在Ce_(0.8)Cu_(0.2)O_(2)氧载体中添加不同质量S-1分子筛,并利用XRD、BET、XPS、SEM、TEM和CH4-TPR&CO_(2)-TPO等表征对氧载体的物化特性和反应性能进行了研究。考察了S-1分子筛添加量对Ce_(0.8)Cu_(0.2)O_(2)氧载体在化学链甲烷重...在Ce_(0.8)Cu_(0.2)O_(2)氧载体中添加不同质量S-1分子筛,并利用XRD、BET、XPS、SEM、TEM和CH4-TPR&CO_(2)-TPO等表征对氧载体的物化特性和反应性能进行了研究。考察了S-1分子筛添加量对Ce_(0.8)Cu_(0.2)O_(2)氧载体在化学链甲烷重整耦合CO_(2)还原反应中的性能的影响。与单纯的Ce_(0.8)Cu_(0.2)O_(2)氧载体相比,添加了0.3 g S-1分子筛后复合氧载体的比表面积明显增大,从15.44 m^(2)/g提高至73.27 m^(2)/g。同时热稳定性和结构稳定性也得到了很大的改善。添加了0.3 g S-1分子筛的复合氧载体CH4转化率由38.93%提升至56.03%,CO_(2)还原过程中CO产率由1.18 mmol/g增加至2.16 mmol/g。展开更多
Autonomous driving technology has made a lot of outstanding achievements with deep learning,and the vehicle detection and classification algorithm has become one of the critical technologies of autonomous driving syst...Autonomous driving technology has made a lot of outstanding achievements with deep learning,and the vehicle detection and classification algorithm has become one of the critical technologies of autonomous driving systems.The vehicle instance segmentation can perform instance-level semantic parsing of vehicle information,which is more accurate and reliable than object detection.However,the existing instance segmentation algorithms still have the problems of poor mask prediction accuracy and low detection speed.Therefore,this paper proposes an advanced real-time instance segmentation model named FIR-YOLACT,which fuses the ICIoU(Improved Complete Intersection over Union)and Res2Net for the YOLACT algorithm.Specifically,the ICIoU function can effectively solve the degradation problem of the original CIoU loss function,and improve the training convergence speed and detection accuracy.The Res2Net module fused with the ECA(Efficient Channel Attention)Net is added to the model’s backbone network,which improves the multi-scale detection capability and mask prediction accuracy.Furthermore,the Cluster NMS(Non-Maximum Suppression)algorithm is introduced in the model’s bounding box regression to enhance the performance of detecting similarly occluded objects.The experimental results demonstrate the superiority of FIR-YOLACT to the based methods and the effectiveness of all components.The processing speed reaches 28 FPS,which meets the demands of real-time vehicle instance segmentation.展开更多
文摘在Ce_(0.8)Cu_(0.2)O_(2)氧载体中添加不同质量S-1分子筛,并利用XRD、BET、XPS、SEM、TEM和CH4-TPR&CO_(2)-TPO等表征对氧载体的物化特性和反应性能进行了研究。考察了S-1分子筛添加量对Ce_(0.8)Cu_(0.2)O_(2)氧载体在化学链甲烷重整耦合CO_(2)还原反应中的性能的影响。与单纯的Ce_(0.8)Cu_(0.2)O_(2)氧载体相比,添加了0.3 g S-1分子筛后复合氧载体的比表面积明显增大,从15.44 m^(2)/g提高至73.27 m^(2)/g。同时热稳定性和结构稳定性也得到了很大的改善。添加了0.3 g S-1分子筛的复合氧载体CH4转化率由38.93%提升至56.03%,CO_(2)还原过程中CO产率由1.18 mmol/g增加至2.16 mmol/g。
基金supported by the Natural Science Foundation of Guizhou Province(Grant Number:20161054)Joint Natural Science Foundation of Guizhou Province(Grant Number:LH20177226)+1 种基金2017 Special Project of New Academic Talent Training and Innovation Exploration of Guizhou University(Grant Number:20175788)The National Natural Science Foundation of China under Grant No.12205062.
文摘Autonomous driving technology has made a lot of outstanding achievements with deep learning,and the vehicle detection and classification algorithm has become one of the critical technologies of autonomous driving systems.The vehicle instance segmentation can perform instance-level semantic parsing of vehicle information,which is more accurate and reliable than object detection.However,the existing instance segmentation algorithms still have the problems of poor mask prediction accuracy and low detection speed.Therefore,this paper proposes an advanced real-time instance segmentation model named FIR-YOLACT,which fuses the ICIoU(Improved Complete Intersection over Union)and Res2Net for the YOLACT algorithm.Specifically,the ICIoU function can effectively solve the degradation problem of the original CIoU loss function,and improve the training convergence speed and detection accuracy.The Res2Net module fused with the ECA(Efficient Channel Attention)Net is added to the model’s backbone network,which improves the multi-scale detection capability and mask prediction accuracy.Furthermore,the Cluster NMS(Non-Maximum Suppression)algorithm is introduced in the model’s bounding box regression to enhance the performance of detecting similarly occluded objects.The experimental results demonstrate the superiority of FIR-YOLACT to the based methods and the effectiveness of all components.The processing speed reaches 28 FPS,which meets the demands of real-time vehicle instance segmentation.
