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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Transformer Network for Remaining Useful Life Prediction of Lithium-Ion Batteries
| Date | Stars |
|---|---|
| 2026-07-24 | 505 |
| 2026-07-25 | 505 |
| 2026-07-28 | 505 |
| 2026-07-30 | 505 |
| 2026-08-06 | 505 |
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# Figure <img src="figures/TransformerRUL.jpg" width = "70%" /> # Results of Transformer **Figures of NASA** <img src="figures/results_nasa.png" width = "100%" /> **Figures of CALCE** <img src="figures/results_calce.png" width = "100%" /> # Supplement Due to the length of the paper, the two parameters of *dropout* and *noise_level* are not discussed. By setting these two parameters, better results can be obtained than in the paper. - *noise level = 0.01*: Setting the value of 1% disturbance is best: too large will degrade performance, too small will have little effect. - *dropout = 1e-4~1e-3*: Set a small value for the network dropout to ensure the robustness of the model. # Packages - pytorch 1.8.0 - pandas 0.24.2 - mixture_of_experts 0.2.1 (for AttMoE, github: https://github.com/lucidrains/mixture-of-experts) # Update - 6/5/2024, add figures of model and prediction - 1/3/2024, upload the open sorce of AttMoE - 24/2/2022，Change some variable names **Dataset CALCE processing reference** https://github.com/konkon3249/BatteryLifePrediction # E-mail Please feel free to contact me: [email protected] # More （更多内容） 1. 马里兰大学锂电池数据集 CALCE，基于 Python 的锂电池寿命预测: https://snailwish.com/437/ 2. NASA 锂电池数据集，基于 Python 的锂电池寿命预测: https://snailwish.com/395/ 3. NASA 锂电池数据集，基于 python 的 MLP 锂电池寿命预测: https://snailwish.com/427/ 4. NASA 和 CALCE 锂电池数据集，基于 Pytorch 的 RNN、LSTM、GRU 寿命预测: https://snailwish.com/497/ 5. 基于 Pytorch 的 Transformer 锂电池寿命预测: https://snailwish.com/555/ 6. 锂电池研究之七——基于 Pytorch 的高斯函数拟合时间序列数据: https://snailwish.com/576/ # Citation ``` @article{chen2022transformer, title={Transformer network for remaining useful life prediction of lithium-ion batteries}, author={Chen, Daoquan and Hong, Weicong and Zhou, Xiuze}, journal={Ieee Access}, volume={10}, pages={19621--19628}, year={2022}, publisher={IEEE} } @article{chen2024attmoe, title={AttMoE: Attention with Mixture of Experts for remaining useful life prediction of lithium-ion batteries}, author={Chen, Daoquan and Zhou, Xiuze}, journal={Journal of Energy Storage}, volume={84}, pages={110780}, year={2024}, publisher={Elsevier} } ```
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matched fp:98595efe2ea7b617, topic:deep-learning, topic:neural-network
matched fp:98595efe2ea7b617, topic:transformer, readme:mixture of experts