• Journal of Korea Multimedia Society
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• v.13 no.4
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• pp.512-525
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• 2010

A PDP(Plasma Display Panel) Frame is critical part of PDP and also produces couple hundred thousand every month. In the process of mass production, product inspection is very important process. Also to increase the reliance, inspection each part and every final product is asked quite often. Purpose of this paper is to use computer vision system to inspect the PDP parts which is Automated visual process inspection. This paper contains the system design for inspecting defects of hole, tab, stud, rivet of PDP Frame. The system also can inspect various kinds of PDP frames. Quick and accurate 100% inspection of all shapes can improve the manufacturing productivity. Inspection results can be stored in a database and analyzed to find the cause of defects. After applying the system to the industry, the result shows the possibility of fast and accuracy of the inspection.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.4
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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.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2007.04a
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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.

• Journal of the Korean Society for Nondestructive Testing
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• v.29 no.2
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• pp.145-152
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• 2009

Currently almost all in-service-inspection techniques, applied in domestic nuclear power plants, are partial to field inspection technique. These kinds of techniques are related to managing nuclear power plants by the operation of foreign-produced inspection devices. There have been so many needsfor development of native in-service-inspection device because there is no native diagnosis device for nuclear power plant inspection yet in Korea. In this research, we developed several core techniques to make an automated ultrasonic pipe inspection system for nuclear power plants. A high performance multi-channel ultrasonic pulser/receiver module, an A/D converter module and a digital main CPU module were developed and the performance of the developed modules was verified. The S/N ratio, noise level and signal acquisition performance of the developed modules showed proper level as we designed in the beginning.

• Korean Journal of Optics and Photonics
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• v.20 no.4
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• pp.223-229
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• 2009

This paper describes the development of a simulator which can numerically calculate an image to be acquired in a machine vision system for automated optical inspection. The simulator is based on a ray tracing technique and composed of three modules which are an illuminating system, a specimen and an imaging system. Kinds of model parameters for modules and their values are carefully chosen from the direct measurement and the observation of related phenomena. Finally, the validity of the simulator is evaluated by logical analysis and by comparison with measured images.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.520-523
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• 2008

As a result of the recent development of the electro-information industry, the hardware of an automated ultrasonic testing system is getting lighter and diversified image processing techniques are applied to its software so that the possible precise totaling and detecting of the flaws are studied. This study proposes an automated ultrasonic testing system of the pipe in order to organize the optimized system, and also describes the data flow and general composition of the software for the design of the system.

• Journal of the Society of Naval Architects of Korea
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• v.59 no.4
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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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• v.4 no.1
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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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• 2006.08a
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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.

• International conference on construction engineering and project management
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• 2022.06a
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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.