• The Journal of the Korea Contents Association
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• v.21 no.9
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• pp.12-23
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• 2021

This study organizes policy tasks currently being promoted by the government to revitalize the domestic VR and AR industries, which are evaluated to be stagnant compared to major overseas countries, and aims to derive priorities through analysis of an AHP survey for experts in the VR/AR field, and to seek countermeasures based on the analysis results. As a result of classification based on various previous studies, press releases, and policy data, it was divided into 5 major categories and 16 sub-categories: technical issues, awareness improvement, legal/institutional improvement, government support, and manpower development. As a result of the AHP analysis, in the major category, the "government support" appeared as the top priority policy task, followed by the "manpower development". In the sub-categories, "training new manpower" was the most important policy implementation task, followed by "enhancing technological competitiveness". This study is meaningful in that it selects and presents prioritized policy tasks that clearly reflect the position and perspective of the industry on the policy-making situation exposed to the limitations of time and resources, while also presenting practical improvement plans.

• Journal of Internet of Things and Convergence
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• v.9 no.1
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• pp.105-115
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• 2023

This study seeks to find a way to induce users to expand their direct participation in sports through the acceptance of digital technology. From July 1 to August 30, 2022, a survey was conducted targeting home training users who applied the Internet of Things (IoT). 129 people participated in the survey through non-face-to-face self-administration method. For data processing, frequency analysis, exploratory factor analysis, reliability analysis, correlation analysis, multiple regression analysis, and 3-step mediation regression analysis were conducted using IBM's SPSS 21.0 program. The results of the study are as follows. First, in the relationship between the home training PPM model and direct participation in sports, ease appeared to have a mediating effect. In the factors of push, simple functionality showed a complete mediating effect, and inefficiency showed a partial mediating effect. Among pull factors, enjoyment and possibility of experience showed a complete mediating effect. In the mooring factors, individual innovativeness showed a complete mediating effect. Second, in the relationship between home training PPM model and direct participation in sports, usefulness showed a mediating effect. In the factors of push, simple functionality showed a complete mediating effect, and inefficiency showed a partial mediating effect. Among pull factors, enjoyment and possibility of experience showed a complete mediating effect. Among the mooring factors, individual innovativeness showed a partial mediating effect. Through this research, it is expected that the sports industry will contribute to the expansion of consumption expenditure and economic growth through the expansion of digital technologies such as NFT, Metaverse, and virtual/augmented reality.

• Journal of Korean Neurosurgical Society
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• v.66 no.6
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• pp.632-641
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• 2023

Central nervous system tumors are identified as tumors of the brain and spinal cord. The associated morbidity and mortality of cerebrospinal tumors are disproportionately high compared to other malignancies. While minimally invasive techniques have initiated a revolution in neurosurgery, artificial intelligence (AI) is expediting it. Our study aims to analyze AI's role in the neurosurgical management of cerebrospinal tumors. We conducted a scoping review using the Arksey and O'Malley framework. Upon screening, data extraction and analysis were focused on exploring all potential implications of AI, classification of these implications in the management of cerebrospinal tumors. AI has enhanced the precision of diagnosis of these tumors, enables surgeons to excise the tumor margins completely, thereby reducing the risk of recurrence, and helps to make a more accurate prediction of the patient's prognosis than the conventional methods. AI also offers real-time training to neurosurgeons using virtual and 3D simulation, thereby increasing their confidence and skills during procedures. In addition, robotics is integrated into neurosurgery and identified to increase patient outcomes by making surgery less invasive. AI, including machine learning, is rigorously considered for its applications in the neurosurgical management of cerebrospinal tumors. This field requires further research focused on areas clinically essential in improving the outcome that is also economically feasible for clinical use. The authors suggest that data analysts and neurosurgeons collaborate to explore the full potential of AI.

• Tunnel and Underground Space
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• v.33 no.4
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• pp.281-298
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• 2023

Continuous research efforts are being devoted to unmanned mobile platforms for lunar exploration. There is an ongoing demand for real-time information processing to accurately determine the positioning and mapping of areas of interest on the lunar surface. To apply deep learning processing and analysis techniques to practical rovers, research on software integration and optimization is imperative. In this study, a foundational investigation has been conducted on real-time analysis of virtual lunar base construction site images, aimed at automatically quantifying spatial information of key objects. This study involved transitioning from an existing region-based object recognition algorithm to a boundary box-based algorithm, thus enhancing object recognition accuracy and inference speed. To facilitate extensive data-based object matching training, the Batch Hard Triplet Mining technique was introduced, and research was conducted to optimize both training and inference processes. Furthermore, an improved software system for object recognition and identical object matching was integrated, accompanied by the development of visualization software for the automatic matching of identical objects within input images. Leveraging satellite simulative captured video data for training objects and moving object-captured video data for inference, training and inference for identical object matching were successfully executed. The outcomes of this research suggest the feasibility of implementing 3D spatial information based on continuous-capture video data of mobile platforms and utilizing it for positioning objects within regions of interest. As a result, these findings are expected to contribute to the integration of an automated on-site system for video-based construction monitoring and control of significant target objects within future lunar base construction sites.

• Journal of Korea Multimedia Society
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• v.23 no.5
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• pp.710-720
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• 2020

Tsunami occurred by a rapid change in the ocean floor is a natural disaster that causes serious damage worldwide. South Korea seems to be out of the range of this damage, but it is quite possible that South Korea will fall within the range due to the long-distance propagation features of tsunami and many earthquakes occurred in Japan. However, the analysis and preparation for tsunami have been still insufficient. In this paper, we propose a tsunami simulation model based on cellular automata for analyzing coastal inundation. The proposed model calculates the range of inundation in coastal areas by propagating the energy of tsunami using the interaction between neighboring cells. We define interaction rules and algorithms for the energy transfer and propose a software tool to effectively utilize the model. In addition, to verify and tune the simulation model, we used the actual tsunami data in 2010 at Dichato, Chile. As a case study, the proposed model was applied to analyze the coastal inundation according to tsunami height in Gwangali Beach, a famous site in Busan. It is expected that the simulation model can be a help to prepare an effective countermeasure against tsunami and be used for a virtual evacuating training.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.103-111
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• 2023

Mountains area, especially walking in open space is important for special active field which is based on mountain terrain. Recent research on pedestrian-path includes elements about pedestrian and various environment by analyzing network, but it is mainly focusing on limited space except for data-poor terrain like a mountain terrain. This paper proposes an architecture to generate walking path considering the slope for mountain terrain open space through virtual network made of mesh. This architecture shows that it reflects real terrain more effective when measuring distance using slope and is possible to generate mountain walking path using open space unlike other existing services, and is verified through the test. The proposed architecture is expected to utilize for pedestrian-path generation way considering mountain terrain open space in case of distress, mountain rescue and tactical training and so on.

• Land and Housing Review
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• v.15 no.2
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• pp.125-137
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• 2024

A conventional Design for Safety (DfS), introduced to eliminate potential hazards in the design phase proactively, has encountered persistent challenges, such as perfunctory risk assessments and hazard identifications based on 2D drawings and inefficient workflow processes. This study proposes a BIM-based approach to Design for Safety (DfS) to address the limitations of conventional methods, aiming to enhance efficiency and achieve practical safety management benefits. The proposed workflow process for BIM-based DfS has been refined and validated for on-site applicability through various case studies, including risk assessments during the design phase and field applications for safety management activities during the construction phase. Specifically, the critical process of risk assessment within the DfS methodology has also been transitioned to a BIM-based approach. This BIM-based risk assessment process has been evaluated through case studies, encompassing safety reviews for structural design, construction equipment operation, and construction methodology with sequence in design projects. Additionally, the proposed BIM-based DfS has demonstrated exceptional on-site applicability and efficiency, as validated by the application of a BIM deliverable embedded in DfS information for CDE-based daily activity briefing, VR-based safety training, AR-based mitigation measures inspections, and other safety management activities in the construction phase.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.8
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• pp.171-180
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• 2020

Smart Factory consists of digital automation solutions throughout the production process, including design, development, manufacturing and distribution, and it is an intelligent factory that installs IoT in its internal facilities and machines to collect process data in real time and analyze them so that it can control itself. The smart factory's equipment works in a physical combination of numerous hardware, rather than a virtual character being driven by a single object, such as a game. In other words, for a specific common goal, multiple devices must perform individual actions simultaneously. By taking advantage of the smart factory, which can collect process data in real time, if reinforcement learning is used instead of general machine learning, behavior control can be performed without the required training data. However, in the real world, it is impossible to learn more than tens of millions of iterations due to physical wear and time. Thus, this paper uses simulators to develop grid sortation systems focusing on transport facilities, one of the complex environments in smart factory field, and design cooperative multi-agent-based reinforcement learning to demonstrate efficient behavior control.

• Journal of Korean Library and Information Science Society
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• v.52 no.4
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• pp.103-126
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• 2021

This study aims to propose a plan to restructure libraries as a complex space for education and culture for 19 public libraries belonging to the Chungcheongnam-do Office of Education. For the purpose of this, case surveys and user surveys of complex facilities of domestic and foreign public institutions were conducted. Based on the findings, a space restructuring strategy was suggested by dividing the library space into the following ways: a space to learn (a comprehensive data room, a theme data inquiry room, a future classroom, a blended learning center, a STEAM training room, and an online lecture learning room), a space to express (a foyer, a maker room, a digital media creation room and an online lecture production room), a space to share (a club room, a group study room, a well-being complex culture space, a convenient living space, rest area, and a browsing area), and a space to enjoy (a performance-thought playground, infant and child archives, a digital virtual experience room, a specialized alcove room, and an outdoor reading room). In addition, a restructuring model of public libraries belonging to the Office of Education was proposed, such as a leading model, a basic model, a joint model, and a minimum model, in consideration of the size of the building, the size of the library, and the level of service and space.

• KNOM Review
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• v.23 no.2
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• pp.18-28
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• 2020

With the development of the virtual community, the benefits that IT technology provides to people in fields such as healthcare, industry, communication, and culture are increasing, and the quality of life is also improving. Accordingly, there are various malicious attacks targeting the developed network environment. Firewalls and intrusion detection systems exist to detect these attacks in advance, but there is a limit to detecting malicious attacks that are evolving day by day. In order to solve this problem, intrusion detection research using machine learning is being actively conducted, but false positives and false negatives are occurring due to imbalance of the learning dataset. In this paper, a Random Oversampling method is used to solve the unbalance problem of the UNSW-NB15 dataset used for network intrusion detection. And through experiments, we compared and analyzed the accuracy, precision, recall, F1-score, training and prediction time, and hardware resource consumption of the models. Based on this study using the Random Oversampling method, we develop a more efficient network intrusion detection model study using other methods and high-performance models that can solve the unbalanced data problem.