• The Journal of the Korea Contents Association
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• v.18 no.2
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• pp.169-177
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• 2018

There is a lot of loss of life due to natural disasters. In particular, flood damage caused by heavy rainfall in urban areas causes serious damage. Therefore, in this study, we conducted a study on the optimal evacuation route for safe evacuation of urban areas. We set up a methodology by reviewing Flo-2D model and A* algorithm. A Flo-2D model was used to derive the hazardous area, and we selected the starting points with many people and suggested ways to select safe evacuation sites. And the route was derived from the starting point to the safe evacuation point by using the A * algorithm. This study could be used not only for evacuation route but also for road maintenance and evacuation facilities.

• Journal of Korean Society of Disaster and Security
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• v.8 no.1
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• pp.1-4
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• 2015

If a disaster occurs in the underground like subway, disaster response system should minimize the casualties. It must quickly guide passengers to a safe evacuation route. But sometimes the system does not work properly. And then they need distributed disaster response system which make decision autonomously. In this study, we proposed the evacuation route analysis and induction algorithms for creationg passenger evacuation route and offering optimal evacuation route.

• Korean Journal of Hazardous Materials
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• v.3 no.1
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• pp.28-36
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• 2015

In early 2011, the Fukushima nuclear power plant had greater damage due to earthquake in Japan, and the awareness of safety has increased. In particular, special response systems should be required to handle disaster situations in plant sites which are likely to occur for large disasters. In this study, a program is designed to set up optimum escape routes, by a smart phone application, when a disaster situation occurs. This program could get information of the cumulative damage from sensors and display the escape route of the smallest damage in real-time on the screen. Utilizing our application in real-time evacuation has advantage in reducing cumulative damage. The optimal evacuation route, focusing on horizontal path, is calculated based on getting the data of fire, detected radioactivity and hazardous gas. Thus, using our application provides information of optimal evacuation to people who even can not hear sensor alarms or do not know geography, without requiring additional costs except fixed sensors or server network deployment cost. As a result, being informed of real-time escape route, the user could behave rapidly with suitable response to individual situation resulting in improved evacuation than simply reacting to existing warning alarms.

• Journal of the Korean Society of Safety
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• v.37 no.2
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• pp.97-105
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• 2022

Human casualties from fires are increasing worldwide. The majority of human deaths occur during the evacuation process, as occupants panic and are unaware of the location of the fire and evacuation routes. Using an Internet of Things (IoT) sensor and reinforcement learning, we propose a method to find the safest evacuation route by considering the fire location, flame speed, occupant position, and walking conditions. The first step is detecting the fire with IoT-based devices. The second step is identifying the occupant's position via a beacon connected to the occupant's mobile phone. In the third step, the collected information, flame speed, and walking conditions are input into the reinforcement learning model to derive the optimal evacuation route. This study makes it possible to provide the safest evacuation route for individual occupants in real time. This study is expected to reduce human casualties caused by fires.

• Journal of the Society of Disaster Information
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• v.16 no.1
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• pp.96-110
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• 2020

Recently, due to global warming, it is easily exposed to various disasters such as fire, flood, and earthquake. In particular, large-scale disasters have continuously been occurring in crowded areas such as traditional markets, facilities for the elderly and children, and public facilities where various people stay. Purpose: This study aims to detect a fire occurred in crowded facilities early in the event to analyze and provide an optimal evacuation route using big data and advanced technology. Method: The researchers propose a new algorithm through context-aware 3D object model technology and A* algorithm optimization and propose a scenario-based optimal evacuation route selection technique. Result: Using the HPA* E algorithm, the evacuation simulation in the event of a fire was reproduced as a 3D model and the optimal evacuation route and evacuation time were calculated for each scenario. Conclusion: It is expected to reduce fatalities and injuries through the evacuation induction technique that enables evacuation of the building in the shortest path by analyzing in real-time via fire detection sensors that detects the temperature, flame, and smoke.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.6
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• pp.637-650
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• 2014

Geospatial research on emergency response in multi-level micro-spatial environments (e.g., multi-story buildings) that aims at understanding and analyzing human movements at the micro level has increased considerably since 9/11. Past research has shown that reducing the time rescuers needed to reach a disaster site within a building (e.g., a particular room) can have a significant impact on evacuation and rescue outcomes in this kind of disaster situations. With the purpose developing emergency response systems that are capable of using complex real-time geospatial information to generate fast-changing scenarios, this study develops a Spatiotemporal Optimal Route Algorithm (SORA) for guiding rescuers to move quickly from various entrances of a building to the disaster site (room) within the building. It identifies the optimal route and building evacuation bottlenecks within the network in real-time emergency situations. It is integrated with a Ubiquitous Sensor Network (USN) based tracking system in order to monitor dynamic geospatial entities, including the dynamic capacities and flow rates of hallways per time period. Because of the limited scope of this study, the simulated data were used to implement the SORA and evaluate its effectiveness for performing 3D topological analysis. The study shows that capabilities to take into account detailed dynamic geospatial data about emergency situations, including changes in evacuation status over time, are essential for emergency response systems.

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.1
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• pp.69-80
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• 2016

Some studies were conducted for the purpose of minimizing total clearance time for rapid evacuation from the indoor spaces when disaster occurs. Most studies took a long time to calculate the optimal evacuation route that derived minimum evacuation time. For this reason, this study proposes an evacuation route assignment algorithm that can shorten the total clearance time in a short operational time. When lots of exits are in the building, this algorithm can shorten the total clearance time by assigning the appropriate pedestrian traffic volume to each exit and balances each exit-load. The graph theory and greedy algorithm were utilized to assign pedestrian traffic volume to each exit in this study. To verify this algorithm, study used a cellular automata-based evacuation simulator and experimented various occupants distribution in a building structure. As a result, the total clearance time is reduced by using this algorithm, compared to the case of evacuating occupants to the exit within shortest distance. And it was confirmed that the operation takes a short time In a large building structure.

• IEMEK Journal of Embedded Systems and Applications
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• v.10 no.5
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• pp.281-296
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• 2015

This paper describes a novel disaster evacuation system using embedded systems such as smart devices for crisis and emergency management. In indoor environments deployed with the Bluetooth Low Energy(BLE) beacons, smart devices detect their indoor positions from beacon messages and interact with Map Server(MS) and Route Server(RS) in the Internet over the LTE and/or Wi-Fi functions. The MS and RS generate an optimal path to the nearest emergency exit based on a novel graph generation method for less route computation, called the Disaster Evacuation Graph(DEG), for each smart device. The DEG also enables efficient processing of some constraints in the computation of route, such as load balancing in situation of different capacities of paths or exits. All data interfaces among three system components, the MS, RS, smart devices, have been defined for modular implementation of our disaster evacuation system. Our experimental system has been deployed and tested in our building thoroughly and gives a good evidence that the modular design of the system and a novel approach to compute emergency route based on the DEG is competitive and viable.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.258-259
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• 2021

Forest lodge are divided into forest resources, auxiliary facilities, and users, and are constantly being used along with well-being culture. In addition, attention to the safety of users is also required, and this study aims to study how users evacuate within a short time (golden time) in situations of natural disasters that may occur in forests. In order to search for the current location of the user and find the best evacuation route, 3D scans of the entire forest lodge(forest resources, auxiliary facilities, etc.) are performed, and the optimal trajectory to the evacuation site is found through recognition of the current location. It is believed that it is possible to provide a quick evacuation guide through a mobile device with gps.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.11
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• pp.1371-1376
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• 2019

In case of fire in a structure, it is difficult to suppress fire because it can not accurately grasp the location of fire in case of fire. In this paper, we propose a system algorithm that can guide the optimal evacuation route in case of deep learning-based (RNN) structure disaster. The present invention provides a service to transmit data detected by sensors to a server in real time by using installed sensor, to transmit and analyze information such as temperature, heat, smoke, toxic gas around the sensor, to identify the safest moving path within a set threshold, to transmit information to LED guide lights and direction indicators in a structure in real time to avoid risk factors. This is because the information of temperature, heat, smoke, and toxic gas in each area of the structure can be grasped, and it is considered that the optimal evacuation route can be guided in case of structure disaster.