• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.7
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• pp.1314-1323
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• 2011

The maintenance of generating units is implicitly related with power system reliability and has a tremendous bearing on the operation of the power system. A technique using a fuzzy search method which is based on fuzzy multi-criteria function has been proposed for GMS (generator maintenance scheduling) in order to consider multi-objective function. In this study, a new technique using combined fuzzy set theory and genetic algorithm(GA) is proposed for generator maintenance scheduling. The genetic algorithm(GA) is expected to make up for that fuzzy search method might search the local solution. The effectiveness of the proposed approach is demonstrated by the simulation results on a practical size test systems.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.7
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• pp.1053-1058
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• 2017

In this paper, the design of iron loss minimization of 600W was performed by using Quasi-Newton method. Stator shoe, the width of stator teeth and yoke, and the length of d-axis flux path were selected as design parameters, and the output characteristics according to each design variable were considered. The objective function was set to minimize iron loss. Using the Quasi-Newton method, the variables converged to the target value while changing simultaneously and multiple times. As the algorithm advanced optimization, the correlation with the behavior of each variable was compared and analyzed.

• Proceedings of the KIEE Conference
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• 1996.11a
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• pp.133-136
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• 1996

In the paper, an artificial neural network (ANN) approach based on Lagrange multiplier method (Lagrangian ANN) is used to solve an economic load dispatch (ELD) problem. Traditionally ELD problem has one convex cost function as its objective function and nonlinear constraints such as power balance and maximum-minimum limits of real power. In this study, modification is given to the Lagrangian ANN proposed by Gong et all[5] to guarantee the convergence to the optimal solution. Simulation results demonstrate the effectiveness of the proposed method applied to the ELD problem.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.842-843
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• 2007

In this paper, the proper rebate level can be decided in programs of energy savings by solving an optimization problem with an objective function, which satisfies a maximum value of total energy savings. And then, each prevalence amount is estimated by using virtual Bass model which is a function of rebate level, instead of the conventional Bass model. Finally, by cost/benefit analysis of the estimated prevalence amounts, the priority order is obtained for the investment of each program. The priority order obtained in this way may result the improvement of investment efficiency for DSM(Demand-Side Management) programs and the reasonable plan decision for supply and demand in power system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.11
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• pp.4355-4371
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• 2020

Phase retrieval, recovering a signal from phaseless measurements, is generally considered to be an NP-hard problem. This paper adopts an amplitude-based nonconvex optimization cost function to develop a new stochastic gradient algorithm, named stochastic reweighted phase retrieval (SRPR). SRPR is a stochastic gradient iteration algorithm, which runs in two stages: First, we use a truncated sample stochastic variance reduction algorithm to initialize the objective function. The second stage is the gradient refinement stage, which uses continuous updating of the amplitude-based stochastic weighted gradient algorithm to improve the initial estimate. Because of the stochastic method, each iteration of the two stages of SRPR involves only one equation. Therefore, SRPR is simple, scalable, and fast. Compared with the state-of-the-art phase retrieval algorithm, simulation results show that SRPR has a faster convergence speed and fewer magnitude-only measurements required to reconstruct the signal, under the real- or complex- cases.

• Transactions of the Korean Society of Automotive Engineers
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• v.22 no.6
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• pp.1-8
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• 2014

Safety performance of a new car is evaluated through USNCAP and their results in the star rating are provided to the consumers. It is very important to obtain high score of USNCAP to appeal their performance to consumers. Therefore the car companies have made the effort to improve their car safety performance. These efforts should satisfy the demand not only to get high score but also to pass the FMVSS, NHTSA regulations on safety. Huge numbers of car crash tests have been conducted on these bases by car companies. However physical tests spend too much cost and time, as an alternative way, the simulation on the car crash could be a solution to reduce the cost and time. Therefore the simulations have been widely conducted in car industry and various researches on this have been reported. In this study, restraint system had been optimized to minimize the injury of female passenger. Belted $5^{th}%ile$ female frontal crash test was selected from various test methods of USNCAP for the study. Initial velocity of the test was 56km/h. The combination injury probability of USNCAP was selected as an objective function and the injury limit value, which was defined in FMVSS, was set to an optimization constraint. Many researches that were similar to this study had been conducted, however most of them had limitation that interaction between airbag and safety belt had not been considered. Contrary to these researches, the interaction was considered in this study.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.6
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• pp.43-53
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• 2022

Rice paddy has been actively converted into upland crop fields as more profitable upland crop cultivation are encouraged along with the decrease in rice consumption. However, the current water supply system remains mainly for paddy water supply, so research on pipeline water supply for upland cultivation is needed. The objective of this study was to optimize storage tank installation locations for pipeline water supply in reservoir irrigation districts. Five of reservoir irrigation districts were selected as the study sites and gridded of 10×10 m in size. Then genetic algorithm was adopted to evaluate the effects of spatial storage tank allocation on total pipeline cost. The lengths of the main and branch pipelines were considered as the objective cost function for the optimization of storage tank installation. Overall the shorter the branch pipeline and the longer the main pipeline, as the number of storage tanks increase. The minimal pipeline cost, i.e., optimal condition was reached when approximately 10% of the storage tank numbers to total upland plots were installed. The methodology presented in this study can be applied to determine the number and spatial arrangement of storage tanks for upland pipeline irrigation system design.

• Journal of Electrical Engineering and Technology
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• v.8 no.5
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• pp.1012-1020
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• 2013

In this work, Self-adaptive Differential Evolutionary (SaDE) algorithm is proposed to solve Optimal Location and Size of Capacitor (OLSC) problem in radial distribution networks. To obtain the SaDE algorithm, two improvements have been applied on control parameters of mutation and crossover operators. To expand the study, three load conditions have been considered, i.e., constant, varying and effective loads. Objective function is introduced for the load conditions. The annual cost is fitness of problem, in addition to this cost, CPU time, voltage profile, active power loss and total installed capacitor banks and their related costs have been used for comparisons. To confirm the ability of each improvements of SaDE, the improvements are studied both in separate and simultaneous conditions. To verify the effectiveness of the proposed algorithm, it is tested on IEEE 10-bus and 34-bus radial distribution networks and compared with other approaches.

• Journal of Korean Institute of Industrial Engineers
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• v.30 no.2
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• pp.93-106
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• 2004

This paper studied a maximal covering location problem with cost restrictions, to maximize level of service within predetermined cost. It is assumed that all demand have to be met. If the demand node is located within a given range, then its demand is assumed to be covered, but if it is not, then its demand is assumed to be uncovered. An uncovered demand is received a service but at an unsatisfactory level. The objective function is to maximize the sum of covered demand, Two heuristics based on the Lagrangean relaxation of allocation and decoupling are presented and tested. Upper bounds are found through a subgradient optimization and lower bounds are by a cutting algorithm suggested in this paper. The cutting algorithm enables the Lagrangean relaxation to be proceeded continually by allowing infeasible solution temporarily when the feasible solution is not easy to find through iterations. The performances are evaluated through computational experiments. It is shown that both heuristics are able to find the optimal solution in a relatively short computational time for the most instances, and that decoupling relaxation outperformed allocation relaxation.

• Journal of Korean Institute of Industrial Engineers
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• v.30 no.2
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• pp.146-158
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• 2004

This paper considers a continuous-review two-echelon inventory control problem with one-to-one replenishment policy incorporated and with lost sales allowed where demand arrives in a stationary Poisson process. The problem is formulated using METRIC-approximation in a combined approach of pricing and (S-l, S) inventory policy, for which a heuristic solution algorithm is derived with respect to the corresponding one-warehouse multi-retailer supply chain. Specifically, decisions on retail pricing and warehouse inventory policies are made in integration to maximize total profit in the supply chain. The objective function of the model consists of sub-functions of revenue and cost (holding cost and penalty cost). To test the effectiveness and efficiency of the proposed algorithm, numerical experiments are performed with two cases. The first case deals with identical retailers and the second case deals with different retailers with different market sizes. The computational results show that the proposed algorithm is efficient and derives quite good decisions.