• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.2
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• pp.407-415
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• 2011

In this study, we propose the design of optimized fuzzy controller for the rotary inverted pendulum system by using differential evolution algorithm. The structure of the differential evolution algorithm has a simple structure and its convergence to optimal values is superb in comparison to other optimization algorithms. Also the differential evolution algorithm is easier to use because it have simpler mathematical operators and have much less computational time when compared with other optimization algorithms. The rotary inverted pendulum system is nonlinear and has a unstable motion. The objective is to control the position of the rotating arm and to make the pendulum to maintain the unstable equilibrium point at vertical position. The output performance of the proposed fuzzy controller is considered from the viewpoint of performance criteria such as overshoot, steady-state error, and settling time through simulation and practical experiment. From the result of both simulation and practical experiment, we evaluate and analyze the performance of the proposed optimal fuzzy controller from the comparison between PGAs and differential evolution algorithms. Also we show the superiority of the output performance as well as the characteristic of differential evolution algorithm.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.52 no.7
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• pp.424-424
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• 2003

Evolutionary design related to the optimal design of Polynomial Neural Networks (PNNs) structure for model identification of complex and nonlinear system is studied in this paper. The PNN structure is consisted of layers and nodes like conventional neural networks but is not fixed and can be changable according to the system environments. three types of polynomials such as linear, quadratic, and modified quadratic is used in each node that is connected with various kinds of multi-variable inputs. Inputs and order of polynomials in each node are very important element for the performance of model. In most cases these factors are decided by the background information and trial and error of designer. For the high reliability and good performance of the PNN, the factors must be decided according to a logical and systematic way. In the paper evolutionary algorithm is applied to choose the optimal input variables and order. Evolutionary (genetic) algorithm is a random search optimization technique. The evolved PNN with optimally chosen input variables and order is not fixed in advance but becomes fully optimized automatically during the identification process. Gas furnace and pH neutralization processes are used in conventional PNN version are modeled. It shows that the designed PNN architecture with evolutionary structure optimization can produce the model with higher accuracy than previous PNN and other works.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.5A
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• pp.399-406
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• 2010

In this paper, the optimization of frictional bearings that applied to improve the seismic performance of conventional bridges were conducted. The nonlinear dynamic analysis of steel bridges and concrete bridges are carried out with the El Centro and artificial earthquake motions, and the reponses of the bridges were optimized by genetic algorithm. The object functions were considered with two parameters, such as shear forces and displacements at bearing, and the optimum object functions were searched by varying the weighting factors of the two parameters. As results, in case of the steel bridges, the optimum results were obtained when larger weight factor was imposed to the shear force. However, in case of concrete bridges, larger weight factor was need to the displacement for optimum results.

• Nuclear Engineering and Technology
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• v.33 no.5
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• pp.547-555
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• 2001

In pressurized light water reactor fuel assembly, spacer grids support nuclear fuel rods both laterally and vertically. The fuel rods are supported by spacer grid springs and grid dimples that are located in the grid cell. The support system allows for some thermal expansion and imbalance of the fuel rods. The imbalance is absorbed by elastic energy to prevent coolant flow- induced vibration damage. Design requirements are defined and a design process is established. The design process includes mathematical optimization as well as practical design method. The shape of the grid spring is designed to maintain its function during the lifetime of the fuel assembly. A structural optimization method is employed for the shape design. Since the optimization is carried out in the linear range of finite element analysis, the optimum solution is verified by nonlinear analysis. A good design is found and the final design is compared with the initial conceptual design. Commercial codes are utilized for structural analysis and optimization.

• Journal of the Korean Society for Railway
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• v.13 no.2
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• pp.222-228
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• 2010

In this paper, we propose a optimization approach for the Electric Locomotive Overhaul Maintenance Facility (ELOMF), which aims at the simulation optimization so as to meet the design specification. In simulation design, we consider the critical path and sensitivity analysis of the critical (dependent) factors and the design (independent) parameters for the parameter selection and reduction of the metamodel. Therefore, we construct the multi-objective non-linear programming. The objective function is normalized for the generalization of design parameter while the constraints are composed of the simulation-based regression metamodel for the critical factors and design factor's domain. Then the effective solution procedure based on the pareto optimal solution set is proposed. This approach provides a comprehensive approach for the optimization of Train Overhaul Maintenance Facility(TOMF)'s design parameters using the simulation and metamoels.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.36 no.5
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• pp.438-447
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• 2008

This study is focused on reliability based design optimization (RBDO) using moving least squares. A response surface is used to derive a limit-state equation for reliability based design optimization. Response surface method (RSM) with least square method (LSM) or Kriging will be used as a response surface. RSM is fast to make the response surface. On the other hand, RSM has disadvantage to make the response surface of nonlinear equation. Kriging can make the response surface in nonlinear equation precisely but needs considerable amount of computations. The moving least square method (MLSM) is made of both methods (RSM with LSM+Kriging). Numerical results by MLSM are compared with those by LMS in Rosenbrock function and six-hump carmel back function. The RBDO of engine duct of smart UAV is pursued in this paper. It is proved that RBDO is useful tool for aerospace structural optimal design problems.

• Structural Engineering and Mechanics
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• v.31 no.1
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• pp.11-24
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• 2009

In China, the oil and natural gas resources of Bohai Bay are mainly marginal oil fields. It is necessary to build both ice-resistant and economical offshore platforms. However, risk is involved in the design, construction, utilization, maintenance of offshore platforms as uncertain events may occur within the life-cycle of a platform under the extreme ice load. In this study, the optimum design model of the expected life-cycle cost for ice-resistant platforms based on cost-effectiveness criterion is proposed. Multiple performance demands of the structure, facilities and crew members, associated with the failure assessment criteria and evaluation functions of costs of construction, consequences of structural failure modes including damage, revenue loss, death and injury as well as discounting cost over time are considered. An efficient approximate method of the global reliability analysis for the offshore platforms is provided, which converts the implicit nonlinear performance function in the conventional reliability analysis to linear explicit one. The proposed life-cycle optimum design formula are applied to a typical ice-resistant platform in Bohai Bay, and the results demonstrate that the life-cycle cost-effective optimum design model is more rational compared to the conventional design.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.23 no.5
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• pp.456-464
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• 2014

Commercial vehicle drivers typically feel more fatigued compared to general-public drivers. because they spend longer periods of time driving and experience more rough road conditions. This study showed that the application of a magnet, a linear spring, and a seat suspension with nonlinear characteristics was the optimal design to increase comfort while driving. The resonant frequency for the optimal design suspension was 2.8 Hz, and the stiffness was analyzed through displacement-load experiments. Vibration transmissibility was analyzed by suspension stiffness and the existing dynamic compression. The magnetic spring type was at 0.875. As a result, the X-type magnetic spring performed better than the existing spring at 0.729.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.3
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• pp.145-156
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• 2000

In this paper, we propose a new methodology which includes the optimal design procedure of Polynomial Neural Networks(PNN) structure for model identification of complex and nonlinear system. The proposed PNN algorithm is based on GMDA(Group Method of Data handling) method and its structure is similar to Neural Networks. But the structure of PNN is not fixed like in conventional Neural Networks and can be generated. The each node of PNN structure uses several types of high-order polynomial such as linear, quadratic and cubic, and is connected as various kinds of multi-variable inputs. In other words, the PNN uses high-order polynomial as extended type besides quadratic polynomial used in GMDH, and the number of input of its node in each layer depends on that of variables used in the polynomial. The design procedure to obtain an optimal model structure utilizing PNN algorithm is shown in each stage. The study is illustrated with the aid of pH neutralization process data besides representative time series data for gas furnace process used widely for performance comparison, and shows that the proposed PNN algorithm can produce the model with higher accuracy than previous other works. And performance index related to approximation and prediction capabilities of model is evaluated and also discussed.

• Journal of Korea Water Resources Association
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• v.32 no.1
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• pp.71-81
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• 1999

The objective of this study is to develop a model which can design an optimal pipe network system of least cost while satisfying all the design constraints including hydraulic constraints using a genetic algorithm technique. Hydraulic constraints interfaced with the simulation program(KYPIPE) checked feasible solution region. Genetic algorithm(GA) technique is a relatively new optimization technique. The GA is known as a very powerful search and optimization technique especially when solving nonlinear programming problems. The model developed in this study selects optimal pipe diameters in the form of commercial discrete sizes using the pipe diameters and the pumping powers as decision variables. The model not only determines the optimal diameters and pumping powers of pipe network system but also satisfies the discharge and pressure requirements at demanding nodes. The model has been applied to an imaginary and an existing pipe network systems. One system is adopted from journal papers which has been used as an example network by many other researchers. Comparison of the results shows compatibility of the model developed in this study. The model is also applied to a system in Goyang city in order to check the model applicability to finding of optimal pumping powers. It has been found that the developed model can be successfully applied to optimal design of pipe network systems in a relatively simple manner.