• Archives of Craniofacial Surgery
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• v.12 no.2
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• pp.116-120
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• 2011

Purpose: Scalp avulsion is a life-threatening injury that may cause trauma to the forehead, eyebrows, and periauricular tissue. It is difficult to treat scalp avulsion as it may lead to severe bleeding. Therefore, emergency scalp replantation surgery is necessary, and we must consider the function, aesthetics, and psychology of the patients. A case of scalp avulsion leading to massive bleeding was encountered by these authors, which led to a failure to achieve the proper operation conditions in an adequate time period. Methods: A 49-year-old female was hospitalized due to having had her head caught in a rotatory machine, causing complete scalp avulsion which included the dorsum of the nose, both eyebrows, and ears. Emergent microsurgical replantation was performed, where a superficial temporal artery and a vein were anastomosed, but the patient's vital signs were too unstable for further operation due to excessive blood loss. Three days after the microanastomosis, venous congestion developed at the replanted scalp, and a medicinal leech was used. Leech therapy resolved the venous congestion. A demarcation then developed between the vitalized scalp tissue and the necrotized area. Debridement was performed 2 times on the necrotized scalp area. Finally, split-thickness skin graft with a dermal acellular matrix ($Matriderm^{(R)}$) was performed on the defective areas, which included the left temporal area, the occipital area, and both eyebrows. Results: The forehead, vertex, right temporal area, and half of the occipital area were successfully replanted, and the hair at the replanted scalp was preserved. As stated above, two-thirds of the scalp survived; the patient could cover the skin graft area with her hair, and could wear a wig. Conclusion: Complete scalp avulsion needs emergent replantation with microsurgical revascularization, but it often leads to serious vital conditions. We report a case with acceptable results, although the microanastomosed vessel was minimal due to the patient's unstable vital signs.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.27-35
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• 2023

In this paper, we propose a process of increasing productivity by applying a deep learning-based defect detection and classification system to the prepreg fiber manufacturing process, which is in high demand in the field of producing composite materials. In order to apply it to toe prepreg manufacturing equipment that requires a solution due to the occurrence of a large amount of defects in various conditions, the optimal environment was first established by selecting cameras and lights necessary for defect detection and classification model production. In addition, data necessary for the production of multiple classification models were collected and labeled according to normal and defective conditions. The multi-classification model is made based on CNN and applies pre-learning models such as VGGNet, MobileNet, ResNet, etc. to compare performance and identify improvement directions with accuracy and loss graphs. Data augmentation and dropout techniques were applied to identify and improve overfitting problems as major problems. In order to evaluate the performance of the model, a performance evaluation was conducted using the confusion matrix as a performance indicator, and the performance of more than 99% was confirmed. In addition, it checks the classification results for images acquired in real time by applying them to the actual process to check whether the discrimination values are accurately derived.