SEnsor Alignment for Multivariate Time-Series Unsupervised Domain Adaptation
Authors: Yucheng Wang, Yuecong Xu, Jianfei Yang, Zhenghua Chen, Min Wu, Xiaoli Li, Lihua Xie
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results demonstrate the state-of-the-art performance of our proposed SEA on two public MTS datasets for MTS-UDA. The code is available at https://github.com/Frank-Wang-oss/SEA. Experimental Results To evaluate the effectiveness of SEA, we test our model on two public datasets, C-MAPSS for remaining useful life prediction (Saxena et al. 2008) and Opportunity HAR for human activity recognition (Roggen et al. 2010). |
| Researcher Affiliation | Collaboration | 1Nanyang Technological University, Singapore 2Institute for Infocomm Research, A*STAR, Singapore 3Centre for Frontier AI Research, A*STAR, Singapore {yucheng003, xuyu0014, yang0478, chen0832}@e.ntu.edu.sg, {wumin, xlli}@i2r.a-star.edu.sg, elhxie@ntu.edu.sg |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | Yes | The code is available at https://github.com/Frank-Wang-oss/SEA |
| Open Datasets | Yes | To evaluate the effectiveness of SEA, we test our model on two public datasets, C-MAPSS for remaining useful life prediction (Saxena et al. 2008) and Opportunity HAR for human activity recognition (Roggen et al. 2010). |
| Dataset Splits | No | To construct the training dataset, we adopt a sliding window with a size of 128 and an overlapping of 50% as (Ragab et al. 2022a) did. The paper does not explicitly state train/validation/test dataset splits with percentages or counts within its text. |
| Hardware Specification | Yes | Furthermore, we built and trained our model based on Pytorch 1.9 and NVIDIA Ge Force RTX 3080Ti GPU. |
| Software Dependencies | Yes | Furthermore, we built and trained our model based on Pytorch 1.9 and NVIDIA Ge Force RTX 3080Ti GPU. |
| Experiment Setup | Yes | Besides, we set batch size as 50, optimizer as Adam, learning rate as 0.001, and training epoch as 10 for training our model. |