Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Recognition of Fire Situation Using Graph Convolutional Network Model
https://orcid.org/http://orcid.org/0009-0004-4546-3866
https://orcid.org/http://orcid.org/0000-0001-9003-1131
https://orcid.org/http://orcid.org/0000-0002-5038-698X
https://orcid.org/http://orcid.org/0000-0002-0540-4640
Fires cause a lot of casualties and economic losses. In order to prevent fire accidents in advance, it is necessary to find out the cause of the fire. Existing fire alarm systems detected fires with temperature, smoke, and flame sensors, but could not distinguish the cause of the fire. In this paper, to solve this problem, the recognition of fire situation was tested using the GCN model. The fire dataset used in the experiment is a fire dataset composed of causes such as boxes, clothing, and electricity. The accuracy of 93.44% was confirmed as a result of the learning experiment with the GCN model, a graph-based deep learning model.
Recognition of Fire Situation Using Graph Convolutional Network Model
https://orcid.org/http://orcid.org/0009-0004-4546-3866
https://orcid.org/http://orcid.org/0000-0001-9003-1131
https://orcid.org/http://orcid.org/0000-0002-5038-698X
https://orcid.org/http://orcid.org/0000-0002-0540-4640
Fires cause a lot of casualties and economic losses. In order to prevent fire accidents in advance, it is necessary to find out the cause of the fire. Existing fire alarm systems detected fires with temperature, smoke, and flame sensors, but could not distinguish the cause of the fire. In this paper, to solve this problem, the recognition of fire situation was tested using the GCN model. The fire dataset used in the experiment is a fire dataset composed of causes such as boxes, clothing, and electricity. The accuracy of 93.44% was confirmed as a result of the learning experiment with the GCN model, a graph-based deep learning model.
Recognition of Fire Situation Using Graph Convolutional Network Model
https://orcid.org/http://orcid.org/0009-0004-4546-3866
https://orcid.org/http://orcid.org/0000-0001-9003-1131
https://orcid.org/http://orcid.org/0000-0002-5038-698X
https://orcid.org/http://orcid.org/0000-0002-0540-4640
Fires cause a lot of casualties and economic losses. In order to prevent fire accidents in advance, it is necessary to find out the cause of the fire. Existing fire alarm systems detected fires with temperature, smoke, and flame sensors, but could not distinguish the cause of the fire. In this paper, to solve this problem, the recognition of fire situation was tested using the GCN model. The fire dataset used in the experiment is a fire dataset composed of causes such as boxes, clothing, and electricity. The accuracy of 93.44% was confirmed as a result of the learning experiment with the GCN model, a graph-based deep learning model.
Recognition of Fire Situation Using Graph Convolutional Network Model
Lect. Notes Electrical Eng.
Park, Ji Su (Herausgeber:in) / Yang, Laurence T. (Herausgeber:in) / Pan, Yi (Herausgeber:in) / Park, James J. (Herausgeber:in) / Kim, Si Jin (Autor:in) / Park, Ji Su (Autor:in) / Kang, Jungho (Autor:in) / Shon, Jin Gon (Autor:in)
International Conference on Computer Science and its Applications and the International Conference on Ubiquitous Information Technologies and Applications ; 2023 ; Nha Trang, Vietnam
29.09.2024
6 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
| DOAJ | 2022
Rockburst Prediction via Multiscale Graph Convolutional Neural Network
| Springer Verlag | 2025
Charging Stations Selection Using a Graph Convolutional Network from Geographic Grid
| DOAJ | 2022