• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1341-1347
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• 2021

The need for an effective disaster management system has grown these days to protect public safety as the number of disasters causing massive damage increases. Since disaster-induced damage can develop in various ways, rapid and accurate countermeasures must be prepared soon after disasters occur. Numerous studies have continuously developed remote sensing and GIS (Geographic Information System)-based techniques for disaster monitoring and damage analysis. This special issue presents the research results on disaster prediction and monitoring based on various remote sensors on different platforms from ground to space and disaster management using GIS techniques. The developed techniques help manage various disasters such as storms, floods, and forest fires and can be combined to achieve an integrated and effective disaster management system.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.2
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• pp.51-60
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• 2021

Effective nonpoint source (NPS) pollution management requires frequent water quality monitoring, which is, however, often costly to be implemented in practice. Statistical techniques and machine learning methods allow us to identify and focus on fundamental environmental variables that have close relationships with NPS pollutants of interest. This study developed surrogate models to predict the concentrations of suspended sediment (SS) and total phosphorus (T-P) from turbidity and runoff discharge rates using multiple linear regression (MLR) and random forest (RF) methods. The RF models provided acceptable performance in predicting SS and T-P, especially when runoff discharge rates were high. The RF models outperformed the MLR models in all the cases. Such finding highlights the potential of RF techniques and models as a tool to identify fundamental environmental variables that are measured in relatively inexpensive ways or freely available but still able to provide information required to quantify the concentrations of NP S pollutants. The analysis of relative importance rates showed that the temporal variations of SS and T-P concentrations could be more effectively explained by that of turbidity than runoff discharge rate. This study demonstrated that the advanced statistical techniques such as machine learning could help to improve the efficiency of NPS pollutants monitoring.

• Magazine of the Korea Concrete Institute
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• v.12 no.3
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• pp.6-15
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• 2000

• Journal of Welding and Joining
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• v.23 no.3
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• pp.26-30
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• 2005

• Proceedings of the Optical Society of Korea Conference
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• 2004.02a
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• pp.80-85
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• 2004

• Journal of Radiation Protection and Research
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• v.14 no.1
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• pp.75-83
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• 1989

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.57 no.2
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• pp.176-180
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• 2008

A field-oriented UHF system for on-line PD monitoring of transformers is designed, which has been installed inside the oil tank of transformer in a substation by two ways: on-line installing mode through the oil-valve and pre-installing mode through the man hole/hand hole cover. This system has successfully captured long intermittent discharge signals that hadn't been detected through conventional techniques, and solved the problem successfully. The results demonstrate that UHF technique has great advantages for on-line PD monitoring of transformers. By adopting the peak detection technique, it becomes easy and effective for the transplantation of the phase-resolved pattern recognition technique from conventional method to UHF method, and then to realize continuous on-line monitoring, source characterization and trending analysis.

• Journal of the Korea institute for structural maintenance and inspection
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• v.2 no.2
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• pp.185-194
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• 1998

This paper shows a case study on quality confirmation and maintenance monitoring of the tunnel underpassing the Han River. First of all, when it comes to the quality confirmation, soil investigation techniques and shotcrete core test will be improved. On the construction stages, quality control procedures are needed. Second, on the maintenance monitoring, it is the main tool to control stability and safety of the tunnel structures throughout the construction period. On the geotechnical monitoring instrumentation, some considerations such as installation of monitoring sections in time, immediate base readings, adequate reading frequency, etc - shall be improved.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.57 no.2
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• pp.202-208
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• 2008

Condition monitoring technologies allow achieving this goal by minimizing downtime through the integrated planning and scheduling of repairs indicated by condition monitoring techniques. Thermal runaways induced by conduction problems deteriorate insulating material and cause disruptive dielectric discharges resulting in arcing faults. Therefore, having the ability to directly measure the temperature of the contacts while in service will provide more information to determine the true condition of the equipment. It allows connective measures to be taken to prevent upcoming failure. Continuous temperature monitoring and event recording provides information on the energized equipment's response to normal and emergency conditions. On-line temperature monitoring helps to coordinate equipment specifications and ratings, determine the real limits of the monitored equipment and optimize facility operations. Using wireless technique eliminates any need for special cables and wires with lower installation costs if compared to other types of online condition monitoring equipment. In addition, wireless temperature monitoring works well under difficult conditions in strategically important locations. Wireless technology for on -line condition monitoring of energized equipment is applicable both as stand alone system and with an interface for power quality monitoring system. The paper presents the results of wireless temperature monitoring: of electrical equipment at a power plant.

• Tribology and Lubricants
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• v.36 no.6
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• pp.297-306
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• 2020

This paper reviews studies on the types and functions of oil sensors used for machine condition monitoring. Machine condition monitoring is essential for maintaining the reliability of machines and can help avoid catastrophic failures while ensuring the safety and longevity of operation. Machine condition monitoring involves several components, such as compliance monitoring, structural monitoring, thermography, non-destructive testing, and noise and vibration monitoring. Real-time monitoring with oil analysis is also utilized in various industries, such as manufacturing, aerospace, and power plants. The three main methods of oil analysis are off-line, in-line, and on-line techniques. The on-line method is the most popular among these three because it reduces human error during oil sampling, prevents incipient machine failure, reduces the total maintenance cost, and does not need complicated setup or skilled analysts. This method has two advantages over the other two monitoring methods. First, fault conditions can be noticed at the early stages via detection of wear particles using wear particle sensors; therefore, it provides early warning in the failure process. Second, it is convenient and effective for diagnosing data regardless of the measurement time. Real-time condition monitoring with oil analysis uses various oil sensors to diagnose the machine and oil statuses; further, integrated oil sensors can be used to measure several properties simultaneously.