• Geomechanics and Engineering
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• v.20 no.3
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• pp.191-205
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• 2020

Soil shear strength parameters play a remarkable role in designing geotechnical structures such as retaining wall and dam. This study puts an effort to propose two accurate and practical predictive models of soil shear strength parameters via hybrid artificial neural network (ANN)-based models namely genetic algorithm (GA)-ANN and particle swarm optimization (PSO)-ANN. To reach the aim of this study, a series of consolidated undrained Triaxial tests were conducted to survey inherent strength increase due to addition of polypropylene fibers to sandy soil. Fiber material with different lengths and percentages were considered to be mixed with sandy soil to evaluate cohesion (as one of shear strength parameter) values. The obtained results from laboratory tests showed that fiber percentage, fiber length, deviator stress and pore water pressure have a significant impact on cohesion values and due to that, these parameters were selected as model inputs. Many GA-ANN and PSO-ANN models were constructed based on the most effective parameters of these models. Based on the simulation results and the computed indices' values, it is observed that the developed GA-ANN model with training and testing coefficient of determination values of 0.957 and 0.950, respectively, performs better than the proposed PSO-ANN model giving coefficient of determination values of 0.938 and 0.943 for training and testing sets, respectively. Therefore, GA-ANN can provide a new applicable model to effectively predict cohesion of fiber-reinforced sandy soil.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.39 no.5
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• pp.435-441
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• 2015

In this study, a method for optimal design of impeller and diffuser blades in the centrifugal compressor using response surface method (RSM) and multi-objective genetic algorithm (MOGA) was evaluated. A numerical simulation was conducted using ANSYS CFX with various values of impeller and diffuser parameters, which consist of leading edge (LE) angle, trailing edge (TE) angle, and blade thickness. Each of the parameters was divided into three levels. A total of 45 design points were planned using central composite design (CCD), which is one of the design of experiment (DOE) techniques. Response surfaces that were generated on the basis of the results of DOE were used to determine the optimal shape of impeller and diffuser blade. The entire process of optimization was conducted using ANSYS Design Xplorer (DX). Through the optimization, isentropic efficiency and pressure recovery coefficient, which are the main performance parameters of the centrifugal compressor, were increased by 0.3 and 5, respectively.

• Journal of Navigation and Port Research
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• v.44 no.3
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• pp.227-234
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• 2020

One of the most important operational goals in container terminals is to maximize the quay side productivity by minimizing the turnaround times of the vessels, for which the operations of the quay cranes (QC) to load/unload containers onto/from the vessels should be conducted efficiently without delays. This paper suggests using a policy-based dispatching method for YTs (Yard Tractor) that deliver containers between QCs and the storage yard. The goal of using such a dispatching policy is to maximize the efficiency of the YT operation and accordingly to minimize the QC delays because of late arrivals of the YTs. In particular, in this paper, we modified the previously proposed policy for its application to real container terminal and verified the effect through simulation experiments using real terminal data.

• Proceedings of the KIEE Conference
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• 2004.11b
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• pp.190-193
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• 2004

Optimal Power Flow (OPF) is becoming more and more important in the deregulation environment of power pool and there is an urgent need of faster solution technique for on-line application. So this paper presents parallel genetic algorithm-tap search for the solution of the OPF. The control variables modeled unit active power outputs, generator-bus voltage magnitudes and transformer-tap settings. A number of functional operating constraints, such as branch flow limits, load bus boltage magnitude limits and generator reactive capabilities are included as penalties in the fitness function. In parallel GA-TS, GA operators are executed for each process. If best fitness of the GA is not changed for several generations, TS operators are executed for the upper three populations to enhance the local searching capabilities. With migration operation, best string of each node is transferred to the neighboring node after predetermined iterations are executed. For parallel computing, we developed a PC-cluster system consisting of 8 PCs. Each PC employs the 2 GHz Pentium IV CPU and is connected with others through ethernet switch based fast ethernet. To show the usefulness of the proposed method, developed algorithm has been tested and compared on an IEEE 30-bus system in the reference paper. From the simulation results, we can find that the proposed algorithm is efficient for the OPF.

• Journal of the Korean Society of Propulsion Engineers
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• v.17 no.4
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• pp.10-15
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• 2013

For design optimization, engineers should require the accurate information of design space and then explore the design space and carry out optimization. Recently, the total design framework, based on design of experiments and optimization, is widely used in industry areas to explore the design space above all. For optimizing turbofan engine design point, the response surface model is constructed by using the 7 level orthogonal array which satisfies the statistical uniformity and orthogonality and gets the dense design space information. The multi-objective genetic algorithm is used to find the optimal solution within the given constraints for finding global optimal one in response surface model. The optimal solution from response surface model is verified with GasTurb simulation result.

• Membrane and Water Treatment
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• v.10 no.1
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• pp.75-82
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• 2019

In a highly urbanized area, land availability is limited for the installation of space consuming stormwater systems for best management practices (BMPs), leading to the consideration of underground stormwater treatment devices connected to the stormwater pipe system. The configuration of a stormwater pipe network determines the hydrological and pollutant transport characteristics of the stormwater discharged through the pipe network, and thus should be an important design consideration for effective management of stormwater quantity and quality. This article presents a multi-objective optimization approach for designing a stormwater pipe network with on-line stormwater treatment devices to achieve an optimal trade-off between the total installation cost and the annual removal efficiency of total suspended solids (TSS). The Non-dominated Sorted Genetic Algorithm-II (NSGA-II) was adapted to solve the multi-objective optimization problem. The study site used to demonstrate the developed approach was a commercial area that has an existing pipe network with eight outfalls into an adjacent stream in Yongin City, South Korea. The stormwater management model (SWMM) was calibrated based on the data obtained from a subcatchment within the study area and was further used to simulate the flow rates and TSS discharge rates through a given pipe network for the entire study area. In the simulation, an underground stormwater treatment device was assumed to be installed at each outfall and sized proportional to the average flow rate at the outfall. The total installation cost for the pipes and underground devices was estimated based on empirical formulas using the flow rates and TSS discharge rates simulated by the SWMM. In the demonstration example, the installation cost could be reduced by up to 9% while the annual TSS removal efficiency could be increased by 4% compared to the original pipe network configuration. The annual TSS removal efficiency was relatively insensitive to the total installation cost in the Pareto-optimal solutions of the pipe network design. The results suggested that the installation cost of the pipes and stormwater treatment devices can be substantially reduced without significantly compromising the pollutant removal efficiency when the pipe network is optimally designed.

• Asian-Australasian Journal of Animal Sciences
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• v.31 no.12
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• pp.1863-1870
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• 2018

Objective: The Bayesian first-order antedependence models, which specified single nucleotide polymorphisms (SNP) effects as being spatially correlated in the conventional BayesA/B, had more accurate genomic prediction than their corresponding classical counterparts. Given advantages of $BayesC{\pi}$ over BayesA/B, we have developed hyper-$BayesC{\pi}$, ante-$BayesC{\pi}$, and ante-hyper-$BayesC{\pi}$ to evaluate influences of the antedependence model and hyperparameters for $v_g$ and $s_g^2$ on $BayesC{\pi}$.Methods: Three public data (two simulated data and one mouse data) were used to validate our proposed methods. Genomic prediction performance of proposed methods was compared to traditional $BayesC{\pi}$, ante-BayesA and ante-BayesB. Results: Through both simulation and real data analyses, we found that hyper-$BayesC{\pi}$, ante-$BayesC{\pi}$ and ante-hyper-$BayesC{\pi}$ were comparable with $BayesC{\pi}$, ante-BayesB, and ante-BayesA regarding the prediction accuracy and bias, except the situation in which ante-BayesB performed significantly worse when using a few SNPs and ${\pi}=0.95$. Conclusion: Hyper-$BayesC{\pi}$ is recommended because it avoids pre-estimated total genetic variance of a trait compared with $BayesC{\pi}$ and shortens computing time compared with ante-BayesB. Although the antedependence model in $BayesC{\pi}$ did not show the advantages in our study, larger real data with high density chip may be used to validate it again in the future.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1260-1283
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• 2019

Low earth orbit (LEO) satellite broadband network is a crucial part of the space information network. LEO satellite constellation design is a top-level design, which plays a decisive role in the overall performance of the LEO satellite network. However, the existing works on constellation design mainly focus on the coverage criterion and rarely take network performance into the design process. In this article, we develop a unified framework for constellation optimization design in LEO satellite broadband networks. Several design criteria including network performance and coverage capability are combined into the design process. Firstly, the quality of service (QoS) metrics is presented to evaluate the performance of the LEO satellite broadband network. Also, we propose a network stability model for the rapid change of the satellite network topology. Besides, a mathematical model of constellation optimization design is formulated by considering the network cost-efficiency and stability. Then, an optimization algorithm based on non-dominated sorting genetic algorithm-II (NSGA-II) is provided for the problem of constellation design. Finally, the proposed method is further evaluated through numerical simulations. Simulation results validate the proposed method and show that it is an efficient and effective approach for solving the problem of constellation design in LEO satellite broadband networks.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.9
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• pp.1193-1201
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• 2020

Cell planning, which determines the cell structure for location management of mobile terminals in mobile communications, has been dealt with as an important research task to determine network performance. Among the factors influencing the cell structure planning in mobile communication, the signal cost for location management plays the most important role. In this paper, we propose an optimization algorithm that minimizes the location management cost of all the cells used to plan the cell structure in the network with reporting cell structure in mobile communication. The proposed algorithm uses a Tabu search algorithm, which is a meta-heuristic algorithm, and the proposed algorithm proposes a new neighborhood generation method to obtain a result close to the optimal solution. In order to evaluate the performance of the proposed algorithm, the simulation was performed in terms of location management cost and algorithm execution time. The evaluation results show that the proposed algorithm outperforms the existing genetic algorithm and simulated annealing.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.1
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• pp.79-89
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• 2022

Soil moisture is significantly related to crop growth and plays an important role in irrigation management. To predict soil moisture, various process-based model has been developed and used in the world. Various models (Land surface model) may have different performance depending on the model parameters and structures that causes the different model output for the same modeling condition. In this study, the three land surface models (Noah Land Surface Model, Soil Water Atmosphere Plant, Community Land Model) were used to compare the model performance (soil moisture prediction) and develop the multi-model simulation. At first, the genetic algorithm was used to estimate the optimal soil parameters for each model, and the parameters were used to predict soil moisture in the study area. Then, we used the multi-model approach based on Bayesian model averaging (BMA). The results derived from this approach showed a better match to the measurements than the results from the original single land surface model. In addition, identifying the strengths and weaknesses of the single model and utilizing multi-model methods can help to increase the accuracy of soil moisture prediction.