• Nuclear Engineering and Technology
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• v.49 no.8
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• pp.1696-1710
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• 2017

An improved Reactor Regulating System (RRS) logic architecture, which is combined with genetic algorithm (GA), is implemented in this work. It is devised to provide an optimal solution to the current RRS. The current system works desirably and has contributed to safe and stable nuclear power plant operation. However, during the ascent and descent section of the reactor power, the RRS output reveals a relatively high steady-state error, and the output also carries a considerable level of overshoot. In an attempt to consolidate conservatism and minimize the error, this work proposes to apply GA to RRS and suggests reconfiguring the system. Prior to the use of GA, reverse engineering is implemented to build a Simulink-based RRS model. Reengineering is followed to produce a newly configured RRS to generate an output that has a reduced steady-state error and diminished overshoot level. A full-scope APR1400 simulator is used to examine the dynamic behaviors of RRS and to build the RRS Simulink model.

• Structural Engineering and Mechanics
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• v.70 no.2
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• pp.209-219
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• 2019

By normalizing the internal hysteresis variable and eliminating the redundant parameter, the normalized Bouc-Wen model is considered to be an improved and more reasonable form of the Bouc-Wen model. In order to facilitate application and further research of the normalized Bouc-Wen model, some key aspects of the model need to be uncovered. In this paper, hysteresis characterization of the normalized Bouc-Wen model is first studied with respect to the model parameters, which reveals the influence of each model parameter to the shape of the hysteresis loops. The parameter identification scheme is then proposed based on an improved genetic algorithm (IGA), and verified by experimental test data. It is proved that the proposed method can be an efficacious tool for identification of the model parameters by matching the reconstructed hysteresis loops with the target hysteresis loops. Meanwhile, the IGA is shown to outperform the standard GA. Finally, a simplified identification method is proposed based on parameter sensitivity, which indicates that the efficiency of the identification process can be greatly enhanced while maintaining comparable accuracy if the low-sensitivity parameters are reasonably restricted to narrower ranges.

• Journal of Korean Association for Spatial Structures
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• v.13 no.3
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• pp.91-98
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• 2013

A multi-input single-output (MISO) semi-active control systems were studied by many researchers. For more improved vibration control performance, a structure requires more than one control device. In this paper, multi-input multi-output (MIMO) semi-active fuzzy controller has been proposed for vibration control of seismically excited small-scale buildings. The MIMO fuzzy controller was optimized by multi-objective genetic algorithm. For numerical simulation, five-story example building structure is used and two MR dampers are employed. For comparison purpose, a clipped-optimal control strategy based on acceleration feedback is employed for controlling MR dampers to reduce structural responses due to seismic loads. Numerical simulation results show that the MIMO fuzzy control algorithm can provide superior control performance to the clipped-optimal control algorithm.

• International Journal of Contents
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• v.2 no.1
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• pp.39-44
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• 2006

Load sharing is a critical resource in computer system. In sender-initiated load sharing algorithms, the sender continues to send unnecessary request messages for load transfer until a receiver is found while the system load is heavy. Meanwhile, in the receiver initiated load sharing algorithms, the receiver continues to send an unnecessary request message for load acquisition until a sender is found while the system load is light. These unnecessary request messages result in inefficient communications, low CPU utilization, and low system throughput in distributed systems. To solve these problems, we propose a genetic algorithm based approach for improved sender-initiated and receiver-initiated load sharing in distributed systems. And we expand this algorithm to an adaptive load sharing algorithm. Compared with the conventional sender-initiated and receiver-initiated algorithms, the proposed algorithm decreases the response time and task processing time.

• Journal of the Society of Naval Architects of Korea
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• v.37 no.3
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• pp.90-98
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• 2000

Nesting and cutting path optimization have a great effect on price competitions and improvement of productivity in various industries such as the shipbuilding, the auto, the clothing, and so on. But the theoretical approach on the development of cutting path optimization algorithm, which can be applied effectively in the shipbuilding, has not been performed enough because parts are so complex and various. In this study, a new solution has been presented to solve the cutting path problem in 2-D cutting by using improved genetic algorithm. The presented optimization algorithm can search not only the cutting sequence of parts but also the position of piercing point by applying the effective neighborhood solution generating method

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1999.04a
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• pp.173-173
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• 1999

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.6
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• pp.159-169
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• 2020

A cold chain logistics (CCL) model for chilled food (-1℃ to 8℃) distributed in China was developed in this study. The CCL model consists of a distribution center (DC) and distribution target points (DT). The objective function of the CCL model is to minimize the total distribution routes of all distributors. To find the optimal result of the objective function, the hybrid genetic algorithm (HGA) approach is proposed. The HGA approach was constructed by combining the improved K-means and genetic algorithm (GA) approaches. In the case study, three scenarios were considered for the CCL model based on the distribution routes and the available distance, and they were solved using the proposed HGA approach. Analysis results showed that the distribution costs and mileage were reduced by approximately 19%, 20% and 16% when the proposed HGA approach was used.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.9
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• pp.2087-2093
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• 2014

In this paper, an orthogonal matching pursuit (OMP) method combined with genetic algorithm (GA), named GAOMP, is proposed for sparse signal recovery. Some recent greedy algorithms such as SP, CoSaMP, and gOMP improved the reconstruction performance by deleting unsuitable atoms at each iteration. However they still often fail to converge to the solution because the support set could not avoid the local minimum during the iterations. Mutating the candidate support set chosen by the OMP algorithm, GAOMP is able to escape from the local minimum and hence recovers the sparse signal. Experimental results show that GAOMP outperforms several OMP based algorithms and the $l_1$ optimization method in terms of exact reconstruction probability.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.1
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• pp.59-64
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• 2002

In the sender-initiated load balancing algorithms, the sender continues to send an unnecessary request message fur load transfer until a receiver is found while the system load is heavy. Meanwhile, in the receiver-initiated load balancing algorithms, the receiver continues to send an unnecessary request message for load acquisition until a sender is found while the system load is light. These unnecessary request messages result in inefficient communications, low CPU utilization, and low system throughput in distributed systems. To solve these problems, in this paper, we propose a genetic algorithm based approach fur improved sender-initiated and receiver-initiated load balancing. The proposed algorithm is used for new adaptive load balancing approach. Compared with the conventional sender-initiated and receiver-initiated load balancing algorithms, the proposed algorithm decreases the response time and increases the acceptance rate.

• 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.