• Imaging Science in Dentistry
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• v.52 no.3
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• pp.239-244
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• 2022

Purpose: Despite the proliferation of numerous morphometric and anthropometric methods for sex identification based on linear, angular, and regional measurements of various parts of the body, these methods are subject to error due to the observer's knowledge and expertise. This study aimed to explore the possibility of automated sex determination using convolutional neural networks(CNNs) based on lateral cephalometric radiographs. Materials and Methods: Lateral cephalometric radiographs of 1,476 Iranian subjects (794 women and 682 men) from 18 to 49 years of age were included. Lateral cephalometric radiographs were considered as a network input and output layer including 2 classes(male and female). Eighty percent of the data was used as a training set and the rest as a test set. Hyperparameter tuning of each network was done after preprocessing and data augmentation steps. The predictive performance of different architectures (DenseNet, ResNet, and VGG) was evaluated based on their accuracy in test sets. Results: The CNN based on the DenseNet121 architecture, with an overall accuracy of 90%, had the best predictive power in sex determination. The prediction accuracy of this model was almost equal for men and women. Furthermore, with all architectures, the use of transfer learning improved predictive performance. Conclusion: The results confirmed that a CNN could predict a person's sex with high accuracy. This prediction was independent of human bias because feature extraction was done automatically. However, for more accurate sex determination on a wider scale, further studies with larger sample sizes are desirable.

• Structural Engineering and Mechanics
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• v.83 no.5
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• pp.693-707
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• 2022

Bonded joints have proven their performance against conventional joining processes such as welding, riveting and bolting. The single-lap joint is the most widely used to characterize adhesive joints in tensile-shear loadings. However, the high stress concentrations in the adhesive joint due to the non-linearity of the applied loads generate a bending moment in the joint, resulting in high stresses at the adhesive edges. Geometric optimization of the bonded joint to reduce this high stress concentration prompted various researchers to perform geometric modifications of the adhesive and adherends at their free edges. Modifying both edges of the adhesive (spew) and the adherends (bevel) has proven to be an effective solution to reduce stresses at both edges and improve stress transfer at the inner part of the adhesive layer. The majority of research aimed at improving the geometry of the plate and adhesive edges has not considered the effect of temperature and water absorption in evaluating the strength of the joint. The objective of this work is to analyze, by the finite element method, the stress distribution in an adhesive joint between two 2024-T3 aluminum plates. The effects of the adhesive fillet and adherend bevel on the bonded joint stresses were taken into account. On the other hand, degradation of the mechanical properties of the adhesive following its exposure to moisture and temperature was found. The results clearly showed that the modification of the edges of the adhesive and of the bonding agent have an important role in the durability of the bond. Although the modification of the adhesive and bonding edges significantly improves the joint strength, the simultaneous exposure of the joint to temperature and moisture generates high stress concentrations in the adhesive joint that, in most cases, can easily reach the failure point of the material even at low applied stresses.

• Journal of Electrochemical Science and Technology
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• v.14 no.1
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• pp.96-104
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• 2023

Synergistically increased oxygen evolution reaction (OER) of manganese oxide (MnO₂) catalyst is introduced with surface-modified halloysite nanotube (Fe₃O₄-HNTs) structure. The flake shaped MnO₂ catalyst is attached on the nanotube template (Fe₃O₄-HNTs) by series of wet chemical and hydrothermal method. The strong interaction between MnO₂ and Fe₃O₄-HNTs maximized active surface area and inter-connectivity for festinate charge transfer reaction for OER. The synergistical effect between Fe₃O₄ layer and MnO₂ catalyst enhance the Mn³⁺/Mn⁴⁺ ratio by partial replacement of Mn ions with Fe. The relatively increased Mn³⁺/Mn⁴⁺ ratio on MnO₂@FHNTs induced 𝜎^* orbital (e_g) occupation close to single electron, improving the OER performances. The MnO₂@FHNTs catalyst exhibited the reduced overpotential of 0.42 V (E vs. RHE) at 10 mA/cm² and Tafel slope of (99 mV/dec), compared with that of MnO₂ with unmodified HNTs (0.65 V, 219 mV/dec) and pristine MnO₂ (0.53 V, 205 mV/dec). The present study provides simple and innovative method to fabricate nano fiberized OER catalyst for a broad application of energy conversion and storage systems.

• Nuclear Engineering and Technology
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• v.55 no.3
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• pp.1140-1151
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• 2023

Fully Ceramic Microencapsulated (FCM) fuel is emerging advanced fuel material for the future nuclear reactors. The fuel pellet in the FCM fuel is composed of matrix and a large number of TRistructural-ISOtopic (TRISO) fuel particles which are randomly dispersed in the SiC matrix. The minimum layer thickness in a TRISO fuel particle is on the order of 10^-5 m, and the length of the FCM pellet is on the order of 10^-2 m. Hence, the heat transfer in the FCM pellet is a multi-scale phenomenon. In this study, three multi-scale heat conduction models including the Multi-region Layered (ML) model, Multi-region Non-layered (MN) model and Homogeneous model for FCM pellet were constructed. In the ML model, the random distributed TRISO fuel particles and coating layers are completely built. While the TRISO fuel particles with coating layers are homogenized in the MN model and the whole fuel pellet is taken as the homogenous material in the Homogeneous model. Taking the results by the ML model as the benchmark, the abilities of the MN model and Homogenous model to predict the maximum and average temperature were discussed. It was found that the MN model and the Homogenous model greatly underestimate the temperature of TRISO fuel particles. The reason is mainly that the conventional equivalent thermal conductivity (ETC) models do not take the internal heat source into account and are not suitable for the TRISO fuel particle. Then the improved ETCs considering internal heat source were derived. With the improved ETCs, the MN model is able to capture the peak temperature as well as the average temperature at a wide range of the linear powers (165 W/cm~ 415 W/cm) and the packing fractions (20%-50%). With the improved ETCs, the Homogenous model is better to predict the average temperature at different linear powers and packing fractions, and able to predict the peak temperature at high packing fractions (45%-50%).

• The Bulletin of The Korean Astronomical Society
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• v.46 no.2
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• pp.46.3-47
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• 2021

Recently a multilayer spectral inversion (MLSI) model has been proposed to infer the physical parameters of plasmas in the solar chromosphere. The inversion solves a three-layer radiative transfer model using the strong absorption line profiles, H alpha and Ca II 8542 Å, taken by the Fast Imaging Solar Spectrograph (FISS). The model successfully provides the physical plasma parameters, such as source functions, Doppler velocities, and Doppler widths in the layers of the photosphere to the chromosphere. However, it is quite expensive to apply the MLSI to a huge number of line profiles. For example, the calculating time is an hour to several hours depending on the size of the scan raster. We apply deep neural network (DNN) to the inversion code to reduce the cost of calculating the physical parameters. We train the models using pairs of absorption line profiles from FISS and their 13 physical parameters (source functions, Doppler velocities, Doppler widths in the chromosphere, and the pre-determined parameters for the photosphere) calculated from the spectral inversion code for 49 scan rasters (~2,000,000 dataset) including quiet and active regions. We use fully connected dense layers for training the model. In addition, we utilize a skip connection to avoid a problem of vanishing gradients. We evaluate the model by comparing the pairs of absorption line profiles and their inverted physical parameters from other quiet and active regions. Our result shows that the deep learning model successfully reproduces physical parameter maps of a scan raster observation per second within 15% of mean absolute percentage error and the mean squared error of 0.3 to 0.003 depending on the parameters. Taking this advantage of high performance of the deep learning model, we plan to provide the physical parameter maps from the FISS observations to understand the chromospheric plasma conditions in various solar features.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1530-1544
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• 2023

Data monitoring is an important foundation of modern science. In most cases, the monitoring data is time-series data, which has high application value. The deep learning algorithm has a strong nonlinear fitting capability, which enables the recognition of time series by capturing anomalous information in time series. At present, the research of time series recognition based on deep learning is especially important for data monitoring. Deep learning algorithms require a large amount of data for training. However, abnormal sample is a small sample in time series, which means the number of abnormal time series can seriously affect the accuracy of recognition algorithm because of class imbalance. In order to increase the number of abnormal sample, a data augmentation method called GANBATS (GAN-based Bi-LSTM and Attention for Time Series) is proposed. In GANBATS, Bi-LSTM is introduced to extract the timing features and then transfer features to the generator network of GANBATS.GANBATS also modifies the discriminator network by adding an attention mechanism to achieve global attention for time series. At the end of discriminator, GANBATS is adding averagepooling layer, which merges temporal features to boost the operational efficiency. In this paper, four time series datasets and five data augmentation algorithms are used for comparison experiments. The generated data are measured by PRD(Percent Root Mean Square Difference) and DTW(Dynamic Time Warping). The experimental results show that GANBATS reduces up to 26.22 in PRD metric and 9.45 in DTW metric. In addition, this paper uses different algorithms to reconstruct the datasets and compare them by classification accuracy. The classification accuracy is improved by 6.44%-12.96% on four time series datasets.

• The Bulletin of The Korean Astronomical Society
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• v.45 no.1
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• pp.59.2-59.2
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• 2020

Disks around protostars are the birthplace of planets. The first step toward planet formation is grain growth from ㎛-sized grains to mm/cm-sized grains in a disk, particularly in dense regions. In order to study whether grains grow and segregate at the protostellar stage, we investigate the ALMA Band 3 (3.1 mm) and 7 (0.87 mm) dust continuum observations of the protostellar disk WL 17 in ρ Ophiuchus L1688 cloud. As reported in a previous study, the Band 3 image shows substructures: a narrow ring and a large central hole. On the other hand, the Band 7 image shows different substructures: a non-axisymmetric ring and an off-center hole. The two-band observations provide a mean spectral index of 2.3, which suggests the presence of mm/cm-sized large grains. Its non-axisymmetric distribution may imply dust segregation between small and large grains. We perform radiative transfer modeling to examine the size and spatial distributions of dust grains in the WL 17 disk. The best-fit model suggests that large grains (>1 cm) exist in the disk, settling down toward the midplane, whereas small grains (~10 ㎛) well mixed with gas are distributed off-center and non-axisymmetrically in a thick layer. The low spectral index and the modeling results suggest that grains rapidly grow at the protostellar stage and that grains differently distribute depending on sizes, resulting in substructures varying with observed wavelengths. To understand the differential grain distributions and substructures, we discuss the effects of the protoplanet(s) expected inside the large hole and the possibility of gravitational instability.

• Journal of Electrochemical Science and Technology
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• v.14 no.4
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• pp.311-319
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• 2023

This work was performed to characterize the electrochemical behavior of AZ31 Mg alloy in neutral aqueous solutions where Cl^-, SO₄^2-, PO₄^3-, and F^- ions were present and pH was adjusted to 6 to exclude the contribution of H⁺ and OH^- ions. Open-circuit potential (OCP) transient, electrochemical impedance spectroscopy (EIS) and potnetiodynamic polarization curves were employed. The OCP value appeared to decrease in the order of F^- > Cl^- > SO₄^2- > PO₄^3- ions while corrosion current density increased in the same order. Electrochemical impedance spectroscopy (EIS) data showed two capacitive arcs in all the solutions and one more inductive arc appeared in PO₄^3--containing solution. By fitting of two capacitive arcs, capacitance of dense film (C_df), resistance of porous film (R_pf) and double layer capacitance (C_dl) and charge transfer resistance (R_ct) beneath the porous films were obtained. A simplified model in which various thicknesses and coverages of dense and porous films are assumed to be present on the AZ31 Mg alloy surface, is suggested to explain the effects of four different anions on the electrochemical behavior of AZ31 Mg alloy.

• Korean Journal of Materials Research
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• v.34 no.4
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• pp.202-207
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• 2024

In this study, we undertook detailed experiments to increase hydrogen production efficiency by optimizing the thickness of titanium dioxide (TiO₂) thin films. TiO₂ films were deposited on p-type silicon (Si) wafers using atomic layer deposition (ALD) technology. The main goal was to identify the optimal thickness of TiO₂ film that would maximize hydrogen production efficiency while maintaining stable operating conditions. The photoelectrochemical (PEC) properties of the TiO₂ films of different thicknesses were evaluated using open circuit potential (OCP) and linear sweep voltammetry (LSV) analysis. These techniques play a pivotal role in evaluating the electrochemical behavior and photoactivity of semiconductor materials in PEC systems. Our results showed photovoltage tended to improve with increasing thickness of TiO₂ deposition. However, this improvement was observed to plateau and eventually decline when the thickness exceeded 1.5 nm, showing a correlation between charge transfer efficiency and tunneling. On the other hand, LSV analysis showed bare Si had the greatest efficiency, and that the deposition of TiO₂ caused a positive change in the formation of photovoltage, but was not optimal. We show that oxide tunneling-capable TiO₂ film thicknesses of 1~2 nm have the potential to improve the efficiency of PEC hydrogen production systems. This study not only reveals the complex relationship between film thickness and PEC performance, but also enabled greater efficiency and set a benchmark for future research aimed at developing sustainable hydrogen production technologies.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.30 no.2
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• pp.216-224
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• 1997

In order to investigate the osmoregulation capability of grey mullet, Mugil cephalus with the different salinities, juvenile fish $(13.6{\pm}0.2\;TL)$ stocked in seawater (SW) were abruptly transferred to each experimental group $0\%SW(0\%_{\circ}),\;25\%SW(7.7\%_{\circ}),\;50\%SW(16.1\%_{\circ})\;and \;100\%SW(32.8\%_{\circ})$ and reared for 60 days. Blood samples were taken by the time schedule after the transfer. Plasma $Na^{+},\;K^{+},\;Cl^{-}$ and osmolality, muscle water content, and the electron microscopical observations of chloride cells were analyzed and made by the time schedule. In $100\%SW$, the maintainable levels of plasma $Na^{+},\;K^{+},\;Cl^{-}$ and osmolality were $167.1{\pm}7.7mM/l,\;9.1{\pm}2.1mM/l,\;137.8{\pm}5.6mM/l\;and\;351{\pm}18\;mOsm/kg$, respectively. These values were significantly changed at $6h\~1\;day$ after the beginning of the experiment with four different salinities. Fish from $0\%\;and\;25\%SW$ had lower osmolalities than those of fish from $50\%\;and\;100\%SW$, and showed the hyposmotic regulation pattern. At the end of the experiment (60 days after transfer), however, no significant difference was found in the concentrations of plasma $Na^{+},\;K^{+}\;and\;Cl^{-}$ among four experimental groups. Hematocrit was increased with salinity (P<0.01). After 10 days, fish from $0\%\;and\;25\%SW$ showed the hypertrophy, fusion and edema of epithelial layer in gill lamella. However, at the 15th day, epithelial layer in gill lamella was back to the normal status. On gill of fish from $0\%SW$, one apical pit held two or three chloride cells in common. Muscle water content was subsequently regulated to near the normal levels within 4 days, and there was no significant difference among four different salinities at the end of the experiment.