• 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.

• Journal of Software Engineering Society
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• v.25 no.4
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• pp.69-74
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• 2012

This article suggests applicability to community detection algorithm from module recovering process of software architecture through compare to software clustering metric and community dectection metric. in addition to, analyze mutual relation and difference between separated module and measurement value of typical clustering algorithms and community detection algorithms. and then only sugeested several kinds basis that community detection algorithm can use to recovering module view of software architecture and, by so comparing measurement value of existing clustering metric and community algorithms, this article suggested correlation of two result data.

• Journal of the Korean institute of surface engineering
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• v.56 no.6
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• pp.353-370
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• 2023

Corrosion detection and analysis is a very important topic in reducing costs and preventing disasters. Recently, image processing techniques have been widely applied to corrosion identification and analysis. In this work, we briefly introduces traditional image processing techniques and machine learning algorithms applied to detect or analyze corrosion in various fields. Recently, machine learning, especially CNN-based algorithms, have been widely applied to corrosion detection. Additionally, research on applying machine learning to region segmentation is very actively underway. The corrosion is reddish and brown in color and has a very irregular shape, so a combination of techniques that consider color and texture, various mathematical techniques, and machine learning algorithms are used to detect and analyze corrosion. We present examples of the application of traditional image processing techniques and machine learning to corrosion detection and analysis.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.9
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• pp.1828-1836
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• 2015

Object tracking algorithms can reduce computational cost by avoiding computation over the whole image through the selection of region of interests based on object detection. So, accurate object detection is an important task for object tracking. The background subtraction algorithm has been widely used in moving object detection using a stationary camera. However, it has the problem of object detection error due to incorrect background modeling, whereas the method of background modeling has been improved by many researches. This paper proposes a new moving object detection algorithm to overcome the drawback of the conventional background subtraction algorithm by combining the background subtraction algorithm with the motion history image algorithm that is usually used in gesture detection. Although the proposed algorithm demands more processing time because of time taken for combining two algorithms, it meet the real-time processing requirement. Moreover, experimental results show that it has higher accuracy compared with the previous two algorithms.

• Journal of KIISE:Computer Systems and Theory
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• v.29 no.10
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• pp.561-572
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• 2002

Deadlock detection in distributed systems is considered difficult since no single site knows the exact information on the whole system state. This paper proposes a time-efficient algorithm for distributed deadlock detection and resolution. The initiator of the algorithm propagates a deadlock detection message and builds a reduced wait-for graph from the information carried by the replies. The time required for deadlock detection is reduced to half of that of the other algorithms. Moreover, any deadlock reachable from the initiator is detected whereas most previous algorithms only find out whether the initiator is involved in deadlock. This feature accelerates the detection of deadlock. Resolution of the detected deadlock is also simplified and precisely specified, while the current algorithms either present no resolution scheme or simply abort the initiator of the algorithm upon detecting deadlock.

• Journal of Broadcast Engineering
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• v.24 no.7
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• pp.1266-1275
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• 2019

With increasing criminal rates and number of CCTVs, much attention has been paid to intelligent surveillance system on the horizon. Object detection and tracking algorithms have been developed to reduce false alarms and accurately help security agents immediately response to undesirable changes in video clips such as crimes and accidents. Many studies have proposed a variety of algorithms to improve accuracy of detecting and tracking objects outside tunnels. The proposed methods might not work well in a tunnel because of low illuminance significantly susceptible to tail and warning lights of driving vehicles. The detection performance has rarely been tested against the tunnel environment. This study investigated a feasibility of object detection and tracking in an actual tunnel environment by utilizing YOLOv3 and Kernelized Correlation Filter. We tested 40 actual video clips to differentiate pedestrians and motorcycles to evaluate the performance of our algorithm. The experimental results showed significant difference in detection between pedestrians and motorcycles without false positive rates. Our findings are expected to provide a stepping stone of developing efficient detection algorithms suitable for tunnel environment and encouraging other researchers to glean reliable tracking data for smarter and safer City.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.23 no.9A
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• pp.2197-2210
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• 1998

In the grand alliance (GA) HDTV receiver, a coherent detection is empolyed for coherent demodulation of vestigial side-band (VSB) signal by using frequency and phaselocked loop(FPLL) operating on the pilot carrier. Additional phase tracking loop (PTL) employed to track out phase noise that has not been removed by the FPLL in theGA system. In this paper, we propose an algorithm for phase detection which utilizes a weighting function. The simplest implementation of the proposed algorithm using te sign of the Q channel component can be tractable by imposing a phase detection gain to the loop gain. It is obserbed that the propsoed algorithm has a robust characteristic against the performance of the digital filters used for Q channel estimation. A second goal of this paper is to introduce a gain control algorithm for the PTL in order to provide an effective implementation of the proposed phase detection algorithm. And we design the PTL through the realization of the simplified digital filter for H/W reduction. The proposed algorithms and the designed PTL are evaluated by computer simulation. In spite of using the simplified H/W structure, simulation results show that the proposed algorithms outperform the coventional PTL algorithms in the phase detection and tracking performance.

• Korean Journal of Remote Sensing
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• v.22 no.5
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• pp.367-372
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• 2006

Cloud detection algorithm is being developed as primary one of the 16 baseline products of CMDPS (COMS Meteorological Data Processing System), which is under development for the real-time application of data will be observed from COMS Meteorological Imager. For cloud detection from satellite data, we studied two different algorithms. One is threshold technique based algorithm, which is traditionally used, and another is artificial neural network model. MPEF scene analysis algorithm is the basic idea of threshold cloud detection algorithm, and some modifications are conducted for COMS. For the neural network, we selected MLP with back-propagation algorithm. Prototype software of each algorithm was completed and evaluated by using the MTSAT-IR and GOES-9 data. Currently the software codes are standardized using Fortran90 language. For the preparation as an operational algorithm, we will setup the validation strategy and tune up the algorithm continuously. This paper shows the outline of the two cloud detection algorithms and preliminary test results of both algorithms.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.411-414
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• 2009

Many victims and property damages are caused in fires every year. In this paper, intelligent fire-detection systems with embedded fire-detection algorithms for early fire detection and alarm is proposed to reduce fire damages by using image processing technique, high speed digital signal processor(DSP) technique, and information technique. The fire detection algorithms used for the proposed systems consist of flame and smoke detection algorithms. If flame or smoke is detected respectively, the corresponding alarm signal can be transferred to management computer. And if flame and smoke is detected simultaneously, the fire alarm signal shall be generated. Through several experiments in the physical environment, it is shown that the proposed system works well without malfunction.

• Journal of the Society of Naval Architects of Korea
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• v.59 no.5
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• pp.330-337
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• 2022

Research on object detection algorithms using 2D data has already progressed to the level of commercialization and is being applied to various manufacturing industries. Object detection technology using 2D data has an effective advantage, there are technical limitations to accurate data generation and analysis. Since 2D data is two-axis data without a sense of depth, ambiguity arises when approached from a practical point of view. Advanced countries such as the United States are leading 3D data collection and research using 3D laser scanners. Existing processing and detection algorithms such as ICP and RANSAC show high accuracy, but are used as a processing speed problem in the processing of large-scale point cloud data. In this study, PointNet a representative technique for detecting objects using widely used 3D point cloud data is analyzed and described. And RandLA-Net, which overcomes the limitations of PointNet's performance and object prediction accuracy, is described a review of detection technology using point cloud data was conducted.