• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.9
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• pp.1317-1324
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• 2017

These days, a penetration of distributed generation(DG) has increased in power system. Due to increased penetration of DG, a whole system is forced to install the maximum hosting capacity of DG. Therefore analysis between the maximum hosting capacity of DG at the target bus and the whole system is important. If we know the maximum hosting capacity, it will be able to satisfy the demand of system planner and customer. In this paper, we use a genetic algorithm to calculate the hosting capacity with optimization program using Design Analysis Kit for Optimization and Terascale Applications(DAKOTA). To consider a real system, we establish constraints and use IEEE 34 node test system. In addition, through the correlation coefficient between the target bus and the other buses, when capacity of DG at the target bus increases, we analyze which capacity of DG at the other buses will be decreased.

• Korean Chemical Engineering Research
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• v.55 no.5
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• pp.652-659
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• 2017

In the present research, in order to predict activity coefficient of inorganic ions in electrolyte solution of a petroleum system, we studied 13 components in the electrolyte solution, including $H_2O$, $CO_2$ (aq), $H^+$, $Na^+$, $Ba^{2+}$, $Ca^{2+}$, $Sr^{2+}$, $Mg^{2+}$, $SO_4$, $CO_3$, $OH^-$, $Cl^-$, and $HCO_3$. To predict the activity coefficient of the components of the petroleum system (a solid/liquid equilibrium system), activity coefficient model of Extended UNIQUAC was studied, along with its adjustable parameters optimized based on a genetic algorithm. The total calculated error associated with optimizing the adjustable parameters of Extended UNIQUAC model considering the 13 components under study at three temperature levels (298.15, 323.15, and 373.15 K) using the genetic algorithm is found to be 0.07.

• Proceedings of the KSME Conference
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• 2007.05b
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• pp.3080-3085
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• 2007

Shape optimization of the 3-dimensional WIG airfoil with 3.0-aspect ratio has been performed by using the multi-objective genetic algorithm. The WIG ship effectively floating above the surface by the ram effect and the virtual additional aspect ratio by a ground is one of next-generation and cost-effective transportations. Unlike the airplane flying out of the ground effect, a WIG ship has possibility to capsize because of unsatisfying the static stability. The WIG ship should satisfy aerodynamic properties as well as a static stability. They tend to strong contradict and it is difficult to satisfy aerodynamic properties and static stability simultaneously. It is inevitable that lift force has to scarify to obtain a static stability. Multi-objective optimization technique that the individual objectives are considered separately instead of weighting can overcome the conflict. Due to handling individual objectives, the optimum cannot be unique but a set of nondominated potential solutions: pareto optimum. There are three objectives; lift coefficient, lift-to-drag ratio and static stability. After a few evolutions, the non-dominated pareto individuals can be obtained. Pareto sets are all the set of possible and excellent solution across the design space. At any selections of the pareto set, these are no better solutions in all design space

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.1
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• pp.61-80
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• 2018

Location information of individual nodes is important in the implementation of necessary network functions. While extensive studies focus on localization techniques in 2D space, few approaches have been proposed for 3D positioning, which brings the location closer to the reality with more complex calculation consumptions for high accuracy. In this paper, an effective range-free localization scheme is proposed for 3D space localization, and the sensitivity of parameters is evaluated. Firstly, we present an improved algorithm (MDV-Hop), that the average distance per hop of the anchor nodes is calculated by root-mean-square error (RMSE), and is dynamically corrected in groups with the weighted RMSE based on group hops. For more improvement in accuracy, we expand particle swarm optimization (PSO) of intelligent optimization algorithms to MDV-Hop localization algorithm, called PMDV-hop, in which the parameters (inertia weight and trust coefficient) in PSO are calculated dynamically. Secondly, the effect of various localization parameters affecting the PMDV-hop performance is also present. The simulation results show that PMDV-hop performs better in positioning accuracy with limited energy.

• 한국전산유체공학회:학술대회논문집
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• 1999.05a
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• pp.171-176
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• 1999

In the present paper, three types of the flow solvers were used to investigate the influence on the airfoil shape optimization. The adopted equations, i.e., Euler , thin layer Navier- Stokes and full Navier-Stokes ones, are solved using implicit LU-ADI decomposition scheme. The feasible direction algorithm with the sinusoidal function was used as an optimization algorithm. The present numerical method was applied to the drag minimization problems under the initial shape of NACA0012 airfoils.

• International Journal of Naval Architecture and Ocean Engineering
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• v.9 no.1
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• pp.11-24
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• 2017

The present work focuses multi-objective optimization of blade sweep for a Wells turbine. The blade-sweep parameters at the mid and the tip sections are selected as design variables. The peak-torque coefficient and the corresponding efficiency are the objective functions, which are maximized. The numerical analysis has been carried out by solving 3D RANS equations based on k-w SST turbulence model. Nine design points are selected within a design space and the simulations are run. Based on the computational results, surrogate-based weighted average models are constructed and the population based multi-objective evolutionary algorithm gave Pareto optimal solutions. The peak-torque coefficient and the corresponding efficiency are enhanced, and the results are analysed using CFD simulations. Two extreme designs in the Pareto solutions show that the peak-torque-coefficient is increased by 28.28% and the corresponding efficiency is decreased by 13.5%. A detailed flow analysis shows the separation phenomena change the turbine performance.

• Wind and Structures
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• v.20 no.4
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• pp.489-508
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• 2015

In this paper, a novel methodology is proposed to obtain optimum location of outriggers. The method utilizes genetic algorithm (GA) for shape and size optimization of outrigger-braced tall structures. In spite of previous studies (simplified methods), current study is based on exact modeling of the structure in a computer program developed on Matlab in conjunction with OpenSees. In addition to that, exact wind loading distribution is calculated in accordance with ASCE 7-10. This is novel since in previous studies wind loading distributions were assumed to be uniform or triangular. Also, a new penalty coefficient is proposed which is suitable for optimization of tall buildings. Newly proposed penalty coefficient improves the performance of GA and results in a faster convergence. Optimum location and number of outriggers is investigated. Also, contribution of factors like central core and outrigger rigidity is assessed by analyzing several design examples. According to the results of analysis, exact wind load distribution and modeling of all structural elements, yields optimum designs which are in contrast of simplified methods results. For taller frames significant increase of wind pressure changes the optimum location of outriggers obtained by simplified methods. Ratio of optimum location to the height of the structure for minimizing weight and satisfying serviceability constraints is not a fixed value. Ratio highly depends on height of the structure, core and outriggers stiffness and lateral wind loading distribution.

• Nuclear Engineering and Technology
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• v.54 no.9
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• pp.3403-3414
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• 2022

The purpose of this study is to model and optimize the block-matching and 3D filtering (BM3D) algorithm and to evaluate its applicability in brain single-photon emission computed tomography (SPECT) images using a fan beam collimator. For quantitative evaluation of the noise level, the coefficient of variation (COV) and contrast-to-noise ratio (CNR) were used, and finally, a no-reference-based evaluation parameter was used for optimization of the BM3D algorithm in the brain SPECT images. As a result, optimized results were derived when the sigma values of the BM3D algorithm were 0.15, 0.2, and 0.25 in brain SPECT images acquired for 5, 10, and 15 s, respectively. In addition, when the sigma value of the optimized BM3D algorithm was applied, superior results were obtained compared with conventional filtering methods. In particular, we confirmed that the COV and CNR of the images obtained using the BM3D algorithm were improved by 2.40 and 2.33 times, respectively, compared with the original image. In conclusion, the usefulness of the optimized BM3D algorithm in brain SPECT images using a fan beam collimator has been proven, and based on the results, it is expected that its application in various nuclear medicine examinations will be possible.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.44 no.5
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• pp.42-49
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• 2007

This paper introduces a study on reliability-driven device placement using simulated annealing algorithm which can be applicable to MCM or electronic systems embedded in a spacecraft running at thermal conduction environment. Reliability of the unit's has been predicted with the devices' junction temperatures calculated from FDM solver and optimized by simulated annealing algorithm. Simulated annealing in this paper adopts swapping devices method as a perturbation. This paper describes and compares the optimization simulation results with respect to two objective functions: minimization of failure rate and minimization of average junction temperature. Annealing temperature variation simulation case and equilibrium coefficient variation simulation case are also presented at the two respective objective functions. This paper proposes a new approach for reliability optimization of MCM and electronic systems considering those simulation results.

• Journal of Power Electronics
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• v.17 no.5
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• pp.1288-1297
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• 2017

The estimation of the remaining useful life (RUL) of lithium-ion (Li-ion) batteries is important for intelligent battery management system (BMS). Data mining technology is becoming increasingly mature, and the RUL estimation of Li-ion batteries based on data-driven prognostics is more accurate with the arrival of the era of big data. However, the support vector machine (SVM), which is applied to predict the RUL of Li-ion batteries, uses the traditional single-radial basis kernel function. This type of classifier has weak generalization ability, and it easily shows the problem of data migration, which results in inaccurate prediction of the RUL of Li-ion batteries. In this study, a novel multi-kernel SVM (MSVM) based on polynomial kernel and radial basis kernel function is proposed. Moreover, the particle swarm optimization algorithm is used to search the kernel parameters, penalty factor, and weight coefficient of the MSVM model. Finally, this paper utilizes the NASA battery dataset to form the observed data sequence for regression prediction. Results show that the improved algorithm not only has better prediction accuracy and stronger generalization ability but also decreases training time and computational complexity.