• 소성∙가공
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• 제5권4호
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• pp.269-280
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• 1996

A new approach to die shape optimal design in extrusion is presented. The approach consists of a FEM analysis model to predict the value of the objective function a design model to relate the die profile with the design variables and a genetic algorithm based optimaization procedure. The approach was described in detail with emphasis on our modified micro genetic algorithm. Comparison with theoretical solutions was made to examine the validity of the predicted optimal die shapes. The approach was then applied to revealing the optimal die shapes with regard to various objective functions including those for which the design sensitivities can not be deter-mined analytically.

• Journal of Animal Science and Technology
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• 제48권2호
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• pp.161-168
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• 2006

Genetic parameters for linear type and composite traits were estimated by using Bayesian inference via Gibbs sampling with a multiple threshold animal model in Holstein cows. Fifteen linear type traits and 5 composite traits were included to estimate genetic variance and covariance components in the model. In this study, 30,204 records were obtained in the cows from 305 sires. Heritability estimates for linear type traits had the estimates as high as 0.28~0.64. Heritability estimates for composite traits were also high, when the traits were assumed to be categorical traits. Final score was more correlated with the composite traits than with the linear type traits.

• Journal of Information Processing Systems
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• 제16권5호
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• pp.1169-1182
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• 2020

The ant colony optimization (ACO) algorithm is a classical metaheuristic optimization algorithm. However, the conventional ACO was liable to trap in the local minimum and has an inherent slow rate of convergence. In this work, we propose a novel combinatorial ACO algorithm (CG-ACO) to alleviate these limitations. The genetic algorithm and the cloud model were embedded into the ACO to find better initial solutions and the optimal parameters. In the experiment section, we compared CG-ACO with the state-of-the-art methods and discussed the parameter stability of CG-ACO. The experiment results showed that the CG-ACO achieved better performance than ACO_R, simple genetic algorithm (SGA), CQPSO and CAFSA and was more likely to reach the global optimal solution.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제5권3호
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• pp.253-258
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• 2005

In this study, we introduce a new category of fuzzy inference systems based on information granulation to carry out the model identification of complex and nonlinear systems. Informal speaking, information granules are viewed as linked collections of objects (data, in particular) drawn together by the criteria of proximity, similarity, or functionality. To identify the structure of fuzzy rules we use genetic algorithms (GAs). Granulation of information with the aid of Hard C-Means (HCM) clustering algorithm help determine the initial parameters of fuzzy model such as the initial apexes of the membership functions and the initial values of polynomial functions being used in the premise and consequence part of the fuzzy rules. And the initial parameters are tuned effectively with the aid of the genetic algorithms and the least square method (LSM). The proposed model is contrasted with the performance of the conventional fuzzy models in the literature.

• 대한안전경영과학회지
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• 제9권4호
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• pp.129-136
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• 2007

This paper considers the sequencing of products in mixed model assembly lines under Just-In-Time (JIT) systems. Under JIT systems, the most important goal for the sequencing problem is to keep a constant rate of usage every part used by the systems. The sequencing problem is solved using Genetic Algorithm Genetic Algorithm is a heuristic method which can provide a near optimal solution in real time. The performance of proposed technique is compared with existing heuristic methods in terms of solution quality. Various examples are presented and experimental results are reported to demonstrate the efficiency of the technique.

• 한국시뮬레이션학회논문지
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• 제12권1호
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• pp.59-71
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• 2003

Recently, a multi facility, multi product and multi period industrial problem has been widely investigated in Supply Chain Management (SCM). One of the key issues in the current SCM research area involves reducing both production and distribution costs. The purpose of this study is to determine the optimum quantity of production and transportation with minimum cost in the supply chain network. We have presented a mathematical model that deals with real world factors and constraints. Considering the complexity of solving such model, we have applied the genetic algorithm approach for solving this model using a commercial genetic algorithm based optimizer. The results for computational experiments show that the real size problems we encountered can be solved in reasonable time.

• 반도체디스플레이기술학회지
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• 제10권2호
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• pp.25-30
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• 2011

This paper presents a tracking vibration estimation system of the track-following system using a tracking loop gain adjustment algorithm and a genetic algorithm. The algorithms are introduced to estimate accurately the tracking vibration quantity in spite of the uncertainties of the tracking actuator. An estimated actuator model can be found by applying a genetic algorithm. Accordingly, the tracking vibration quantity can be estimated from the measured tracking error, the tracking controller and the estimated actuator model. The proposed tracking vibration estimation method is applied to the track-following system of an optical recording device and is evaluated through the experimental result.

• 전기학회논문지
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• 제62권2호
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• pp.177-183
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• 2013

We describe a new method for selecting design variables for shape optimization of high-voltage gas circuit breaker using a Kriging meta-model and a genetic algorithm. Firstly we sample balance design variables using the Latin Hypercube Sampling. Secondly, we build meta-model using the Kriging. Thirdly, we search the optimal design variables using a genetic algorithm. To obtain the more exact design variable, we adopt the boundary shifting method. With the proposed optimization frame, we can get the improved interruption design and reduce the design time by 80%. We applied the proposed method to the optimization of multivariate optimization problems as well as shape optimization of a high - voltage gas circuit breaker.

• Design & Manufacturing
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• 제17권3호
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• pp.41-47
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• 2023

This paper presents the development of a controller that can control the temperature of an heating system based on a thermoelectric module. Temperature controller using Peltier has various external factors such as external temperature, characteristics of an aluminum plate, installation location of temperature sensors, and combination method between the aluminum plate and heating element. Therefore, it is difficult to apply the simulation and simulation results of heating system using Peltier at control algorithm. In general, almost temperature controller is using PID algorithm that finds control gain value heuristically. In this paper, it is proposed mathematical model that explain correlate between the temperature of the heating system and input voltage. And then, optimal parameter of estimated thermal model of the aluminum plate are searched by using genetic algorithm. In addition, based on this estimated model, the optimal PID control gain are inferred using a genetic algorithm. All of the sequence are simulated and verified with proposed real system.

• Asian-Australasian Journal of Animal Sciences
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• 제19권12호
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• pp.1678-1684
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• 2006

The objectives of this study were to estimate genetic parameters and sire breeding values for average daily gain (ADG) and carcass traits using sire-maternal grandsire model with REML approach, sire model with REML approach, sire model without relationships among sires and with REML and ANOVA approach, and to investigate advantages and disadvantages of these methods. Data were collected from 42,325 Japanese Black steers and heifers finished and slaughtered from 1991 to 2004. Traits analyzed in this study were average daily gain (ADG) during the fattening period, live weight at slaughter (LW), cold carcass weight (CW), estimated lean yield percentage (LYE), longissimus muscle area (LMA), subcutaneous fat thickness (SFT), rib thickness (RT), and marbling score (BMS). Bivariate analyses were also performed to obtain genetic and phenotypic correlation coefficients among traits. Estimated breeding values were obtained from each model, and simple and rank correlations among breeding values from each model were calculated. Estimates of heritability using the four models ranged from 0.25 to 0.31 in ADG, from 0.21 to 0.24 in LW, from 0.23 to 0.27 in CW, from 0.10 to 0.17 in DP, from 0.40 to 0.42 in LYE, from 0.19 to 0.31 in LMA, from 0.31 to 0.34 in SFT, from 0.26 to 0.33 in RT, and from 0.18 to 0.44 in BMS. The differences in heritability estimates using the four models seemed to be feasible in ADG, CW, DP, LMA, RT, and BMS. Genetic correlation coefficients of ADG with CW, SFT, RT and BMS were moderate to high and positive while the genetic correlation coefficients between ADG and LYE was low and negative. Correlation coefficients of BMS with SFT were negligible for both genetic and phenotypic correlations. The correlations of estimates evaluated from sire models with those from sire-maternal grandsire model were not large enough to convincing that breeding values using a sire model were corresponding to those using a sire-maternal grand sire model. If information of maternal grand sires are not available, the sire model with incomplete pedigree information included only sire of sire (Model 2) is optimal among the sire models evaluated in this study.