• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.667-669
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• 2021

Recently, RFID/USN technology has been applied in various fields such as logistics, environment, education, home network, disaster prevention, military, and medical care, but despite the remarkable development of RFID/USN technology, it is difficult to apply it to marine industry due to the characteristics of poor marine environment. Therefore, satellites are mainly used in the marine sector, and existing communication networks are used in the coast, so measures for forming a shelf-only short-range network in the ocean are being considered. In this paper, we consider the use of drones as mobile base stations of USN as a base station role using USN in existing PS-LTE and LTE networks.Since autonomous navigation vessels are aiming for the intelligent system, the number of crew and labor force should be reduced and the function of autonomous network formation in the form of more stable and intelligent ICT convergence technology should be strengthened.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1873-1893
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• 2023

The development of lightweight, low energy and small-sized sensors incorporated with the wireless networks has brought about a phenomenal growth of Wireless Sensor Networks (WSNs) in its different fields of applications. Moreover, the routing of data is crucial in a wide number of critical applications that includes ecosystem monitoring, military and disaster management. However, the time-delay, energy imbalance and minimized network lifetime are considered as the key problems faced during the process of data transmission. Furthermore, only when the functionality of cluster head selection is available in WSNs, it is possible to improve energy and network lifetime. Besides that, the task of cluster head selection is regarded as an NP-hard optimization problem that can be effectively modelled using hybrid metaheuristic approaches. Due to this reason, an Improved Coyote Optimization Algorithm-based Clustering Technique (ICOACT) is proposed for extending the lifetime for making efficient choices for cluster heads while maintaining a consistent balance between exploitation and exploration. The issue of premature convergence and its tendency of being trapped into the local optima in the Improved Coyote Optimization Algorithm (ICOA) through the selection of center solution is used for replacing the best solution in the search space during the clustering functionality. The simulation results of the proposed ICOACT confirmed its efficiency by increasing the number of alive nodes, the total number of clusters formed with the least amount of end-to-end delay and mean packet loss rate.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.5D
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• pp.749-757
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• 2008

Recently, flood damages have increased with heavy rainfall and typhoon influences, and it requires that visualization information to the flood inundation area of downstream in dam discharge. This study developed 3D GIS system that can visualize flood inundation area for Namgang Dam downstream. First, DEMs extracted from NGIS digital maps and IKONOS satellite images were optimized to mount in iWorld engine using TextureMaker and HeightMaker modules. And flood inundation area of downstream could be efficiently extracted with real-time flooding water level using Coordinate Operation System for Flood control In Multi-reservoir (COSFIM) and Flood Wave routing model (FLDWAV) in river cross section. This visualization information of flood inundation area can be used to examine flood weakness district needed in real time Dam operation and be applied to establish the rapid flood disaster countermeasures efficiently.

• Journal of the Korea Society of Computer and Information
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• v.29 no.3
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• pp.11-20
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• 2024

Among the generative models in Artificial Intelligence (AI), especially Generative Adversarial Network (GAN) has been successful in various applications such as image processing, density estimation, and style transfer. While the GAN models including Conditional GAN (CGAN), CycleGAN, BigGAN, have been extended and improved, researchers face challenges in real-world applications in specific domains such as disaster simulation, healthcare, and urban planning due to data scarcity and unstable learning causing Image distortion. This paper proposes a new progressive learning methodology called Progressive Step Training (PST) based on the Auxiliary Classifier GAN (ACGAN) that discriminates class labels, leveraging the progressive learning approach of the Progressive Growing of GAN (PGGAN). The PST model achieves 70.82% faster stabilization, 51.3% lower standard deviation, stable convergence of loss values in the later high resolution stages, and a 94.6% faster loss reduction compared to conventional methods.

• Journal of the Korea Society of Computer and Information
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• v.29 no.11
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• pp.79-88
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• 2024

With the advancement of unmanned aerial vehicle technology, swarm drones are increasingly being deployed across various domains, including disaster response and military operations, where their effectiveness is particularly pronounced. These swarm drones leverage real-time data sharing and decision-making capabilities to execute tactical missions, making collaborative behavior essential in complex battlefield environments. However, traditional rule-based behavior mechanisms face limitations as environmental complexity escalates. This paper explores the potential of applying multi-agent reinforcement learning (MARL) to swarm drone models and proposes strategies to enhance their mission success rates. By utilizing QPLEX and Prioritized Experience Replay (PER), we present methods aimed at improving learning efficiency. Validation through the SMACv2 simulator reveals that the proposed approach achieves faster learning convergence and higher mission success rates compared to existing MARL algorithms.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.5
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• pp.1021-1035
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• 2024

This paper proposes the TEE-BOP (Trusted Execution Environment-Based Blockchain Offline Payment) protocol for blockchain-based offline payments. TEE-BOP securely manages offline balances within a Trusted Execution Environment (TEE) and efficiently verifies initial deposit proofs recorded on the blockchain using Merkle trees. Additionally, it ensures secure and tamper-proof transactions in offline environments by guaranteeing the reliability of keys and the system through TEE Attestation. Unlike previous studies, TEE-BOP enhances real-world applicability by eliminating dependence on central authorities and avoiding assumptions of ideal models. The protocol solves the double-spending problem through multi-layered defense mechanisms and addresses forgery prevention by allowing recipients to directly verify data consistency between the TEE and the blockchain. This enables reliable blockchain-based offline payments in areas with unstable network infrastructure. It demonstrates that this research can expand the application of blockchain technology and contribute to improving access to financial services in developing countries or disaster situations.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.11
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• pp.728-739
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• 2020

Using webometric big data, this study examines the diversity of online information sources about the novel coronavirus causing the COVID-19 pandemic. Specifically, it focuses on some 28 countries where confirmed coronavirus cases occurred in February 2020. In the results, the online visibility of Australia, Canada, and Italy was the highest, based on their producing the most relevant information. There was a statistically significant correlation between the hit counts per country and the frequency of visiting the domains that act as information channels. Interestingly, Japan, China, and Singapore, which had a large number of confirmed cases at that time, were providing web data related to the novel coronavirus. Online sources were classified using an N-tuple helix model. The results showed that government agencies were the largest supplier of coronavirus information in cyberspace. Furthermore, the two-mode network technique revealed that media companies, university hospitals, and public healthcare centers had taken a positive attitude towards online circulation of coronavirus research and epidemic prevention information. However, semantic network analysis showed that health, school, home, and public had high centrality values. This means that people were concerned not only about personal prevention rules caused by the coronavirus outbreak, but also about response plans caused by life inconveniences and operational obstacles.

• Journal of the Korea Society of Computer and Information
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• v.25 no.8
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• pp.159-172
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• 2020

Social networking services (SNS) are developing significantly with the Internet and smartphones. It's a friendly social media, but if you think deeply about it, you'll find that it has a variety of faces. It is a communication tool between users, a medium for delivering information, an infrastructure for providing applications, and a community where people with common interests gather. In recent years, business tools, shopping and payment methods are also being swallowed. The influence of the spread of SNS on the real world is also expanding, and the work being dealt with from a sociological perspective is also increasing. Also, if you pay attention to the technical aspects of SNS, it is composed of various technical elements, such as infrastructure that handles large-scale access, user interface that supports comfortable use, and big data analysis to understand people's behavior more deeply. However, I usually use it as usual. However, if you look through SNS, you can see that the situation is surprisingly profound and multifaceted. This study began by looking at the history and current status of SNS and attempted to find its status through comparison with other media. From the point of view of relationship with society, it can be a risk and legal issue when using SNS, such as crimes using bad social media or social media. It is also necessary to comment on the activities on SNS or the guidelines established by the operators. Therefore, various legal issues on SNS will be discussed. Also, as an example of using SNS, I will introduce an example of using SNS in disaster response. From a more technical point of view, you will receive commentary on SNS's network-based technology and SNS's information use, and these articles will help you understand and use SNS safely and help you further utilize or develop SNS.

• Spatial Information Research
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• v.22 no.6
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• pp.33-43
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• 2014

As the number of people who live indoor space has been increased, the interest in 3D indoor spatial information has been grown. Object-Oriented 3D indoor space modeling including indoor spatial information has performed in level of detail 4, and modeling data is able to be constructed based on various raw data which are as-built drawing, laser scanning, BIM data, and camera. 3D indoor space modeling has been worked based on established indoor space modeling process, and the result can be used for various application fields such as indoor space pedestrian navigation, facility management, disaster management, and so on. However, the modeling process has limitations to perform indoor space modeling efficiently, because the process is complicated and wastes time at modeling work. In this paper, we propose evaluation on practical use of raw data for 3D indoor space modeling purpose on supporting efficient indoor space modeling through analyzing the established process. Therefore, we define the requirements to evaluate the practical use of raw data and propose the verification method. In addition, as-built drawing which has been used in Seoul 3D indoor space modeling project will be applied to proposed method as a raw data.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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• pp.555-562
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• 2021

Recently, in the field of geospatial information construction, unmanned aerial vehicles have been increasingly used because they enable rapid data acquisition and utilization. In this study, photogrammetry was performed using fixed-wing, rotary-wing, and VTOL (Vertical Take-Off and Landing) unmanned aerial vehicles, and geospatial information was constructed using two types of unmanned aerial vehicle LiDAR (Light Detection And Ranging) sensors. In addition, the accuracy was evaluated to present the utility of spatial information constructed through unmanned aerial photogrammetry and LiDAR. As a result of the accuracy evaluation, the orthographic image constructed through unmanned aerial photogrammetry showed accuracy within 2 cm. Considering that the GSD (Ground Sample Distance) of the constructed orthographic image is about 2 cm, the accuracy of the unmanned aerial photogrammetry results is judged to be within the GSD. The spatial information constructed through the unmanned aerial vehicle LiDAR showed accuracy within 6 cm in the height direction, and data on the ground was obtained in the vegetation area. DEM (Digital Elevation Model) using LiDAR data will be able to be used in various ways, such as construction work, urban planning, disaster prevention, and topographic analysis.