• International Journal of Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.237-246
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• 2024

In recent years, the frequent occurrence of fire accidents in university libraries has posed significant threats to the safety of students' lives and property, alongside negative social impacts. Accurately analyzing the factors affecting evacuation during library fires and proposing optimized measures for safe evacuation is thus crucial. This paper utilizes a specific university library as a case study, simulating fire evacuation scenarios using the Pathfinder software, to assess and validate evacuation strategies and propose relevant optimizations. Pathfinder, developed by Thunderhead Engineering in the United States, is an intuitive and straightforward personnel emergency evacuation assessment system, offering advanced visualization interfaces and 3D animation effects. This study aims to construct evacuation models and perform simulation analysis for the selected university library using Pathfinder. The library's structural layout, people flow characteristics, and the nature of fire and smoke spread are considered in the analysis. Additionally, evacuation scenarios involving different fire outbreak locations and the status of emergency exits are examined. The findings underscore the importance of effective evacuation in fire situations, highlighting how environmental conditions, individual characteristics, and behavioral patterns significantly influence evacuation efficiency. Through these investigations, the study enhances understanding and optimization of evacuation strategies in fire scenarios, thereby improving safety and efficiency. The research not only provides concrete and practical guidelines for building design, management, and emergency response planning in libraries but also offers valuable insights for the design and management of effective evacuation systems in buildings, crucial for ensuring occupant safety and minimizing loss of life in potential hazard situations

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.209-217
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• 2024

Recent years have seen a notable increase in fire incidents in university cafeterias, yet the social attention to these occurrences remains limited. Despite quick responses to these incidents preventing loss of life, the need for large-scale evacuation in such high foot traffic areas can cause significant disruptions, economic losses, and panic among students. The potential for stampedes and unpredictable damage during inadequate evacuations underscores the importance of fire safety and evacuation research in these settings. Previous studies have explored evacuation models in various university environments, emphasizing the influence of environmental conditions, personal characteristics, and behavioral patterns on evacuation efficiency. However, research specifically focusing on university cafeterias is scarce. This paper addresses this gap by employing Pathfinder software to analyze fire spread and evacuation safety in a university cafeteria. Pathfinder, an advanced emergency evacuation assessment system, offers realistic 3D simulations, crucial for intuitive and scientific evacuation analysis. The studied cafeteria, encompassing three floors and various functional areas, often exceeds a capacity of 1500 people, primarily students, during peak times. The study includes constructing a model of the cafeteria in Pathfinder and analyzing evacuation scenarios under different fire outbreak conditions on each floor. The paper sets standard safe evacuation criteria (ASET > RSET) and formulates three distinct evacuation scenarios, considering different fire outbreak locations and initial evacuation times on each floor. The simulation results reveal the impact of the fire's location and the evacuation preparation time on the overall evacuation process, highlighting that fires on higher floors or longer evacuation preparation times tend to reduce overall evacuation time.In conclusion, the study emphasizes a multifaceted approach to improve evacuation safety and efficiency in educational settings. Recommendations include expanding staircase widths, optimizing evacuation routes, conducting regular drills, strengthening command during evacuations, and upgrading emergency facilities. The use of information and communication technology for managing emergencies is also suggested. These measures collectively form a comprehensive framework for ensuring safety in educational institutions during fire emergencies.

• Journal of Technologic Dentistry
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• v.46 no.1
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• pp.1-7
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• 2024

Purpose: The goal of this study was to determine the clinical acceptability of various cement space settings for the marginal and internal fit of a zirconia core manufactured using additive manufacturing. Methods: The maxillary right incisor served as the master model. After scanning the maxillary right incisor with a dental 3D (three-dimensional) scanner, the stereo lithography file was created using different cement space settings of 40, 120, and 200 ㎛ using computer-aided design software (Dental System 2018; 3Shape). The marginal and internal fit of the 3 groups were determined using the silicon replica technique. Measurement points were divided into the following three categories: margin, axial wall, and incisal. To ensure more accurate measurements, these three measurement points were divided into 8 points. The Shapiro-Wilk, one-way ANOVA, and Tukey's honestly significant difference test (for all tests α=0.05) were the statistical analyses that were included in the study. Results: The CS (cement space)-200 group had better marginal and internal fit than the CS-40 and CS-120 groups, and there were statistically significant differences at the marginal and incisal points, except for the axial wall points. CS-200 group, both marginal and internal fit were within 120 ㎛, which is the clinically acceptable value. Conclusion: This study suggests that a 200 ㎛ cement space setting is ideal for optimal marginal and internal fit of 3D-printed ceramic crowns.

• Earthquakes and Structures
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• v.26 no.3
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• pp.239-249
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• 2024

A new layered shear continuum model box was developed to address the dynamic response issues of buried oil and gas pipelines under multi-point excitation. Vibration table tests were conducted to investigate the seismic response of buried pipelines and the surrounding soil under longitudinal multi-point excitation. A nonlinear model of the pipeline-soil interaction was established using ABAQUS finite element software for simulation and analysis. The seismic response characteristics of the pipeline and soil under longitudinal multi-point excitation were clarified through vibration table tests and simulation. The results showed good consistency between the simulation and tests. The acceleration of the soil and pipeline exhibited amplification effects at loading levels of 0.1 g and 0.2 g, which significantly reduced at loading levels of 0.4 g and 0.62 g. The peak acceleration increased with increasing loading levels, and the peak frequency was in the low-frequency range of 0 Hz to 10 Hz. The amplitude in the frequency range of 10 Hz to 50 Hz showed a significant decreasing trend. The displacement peak curve of the soil increased with the loading level, and the nonlinearity of the soil resulted in a slower growth rate of displacement. The strain curve of the pipeline exhibited a parabolic shape, with the strain in the middle of the pipeline about 3 to 3.5 times larger than that on both sides. This study provides an effective theoretical basis and test basis for improving the seismic resistance of buried oil and gas pipelines.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.1
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• pp.21-29
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• 2024

With the development of ICT and network, security management of IT infrastructure that has grown in size is becoming very difficult. Many companies and public institutions are having difficulty managing system and network security. In addition, as the complexity of hardware and software grows, it is becoming almost impossible for a person to manage all security. Therefore, AI is essential for network security management. However, since it is very dangerous to operate an attack model in a real network environment, cybersecurity emulation research was conducted through reinforcement learning by implementing a real-life network environment. To this end, this study applied reinforcement learning to the network environment, and as the learning progressed, the agent accurately identified the vulnerability of the network. When a network vulnerability is detected through AI, automated customized response becomes possible.

• Journal of Information Processing Systems
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• v.20 no.3
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• pp.375-390
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• 2024

Edge computing architecture has effectively alleviated the computing pressure on cloud platforms, reduced network bandwidth consumption, and improved the quality of service for user experience; however, it has also introduced new security issues. Existing anomaly detection methods in big data scenarios with cloud-edge computing collaboration face several challenges, such as sample imbalance, difficulty in dealing with complex network traffic attacks, and difficulty in effectively training large-scale data or overly complex deep-learning network models. A lightweight deep-learning model was proposed to address these challenges. First, normalization on the user side was used to preprocess the traffic data. On the edge side, a trained Wasserstein generative adversarial network (WGAN) was used to supplement the data samples, which effectively alleviates the imbalance issue of a few types of samples while occupying a small amount of edge-computing resources. Finally, a trained lightweight deep learning network model is deployed on the edge side, and the preprocessed and expanded local data are used to fine-tune the trained model. This ensures that the data of each edge node are more consistent with the local characteristics, effectively improving the system's detection ability. In the designed lightweight deep learning network model, two sets of convolutional pooling layers of convolutional neural networks (CNN) were used to extract spatial features. The bidirectional long short-term memory network (BiLSTM) was used to collect time sequence features, and the weight of traffic features was adjusted through the attention mechanism, improving the model's ability to identify abnormal traffic features. The proposed model was experimentally demonstrated using the NSL-KDD, UNSW-NB15, and CIC-ISD2018 datasets. The accuracies of the proposed model on the three datasets were as high as 0.974, 0.925, and 0.953, respectively, showing superior accuracy to other comparative models. The proposed lightweight deep learning network model has good application prospects for anomaly traffic detection in cloud-edge collaborative computing architectures.

• Journal of Radiation Industry
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• v.17 no.3
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• pp.265-273
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• 2023

To effectively and safely manage the radiation exposure to nuclear power plant (NPP) workers in accidents, major overseas NPP operators such as the United States, Germany, and France have developed and applied realistic 3D model radiation dose assessment software for workers. Continuous research and development have recently been conducted, such as performing NPP accident management using 3D-VR based on As Low As Reasonably Achievable (ALARA) planning tool. In line with this global trend, it is also required to secure technology to manage radiation exposure of workers in Korea efficiently. Therefore, in this paper, it is described the application method and assessment results of radiation exposure scenarios for workers in response to accidents assessment technology, which is one of the fundamental technologies for constructing a realistic platform to be utilized for radiation exposure prediction, diagnosis, management, and training simulations following accidents. First, the post-accident sampling after the Loss of Coolant Accident(LOCA) was selected as the accident and response scenario, and the assessment area related to this work was established. Subsequently, the structures within the assessment area were modeled using MCNP, and the radiation source of the equipment was inputted. Based on this, the radiation dose distribution in the assessment area was assessed. Afterward, considering the three principles of external radiation protection (time, distance, and shielding) detailed work scenarios were developed by varying the number of workers, the presence or absence of a shield, and the location of the shield. The radiation exposure doses received by workers were compared and analyzed for each scenario, and based on the results, the optimal accident response scenario was derived. The results of this study plan to be utilized as a fundamental technology to ensure the safety of workers through simulations targeting various reactor types and accident response scenarios in the future. Furthermore, it is expected to secure the possibility of developing a data-based ALARA decision support system for predicting radiation exposure dose at NPP sites.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.701-710
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• 2024

This study conducted a survey of companies in the aviation, drone, and Urban Air Mobility (UAM) sectors to analyze the educational and workforce needs, identifying essential competencies and technical training required. The study also proposed potential areas for collaboration between universities and industry regarding educational methods. Key findings and implications of the survey were derived. The results indicated that the most critical consideration for hiring was job-specific skills in the respective field. The most essential quality for workforce training was identified as enhancing the ability to use various equipment and software related to the major field. In the UAM sector, there was a high demand for personnel and education related to aircraft and components, with the highest demand being for lightweight manufacturing technology for aircraft. This study can serve as foundational data for addressing the educational needs in this field.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.4
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• pp.77-82
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• 2024

Recent advancements in artificial intelligence (AI) technology have led to an increase in the implementation of AI applications in mobile environments. However, due to the limited resources in mobile devices compared to desktops and servers, there is growing interest in research aimed at efficiently executing AI workloads on mobile platforms. While most studies focus on offloading to edge or cloud solutions to mitigate computing resource constraints, research on the characteristics of file I/O related to storage access in mobile settings remains underexplored. This paper analyzes file I/O traces generated during the execution of deep learning applications in mobile environments and investigates how they differ from traditional mobile workloads. We anticipate that the findings of this study will be utilized to design future smartphone system software more efficiently, considering the file access characteristics of deep learning.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.4
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• pp.113-119
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• 2024

In this paper, a model was devised to change the numerical values that affect the game balance by using Chat-GPT for game balancing. Based on the usability of Chat-GPT shown in several studies and cases using Chat-GPT, Chat-GPT is automated to directly adjust detailed and objective in-game numerical values. In this paper, the format of Chat-GPT responses was consistently adjusted so that the numerical values required for game balancing could be obtained directly from the answers. As an experimental method, it was confirmed that four players autonomously designed the game level through five rounds to adjust the balance. These studies suggest the possibility that games can be produced using Chat-GPT in the future.