• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.2
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• pp.360-368
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• 2008

In this paper, in order that the various QoS(Qualify of Service)s that are required by different traffic class are guaranteed in the random access channels in multi-service multi-user OFDMA systems, the backoff-based random access algorithm is proposed and corresponding performance is analyzed in terms of the access success probability, the throughput, the average delay and the blocking probability. Through the numerical analysis, it is shown that the proposed backoff-based random access algorithm can provide the differentiated QoSs to random access attempts according to their service class.

• The Journal of Information Systems
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• v.29 no.4
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• pp.153-169
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• 2020

Purpose Many students have concerned about the quality and operation of online courses with universities offering online courses to avoid the spread of COVID-19. To deepen our understanding of university online class systems, this research aims to assess students' satisfaction with online class systems and continuous usage intention on the basis of the perceived benefits and expectation confirmation theory. Design/methodology/approach This paper empirically analyzes the impact of each perceived benefit on user satisfaction and the intention to use it continuously by dividing the perceived benefits considered in existing literature into utilitarian benefits (convenience), emotional benefits (pleasure), and symbolic benefits (personal benefits). Moreover, the perceived expectations and performance have also been assessed with its impact on satisfaction and the intention to continue use. Data collected from 241 university students were empirically tested against a research model. Findings Analysis results showed that perceived advantages (comfort, enjoyment and personalized benefit) significantly affect user satisfaction and that perceived benefits have positive effects on the intention to continue use whereas the expected confirmation do not significantly influence on the intention to continue use.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.13 no.3
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• pp.45-55
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• 2003

Wireless LAM has the advantage of extension, flexibility and easiness of installation and maintenance. However, due to the characteristics of wireless media, it is vulnerable to security attacks. PKI(Public Key Infrastructure) is estimated to be a good solution offering security function to wireless LAM including global roaming. It offers high security functions as authentication confidentiality and digital signature while it generates big overheads such as CRL search and certificate verification. The overheads can not be avoided during the initial authentication. However, when we consider the case of handoff, it can be minimized through the fast handoff. In this paper, we design a fast handoff authentication mechanism based on PKI in the wireless LAM and analyze the performance of the scheme.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.204-209
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• 2023

Dynamic Bandwidth Allocation (DBA) methods in telecommunication network & systems have emerged with mechanisms for sharing limited resources in a rapidly growing number of users in today's access networks. Since the DBA research trends are incredibly fast-changing literature where almost every day new areas and terms continue to emerge. Co - citation analysis offers a significant support to researchers to distinguish intellectual bases and potentially leading edges of a specific field. We present the visualization based analysis for DBA algorithms in telecommunication field using mainstream co-citation analysis tool-CiteSpace and web of science (WoS) analysis. Research records for the period of decade (2009-2018) for this analysis are sought from WoS. The visualization results identify the most influential DBA algorithms research studies, journals, major countries, institutions, and researchers, and indicate the intellectual bases and focus entirely on DBA algorithms in the literature, offering guidance to interested researchers on more study of DBA algorithms.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.97-99
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• 2021

As the aging society continues in recent years, interest in dementia is increasing. Among them, Alzheimer's disease is a degenerative brain disease that accounts for the largest percentage of all dementia patients, with the medical community currently not offering clear prevention and treatment for Alzheimer's disease, and the importance of early treatment and early prevention is emphasized. In this paper, we intend to find the most efficient activation function by combining various activation functions centering on convolutional neural networks using MRI datasets of normal people and patients with Alzheimer's disease. In addition, it is intended to be used as a dementia classification modeling suitable for the medical field in the future through Alzheimer's dementia classification modeling.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1794-1806
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• 2023

This study presents a method for capturing photographs of users as input and converting them into 2D character animation sprites using a generative adversarial network-based artificial intelligence network. Traditionally, 2D character animations have been created by manually creating an entire sequence of sprite images, which incurs high development costs. To address this issue, this study proposes a technique that combines motion videos and sample 2D images. In the 2D sprite generation process that uses the proposed technique, a sequence of images is extracted from real-life images captured by the user, and these are combined with character images from within the game. Our research aims to leverage cutting-edge deep learning-based image manipulation techniques, such as the GAN-based motion transfer network (impersonator) and background noise removal (U² -Net), to generate a sequence of animation sprites from a single image. The proposed technique enables the creation of diverse animations and motions just one image. By utilizing these advancements, we focus on enhancing productivity in the game and animation industry through improved efficiency and streamlined production processes. By employing state-of-the-art techniques, our research enables the generation of 2D sprite images with various motions, offering significant potential for boosting productivity and creativity in the industry.

• The Journal of Information Systems
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• v.32 no.2
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• pp.25-43
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• 2023

Purpose In online delivery platforms, platform workers are required to perform both platform labor, which is compensated with immediate wages, and shadow work which is a kind of unpaid job to support the platform labor. Thus, the objective of this study is to empirically examine how platform workers' platform labor and shadow work affect their job burnout and their turnover intention in the online delivery platform context. Design/methodology/approach This study developed a research model by employing platform labor and shadow work to influence job burnout and turnover intention. This study also tested the model by partial least square techniques after collecting 169 cross-sectional data from food delivery riders in Korea. Findings This study found that both platform labor and shadow work affected platform workers' job burnout. In addition, the results showed that shadow work influenced their turnover intention while platform labor did not affect the turnover intention. Based on the results, this study contributed to relevant researchers who are interested in platform contexts by offering measurable constructs on platform labor and shadow work. In addition, this study could provide practitioners with practical implications on managing platform workers.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.182-189
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• 2023

A recently research, object detection and segmentation have emerged as crucial technologies widely utilized in various fields such as autonomous driving systems, surveillance and image editing. This paper proposes a program that utilizes the QT framework to perform real-time object detection and precise instance segmentation by integrating YOLO(You Only Look Once) and Mask R CNN. This system provides users with a diverse image editing environment, offering features such as selecting specific modes, drawing masks, inspecting detailed image information and employing various image processing techniques, including those based on deep learning. The program advantage the efficiency of YOLO to enable fast and accurate object detection, providing information about bounding boxes. Additionally, it performs precise segmentation using the functionalities of Mask R CNN, allowing users to accurately distinguish and edit objects within images. The QT interface ensures an intuitive and user-friendly environment for program control and enhancing accessibility. Through experiments and evaluations, our proposed system has been demonstrated to be effective in various scenarios. This program provides convenience and powerful image processing and editing capabilities to both beginners and experts, smoothly integrating computer vision technology. This paper contributes to the growth of the computer vision application field and showing the potential to integrate various image processing algorithms on a user-friendly platform

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.8
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• pp.2381-2398
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• 2024

The construction of virtual indoor spaces is crucial for the development of metaverses, virtual production, and other 3D content domains. Traditional methods for creating these spaces are often cost-prohibitive and labor-intensive. To address these challenges, we present a pipeline for generating digital twins of real indoor environments from RGB-D camera-scanned data. Our pipeline synergizes space structure estimation, 3D object detection, and the inpainting of missing areas, utilizing deep learning technologies to automate the creation process. Specifically, we apply deep learning models for object recognition and area inpainting, significantly enhancing the accuracy and efficiency of virtual space construction. Our approach minimizes manual labor and reduces costs, paving the way for the creation of metaverse spaces that closely mimic real-world environments. Experimental results demonstrate the effectiveness of our deep learning applications in overcoming traditional obstacles in digital twin creation, offering high-fidelity digital replicas of indoor spaces. This advancement opens for immersive and realistic virtual content creation, showcasing the potential of deep learning in the field of virtual space construction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.8
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• pp.2230-2252
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• 2024

Advances in deep learning technology have enabled the generation of more realistic deepfakes, which not only endanger individuals' identities but also exploit vulnerabilities in face recognition systems. The majority of existing deepfake detection methods have primarily focused on RGB-based analysis, offering unreliable performance in terms of detection accuracy and time. To address the issue, a grayscale-based deepfake detection method has recently been proposed. This method significantly reduces detection time while providing comparable accuracy to RGB-based methods. However, despite its significant effectiveness, the "key components" that directly affect the performance of grayscale-based deepfake detection have not been systematically analyzed. In this paper, we target three key components: RGB-to-grayscale conversion method, brightness level in grayscale, and resolution level in grayscale. To analyze their impacts on the performance of grayscale-based deepfake detection, we conducted comprehensive evaluations, including component-wise analysis and comparative analysis using real-world datasets. For each key component, we quantitatively analyzed its characteristics' performance and identified differences between them. Moreover, we successfully verified the effectiveness of an optimal combination of the key components by comparing it with existing deepfake detection methods.