Mobile QR Code QR CODE : Korean Journal of Air-Conditioning and Refrigeration Engineering
Korean Journal of Air-Conditioning and Refrigeration Engineering

Korean Journal of Air-Conditioning and Refrigeration Engineering

ISO Journal TitleKorean J. Air-Cond. Refrig. Eng.
  • Open Access, Monthly
Open Access Monthly
  • ISSN : 1229-6422 (Print)
  • ISSN : 2465-7611 (Online)

References

1 
National Fire Agency, 2022, National Fire Agency Statistical Year Book, Available: https://kosis.kr/statHtml/statHtml.do?orgId=156&tblId=TX_15601_A004&conn_path=I3.URL
2 
Ministry of Land, Infrastructure and Transport., 2022, Statistics on Buildings, Available: https://stat.molit.go.kr/portal/main/portalMain.dDOI
3 
Lee, H. K., Choi, J. H., and Cho, H. S., 2020, Research for Automatic Generation of 2D Design Drawings of Firefighting Equipment using AI, Proceedings of the Korea Institute of Fire Science and Engineering Conference, Vol. 1, pp. 197-206.URL
4 
National Fire Agency, 2022, Korean Firefighting Facility Design Procedure, Available: https://www.nfa.go.kr.URL
5 
McCarthy, J., 1987, Generality in Artificial Intelligence, Communications of the ACM, Vol. 30, No. 12, pp. 1030-1035.DOI
6 
Ko, K. S., Yang, J. K., Hwang, D. H., Ko, H. S., Ga. C. O., and Jo, J. P., 2020, Building Fire Prediction Model Study using AI, The Journal of Korean Institute of Communications and Information Sciences, Vol. 45, No. 7, pp. 1210-1218.DOI
7 
Park. J. C. and Kang. D, S., 2021, Real-Time Video Fire Detection based on YOLO in Antifire Surveillance Systems, Proceedings of KIIT Conference, pp. 179-181.URL
8 
Kim, Y. J. and Kim, E, G., 2016, Image based Fire Detection using Convolutional Neural Network, Journal of the Korea Institute of Information and Communication Engineering, Vol. 20, No. 9, pp. 1649-1656.DOI
9 
Roh. J. H., Min. S. H., and Kong. M. S., 2022, Analysis of Fire Prediction Performance of Image Classification Models based on Convolution Neural Network, Fire Science and Engineering, Vol. 36, No. 6 pp. 70-77.DOI
10 
Kim, T. H., H, S. M., and Yeon, S. C., 2022, Development of Optimal Rescue Pathfinding Algorithm using Deep-Learning Architectural Plan Recognition, Journal of the Architectural Institute of Korea, Vol. 42, No. 1, pp. 562-563.URL
11 
Zhao, Y.F., Deng, X., and Lai, H., 2020, A Deep Learning-Based Method to Detect Components from Scanned Structural Drawings for Reconstructing 3D Models, Applied Sciences, Vol. 10, No. 6, p. 2066.DOI
12 
Schönfelder, P. and König, M., 2022, Deep Learning-based Text Detection on Architectural Floor Plan Images, In IOP Conference Series: Earth and Environmental Science, Vol. 1101, No. 8, p. 082017.DOI
13 
Python, 2022, Python Documentation, Available: https://www.python.org/.URL
14 
Anaconda, 2022, Anaconda Documentation, Available: https://www.anaconda.com/use-cases.URL
15 
Paliwal, S., Vishwanath, D., Rahul, R., Sharma, M., and Vig, L., 2019, TableNet: Deep Learning Model for End-to-End Table Detection and Tabular Data Extraction from Scanned Document Images, In 2019 International Conference on Document Analysis and Recognition, pp. 128-133.DOI
16 
OpenCV., 2022, OpenCV Documentation, Available: https://docs.opencv.org/4.x/.URL
17 
EasyOCR., 2022, EasyOCR API Documentation, Available: https://www.jaided.ai/easyocr/documentation/.URL
18 
Baek, Y. M., Lee, B. D., Han. D. Y., Yun, S. D., and Lee, H. S., 2019, Character Region Awareness for Text Detection, In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9365-9374.URL
19 
Haldar, R. and Mukhopadhyay, D., 2011, Levenshtein Distance Technique in Dictionary Lookup Methods: An improved Approach, Available: arXiv preprint arXiv:1101.1232.DOI
20 
Visa, S., Ramsay, B., Ralescu, A. L., and Van Der Knaap, E., 2011, Confusion Matrix-Based Feature Selection. Proceedings of the Twenty second Midwest Artificial Intelligence and Cognitive Science Conference, Vol. 710, No. 1, pp. 120-127.URL
21 
Pytorch., 2022, Pytorch Documentation, Available: https://pytorch.org/docs/stable/generated/torch.nn.CTCLoss.html.URL