• Journal of The Geomorphological Association of Korea
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• v.18 no.4
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• pp.203-212
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• 2011

The objective of this paper is to identify the present-day surficial sediment distribution patterns of the tidal river, Gongneungcheon, through the grain size and statistical analysis. Four major findings of this study are as follows; First, the composition of sediments over the study area are mainly silt in texture. Second, the surficial sediment distribution reveals that grain size becomes coarser as they approach seawards not only in summer but also in winter. It can be concluded that tidal flows play a significant role, especially in winter, in the distribution of surficial sediments in Gongneungcheon. However, samples obtained in summer were relatively small in mean size and showed better sorting compared to those obtained in winter. Third, the mean sizes of the samples on the transects decrease as the distance from the channel increases. Finally, the artificial structure such as a floodgate affects the distribution of the sediments.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.543-548
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• 2023

This study emphasizes the need to understand digital transformation and addresses the development process of functional mobile games with the theme of digital transformation. Through previous research, we found that existing mobile games do not reflect the changes in industries and jobs in the future. In this study, we designed and developed an educational mobile game that can help young people develop digital literacy skills and understand core skills for future changes in industrial structure and jobs due to digital transformation. It utilizes Unity 3D and combines the best features of puzzle, card, and board games. While there may be limitations in terms of expertise in developing educational content, it is important to note that we have presented content that can provide a futuristic view of careers through a game that is highly preferred by students. Considering the direct and indirect educational effects of the game, it is expected to be useful as a vocational education content in the classroom through tablet devices that have recently been distributed in schools.

• Journal of Advanced Navigation Technology
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• v.27 no.5
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• pp.629-634
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• 2023

In this paper, the design of a miniaturized wideband tapered slot antenna using slots combining various types of fan-shaped structures was studied. To miniaturize the tapered slot antenna and make it operate at low frequencies, slots combining fan-shaped structures were added to the ground plane of the tapered slot antenna. The miniaturization design process of the final proposed antenna was systematically explained by comparing the input reflection coefficient and gain variations when each fan-shaped structure was appended, compared to when there was no slot. The proposed miniaturized wideband tapered slot antenna using slots combining the fan-shaped structures was fabricated on an RF-35 substrate and its measured characteristics were compared with the simulation results. Experiment results show that the frequency band with a voltage standing wave ratio (VSWR) less than 2 was 2.59-11.39 GHz, and gain within the band was measured to be 3.3-7.0 dBi. The proposed miniaturized wideband tapered slot antenna can be reduced in size by 36.9%, compared to when there are no slots in the ground plane.

• Journal of Advanced Navigation Technology
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• v.26 no.6
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• pp.516-521
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• 2022

In this paper, a compact 4-bit chipless RFID(radio frequency identification) tag using a modified ELC(electric field-coupled inductive-capacitive) resonator and multiple slot resonators is proposed. The modified ELC resonator uses an interdigital-capacitor structure in the conventional ELC resonator to lower the resonance peak frequency of the RCS. The multiple slot resonators are designed by etching three slots with different lengths into an inverted U-shaped conductor. The resonant peak frequency of the RCS for the modified ELC resonator is 3.216 GHz, whereas those of the multiple slot resonators are set at 4.122 GHz, 4.64 GHz, and 5.304 GHz, respectively. The proposed compact four-bit tag is fabricated on an RF-301 substrate with dimensions of 50 mm×20 mm and a thickness of 0.8 mm. Experiment results show that the resonant peak frequencies of the fabricated four-bit chipless RFID tag are 3.285 GHz, 4.09 GHz, 4.63 GHz, and 5.31 GHz, respectively, which is similar to the simulation results with errors in the range between 0.78% and 2.16%.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.1-7
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• 2023

The advent of deep learning technologies has led to the development of various medical applications, making healthcare services more convenient and effective. Among these applications, heart rate estimation is considered a vital method for assessing an individual's health. Traditional methods, such as photoplethysmography through smart watches, have been widely used but are invasive and require additional hardware. Recent advancements allow for contactless heart rate estimation through facial image analysis, providing a more hygienic and convenient approach. In this paper, we propose a lightweight methodology capable of accurately estimating heart rate in mobile environments, using a specialized 2-channel network structure based on 2D convolution. Our method considers both subtle facial movements and color changes resulting from blood flow and muscle contractions. The approach comprises two major components: an Encoder for analyzing image features and a regression layer for evaluating Blood Volume Pulse. By incorporating both features simultaneously our methodology delivers more accurate results even in computing environments with limited resources. The proposed approach is expected to offer a more efficient way to monitor heart rate without invasive technology, particularly well-suited for mobile devices.

• Geomechanics and Engineering
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• v.37 no.1
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• pp.49-64
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• 2024

Rocks undergoing repeated loading and unloading over an extended period, such as due to earthquakes, human excavation, and blasting, may result in the gradual accumulation of stress and deformation within the rock mass, eventually reaching an unstable state. In this study, a CNN-CCM is proposed to address the mechanical behavior. The structure and hyperparameters of CNN-CCM include Conv2D layers × 5; Max pooling2D layers × 4; Dense layers × 4; learning rate=0.001; Epoch=50; Batch size=64; Dropout=0.5. Training and validation data for deep learning include 71 rock samples and 122,152 data points. The AI Rock Constitutive Model learned by CNN-CCM can predict strain values(ε₁) using Mass (M), Axial stress (σ₁), Density (ρ), Cyclic number (N), Confining pressure (σ₃), and Young's modulus (E). Five evaluation indicators R², MAPE, RMSE, MSE, and MAE yield respective values of 0.929, 16.44%, 0.954, 0.913, and 0.542, illustrating good predictive performance and generalization ability of model. Finally, interpreting the AI Rock Constitutive Model using the SHAP explaining method reveals that feature importance follows the order N > M > σ₁ > E > ρ > σ₃.Positive SHAP values indicate positive effects on predicting strain ε₁ for N, M, σ₁, and σ₃, while negative SHAP values have negative effects. For E, a positive value has a negative effect on predicting strain ε₁, consistent with the influence patterns of conventional physical rock constitutive equations. The present study offers a novel approach to the investigation of the mechanical constitutive model of rocks under cyclic loading and unloading conditions.

• Geomechanics and Engineering
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• v.36 no.2
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• pp.183-204
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• 2024

In current investigation, a novel beam finite element model is formulated to analyze the buckling and free vibration responses of functionally graded porous beams resting on Winkler-Pasternak elastic foundations. The novelty lies in the formulation of a simplified finite element model with only three degrees of freedom per node, integrating both C⁰ and C¹ continuity requirements according to Lagrange and Hermite interpolations, respectively, in isoparametric coordinate while emphasizing the impact of z-coordinate-dependent porosity on vibration and buckling responses. The proposed model has been validated and demonstrating high accuracy when compared to previously published solutions. A detailed parametric examination is performed, highlighting the influence of porosity distribution, foundation parameters, slenderness ratio, and boundary conditions. Unlike existing numerical techniques, the proposed element achieves a high rate of convergence with reduced computational complexity. Additionally, the model's adaptability to various mechanical problems and structural geometries is showcased through the numerical evaluation of elastic foundations, with results in strong agreement with the theoretical formulation. In light of the findings, porosity significantly affects the mechanical integrity of FGP beams on elastic foundations, with the advanced beam element offering a stable, efficient model for future research and this in-depth investigation enriches porous structure simulations in a field with limited current research, necessitating additional exploration and investigation.

• Journal of Korean Elementary Science Education
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• v.43 no.2
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• pp.322-336
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• 2024

This study explored the educational effects of using ChatGPT in science lessons for elementary school students. The participants included 25 sixth-grade students studying at an elementary school in Metropolitan City D. This study examined the impacts of elementary science lessons on the cognitive development of elementary school students and their perceptions of using ChatGPT in their science classes. We found that science lessons that used ChatGPT aided the cognitive development of the participating elementary students. These students responded positively to the classes using ChatGPT. The results were then divided into those who perceived ChatGPT positively, those who perceived it negatively, and those who recognized both positive and negative aspects. Students who perceived it negatively mainly remained at the memorization level, and those who recognized both positive and negative aspects posed higher-level questions to ChatGPT.

• Journal of the Korea institute for structural maintenance and inspection
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• v.11 no.1
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• pp.103-112
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• 2007

Recently structures become longer and higher because of the developments of new materials and construction techniques. However, such modern structures are susceptible to excessive structural vibrations, which may induce problems of serviceability and structural damages. In this paper we attempt to control structural vibration using the probabilistic neural network(PNN) and the artificial neural network(ANN) based on the training pattern that consist of only the structural state vector and the control force. The state vectors of the structure and control forces made by linear quadratic regulator(LQR) algorithm are used for training pattern of PNN and ANN. The proposed algorithm is applied for the vibration control of the three story shear building under Northridge earthquake. Control results by the proposed PNN and ANN are compared with each other.

• Journal of Biomedical Engineering Research
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• v.45 no.4
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• pp.139-147
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• 2024

This study enhances AI algorithms for extracting spinal regions from Whole Spine X-rays, aiming for higher accuracy while minimizing learning and detection times. Whole Spine X-rays, critical for diagnosing conditions such as scoliosis and kyphosis, necessitate precise differentiation of spinal contours. The conventional manual methodology encounters challenge due to the overlap of anatomical structures, prompting the integration of AI to overcome these limitations and enhance diagnostic precision. In this study, 1204 AP and 500 LAT Whole Spine X-ray images were meticulously labeled, spanning the third cervical to the fifth lumbar vertebrae. We based our efforts on the HR-Net algorithm, which exhibited the highest accuracy, and proceeded to simplify its network architecture and enhance the block structure for optimization. The optimized HR-Net algorithm demonstrates an improvement, increasing accuracy by 2.98% for the AP dataset and 1.59% for the LAT dataset compared to its original formulation. Additionally, the modification resulted in a substantial reduction in learning time by 70.06% for AP images and 68.43% for LAT images, along with a decrease in detection time by 47.18% for AP and 43.07% for LAT images. The time taken per image for detection was also reduced by 47.09% for AP and 43.07% for LAT images. We suggest that the application of the proposed HR-Net in this study can lead to more accurate and efficient extraction of spinal regions in Whole Spine X-ray images. This can become a crucial tool for medical professionals in the diagnosis and treatment of spinal-related conditions, and it will serve as a foundation for future research aimed at further improving the accuracy and speed of spinal region segmentation.