• 한국신재생에너지학회:학술대회논문집
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• 2011.11a
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• pp.139.1-139.1
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• 2011

Electric vehicle use independent electricity of high voltage. if isolation of electricity is destructed, devices and people are considerably damaged. Therefore, detection of ground fault is necessary for electric vehicle. As the existing detection method of ground fault can not detect ground fault when isolation of both positive side and negative side of electricity is destructed, and change of voltage of electricity. This paper proposed detection method for ground fault of both two sides of electricity and change of voltage. The proposed method is verified by analysis of equivalent circuit.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.50 no.1
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• pp.15-22
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• 2001

In this paper, a VSC(Voltage Sag Corrector) is discussed for the purpose of power quality enhancement. A fast detecting technique of voltage sag is accomplished through the detection of instantaneous value on synchronous reference frame. A robust characteristic against the noise is available by inserting the first order low pass filter in the detection circuit. The formula and the filter design process is described properly with the mathematical equations. Because the VSC system supply the active power to load, it is required to design the proper size of the energy storage system, In this paper, the capacitor bank is used as an energy storage system, and the size of the capacitor is designed from the point of view of input/output energy as the output power rating and the amplitude and duration time of the voltage sag. The simulation is accomplished by PSCAD/EMTDC.

• Proceedings of the KSR Conference
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• 2009.05a
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• pp.1970-1976
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• 2009

This paper describes a series arc detection algorithm in a low-voltage wiring system. We designed and fabricated an arc detection circuit which consists of a high-pass filter with the low cut-off frequency of 170 kHz to attenuate power frequency. The series arcing phenomena was simulated by an arc generator specified in UL1699. In the experiment, various loads such as resistive loads, resistive loads controlled by a dimmer, and vacuum cleaners were used. Whether the signal is arc or noise is discriminated by pulse counts and periodicity of the detected signal.

• Proceedings of the KSR Conference
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• 2004.10a
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• pp.1229-1234
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• 2004

Accurate and reliable tracking system is essential for the central train control system. There are two systems widely being used. one is the fixed block system which utilizes the track circuit for the detection of train position. the other is the moving block system which has the advantage over the former since it enables shorter radio signal for the train position detection. In this paper a new algorithm is proposed which uses signal's phase difference of arrival to detect the train position. Experimental verification of the algorithm is presented in the paper.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.3
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• pp.498-504
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• 2015

This paper proposed an Spread Spectrum Time Domain Reflectometry (SSTDR) using time-frequency correlation analysis in order to have more accurate fault determination and location detection than classical SSTDR despite increased signal attenuation due to the long distance to cable fault location. The proposed method was validated through comparison with classical SSTDR methods in open- and short-circuit fault detection experiments of low-voltage power cables. The experimental results showed that the proposed method can detect correlation coefficients at fault locations accurately despite reflected signal attenuation so that cable faults can be detected more accurately and clearly in comparison to existing methods.

• Proceedings of the KIPE Conference
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• 2005.07a
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• pp.195-197
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• 2005

This paper proposed the new harmonic detection system for the source current with the large frequency variation. The harmonic detection system is designed of the analog circuit and is composed of a low pass filter, a DC-level converter using the PLL, the Voltage controlled GIC.

• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.1114-1117
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• 1998

Recent applications of neural networks to power system fault diagnosis have provided positive results and have shown advantages in process speed over conventional approaches. This paper describes the application of neural network to fault detection and classification in distribution lines using the fundamental component, 2-5th harmonics index, even and odd harmonics index, and zero phase current. The Electromagnetic Transients Program (EMTP) is used to obtain fault patterns for the training and testing of neural networks. The proposed fault detection and classification method in distribution lines is obtained by analysing the difference among normal, HIF, ground fault, short circuit fault condition.

• Proceedings of the KSR Conference
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• 2008.11b
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• pp.225-229
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• 2008

This paper describes a series arc detection algorithm in a low-voltage wiring system. We designed and fabricated an arc detection circuit which consists of a high-pass filter with the low cut-off frequency of 3 kHz to attenuate power frequency. The series arcing phenomena was simulated by an arc generator specified in UL1699. In the experiment, various loads such as resistive loads, resistive loads controlled by a dimmer, and vacuum cleaners were used. Whether the signal is arc or noise is discriminated by pulse counts and periodicity of the detected signal.

• Journal of Korea Multimedia Society
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• v.24 no.7
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• pp.868-879
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• 2021

PCB (Printed Circuit Board) inspection using a deep learning model requires a large amount of data and storage. When the amount of stored data increases, problems such as learning time and insufficient storage space occur. In this study, the existing object detection model is changed to a continual learning model to enable the recognition and classification of PCB components that are constantly increasing. By changing the structure of the object detection model to a knowledge distillation model, we propose a method that allows knowledge distillation of information on existing classified parts while simultaneously learning information on new components. In classification scenario, the transfer learning model result is 75.9%, and the continual learning model proposed in this study shows 90.7%.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.600-602
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• 2019

Metals are used in a variety of industrial sites and daily life. Metals are often used in machinery, automobile parts, wires, and robots. Metal causes small damage to the metal surface for a variety of reasons, such as the processing process and the user's operating environment. In this paper, phase detection for the inspection of defects in metals has been implemented. Using the electrical conductivity of metal, a circuit whose phase varies with the depth and size of the defect, and with the changed phase, the depth and size of the defect can be estimated.