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전체 글317

[NIPS-2017] Attention is all you need 논문 전문 : https://arxiv.org/abs/1706.03762 [출처] https://doi.org/10.48550/arXiv.1706.03762 ※ The picture and content of this article are from the original paper. This article is more of an intuitive understanding than academic analysis. [논문 정리] Attention is all you need Attention is all you need라는 제목만으로도 오금이 저릴정도로 슈퍼 논문입니다. 제가 생각하기에는, GAN과 더불어 2000년대 나온 최고의 논문이 아닐까 합니다. 후대에는 이 시점을 AGI(Artificial Ge.. 2023. 12. 23.
[Power Sources-2013] Lithium ion battery pack power fade fault identification based on Shannon entropy in electric vehicles 논문 전문 : https://www.sciencedirect.com/science/article/abs/pii/S0378775312014231 [출처] Yuejiu Zheng, Xuebing Han, Languang Lu, Jianqiu Li, Minggao Ouyang, Lithium ion battery pack power fade fault identification based on Shannon entropy in electric vehicles,Journal of Power Sources,Volume 223,2013,Pages 136-146,ISSN 0378-7753,https://doi.org/10.1016/j.jpowsour.2012.09.015. ※ The picture and conten.. 2023. 12. 22.
[IEEE-2021] Multisource Domain Adaption for Health Degradation Monitoring of Lithium-Ion Batteries 논문 전문 : https://ieeexplore.ieee.org/document/9445018 [출처] Z. Ye, J. Yu and L. Mao, "Multisource Domain Adaption for Health Degradation Monitoring of Lithium-Ion Batteries," in IEEE Transactions on Transportation Electrification, vol. 7, no. 4, pp. 2279-2292, Dec. 2021, doi: 10.1109/TTE.2021.3085430. ※ The picture and content of this article are from the original paper. This article is more of an.. 2023. 12. 16.
[CVPR-2018] ESRGAN : Enhanced Super-Resolution Generative Adversarial Networks 논문 전문 : https://arxiv.org/abs/1809.00219 [출처] https://doi.org/10.48550/arXiv.1809.00219 ※ The picture and content of this article are from the original paper. This article is more of an intuitive understanding than academic analysis. [논문 요약] ESRGAN : Enhanced Super-Resolution Generative Adversarial Networks 본 논문은 GAN을 활용한 Super Resolution(이하 SR)에 거의 시초격인 논문입니다. GAN 네트워크야 CNN으로 짜면 얼마든지 원하는 모델을 만들.. 2023. 12. 11.
[CVPR-2014] Image Super-Resolution Using Deep Convolutional Networks 논문 전문 : https://arxiv.org/abs/1501.00092 [출처] https://doi.org/10.48550/arXiv.1501.00092 ※ The picture and content of this article are from the original paper. This article is more of an intuitive understanding than academic analysis. [논문 요약] Image Super-Resolution Using Deep Convolutional Networks 지금으로부터 10년전 나온 논문입니다. Super Resolution(이하 SR)에 딥러닝을 활용한 시조새 격의 논문이라고 할 수 있는데요, EDSR이나 최근 SR 논문들은 mu.. 2023. 12. 8.
[CVPR-2017] Enhanced Deep Residual Network for Single Image Super-Resolution 논문 전문 : https://arxiv.org/abs/1707.02921 [출처] https://doi.org/10.48550/arXiv.1707.02921 ※ The picture and content of this article are from the original paper. This article is more of an intuitive understanding than academic analysis. [논문 요약] Enhanced Deep Residual Network for Single Image Super-Resolution 서울대학교에서 나온 논문으로, 무려 현 글 작성시점 기준 Citation 5772회에 해당하는 슈퍼논문입니다. EDSR이라는 약어로 많이 불리며, Super Res.. 2023. 12. 4.
[IJETAE-2022] State of Charge Prediction of Lead Acid Battery using Transformer Neural Network for Solar Smart Dome 4.0 논문 전문 : https://engrxiv.org/preprint/view/2606 [출처] https://doi.org/10.31224/2606 ※ The picture and content of this article are from the original paper. [논문 요약] State of Charge Prediction of Lead Acid Battery using Transformer Neural Network for Solar Smart Dome 4.0 Transformer를 사용해서 SOC를 추정하는 논문입니다. 지극히 개인적으로 생각하기에, 학술적인 가치가 있다고 보긴 힘듭니다. 그냥 Regression할때 Transformer를 쓴것말고는 아무 기술적 차이가 없기때문입니다. 그럼.. 2023. 11. 29.
[CVPR-2021] Transfer Learning and Vision Transformer based State-of-Health prediction of Lithium-Ion Batteries 논문 전문 : https://arxiv.org/pdf/2209.05253.pdf [출처] https://doi.org/10.48550/arXiv.2209.05253 ※ The picture and content of this article are from the original paper. [논문 요약] Transfer Learning and Vision Transformer based State-of-Health prediction of Lithium-Ion Batteries 재미있게도 이 논문은 Vision에 관한것이 아닌데도 CVPR에 투고된 논문입니다. Vision Transformer를 쓴다는 이유때문인것같은데.. 혹은 Pattern Recognition이라서인가... 사실 Pattern Rec.. 2023. 11. 27.
[MDPI-2023] Online State-of-Health Estimation for Fast-Charging Lithium-Ion Batteries Based on a Transformer–Long Short-Term Memory Neural Network 논문 전문 : https://www.mdpi.com/2313-0105/9/11/539 [출처] Fan, Y.; Li, Y.; Zhao, J.; Wang, L.; Yan, C.; Wu, X.; Zhang, P.; Wang, J.; Gao, G.; Wei, L. Online State-of-Health Estimation for Fast-Charging Lithium-Ion Batteries Based on a Transformer–Long Short-Term Memory Neural Network. Batteries 2023, 9, 539. https://doi.org/10.3390/batteries9110539 ※ The picture and content of this article are from t.. 2023. 11. 25.

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