• Journal of the Korean Society of Safety
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• v.20 no.1 s.69
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• pp.30-35
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• 2005

This study proposes construction of through transmission for weld defects detection of rail weld zone from ultrasonic signals. For these purposes, the ultrasonic signals for defects(porosity and crack) of weld zone in rails are acquired in the type of time series data and echo strength. 6 lines in the distance amplitude characteristics curve(DACC) indicated damage evaluation standard of weld zone in rails. The acquired ultrasonic signals agree flirty well with the mesured results of reference block and sensitivity block(defect location beam propagation distance, echo strength, etc). The proposed construction of through transmission in this study can be used for weld defects detection of rail weld zone.

• Journal of Welding and Joining
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• v.18 no.5
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• pp.77-83
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• 2000

This study proposes the analysis and evaluation method of time series ultrasonic signal using the attractor analysis. Features extracted from time series signal analyze quantitatively characteristics of weld defects. For this purpose, analysis objective in this study is fractal dimension and attractor quadrant feature. Trajectory changes in the attractor indicated a substantial difference in fractal characteristics resulting from distance shifts such as parts of head and flange even though the types of defects are identified. These difference in characteristics of weld defects enables the evaluation of unique characteristics of defects in the weld zone. In quantitative fractal feature extraction, feature values of 3.848 in the case of part of head(crack) and 4.102 in the case of part of web(side hole) and 3.711 in the case of part of flange(crack) were proposed on the basis of fractal dimensions. Proposed attractor analysis and DAC in this study can enhance the precision rate of ultrasonic evaluation for defect signals of rail weld zone such as side hole and crack.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.3
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• pp.559-565
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• 2015

To defend against information leakage or malware inflow by USB memory, security technologies such as copy protection and device control have being researched and developed. However, countermeasure are insufficient despite being recognized as a fatal security-hole for BadUSB presented at the Black Hat Security Conference 2014. To solve this problem, the countermeasures for BadUSB vulnerability are proposed.

• Proceedings of the KIEE Conference
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• 2007.10a
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• pp.425-426
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• 2007

본 논문에서는 4 m 길이의 핵연료 집합체를 지지하는 스페이서 그리드 외관의 이상 상태를 자동으로 검출하기 위한 영상처리 알고리즘에 대해 기술한다. 원자로 가동 중에는 중성자 조사와 고온 고압 냉각수의 열 유동에 의한 물리적 작용으로 정방 격자구조로 되어 있는 연료봉이 늘어나거나 휘어져 연료봉 틈새 간격이 좁아지고 냉각수의 원활한 유동이 방해를 받게 됨에 따라 연료봉을 지지하는 스페이서 그리드의 변형이 초래될 수 있다. 스페이서 그리드 간격의 변화를 자동으로 추출하는 영상처리 알고리즘을 개발하여 스페이서 그리드의 이상 상태를 자동으로 인식할 수 있게 하였다. 실험 영상으로는 스웨덴 Ahlberg 사의 4면 검사장치에 의해 수행된 경수로형 핵연료 집합체의 검사 동영상을 이용하였다.

• Proceedings of the KIEE Conference
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• 2000.07c
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• pp.1930-1932
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• 2000

PD in defects of solid insulation system is very harmful since it leads to deteriorate insulation system by. the discharge electrons and ions bombarding the insulation surface and the action of chemical products that are formed by discharges. PD is used to detect and recognize defects and degradation of insulation system However, there are still marked difficulties to recognize defects by PD methode. In this paper, we investigated properties of PD of defects in solid insulation by using statical methods and classified PD patterns of surface discharge, electrical tree and void discharge with source of discharge, we used specific distributions such as $H_n(q)$, $H_{an}(\phi)$, $H_n(\phi)$, $H_a(\phi)$ to recognize defects of solid insulation system.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2005.05a
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• pp.91-96
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• 2005

In this study, researchers developed the estimative algorithm for artificial defects in semiconductor packages and performed it by pattern recognition technology. For this purpose, the estimative algorithm was included that researchers made software with MATLAB. The software consists of some procedures including ultrasonic image acquisition, equalization filtering, Self-Organizing Map and Backpropagation Neural Network. Self-Organizing Map and Backpropagation Neural Network are belong to methods of Neural Networks. And the pattern recognition technology has applied to classify three kinds of detective patterns in semiconductor packages: Crack, Delamination and Normal. According to the results, we were confirmed that estimative algorithm was provided the recognition rates of $75.7\%$ (for Crack) and $83_4\%$ (for Delamination) and $87.2\%$ (for Normal).

• 한국IT서비스학회:학술대회논문집
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• 2008.05a
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• pp.254-259
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• 2008

정해진 일정과 비용으로 원하는 기능의 시스템을 생산성과 품질은 최대화하고 결함은 최소화로 개발하는 것이 SW 개발 프로젝트의 궁극적인 목적이다. 그러나 IT 환경 및 SW 시장의 급속한 변화와 새로운 비즈니스 모텔의 대두로, 다양한 프로젝트 이해관련자들의 요구를 충족시키며 성공적으로 프로젝트를 완료하기에 어려움이 가중되고 있다. 본 연구에서는 국내 기업들을 대상으로 SW 개발 프로젝트에 대한 인식 및 관리 실태 파악과 그 원인을 분석하기 위한 조사연구를 실시했다. 수요자 중심의 성공적 프로젝트 수행 환경을 위한 마인드 확산과 SW 개발 프로젝트에 대한 국내 현실 파악을 통해 성공적 프로젝트 수행과 SW 품질 향상을 목표로 한다.

• Journal of Educational Research in Mathematics
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• v.10 no.1
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• pp.11-34
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• 2000

수학학습장애는 독해장애와 더불어 학습장애의 주요영역으로 인식되고 있다. 그러나 독해 장애에 대한 연구와 비교하였을 때 상대적으로 주목받지 못한 분야로, 과연 수학학습장애는 무엇이며, 수학학습장애 아를 판별하는 기준을 어떻게 설정할 것인가의 문제가 여전히 논란이 되고 있다. 본고에서는 수학학습장애에 대한 최근의 세 가지 관점-신경학적 관점, 발달심리적 관점, 교육적 관점-의 고찰을 통해, 수학학습장애를 진단하기 위한 하나의 통합적 관점이 필요함을 제안하고 있다. 이것은 수학학습장애를 신경적 결함으로만 해석하려는 전통적 관점에 대한 발달심리학자들의 비판을 수용한 것이며, 오늘날 파행적인 교육체계에서 희생되고 소외되어온 학생계층을 포용하기 위한 한 가지 제안이 될 것이다.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.10a
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• pp.721-726
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• 2005

In this study, researchers classifying the artificial flaws in semiconductor. packages are performed by pattern recognition technology. For this purposes, image pattern recognition package including the user made software was developed and total procedure including ultrasonic image acquisition, equalization filtration, binary process, edge detection and classifier design is treated by Backpropagation Neural Network. Specially, it is compared with various weights of Backpropagation Neural Network and it is compared with threshold level of edge detection in preprocessing method for entrance into Multi-Layer Perceptron(Backpropagation Neural network). Also, the pattern recognition techniques is applied to the classification problem of defects in semiconductor packages as normal, crack, delamination. According to this results, it is possible to acquire the recognition rate of 100% for Backpropagation Neural Network.

• Proceedings of the KSR Conference
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• 1998.11a
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• pp.87-95
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• 1998

This study proposes the analysis and evaluation method of time series ultrasonic signal using the attractor analysis. Features extracted from time series signal analyze quantitatively characteristics of welding defects. For this purpose, analysis objective in this study is fractal dimension and attractor Quadrant feature. Trajectory changes in the attractor indicated that even the same type of defects carried substantial difference in fractal characteristics resulting from distance shifts such as parts of head and flange. Such differences in characteristics of weld defects enables the evaluation of unique features of defects in the weld zone. In quantitative fractal feature extraction, feature values of 3.848 in the case of part of head(crack) and 4.102 in the case of part of web(side hale) and 3.711 in the case of part of flange(crack) were proposed on the basis of fractal dimensions. Proposed attractor feature extraction in this study can enhance the precision rate of ultrasonic evalaution for defect signals of rail weld zone such as side hole and crack.