• Journal of the Korean Society for Nondestructive Testing
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• v.17 no.4
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• pp.262-269
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• 1997

An eddy current probe ($8{\times}1$ multiple-element, surface scan) was successfully designed and fabricated at the KEPRI using the impedance equivalent circuit theory. The probe is intended for the detection of circumferential deformations (cross-section view) of the heat exchanger tubing that can occur due to corrosion, erosion, and denting. Optimum design parameters providing the highest sensitivity and signal-to-noise ratio, such as the coil dimensions, electrical characteristics, and test frequencies, were determined based on initial laboratory experiments conducted on the test specimen (SS304 tubing: OD : 9.68mm, wall-thickness : 0.47mm) containing artificial flaws (e.g., dents and corroded surface on tube OD) using the available Zetec-made probe. Using this parameters, a new probe was made and tested on an unknown specimen. The result indicated that the new probe is capable of detecting the circumferential deformation with the error of ${\pm}0.2%$ (0.022mm) of the tube O.D.

• Journal of the Institute of Convergence Signal Processing
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• v.4 no.1
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• pp.15-26
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• 2003

Ultrasonic inspection methods are widely used for detecting flaws in materials. The signal analysis step plays a crucial part in the data interpretation process. A number of signal processing methods have been proposed to classify ultrasonic flaw signals. One of the more popular methods involves the extraction of an appropriate set of features followed by the use of a neural network for the classification of the signals in the feature space. This paper describes an alternative approach which uses the least mean square (LMS) method and expectation maximization (EM) algorithm with the model based deconvolution which is employed for classifying nondestructive evaluation (NDE) signals from steam generator tubes in a nuclear power plant. The signals due to cracks and deposits are not significantly different. These signals must be discriminated to prevent from happening a huge disaster such as contamination of water or explosion. A model based deconvolution has been described to facilitate comparison of classification results. The method uses the space alternating generalized expectation maximization (SAGE) algorithm In conjunction with the Newton-Raphson method which uses the Hessian parameter resulting in fast convergence to estimate the time of flight and the distance between the tube wall and the ultrasonic sensor Results using these schemes for the classification of ultrasonic signals from cracks and deposits within steam generator tubes are presented and showed a reasonable performances.

• Journal of the Korean Society for Nondestructive Testing
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• v.25 no.1
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• pp.27-35
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• 2005

Ultrasonic inspection methods are widely used for detecting flaws in materials. The signal analysis step plays a crucial part in the data interpretation process. A number of signal processing methods have been proposed to classify ultrasonic flaw signals. One of the more popular methods involves the extraction of an appropriate set of features followed by the use of a neural network for the classification of the signals in the feature spare. This paper describes an alternative approach which uses the least mean square (LMS) method and exportation maximization (EM) algorithm with the model based deconvolution which is employed for classifying nondestructive evaluation (NDE) signals from steam generator tubes in a nuclear power plant. The signals due to cracks and deposits are not significantly different. These signals must be discriminated to prevent from happening a huge disaster such as contamination of water or explosion. A model based deconvolution has been described to facilitate comparison of classification results. The method uses the space alternating generalized expectation maximiBation (SAGE) algorithm ill conjunction with the Newton-Raphson method which uses the Hessian parameter resulting in fast convergence to estimate the time of flight and the distance between the tube wall and the ultrasonic sensor. Results using these schemes for the classification of ultrasonic signals from cracks and deposits within steam generator tubes are presented and showed a reasonable performances.

• Journal of the Korean Society for Nondestructive Testing
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• v.29 no.2
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• pp.153-161
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• 2009

After several PWSCCs were found in Bugey(France), Ringhals(Sweden), Tihange(Belgium), Oconee, Arkansas, Crystal Fever, Davis-Basse, VC Summer(U.S.A.), Thuruga(Japan), USNRC and PNNL started the research on PWSCC, that is, PINC project. The aim of this project is to fabricate and obtain representative NDE mock-ups with flaws to simulate tight PWSCC cracks, to identify and quantitatively assess NDE methods for accurately detecting, sizing and characterizing tight cracks such as PWSCC, to document the range of locations and crack morphologies associated with PWSCC and observed responses and to incorporate findings from other ongoing PWSCC research programs, as appropriate. By participating in PINC project, Korean morphology technique about PWSCC and NDE technique have improved and become similar lever with other advanced country. Therefore, the evaluation technique of integrity for nickel alloy component has been improved by cooperation with university, research institute and industries.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.11
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• pp.94-103
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• 2017

As automatic hacking tools and techniques have been improved, the number of new vulnerabilities has increased. The CVE registered from 2010 to 2015 numbered about 80,000, and it is expected that more vulnerabilities will be reported. In most cases, patching a vulnerability depends on the developers' capability, and most patching techniques are based on manual analysis, which requires nine months, on average. The techniques are composed of finding the vulnerability, conducting the analysis based on the source code, and writing new code for the patch. Zero-day is critical because the time gap between the first discovery and taking action is too long, as mentioned. To solve the problem, techniques for automatically detecting and analyzing software (SW) vulnerabilities have been proposed recently. Cyber Grand Challenge (CGC) held in 2016 was the first competition to create automatic defensive systems capable of reasoning over flaws in binary and formulating patches without experts' direct analysis. Darktrace and Cylance are similar projects for managing SW automatically with artificial intelligence and machine learning. Though many foreign commercial institutions and academies run their projects for automatic binary analysis, the domestic level of technology is much lower. This paper is to study developing automatic detection of SW vulnerabilities and defenses against them. We analyzed and compared relative works and tools as additional elements, and optimal techniques for automatic analysis are suggested.