• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.8B
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• pp.482-491
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• 2007

This paper proposes a k-disjoint-path routing algorithm that provides energy efficient and reliable message transmission in wireless sensor networks. The proposed algorithm sends messages through a single path without the occurrence of critical events. However, it sends through k disjoint paths(k>1) under the occurrence of critical events. The proposed algorithm detects the occurrence of critical events by monitoring changing data patterns, and calculates k from a well-defined fault model and the target-delivery ratio. Our simulations reveal that the proposed algorithm is more resilient to node failure than other routing algorithms, and it also decreases energy consumption and reduces the average delay much more than multi-path and path-repair algorithms.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.7
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• pp.1483-1490
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• 2003

In existent industry spot, device administrators should be ever-ready for monitor and control of devices. Thus, it is inefficient because there are much human strength waste and time loss. To solve these problem, we develop a real time device management system based on wire and wireless internet. If any device brutes down, this system senses breakdown automatically and notify it to device administrators via mobile phones. Then the administrator does emergency measures for breakdown device though wire and wireless internet. For the purpose, we develop each local device control system. device management system, mobile system, and web system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1777-1783
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• 2021

The use of drones is gradually increasing for the efficient maintenance of large-scale renewable energy power generation complexes. For a long time, photovoltaic panels have been photographed with drones to manage panel loss and contamination. Various approaches using artificial intelligence are being tried for efficient maintenance of large-scale photovoltaic complexes. Recently, semantic segmentation-based application techniques have been developed to solve the image classification problem. In this paper, we propose a classification model using semantic segmentation to determine the presence or absence of failures such as arcs, disconnections, and cracks in solar panel images obtained using a drone equipped with a thermal imaging camera. In addition, an efficient classification model was implemented by tuning several factors such as data size and type and loss function customization in U-Net, which shows robust classification performance even with a small dataset.

• Current Industrial and Technological Trends in Aerospace
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• v.8 no.1
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• pp.77-85
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• 2010

The current GPS constellation consists of 32 Block IIA/IIR/IIR-M satellites including 12 Block IIA satellites on service over 15 years. The satellites in poor space conditions may suffer from anomalies, especially influenced by aging atomic clocks which are of importance positioning and timing. Recently, the IGS Ultra-rapid predicted products have not shown acceptably high quality prediction performance because the Block IIA cesium clocks may be easily affected by various factors such as temperature and environment. The anomalies of aging clocks involve lower performance of positioning in the GPS applications. We, thus, describe satellite clock behaviors and anomalies induced by aging clocks and their detection technologies to avoid such anomalies.

• Proceedings of the KIEE Conference
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• 2007.04b
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• pp.143-145
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• 2007

화력발전소 S/Y(switchyard)의 362 kV GIS에 대한 부분방전 정밀측정을 수행하였다. 2005년 부터 화력발전소 S/Y GIS의 고장예방을 위해 설치 운전승인 UHF 부분방전 상시감시시스템에서 상당히 큰 부분 방전신호가 지속적으로 감지되었다. 상시감시시스템에서 방전신호의 원인을 부유전극으로 추정하였으며, 이에 해당 사업소에서 감지된 방전신호의 노이즈 여부 및 방전 발생위치 추정을 위한 기술지원을 요청하였다. 이에 따라 전력연구원은 방전신호를 정밀 측정한 후 PRPD(phase resolved partial discharge) 분석을 통해 부유전극이 GIS 내부에 존재함을 확인하였으며, TOA(time of arrival)법에 의해 제 1 발전기 step-up 변압기부터 인근 가스절연모선 사이에 결함이 존재하는 것으로 추정하였다. 전력연구원의 측정결과를 바탕으로 발전소측에서 해당 개소를 분해 점검한 결과 가스절연모선의 중앙도체를 지지하는 supporting insulator 금구류의 접촉불량에 의한 부유전극 결함을 발견하고 보수하였다. 보수후 상시감시시스템에서 방전신호가 검출되지 않음에 따라 방전원인이 제거되었음을 확인하였다.

• The Journal of Bigdata
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• v.6 no.1
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• pp.23-35
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• 2021

In the era of the 4th industrial revolution, smart factories have received great attention, where production and manufacturing technology and ICT converge. With the development of IoT technology and big data, automation of production systems has become possible. In the advanced manufacturing industry, production systems are subject to unscheduled performance degradation and downtime, and there is a demand to reduce safety risks by detecting and reparing potential errors as soon as possible. This study designs a model based on supervised and unsupervised learning for detecting anomalies. The accuracy of XGBoost, LightGBM, and CNN models was compared as a supervised learning analysis method. Through the evaluation index based on the confusion matrix, it was confirmed that LightGBM is most predictive (97%). In addition, as an unsupervised learning analysis method, MD, AE, and LSTM-AE models were constructed. Comparing three unsupervised learning analysis methods, the LSTM-AE model detected 75% of anomalies and showed the best performance. This study aims to contribute to the advancement of the smart factory by combining supervised and unsupervised learning techniques to accurately diagnose equipment failures and predict when abnormal situations occur, thereby laying the foundation for preemptive responses to abnormal situations. do.

• The Journal of Society for e-Business Studies
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• v.23 no.2
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• pp.111-121
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• 2018

Many problems in rotating machinery such as aircraft engines, wind turbines and motors are caused by bearing defects. The abnormalities of the bearing can be detected by analyzing signal data such as vibration or noise, proper pre-processing through a few signal processing techniques is required to analyze their frequencies. In this paper, we introduce the condition monitoring method for diagnosing the failure of the rotating machines by analyzing the vibration signal of the bearing. From the collected signal data, the normal states are trained, and then normal or abnormal state data are classified based on the trained normal state. For preprocessing, a Hamming window is applied to eliminate leakage generated in this process, and the cepstrum analysis is performed to obtain the original signal of the signal data, called the formant. From the vibration data of the IMS bearing dataset, we have extracted 6 statistic indicators using the cepstral coefficients and showed that the application of the Mahalanobis distance classifier can monitor the bearing status and detect the failure in advance.

• Journal of Navigation and Port Research
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• v.46 no.4
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• pp.367-374
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• 2022

The purpose of this study was to propose a deep learning algorithm that applies to the fault diagnosis of fuel pumps and purifiers of autonomous ships. A deep learning algorithm reflecting the time dependence of the measured signal was configured, and the failure pattern was trained using the vibration signal, measured in the equipment's regular operation and failure state. Considering the sequential time-dependence of deterioration implied in the vibration signal, this study adopts Conv1D with sliding window computation for fault detection. The time dependence was also reflected, by transferring the measured signal from two-dimensional to three-dimensional. Additionally, the optimal values of the hyper-parameters of the Conv1D model were determined, using the grid search technique. Finally, the results show that the proposed data preprocessing method as well as the Conv1D model, can reflect the sequential dependency between the fault and its effect on the measured signal, and appropriately perform anomaly as well as failure detection, of the equipment chosen for application.

• Journal of Broadcast Engineering
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• v.25 no.1
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• pp.13-24
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• 2020

There are about nine million power line poles and 1.3 million kilometers of the power line for electric power distribution in Korea. Maintenance of such a large number of electric power facilities requires a lot of manpower and time. Recently, various fault diagnosis techniques using artificial intelligence have been studied. Therefore, in this paper, proposes a power line deflection detect system using artificial intelligence and computer vision technology in images taken by vision system. The proposed system proceeds as follows. (i) Detection of transmission tower using object detection system (ii) Histogram equalization technique to solve the degradation in image quality problem of video data (iii) In general, since the distance between two transmission towers is long, a panoramic video stitching process is performed to grasp the entire power line (iv) Detecting deflection using computer vision technology after applying power line detection algorithm This paper explain and experiment about each process.

• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.135-143
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• 2022

As the world has implemented social distancing and telecommuting due to the spread of COVID-19, real-time streaming sessions based on routing protocols have increased dependence on the Internet due to the activation of video and voice-related content services and cloud computing. BGP is the most widely used routing protocol, and although many studies continue to improve security, there is a lack of visual analysis to determine the real-time nature of analysis and the mis-detection of algorithms. In this paper, we analyze BGP data, which are powdered as normal and abnormal, on a real-world basis, using an anomaly detection algorithm that combines statistical and post-processing statistical techniques with Rule-based techniques. In addition, we present an interactive spatio-temporal analysis plan as an intuitive visualization plan and analysis result of the algorithm with a map and Sankey Chart-based visualization technique.