• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.4 no.2
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• pp.34-39
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• 2008

Steam Generator(SG) tubes are classified as one of the key components in nuclear power plants, and they should be periodically examined by the intensified management program for the assurance and diagnosis of their structural integrity. In this study, we use the optimum analysis technique to draw the detection and categorization of bowing(BOW) signals; abnormal tube-to-tube proximity in the SG upper bundle free span area. The locations in which BOW signals are detected likely have latent degradation of ODSCC(Outer Diameter Stress Corrosion Cracking). For the sake of timely and correct detection of BOW signals and diagnosis of ODSCC, we carried out the experimental demonstrations using a reduced mock-up. And we validated the MRPC(Motorized Rotating Pancake Coil) analysis technique is better than the bobbin. Hence, it comes to conclusion that the optimum analysis technique can be a good alternative for the reliable SG tube examination.

• Journal of the Korean Professional Engineers Association
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• v.31 no.3
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• pp.65-74
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• 1998

• Journal of the Korean Professional Engineers Association
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• v.32 no.2
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• pp.49-58
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• 1999

• Journal of the Korean Professional Engineers Association
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• v.32 no.1
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• pp.57-66
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• 1999

• Journal of the Korean Professional Engineers Association
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• v.31 no.4
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• pp.58-63
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• 1998

• Journal of the Korean Professional Engineers Association
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• v.31 no.5
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• pp.49-55
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• 1998

• Journal of the Korean Professional Engineers Association
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• v.31 no.6
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• pp.69-77
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• 1998

• Proceedings of the Korean Vacuum Society Conference
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• 2015.08a
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• pp.142.2-142.2
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• 2015

Plasma etch endpoint detection (EPD) of SiO2 and PR layer is demonstrated by plasma impedance monitoring in this work. Plasma etching process is the core process for making fine pattern devices in semiconductor fabrication, and the etching endpoint detection is one of the essential FDC (Fault Detection and Classification) for yield management and mass production. In general, Optical emission spectrocopy (OES) has been used to detect endpoint because OES can be a simple, non-invasive and real-time plasma monitoring tool. In OES, the trend of a few sensitive wavelengths is traced. However, in case of small-open area etch endpoint detection (ex. contact etch), it is at the boundary of the detection limit because of weak signal intensities of reaction reactants and products. Furthemore, the various materials covering the wafer such as photoresist (PR), dielectric materials, and metals make the analysis of OES signals complicated. In this study, full spectra of optical emission signals were collected and the data were analyzed by a data-mining approach, modified K-means cluster analysis. The K-means cluster analysis is modified suitably to analyze a thousand of wavelength variables from OES. This technique can improve the sensitivity of EPD for small area oxide layer etching processes: about 1.0 % oxide area. This technique is expected to be applied to various plasma monitoring applications including fault detections as well as EPD.

• Journal of Korean Society of Transportation
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• v.26 no.2
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• pp.177-186
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• 2008

Video Image Detection System can be used for various traffic managements including traffic operation and traffic safety. Video Image Detection Technique can be divide by Tripwire System and Tracking System. Autoscope, which is widely used in the market, utilizes the Tripwire System. In this study, we developed an individual vehicle tracking system that can collect microscopic traffic information and also developed another image detection technology under the Tripwire System. To prove the accuracy and reliability of the newly developed systems, we compared the traffic data of the systems with those generated by Autoscope. The results showed that 0.35% of errors compared with the real traffic counts and 1.78% of errors with Autoscope. Performance comparisons on speed from the two systems showed the maximum errors of 1.77% with Autoscope, which confirms the usefulness of the newly developed systems.

• KIISE Transactions on Computing Practices
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• v.23 no.2
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• pp.128-133
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• 2017

In recent years, the number of systems for the analysis of large volumes of data is increasing. Hadoop, a representative big data system, stores and processes the large data in the distributed environment of multiple servers, where system-resource management is very important. The authors attempted to detect anomalies from the rapid changing of the log data that are collected from the multiple servers using simple but efficient anomaly-detection techniques. Accordingly, an Apache Hive storage architecture was designed to store the log data that were collected from the multiple servers in the Hadoop ecosystem. Also, three anomaly-detection techniques were designed based on the moving-average and 3-sigma concepts. It was finally confirmed that all three of the techniques detected the abnormal intervals correctly, while the weighted anomaly-detection technique is more precise than the basic techniques. These results show an excellent approach for the detection of log-data anomalies with the use of simple techniques in the Hadoop ecosystem.