With the rapid increase of the amount of vehicles in urban areas,the pollution of vehicle emissions is becoming more and more serious.Precise prediction of the spatiotemporal evolution of urban traffic emissions plays...With the rapid increase of the amount of vehicles in urban areas,the pollution of vehicle emissions is becoming more and more serious.Precise prediction of the spatiotemporal evolution of urban traffic emissions plays a great role in urban planning and policy making.Most existing methods usually focus on estimating vehicle emissions at historical or current moments which cannot well meet the demands of future planning.Recent work has started to pay attention to the evolution of vehicle emissions at future moments using multiple attributes related to emissions,however,they are not effective and efficient enough in the combination and utilization of different inputs.To address this issue,we propose a joint framework to predict the future evolution of vehicle emissions based on the GPS trajectories of taxis with a multi-channel spatiotemporal network and the motor vehicle emission simulator(MOVES)model.Specifically,we first estimate the spatial distribution matrices with GPS trajectories through map-matching algorithms.These matrices can reflect the attributes related to the traffic status of road networks such as volume,speed and acceleration.Then,our multi-channel spatiotemporal network is used to efficiently combine three key attributes(volume,speed and acceleration)through the feature sharing mechanism and generate a precise prediction of them in the future period.Finally,we adopt an MOVES model to estimate vehicle emissions by integrating several traffic factors including the predicted traffic states,road networks and the statistical information of urban vehicles.We evaluate our model on the Xi′an taxi GPS trajectories dataset.Experiments show that our proposed network can effectively predict the temporal evolution of vehicle emissions.展开更多
In recent years,human motion prediction has become an active research topic in computer vision.However,owing to the complexity and stochastic nature of human motion,it remains a challenging problem.In previous works,h...In recent years,human motion prediction has become an active research topic in computer vision.However,owing to the complexity and stochastic nature of human motion,it remains a challenging problem.In previous works,human motion prediction has always been treated as a typical inter-sequence problem,and most works have aimed to capture the temporal dependence between successive frames.However,although these approaches focused on the effects of the temporal dimension,they rarely considered the correlation between different joints in space.Thus,the spatio-temporal coupling of human joints is considered,to propose a novel spatio-temporal network based on a transformer and a gragh convolutional network(GCN)(STTG-Net).The temporal transformer is used to capture the global temporal dependencies,and the spatial GCN module is used to establish local spatial correlations between the joints for each frame.To overcome the problems of error accumulation and discontinuity in the motion prediction,a revision method based on fusion strategy is also proposed,in which the current prediction frame is fused with the previous frame.The experimental results show that the proposed prediction method has less prediction error and the prediction motion is smoother than previous prediction methods.The effectiveness of the proposed method is also demonstrated comparing it with the state-of-the-art method on the Human3.6 M dataset.展开更多
产品BOM(Bill of Material)中结点可能在不同层次重复出现,传统的树形结构不便于表达这些结点之间的关系。作者在分析传统产品BOM树模型的局限性的基础上,研究了产品BOM的本质结构,提出了产品BOM赋权有向图通用模型,给出了其相应的关系...产品BOM(Bill of Material)中结点可能在不同层次重复出现,传统的树形结构不便于表达这些结点之间的关系。作者在分析传统产品BOM树模型的局限性的基础上,研究了产品BOM的本质结构,提出了产品BOM赋权有向图通用模型,给出了其相应的关系数据库实现,研究了产品BOM赋权有向图模型的维护方法,讨论了基于企业实际的模型属性扩展,给出了其遍历和搜索的思想、相关算法以及编程语言实现方法,为PDM(Product Data Management)/CAPP(Computer Aided Process Planning)/CAD(Computer Aided Design)/MRPⅡ(Manufacturing Resource Plan)/CIMS(Computer Integrated Manufac-turing System)/ERP(Enterprise Resource Plan)系统集成提供了前提和基础。展开更多
基金This work was supported by National Key R&D Program of China under Grant(Nos.2018AAA0100800,2018YFE0106800)National Natural Science Foundation of China(Nos.61725304,61673361 and 62033012)Major Special Science and Technology Project of Anhui,China(No.912198698036).
文摘With the rapid increase of the amount of vehicles in urban areas,the pollution of vehicle emissions is becoming more and more serious.Precise prediction of the spatiotemporal evolution of urban traffic emissions plays a great role in urban planning and policy making.Most existing methods usually focus on estimating vehicle emissions at historical or current moments which cannot well meet the demands of future planning.Recent work has started to pay attention to the evolution of vehicle emissions at future moments using multiple attributes related to emissions,however,they are not effective and efficient enough in the combination and utilization of different inputs.To address this issue,we propose a joint framework to predict the future evolution of vehicle emissions based on the GPS trajectories of taxis with a multi-channel spatiotemporal network and the motor vehicle emission simulator(MOVES)model.Specifically,we first estimate the spatial distribution matrices with GPS trajectories through map-matching algorithms.These matrices can reflect the attributes related to the traffic status of road networks such as volume,speed and acceleration.Then,our multi-channel spatiotemporal network is used to efficiently combine three key attributes(volume,speed and acceleration)through the feature sharing mechanism and generate a precise prediction of them in the future period.Finally,we adopt an MOVES model to estimate vehicle emissions by integrating several traffic factors including the predicted traffic states,road networks and the statistical information of urban vehicles.We evaluate our model on the Xi′an taxi GPS trajectories dataset.Experiments show that our proposed network can effectively predict the temporal evolution of vehicle emissions.
基金This work was supported in part by the Key Program of NSFC(Grant No.U1908214)Program for Innovative Research Team in University of Liaoning Province(LT2020015)+1 种基金the Support Plan for Key Field Innovation Team of Dalian(2021RT06)the Science and Technology Innovation Fund of Dalian(Grant No.2020JJ25CY001).
文摘In recent years,human motion prediction has become an active research topic in computer vision.However,owing to the complexity and stochastic nature of human motion,it remains a challenging problem.In previous works,human motion prediction has always been treated as a typical inter-sequence problem,and most works have aimed to capture the temporal dependence between successive frames.However,although these approaches focused on the effects of the temporal dimension,they rarely considered the correlation between different joints in space.Thus,the spatio-temporal coupling of human joints is considered,to propose a novel spatio-temporal network based on a transformer and a gragh convolutional network(GCN)(STTG-Net).The temporal transformer is used to capture the global temporal dependencies,and the spatial GCN module is used to establish local spatial correlations between the joints for each frame.To overcome the problems of error accumulation and discontinuity in the motion prediction,a revision method based on fusion strategy is also proposed,in which the current prediction frame is fused with the previous frame.The experimental results show that the proposed prediction method has less prediction error and the prediction motion is smoother than previous prediction methods.The effectiveness of the proposed method is also demonstrated comparing it with the state-of-the-art method on the Human3.6 M dataset.
