• Advances in concrete construction
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• v.16 no.4
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• pp.189-203
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• 2023

This study investigates the bending and stability properties of cylindrical constructions, with a focus on their use in the design and implementation of sporting equipment. The work focuses on a cylindrical construction resembling nanomotors, similar to components seen in sports equipment, using mathematical modeling based on high-order beam theory and nonlocal strain gradient theory. The analysis provides important insights into the dynamic behavior of these systems, revealing light on the impact of numerous factors such as rotational velocity, section change rate, and structural dimensions. The results show a relationship between angular velocity growth and section change rate, which leads to an increase in fundamental frequency values. Furthermore, the research emphasizes the effect of structural factors on dynamic deflection, giving critical information for increasing the stability and performance of sporting equipment. This study adds to the area of sports engineering by providing a more nuanced understanding of how cylindrical constructions react under diverse settings. The results will help to guide the design and manufacturing processes of sports equipment, assuring improved stability and performance for players across a wide range of sports.

• Journal of the Korea Society of Computer and Information
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• v.29 no.5
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• pp.85-91
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• 2024

With the current surge in leisure sports activities involving firearms and the costly shooting practices in the military, there's a growing interest in using virtual reality as a cost-effective alternative. This study proposes a system that addresses the drawbacks of existing shooting practice setups, such as dim spaces and high installation costs, by making it feasible on large display screens. The system integrates IR receivers and guns for practice, ensuring usability and efficiency through an application. Additionally, an accuracy adjustment feature enhances precise coordination recognition. As a result, this cyber light gun system offers an affordable solution for outdoor training.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.6
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• pp.1675-1691
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• 2024

The metaverse is an emerging interactive domain that enables people to participate in an array of activities utilizing cutting-edge technologies. Generation Z perceives no substantial distinction between their virtual and actual identities, regarding the virtual world as an extension of reality. As an attempt to apply Bourdieu's theory of cultural taste and cultural capital to the area of the metaverse avatar, investigates the impact of users' cultural tastes on the avatars they create and experience in the metaverse. The research employed both focus group interviews and individual in-depth interviews with users of Generation Z. The study demonstrated that Generation Z users exhibit unrestricted engagement in the metaverse, although their behavior is significantly affected by their economic situation. One's cultural tastes, influenced by diverse interactions with their parents, greatly impact how they engage in cultural activities in the metaverse. Three categories were identified from the perception of avatars: Idealized Self-Representation Avatars, Atypical Self-Representation Avatars, and Integrated Self-Representation Avatars. Perceiving avatars as an extension of the self was associated with higher cultural capital. Participants held divergent perspectives on the metaverse, with certain individuals regarding it as a realm of imagination or a limitless arena for activities.

• Advances in environmental research
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• v.12 no.2
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• pp.113-122
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• 2023

Biometric authentication has become an essential part of modern-day security systems, especially in financial institutions like banks. A face recognition-based ATM is a biometric authentication system, that uses facial recognition technology to verify the identity of bank account holders during ATM transactions. This technology offers a secure and convenient alternative to traditional ATM transactions that rely on PIN numbers for verification. The proposed system captures users' pictures and compares it with the stored image in the bank's database to authenticate the transaction. The technology also offers additional benefits such as reducing the risk of fraud and theft, as well as speeding up the transaction process. However, privacy and data security concerns remain, and it is important for the banking sector to instrument solid security actions to protect customers' personal information. The proposed system consists of two stages: the first stage captures the user's facial image using a camera and performs pre-processing, including face detection and alignment. In the second stage, machine learning algorithms compare the pre-processed image with the stored image in the database. The results demonstrate the feasibility and effectiveness of using face recognition for ATM authentication, which can enhance the security of ATMs and reduce the risk of fraud.

• Journal of the Korea Institute of Military Science and Technology
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• v.27 no.1
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• pp.15-23
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• 2024

In order to avoid the high observability due to the cavity resonance or electromagnetic wave leakages from the bridge of a battleship or the cockpit of an aircraft, this paper presents a transparent conductive oxide coated structure to prevent the incoming/outgoing electromagnetic waves. Currently, most of the RCS reduction technologies were focused on radar absorbing material such as paints based on conductive or magnetic materials in the fuselage, and there is not much research on countermeasures for achieving the low observability of materials that required optical transparency in actual weapon systems. In this study, the transmission/reflection and absorption performance of the ITO coated structure according to the change of the surface resistance of the transparent conductor were analyzed. Finally, the relationship between the electromagnetic and optical characteristics was established through fabrication and measurement.

• Journal of the Society of Disaster Information
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• v.20 no.1
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• pp.147-155
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• 2024

Purpose: The purpose of this study is to analyze the danger signs of buildings and present a plan to install a building monitoring system to develop measurement technology for small private buildings in the blind spot of disaster safety. Method: The cause of building risk behavior, components of monitoring measuring equipment, location of measuring equipment installation, management plan, etc. are presented. Result: Measuring instruments essentially include acceleration sensors, tilt sensors, gyro sensors, GPS, etc. The measuring instrument should take into account the height and cross-sectional area of the pillar. Conclusion: The results of this study can strengthen disaster safety capabilities in preparation for disasters arising from building collapses that may occur in small private buildings.

• Annual Conference of KIPS
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• 2024.05a
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• pp.235-238
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• 2024

This paper investigates the integration of ARIA cryptography within hardware secure modules to bolster IoT security. We present a comparative analysis of two prominent IoT communication protocols, MQTT and LwM2M, augmented with ARIA cryptography. The study evaluates their performance, security, and scalability in practical IoT applications. Our experimental setup comprises FPGA-enabled hardware secure modules interfaced with Raspberry Pi acting as an MQTT and LwM2M client. We utilize the Mosquitto MQTT server and an LwM2M server deployed on AWS IoT. Through rigorous experimentation, we measure various performance metrics, including latency, throughput, and resource utilization. Additionally, security aspects are scrutinized, assessing the resilience of each protocol against common IoT security threats. Our findings highlight the efficacy of ARIA cryptography in bolstering IoT security and reveal insights into the comparative strengths and weaknesses of MQTT and LwM2M protocols. These results contribute to the development of robust and secure IoT systems, paving the way for future research in this domain.

• Journal of Korea Society of Digital Industry and Information Management
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• v.20 no.3
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• pp.13-23
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• 2024

For the purpose of predicting credit card customer churn accurately through data analysis Detecting and tracking objects in continuous video is essential in self-driving cars, security and surveillance systems, sports analytics, medical image processing, and more. Correlation tracking methods such as Normalized Cross Correlation(NCC) and Sum of Absolute Differences(SAD) are used as an effective way to measure the similarity between two images. NCC, a representative correlation tracking method, has been useful in real-time environments because it is relatively simple to compute and effective. However, correlation tracking methods are sensitive to rotation and size changes of objects, making them difficult to apply to real-time changing videos. To overcome these limitations, this paper proposes an object tracking method using the Histogram of Oriented Gradients(HOG) feature to effectively obtain object data and the Convolution Neural Network(CNN) algorithm. By using the two algorithms, the shape and structure of the object can be effectively represented and learned, resulting in more reliable and accurate object tracking. In this paper, the performance of the proposed method is verified through experiments and its superiority is demonstrated.

• Journal of Information Processing Systems
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• v.20 no.4
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• pp.501-513
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• 2024

In recent years, graph neural networks (GNNs) have been extensively used to analyze graph data across various domains because of their powerful capabilities in learning complex graph-structured data. However, recent research has focused on improving the performance of a single GNN with only two or three layers. This is because stacking layers deeply causes the over-smoothing problem of GNNs, which degrades the performance of GNNs significantly. On the other hand, ensemble methods combine individual weak models to obtain better generalization performance. Among them, gradient boosting is a powerful supervised learning algorithm that adds new weak models in the direction of reducing the errors of the previously created weak models. After repeating this process, gradient boosting combines the weak models to produce a strong model with better performance. Until now, most studies on GNNs have focused on improving the performance of a single GNN. In contrast, improving the performance of GNNs using multiple GNNs has not been studied much yet. In this paper, we propose gradient boosted graph neural networks (GBGNN) that combine multiple shallow GNNs with gradient boosting. We use shallow GNNs as weak models and create new weak models using the proposed gradient boosting-based loss function. Our empirical evaluations on three real-world datasets demonstrate that GBGNN performs much better than a single GNN. Specifically, in our experiments using graph convolutional network (GCN) and graph attention network (GAT) as weak models on the Cora dataset, GBGNN achieves performance improvements of 12.3%p and 6.1%p in node classification accuracy compared to a single GCN and a single GAT, respectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.8
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• pp.2366-2380
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• 2024

In this study, a high-speed template matching system is proposed for wafer-vision alignment. The proposed system is designed to rapidly locate markers in semiconductor equipment used for wafer-vision alignment. We optimized and implemented a template-matching algorithm for the high-speed processing of high-resolution wafer images. Owing to the simplicity of wafer markers, we removed unnecessary components in the algorithm and designed the system using a field-programmable gate array (FPGA) to implement high-speed processing. The hardware blocks were designed using the Xilinx ZCU104 board, and the pyramid and matching blocks were designed using programmable logic for accelerated operations. To validate the proposed system, we established a verification environment using stage equipment commonly used in industrial settings and reference-software-based validation frameworks. The output results from the FPGA were transmitted to the wafer-alignment controller for system verification. The proposed system reduced the data-processing time by approximately 30% and achieved a level of accuracy in detecting wafer markers that was comparable to that achieved by reference software, with minimal deviation. This system can be used to increase precision and productivity during semiconductor manufacturing processes.