• Journal of the Semiconductor & Display Technology
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• v.20 no.3
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• pp.125-130
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• 2021

Scaffold is used to produce bio sensor. Scaffold is required high dimensional accuracy. 3D printer is used to manufacture scaffold. 3D printer can't detect defect during printing. Defect detection is very important in scaffold printing. Real-time defect detection is very necessary on industry. In this paper, we proposed the method for real-time scaffold defect detection. Real-time defect detection model is produced using CNN(Convolution Neural Network) algorithm. Performance of the proposed model has been verified through evaluation. Real-time defect detection system are manufactured on hardware. Experiments were conducted to detect scaffold defects in real-time. As result of verification, the defect detection system detected scaffold defect well in real-time.

• Journal of the Semiconductor & Display Technology
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• v.21 no.2
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• pp.68-73
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• 2022

In this study, we produced the surface defect detection model to automatically detect defect bone plates using a deep learning algorithm. Bone plates with a width and a length of 50 mm are most used for fracture treatment. Normal bone plates and defective bone plates were printed on the 3d printer. Normal bone plates and defective bone plates were photographed with 1,080 pixels using the webcam. The total quantity of collected images was 500. 300 images were used to learn the defect detection model. 200 images were used to test the defect detection model. The mAP(Mean Average Precision) method was used to evaluate the performance of the surface defect detection model. As the result of confirming the performance of the surface defect detection model, the detection accuracy was 96.3 %.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.1
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• pp.55-62
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• 2013

Automatic inspection system is required for increment of copper plate production and demand expansion. Thus 3D surface form and defect detection of copper plate calls for 3D image and GUI analysis. Limitation of 2D analysis, such as error occurrence and decision difficulty makes eye inspection automatic. Automatic inspection is able to raise accurate inspection rate and productivity efficiency elevation. In this paper defect classification is defined and inspection system is implemented. Defect analysis algorithms and GUI for 3D image analysis is developed and tested.

• Journal of International Society for Simulation Surgery
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• v.1 no.1
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• pp.13-15
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• 2014

Nowadays, with advanced 3D printing techniques, the custom-made implant can be manufactured for the patient. Especially in skull reconstruction, it is difficult to design the implant due to complicated geometry. In large defect, an autograft is inappropriate to cover the defect due to donor morbidity. We present the process of manufacturing the 3D custom-made implant for skull reconstruction. There was one patient with skull defect repaired using custom-made 3D titanium implant in the plastic and reconstructive surgery department. The patient had defect of the left parieto-temporal area after craniectomy due to traumatic subdural hematoma. Custom-made 3D titanium implants were manufactured by Medyssey Co., Ltd. using 3D CT data, Mimics software and an EBM (Electron Beam Melting) machine. The engineer and surgeon reviewed several different designs and simulated a mock surgery on 3D skull model. During the operation, the custom-made implant was fit to the defect properly without dead space. The operative site healed without any specific complications. In skull reconstruction, autograft has been the treatment of choice. However, it is not always available and depends on the size of defect and donor morbidity. As 3D printing technique has been advanced, it is useful to manufacture custom-made implant for skull reconstruction.

• Journal of Korean Society of Water and Wastewater
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• v.31 no.6
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• pp.619-628
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• 2017

As the 3D laser scanning technology capable of databaseing large sewage box culverts becomes possible, it is necessary to develop a standardization manual that can clearly distinguish the structural and operational defect types of box culver and analyze the defect data. In this study, we collected and analyzed defects in sewage box culverts of 14,827m in total by selecting three districts in Korea. The major defects were surface damages, and their defect densities were $2.17m^2/m$, $0.27m^2/m$ and $0.10m^2/m$ for aggregate exposure, Steel reinforcement exposure, and Steel reinforcement projecting. In order to support the decision of the box culverment management, it was divided into five grades and each defect code and defect score were allocated. The results of this study are useful for the diagnosis of the sewage box culverts in Korea and it is expected to support a decision making for management.

• Journal of Cardiovascular Imaging
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• v.31 no.4
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• pp.180-187
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• 2023

BACKGROUND: Two-dimensional (2D) transesophageal echocardiography (TEE) is commonly used for assessing patients undergoing transcatheter atrial septal defect (ASD) device closure. 3D TEE, albeit providing high resolution en-face images of ASD, is used in only a fraction of cases. We aimed to perform a comparative analysis between 3D and 2D TEE assessment for ASD device planning. METHODS: This was a prospective, observational study conducted over a period of one year. Patients deemed suitable for device closure underwent 2D and 3D TEE at baseline. Defect characteristics, assessed separately in both modalities, were compared. Using regression analysis, we aimed to derive an equation for predicting device size using 3D TEE parameters. RESULTS: Thirty patients were included in the study, majority being females (83%). The mean age of the study population was 40.5 ± 12.05 years. Chest pain, dyspnea and palpitations were the common presenting complaints. All patients had suitable rims on 2D TEE. A good agreement was noted between 2D and 3D TEE for measured ASD diameters. 3D TEE showed that majority of defects were circular in shape (60%). The final device size used had high degree of correlation with 3D defect area and circumference. An equation was devised to predict device size using 3D defect area and circumference. The mean device size obtained from the equation was similar to the actual device size used in the study population (p = 0.31). CONCLUSIONS: Device sizing based on 3D TEE parameters alone is equally effective for transcatheter ASD closure as compared to 2D TEE.

• Journal of Korean Neurosurgical Society
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• v.58 no.6
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• pp.528-533
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• 2015

Objective : We sought to examine anatomic variations of the atlas and the clinical significance of these variations. Methods : We retrospectively reviewed 1029 cervical 3-dimensional (3D) CT images. Cervical 3D CT was performed between November 2011 and August 2014. Arcuate foramina were classified as partial or complete and left and/or right. Occipitalization of the atlas was classified in accordance with criteria specified by Mudaliar et al. Posterior arch defects of the atlas were classified in accordance with criteria specified by Currarino et al. Results : One hundred and eight vertebrae (108/1029, 10.5%) showed an arcuate foramen. Bilateral arcuate foramina were present in 41 of these vertebrae and the remaining 67 arcuate foramina were unilateral (right 31, left 36). Right-side arcuate foramina were partial on 18 sides and complete on 54 sides. Left-side arcuate foramina were partial on 24 sides and complete on 53 sides. One case of atlas assimilation was found. Twelve patients (12/1029, 1.17%) had a defect of the atlantal posterior arch. Nine of these patients (9/1029, 0.87%) had a type A posterior arch defect. We also identified one type B, one type D, and one type E defect. Conclusion : Preoperative diagnosis of occipitalization of the atlas and arcuate foramina using 3D CT is of paramount importance in avoiding neurovascular injury during surgery. It is important to be aware of posterior arch defects of the atlas because they may be misdiagnosed as a fracture.

• Journal of Korean Society for Quality Management
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• v.43 no.3
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• pp.273-288
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• 2015

Purpose: In this study, we apply environmental stress screening (ESS) to battle management system (BMS) of a tank and use the ESS profile based on production process data, guided by MIL-HDBK-781/344/2164. Methods: To optimize ESS Profile of the BMS of a tank, we estimate ESS model parameters (e.g., defect density, screening strength) using primary production failure reporting and corrective action system (FRACAS) data of military supply contract firm. Results: First, we collect the Primary production FRACAS data of military supply contract firm. Second, we compute curve fitting approach to find patent defect density and latent defect density using FRACAS data. Third, we solve the equation of Defect Density(patent defect density + latent defect density)($D_{IN}$) and Screening Strength(SS) Using second step data. As a result of analysis according to the order, we calculate $D_{IN}$(Temperature stress case : 74.02, Vibration stress : 10.252) and : SS(Temperature stress case : 0.4632, Vibration stress : 0.4142) and confirm the Condition II-D based on MIL-HDBK-344. According to Condition II-D, it is necessary to modify existing ESS profile through decreasing the $D_{IN}$ and increasing the SS. Conclusion: Identification of defect causes through ESS approach reduce defect densities for production. It provides feedback to a lessons-learned data base to avoid similar problems on next generation tank BMS.

• Journal of Magnetics
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• v.16 no.4
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• pp.332-336
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• 2011

We studied the effect on the electronic and magnetic properties of the N defect in clean and Cu-doped wurtzite III-nitrides by using the first-principles calculations. When it is doped two Cu atoms in the nearest neighboring sites, the system of AlN, GaN, or InN with the N vacancy is energetically more favorable than that without the N vacancy site. When the Cu concentration increases, the total magnetic moment of a supercell becomes small. The ferromagnetism of Cu atom is very low due to the weak 3d-3d coupling. It is noticeable that the spin-exchange interaction between the Cu-3d and N defect states is important.

• Journal of the Semiconductor & Display Technology
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• v.19 no.3
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• pp.77-81
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• 2020

In this paper, we create scaffold defect classification models using machine learning based data. We extract the characteristic from collected scaffold external images using USB camera. SVM, KNN, MLP algorithm of machine learning was using extracted features. Classification models of three type learned using train dataset. We created scaffold defect classification models using test dataset. We quantified the performance of defect classification models. We have confirmed that the SVM accuracy is 95%. So the best performance model is using SVM.