• Journal of the Korean Society for Precision Engineering
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• v.19 no.11
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• pp.192-199
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• 2002

This paper demonstrates that hybrid neural-genetic multimodel parameter estimation algorithm can be applied to structured system Identification of electro-hydraulic servo system. This algorithm are consist of a recurrent incremental credit assignment (ICRA) neural network and a genetic algorithm. The ICRA neural network evaluates each member of a generation of model and genetic algorithm produces new generation of model. We manufactured electro-hydraulic servo system and the hybrid neural-genetic multimodel parameter estimation algorithm is applied to the task to find the parameter values(mass, damping coefficient, bulk modulus, spring coefficient) which minimize total square error.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.6
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• pp.442-447
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• 2003

This paper demonstrates that a modified hybrid neural-genetic multimodel parameter estimation algorithm can be applied to structured system identification of an electro-hydraulic servo system. This algorithm is consists of a recurrent incremental credit assignment(ICRA) neural network and a genetic algorithm. The ICRA neural network evaluates each member of a generation of model and genetic algorithm produces new generation of model. The modified hybrid neural-genetic multimodel parameter estimation algorithm is applied to an electro-hydraulic servo system the task to find the parameter values such as mass, damping coefficient, bulk modulus, spring coefficient and disturbance, which minimizes the total square error.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.475-478
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• 2015

The objective of this study is to apply a hybrid model for estimating solar radiation and investigate their accuracy. A hybrid model is wavelet-based support vector machines (WSVMs). Wavelet decomposition is employed to decompose the solar radiation time series into approximation and detail components. These decomposed time series are then used as inputs of support vector machines (SVMs) modules in the WSVMs model. Results obtained indicate that WSVMs can successfully be used for the estimation of daily global solar radiation at Champaign and Springfield stations in Illinois.

• Journal of Korea Society of Industrial Information Systems
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• v.24 no.5
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• pp.53-64
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• 2019

The term 're-use' here signifies the re-use of end-of-life products without changing their form after they have been thoroughly inspected and cleaned. In the re-use network model, the distributor determines the product order quantity on the network through which new products are received from the suppliers or products are supplied to the customers through re-use of the recovered products. In this paper, we propose a reusable network model for reusable products that considers the total logistics cost from the forward logistics to the reverse logistics. We also propose a reusable network model that considers the processing and disposal costs for reuse in an optimal inventory management environment. The authors employe Genetic Algorithm (GA), which is one of the optimization techniques, to verify the validity of the proposed model. And in order to investigate the effect of the parameters on the solution, the priority-based GA (priGA) under three different parameters and the modified Hybrid GA (mhGA), in which parameters are adjusted for each generation, were applied to four examples with varying sizes in the simulation.

• Journal of Korea Society of Industrial Information Systems
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• v.26 no.1
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• pp.55-65
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• 2021

In this paper, a methodology for reinforcing reverse logistics (RL) activities in a closed-loop supply chain (CLSC) model is proposed. For the methodology, the activities of the recovery center (RC) which can be considered as one of the facilities in the RL are reinforced. By the reinforced activities in the RC, the recovered parts and products after checking and recovering processes of the returned product from customer can be reused in the forward logistics (FL) of the CLSC model. A mathematical formulation is suggested for representing the CLSC model with reinforced RL activities, and implemented using a hybrid genetic algorithm (HGA) approach. In numerical experiment, two different scales of the CLSC model are presented and the performance of the HGA approach is compared with those of some conventional approaches. The experimental results show that the former outperforms the latter in most of performance measures. The robustness of the CLSC model is also proved by regulating various rates of the recovered parts and products in the RC.

• Transactions of the Korean Society of Automotive Engineers
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• v.12 no.3
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• pp.44-50
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• 2004

The purpose of this study is to improve the prediction ability of the atomization and vaporization processes of GDI spray under high-pressure and high-temperature conditions. Several models have been introduced and compared. The atomization process was modeled using hybrid breakup model that is composed of Conical Sheet Disintegration (CSD) model and Aerodynamically Progressed TAB(APTAB) model. The vaporization process was modeled using Spalding model, modified Spalding model and Abramzon ＆ Sirignano model. Exciplex fluorescence method was used for comparing the calculated with the experimental results. The experiment and calculation were performed at the ambient pressure of 0.5 MPa and 1.0 MPa and the ambient temperature of 473k. Comparison of caldulated and experimental spray characteristics was carried out and Abramzon ＆ Sirignano model and modified Spalding model had the better prediction ability for vaporization process than Spalding model.

• Journal of Korea Society of Industrial Information Systems
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• v.26 no.3
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• pp.9-22
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• 2021

This study proposes a supply chain network (SCN) model considering supply disruption in assembly industry. For supply disruption, supplier disruption and its route disruption are simultaneously taken into consideration in the SCN model. With the simultaneous consideration, the SCN model can achieve its flexibility and efficiency. A mathematical formulation is suggested for representing the SCN model, and a proposed hybrid genetic algorithm (pro-HGA) is used for implementing the mathematical formulation. In numerical experiment, the performance of the pro-HGA approach is compared with those of some conventional approaches using the SCN models with various scales, and a sensitivity analysis considering the change of the numbers of suppliers and backup routes is done. Experimental results show that the performances of the pro-HGA approach are superior to those of the conventional approaches, and the flexibility and efficiency of the SCN model considering supply disruption are proved. Finally, the significance of this study is summarized and a potential future research direction is mentioned in conclusion.

• Proceedings of the Korean Society For Composite Materials Conference
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• 2005.04a
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• pp.76-79
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• 2005

Bending deformation and energy absorption characteristics of aluminum-composite hybrid tube beams have been analyzed for improvement in the bending performance of aluminum space frame by using experimental tests combined with theoretical and finite element analyses. Hybrid tube beams composed of glass fabric/epoxy layer wrapped around on aluminum tube were made in autoclave with the recommended curing cycle. Basic properties of aluminum material used for initial input data of the finite element simulation and theoretical analysis were obtained from the true stress-true strain curve of specimen which had bean extracted from the Al tube beam. A modified theoretical model was developed to predict the resistance to the collapse of hybrid tube beams subjected to a bending load. Theoretical moment-rotation angle curves of hybrid tube beams were in good agreement with experimental ones, which was comparable to the results obtained from finite element simulation. Hybrid tube beams strengthened by composite layer on the whole web and flange showed an excellent bending strength and energy absorption capability.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.6
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• pp.433-439
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• 2004

The Helicopter Simulator System is non-linear and complex. Futhermore, because of absence of its accurate mathematical model, it is difficult to control accurately its attitudes such as elevation angle and azimuth one. Therefore, we proposed a Hybrid GA-PID WAVENET(Genetic Algorithm Proportional Integral Derivative Wavelet Neural Network)control technique to control efficiently these angles. The proposed Hybrid GA-PID WAVENET is made through the following process. First, the WAVENET fundamental functions are defined. And their dilation and translation values are adjusted by GA to construct the optimal WAVENET controller. Secondly, the proportional, integral, and derivative gain coefficients of PR controller are tuned optimally. Finally, WAVENET controller which has a good transient characteristic and GA-PE controller which has a good steady state characteristic is adequately combined in hybrid type. Through the computer simulations, it is proved that the Hybrid GA-PE WAVENET control technique has a more excellent dynamic response than PID control technique and GA-PID one.

• Journal of Environmental Science International
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• v.24 no.8
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• pp.1023-1036
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• 2015

A reliable streamflow forecasting is essential for flood disaster prevention, reservoir operation, water supply and water resources management. This study proposes a hybrid model for river stage forecasting and investigates its accuracy. The proposed model is the wavelet packet-based artificial neural network(WPANN). Wavelet packet transform(WPT) module in WPANN model is employed to decompose an input time series into approximation and detail components. The decomposed time series are then used as inputs of artificial neural network(ANN) module in WPANN model. Based on model performance indexes, WPANN models are found to produce better efficiency than ANN model. WPANN-sym10 model yields the best performance among all other models. It is found that WPT improves the accuracy of ANN model. The results obtained from this study indicate that the conjunction of WPT and ANN can improve the efficiency of ANN model and can be a potential tool for forecasting river stage more accurately.