• Journal of the Korean Society of Systems Engineering
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• v.12 no.2
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• pp.115-125
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• 2016

An improved RRS (Reactor Regulating System) logic is implemented in this work using systems engineering approach along with GA (Genetic Algorithm) deemed as providing an optimal solution to a given system. The current system works desirably and has been contributed to the safe and stable NPP operation. However, during the ascent and decent 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 research proposes applying genetic algorithm to RRS and suggests reconfiguring the system. Prior to the use of GA, reverse-engineering is implemented to build a Simulink-based RRS model and re-engineering is followed to apply the GA and to produce a newly-configured RRS generating an output that has a reduced steady state error and diminished overshoot level.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.26 no.2
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• pp.1-8
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• 2003

The operation and management of a plant require proper accounting for the constraints coming from reliability requirements as well as from budget and resource considerations. Most of the mathematical methods to decide the inspection time interval for plant maintenance by reliability theory are too complicated to be solved. Moreover, the mathematical and theoretical models are not usually cases in the practical applications. In order to overcome these problems, we propose a new the decision-making method of optimal inspection interval to minimize the maintenance cost by reliability theory and genetic algorithm (GA). The most merit of the proposed method is to decide the inspection interval for a plant machine of which failure rate $\lambda$(t) conforms to any probability distribution. Therefore, this method is more practical. The efficiency of the proposed method is verified by comparing the results obtained by GA-based method with the inspection model haying regular time interval.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.70.1-70
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• 2002

In the fuzzy applications and theories, it is very important to consider how to design the optimal fuzzy model from short training data, in order to construct the reasonable fuzzy model for identifying the practical process. There are several concerns to be confirmed for efficient fuzzy model design. One of concern is the optimization problem of the fuzzy model. In various applications, the genetic algorithm is widely applied to obtain optimal fuzzy model and other cases that adopt evolutionary mechanism of the nature. If we use natural selection and multiplication operation of the genetic algorithm, early convergence to local minimum can be occurred. In other word, we can find only optimum...

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.15 no.9
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• pp.856-862
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• 2004

This paper introduces a program by which we can extract the optimal parameters from the statistical model, the amplitude probability distribution(APD), of the electromagnetic noise using genetic algorithm. The genetic algorithm used in this program has the advantages of the reduction of calculation time, the automation of extraction process, and the operation of global optimization.

• Proceedings of the KIEE Conference
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• 1995.11a
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• pp.64-66
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• 1995

This paper presents a new approach to evaluate optimal basic reliability indices of electric distribution systems using genetic algorithm. The use of optimal reliability evaluation is an important aspect of distribution system planning and operation to determine adequacy reliability level of each area. In this paper, the reliability model is based on the analytical method, connecting component failure to load point outage in each section. The proposed method applies genetic algorithm to calculate the optimal values of basic reliability indices, ie. failure rate and repair time, for a load point in the power distribution system, subject to minimizing interruption cost. Test results for the model system are reported in the paper compared with a direct optimization method(gradient projection).

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.524-524
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• 2000

We have been developing a method to build one-step-ahead prediction models for time series using genetic programming (GP). Our model building method consists of two stages. In the first stage, functional forms of the models are inherited from their parent models through crossover operation of GP. In the second stage, the parameters of the newborn model arc optimized based on an iterative method just like the back propagation. The proposed method has been applied to various kinds of time series problems. An application to the seismic ground motion was presented in the KACC'99, and since then the method has been improved in many aspects, for example, additions of new node functions, improvements of the node functions, and new exploitations of many kinds of mutation operators. The new ideas and trials enhance the ability to generate effective and complicated models and reduce CPU time. Today, we will present a couple of financial applications, espc:cially focusing on gold price prediction in Tokyo market.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.6
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• pp.197-208
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• 1999

This paper presents an operation sequence-based approach for determining machine cell layout in a cellular manufacturing environment. The proposed model considers the sequence of operations in evaluating the intercell and intracell movements. In this paper, design of cellular layout has an objective of minimization of total material flow among facilities, where the total material flow is defined as a weighted sum of both intercell and intracell part movements. The proposed algorithm is developed by using genetic algorithm and can be used to design an optimal cellular layout which can cope with changes of shop floor situation by considering constraints such as the number of machine cells and the number of machines in a machine cell.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.2
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• pp.58-66
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• 2001

This paper presents an application of a newly designed Adaptive Genetic Algorithm (AGA) to solve the Optimal Feeder Routing (OFR) problem for distribution system planning. The main objective of the OFR problem usually is to minimize the total cost that is the sum of investment costs and system operation costs. We propose a properly designed AGA, in this paper, which can handle the horizon-year expansion planning problem of power distribution network in which the location of substation candidates, the location and amount of forecasted demands are given. In the proposed AGA, we applied adaptive operators using specially designed adaptive probabilities. we also a Simplified Load Flow (SLF) technique for radial networks to improve a searching efficiency of AGA. The proposed algorithm has been evaluated with the practical 32, 69 bus test system to show favorable performance. It is also shown that the proposed method for the OFR can also be used for the network reconfiguration problem in distribution system.

• Proceedings of the KIEE Conference
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• 2001.11b
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• pp.330-332
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• 2001

Distribution systems is consist of network in physical and radial in electrical aspect. Therefore radial operation is realized by changing the status of sectionalizing switches, and is usually done for loss reduction in the system. In this paper, we propose a optimal method for distribution systems reconfiguration. Specifically we use genetic algorithm method to solve distribution systems reconfiguration for loss minimization problem. A genetic algorithm(GA) is set up, in which some improvements are made on string coding, fitness function and mutation pattern. As a result, premature convergence is avoided.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.29 no.1
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• pp.63-71
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• 2006

In this paper, we consider a planning problem arising from printed circuit board manufacturing industries. Given a set of several types of PCBs, component feeders and surface mounting machines in series in a PCB assembly line, the problem is to define the feeder allocation and job sequence with the objective of minimizing the total operation time of the line. We formulate the problem as a mathematical model. And, the problem is proven to be NP-hard, so a genetic algorithm is developed. Finally, we give test results to evaluate the performance of the genetic algorithm.