• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.171-171
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• 2000

We have developed m innovative surface inspection system for automated quality control for steel products in POSCO. We had ever installed the various kinds of surface inspection systems, such as a linear CCD and a laser typed surface inspection systems at cold rolled strips production lines. But, these systems cannot fulfill the sufficient detection and classification rate, and real time processing performance. In order to increase detection and classification rate, we have used the Dark, Bright and Transition Field illumination and area type CCD camera, and fur the real time image processing, parallel computing has been used. In this paper, we introduced the automatic surface inspection system and real time image processing technique using the Object Detection, Defect Detection, Classification algorithms and its performance obtained at the production line.

• Journal of the Korean Society for Nondestructive Testing
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• v.35 no.3
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• pp.193-199
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• 2015

Active thermography techniques have the capability of inspecting a broad range simultaneously. By evaluating the phase difference between the defected area and the healthy area, the technique indicates the qualitative location and size of the defect. Previously, the development of the defect detection method used a variety of materials and the test specimen was done. In this study, the proposed technique of lock-in is verified with artificial specimens that have different size and depth of subsurface defects. Finally, the defect detection capability was evaluated using comparisons of the phase image and the amplitude image according to the size and depth of defects.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.2059-2064
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• 2004

Texture analysis is an important role for automatic visual insfection. This paper presents an application of wavelet extension and Gray level co-occurrence matrix (GLCM) for detection of defect encountered in textured images. Texture characteristic in low quality images is not to easy task to perform caused by noise, low frequency and small dimension. In order to solve this problem, we have developed a procedure called wavelet image extension. Wavelet extension procedure is used to determine the frequency bands carrying the most information about the texture by decomposing images into multiple frequency bands and to form an image approximation with higher resolution. Thus, wavelet extension procedure offers the ability to robust feature extraction in images. Then the features are extracted from the co-occurrence matrices computed from the sub-bands which performed by partitioning the texture image into sub-window. In the detection part, Mahalanobis distance classifier is used to decide whether the test image is defective or non defective.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2007.06a
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• pp.307-308
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• 2007

• Journal of the Korean Physical Society
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• v.73 no.11
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• pp.1644-1649
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• 2018

Aiming at infrared thermal images with different buried depth defects, we study a variety of image segmentation algorithms based on the threshold to develop global search ability and the ability to find the defect area accurately. Firstly, the iterative thresholding method, the maximum entropy method, the minimum error method, the Ostu method and the minimum skewness method are applied to image segmentation of the same infrared thermal image. The study shows that the maximum entropy method and the minimum error method have strong global search capability and can simultaneously extract defects at different depths. However none of these five methods can accurately calculate the defect area at different depths. In order to solve this problem, we put forward a strategy of "divide and conquer". The infrared thermal image is divided into several local thermal maps, with each map containing only one defect, and the defect area is calculated after local image processing of the different buried defects one by one. The results show that, under the "divide and conquer" strategy, the iterative threshold method and the Ostu method have the advantage of high precision and can accurately extract the area of different defects at different depths, with an error of less than 5%.

• Journal of the Korean Society for Railway
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• v.10 no.1 s.38
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• pp.1-6
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• 2007

Railway wheels in service are regularly checked by ultrasonic testing, acoustic emission and eddy current testing method and so on. However, ultrasonic testing is sometimes inadequate for sensitively detecting the cracks in railway wheel which is mainly because of the fact of crack closure. Recently, many researchers have actively fried to improve precision for defect detection of railway wheel. The development of a nondestructive measurement tool for wheel defects and its use for the maintenance of railway wheels would be useful to prevent wheel failure. The induced current focusing potential drop(ICFPD) technique is a new non-destructive tasting technique that can detect defects in railway wheels by applying on electro-magnetic field and potential drops variation. In the present paper, the ICFPD technique is applied to the detection of surface and internal defects for railway wheels. To defect the defects for railway wheels, the sensor for ICFPD is optimized and the tests are carried out with respect to 4 surface defects and 6 internal defects each other. The results show that the surface crack depth of 0.5 mm and internal crack depth of 0.7 mm in wheel tread could be detected by using this method. The ICFPB method is useful to detect the defect that initiated in the tread of railway wheels

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.1
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• pp.55-62
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• 2013

Automatic inspection system is required for increment of copper plate production and demand expansion. Thus 3D surface form and defect detection of copper plate calls for 3D image and GUI analysis. Limitation of 2D analysis, such as error occurrence and decision difficulty makes eye inspection automatic. Automatic inspection is able to raise accurate inspection rate and productivity efficiency elevation. In this paper defect classification is defined and inspection system is implemented. Defect analysis algorithms and GUI for 3D image analysis is developed and tested.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.6
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• pp.604-608
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• 2008

The TFT-LCD image allows non-uniform illumination variation and that is one of main difficulties of finding defect region. The SQI (self quotient image) has the HPF (high pass filter) shape and is used to reduce low frequency-lightness component. In this paper, we proposed the TFT-LCD defect-enhancement algorithm using characteristics of the SQI, that is the SQI has low-frequency flattening effect and maintains local variation. The proposed method has superior flattening effect and defect-enhancement effect compared with previous the TFT-LCD image preprocessing.

• Journal of the Korean Society for Nondestructive Testing
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• v.27 no.3
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• pp.246-254
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• 2007

The scanning laser source (SLS) technique is a promising new laser ultrasonic tool for the detection of small surface-breaking defects. The SLS approach is based on monitoring the changes in laser-generated ultrasound as a laser source is scanned over a defect. Changes in amplitude and frequency content are observed for ultrasound generated by the laser over uniform and defective areas. The SLS technique uses a point or a short line-focused high-power laser beam which is swept across the test specimen surface and passes over surface-breaking or subsurface flaws. The ultrasonic signal that arrives at the Rayleigh wave speed is monitored as the SLS is scanned. It is found that the amplitude and frequency of the measured ultrasonic signal have specific variations when the laser source approaches, passes over and moves behind the defect. In this paper, the setup for SLS experiments with full B-scan capability is described and SLS signatures from small surface-breaking and subsurface flaws are discussed using a point or short line focused laser source.

• Journal of KIISE:Software and Applications
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• v.33 no.7
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• pp.605-614
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• 2006

The success of a software development project can be determined by the use of QCD. And as a software's size and complexity increase, the importance of early quality assurance rises. Therefore, more effort should be given to prevention, as opposed to correction. In order to provide a framework for the prevention of defects, defect detection activities such as peer review and testing, along with analysis of previous defects, is required. This entails a systematization and use of quality data from previous development efforts. FMEA, which is utilized for system safety assurance, can be applied as a means of software defect prevention. SW-FMEA (Software Failure Mode Effect Analysis) attempts to prevent defects by predicting likely defects. Presently, it has been applied to requirement analysis and design. SW-FMEA utilizes measured data from development activities, and can be used for defect prevention on both the development and management sides, for example, in planning, analysis, design, peer reviews, testing, risk management, and so forth. This research discusses about related methodology and proposes defect prevention model based on SW-FMEA. Proposed model is extended SW-FMEA that focuses on system analysis and design. The model not only supports verification and validation effectively, but is useful for reducing defect detection.