Lithium sulfur battery （LSB） offers several advantages such as very high energy density, low-cost, and environmental-friendliness. However, it suffers from serious degradation of its reversible capacity because of t...Lithium sulfur battery （LSB） offers several advantages such as very high energy density, low-cost, and environmental-friendliness. However, it suffers from serious degradation of its reversible capacity because of the dissolution of reaction intermediates, lithium polysulfides, into the electrolyte. To solve this limitation, there are many studies using graphene-based materials due to their excellent mechanical strength and high conductivity. Compared with graphene, graphene oxide （GO） contains various oxygen functional groups, which enhance the reaction with lithium polysulfides. Here, we investigated the positive effect of using GO mixed with carbon black on the performance of cathode in LSB. We have observed a smaller drop of capacity in GO mixed sulfur cathode. We further demonstrate that the mechanistic origin of reversibility improvement, as confirmed through CV and Raman spectra, can be explained by the stabilization of sulfur in lithium polysulfide intermediates by oxygen functional groups of GO to prevent dissolution. Our findings suggest that the use of graphene oxide-based cathode is a promising route to significantly improve the reversibility of current LSB.展开更多
Action recognition has become a current research hotspot in computer vision.Compared to other deep learning methods,Two-stream convolutional network structure achieves better performance in action recognition,which di...Action recognition has become a current research hotspot in computer vision.Compared to other deep learning methods,Two-stream convolutional network structure achieves better performance in action recognition,which divides the network into spatial and temporal streams,using video frame images as well as dense optical streams in the network,respectively,to obtain the category labels.However,the two-stream network has some drawbacks,i.e.,using dense optical flow as the input of the temporal stream,which is computationally expensive and extremely time-consuming for the current extraction algorithm and cannot meet the requirements of real-time tasks.In this paper,instead of the dense optical flow,the Motion Vectors(MVs)are used and extracted from the compressed domain as temporal features,which greatly reduces the extraction time.However,the motion pattern that MVs contain is coarser,which leads to low accuracy.In this paper,we propose two strategies to improve the accuracy:firstly,an accumulated strategy is used to enhance the motion information and continuity of MVs;secondly,knowledge distillation is used to fuse the spatial information into the temporal stream so that more information(e.g.,motion details,colors,etc.)is obtainable.Experimental results show that the accuracy of MV can be greatly improved by the strategies proposed in this paper and the final recognition for human actions accuracy is guaranteed without using optical flow.展开更多
基金supported by the Core Technology Development Program for Next-Generation Energy Storage of the Research Institute for Solar and Sustainable Energies (RISE) at GISTthe DOST UPD ERDT Faculty Development Program
文摘Lithium sulfur battery （LSB） offers several advantages such as very high energy density, low-cost, and environmental-friendliness. However, it suffers from serious degradation of its reversible capacity because of the dissolution of reaction intermediates, lithium polysulfides, into the electrolyte. To solve this limitation, there are many studies using graphene-based materials due to their excellent mechanical strength and high conductivity. Compared with graphene, graphene oxide （GO） contains various oxygen functional groups, which enhance the reaction with lithium polysulfides. Here, we investigated the positive effect of using GO mixed with carbon black on the performance of cathode in LSB. We have observed a smaller drop of capacity in GO mixed sulfur cathode. We further demonstrate that the mechanistic origin of reversibility improvement, as confirmed through CV and Raman spectra, can be explained by the stabilization of sulfur in lithium polysulfide intermediates by oxygen functional groups of GO to prevent dissolution. Our findings suggest that the use of graphene oxide-based cathode is a promising route to significantly improve the reversibility of current LSB.
基金This work is supported by the Inner Mongolia Natural Science Foundation of China under Grant No.2021MS06016the CERNET Innovation Project(NGII20190625).
文摘Action recognition has become a current research hotspot in computer vision.Compared to other deep learning methods,Two-stream convolutional network structure achieves better performance in action recognition,which divides the network into spatial and temporal streams,using video frame images as well as dense optical streams in the network,respectively,to obtain the category labels.However,the two-stream network has some drawbacks,i.e.,using dense optical flow as the input of the temporal stream,which is computationally expensive and extremely time-consuming for the current extraction algorithm and cannot meet the requirements of real-time tasks.In this paper,instead of the dense optical flow,the Motion Vectors(MVs)are used and extracted from the compressed domain as temporal features,which greatly reduces the extraction time.However,the motion pattern that MVs contain is coarser,which leads to low accuracy.In this paper,we propose two strategies to improve the accuracy:firstly,an accumulated strategy is used to enhance the motion information and continuity of MVs;secondly,knowledge distillation is used to fuse the spatial information into the temporal stream so that more information(e.g.,motion details,colors,etc.)is obtainable.Experimental results show that the accuracy of MV can be greatly improved by the strategies proposed in this paper and the final recognition for human actions accuracy is guaranteed without using optical flow.
