• Journal of the Korea Institute of Military Science and Technology
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• v.20 no.4
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• pp.566-578
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• 2017

For optimizing the operation plan when strike packages attack multiple targets, this article suggests a new mathematical model and a parallel hybrid genetic algorithm (PHGA) as a solution methodology. In the model, a package can assault multiple targets on a sortie and permitted the use of mixed munitions for a target. Furthermore, because the survival probability of a package depends on a flight route, it is formulated as a mixed integer programming which is synthesized the models for vehicle routing and weapon-target assignment. The hybrid strategy of the solution method (PHGA) is also implemented by the separation of functions of a GA and an exact solution method using ILOG CPLEX. The GA searches the flight routes of packages, and CPLEX assigns the munitions of a package to the targets on its way. The parallelism enhances the likelihood seeking the optimal solution via the collaboration among the HGAs.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.7
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• pp.339-349
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• 2001

This paper suggests an optimal identification method for complex and nonlinear system modeling that is based on Fuzzy-Neural Networks(FNN). The proposed Hybrid Identification Algorithm is based on Yamakawa's FNN and uses the simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. In this paper, the FNN modeling implements parameter identification using HCM algorithm and hybrid structure combined with two types of optimization theories for nonlinear systems. We use a HCM(Hard C-Means) clustering algorithm to find initial apexes of membership function. The parameters such as apexes of membership functions, learning rates, and momentum coefficients are adjusted using hybrid algorithm. The proposed hybrid identification algorithm is carried out using both a genetic algorithm and the improved complex method. Also, an aggregated objective function(performance index) with weighting factor is introduced to achieve a sound balance between approximation and generalization abilities of the model. According to the selection and adjustment of a weighting factor of an aggregate objective function which depends on the number of data and a certain degree of nonlinearity(distribution of I/O data), we show that it is available and effective to design an optimal FNN model structure with mutual balance and dependency between approximation and generalization abilities. To evaluate the performance of the proposed model, we use the time series data for gas furnace, the data of sewage treatment process and traffic route choice process.

• Advances in concrete construction
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• v.11 no.1
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• pp.19-32
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• 2021

In the present experimental study, the high performance hybrid fiber reinforced concrete (HPHFRC) is prepared using the Modified Andreasen and Andersen (A&A) particle packing model. Total of 16 trial mixes of HPHFRC with Indian standard sand (SS) and natural river sand (NS) are prepared to achieve the selection criteria (flow percent>150 and compressive strength>80 MPa). Based on the flow percent and compressive strength criteria, the selected mixes evaluated to study the effect of usage of natural river sand (NS) and the expensive Indian standard sand (SS) on the mechanical, durability, and microstructure property of designed HPHFRC. It has been found that the Modified A&A model is reliable to design the mix for HPHFRC with excellent mechanical, durability, and microstructure properties. In addition to that, a moderate difference in the mechanical and durability properties of NS contained HPHFRC and SS contained HPHFRC is found. Based on the obtained results of NS contained HPHFRC, it can be concluded that the use of natural river sand (NS) can be successfully adopted for the production of HPHFRC, resulted in a reduction of the production cost without compromising the excellent performance of HPHFRC.

• Computers and Concrete
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• v.8 no.5
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• pp.597-616
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• 2011

With respect to rehabilitation, strengthening and retrofitting of existing and deteriorated columns in buildings and bridges, CFRP sheets have been found effective in enhancing the performance of existing RC columns by wrapping and bonding CFRP sheets externally around the concrete. Concrete columns and piers that are confined by both lateral steel reinforcement and CFRP are sometimes referred to as "hybrid" concrete columns. With the availability of experimental data on concrete columns confined by steel reinforcement and/or CFRP, the study presents modeling using artificial neural networks (ANNs) to predict the compressive strength of hybrid circular RC columns. The prediction of the ultimate confined compressive strength of RC columns is very important especially when this value is used in estimating the capacity of structures. The present ANN model used as parameters for the confining materials the lateral steel ratio (${\rho}_s$) and the FRP volumetric ratio (${\rho}_{FRP}$). The model gave good predictions for three types of confined columns: (a) columns confined with steel reinforcement only, (b) CFRP confined columns, and (c) hybrid columns confined by both steel and CFRP. The model may be used for predicting the compressive strength of existing circular RC columns confined with steel only that will be strengthened or retrofitted using CFRP.

• Journal of Satellite, Information and Communications
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• v.3 no.1
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• pp.1-7
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• 2008

This Paper studied the design and measured results of a flight model for Ku-Band Linearized Channel Amplifier (LCAMP) for communication satellite onboard system. All MMICs, i.e. Variable Gain Amplifier (VGA), Variable Voltage Attenuator (VVA) with analog/digital attenuator, Branch line Hybrid Coupler and Detector for Pre-distorter are fabricated using Thin-Film Hybrid process. The performance of the fabricated module is verified through Radio Frequency circuit simulations and electrical function test in space environment for flight model at 12.25 to 12.75 GHz.

• Journal of international Conference on Electrical Machines and Systems
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• v.1 no.2
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• pp.91-97
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• 2012

This paper presents a state space model of a two quadrant chopper and PWM inverter-fed Interior Permanent Magnet Synchronous Motor (IPMSM) drive system and its application to hybrid vehicles. The drive system has two different state equations for motoring and regenerating action. This paper presents a common state equation by using State Space Averaging method. Using this model of the IPMSM drive system, detailed simulation and controller design of the drive system, including PWM inverter switching, are given. The validity of this model and usefulness, according to a comparison among Maximum Torque/Ampere control, Maximum Torque/Flux control, and Maximum Efficiency optimization, are confirmed from simulation results.

• Advances in nano research
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• v.7 no.6
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• pp.419-429
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• 2019

The effect of an increasing percentage of nanofiller (glass cenosphere) with Glass/Epoxy hybrid composite curved panels modeled mathematically using the multiscale concept and subsequent numerical eigenvalues of different geometrical configurations (cylindrical, spherical, elliptical, hyperboloid and flat) predicted in this research article. The numerical model of Glass/Epoxy/Cenosphere is derived using the higher-order polynomial type of kinematic theory in association with isoparametric finite element technique. The multiscale mathematical model utilized for the customized computer code for the evaluation of the frequency data. The numerical model validation and consistency verified with experimental frequency data and convergence test including the experimental elastic properties. The experimental frequencies of the multiscale nano filler-reinforced composite are recorded through the impact hammer frequency test rig including CDAQ-9178 (National Instruments) and LABVIEW virtual programming. Finally, the nano cenosphere filler percentage and different design associated geometrical parameters on the natural frequency data of hybrid composite structural configurations are illustrated through a series of numerical examples.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.23 no.2
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• pp.123-137
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• 2010

The hybrid structural experiment decomposes a structure into independent substructures that can be tested or simulated. The substructures being tested or simulated may be distributed at different facilities of different locations, and are managed by the simulation coordinator. There exist interactions among the simulation coordinator and the substructures since they give and receive the commands and feedbacks during the experimental process. These interactions are described in this paper for an example hybrid structural experiment using the classes and objects in the Lehigh Model which is one of the data models for structural experiments. The simulation coordinator and the substructures have the objects for the interaction data files, and are linked together through the same types of the interface links. The objects for the interactions presented in this paper can be implemented in a consistent way, and be used for developing the computer system for the hybrid structural experiments.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.10
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• pp.1257-1264
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• 2018

This paper proposes Autoregressive Integrated Moving Average (ARIMA)-based forecasting algorithms using meteorological indices to predict seasonal peak load. First of all, this paper observes a seasonal pattern of the peak load that appears intensively in winter and summer, and generates ARIMA models to predict the peak load of summer and winter. In addition, this paper also proposes hybrid ARIMA-based models (ARIMA-Hybrid) using a discomfort index and a sensible temperature to enhance the conventional ARIMA model. To verify the proposed algorithm, both ARIMA and ARIMA-Hybrid models are developed based on peak load data obtained from 2006 to 2015 and their forecasting results are compared by using the peak load in 2016. The simulation result indicates that the proposed ARIMA-Hybrid models shows the relatively improved performance than the conventional ARIMA model.

• Steel and Composite Structures
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• v.44 no.3
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• pp.389-406
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• 2022

Recycling concrete construction waste is an encouraging step toward green and sustainable building. A lot of research has been done on recycled aggregate concretes (RACs), but not nearly as much has been done on concrete made with recycled aggregate. Recycled aggregate concrete, on the other hand, has been found to have a lower mechanical productivity compared to conventional one. Accurately estimating the mechanical behavior of the concrete samples is a most important scientific topic in civil, structural, and construction engineering. This may prevent the need for excess time and effort and lead to economic considerations because experimental studies are often time-consuming, costly, and troublous. This study presents a comprehensive data-mining-based model for predicting the splitting tensile strength of recycled aggregate concrete modified with glass fiber and silica fume. For this purpose, first, 168 splitting tensile strength tests under different conditions have been performed in the laboratory, then based on the different conditions of each experiment, some variables are considered as input parameters to predict the splitting tensile strength. Then, three hybrid models as GWO-RF, GWO-MLP, and GWO-SVR, were utilized for this purpose. The results showed that all developed GWO-based hybrid predicting models have good agreement with measured experimental results. Significantly, the GWO-RF model has the best accuracy based on the model performance assessment criteria for training and testing data.