Accurate capacity and State of Charge(SOC)estimation are crucial for ensuring the safety and longevity of lithium-ion batteries in electric vehicles.This study examines ten machine learning architectures,Including Dee...Accurate capacity and State of Charge(SOC)estimation are crucial for ensuring the safety and longevity of lithium-ion batteries in electric vehicles.This study examines ten machine learning architectures,Including Deep Belief Network(DBN),Bidirectional Recurrent Neural Network(BiDirRNN),Gated Recurrent Unit(GRU),and others using the NASA B0005 dataset of 591,458 instances.Results indicate that DBN excels in capacity estimation,achieving orders-of-magnitude lower error values and explaining over 99.97%of the predicted variable’s variance.When computational efficiency is paramount,the Deep Neural Network(DNN)offers a strong alternative,delivering near-competitive accuracy with significantly reduced prediction times.The GRU achieves the best overall performance for SOC estimation,attaining an R^(2) of 0.9999,while the BiDirRNN provides a marginally lower error at a slightly higher computational speed.In contrast,Convolutional Neural Networks(CNN)and Radial Basis Function Networks(RBFN)exhibit relatively high error rates,making them less viable for real-world battery management.Analyses of error distributions reveal that the top-performing models cluster most predictions within tight bounds,limiting the risk of overcharging or deep discharging.These findings highlight the trade-off between accuracy and computational overhead,offering valuable guidance for battery management system(BMS)designers seeking optimal performance under constrained resources.Future work may further explore advanced data augmentation and domain adaptation techniques to enhance these models’robustness in diverse operating conditions.展开更多
The objective of this study is to demonstrate that tensile stress resulting due to applied force on cornea can be accurately measured by using a time-domain common-path optical coherence tomography （OCT） system with...The objective of this study is to demonstrate that tensile stress resulting due to applied force on cornea can be accurately measured by using a time-domain common-path optical coherence tomography （OCT） system with an external contact reference. The unique design of the common-path OCT is utilized to set up an imaging system in which a chicken eye is placed adjacent to a glass plate serving as the external reference plane for the imaging system. As the force is applied to the chicken eye, it presses against the reference glass plate. The modified OCT image obtained is used to calculate the size of contact area, which is then used to derive the tensile stress on the cornea. The drop in signal levels upon contact of reference glass plate with the tissue are extremely sharp because of the sharp decline in reference power levels itself, thus providing us with an accurate measurement of contact area. The experimental results were in good agreement with the numerical predictions. The results of this study might be useful in providing new insights and ideas to improve the precision and safety of currently used ophthalmic surgical techniques. This research outlines a method which could be used to provide high resolution OCT images and a precise feedback of the forces applied to the cornea simultaneously.展开更多
文摘Accurate capacity and State of Charge(SOC)estimation are crucial for ensuring the safety and longevity of lithium-ion batteries in electric vehicles.This study examines ten machine learning architectures,Including Deep Belief Network(DBN),Bidirectional Recurrent Neural Network(BiDirRNN),Gated Recurrent Unit(GRU),and others using the NASA B0005 dataset of 591,458 instances.Results indicate that DBN excels in capacity estimation,achieving orders-of-magnitude lower error values and explaining over 99.97%of the predicted variable’s variance.When computational efficiency is paramount,the Deep Neural Network(DNN)offers a strong alternative,delivering near-competitive accuracy with significantly reduced prediction times.The GRU achieves the best overall performance for SOC estimation,attaining an R^(2) of 0.9999,while the BiDirRNN provides a marginally lower error at a slightly higher computational speed.In contrast,Convolutional Neural Networks(CNN)and Radial Basis Function Networks(RBFN)exhibit relatively high error rates,making them less viable for real-world battery management.Analyses of error distributions reveal that the top-performing models cluster most predictions within tight bounds,limiting the risk of overcharging or deep discharging.These findings highlight the trade-off between accuracy and computational overhead,offering valuable guidance for battery management system(BMS)designers seeking optimal performance under constrained resources.Future work may further explore advanced data augmentation and domain adaptation techniques to enhance these models’robustness in diverse operating conditions.
文摘The objective of this study is to demonstrate that tensile stress resulting due to applied force on cornea can be accurately measured by using a time-domain common-path optical coherence tomography （OCT） system with an external contact reference. The unique design of the common-path OCT is utilized to set up an imaging system in which a chicken eye is placed adjacent to a glass plate serving as the external reference plane for the imaging system. As the force is applied to the chicken eye, it presses against the reference glass plate. The modified OCT image obtained is used to calculate the size of contact area, which is then used to derive the tensile stress on the cornea. The drop in signal levels upon contact of reference glass plate with the tissue are extremely sharp because of the sharp decline in reference power levels itself, thus providing us with an accurate measurement of contact area. The experimental results were in good agreement with the numerical predictions. The results of this study might be useful in providing new insights and ideas to improve the precision and safety of currently used ophthalmic surgical techniques. This research outlines a method which could be used to provide high resolution OCT images and a precise feedback of the forces applied to the cornea simultaneously.
