• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1996년도 추계학술대회 논문집
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• pp.306-312
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• 1996

The performance of the failure detection algorithm may be greatly influenced by the model uncertainty. It is very important to design a robust failure detection system to the model uncertainty. In this paper, a design procedure to generate failure detection algorithm is proposed. The design procedure suggested is based on the concept of the‘threshold selector［1］’. The H$\infty$ control algorithm is used to derive a threshold selector which is robust to the model uncertainty, The threshold selector derived can be used to develop a failure detection system together with the weighted cumulative sum algorithm［3］. Computer simulation study showed that the failure detection system designed for an ISA(Integrated Servo Actuator) system by using the proposed method is robust to the model uncertainty.

• EDISON SW 활용 경진대회 논문집
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• 제4회(2015년)
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• pp.500-509
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• 2015

This paper presents the single objective design optimization of plate-fin heat sink equipped with fan cooling system using Genetic Algorithm. The proper heat sink and fan model are selected based on the previous studies. And the thermal resistance of heat sinks and fan efficiency during operation are calculated according to specific design parameters. The objective function is combination of thermal resistance and fan efficiency which have been taken to measure the performance of the heat sink. And Decision making procedure is suggested considering life time of semiconductor and Fan Operating cost. And also Analytical Model used for optimization is validated by Fluent, Ansys 13.0 and this model give a quite reasonable and reliable design.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 하계학술대회 논문집 A
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• pp.125-127
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• 2003

The Conventional time-domain simulation of transient stability requires iterative calculation procedures to consider the saliency of generator. Recently, a non-iterative algorithm has successfully developed to take into account the generator saliency exactly with the use of $E_q'$-model. This study proposes an extended non-iterative algorithm by adopting the two-axis generator model. Given internal voltages and rotor angles of the generators, network voltages and generator currents can be directly calculated by solving a linear algebraic equation, which enables us to reduce the computation time remarkably.

• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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• 제2B권4호
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• pp.168-173
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• 2002

A new identification algorithm for the Preisach model is presented. The algorithm treats the identification procedure of the Preisach model as an inverse problem where the independent variables are parameters of the distribution function and the objective function is constructed using only the initial magnetization curve or only tile major loop of the hysteresis curve as well as the whole reversal curves. To parameterize the distribution function, the Bezier spline and Gaussian function are used for the coercive and interaction fields axes, respectively. The presented algorithm is applied to the ferrite permanent magnets, and the distribution functions are correctly found from the major loop of the hysteresis curve or the initial magnetization curve.

• 전기학회논문지
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• 제61권7호
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• pp.1007-1013
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• 2012

Modeling of photovoltaic power systems is significant to analytically predict its dynamics in practical applications. This paper presents a novel modeling algorithm of such system by using probability and statistic theories. We first establish a linear model basically composed of Fourier parameter sets for mapping the input/output variable of photovoltaic systems. The proposed model includes solar irradiation and ambient temperature of photovoltaic modules as an input vector and the inverter power output is estimated sequentially. We deal with these measurements as random variables and derive a parameter learning algorithm of the model in terms of statistics. Our learning algorithm requires computation of an expectation and joint expectation against solar irradiation and ambient temperature, which are analytically solved from the integral calculus. For testing the proposed modeling algorithm, we utilize realistic measurement data sets obtained from the Seokwang Solar power plant in Youngcheon, Korea. We demonstrate reliability and superiority of the proposed photovoltaic system model by observing error signals between a practical system output and its estimation.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 하계학술대회 논문집 A
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• pp.289-291
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• 2004

ln this paper, A dc-offset elimination novel algorithm based on an An model is proposed. The algorithm can eliminate dc-offset rapidly than other algorithms. The signal of fault current can be presented as a linear equation combined sinusoidal with exponential signals. Then, the linear equation can be presented an auto-regressive(AR) model and do-offset can be calculated by the equation of AR model. So it is possible to be removed the dc-offset from the original current signal. Performance evaluation of the algorithm was tested on condition that A-phase ground fault on 154kV 25km overhead transmission line.

• Journal of Information Processing Systems
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• 제18권4호
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• pp.562-574
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• 2022

As traditional ways of evaluation prove to be ineffective in evaluating the effect of interactive multimedia physical education (PE) teaching, this study develops a new evaluation model based on the simulated annealing algorithm. After the evaluation subjects and the principle of the evaluation system are determined, different subjects are well chosen to constitute the evaluation system and given the weight. The backpropagation neural network has been improved through the simulated annealing algorithm, whose improvement indicates the completion of the evaluation model. Simulation results show that the evaluation model is highly efficient. Compared with traditional evaluation models, the proposed one enhances students' performance in PE classes by 50%.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2004년도 추계학술대회 학술발표 논문집 제14권 제2호
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• pp.303-308
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• 2004

Fuzzy logic, neural network, fuzzy-neural network play an important as the key technology of linguistic modeling for intelligent control and decision making in complex systems. The fuzzy-neural network (FNN) learning represents one of the most effective algorithms to build such linguistic models. This paper proposes learning approach of fuzzy-neural network by immune algorithm. The proposed learning model is presented in an immune based fuzzy-neural network (FNN) form which can handle linguistic knowledge by immune algorithm. The learning algorithm of an immune based FNN is composed of two phases. The first phase used to find the initial membership functions of the fuzzy neural network model. In the second phase, a new immune algorithm based optimization is proposed for tuning of membership functions and structure of the proposed model.

• 한국정밀공학회지
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• 제12권11호
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• pp.110-117
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• 1995

This study presents and algorithm and related techniques which could satisfy the important properties of check weighers and conveyor scales. The algorithm of Recursive Least Squares Regression is applied for the weighing system simulated as a dynamic model of the second order. Using the model and the algorithm, model parameters and then the mass being weighed can be determined from the step input. The performance of the algorithm was tested on a check weigher. Discussions were extended to the development of noise reduction techniques and to the lagged introduction of objects on the moving plate. It turns out that the algorithm shows several desirable features suitable for real-time signal processing with a microcomputer, which are high precision and stability in noisy environment.

• 산업경영시스템학회지
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• 제27권2호
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• pp.102-113
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• 2004

This paper addresses the transportation planning that is based on genetic algorithm for determining transportation time and transportation amount of minimizing cost of distribution system. The vehicle routing of minimizing the transportation distance of vehicle is determined. A distribution system is consisted of a distribution center and many retailers. The model is assumed that the time horizon is discrete and finite, and the demand of retailers is dynamic and deterministic. Products are transported from distribution center to retailers according to transportation planning. Cost factors are the transportation cost and the inventory cost, which transportation cost is proportional to transportation distance of vehicle when products are transported from distribution center to retailers, and inventory cost is proportional to inventory amounts of retailers. Transportation time to retailers is represented as a genetic string. The encoding of the solutions into binary strings is presented, as well as the genetic operators used by the algorithm. A mathematical model is developed. Genetic algorithm procedure is suggested, and a illustrative example is shown to explain the procedure.