• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.1C
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• pp.53-61
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• 2006

This study is aimed at to examine the stress redistribution mechanism for tunneling in an unconsolidated ground with inclined layers through model tests. To make the unconsolidated ground, two dimensional model ground is prepared with aluminum rods and blocks, which are frictional resistance free between testing apparatus walls and ground materials, by establishing the ground materials self-supporting. It is carried out to measure the ground deformation and the stress redistribution for model ground with tunneling by measuring apparatus respectively. For the ground deformation, surface settlements are measured to examine the deformation features during tunnel excavation. For the stress redistribution, the earth pressure acting on both the tunneling part and its surrounding parts is measured to examine their mutual relationship. Based on test results, precise examination is conducted on the stress redistribution mechanism in the unconsolidated ground with inclined layers during tunnel construction.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.4C
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• pp.275-282
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• 2006

Since tunnels are linear type structures that have a long extent in comparison to their excavation or inner section, tunnels must be constructed in various ground conditions. In this study, laboratory model tests and theoretical analysis on a tunnel loads are carried out in the unconsolidated ground with inclined layers for tunnel excavation. Laboratory model tests are performed with the variation in the angle of the inclined layers and tunnel depth for the model ground with inclined layers. As for the ground materials, two dimensional model ground is prepared with aluminum rods and blocks with no cohesion, which are frictional resistance free between testing apparatus walls and ground materials, by establishing the ground materials self-supporting. Moreover tunnel load equation are newly induced so that comparisons between model test results and the theoretical results are conducted as well.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5B
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• pp.539-549
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• 2006

The performance of a pontoon-type floating breakwater (FB) is investigated numerically with the use of a second-order time domain model. The model has been developed based on potential theory, perturbation theory and boundary element method. This study is focused on the effects of weakly nonlinear wave on the hydrodynamic characteristics of the FB. Hydrodynamic forces, motion responses, surface elevation, and wave transmission coefficient around the floating breakwater are evaluated for various wave and geometric parameters. It is shown that the second-order wave component is of significant importance in calculating magnitudes of the hydrodynamic forces, mooring forces and the maximum response of a structure. The weak non-linearity of incident waves, however, can have little influence on the efficiency of the FB. From numerical simulations, the ratio of draft and depth, the relationship of wave number and width are presented for providing an effective means of reducing wave energy.

• Journal of Biomedical Engineering Research
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• v.44 no.6
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• pp.392-403
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• 2023

This study presents a new hybrid fNIRS-EEG system to meet the demand for a lightweight and low-cost sleep pattern monitoring device. For multiple-channel configuration, a six-channel electroencephalogram (EEG) and a functional near-infrared spectroscopy (fNIRS) system with eight photodiodes (PD) and four dual-wavelength LEDs are designed. To enhance the convenience of signal measurement, the device is miniaturized into a patch-like form, enabling simultaneous measurement on the forehead. Due to its fully integrated functionality, the developed system is advantageous for performing sleep stage classification with high-temporal and spatial resolution data. This can be realized by utilizing a two-dimensional (2D) brain activation map based on the concentration changes in oxyhemoglobin and deoxyhemoglobin during sleep stage transitions. For the system verification, the phantom model with known optical properties was tested at first, and then the sleep experiment for a human subject was conducted. The experimental results show that the developed system qualifies as a portable hybrid fNIRS-EEG sleep pattern monitoring device.

• Imaging Science in Dentistry
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• v.53 no.4
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• pp.375-381
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• 2023

Purpose: This study assessed the diagnostic performance of stitched and non-stitched cross-sectional cone-beam computed tomography (CBCT) images of non-displaced ovine mandibular fractures. Materials and Methods: In this ex vivo study, non-displaced fractures were artificially created in 10 ovine mandibles (20 hemi-mandibles) using a hammer. The control group comprised 8 hemi-mandibles. The non-displaced fracture lines were oblique or vertical, <0.5 mm wide, 10-20 mm long, and only in the buccal or lingual cortex. Fracture lines in the ramus and posterior mandible were created to be at the interface or borders of the 2 stitched images. CBCT images were obtained from the specimens with an 80 mm×80 mm field of view before and after fracture induction. OnDemand software (Cybermed, Seoul, Korea) was used for stitching the CBCT images. Four observers evaluated 56 (28 stitched and 28 non-stitched) images to detect fracture lines. The diagnostic performance of stitched and non-stitched images was assessed by calculating the area under the receiver operating characteristic curve (AUC). Sensitivity and specificity values were also calculated (alpha=0.05). Results: The AUC was calculated to be 0.862 and 0.825 for the stitched and non-stitched images, respectively (P=0.747). The sensitivity and specificity were 90% and 75% for the non-stitched images and 85% and 87% for the stitched images, respectively. The inter-observer reliability was shown by a Fleiss kappa coefficient of 0.79, indicating good agreement. Conclusion: No significant difference was found in the diagnostic performance of stitched and non-stitched cross-sectional CBCT images of non-displaced fractures of the ovine mandible.

• Nutrition Research and Practice
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• v.17 no.5
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• pp.934-944
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• 2023

BACKGROUND/OBJECTIVES: Maintaining total muscle mass in the older adults with swallowing difficulty (dysphagia) is important for preserving swallowing function. Increasing protein intake can help sustain lean body mass in the older adults. The aim of this study was to evaluate the effect of various high-protein texture-modified foods (HPTMFs) on muscle mass and perform dietary assessment in ≥ 65-yrs-old patients with dysphagia. SUBJECTS/METHODS: Participants (n = 10) received the newly developed HPTMFs (average 595.23 ± 66.75 kcal/day of energy, 54.22 ± 6.32 g/day of protein) for 10 days. Relative handgrip strength (RHS), mid-upper arm circumference (MUAC), body composition, mini nutritional assessment (MNA), mini dietary assessment (MDA), and Euro Quality-of-Life questionnaire 5-dimensional classification (EQ-5D) were assessed. RESULTS: After 10 days, an increase in MUAC (26.36 ± 2.35 cm to 28.50 ± 3.17 cm, P = 0.013) and RHS (0.38 ± 0.24 kg/kg body weight to 0.42 ± 0.22 kg/kg body weight, P = 0.046) was observed. Although MNA, MDA, EQ-5D, subjective health status, muscle mass, and calf circumference showed a tendency to increase after intervention, no significant differences were found. CONCLUSIONS: These results suggest that the HPTMFs can be used for improving the nutritional and health status in patients with dysphagia.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.311-326
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• 2024

The rapid development of neural network technology promotes the neural network model driven by big data to overcome the texture effect of complex objects. Due to the limitations in complex scenes, it is necessary to establish custom template matching and apply it to the research of many fields of computational vision technology. The dependence on high-quality small label sample database data is not very strong, and the machine learning system of deep feature connection to complete the task of texture effect inference and speculation is relatively poor. The style transfer algorithm based on neural network collects and preserves the data of patterns, extracts and modernizes their features. Through the algorithm model, it is easier to present the texture color of patterns and display them digitally. In this paper, according to the texture effect reasoning of custom template matching, the 3D visualization of the target is transformed into a 3D model. The high similarity between the scene to be inferred and the user-defined template is calculated by the user-defined template of the multi-dimensional external feature label. The convolutional neural network is adopted to optimize the external area of the object to improve the sampling quality and computational performance of the sample pyramid structure. The results indicate that the proposed algorithm can accurately capture the significant target, achieve more ablation noise, and improve the visualization results. The proposed deep convolutional neural network optimization algorithm has good rapidity, data accuracy and robustness. The proposed algorithm can adapt to the calculation of more task scenes, display the redundant vision-related information of image conversion, enhance the powerful computing power, and further improve the computational efficiency and accuracy of convolutional networks, which has a high research significance for the study of image information conversion.

• The Journal of the Acoustical Society of Korea
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• v.28 no.5
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• pp.416-423
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• 2009

The ultrasonic pressure fields for the 3 MHz linear phased array transducer with sixteen piezoelectric elements of which one may not be operated by defect were simulated theoretically and measured experimentally using a visualization system of the Schlieren method. The simulation results for steering angles of $0^{\circ}$ and $30^{\circ}$ show that the side-lobe patterns of the transducer including a defective element is quite different from the transducer with all normal elements, and those patterns are in good agreement with the results of visualization. It is shown that the defective elements in a linear array transducer can be detected by comparison of the simulated and the visualized side-lobe patterns in two dimensional acoustic fields.

• Smart Media Journal
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• v.13 no.5
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• pp.45-51
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• 2024

Recently, realistic contents such as virtual reality(VR) and augmented reality (AR) are widely used for education to provide beneficial learning environments with thee-dimensional(3D) information and interactive technology. Specially, AR technology will be helpful to intuitively understand by adding virtual objects registered in the real learning environment with effective ways. In this paper, we developed an AR learning system using 3D spatial information in the 2D based textbook for studying math related to geometry. In order to increase spatial learning effect, we applied to solid shapes such as prisms and pyramids in mathematics education process. Also, it allows participants to use various shapes and expression methods (e.g., wireframe mode) with interaction. We conducted the experiment with our AR system, evaluated achievement and interest. Our experimental study showed positive results, our results are expected to provide effective learning methods in various classes through realistic visualization and interaction methods.

• Korean Journal of Agricultural Science
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• v.50 no.4
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• pp.675-696
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• 2023

Terpenoids, also referred to as terpenes, are a large family of naturally occurring chemical compounds present in the essential oils extracted from medicinal plants. In this study, a nondestructive methodology was created by combining ATR-FT-IR (attenuated total reflectance-Fourier transform infrared), and Raman spectroscopy for the terpenoids assessment in medicinal plants essential oils from ten different geographical locations. Partial least squares regression (PLSR) and support vector regression (SVR) were used as machine learning methodologies. However, a deep learning based model called as one-dimensional convolutional neural network (1D CNN) were also developed for models comparison. With a correlation coefficient (R²) of 0.999 and a lowest RMSEP (root mean squared error of prediction) of 0.006% for the prediction datasets, the SVR model created for FT-IR spectral data outperformed both the PLSR and 1 D CNN models. On the other hand, for the classification of essential oils derived from plants collected from various geographical regions, the created SVM (support vector machine) classification model for Raman spectroscopic data obtained an overall classification accuracy of 0.997% which was superior than the FT-IR (0.986%) data. Based on the results we propose that FT-IR spectroscopy, when coupled with the SVR model, has a significant potential for the non-destructive identification of terpenoids in essential oils compared with destructive chemical analysis methods.