Summary of Iip-mixer:intra-inter Patch Mixing Architecture For Battery Remaining Useful Life Prediction, by Guangzai Ye et al.
IIP-Mixer:Intra-Inter Patch Mixing Architecture for Battery Remaining Useful Life Prediction
by Guangzai Ye, Li Feng, Jianlan Guo, Yuqiang Chen
First submitted to arxiv on: 27 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed “Intra-Inter Patch Mixer” (IIP-Mixer) architecture is a novel approach for estimating the Remaining Useful Life (RUL) of lithium-ion batteries. The IIP-Mixer is an MLP-based model that extracts information by mixing operations along both intra-patch and inter-patch dimensions, enabling the capture of local temporal patterns in short-term periods and global temporal patterns in long-term periods. To address the varying importance of features in RUL prediction, a weighted loss function is introduced in the MLP-Mixer-based architecture, which marks the first time such an approach has been employed. The IIP-Mixer achieves competitive performance in battery RUL prediction, outperforming other popular time-series frameworks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to predict how long lithium-ion batteries will last. This is important because it helps keep batteries safe and working well. The team uses a special kind of artificial intelligence called MLP-Mixer to do this. They mix together information from different parts of the data to make predictions about what will happen in the future. This approach works really well and can help us better understand how batteries work. |
Keywords
* Artificial intelligence * Loss function * Time series
