• Imaging Science in Dentistry
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• v.52 no.3
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• pp.245-258
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• 2022

Purpose: This study compared the root canal anatomy between cone-beam computed tomography (CBCT) and micro-computed tomography (micro-CT) images before and after biomechanical preparation and root canal filling. Materials and Methods: Isthmus-containing mesial roots of mandibular molars(n=14) were scanned by micro-CT and 3 CBCT devices: 3D Accuitomo 170 (ACC), NewTom 5G (N5G) and NewTom VGi evo (NEVO). Two calibrated observers evaluated the images for 2-dimensional quantitative parameters, the presence of debris or root perforation, and filling quality in the root canal and isthmus. The kappa coefficient, analysis of variance, and the Tukey test were used for statistical analyses(α=5%). Results: Substantial intra-observer agreement (κ=0.63) was found between micro-CT and ACC, N5G, and NEVO. Debris detection was difficult using ACC (42.9%), N5G (40.0%), and NEVO (40%), with no agreement between micro-CT and ACC, N5G, and NEVO (0.05<κ<0.12). After biomechanical preparation, 2.4%-4.8% of CBCT images showed root perforation that was absent on micro-CT. The 2D parameters showed satisfactory reproducibility between micro-CT and ACC, N5G, and NEVO (intraclass correlation coefficient: 0.60-0.73). Partially filled isthmuses were observed in 2.9% of the ACC images, 8.8% of the N5G and NEVO images, and 26.5% of the micro-CT images, with no agreement between micro-CT and ACC, and poor agreement between micro-CT and N5G and NEVO. Excellent agreement was found for area, perimeter, and the major and minor diameters, while the roundness measures were satisfactory. Conclusion: CBCT images aided in isthmus detection and classification, but did not allow their classification after biomechanical preparation and root canal filling.

• The Journal of the Acoustical Society of Korea
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• v.41 no.5
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• pp.492-499
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• 2022

This study experimentally investigates the effect of dimensionality reduction of vibration signal on fault diagnosis of a marine engine. By using the principal component analysis, a vibration signal having the dimension of 513 is converted into a low-dimensional signal having the dimension of 1 to 15, and the variation in fault diagnosis accuracy according to the dimensionality change is observed. The vibration signal measured from a full-scale marine generator diesel engine is used, and the contribution of the dimension-reduced signal is quantitatively evaluated using two kinds of variable importance analysis algorithms which are the integrated gradients and the feature permutation methods. As a result of experimental data analysis, the accuracy of the fault diagnosis is shown to improve as the number of dimensions used increases, and when the dimension approaches 10, near-perfect fault classification accuracy is achieved. This shows that the dimension of the vibration signal can be considerably reduced without degrading fault diagnosis accuracy. In the variable importance analysis, the dimension-reduced principal components show higher contribution than the conventional statistical features, which supports the effectiveness of the dimension-reduced signals on fault diagnosis.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.31 no.4
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• pp.149-153
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• 2021

ε-Ga₂O₃ thin films were grown on 4H-SiC substrates by metal organic chemical vapor deposition (MOCVD) and crystalline quality were evaluated depend on growth conditions. It was found that the best conditions of the ε-Ga₂O₃ were grown at a growth temperature of 665℃ and an oxygen flow rate of 200 sccm. Two-dimensional growth was completed after the merge of hexagonal nuclei, and the arrangement direction of hexagonal nuclei was closely related to the crystal direction of the substrate. However, it was confirmed that crystal structure of the ε-Ga₂O₃ had an orthorhombic rather than hexagonal. Crystal phase transformation was performed by thermal treatment. And a β-Ga₂O₃ thin film was grown directly on 4H-SiC for the comparison to the phase transformed β-Ga₂O₃ thin film. The phase transformed β-Ga₂O₃ film showed better crystal quality than directly grown one.

• Journal of the Korea Society for Simulation
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• v.31 no.4
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• pp.23-31
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• 2022

The development of innovative technology through the 4th Industrial Revolution is actively used in the defense field. In particular, surveillance and reconnaissance capabilities using drones will be of great help to the development of military combat capabilities, such as preparing for future military personnel reductions and reinforcing alert capabilities. In this study, we analyze the combat efficiency of drones how helpful drones can be to the military operations through simulations. Drones and enemy move in the efficient shortest path within a two-dimensional space in which operational areas are mapped into number such as detection probability. Based on the detection probability of an enemy infiltrating along the path with the lowest detection probability, the detection probability change that occurs whenever a drone is additionally deployed is presented, and we analyze the combat efficiency according to the additional drone input. Simulation proves that the increase in combat efficiency decreases as more drones are added in small operational areas such as company-level operational areas. This study is expected to contribute to the efficient operation of a limited number of drones in company-level units and to help determine the most desirable quantity of drones for additional combat power improvement.

• Journal of Navigation and Port Research
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• v.46 no.6
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• pp.471-482
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• 2022

This study aims to investigate the characteristics of wave generation in a deep ocean engineering basin and to develop a meta-model of the transfer function of the wavemaker that reflects the geometric characteristics of the deep ocean engineering basin. To this end, the two-dimensional frequency domain boundary element method was applied to achieve an efficient analysis that reflects the geometric characteristics of the deep ocean engineering basin. The developed numerical method was validated through comparison with the analytical solution. Numerical analyses were conducted for the boundary value problem of the wavemaker according to various periods and the positions of the movable bottom. The numerical results were used to investigate the effect of the geometric characteristics of the deep ocean engineering basin on the transfer function of the wavemaker, and the effect of depth on wave generation was checked by changing the position of the movable bottom. To efficiently utilize the various results of the boundary element method, a meta-model, an approximate model of the transfer function of the wave maker, was developed using a thin plate spline interpolation model. The validity of the developed meta-model was confirmed through a comparison of the results of the model tests.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.32 no.4
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• pp.136-142
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• 2022

The crystal structure, grain growth behavior, and dielectric properties of BaTiO₃ have been studied with the addition of Dy₂O₃. The powders were synthesized at ratios of (100-x)BaTiO₃-xDy₂O₃ (mol%, x = 0, 0.5, 1.0, 2.0) by a conventional solid-state synthesis, and the powder compacts were sintered at 1250℃ for 2 hours in air. As the amount of added Dy₂O₃ was increased, the crystal structure of the sintered samples changed from a tetragonal to a pseudo-cubic structure, and the tetragonality decreased. In addition, a secondary phase of Ba₁₂Dy_4.67Ti₈O₃₅ appeared when Dy₂O₃ was added. The average grain size after sintering decreased and abnormal grains appeared as the amount of Dy₂O₃ increased. It can be explained that the grain growth behavior of the Dy₂O₃ added-BaTiO₃ occurs due to the two-dimensional nucleation and growth, and is governed by the interface reaction. Further, the correlation between crystal structure, microstructure, and dielectric properties was discussed.

• Smart Structures and Systems
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• v.29 no.4
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• pp.625-640
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• 2022

Maglev rail joints are vital components serving as connections between the adjacent F-type rail sections in maglev guideway. Damage to maglev rail joints such as bolt looseness may result in rough suspension gap fluctuation, failure of suspension control, and even sudden clash between the electromagnets and F-type rail. The condition monitoring of maglev rail joints is therefore highly desirable to maintain safe operation of maglev. In this connection, an online damage detection approach based on three-dimensional (3D) convolutional neural network (CNN) and time-frequency characterization is developed for simultaneous detection of multiple damage of maglev rail joints in this paper. The training and testing data used for condition evaluation of maglev rail joints consist of two months of acceleration recordings, which were acquired in-situ from different rail joints by an integrated online monitoring system during a maglev train running on a test line. Short-time Fourier transform (STFT) method is applied to transform the raw monitoring data into time-frequency spectrograms (TFS). Three CNN architectures, i.e., small-sized CNN (S-CNN), middle-sized CNN (M-CNN), and large-sized CNN (L-CNN), are configured for trial calculation and the M-CNN model with excellent prediction accuracy and high computational efficiency is finally optioned for multiple damage detection of maglev rail joints. Results show that the rail joints in three different conditions (bolt-looseness-caused rail step, misalignment-caused lateral dislocation, and normal condition) are successfully identified by the proposed approach, even when using data collected from rail joints from which no data were used in the CNN training. The capability of the proposed method is further examined by using the data collected after the loosed bolts have been replaced. In addition, by comparison with the results of CNN using frequency spectrum and traditional neural network using TFS, the proposed TFS-CNN framework is proven more accurate and robust for multiple damage detection of maglev rail joints.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.119-137
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• 2022

Recent recommendation system studies apply various deep learning models to represent user and item interactions better. One of the noteworthy studies is ONCF(Outer product-based Neural Collaborative Filtering) which builds a two-dimensional interaction map via outer product and employs CNN (Convolutional Neural Networks) to learn high-order correlations from the map. However, ONCF has limitations in recommendation performance due to the problems with CNN and the absence of side information. ONCF using CNN has an inductive bias problem that causes poor performances for data with a distribution that does not appear in the training data. This paper proposes to employ a Vision Transformer (ViT) instead of the vanilla CNN used in ONCF. The reason is that ViT showed better results than state-of-the-art CNN in many image classification cases. In addition, we propose a new architecture to reflect side information that ONCF did not consider. Unlike previous studies that reflect side information in a neural network using simple input combination methods, this study uses an independent auxiliary classifier to reflect side information more effectively in the recommender system. ONCF used a single latent vector for user and item, but in this study, a channel is constructed using multiple vectors to enable the model to learn more diverse expressions and to obtain an ensemble effect. The experiments showed our deep learning model improved performance in recommendation compared to ONCF.

• The Journal of the Korea Contents Association
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• v.22 no.9
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• pp.179-193
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• 2022

Amid the explosive growth of various metaverse platforms, there is no unified indicator to measure, analyze, and evaluate based on customer experience. Therefore, the usability evaluation factors in metaverse were identified through a heuristic methodology and literature review, to evaluate the metaverse, a two-to three-dimensional virtual world platform. A measurable system was established by subdividing 20 items in 5 fields, including user control, information structure, design and content, and usage environment, derived through Delphi technique. Based on this, after experiencing the actual contents of major metaverse platforms such as Roblox and Zepeto, usability was evaluated and comparative verification was conducted. As a result, it was estimated that metaverse user experience could be improved as its utility was derived relatively high in terms of user control and content. This study constitutes a theoretical contribution by extending the usability evaluation system, which has been widely used in the field of service design, to the fields of extended reality and mixed reality. At the same time, it has practical key findings of providing basic judgment standards to stakeholders in the metaverse field, as well as policy implications for digital capability enhancement and industry revitalization.

• Journal of Korean Society of Disaster and Security
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• v.15 no.2
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• pp.37-44
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• 2022

As the frequency of torrential rains and typhoons increases due to climate change, the frequency of occurrence of debris flow is also increasing. In particular, in the case of Kangwon-do, the occurrence of damage caused by mountain disasters is increasing as it has a topographical characteristic where the mountains and the coast are in contact. In order to analyze the flow characteristics in the sedimentary part of the debris flow, input data were constructed through numerical maps and field data, and a two-dimensional model, FLO-2D, was simulated. The damaged area was divided into the inflow part of the debris flow, the village center, and the vicinity of the port, and the flow center and flow velocity of the debris flow were simulated and compared with field survey data. As a result, the maximum flow depth was found to be 2.4 m at the debris flow inlet, 2.7 m at the center of the village, and 1.4 m at the port adjacent to the port so the results were similar when compared to the field survey. And in the case of the maximum flow velocity, it was calculated as 3.6 m/s at the debris flow inlet, 4.9 m/s in the center of the village and 1.2 m/s in the vicinity of the port, so It was confirmed that the maximum flow center occurred in the section where the maximum flow rate appeared.