作为目前主流的大数据流式计算平台之一,Storm在设计之初以性能为目的进行研究而忽视了高能耗的问题,但是其高能耗问题已经开始制约着平台的发展.针对这一问题,分别建立了任务分配模型、拓扑信息监控模型、数据恢复模型以及能耗模型,并...作为目前主流的大数据流式计算平台之一,Storm在设计之初以性能为目的进行研究而忽视了高能耗的问题,但是其高能耗问题已经开始制约着平台的发展.针对这一问题,分别建立了任务分配模型、拓扑信息监控模型、数据恢复模型以及能耗模型,并进一步提出了基于Storm平台的数据恢复节能策略(energy-efficient strategy based on data recovery in Storm,DR-Storm),包括吞吐量检测算法与数据恢复算法.其中吞吐量检测算法根据拓扑信息监控模型反馈的拓扑信息计算集群吞吐量,并通过信息反馈判断是否终止整个集群内拓扑的任务.数据恢复算法根据数据恢复模型选择备份节点用于数据存储,并通过拓扑信息监控模型反馈的信息判断集群拓扑是否进行数据恢复.此外,DR-Storm通过备份节点内存恢复集群拓扑内的数据,并根据大数据流式计算的系统延迟与能效评估DR-Storm.实验结果表明:与现有研究成果相比,DR-Storm在减少系统计算延迟、降低集群功率的同时,有效节约了能耗.展开更多
Lane detection is a fundamental necessary task for autonomous driving.The conventional methods mainly treat lane detection as a pixel-wise segmentation problem,which suffers from the challenge of uncontrollable drivin...Lane detection is a fundamental necessary task for autonomous driving.The conventional methods mainly treat lane detection as a pixel-wise segmentation problem,which suffers from the challenge of uncontrollable driving road environments and needs post-processing to abstract the lane parameters.In this work,a series of lines are used to represent traffic lanes and a novel line deformation network(LDNet) is proposed to directly predict the coordinates of lane line points.Inspired by the dynamic behavior of classic snake algorithms,LDNet uses a neural network to iteratively deform an initial lane line to match the lane markings.To capture the long and discontinuous structures of lane lines,1 D convolution in LDNet is used for structured feature learning along the lane lines.Based on LDNet,a two-stage pipeline is developed for lane marking detection:(1) initial lane line proposal to predict a list of lane line candidates,and(2) lane line deformation to obtain the coordinates of lane line points.Experiments show that the proposed approach achieves competitive performances on the TuSimple dataset while being efficient for real-time applications on a GTX 1650 GPU.In particular,the accuracy of LDNet with the annotated starting and ending points is up to99.45%,which indicates the improved initial lane line proposal method can further enhance the performance of LDNet.展开更多
文摘作为目前主流的大数据流式计算平台之一,Storm在设计之初以性能为目的进行研究而忽视了高能耗的问题,但是其高能耗问题已经开始制约着平台的发展.针对这一问题,分别建立了任务分配模型、拓扑信息监控模型、数据恢复模型以及能耗模型,并进一步提出了基于Storm平台的数据恢复节能策略(energy-efficient strategy based on data recovery in Storm,DR-Storm),包括吞吐量检测算法与数据恢复算法.其中吞吐量检测算法根据拓扑信息监控模型反馈的拓扑信息计算集群吞吐量,并通过信息反馈判断是否终止整个集群内拓扑的任务.数据恢复算法根据数据恢复模型选择备份节点用于数据存储,并通过拓扑信息监控模型反馈的信息判断集群拓扑是否进行数据恢复.此外,DR-Storm通过备份节点内存恢复集群拓扑内的数据,并根据大数据流式计算的系统延迟与能效评估DR-Storm.实验结果表明:与现有研究成果相比,DR-Storm在减少系统计算延迟、降低集群功率的同时,有效节约了能耗.
基金Supported by the Science and Technology Research Project of Hubei Provincial Department of Education (No.Q20202604)。
文摘Lane detection is a fundamental necessary task for autonomous driving.The conventional methods mainly treat lane detection as a pixel-wise segmentation problem,which suffers from the challenge of uncontrollable driving road environments and needs post-processing to abstract the lane parameters.In this work,a series of lines are used to represent traffic lanes and a novel line deformation network(LDNet) is proposed to directly predict the coordinates of lane line points.Inspired by the dynamic behavior of classic snake algorithms,LDNet uses a neural network to iteratively deform an initial lane line to match the lane markings.To capture the long and discontinuous structures of lane lines,1 D convolution in LDNet is used for structured feature learning along the lane lines.Based on LDNet,a two-stage pipeline is developed for lane marking detection:(1) initial lane line proposal to predict a list of lane line candidates,and(2) lane line deformation to obtain the coordinates of lane line points.Experiments show that the proposed approach achieves competitive performances on the TuSimple dataset while being efficient for real-time applications on a GTX 1650 GPU.In particular,the accuracy of LDNet with the annotated starting and ending points is up to99.45%,which indicates the improved initial lane line proposal method can further enhance the performance of LDNet.
