• 대한전기학회
Mobile QR Code QR CODE : The Transactions of the Korean Institute of Electrical Engineers
  • COPE
  • kcse
  • 한국과학기술단체총연합회
  • 한국학술지인용색인
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References

1 
D. H. Lee, H. S. Yeo, S. H. Kim, S. A. Kim, “A Study on the Framework for Real-time Railway Safety Integrated Monitoring and Control System,” Journal of the Korea Society for Urban Railway, vol. 3, no. 2, pp. 367~374, 2015.URL
2 
Yunjung Park, Sangahm Kim, “Research on Development and Verification of Data Transmission Standard for Real-time Integrated Railway Safety Monitoring and Control System,” Journal of the Korean Society for Railway, vol. 20, no. 6, pp. 749~763, 2017.DOI:10.7782/jksr.2017.20.6.749URL
3 
Yunjung Park, Damsub Lim, Dugki Min, Sang Ahm Kim, “Research on Design of DDS-based Conventional Railway Signal Data Specification for Real-time Railway Safety Monitoring and Control,” Journal of the Korea Institute of Information and Communication Engineering, vol. 20, no. 4, pp. 739~746, 2016.DOI:10.6109/jkiice.2016.20.4.739DOI
4 
Sang Log Kwak, “Risk Estimation Study on Railway Track Worker Hit by Train,” Journal of the Korean Society of Safety, vol. 35, no. 3, pp. 72~78, 2020.DOI:10.14346/JKOSOS.2020.35.3.72DOI
5 
Dong-myung Kim, “A Study on the Construction and Application ofthe Protection System for the Workers adjacentto Train Operation Line,” Master's thesis, Korea National University of Transportation, 2022.URL
6 
Tsung-Yi Lin, et al., “Microsoft coco: Common Objects in Context,” Computer Vision-ECCV2014 Conference paper, vol. 8693, pp. 749~755, 2014.DOI:10.1007/978-3-319-10602-1_48URL
7 
Alexey Dosovitskiy, et al., “Flownet: Learning Optical Flow with Convolutional Networks,” 2015 IEEE International Conference on Computer Vision (ICCV), pp. 2758~2766, 2015.DOI:10.1109/iccv.2015.316URL
8 
Eddy Ilg, et al., “Flownet 2.0: Evolution of Optical Flow Estimation with Deep Networks,” 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2462~2470, 2017.DOI:10.1109/cvpr.2017.179URL
9 
Deqing Sun, et al., “PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume,” 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8934~8943, 2018.DOI:10.1109/cvpr.2018.00931URL
10 
Tak-Wai Hui , Xiaoou Tang, Chen Change Loy, “LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation,” 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8981~8989, 2018.DOI:10.1109/cvpr.2018.00936URL
11 
Lingtong Kong, Chunhua Shen, Jie Yang, “FastFlowNet: A LightweightNetwork for Fast Optical Flow Estimation,” 2021 IEEE International Conference on Robotics and Automation (ICRA), 2021.DOI:10.1109/icra48506.2021.9560800DOI
12 
Chien-Yao Wang, Alexey Bochkovskiy, Hong-Yuan Mark Liao, “YOLOv7: Trainable bag-of-freebies Sets New state-of-the-art for Real-time Object Detectors,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7464~7475, 2023.DOI:10.1109/cvpr52729.202300721URL