• Journal of Biomedical Engineering Research
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• v.36 no.2
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• pp.43-47
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• 2015

Osteoarthritis is the most common chronic joint disease in the world. With its progression, cartilage thickness tends to diminish, which causes severe pain to human being. One way to examine the stage of osteoarthritis is to measure the cartilage thickness. When it comes to inter-subject study, however, it is not easy task to compare cartilage thickness since every human being has different cartilage structure. In this paper, we propose a method to assess cartilage defect using MRI inter-subject thickness comparison. First, we used manual segmentation method to build accurate atlas images and each segmented image was labeled as articular surface and bone-cartilage interface in order to measure the thickness. Secondly, each point in the bone-cartilage interface was assigned the measured thickness so that the thickness does not change after registration. We used affine transformation and SyGN to get deformation fields which were then applied to thickness images to have cartilage thickness atlas. In this way, it is possible to investigate pixel-by-pixel thickness comparison. Lastly, the atlas images were made according to their osteoarthritis grade which indicates the degree of its progression. The result atlas images were compared using the analysis of variance in order to verify the validity of our method. The result shows that a significant difference is existed among them with p < 0.001.

• Steel and Composite Structures
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• v.34 no.2
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• pp.261-277
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• 2020

The main objective of this research paper is to consider vibration analysis of vacancy defected graphene sheet as a nonisotropic structure via molecular dynamic and continuum approaches. The influence of structural defects on the vibration of graphene sheets is considered by applying the mechanical properties of defected graphene sheets. Molecular dynamic simulations have been performed to estimate the mechanical properties of graphene as a nonisotropic structure with single- and double- vacancy defects using open source well-known software i.e., large-scale atomic/molecular massively parallel simulator (LAMMPS). The interactions between the carbon atoms are modelled using Adaptive Intermolecular Reactive Empirical Bond Order (AIREBO) potential. An isogeometric analysis (IGA) based upon non-uniform rational B-spline (NURBS) is employed for approximation of single-layered graphene sheets deflection field and the governing equations are derived using nonlocal elasticity theory. The dependence of small-scale effects, chirality and different defect types on vibrational characteristic of graphene sheets is investigated in this comprehensive research work. In addition, numerical results are validated and compared with those achieved using other analysis, where an excellent agreement is found. The interesting results indicate that increasing the number of missing atoms can lead to decrease the natural frequencies of graphene sheets. It is seen that the degree of the detrimental effects differ with defect type. The Young's and shear modulus of the graphene with SV defects are much smaller than graphene with DV defects. It is also observed that Single Vacancy (SV) clusters cause more reduction in the natural frequencies of SLGS than Double Vacancy (DV) clusters. The effectiveness and the accuracy of the present IGA approach have been demonstrated and it is shown that the IGA is efficient, robust and accurate in terms of nanoplate problems.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.9
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• pp.44-51
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• 2019

Three types of samples with defects were measured by lock-in med-IR (infrared) thermography with various lock-in frequencies for different materials. The lock-in method can be used to detect defects when an external energy source is applied to the object, the non-uniformity of the incident thermal energy distribution is eliminated, and the camera's measurement cycle is synchronized with the load cycle of the incident energy source. For inspecting samples with defects, results of thermal images are analyzed when three types of materials, i.e., SM45C, STS316L, and AL6061 are tested and three lock-in frequencies, i.e., 0.08, 0.1, and 0.12 Hz are applied. In this study, the optimal lock-in frequencies were determined by comparing the results of each material and lock-in frequency measured using the mid-IR camera.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.2
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• pp.267-275
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• 2023

As aged infrastructures have been increased, the importance of accurate maintenance costs and proper budget allocation for infrastructure become prominent under limited resources. This study proposed a mapping algorithm between representative defects, repair methods, and the estimated maintenance costs for concrete bridges. In this regard, using BMS (Bridge Management System) data analysis, bridge repair methods were classified and matched with defects according to their locations, types, and sizes. In addition, the maintenance costs were estimated based on the amount of work-load and quantity per unit using CSPR (Cost Standard Production Rate). As a result, the level of accuracy was an average of 85.1 % compared with the actual bill of quantity for Seoul bridge maintenance. The accuracy of maintenance costs is expected to be enhanced by considering the various site conditions such as pier height, extra charge conditions, additional equipment, etc.

• Journal of Biomedical Engineering Research
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• v.16 no.2
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• pp.231-238
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• 1995

Tc-99m-MIBI (Sestamibi) myocardial SPECT along with TI-201 tomographic Imaging has demonstrated wide application and high image quality sufficient for the diagnosis of myocardial perfusion defect, which consequently reflects regional myocardial blood flow. The qualitative values of myocardial SPECT with Tc-99m-MIBI as well ds the quantitative cases depend in some degree on the reconstruction techniques of multiple projections. Filtered backprojection (FBP) Is the common standard method for reconstruction rather than the complicated and time-consuming arithmetic methods. In FBP it is known that the distribution of radioactivity in reconstructed transverse slices varies with the selected litter parameters such as cutoff frequencies and order (Butterworth case) The cutoff frequencies used in clinicAl practice partially remove and decrease the true radioactive distribution and alter the pixel counts, which lead to underestimation of true counts in specific myocardial regions. In this study, we have investigated the effect of cutoff frequencies of reconstruction filter on the artifactually induced perfusion defects, which are often demonstrated near inferior and/or inferoseptal cardiac walls due to the intense hepatic uptake of Tc-99m-MIBI. A computerized method for Identifying the relative degree of artifactual perfusion defect and for comparing those degrees along with the relative amount of hepatic uptake to myocardium was developed and patient images were studied to observe the quantitative degree of underestimation of myocardial perfusion, and to propose some reasonable threshold of cutoff frequency in the diagnosis of perfusion defect quantitatively. We concluded that from the quantitative viewpoint cutoff frequencies may be used as high as possible with the sacrifice of homogeneity of image quality, and those frequencies lower than the common 0.3 Wyquist frequency would reveal severe degradation of radioactive distribution near inferior and/or inferoseptal myocardium when applying Butterworth or low pass filter.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.6
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• pp.834-841
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• 2022

Recently, applying artificial intelligence technologies in various fields of production has drawn an upsurge of research interest due to the increase for smart factory and artificial intelligence technologies. A great deal of effort is being made to introduce artificial intelligence algorithms into the defect detection task. Particularly, detection of defects on the surface of metal has a higher level of research interest compared to other materials (wood, plastics, fibers, etc.). In this paper, we compare and analyze the speed and performance of defect classification by combining machine learning techniques (Support Vector Machine, Softmax Regression, Decision Tree) with dimensionality reduction algorithms (Principal Component Analysis, AutoEncoders) and two convolutional neural networks (proposed method, ResNet). To validate and compare the performance and speed of the algorithms, we have adopted two datasets ((i) public dataset, (ii) actual dataset), and on the basis of the results, the most efficient algorithm is determined.

• 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.

• Tribology and Lubricants
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• v.36 no.2
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• pp.105-115
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• 2020

This paper presents a numerical study on the rotordynamic analysis of a dual-spool turbofan engine in the context of blade defect events. The blades of an axial-type aeroengine are typically well aligned during the compressor and turbine stages. However, they are sometimes exposed to damage, partially or entirely, for several operational reasons, such as cracks due to foreign objects, burns from the combustion gas, and corrosion due to oxygen in the air. Herein, we designed a dual-spool rotor using the commercial 3D modeling software CATIA to simulate blade defects in the turbofan engine. We utilized the rotordynamic parameters to create two finite element Euler-Bernoulli beam models connected by means of an inter-rotor bearing. We then applied the unbalanced forces induced by the mass eccentricities of the blades to the following selected scenarios: 1) fully balanced, 2) crack in the low-pressure compressor (LPC) and high pressure compressor (HPC), 3) burn on the high-pressure turbine (HPT) and low pressure compressor, 4) corrosion of the LPC, and 5) corrosion of the HPC. Additionally, we obtained the transient and steady-state responses of the overall rotor nodes using the Runge-Kutta numerical integration method, and employed model reduction techniques such as component mode synthesis to enhance the computational efficiency of the process. The simulation results indicate that the high-vibration status of the rotor commences beyond 10,000 rpm, which is identified as the first critical speed of the lower speed rotor. Moreover, we monitored the unbalanced stages near the inter-rotor bearing, which prominently influences the overall rotordynamic status, and the corrosion of the HPC to prevent further instability. The high-speed range operation (>13,000 rpm) coupled with HPC/HPT blade defects possibly presents a rotor-case contact problem that can lead to catastrophic failure.

• Journal of Periodontal and Implant Science
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• v.46 no.6
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• pp.382-395
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• 2016

Purpose: Alendronate has been proposed as a local and systemic drug treatment used as an adjunct to scaling and root planing (SRP) for the treatment of periodontitis. However, its effectiveness has yet to be conclusively established. The purpose of the present meta-analysis was to assess the effectiveness of SRP with alendronate on periodontitis compared to SRP alone. Methods: Five electronic databases were used by 2 independent reviewers to identify relevant articles from the earliest records up to September 2016. Randomized controlled trials (RCTs) comparing SRP with alendronate to SRP with placebo in the treatment of periodontitis were included. The outcome measures were changes in bone defect fill, probing depth (PD), and clinical attachment level (CAL) from baseline to 6 months. A fixed-effect or random-effect model was used to pool the extracted data, as appropriate. Mean differences (MDs) with 95% confidence intervals (CIs) were calculated. Heterogeneity was assessed using the Cochrane ${\chi}^2$ and $I^2$ tests. Results: After the selection process, 8 articles were included in the meta-analysis. Compared with SRP alone, the adjunctive mean benefits of locally delivered alendronate were 38.25% for bone defect fill increase (95% CI=33.05%-43.45%; P<0.001; $I^2=94.0%$), 2.29 mm for PD reduction (95% CI=2.07-2.52 mm; P<0.001; $I^2=0.0%$) and 1.92 mm for CAL gain (95% CI=1.55-2.30 mm; P<0.001; $I^2=66.0%$). In addition, systemically administered alendronate with SRP significantly reduced PD by 0.36 mm (95% CI=0.18-0.55 mm; P<0.001; $I^2=0.0%$) and increased CAL by 0.39 mm (95% CI=0.11-0.68 mm; P=0.006; $I^2=6.0%$). Conclusions: The collective evidence regarding the adjunctive use of alendronate locally and systemically with SRP indicates that the combined treatment can improve the efficacy of non-surgical periodontal therapy on increasing CAL and bone defect fill and reducing PD. However, precautions must be exercised in interpreting these results, and multicenter studies evaluating this specific application should be carried out.

• Journal of the Korea Institute of Building Construction
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• v.21 no.3
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• pp.231-239
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• 2021

Entering the era of the fourth industrial revolution, information and communication technologies such as CCTV, home network systems and equipment are being used in the construction industry. In particular, in order to increase the autonomy of information and communication technologies in apartments, the government has announced an administrative revision of information and communication-related laws, and companies are focusing on developing technologies such as smart home services. In addition, most domestic and foreign studies on the information and communication work were mainly conducted on technology and management. However there is a lack of research on physical defects affecting the quality of ICT. Therefore, this study collected the defect data registered in the project management system of three domestic construction companies and classified them according to the standards of the Enforcement Decree of the Apartment House Management Act. According to the analysis of the frequency of defects work type, 88.10% of defects occurred in home network equipment work. In addition, analysis of defects type in the four detailed works showed the highest number of operation error. The cause was analyzed and prevention measures and countermeasures were presented in parts of design, construction, and maintenance. The results of this study will improve the quality of apartment housing and be used as basic data for future research on practical defect minimization and prevention measures.