• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.4
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• pp.2356-2363
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• 2015

This study deals the optimization design with the simulation based design of a coil spring inserted into the lock nut for preventing the screw thread from loosening at the bolted joint when the high-strength steel bolt with the property class of 10.9 is used and the screw torque of 640 to 800 (Nm) is applied. In this study, structural analysis of assembly composed of bolt, nut and coil spring is carried out to evaluate its safety factors on the basis of the equivalent stress with commercial finite element analysis software. And the design strategy to extract the design improvement from these simulation results is established. An iterative process performed with the proposed design strategy is also proposed for improving the performance of the existing design. At the proposed procedure, the feasible design parameters using response surface method are found, and then these parameters are verified to be optimal or not by comparing with the response values and the simulation results obtained from the feasible parameters.

• Computational Structural Engineering
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• v.10 no.2
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• pp.233-242
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• 1997

The main purpose of this paper is to investigate the dynamic optimal design of continuous beams. The computer-aided optimization technique is used to obtain the near-optimal parameters of continuous beam. The computer program is developed to obtain the natural frequency parameters and the forced vibration responses to a transit point load for the continuous beam with variable support spacing, mass and stiffness. The model test data is in good agreement with the computer calculation, which serves to validate the mathematical analysis. The optimization function to describe the design efficiency is defined as a linear combination of four dimensionless span characteristics; the maximum dynamic stress; the stress difference between span segments; the rms deflection under the transit point load; and the total span mass. Studies of three span beams show that the beam with near-optimal parameters can improve design efficiency when compared to a uniform beam with even spacing of the same total span length.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.8
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• pp.561-570
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• 2004

In this paper, new architectures and comprehensive design methodologies of Genetic Algorithms(GAs) based Genetically optimized Neurofuzzy Networks(GoNFN) are introduced, and a series of numeric experiments are carried out. The proposed GoNFN is based on the rule-based Neurofuzzy Networks(NFN) with the extended structure of the premise and the consequence parts of fuzzy rules being formed within the networks. The premise part of the fuzzy rules are designed by using space partitioning in terms of fuzzy sets defined in individual variables. In the consequence part of the fuzzy rules, three different forms of the regression polynomials such as constant, linear and quadratic are taken into consideration. The structure and parameters of the proposed GoNFN are optimized by GAs. GAs being a global optimization technique determines optimal parameters in a vast search space. But it cannot effectively avoid a large amount of time-consuming iteration because GAs finds optimal parameters by using a given space. To alleviate the problems, the dynamic search-based GAs is introduced to lead to rapidly optimal convergence over a limited region or a boundary condition. In a nutshell, the objective of this study is to develop a general design methodology o GAs-based GoNFN modeling, come up a logic-based structure of such model and propose a comprehensive evolutionary development environment in which the optimization of the model can be efficiently carried out both at the structural as well as parametric level for overall optimization by utilizing the separate or consecutive tuning technology. To evaluate the performance of the proposed GoNFN, the models are experimented with the use of several representative numerical examples.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.6
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• pp.415-424
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• 2004

In this paper, we introduce and investigate a new category of rule-based fuzzy inference system based on Information Granulation(IG). The proposed rule-based fuzzy modeling implements system structure and parameter identification in the efficient form of “If..., then...” statements, and exploits the theory of system optimization and fuzzy implication rules. The form of the fuzzy rules comes with three types of fuzzy inferences: a simplified one that involves conclusions that are fixed numeric values, a linear one where the conclusion part is viewed as a linear function of inputs, and a regression polynomial one as the extended type of the linear one. By the nature of the rule-based fuzzy systems, these fuzzy models are geared toward capturing relationships between information granules. The form of the information granules themselves becomes an important design features of the fuzzy model. Information granulation with the aid of HCM(Hard C-Means) clustering algorithm hell)s determine the initial parameters of rule-based fuzzy model such as the initial apexes of the membership functions and the initial values of polynomial function being used in the Premise and consequence Part of the fuzzy rules. And then the initial Parameters are tuned (adjusted) effectively with the aid of the improved complex method(ICM) and the standard least square method(LSM). In the sequel, the ICM and LSM lead to fine-tuning of the parameters of premise membership functions and consequent polynomial functions in the rules of fuzzy model. An aggregate objective function with a weighting factor is proposed in order to achieve a balance between performance of the fuzzy model. Numerical examples are included to evaluate the performance of the proposed model. They are also contrasted with the performance of the fuzzy models existing in the literature.

• Journal of the korean society of oceanography
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• v.32 no.4
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• pp.163-170
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• 1997

Some CMP gathers acquired from shallow marine seismic reflection survey in offshore Korea do not show the hyperbolic trend of moveout. It originated from so-called swell effect of source and streamer, which are towed under rough sea surface during the data acquisition. The observed time deviations of NMO-corrected traces can be entirely ascribed to the swell effect. To correct these time deviations, a residual statics is introduced using Genetic Algorithms (GA) into the swell correction. A new class of global optimization methods known as GA has recently been developed in the field of Artificial Intelligence and has a resemblance with the genetic evolution of biological systems. The basic idea in using GA as an optimization method is to represent a population of possible solutions or models in a chromosome-type encoding and manipulate these encoded models through simulated reproduction, crossover and mutation. GA parameters used in this paper are as follows: population size Q=40, probability of multiple-point crossover P$_c$=0.6, linear relationship of mutation probability P$_m$ from 0.002 to 0.004, and gray code representation are adopted. The number of the model participating in tournament selection (nt) is 3, and the number of expected copies desired for the best population member in the scaling of fitness is 1.5. With above parameters, an optimization run was iterated for 101 generations. The combination of above parameters are found to be optimal for the convergence of the algorithm. The resulting reflection events in every NMO-corrected CMP gather show good alignment and enhanced quality stack section.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.20 no.1
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• pp.9-18
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• 2007

This study shows an implementation of partial holes in an initial design domain in order to improve convergences of topology optimization algorithms. The method is associated with a bubble method as introduced by Eschenauer et al. to overcome slow convergence of boundary-based shape optimization methods. However, contrary to the bubble method, initial holes are only implemented for initializations of optimization algorithm in this approach, and there is no need to consider a characteristic function which defines hole's deposition during every optimization procedure. In addition, solid and void regions within the initial design domain are not fixed but merged or split during optimization Procedures. Since this phenomenon activates finite changes of design parameters without numerically calculating movements and positions of holes, convergences of topology optimization algorithm can be improved. In the present study, material topology optimization designs of Michell-type beam utilizing the initial design domain with initial holes of varied sizes and shapes is carried out by using SIMP like a density distribution method. Numerical examples demonstrate the efficiency and simplicity of the present method.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.43 no.6
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• pp.568-583
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• 2015

In this paper, the conceptual design procedure and results of Earth observation satellite through Multidisciplinary Design Optimization (MDO) are described. The conceptual design equations for major parameters are developed based on the established database of Earth observation satellite so far. The MDO conceptual design tool for Earth observation satellite was developed by applying the Collaborative Optimization (CO) architecture amongst several MDO architecture techniques available today. The objective for this research was set to minimize the total mass of satellite as well as satisfy all design constraints by utilizing the Sequential Quadratic Programming (SQP) algorithm. Eventually the effectiveness of MDO conceptual design tool was verified through proposing a comparison between the conceptual design results with MDO applied and the design specification of ASNARO-1 & IKONOS-2 Earth observation satellite.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.20 no.4
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• pp.390-395
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• 2009

Design and optimization of a broadband meander-line twist reflector was performed for X-band application. Based on the equivalent transmission line model, the polarization twist performance was evaluated. Genetic analysis, particles swarm, and hybrid swarm optimizations were employed to obtain the optimized geometrical parameters. The optimized design exhibits low cross-polarization level below - 25 dB between 8.45 and 11.38 GHz. The polarization twist loss was below 0.2 dB. Comparison between computed and simulated results was also discussed.

• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1406-1420
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• 2017

Artificial neural networks (ANNs) play an important role in the fields of function approximation, prediction, and classification. ANN performance is critically dependent on the input parameters, including the number of neurons in each layer, and the optimal values of weights and biases assigned to each neuron. In this study, we apply the particle swarm optimization method, a popular optimization algorithm for determining the optimal values of weights and biases for every neuron in different layers of the ANN. Several regression models, including general linear regression, Fourier regression, smoothing spline, and polynomial regression, are conducted to evaluate the proposed method's prediction power compared to multiple linear regression (MLR) methods. In addition, residual analysis is conducted to evaluate the optimized ANN accuracy for both training and test datasets. The experimental results demonstrate that the proposed method can effectively determine optimal values for neuron weights and biases, and high accuracy results are obtained for prediction applications. Evaluations of the proposed method reveal that it can be used for prediction and estimation purposes, with a high accuracy ratio, and the designed model provides a reliable technique for optimization. The simulation results show that the optimized ANN exhibits superior performance to MLR for prediction purposes.

• Advances in aircraft and spacecraft science
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• v.4 no.6
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• pp.701-709
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• 2017

One of the components that used in the satellite thermal control subsystem is the Mechanically Pumped Fluid Loop (MPFL) system; this system mostly used in geosynchronous orbit (GEO) satellites, and can transfer heat from a hot point to a cold point using the fluid which circulated in a closed loop. Heat radiates to the deep space at the cold plate to cool down the fluid temperature. In this research, the radiative heatexchanger (RHX) for a MPFL system is optimized. The genetic algorithm has been used for minimizing the total mass and pressure drop by considering a constant transferred heat rate at the heat exchanger. The optimization has been done in two cases. In case I, two parameters are considered as a goal function, so optimization is performed using NSGA-II method. Results of optimization are shown in the pareto diagram. In case II, the diameter of pipe is considered constant, so the optimized value for distances of the parallel pipes is obtained by using the genetic algorithm, in which the system has the least total mass. Results show that in the RHX, by increasing the pipe diameter, pressure drop decreases and total mass increases. Also by considering a constant value for pipe diameter, an optimum distance between pipes and pipe length are obtained in which the system has a minimum mass.