• 의료ㆍ복지 건축 : 한국의료복지건축학회 논문집
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• 제21권1호
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• pp.7-15
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• 2015

Purpose : When natural disaster occurs, the victims are evacuated to temporary shelters such as indoor gymnasiums or large space buildings until their homes are recovered. If someone in this temporary shelter is infected with an airborne infectious disease, it becomes easier for the disease to spread to the other people in the shelter than it would be under normal conditions. Therefore, temporary shelters need to provide not only water and food but also hygienic indoor conditions. Methods : In this study, the use of mechanical systems such as ultraviolet germicidal irradiation (UVGI) systems and air cleaners were simulated using numerical analysis to find out how these systems can control airborne infection in temporary shelters. An indoor gymnasium was selected as a temporary shelter for the numerical simulation model considering Korea's post-disaster response system. Influenza A virus was assumed as an airborne infectious disease and the diffusion of the virus was made by one person in the shelter. Results : The result of this study showed that the UVGI systems disinfected the virus more effectively than the air cleaners by creating a more stable airflow after the disinfection process. The air cleaners could remove the virus but since it created an unstable airflow in the temporary shelter, the virus was condensed to a certain area to show a higher virus concentration level than the source location. Implications : In the temporary shelter, it is necessary to use UVGI systems or air cleaners for hygienic indoor conditions.

• 반도체디스플레이기술학회지
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• 제22권3호
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• pp.112-118
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• 2023

As technology advances, the need for enhanced preparedness against cyber-attacks becomes an increasingly critical problem. Therefore, it is imperative to consider various circumstances and to prepare for cyber-attack strategic technology. This paper proposes a method to solve network security problems by applying reinforcement learning to cyber-security. In general, traditional static cyber-security methods have difficulty effectively responding to modern dynamic attack patterns. To address this, we implement cyber-attack scenarios such as 'Tiny Alpha' and 'Small Alpha' and evaluate the performance of various reinforcement learning methods using Network Attack Simulator, which is a cyber-attack simulation environment based on the gymnasium (formerly Open AI gym) interface. In addition, we experimented with different RL algorithms such as value-based methods (Q-Learning, Deep-Q-Network, and Double Deep-Q-Network) and policy-based methods (Actor-Critic). As a result, we observed that value-based methods with discrete action spaces consistently outperformed policy-based methods with continuous action spaces, demonstrating a performance difference ranging from a minimum of 20.9% to a maximum of 53.2%. This result shows that the scheme not only suggests opportunities for enhancing cybersecurity strategies, but also indicates potential applications in cyber-security education and system validation across a large number of domains such as military, government, and corporate sectors.