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전체 글288

[MDPI-2023] State of Health Assessment for Lithium-Ion Batteries Using Incremental Energy Analysis and Bidirectional Long Short-Term Memory 논문 전문 : https://www.mdpi.com/2032-6653/14/7/188 [출처] Li, Y.; Luo, L.; Zhang, C.; Liu, H. State of Health Assessment for Lithium-Ion Batteries Using Incremental Energy Analysis and Bidirectional Long Short-Term Memory. World Electr. Veh. J. 2023, 14, 188. https://doi.org/10.3390/wevj14070188 ※ The picture and content of this article are from the original paper. This article is more of an intuitiv.. 2024. 3. 4.
[Energy-2020] Preheating strategy of variable-frequency pulse for lithium battery in cold weather 논문 전문 : https://onlinelibrary.wiley.com/doi/abs/10.1002/er.5715 [출처] https://doi.org/10.1002/er.5715 ※ 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] Preheating strategy of variable-frequency pulse for lithium battery in cold weather 본 논문은 저온에서 배터리 자체발열만을 통해 승온을 하는 방식에 관한 연구입니다. 특히, 어떤 조건에서는 .. 2024. 2. 29.
[Acta-2013] Heating strategies for Li-ion batteries operated from subzero temperatures 논문 전문 : https://www.sciencedirect.com/science/article/abs/pii/S0013468613005707 [출처] Yan Ji, Chao Yang Wang,Heating strategies for Li-ion batteries operated from subzero temperatures, Electrochimica Acta,Volume 107,2013,Pages 664-674,ISSN 0013-4686, https://doi.org/10.1016/j.electacta.2013.03.147. ※ The picture and content of this article are from the original paper. This article is more of an i.. 2024. 2. 26.
[IEEE-2023] Cross-Domain Prognostic Method of Lithium-Ion Battery in New Energy Electric Aircraft With Domain Adaption 논문 전문 : https://ieeexplore.ieee.org/document/10130743 [출처] Y. Zhu, X. Li, Y. Zhang and W. Zhang, "Cross-Domain Prognostic Method of Lithium-Ion Battery in New Energy Electric Aircraft With Domain Adaptation," in IEEE Sensors Journal, vol. 23, no. 13, pp. 14487-14498, 1 July1, 2023, doi: 10.1109/JSEN.2023.3277131. ※ The picture and content of this article are from the original paper. This article.. 2024. 2. 5.
[ECS-2023] Short-Time Fourier Transform Analysis of Current Charge/Discharge Response of Lithium-Sulfur Batteries 논문 전문 : https://iopscience.iop.org/article/10.1149/1945-7111/ad07ad [출처] Anis Allagui et al 2023 J. Electrochem. Soc. 170 110511 ※ 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] Short-Time Fourier Transform Analysis of Current Charge/Discharge Response of Lithium-Sulfur Batteries 리튬 황배터리에 대해 .. 2024. 2. 1.
[NIPS-2020] Denoising Diffusin Probabilistic Models 논문 전문 : https://papers.nips.cc/paper_files/paper/2014/hash/5ca3e9b122f61f8f06494c97b1afccf3-Abstract.html [출처] https://doi.org/10.48550/arXiv.2006.11239 ※ 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] Denoising Diffusion Probablistic Models 2020년 이후 나온 논문들 중 가장 핫한 AI 모델을 꼽으라면 당연코 Diffusion M.. 2024. 1. 29.
[NIPS-2014] Generative Adversarial Nets 논문 전문 : https://papers.nips.cc/paper_files/paper/2014/hash/5ca3e9b122f61f8f06494c97b1afccf3-Abstract.html [출처] https://doi.org/10.48550/arXiv.1406.2661 ※ 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] Generative Adversarial Nets Citation 68,000이 넘는 슈퍼 논문입니다. 저자들만봐도 모두 한명한명 현재 대가라고 불리는 사람들입니다... 2024. 1. 25.
[KDD-2019] Time-Series Anomaly Detection Service at Microsoft 논문 전문 : https://arxiv.org/abs/1906.03821 [출처] KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data MiningJuly 2019Pages 3009–3017https://doi.org/10.1145/3292500.3330680 ※ 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] Time-Series Anomaly Detection.. 2024. 1. 22.
[AAAI-2019] TabNet : Attentive Interpetable Tabular Learning 논문 전문 : https://arxiv.org/abs/1908.07442 [출처] https://doi.org/10.1609/aaai.v35i8.16826 ※ 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] TabNet : Attentive Interpretable Tabular Learning Tabular Data를 다루는 분들에게는 꽤나 유명한(Citation 800이상) 구글에서 나온 TabNet 논문입니다. 당시에는 Tab Data 기준으로는 SOTA 성능이였습니다만, Tab.. 2024. 1. 18.

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