References
- Wong, Cynthia A., "Advances in labor analgesia." International journal of women's health, 2010, pp. 139-154.
- The World Fire Statistics for 2019. https://www.ctif.org/sites/default/files/2021-06/CTIF_Report26_KOREA.pdf (07.06.2021).
- Nazir, Amril, et al., "Early fire detection: a new indoor laboratory dataset and data distribution analysis," Fire, vol. 5, no. 1, 2022.
- GAUR, Anshul, et al., "Fire sensing technologies: A review," IEEE Sensors Journal, vol. 19, no. 9, 2019, pp. 3191-3202.
- Muhammad, Khan, et al., "Convolutional neural networks based fire detection in surveillance videos," IEEE Access, vol. 6, 2018, pp. 18174-18183.
- Li, Pu, and Wangda Zhao, "Image fire detection algorithms based on convolutional neural networks," Case Studies in Thermal Engineering, vol. 19, 2020, 100625.
- T. H. Lee, C.-S. Park, "Real-Time Fire Detection Method Using YOLOv8," Journal of the Semiconductor & Display Technology, vol. 22, no. 2, 2023.
- I.-H. Park, et al., "Deep-learning based In-situ Monitoring and Prediction System for the Organic Light Emitting Diode," Journal of the Semiconductor & Display Technology, vol. 19, no. 4, 2020, pp. 126-129.
- H. Ahn, Y.-H Lee, "A Research of CNN-based Object Detection for Multiple Object Tracking in Image : A Research of CNN-based Object Detection for Multiple Object Tracking in Image," Journal of the Semiconductor & Display Technology, vol. 18, no. 3, 2019, pp. 110-114.
- Y.-H. Lee, Y. Kim, "Comparison of CNN and YOLO for Object Detection," Journal of the Semiconductor & Display Technology, vol. 19, no. 1, 2020, pp. 85-92.
- Fonollosa, Jordi, Ana Solorzano, and Santiago Marco, "Chemical sensor systems and associated algorithms for fire detection: A review," Sensors, vol. 18, no. 2, 2018, 553.
- Wu, Lesong, Lan Chen, and Xiaoran Hao, "Multi-sensor data fusion algorithm for indoor fire early warning based on BP neural network," Information, vol. 12, no. 2, 2021, 59.
- Saeed, Faisal, et al., "Convolutional neural network based early fire detection," Multimedia Tools and Applications, vol. 79, no. 13, 2020, pp. 9083-9099.
- Vorwerk, Pascal, et al., "Classification in Early Fire Detection Using Multi-Sensor Nodes-A Transfer Learning Approach," Sensors, vol. 24, no. 5, 2024, 1428.
- K. N. Kim, "Principles and applications of gas sensors," Monthly Instrumentation Technology, 2004.
- D. Sarangi, et al., "Characterization studies of diamond-like carbon films grown using a saddle-field fast-atom-beam source," Journal of Vacuum Science & Technology A: Vacuum, Surfaces, and Films, vol. 18, no. 5, 2000, pp. 2302-2311.
- Batzill, Matthias, "Surface science studies of gas sensing materials: SnO2," Sensors, vol. 6, no. 10, 2006, pp. 1345-1366.
- H.-W. Jang, "Development trends of semiconductor gas sensors," Electrical & Electronic Materials, vol. 24, no. 1, 2011, pp. 34-42.
- Y. B. Yahmed, et al., "ADAPTIVE SLIDING WINDOW ALGORITHM FOR WEATHER DATA SEGMENTATION," Journal of Theoretical & Applied Information Technology, vol. 80, no. 2, 2015.
- K. Lee, et al., "Highly sensitive sensors based on metal-oxide nanocolumns for fire detection," Sensors, vol. 17, no. 2, 2017, 303.
- B. J. Kang, H.-C. Cho, "Development of a Deep Learning Prediction Model to Recognize Dangerous Situations in a Gas-use Environment," Journal of the Semiconductor & Display Technology, vol. 21, no. 1, 2022, pp. 132-135.
- M. J. Kim, et al., "Study on the Failure Diagnosis of Robot Joints Using Machine Learning," Journal of the Semiconductor & Display Technology, vol. 22, no. 4, 2023, pp. 113-118.