• Proceedings of the KIEE Conference
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• 1987.11a
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• pp.463-468
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• 1987

In this paper, a high impedance fault detection on 22.9kV multigrounded distribution system that has been very difficult by any existing conventional protective relaying systems is studied. Because the fault current is very low, it cannot be distinguished from neutral current caused by load unvalanced on multigrounded distribution system. We developed the new and best algorithms of high impedance ground fault detection. This algorithms are 'the even order power method, even order ratio method', 'and even order ratio varience method'. Using this algorithms, a detection device for high impedance faults is constructed and tested in the laboratory. And continually, it is installed and has been tested in KEPCO substations.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.6
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• pp.241-251
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• 2002

The effect of sensor faults in direct torque control(DTC) based induction motor drives is analyzed and a new Instrument fault detection isolation scheme(IFDIS) is proposed. The proposed IFDIS, which operated in real-time, detects and isolates the incipient fault(s) of speed sensor and current sensors that provide the feedback information. The scheme consists of an adaptive gain scheduling observer as a residual generator and a special sequential test logic unit. The observer provides not only the estimate of stator flux, a key variable in DTC system, but also the estimates of stator current and rotor speed that are useful for fault detection. With the test logic, the IFDIS has the functionality of fault isolation that only multiple estimator based IFDIS schemes can have. Simulation results for various type of sensor faults show the detection and isolation performance of the IFDIS and the applicability of this scheme to fault tolerant control system design.

• Structural Monitoring and Maintenance
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• v.2 no.4
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• pp.383-397
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• 2015

This article identifies and addresses current limitations on the use of numerical models for validation and/or calibration of damage detection methodologies that are based on the analysis of the high frequency response of the structure to identify the occurrence of abrupt anomalies. Distributed-plasticity non-linear fiber-based models in combination with experimental data from a full-scale reinforced concrete column test are used to point out current modeling techniques limitations. It was found that the numerical model was capable of reproducing the global and local response of the structure at a wide range of inelastic demands, including the occurrences of rebar ruptures. However, when abrupt sudden damage occurs, like rebar fracture, a high frequency pulse is detected in the accelerations recorded in the structure that the numerical model is incapable of reproducing. Since the occurrence of such pulse is fundamental on the detection of damage, it is proposed to add this effect to the simulated response before it is used for validation purposes.

• Electronics and Telecommunications Trends
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• v.36 no.2
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• pp.75-82
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• 2021

Online action detection (OAD) in a streaming video is an attractive research area that has aroused interest lately. Although most studies for action understanding have considered action recognition in well-trimmed videos and offline temporal action detection in untrimmed videos, online action detection methods are required to monitor action occurrences in streaming videos. OAD predicts action probabilities for a current frame or frame sequence using a fixed-sized video segment, including past and current frames. In this article, we discuss deep learning-based OAD models. In addition, we investigated OAD evaluation methodologies, including benchmark datasets and performance measures, and compared the performances of the presented OAD models.

• Journal of Korea Multimedia Society
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• v.22 no.4
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• pp.432-442
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• 2019

Most current human action recognition methods based on deep learning methods. It is required, however, a very high computational cost. In this paper, we propose an action change detection method to reduce repetitive human action recognition tasks. In reality, simple actions are often repeated and it is time consuming process to apply high cost action recognition methods on repeated actions. The proposed method decides whether action has changed. The action recognition is executed only when it has detected action change. The action change detection process is as follows. First, extract the number of non-zero pixel from motion history image and generate one-dimensional time-series data. Second, detecting action change by comparison of difference between current time trend and local extremum of time-series data and threshold. Experiments on the proposed method achieved 89% balanced accuracy on action change data and 61% reduced action recognition repetition.

• The Transactions of the Korean Institute of Power Electronics
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• v.27 no.1
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• pp.80-83
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• 2022

This paper proposes an initial rotor polarity detection algorithm of a single-phase permanent magnet synchronous motor (SP-PMSM) related to stable open-loop starting for sensorless operation. Generally, the SP-PMSM needs an asymmetric air-gap structure to can avoid the initial starting failure at zero torque point. Therefore, the rotor polarity information can be obtained by using the DC offset current direction of a stator current through a high frequency voltage injection into an SP-PMSM with an asymmetric air gap. In this paper, the proposed rotor initial polarity detection algorithm is verified through several experimental results.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.3
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• pp.149-154
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• 2022

In recent years, the application of deep learning method to computer vision has shown to achieve great performances. Thus, many research projects have also applied deep learning technology to railroad defect detection. In this paper, we have reviewed the researches that applied computer vision based deep learning method to railroad defect detection and inspection, and have discussed the current trend and the direction of those researches. Many research projects were targeted to operate automatically without visual inspection of human and to work in real-time. Therefore, methods to speed up the computation were also investigated. The reduction of the number of learning parameters was considered important to improve computation efficiency. In addition to computation speed issue, the problem of annotation was also discussed in some research projects. To alleviate the problem of time consuming annotation, some kinds of automatic segmentation of the railroad defect or self-supervised methods have been suggested.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.4
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• pp.584-590
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• 2013

Depending on the operating characteristics, track circuit is installed for the purpose of control directly or indirectly of the signal device, point switch machine and other security device. These are mainly used for train detection, transmission of information, broken train detection and transmission of return current. Especially, the return current is related to signal system, power system and catenary line, and track circuit systems. It is one of the most important component shall be dealt for the safety of track side staff and for the protection of railway-related electrical system according to electrification. Therefore, an accurate analysis of the return current is needed to prevent the return current unbalance and the system induced disorder and failure due to an over current condition. Also, if the malfunction occurred by the return current harmonics, it can cause problems including train operation interruption. In this paper, we presented measurement and analysis method at return current and it's harmonics by train operation. By the test criteria, we evaluated for safety. Hereafter, it is expected to contribute to the field associated with it.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.6
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• pp.605-610
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• 2016

The ELCB(: Earth Leakage Circuit Breaker) is the only way being used to prevent accidents from happening caused by electrical disaster. However, the existing ELCB has a limit to prevent damages to life and property due to a electric fire and a human body electric shock caused by the resistive leakage current, because of detecting the total leakage current in the block range of 15mA~30mA. It also has problems such as reduced productivity and reliability due to malfunctions by capacitive leakage currents. In this study, we have implemented the algorithm for the resistive leakage current detection technique and developed the resistive leakage current detection module based on a MSP430 processor, 16bit signal processor and this module can be operated in a desired block threshold within 0.03 seconds as specified in KS C 4613.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.3
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• pp.374-379
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• 2007

This paper focuses on the improvement of fault detection algorithm in FRTU(feeder remote terminal unit) on the feeder of distribution power system. FRTU is applied to fault detection schemes for phase fault and ground fault. Especially, cold load pickup and inrush restraint functions distinguish the fault current from the normal load current. FRTU shows FI(Fault Indicator) when the fault current is over pickup value or inrush current. STFT(Short Time Fourier Transform) analysis provides the frequency and time Information. FCM(Fuzzy C-Mean clustering) algorithm extracts characteristics of harmonics. The neural network system as a fault detector was trained to distinguish the inruih current from the fault status by a gradient descent method. In this paper, fault detection is improved by using FCM and neural network. The result data were measured in actual 22.9kV distribution power system.