• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.5
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• pp.85-94
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• 2008

When an RFID reader uses the same frequency or adjacent frequency with neighbor readers, the interference might occur. These interferences cause the RFID reader collision and errors during tag recognition. Therefore, the international standard for RFID and some papers proposed the methods to reduce the reader collision. The reader interference is closely related to the distance between the readers haying interference and used frequency band. In the previous RFID reader anti-collision algorithms, the location of readers inducing interference which is closely related to interference of readers is not considered. Only the reader collision is tried to reduce through frequency transfer after collisions occur or modification of frame size in relation to collision probability based a TDM(Time Division Multiplex). In this paper, the genetic algorithm using two-dimensional chromosome which reflect readers' location is proposed to prevent reader collision effectively. By executing evolutionary operation with two-dimensional chromosome, the location information having influence on reader interference can be used. The repair operation in the proposed algorithm makes all reader stably recognize their tags.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.4
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• pp.537-545
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• 2013

The elastic recovery of a metal seal is a factor that can be used to assess its sealing performance. In this study, a compliant mechanism topology optimization has been performed to find a structure of a metal O-ring seal that can maintain excellent sealing performance with a maximized elastic recovery over extended operation. An evolutionary structural optimization (ESO) was used as a topology optimization algorithm with two different types of objective functions considering both flexibility and stiffness. In particular, a circular design domain was adopted to consider the outer shape of the metal O-ring seal. The elastic recovery of the optimal topology was calculated and compared to that of a commercial product.

• Journal of Electrical Engineering and Technology
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• v.2 no.4
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• pp.413-419
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• 2007

The optimal power flow (OPF) problem was introduced by Carpentier in 1962 as a network constrained economic dispatch problem. Since then, it has been intensively studied and widely used in power system operation and planning. In the past few decades, many stochastic optimization methods such as Genetic Algorithm (GA), Evolutionary Programming (EP), and Particle Swarm Optimization (PSO) have been applied to solve the OPF problem. In particular, PSO is a newly proposed population based stochastic optimization algorithm. The main idea behind it is based on the food-searching behavior of birds and fish. Compared with other stochastic optimization methods, PSO has comparable or even superior search performance for some hard optimization problems in real power systems. Nowadays, some modifications such as breeding and selection operators are considered to make the PSO superior and robust. In this paper, we propose the Modified PSO (MPSO), in which the mutation operator of GA is incorporated into the conventional PSO to improve the search performance. To verify the optimal solution searching ability, the proposed approach has been evaluated on an IEEE 3D-bus test system. The results showed that performance of the proposed approach is better than that of the standard PSO.

• Industrial Engineering and Management Systems
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• v.14 no.1
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• pp.1-10
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• 2015

In this paper, we consider multi-item inventory management. When managing a multi-item inventory, we coordinate replenishment orders of items supplied by the same supplier. The associated problem is called the joint replenishment problem (JRP). One often-used approach to the JRP is to apply a can-order policy. Under a can-order policy, some items are re-ordered when their inventory level drops to or below their re-order level, and any other item with an inventory level at or below its can-order level can be included in this order. In the present paper, we propose a method for finding the optimal parameter of a can-order policy, the can-order level, for each item in a lost-sales model. The main objectives in our model are minimizing the number of ordering, inventory, and shortage (i.e., lost-sales) respectively, compared with the conventional JRP, in which the objective is to minimize total cost. In order to solve this multi-objective optimization problem, we apply a genetic algorithm. In a numerical experiment using actual shipment data, we simulate the proposed model and compare the results with those of other methods.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.10
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• pp.1699-1708
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• 2007

The optimal power flow(OPF) problem was introduced by Carpentier in 1962 as a network constrained economic dispatch problem. Since then, the OPF problem has been intensively studied and widely used in power system operation and planning. In these days, 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. To solve OPF problem, many heuristic optimization methods have been developed, such as Genetic Algorithm(GA), Evolutionary Programming(EP), Evolution Strategies(ES), and Particle Swarm Optimization(PSO). Especially, PSO algorithm is a newly proposed population based heuristic optimization algorithm which was inspired by the social behaviors of animals. However, population based heuristic optimization methods require higher computing time to find optimal point. This shortcoming is overcome by a straightforward parallel processing of PSO algorithm. The developed parallel PSO algorithm is implemented on a PC cluster system with 6 Intel Pentium IV 2GHz processors. The proposed approach has been tested on the IEEE 30-bus system. The results showed that computing time of parallelized PSO algorithm can be reduced by parallel processing without losing the quality of solution.

• Journal of Electrical Engineering and Technology
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• v.9 no.6
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• pp.2142-2153
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• 2014

Linearization of transducer characteristic plays a vital role in electronic instrumentation because all transducers have outputs nonlinearly related to the physical variables they sense. If the transducer output is nonlinear, it will produce a whole assortment of problems. Transducers rarely possess a perfectly linear transfer characteristic, but always have some degree of non-linearity over their range of operation. Attempts have been made by many researchers to increase the range of linearity of transducers. This paper presents a method to compensate nonlinearity of Linear Variable Displacement Transducer (LVDT) based on Extreme Learning Machine (ELM) method, Differential Evolution (DE) algorithm and Artificial Neural Network (ANN) trained by Genetic Algorithm (GA). Because of the mechanism structure, LVDT often exhibit inherent nonlinear input-output characteristics. The best approximation capability of optimized ANN technique is beneficial to this. The use of this proposed method is demonstrated through computer simulation with the experimental data of two different LVDTs. The results reveal that the proposed method compensated the presence of nonlinearity in the displacement transducer with very low training time, lowest Mean Square Error (MSE) value and better linearity. This research work involves less computational complexity and it behaves a good performance for nonlinearity compensation for LVDT and has good application prospect.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2006.06a
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• pp.273-274
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• 2006

The sea is origin of all lift, and 90% of the all living organisms are in the sea. The biosynthesis is very different. Many organisms are kept on a lower or developed to another evolutionary level than on shore. Our society is increasing demand and need for marine food and this food has to product at onshore or offshore fish farming sites. Ocean fish farms have a special operation properties such as a good quality water, net cage, sheltered locations and feeding system. The farming site is controlled and monitored for fish welfare as ammonia($NH_3$), temperature, the speed of a running fluid. Specially, the fish farm is seriously influenced by ammonia. In this paper, $NH_3$ monitoring system for ocean fish farm is researched for the suitable fish farming sites, and test equipment is designed for achieving practical data. The equipment wit monitoring algorithm is expected to the useful system for ocean fish farm.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.6
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• pp.600-607
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• 2006

For the effective operation of manufacturing system, FMS(Flexible Manufacturing System) and CIM(Computer Integrated Manufacturing) system are developed. In these systems, a machine tool is the target of integration in last 3 decades. In nowadays, the conventional concept of machine tools is changing to the autonomous manufacturing device based on knowledge-evolution through applying advanced information technology in which open architecture controller, high speed network and internet technology are contained. In this environment, a machine tool is not the target of integration but the subject of cooperation. In the future, a machine tool will be more improved in the form of a knowledge-evolution based device. In order to develop the knowledge-evolution based machine tools, this paper proposes the structure of knowledge evolution in M2M(Machine To Machine) and the scheme of a dialogue agent among agent-based modules such as a sensory module, a dialogue module, and an expert system. The dialogue agent has a role of interfacing with another machine for cooperation. To design the dialogue agent module in M2M environment, FIPA-OS and ping agent based on FIPA-OS are analyzed in this study. Through this, it is expected that the dialogue agent module can be more efficiently designed and the knowledge-evolution based machine tools can be hereafter more easily implemented.

• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.1864-1873
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• 2018

Energy shifting is an innovative method used to obtain the highest profit from the operation of energy storage systems (ESS) by controlling the charge and discharge schedules according to the electricity prices in a given period. Therefore, in this study, we propose an optimal charge and discharge scheduling method that performs energy shift operations derived from an ESS efficiency model. The efficiency model reflects the construction of power conversion systems (PCSs) and lithium battery systems (LBSs) according to the rated discharge time of a MWh-scale ESS. The PCS model was based on measurement data from a real system, whereas for the LBS, we used a circuit model that is appropriate for the MWh scale. In addition, this paper presents the application of a genetic algorithm to obtain the optimal charge and discharge schedules. This development represents a novel evolutionary computation method and aims to find an optimal solution that does not modify the total energy volume for the scheduling process. This optimal charge and discharge scheduling method was verified by various case studies, while the model was used to realize a higher profit than that realized using other scheduling methods.

• Nuclear Engineering and Technology
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• v.54 no.3
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• pp.909-916
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• 2022

To study the developments and variations of unsafe behaviors in nuclear power plants thus reduce the possibility of human-related accidents, this paper, based on the Game Theory, focused on the changes in benefits of the Department of Management, Operational and Emergency in a nuclear power plant, and established the expected revenue functions of these departments. Additionally, the preventive measures of unsafe behaviors in nuclear power plants were also presented in terms of these 3 departments. Results showed that the violations of the Operation Department (OD) and the Emergency Department (ED) were not only relevant with the factors such as their own risks, costs, and the responsibility-sharing due to accidents, but also affected by the safety investments from the Management Department (MD). Furthermore, results also showed that the accident-induced responsibility-sharing of both the OD and the ED would rise, if the MD increased the investments in safety. As a result, the probability of violation behaviors of these 3 departments would be attenuated consciously, which would reduce the unsafe behaviors in the nuclear power plants significantly.