• Journal of the Institute of Electronics Engineers of Korea SC
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• v.46 no.4
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• pp.58-63
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• 2009

The appearance inspection of various electronic products and parts was executed by the eyesight of human. The appearance inspection is applied to the most electronic component of LCD Panel, flexible PCB and remote control. If the appearance of electronic products of small and minute size is inspected by the eyesight of human, we can't expect the stable inspection result because inspection result is changed by condition of physical and spirit of the checker. Therefore currently machine vision systems are used to many appearance inspection fields instead of inspection by human. The many problems of inspection by the checker are not occurred in machine vision circumstance. However, the inspection by automatic machine vision system is mainly influenced by illumination of workplace. In this paper, we propose a histogram transform method for improving accuracy of machine visual inspection.

• Proceedings of the KAIS Fall Conference
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• 2007.05a
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• pp.32-34
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• 2007

본 논문에서는 자동차 핵심부품 중 하나인 알터네이터 수풀의 육안검사를 대체하기 위한 머신비전시스템을 개발하는 목적이 있다. 플라스틱 사출물의 경우 일반적으로 미성형, 찍힘, 뜯김, 크랙 등의 불량 유형이 발생하는 데, 이를 전수검사하기 위한 머신비전 시스템의 설계와 검사 알고리즘을 개발하고자 한다. 개발된 시스템은 산업현장에 적용하여 절대적판정의 안정성을 도모하고, 생산성 향상 및 부품의 표준화를 확립하는 데 기여할 수 있다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.37-38
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• 2023

본 논문에서는 딥러닝을 기반으로 하는 키포인트 인식 모델을 산업용 품질검사 머신비전에 응용하는 방법을 제안한다. 전이학습 방법을 이용하여 딥러닝 모델의 인식률을 높이는 방법을 제시하였고, 전이시킨 특성 추출 모델에 대해 추가로 데이터 세트에 대한 학습을 진행하는 것이 특성추출 모델의 초기 ImageNet 가중치를 동결시켜 학습하는 것보다 학습 속도나 정확도가 높다는 것을 보여준다. 실험을 통해 딥러닝을 응용하는 산업용 품질 검사 공정에는 특성추출 모델의 추가 학습이 중요하다는 점을 확인할 수 있었다.

• The Journal of the Convergence on Culture Technology
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• v.8 no.1
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• pp.409-415
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• 2022

In computer vision, an image of a measurement target is acquired using a camera. And feature values, vectors, and regions are detected by applying algorithms and library functions. The detected data is calculated and analyzed in various forms depending on the purpose of use. Computer vision is being used in various places, especially in the field of automatically recognizing automobile parts or measuring the quality. Computer vision is being used as the term machine vision in the industrial field, and it is connected with artificial intelligence to judge product quality or predict results. In this study, a vision system for judging the quality of automobile parts was built, and the results were compared by applying five machine learning classification models to the produced data.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.45 no.6
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• pp.16-25
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• 2008

The micro needle means the ultrafine probe of 'Probe Card'. The size of micro needle is so minute that it is difficult to minute that it is difficult to inspect it with eyes. On the other hand it is very critical if there is a defect in micro needle of probe card. In this study, we developed an automatic visual inspection system for finding defect status in probe angle, tip length and diameter. Through the experimental results, we could find that our proposed method is better than human-eyes inspection method in accuracy and inspection speed, and also in robustness to lighting circumstances.

• 전자공학회논문지 IE
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• v.48 no.1
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• pp.16-24
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• 2011

In this paper, we developed a auto sorting system for T type welding nut using a vision inspector. We used edge and thread detection with histogram of image which is captured by machine vision camera. We also used a binary morphology operation for a detection of spot. As a result we performed numeric inspection of 0.1mm accuracy. This is impossible in old sorting system and inspector with naked eye. Also, we reduced the manufacturing unit cost to 25% and improved a production efficiency to 330%.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2015.10a
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• pp.33-34
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• 2015

The voyage and cargo volume passing through the Arctic route (NSR) have been gradually increased. The ship-ice collision is one of the most biggest factors threatening the safety navigation of ice-going ships. A lot of researchers are trying to reveal the ship-ice collision mechanism. In this study, some tests that a model ship is forced to collide with disk-shaped synthetic ice are carried out in a towing tank. Then, ice floe's motion (velocity and trajectory) is measured by machine vision inspection.

• Smart Media Journal
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• v.11 no.9
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• pp.47-55
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• 2022

Quality control in the smart factory manufacturing process is an important factor. Currently, quality inspection of home appliance manufacturing parts produced by the mold process is mostly performed with the naked eye of the operator, resulting in a high error rate of inspection. In order to improve the quality competition, an automatic defect detection system was designed and implemented. The proposed system acquires an image by photographing an object with a high-performance scan camera at a specific location, and reads defective products due to scratches, dents, and foreign substances according to the vision inspection algorithm. In this study, the depth-based branch decision algorithm (DBD) was developed to increase the recognition rate of defects due to scratches, and the accuracy was improved.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.1
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• pp.59-63
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• 2014

Elliptical gears are used in the oval flowmeter and oval flow meter inspects volume of water thanks to space by the elliptical shape. The purpose of this study is to judge accuracy of processing of the elliptical gear and develop inspection system using machine vision. Demand of machine vision is increasing while the factory automation is spreading and principle factor in-process inspection. But, gear inspection using the machine vision rarely used because of complex shape of gear. In this study, it seems possible that elliptical gear is inspected by inspection software using machine vision and inspection program can judge accuracy of processing of the elliptical gear designed this study.

• The monthly packaging world
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• s.318
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• pp.77-83
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• 2019