• 한국멀티미디어학회논문지
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• 제13권4호
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• pp.512-525
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• 2010

PDP(Plasma Display Panel) 프레임은 PDP의 핵심 부품으로 매월 수 십 만개 이상을 생산하고 있다. 대량생산 과정에서 제품의 검사 과정은 매우 중요한 공정 단계이며 제품의 신뢰성을 높이기 위해 모든 부품과 완제품에 대한 전수 검사가 종종 요구되고 있다. 본 논문에서 구현 하고자 하는 시각 검사 공정의 자동화는 생산 현장에서 컴퓨터 비전 기술을 이용하여 PDP 부품의 일부인 프레임의 결함을 검사한다. 본 논문은 PDP 프레임에 있는 홀(Hole), 탭홀(Tab), 스터드(STUD), 리벳(Rivet)의 결함을 검사하는 시스템을 설계하고 구현 하였다. 본 시스템은 다양한 종류의 PDP 프레임을 학습하고 검사 할 수 있다. 모든 제품에 대한 신속하고 정확하게 100% 검사를 함으로써 공정의 효율성을 높이고 검사결과의 데이터베이스화로 신속한 결함의 원인파악과 조치가 가능하도록 하였다. 시스템을 산업체에 적용한 결과 신속하고 정확한 검사가 가능함을 보였다.

• 제어로봇시스템학회논문지
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• 제3권4호
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• pp.406-414
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• 1997

A real-time inspection system is developed using line scan cameras. Several improved algorithms are proposed for real-time detection of defects in this automated inspection system. The major improved algorithms include the preprocessing, the threshold decision, and the clustering algorithms. The preprocessing algorithms are for exact binarization and the threshold decision algorithm is for fast detection of defects in 1-D binary images. The clustering algorithm is also developed for fast classifying of the defects. The system is applied to PCBs(Printed Circuit Boards) inspection. The typical defects in PCBs are pits, dent, wrinkle, scratch, and black spots. The results show that most defects are detected and classified successfully.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2007년도 정기 학술대회 논문집
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• pp.90-95
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• 2007

Conventional bridge inspection involves the physical positioning of an inspector by the hydraulic telescoping boom of a "snooper truck" thereby providing visual access to bridge components. The process is time consuming, hazardous, and may be affected by lighting conditions, Therefore, it is of great interest that an automated and/or teleoperated inspection robot be developed to replace the manual inspection procedure. This paper describes the advanced bridge inspection robot system under development and other related activities currently undergoing at the Bridge Inspection Robot Development Interface (BIRDI). BIRDI is a research consortium with its home in the Department of Civil and Environmental System Engineering at Hanyang University at Ansan. Its primary goal is to develop advanced robot systems for bridge inspection and monitoring for immediate field application and commercialization. The research program includes research areas such as advanced inspection robot and motion control system, sensing technologies for monitoring and assessment, and integrated system for bridge maintenance. The center embraces 12 institutions, which consist of 7 universities, 2 research institutes, and 3 private enterprises. Research projects are cross-disciplinary and include experts from structural engineering, mechanical engineering, electronic and control engineering. This research project will contribute to advancement of infrastructure maintenance technology, enhancement of construction industry competitiveness, and promotion of national capacity for technology innovation.

• 비파괴검사학회지
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• 제29권2호
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• pp.145-152
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• 2009

국내 원전 가동중검사 기술은 대부분이 선진국에서 도입한 비파괴검사 장비를 진단현장에 적용하는 운영기술로 지금까지 선진 운영 기술의 습득에 중점을 두어, 검사 시스템 하드웨어 및 소프트웨어의 국내 제작 기술은 매우 미흡하였다. 때문에 국산 고유모델의 원전 가동중검사용 진단장치 개발의 필요성이 끊임없이 제기되었다. 본 연구에서는, 원전 배관 자동 초음파검사 시스템의 핵심 기술인 고성능 다채널 초음파 펄서/리시버와 A/D converter 보드, 디지털 제어보드를 개발하고 그 성능을 검증하였다. 검증 실험 결과는 개발된 시스템이 설계 목적에 부합하는 성능을 보이는 것으로 확인되었다.

• 한국광학회지
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• 제20권4호
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• pp.223-229
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• 2009

자동 광검사 장비의 검사 영상 획득과정을 전산모사하는 시뮬레이터를 개발하였다. 시뮬레이터는 광선 추적법을 기반으로 조명 광학계, 검사 대상체 및 결상 광학계의 3가지 모듈로 구성하였다. 각 모듈이 갖는 광학적 특성을 실제와 일치시키기 위해, 다양한 물리적 모델과 실측 결과를 반영하여 주요 인자들의 종류와 범위를 결정하였다. 전산모사 결과의 타당성은 논리적인 검증과정과 실험 결과와의 비교를 통해 확인할 수 있었다.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2008년도 추계종합학술대회 B
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• pp.520-523
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• 2008

최근까지 전자정보산업의 발달로 자동 초음파검사 하드웨어는 갈수록 경량화 되어 가고 있고, 소프트웨어는 다양한 최신 이미지 처리 기법이 적용되어 정확한 결함 위치 및 크기 측정이 가능하도록 연구되어지고 있다. 본 논문에서는 원전 배관 자동 검사에 최적화된 시스템을 구성하기 위해 배관자동 초음파 검사 시스템을 제안하고, 시스템 설계를 위해 소프트웨어의 데이터 흐름과 일반적인 구성에 대해서 기술한다.

• 대한조선학회논문집
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• 제59권4호
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• pp.192-199
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• 2022

With the advent of autonomous ships, it is emerging as one of the very important issues not only to operate with a minimum crew or unmanned ships, but also to secure the safety of ships to prevent marine accidents. On-site inspection of the hull is mainly performed by the inspector's visual inspection, and video information is recorded using a small camera if necessary. However, due to the shortage of inspection personnel, time and space constraints, and the pandemic situation, the necessity of introducing an automated inspection system using artificial intelligence and remote inspection is becoming more important. Furthermore, research on hardware and software that enables the automated inspection system to operate normally even under the harsh environmental conditions of a ship is absolutely necessary. For automated inspection systems, it is important to review artificial intelligence technologies and equipment that can perform a variety of hull failure detection and classification. To address this, it is important to classify the hull failure. Based on various guidelines and expert opinions, we divided them into 6 types(Crack, Corrosion, Pitting, Deformation, Indent, Others). It was decided to apply object detection technology to cracks of hull failure. After that, YOLOv5 was decided as an artificial intelligence model suitable for survey and a common hull crack dataset was trained. Based on the performance results, it aims to present the possibility of applying artificial intelligence in the field by determining and testing the equipment required for survey.

• International Journal of Control, Automation, and Systems
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• 제4권1호
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• pp.96-104
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• 2006

We propose a path planning method to improve the productivity of AOI (automated optical inspection) machines in PCB (printed circuit board) assembly lines. The path-planning problem is the optimization problem of finding inspection clusters and the visiting sequence of cameras to minimize the overall working time. A unified method is newly proposed to determine the inspection clusters and visiting sequence simultaneously. We apply a hybrid genetic algorithm to solve the highly complicated optimization problem. Comparative simulation results are presented to verify the usefulness of the proposed method.

• 한국정보디스플레이학회:학술대회논문집
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• 한국정보디스플레이학회 2006년도 6th International Meeting on Information Display
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• pp.328-332
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• 2006

Inspection in the back end of the LCD is moving from human based to automated. This is driven by the desire to eliminate human operators and the need to have repeatable, reliable data for fab optimization. The number of systems required varies by the fab module location, product mix, and repair capability.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.304-311
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• 2022

Construction inspection is a crucial stage that ensures that all contractual requirements of a construction project are verified. The construction inspection capabilities among state highway agencies have been greatly affected due to budget reduction. As a result, efficient inspection practices such as risk-based inspection are required to optimize the use of limited resources without compromising inspection quality. Automated prioritization of textual requirements according to their criticality would be extremely helpful since contractual requirements are typically presented in an unstructured natural language in voluminous text documents. The current study introduces a novel model for predicting the risk level of requirements using machine learning (ML) algorithms. The ML algorithms tested in this study included naïve Bayes, support vector machines, logistic regression, and random forest. The training data includes sequences of requirement texts which were labeled with risk levels (such as very low, low, medium, high, very high) using the fuzzy logic systems. The fuzzy model treats the three risk factors (severity, probability, detectability) as fuzzy input variables, and implements the fuzzy inference rules to determine the labels of requirements. The performance of the model was examined on labeled dataset created by fuzzy inference rules and three different membership functions. The developed requirement risk prediction model yielded a precision, recall, and f-score of 78.18%, 77.75%, and 75.82%, respectively. The proposed model is expected to provide construction inspectors with a means for the automated prioritization of voluminous requirements by their importance, thus help to maximize the effectiveness of inspection activities under resource constraints.