• Proceedings of the KIEE Conference
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• 대한전기학회 2006년도 심포지엄 논문집 정보 및 제어부문
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• pp.231-233
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• 2006

Arc Fault Current is an electric discharge which is occurred in two opposite electrode. In this paper, arc current control algorithm is designed for the interruption of arc fault current which is occurred in the low voltage network. This arc is one of the main causes of electric fire. General arc current sensor has troubles for detecting arc currents, thus we would like to propose the arc current detection method without current sensor. In this paper, arc discharge currents within power lines are being detected through the arc current control algorithm.

• Proceedings of the KIEE Conference
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• 대한전기학회 2008년도 제39회 하계학술대회
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• pp.155-156
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• 2008

비접지 계통에서 대부분의 고장은 1선 지락 고장이며 1선 지락 고장 시 고장 전류의 크기가 매우 작기 때문에 고장 검출에 어려움이 있다. 본 논문에서는 영상전류를 이용하여 새로운 FI 검출 알고리즘을 제안한다. 제안된 알고리즘은 선간전압과 영상전류만을 이용하여 단말에서 고장회선, 고장상, 고장구간을 한 번에 판별하여 FI를 검출한다. 제안된 알고리즘은 Matlab/Simulilnk를 통해 계통을 모의하여 검증하였다.

• Journal of Advanced Navigation Technology
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• 제16권1호
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• pp.145-150
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• 2012

This paper explains a performance analysis of an error detection system running on nested session management of cloud computing collaboration environment using rule-based DEVS modeling and simulation techniques. In DEVS, a system has a time base, inputs, states, outputs, and functions. This paper explains the design and implementation of the FDA(Fault Detection Agent). FDA is a system that is suitable for detecting software error for multimedia remote control based on nested session management of cloud computing collaboration environment.

• Proceedings of the KIEE Conference
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• 대한전기학회 1997년도 하계학술대회 논문집 B
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• pp.485-487
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• 1997

Schemes for detecting and diagnosing faults are presented. Faults are detected when residuals change significantly and thresholds are exceed. Two stage artificial neural networks are applied to diagnose faults. The idealized steady state patterns of residuals are defined and learned by ANNs using back propagation algorithm. The first stage ANN is trained to classify the subsystem in which the various faults are located. The first stage ANN could be also used to detect faults with threshold, checking. The second stage ANNs are trained to discriminate the specific cause of a fault at the subsystem level.

• Proceedings of the IEEK Conference
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• 대한전자공학회 2002년도 ITC-CSCC -3
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• pp.1980-1983
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• 2002

When a feedback bridging fault is activated in a circuit, logical oscillation may occur at a signal line. If the oscillation appears, the fault may not be detected by logic testing. In order to detect such bridging faults, output logic values of the circuit should be measured at higher frequency than frequency of the logical oscillation. In this paper, a method fur estimating the maximum frequency of logical oscillation is proposed to detect such bridging faults in a circuit by logic testing. Also, it is shown by some experiments that such bridging faults can be detected by measuring output logic values at the frequency obtained by the method.

• Proceedings of the Korea Information Processing Society Conference
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• 한국정보처리학회 2002년도 춘계학술발표논문집 (상)
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• pp.381-384
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• 2002

This paper describes the motivation, design and performance of bi-active Load balancer in Linux Virtual Server. The goal of bi-active Load balancer is to provide a framework to build highly scalable, fault-tolerant services using a large cluster of commodity servers. The TCP/IP stack of Linux Kernel is extended to support three IP load balancing techniques, which can make parallel services of different kinds of server clusters to appear as a service on a single IP address. Scalability is achieved by transparently adding or removing a node in the cluster. and high availability is provided by detecting node or daemon failures and reconfiguring the system appropriately. Extensive simulation reveals that the proposed approach improves the reply rate about 20% compared to earlier design.

• Journal of Power Electronics
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• 제12권6호
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• pp.982-991
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• 2012

In this study, a new method has been presented for the detection of broken rotor bar (BRB) faults in inverter driven induction motors controlled via Field Oriented Control (FOC). To this end, a FOC controlled induction motor with a BRB fault was modeled using the Matlab/Simulink program. Experiments were carried out using the prepared simulation model at various loads and operating speeds. The motor current and speeds were monitored for healthy, 1, 2 and 3 BRB faults. The Resampling Based Order Tracking Analysis (RB-OTA) method was applied to the monitored signals. The obtained results were compared by using the classic Fast Fourier Transform (FFT) method. When the obtained results were analyzed via the FFT method no information regarding any faults was determined in the run up or run down regions of the motor and the presented method gave very good results. The reliability of the proposed method was validated with experimental results. The main innovative part of this study is that the RB-OTA method was implemented on the induction motor current signal for detecting BRB faults.

• Proceedings of the KIEE Conference
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• 대한전기학회 1996년도 추계학술대회 논문집 학회본부
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• pp.152-156
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• 1996

Identification of incipient faults and various events on the distribution feeders is very important to develop the prediction method of fault symptom. In this paper, the configuration of data acquisition system to get the real field data is introduced. And the Quantification of incipient faults is also discussed. Based on the acquired field data, how the time domain parameters of voltage and current signals are applied to this research is partly introduced.

• Journal of Positioning, Navigation, and Timing
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• 제12권3호
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• pp.257-269
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• 2023

This paper presents a method for detecting faults in data obtained from the Automatic Identification System (AIS) of surface vessels. The data include latitude, longitude, Speed Over Ground (SOG), and Course Over Ground (COG). We derive two methods that utilize two models: a constant state model and a derivative augmented model. The constant state model incorporates noise variables to account for state changes, while the derivative augmented model employs explicit variables such as first or second derivatives, to model dynamic changes in state. Generally, the derivative augmented model detects faults more promptly than the constant state model, although it is vulnerable to potentially overlooking faults. The effectiveness of this method is validated using AIS data collected at a harbor. The results demonstrate that the proposed approach can automatically detect faults in AIS data, thus offering partial assistance for enhancing navigation safety.