• KNOM Review
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• v.22 no.3
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• pp.1-12
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• 2019

Recently, researching using big data and AI has emerged as a major issue in the ICT field. But, the size of big data for research is growing exponentially. In addition, users of data transmission of existing network method suggest that the problem the time taken to send and receive big data is slower than the time to copy and send the hard disk. Accordingly, researchers require dynamic and flexible network technology that can transmit data at high speed and accommodate various network structures. SDN/NFV technologies can be programming a network to provide a network suitable for the needs of users. It can easily solve the network's flexibility and security problems. Also, the problem with performing AI is that centralized data processing cannot guarantee real-time, and network delay occur when traffic increases. In order to solve this problem, the edge-computing technology, should be used which has moved away from the centralized method. In this paper, we investigate the concept and research trend of SDN, NFV, and edge-computing technologies, and analyze the trends of data central network technologies used by combining these three technologies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1216-1233
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• 2023

Conventional occupational therapy (OT) is conducted under the observation of an occupational therapist, and there are limitations in measuring and analyzing details such as degree of hand tremor and movement tendency, so this important information may be lost. It is therefore difficult to identify quantitative performance indicators, and the presence of observers during performance sometimes makes the subjects feel that they have to achieve good results. In this study, by using the Unity3D and artificial intelligence (AI) speaker, we propose a system that allows the subjects to steadily use it by themselves and helps the occupational therapist objectively evaluate through quantitative data. This system is based on the OT of the sensory integration approach. And the purpose of this system is to improve children's activities of daily living by providing various feedback to induce sensory integration, which allows them to develop the ability to effectively use their bodies. A dynamic OT cognitive assessment tool for children used in clinical practice was implemented in Unity3D to create an OT environment of virtual space. The Leap Motion Controller allows users to track and record hand motion data in real time. Occupational therapists can control the user's performance environment remotely by connecting Unity3D and AI speaker. The experiment with the conventional OT tool and the system we proposed was conducted. As a result, it was found that when the system was performed without an observer, users can perform spontaneously and several times feeling ease and active mind.

• Imaging Science in Dentistry
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• v.53 no.1
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• pp.77-82
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• 2023

Purpose: This study evaluated anatomical variations in the root canals of the lower premolars and molars in a Brazilian sub-population using cone-beam computed tomography (CBCT). Materials and Methods: In total, 121 CBCT images of patients were selected from a database. All images contained lower first and second premolars and molars on both sides of the arch, fully developed roots, and no treatment, resorption, or calcifications. In each image, the root canals of the lower premolars and molars were evaluated according to the Vertucci classification in On-Demand 3D software in the multiplanar reconstruction with dynamic navigation. Twenty-five percent of the images were re-assessed to analyze intraobserver confidence with the kappa test. Data were statistically evaluated with linear regression to evaluate the correlations of anatomic variations with age and sex, and the Wilcoxon test to analyze the laterality of variations, with a significance level of 5%. Results: The intraobserver agreement (0.94) was excellent. In general, the root canals of lower premolars and molars showed a higher prevalence of type I than other Vertucci classification types, followed by type V in premolars and type II in molars. When the molar roots were evaluated separately, type II was more frequent in mesial roots and type I in distal roots. Although age showed no correlations with the results, sex and laterality showed correlations with tooth 45 and the lower second premolars, respectively. Conclusion: The lower premolars and molars of a Brazilian sub-population showed a wide range of root canal anatomic variations.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.219-230
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• 2023

Wireless Body Area Networks (WBANs) have made it easier for healthcare workers and patients to monitor patients' status continuously in real time. WBANs have complex and diverse network structures; thus, management and control can be challenging. Therefore, considering emerging Software-defined networks (SDN) with WBANs is a promising technology since SDN implements a new network management and design approach. The SDN concept is used in this study to create more adaptable and dynamic network architectures for WBANs. The study focuses on comparing the performance of two SDN controllers, POX and Ryu, using Mininet, an open-source simulation tool, to construct network topologies. The performance of the controllers is evaluated based on bandwidth, throughput, and round-trip time metrics for networks using an OpenFlow switch with sixteen nodes and a controller for each topology. The study finds that the choice of network controller can significantly impact network performance and suggests that monitoring network performance indicators is crucial for optimizing network performance. The project provides valuable insights into the performance of SDN-based WBANs using POX and Ryu controllers and highlights the importance of selecting the appropriate network controller for a given network architecture.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.1
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• pp.17-24
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• 2024

This paper assesses the structural performance of the Daeungbojeon Hall of Magoksa in Gongju, a representative multi-tiered roof traditional timber structure from the Joseon Dynasty, under vertical loads. Employing midas Gen, a structural analysis software, we developed a three-dimensional analysis model closely resembling the actual structure. Static analysis was employed to evaluate the safety and serviceability of the main vertical and horizontal members under vertical loads. While all members met the safety and serviceability criteria, structural weaknesses were identified in the Daelyang of the lower floor, particularly as a transitional beam, necessitating improvement. For the evaluation of dynamic behavior characteristics, eigenvalue analysis was conducted, assuming a relative rotational stiffness of 5% at the main joints. The natural period was determined to be 1.105 seconds, placing it within the category of a Hanok of similar size. The first mode manifested as a translational movement in the forward and backward direction of the building.

• Quality Improvement in Health Care
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• v.30 no.1
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• pp.55-75
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• 2024

Purpose: This study aimed to elucidate the intention to use non-face-to-face treatment, which was temporarily allowed during the coronavirus disease 2019 pandemic. Based on the social cognitive theory, individual behavioral changes occur through the dynamic interaction of individual, environmental, and behavioral factors. Thus, we investigated the impact of personal, environmental, and behavioral factors on the acceptance of non-face-to-face treatment. Methods: A Web survey was conducted using Korea Research Panel between December 26 and 29, 2022, to examine the conceptual framework. The survey targeted adults aged 19 and older, regardless of whether they had used non-face-to-face treatment. A total of 502 responses were collected. Further, a three-step hierarchical regression analysis was conducted using SPSS Windows software version 25.0. Results: The study showed that 131 out of 502 respondents had experience using non-face-to-face treatment, while 371 did not. The factors that influenced the intention to accept non-face-to-face treatment included the general characteristics of the participants (women, underlying disease), personal factors (usefulness, cost savings, knowledge), and environmental factors (social norms, trust, perceived risk). The model demonstrated an explanatory power of 65%. Conclusion: The results of this study directly show that intention is linked to behavior through the interaction between personal and environmental factors. Further research is needed to explore additional factors influencing the intention to accept non-face-to-face treatment, enabling its effective use in preventing and treating various diseases, including infectious diseases.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.197-205
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• 2024

Development of AI and big data-based algorithms to advance and optimize the recognition and detection performance of various static/dynamic vehicles in front and around the vehicle at a time when securing driving safety is the most important point in the development and commercialization of autonomous vehicles. etc. are being studied. However, there are many research cases for recognizing the same vehicle by using the unique advantages of radar and camera, but deep learning image processing technology is not used, or only a short distance is detected as the same target due to radar performance problems. Therefore, there is a need for a convergence-based vehicle recognition method that configures a dataset that can be collected from radar equipment and camera equipment, calculates the error of the dataset, and recognizes it as the same target. In this paper, we aim to develop a technology that can link location information according to the installation location because data errors occur because it is judged as the same object depending on the installation location of the radar and CCTV (video).

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.1
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• pp.106-123
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• 2024

This article takes the talk show "Beast Town" as an example to introduce the overall technical solution, technical difficulties and countermeasures for the combination of cartoon virtual characters and virtual studio technology, providing reference and experience for the multi-scenario application of digital humans. Compared with the live broadcast that combines reality and reality, we have further upgraded our virtual production technology and digital human-driven technology, adopted industry-leading real-time virtual production technology and monocular camera driving technology, and launched a virtual cartoon character talk show - "Beast Town" to achieve real Perfectly combined with virtuality, it further enhances program immersion and audio-visual experience, and expands infinite boundaries for virtual manufacturing. In the talk show, motion capture shooting technology is used for final picture synthesis. The virtual scene needs to present dynamic effects, and at the same time realize the driving of the digital human and the movement with the push, pull and pan of the overall picture. This puts forward very high requirements for multi-party data synchronization, real-time driving of digital people, and synthetic picture rendering. We focus on issues such as virtual and real data docking and monocular camera motion capture effects. We combine camera outward tracking, multi-scene picture perspective, multi-machine rendering and other solutions to effectively solve picture linkage and rendering quality problems in a deeply immersive space environment. , presenting users with visual effects of linkage between digital people and live guests.

• Korean Journal of Radiology
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• v.24 no.11
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• pp.1131-1141
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• 2023

Objective: Cortical iron deposition has recently been shown to occur in Alzheimer's disease (AD). In this study, we aimed to evaluate how cortical gray matter iron, measured using quantitative susceptibility mapping (QSM), differs in the clinical cognitive impairment spectrum. Materials and Methods: This retrospective study evaluated 73 participants (mean age ± standard deviation, 66.7 ± 7.6 years; 52 females and 21 males) with normal cognition (NC), 158 patients with mild cognitive impairment (MCI), and 48 patients with AD dementia. The participants underwent brain magnetic resonance imaging using a three-dimensional multi-dynamic multi-echo sequence on a 3-T scanner. We employed a deep neural network (QSMnet+) and used automatic segmentation software based on FreeSurfer v6.0 to extract anatomical labels and volumes of interest in the cortex. We used analysis of covariance to investigate the differences in susceptibility among the clinical diagnostic groups in each brain region. Multivariable linear regression analysis was performed to study the association between susceptibility values and cognitive scores including the Mini-Mental State Examination (MMSE). Results: Among the three groups, the frontal (P < 0.001), temporal (P = 0.004), parietal (P = 0.001), occipital (P < 0.001), and cingulate cortices (P < 0.001) showed a higher mean susceptibility in patients with MCI and AD than in NC subjects. In the combined MCI and AD group, the mean susceptibility in the cingulate cortex (β = -216.21, P = 0.019) and insular cortex (β = -276.65, P = 0.001) were significant independent predictors of MMSE scores after correcting for age, sex, education, regional volume, and APOE4 carrier status. Conclusion: Iron deposition in the cortex, as measured by QSMnet+, was higher in patients with AD and MCI than in NC participants. Iron deposition in the cingulate and insular cortices may be an early imaging marker of cognitive impairment related neurodegeneration.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.267-277
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• 2024

This paper explores the use of machine learning in game production to create goal-oriented, realistic animations for skeleton monsters. The purpose of this research is to enhance realism by implementing intelligent movements in monsters within game development. To achieve this, we designed and implemented a learning model for skeleton monsters using reinforcement learning algorithms. During the machine learning process, various reward conditions were established, including the monster's speed, direction, leg movements, and goal contact. The use of configurable joints introduced physical constraints. The experimental method validated performance through seven statistical graphs generated using machine learning methods. The results demonstrated that the developed model allows skeleton monsters to move to their target points efficiently and with natural animation. This paper has implemented a method for creating game monster animations using machine learning, which can be applied in various gaming environments in the future. The year 2024 is expected to bring expanded innovation in the gaming industry. Currently, advancements in technology such as virtual reality, AI, and cloud computing are redefining the sector, providing new experiences and various opportunities. Innovative content optimized for this period is needed to offer new gaming experiences. A high level of interaction and realism, along with the immersion and fun it induces, must be established as the foundation for the environment in which these can be implemented. Recent advancements in AI technology are significantly impacting the gaming industry. By applying many elements necessary for game development, AI can efficiently optimize the game production environment. Through this research, We demonstrate that the application of machine learning to Unity and game engines in game development can contribute to creating more dynamic and realistic game environments. To ensure that VR gaming does not end as a mere craze, we propose new methods in this study to enhance realism and immersion, thereby increasing enjoyment for continuous user engagement.