Jeng Shyr-Long
Department of Mechanical Engineering, Lunghwa University of Science and Technology, Taoyuan City 333326, Taiwan.
Sensors (Basel). 2025 Jun 30;25(13):4073. doi: 10.3390/s25134073.
This study introduces a Multiscale Dual-Attention U-Net (TS-MSDA U-Net) model for long-term time series synthesis. By integrating multiscale temporal feature extraction and dual-attention mechanisms into the U-Net backbone, the model captures complex temporal dependencies more effectively. The model was evaluated in two distinct applications. In the first, using multivariate datasets from 70 real-world electric vehicle (EV) trips, TS-MSDA U-Net achieved a mean absolute error below 1% across key parameters, including battery state of charge, voltage, acceleration, and torque-representing a two-fold improvement over the baseline TS-p2pGAN. While dual-attention modules provided only modest gains over the basic U-Net, the multiscale design enhanced overall performance. In the second application, the model was used to reconstruct high-resolution signals from low-speed analog-to-digital converter data in a prototype resonant CLLC half-bridge converter. TS-MSDA U-Net successfully learned nonlinear mappings and improved signal resolution by a factor of 36, outperforming the basic U-Net, which failed to recover essential waveform details. These results underscore the effectiveness of transformer-inspired U-Net architectures for high-fidelity multivariate time series modeling in both EV analytics and power electronics.
本研究介绍了一种用于长期时间序列合成的多尺度双注意力U-Net（TS-MSDA U-Net）模型。通过将多尺度时间特征提取和双注意力机制集成到U-Net主干中，该模型能更有效地捕捉复杂的时间依赖性。该模型在两个不同的应用中进行了评估。在第一个应用中，使用来自70次实际电动汽车（EV）行程的多变量数据集，TS-MSDA U-Net在包括电池荷电状态、电压、加速度和扭矩等关键参数上实现了低于1%的平均绝对误差，比基线TS-p2pGAN有两倍的提升。虽然双注意力模块相对于基本U-Net仅带来适度的增益，但多尺度设计提升了整体性能。在第二个应用中，该模型用于从原型谐振CLLC半桥转换器中的低速模数转换器数据重建高分辨率信号。TS-MSDA U-Net成功学习了非线性映射，并将信号分辨率提高了36倍，优于未能恢复基本波形细节的基本U-Net。这些结果强调了受变压器启发的U-Net架构在电动汽车分析和电力电子领域的高保真多变量时间序列建模中的有效性。
