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

References

1 
Mar, Nang Seng Siri, Prasad KDV Yarlagadda, and Clinton Fookes, “Design and development of automatic visual inspection system for PCB manufacturing,” Robotics and computer-integrated manufacturing, vol. 27, no. 5, pp. 949-962, 2011DOI
2 
Darwish, Ahmed M, and Anil K. Jain, “A rule based approach for visual pattern inspection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 10, no. 1, pp. 56-68, 1988DOI
3 
Hütten, Nils, et al., “Deep Learning for Automated Visual Inspection in Manufacturing and Maintenance: A Survey of Open-Access Papers,” Applied System Innovation, vol. 7, no. 1, Nov. 2024DOI
4 
Jang, Jiyong, and Sungroh Yoon, “Feature concentration for supervised and semisupervised learning with unbalanced datasets in visual inspection,” IEEE Transactions on Industrial Electronics, vol. 68, no. 8, pp. 7620-7630, 2020DOI
5 
Hoens, T. Ryan, Robi Polikar, and Nitesh V. Chawla, “Learning from streaming data with concept drift and imbalance: an overview,” Progress in Artificial Intelligence, vol. 1, pp. 89-101, 2012DOI
6 
Pang, Guansong, et al., “Deep learning for anomaly detection: A review,” ACM computing surveys (CSUR), vol. 54, no. 2, pp. 1-38, 2021DOI
7 
Liu, Jiaqi, et al., “Deep industrial image anomaly detection: A survey,” Machine Intelligence Research, vol. 21, no. 1, pp. 104-135, 2024DOI
8 
He, Kaiming, et al., “Deep residual learning for image recognition,” Proceedings of the IEEE conference on computer vision and pattern recognition, 2016URL
9 
Woo, Sanghyun, et al., “Cbam: Convolutional block attention module,” Proceedings of the European conference on computer vision (ECCV), 2018URL
10 
Kabir, HM Dipu, et al., “Spinalnet: Deep neural network with gradual input,” IEEE Transactions on Artificial Intelligence, vol. 4, no. 5, pp. 1165-1177, 2022URL
11 
Defard, Thomas, et al., “Padim: a patch distribution modeling framework for anomaly detection and localization,” International Conference on Pattern Recognition. Cham: Springer International Publishing, 2021DOI
12 
Roth, Karsten, et al., “Towards total recall in industrial anomaly detection,” Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022URL
13 
Chickering, David Maxwell, “Optimal structure identification with greedy search,” Journal of machine learning research, vol. 3, pp. 507-554, Nov. 2002URL
14 
Fix, Evelyn, Discriminatory analysis: nonparametric discrimination, consistency properties, USAF school of Aviation Medicine, vol. 1, 1985URL
15 
Ruby, Usha, and Vamsidhar Yendapalli, “Binary cross entropy with deep learning technique for image classification,” Int. J. Adv. Trends Comput. Sci. Eng., vol. 9, no. 10, 2020URL
16 
Reiss, Tal, and Yedid Hoshen, “Mean-shifted contrastive loss for anomaly detection,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, no. 2, 2023DOI
17 
Youden, William J., “Index for rating diagnostic tests,” Cancer, vol. 3, no. 1, pp. 32-35, 1950URL
18 
Tan, M,. and EfficientNet Le Q V, “rethinking model scaling for convolutional neural networks,” arXiv preprint arXiv:1905.11946, 2019(1905)URL
19 
Dosovitskiy, Alexey, “An image is worth 16x16 words: Transformers for image recognition at scale,” arXiv preprint arXiv:2010.11929, 2020URL