• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.3
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• pp.383-388
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• 2014

This paper describes a wave modeling method using low-cost sensors. Wave modeling is applied to the wave monitoring system for accurate measurement of ocean wave parameters. The observation of ocean wave parameters is necessary to improve the accuracy of forecast of ocean wave condition. However, the ocean wave parameters measured by a low-cost wave monitoring system suffer from several errors. Therefore we introduce a wave modeling method to compensate the ocean wave parameters corrupted by errors. The proposed method is analyzed using experiments within controlled environment. It is verified that the accuracy of low-cost wave monitoring system can be increased by the proposed method.

• Journal of the Semiconductor & Display Technology
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• v.11 no.3
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• pp.45-50
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• 2012

In this paper, we propose a real-time ECG monitoring system for personal health records. This study aims to provide services that help patients to monitor their own physical condition and manage their own health records consistently, whereas existing medical services are Medical Institute-Centric model. The system is composed of web server, smart phone, and ECG meter, and web page. Without time and space restraints, It provides us with managing personal health records by performing patient's ECG measurement and real-time monitoring. And also Real-time bidirectional communication between smart phone and web page can be performed rapidly by applying the ECG monitoring with WebSocket Technology that follows HTML5 standard. Through this system, It can handle patient in need immediately.

• Proceedings of the KIEE Conference
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• 2003.07c
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• pp.1644-1646
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• 2003

This paper presents recent studies on the on-line insulation monitoring and diagnostic systems for transformers developed by HHI. Sufficiently high sensitivity and accuracy for practical use were achieved for the system, combined with communication networks to provide an on-line remote monitoring system. Several alarm criteria are formulated to enable a superimposed monitoring system to perform decisive action. The reasons for monitoring the condition and maintaining the health of electrical apparatus were discussed. The experience at the fields and the criteria for the judgment are also discussed in detail.

• Proceedings of the KIEE Conference
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• 2004.05b
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• pp.91-93
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• 2004

This study presents recent trends on the on-line monitoring and diagnostic systems for oil-immersed transformers. Specially, our system on Yeo Su thermal power plant is introduced for high sensitivity and accuracy of ours. It is combined with communication networks to provide an on-line remote monitoring system. Several alarm criteria are formulated to enable a superimposed monitoring system to perform decisive action. The reasons for monitoring the condition and maintaining the health of electrical apparatus were discussed. The experience at the fields and the criteria for the judgment are also discussed in detail.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.190-192
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• 2009

Development of safety monitoring system for workers in the ship building industry is currently under progress using RFID(Radio Frequency IDentification) for successful development of the U-BUSS(Ubiquitous- Safety User Safety System). For decades, RFID technology has become a key technology to provide the real-time location system of worker and is variously used for safety monitoring system to increase productivity, improve the blasting quality and enhance the safety of working condition in the ship building industry In this paper, 2.45GHz band RTLS(Real Time Location System) technologies and the ubiquitous safety monitoring system of the ship yard's blasting cell are described.

• Journal of Electrical Engineering and Technology
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• v.8 no.4
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• pp.813-823
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• 2013

On-line monitoring system is important for high voltage vacuum circuit breakers (HVCBs) in operation condition assessment and fault diagnosis. A distributed multilayer system with client/server architecture is developed on rated voltage 10kV HVCB with spring operating mechanism. It can collect data when HVCB switches, calculate the necessary parameters, show the operation conditions and provide abundant information for fault diagnosis. Ensemble empirical mode decomposition (EEMD) is used to detect the singular point which is regarded as the contact moment. This method has been applied to on-line monitoring system successfully and its satisfactory effect has been proved through experiments. SVM and FCM are both effective methods for fault diagnosis. A combinative algorithm is designed to judge the faults of HVCB's operating mechanism. The system's precision and stability are confirmed by field tests.

• Smart Structures and Systems
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• v.24 no.5
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• pp.567-585
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• 2019

In the past two decades, structural health monitoring (SHM) systems have been widely installed on various civil infrastructures for the tracking of the state of their structural health and the detection of structural damage or abnormality, through long-term monitoring of environmental conditions as well as structural loadings and responses. In an SHM system, there are plenty of sensors to acquire a huge number of monitoring data, which can factually reflect the in-service condition of the target structure. In order to bridge the gap between SHM and structural maintenance and management (SMM), it is necessary to employ advanced data processing methods to convert the original multi-source heterogeneous field monitoring data into different types of specific physical indicators in order to make effective decisions regarding inspection, maintenance and management. Conventional approaches to data analysis are confronted with challenges from environmental noise, the volume of measurement data, the complexity of computation, etc., and they severely constrain the pervasive application of SHM technology. In recent years, with the rapid progress of computing hardware and image acquisition equipment, the deep learning-based data processing approach offers a new channel for excavating the massive data from an SHM system, towards autonomous, accurate and robust processing of the monitoring data. Many researchers from the SHM community have made efforts to explore the applications of deep learning-based approaches for structural damage detection and structural condition assessment. This paper gives a review on the deep learning-based SHM of civil infrastructures with the main content, including a brief summary of the history of the development of deep learning, the applications of deep learning-based data processing approaches in the SHM of many kinds of civil infrastructures, and the key challenges and future trends of the strategy of deep learning-based SHM.

• Proceedings of the KSR Conference
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• 2010.06a
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• pp.989-995
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• 2010

This paper is focused on a simplified measurement of rail irregularity by some axle-box accelerometers for high-speed rail condition monitoring with in-service high-speed trains. Generally, the rail condition monitoring has been done by a special railway inspection vehicle with a 10m versine method. But, the monitoring method needs some expensive measurement system, and have been performed only at night due to its speed limit. In this research, a simplified measurement of rail irregularity using axle-box accelerometers is proposed to monitor the rail condition with in-service high-speed trains. The acceleration is measured by using two accelerometers on a axle-box, and stored in an on-board data acquisition system. The displacement is estimated from the acceleration data by a combination of Kalman filter and the frequency selective filter. The estimated results are compared with the measurement from a laser rail inspection system which is near the axle-box. From the comparison, the proposed method shows promise as a tool for the simplified measurement of rail irregularity at high-speed.

• Journal of Drive and Control
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• v.18 no.1
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• pp.24-30
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• 2021

There is an increasing interest in condition-based maintenance for the prevention of economic loss due to failure. Moreover, immense research is being carried out in related technologies in the field of construction machinery. In particular, data-based failure diagnosis methods that employ AI (machine & deep learning) algorithms are in the spotlight. In this study, we have focused on the failure diagnosis and mode classification of reduction gear of excavator's travel device by using the AI algorithm. In addition, a remote monitoring system has been developed that can monitor the status of the reduction gear by using the developed diagnosis algorithm. The failure diagnosis algorithm was performed in the process of data acquisition of normal and abnormal under various operating conditions, data processing and analysis by the wavelet transformation, and learning. The developed algorithm was verified based on three-evaluation conditions. Finally, we have built a system that can check the status of the reduction gear of travel devices on the web using the Edge platform, which is embedded with the failure diagnosis algorithm and cloud.

• Journal of Korean Society of Disaster and Security
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• v.16 no.4
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• pp.147-156
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• 2023

Because the behavior of cable bridges is dominated by dynamic response and is relatively complex, short- and long-term field monitoring are often required to evaluate the bridge condition. If a permanent SHMS (Structural Health Monitoring System) is not installed, a portable monitoring system is needed for the checking of bridge condition. In this case, it can be difficult to operate the portable monitoring system due to limited conditions such as power and communication according to the location and type of the bridge. In this study, the portable-based smart structural response monitoring system is developed that can be effectively used for short- and long-term monitoring of cable bridges in Korea and Southeast Asia. The developed system is a multi-channel portable data acquisition and analyzer that can be operated for a long time in the field using its own power supply system, and is included with the automated analysis algorithm for the dynamic characteristics of cable bridges using real-time data. In order to evaluate the field applicability of the developed system, field demonstration was conducted on cable bridges in Korea and Vietnam. Through the demonstration, the reliability and efficiency of field operation of the developed system were confirmed, and additionally, the possibility of application to overseas markets was confirmed in cable bridge monitoring field.