
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• pp.495-511
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• 2013

In this paper, a new efficient optimization algorithm called Teaching-Learning-Based Optimization (TLBO) is used for the least weight design of trusses with continuous design variables. The TLBO algorithm is based on the effect of the influence of a teacher on the output of learners in a class. Several truss structures are analyzed to show the efficiency of the TLBO algorithm and the results are compared with those reported in the literature. It is concluded that the TLBO algorithm presented in this study can be effectively used in the weight minimization of truss structures.

• Communications for Statistical Applications and Methods
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• 제6권1호
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• pp.53-68
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• 1999

To improve the slow convergence property of the steepest ascent type algorithm for continuous D-optimal design problems. we develop a new algorithm. We apply the nonlinear system of equations as the necessary condition of optimality and develop the two-point algorithm that solves the problem of clustering. Because of the nature of the steepest coordinate ascent algorithm avoiding the problem of clustering itself helps the improvement of convergence speed. The numerical examples show the performances of the new method is better than those of various steepest ascent algorithms.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 II
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• pp.1157-1160
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• 2003

We proposed a study on optimal clock period selection algorithm for low power RTL design. The proposed algorithm use the way of maintaining the throughput by reducing supply voltage after improve the system performance in order to minimize the power consumption. In this paper, it select the low power to use pipeline in the transformation of architecture. Also, the algorithm is important the clock period selection in order to maximize the resource sharing. however, it execute the optimal clock period selection algorithm.

• 한국CDE학회논문집
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• 제6권2호
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• pp.125-132
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• 2001

An algorithm for generating all hexahedral meshes for three dimensional objects has been presented. This algorithm is based on the sweeping and the grafting method. In sweeping process internal nodes generating method has been modified by employing the distances between nodes on connecting surfaces and on source surfaces. In addition to the sweeping processes grafting algorithm is also modified to obtain more effective meshes by refining elements near grafting surfaces. With this method two and a half dimensional hexahedral meshes for three dimensional objects can be generated effectively. Sample meshes are constructed to demonstrate the mesh generating capability of the proposed algorithm.

• Communications for Statistical Applications and Methods
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• 제7권1호
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• pp.357-370
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• 2000

This study has adapted a genetic algorithm for an optimal design for the first time. the models that was used a simulation are the first and second order response surfaces model, Using an genetic algorithm in D-opimal it is more efficient than previous algorithms to get an object function. Not like other algorithm without any restrictions like troublesome about the initial solution not falling into a local optimal solution it's the most suitable algorithm.

• 한국CDE학회논문집
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• 제7권4호
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• pp.248-253
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• 2002

Delaunay triangulation is well balanced in the sense that the triangles tend toward equiangularity. And so, Delaunay triangulation hasn't some slivers triangle. It's commonly used in various field of CAD applications, such as reverse engineering, shape reconstruction, solid modeling and volume rendering. For Example, In this paper, an improved Delaunay triangulation is proposed in 2-dimensions. The suggested algorithm subdivides a uniform grids into sub-quad grids, and so efficient where points are nonuniform distribution. To get the mate from quad-subdivision algorithm, the area where triangulation-patch will be most likely created should be searched first.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1993년도 하계학술대회 논문집 A
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• pp.341-344
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• 1993

This paper presents the general MRAC algorithm design, it's real time implementation and investigates the effect of purely deterministic disturbances to adaptive control algorithm. The design of adaptive control algorithm to reject the disturbances properly is also presented. In real time application, adaptive control algorithm is considered to investigate its performance by using DC motor. Disturbance rejection algorithm is investigated in simulation.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 1997년도 추계학술대회논문집; 한국과학기술회관; 6 Nov. 1997
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• pp.184-189
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• 1997

This paper introduces a new combined method by genetic algorithm and random tabu search method as optimization algorithm. Genetic algorithm can search the global optimum and tabu search method is very fast in speed. The optimizing ability of new combined method is identified by comparing other optimizing algorithm and used for optimum design of damping plate.

• International Journal of Control, Automation, and Systems
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• 제3권3호
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• pp.403-410
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• 2005

Nowadays, versatile robots are developed around the world. Novel algorithms are needed for controlling such robots. A 2-Layer fuzzy controller can deal with many inputs as well as many outputs, and its overall structure is much simpler than that of a general fuzzy controller. The main problem encountered in fuzzy control is the design of the fuzzy controller. In this paper, the fuzzy controller is designed by the schema co-evolutionary algorithm. This algorithm can quickly and easily find a global solution. Therefore, the schema co-evolutionary algorithm is used to design a 2-layer fuzzy controller in this study. We apply it to a mobile robot and verify the efficacy of the 2-layer fuzzy controller and the schema co-evolutionary algorithm through the experiments.

• 전기학회논문지
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• 제57권3호
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• pp.446-452
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• 2008

To design a spiral inductor a genetic algorithm is applied with fast computing technique. For the inductance extraction of the given geometry the fast multipole method is used, also the distributed computing technique using 10 personal computers is introduced for the massive computation of the genetic algorithm. A few important design parameters are used as genes for the optimization in the genetic algorithm. The target function is chosen as mean square error of the inductance at several sampling frequency points. A large-scaled inductor is fabricated and compared with the simulated data.