Deep learning has emerged as a powerful tool for predicting the remaining useful life(RUL)of batteries,contingent upon access to ample data.However,the inherent limitations of data availability from traditional or acc...Deep learning has emerged as a powerful tool for predicting the remaining useful life(RUL)of batteries,contingent upon access to ample data.However,the inherent limitations of data availability from traditional or accelerated life testing pose significant challenges.To mitigate the prediction accuracy issues arising from small sample sizes in existing intelligent methods,we introduce a novel data augmentation framework for RUL prediction.This framework harnesses the inherent high coincidence of degradation patterns exhibited by lithium-ion batteries to pinpoint the knee point,a critical juncture marking a significant shift in the degradation trajectory.By focusing on this critical knee point,we leverage the power of normalizing flow models to generate virtual data,effectively augmenting the training sample size.Additionally,we integrate a Bayesian Long Short-Term Memory network,optimized with Box-Cox transformation,to address the inherent uncertainty associated with predictions based on augmented data.This integration allows for a more nuanced understanding of RUL prediction uncertainties,offering valuable confidence intervals.The efficacy and superiority of the proposed framework are validated through extensive experiments on the CS2 dataset from the University of Maryland and the CrFeMnNiCo dataset from our laboratory.The results clearly demonstrate a substantial improvement in the confidence interval of RUL predictions compared to pre-optimization,highlighting the ability of the framework to achieve high-precision RUL predictions even with limited data.展开更多
针对简单运动模型在复杂驾驶环境多目标跟踪表现不佳的问题,提出了一种基于恒定转弯率和加速度(constant turn rate and acceleration,CTRA)模型的点云多目标跟踪方法。通过采用包含角速度信息的运动模型来描述目标的运动轨迹,可提高在...针对简单运动模型在复杂驾驶环境多目标跟踪表现不佳的问题,提出了一种基于恒定转弯率和加速度(constant turn rate and acceleration,CTRA)模型的点云多目标跟踪方法。通过采用包含角速度信息的运动模型来描述目标的运动轨迹,可提高在目标转弯时的跟踪精度。同时,利用检测算法提供的速度信息,在轨迹更新时对物体速度进行校正,以改善在目标速度突变时的跟踪效果。此外,采用基于置信度的两阶段匹配策略,以降低低置信度检测框对跟踪结果的影响。在nuScenes验证集上对所提出的三维目标检测与跟踪算法进行了性能评估,并通过消融实验验证了算法中各模块的有效性。实验结果表明,基于CTRA模型的点云多目标跟踪算法在跟踪精度上优于基于简单模型的算法,在目标转弯和速度突变场景下的跟踪效果显著提升,且跟踪过程中身份切换次数大幅减少。展开更多
基金supported by the National Natural Science Foundation of China(Grant No.62227814,52205040,22279070,and U21A20170)the Natural Science Basic Research Program of Shaanxi(2023-JC-QN-0140)+3 种基金the Young Talent Fund of Xi’an Association for Science and Technology(Grant No.959202313096)the Key Projects of the Shaanxi Province Natural Science Foundation(Grant No.2025JC-QYXQ-038)the Open Foundation of the State Key Laboratory of Fluid Power and Mechatronic Systems(Grant No.GZKF-202430)the National Key Research and Development Program of China(Grant No.2024YFB3311204)。
文摘Deep learning has emerged as a powerful tool for predicting the remaining useful life(RUL)of batteries,contingent upon access to ample data.However,the inherent limitations of data availability from traditional or accelerated life testing pose significant challenges.To mitigate the prediction accuracy issues arising from small sample sizes in existing intelligent methods,we introduce a novel data augmentation framework for RUL prediction.This framework harnesses the inherent high coincidence of degradation patterns exhibited by lithium-ion batteries to pinpoint the knee point,a critical juncture marking a significant shift in the degradation trajectory.By focusing on this critical knee point,we leverage the power of normalizing flow models to generate virtual data,effectively augmenting the training sample size.Additionally,we integrate a Bayesian Long Short-Term Memory network,optimized with Box-Cox transformation,to address the inherent uncertainty associated with predictions based on augmented data.This integration allows for a more nuanced understanding of RUL prediction uncertainties,offering valuable confidence intervals.The efficacy and superiority of the proposed framework are validated through extensive experiments on the CS2 dataset from the University of Maryland and the CrFeMnNiCo dataset from our laboratory.The results clearly demonstrate a substantial improvement in the confidence interval of RUL predictions compared to pre-optimization,highlighting the ability of the framework to achieve high-precision RUL predictions even with limited data.
文摘针对简单运动模型在复杂驾驶环境多目标跟踪表现不佳的问题,提出了一种基于恒定转弯率和加速度(constant turn rate and acceleration,CTRA)模型的点云多目标跟踪方法。通过采用包含角速度信息的运动模型来描述目标的运动轨迹,可提高在目标转弯时的跟踪精度。同时,利用检测算法提供的速度信息,在轨迹更新时对物体速度进行校正,以改善在目标速度突变时的跟踪效果。此外,采用基于置信度的两阶段匹配策略,以降低低置信度检测框对跟踪结果的影响。在nuScenes验证集上对所提出的三维目标检测与跟踪算法进行了性能评估,并通过消融实验验证了算法中各模块的有效性。实验结果表明,基于CTRA模型的点云多目标跟踪算法在跟踪精度上优于基于简单模型的算法,在目标转弯和速度突变场景下的跟踪效果显著提升,且跟踪过程中身份切换次数大幅减少。
