• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2152-2154
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• 2002

신경망을 이용한 적응제어는 학습능력에 따라 외란작용에 스스로 대처하고, 정밀한 제어가 가능하지만 학습파라미터가 최적화되기 전에는 불안정한 제어응답을 보인다. 퍼지논리는 전문가의 경험을 논리화한 것으로 제어특성은 좋으나, 외란에 대한 적응력이 부족하여 계속적인 오프셋이 발생할 수 있다. 따라서, 퍼지와 신경망을 시스템의 동특성에 따라 혼용한 제어방식을 제시하고, 시뮬레이션으로 시간지연이 있는 CSTH의 온도와 비선형 공정인 pH 중화공정에 적용하여 단순신경망 제어어보다 개선된 제어응답 특성을 얻었다.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2747-2749
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• 2000

The KEPRI has been developing the medium/small scale Distribution Automation System(DAS) that adopts wireless transmission media for exchanging information between master and remote terminals. It was concluded that Short Message Service(SMS) of Personal Communication Service(PCS) was the best wireless transmission media for the medium/small scale DAS in the last year. However. we had problem that PCS had long transmission delay. Therefore. SMS phones will be substituted with Radio Link Protocol(RLP) modems having transmission delay less than 5 seconds. This paper describes wireless networks for DAS, practical experience.

• Journal of Electrical Engineering and Technology
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• v.7 no.3
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• pp.406-410
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• 2012

This paper looks into the field inside the wide rectangular box structure that is excited by the High Power Electromagnetic(HPEM) source as a potential threat to electric grid and communication networks causing malfunction or destruction. The rectangular box is assumed power/ground planes and its internal field is calculated by the cavity model with the lightning strike excitation as an HPEM pulse. The accuracy of the calculation method employed here is validated through a $156mm{\times}106mm{\times}508{\mu}m$ parallel metallic plate case which is manufactured and tested, and is applied to the size of a building. With the help of the cavity model that takes into account loading, the level of the electric field is shown to decrease when a metal pillar is loaded between the power and ground planes.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.14 no.8
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• pp.626-633
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• 2002

The electric power load during the peak time in summer is strongly affected by cooling load, which decreases the preparation ratio of electricity and brings about the failure in the supply of electricity in the electric power system. The ice-storage system and heat pump system etc. are used to settle this problem. In this study, the method of estimating temperature and humidity to forecast the cooling load of ice storage system is suggested. And also the method of forecasting the cooling load using neural network is suggested. For the simulation, the cooling load is calculated using actual temperature and humidity, The forecast of the temperature, humidity and cooling load are simulated. As a result of the simulation, the forecasted data is approached to the actual data.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.2
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• pp.264-272
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• 1998

This paper proposes a fault detection and recovery mechanism for a fault-tolerant Mini-MAP system, and provides detailed techniques for its implementation. This paper considers the fault-tolerant Mini-MAP system which has dual layer structure from the LLC sublayer down to the physical layer to cope with the faults of those layers. For a good fault detection, a redundant and hierarchical fault supervision architecture is proposed and its implementation technique for a stable detection operation is provided. Information for the fault location is provided from data reported with a fault detection and obtained by an additional network diagnosis. The faults are recovered by the stand-by sparing method applied for a dual network composed of two equivalent networks. A network switch mechanism is proposed to achieve a reliable and stable network function. A fault-tolerant Mini-MAP system is implemented by applying the proposed fault detection and recovery mechanism.

• International Journal of Air-Conditioning and Refrigeration
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• v.11 no.4
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• pp.170-177
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• 2003

The electric power load during the summer peak time is strongly affected by cooling load, which decreases the preparation ratio of electricity and brings about the failure in the supply of electricity in the electric power system. The ice storage system and heat pump system etc. are used to settle this problem. In this study, the method of estimating temperature and humidity to forecast the cooling load of ice storage system is suggested. The method of forecasting the cooling load using neural network is also suggested. The daily cooling load is mainly dependent on actual temperature and humidity of the day. The simulation is started with forecasting the temperature and humidity of the following day from the past data. The cooling load is then simulated by using the forecasted temperature and humidity data obtained from the simulation. It was observed that the forecasted data were closely approached to the actual data.

• Journal of Power Electronics
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• v.14 no.4
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• pp.754-763
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• 2014

In this paper a method for the development of a static VAR compensator (SVC) control algorithm is presented. The proposed algorithm has been designed with the objective of eliminating the negative impact of electric arc furnaces on the power system. First, a mathematical model of the proposed SVC controller in the d-q synchronous rotating coordinate system is developed. An analysis under dynamic and steady state conditions is also carried out. The efficiency of the presented controller is demonstrated by means of computer simulations of an actual steel-factory network model. The major advantages of the proposed controller are better flicker compensation, increased ability to regulate voltage and the need for only one-point network measurements.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.16 no.3
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• pp.61-66
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• 2002

Electric power distribution networks are prime examples of systems where a very high degree of reliability is expected. Reliability is the probability of a device or system performing its function adequately for the period of time intended and intented operating conditions intented. This paper shows that a better meshed distribution configuration over the case study of radial configuration distribution system was selected by comparing the indices obtained from EDSA\`s reliability worth assessment of distribution systems program.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.1904-1921
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• 2019

In this paper, we investigate the problem of secure communications in multiple-input-multiple-output interference networks from the perspective of physical layer security. Specifically, the legitimate transmitter-receiver pairs are divided into different categories of active and inactive. To enhance the security performances of active pairs, inactive pairs serve as cooperative jammers and broadcast artificial noises to interfere with the eavesdropper. Besides, active pairs improve their own security by using joint transceivers. The encoding of active pairs and inactive pairs are designed by maximizing the difference of mean-squared errors between active pairs and the eavesdropper. In detail, the transmit precoder matrices of active pairs and inactive pairs are solved according to game theory and linear programming respectively. Experimental results show that the proposed algorithm has fast convergence speed, and the security performances in different scenarios are effectively improved.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.236-241
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• 2024

Due to the recent emphasis on carbon neutrality and environmental regulations, the global electric vehicle (EV) market is experiencing rapid growth. This surge has raised concerns about the recycling and disposal methods for EV batteries. Unlike traditional internal combustion engine vehicles, EVs require unique and safe methods for the recovery and disposal of their batteries. In this process, predicting the lifespan of the battery is essential. Impedance and State of Charge (SOC) analysis are commonly used methods for this purpose. However, predicting the lifespan of batteries with complex chemical characteristics through electrical measurements presents significant challenges. To enhance the accuracy and precision of existing measurement methods, this paper proposes using a Long Short-Term Memory (LSTM) model, a type of deep learning-based recurrent neural network, to diagnose battery performance. The goal is to achieve safe classification through this model. The designed structure was evaluated, yielding results with a Mean Absolute Error (MAE) of 0.8451, a Root Mean Square Error (RMSE) of 1.3448, and an accuracy of 0.984, demonstrating excellent performance.