A 3D-view is helpful to instantly grasp what is presented in a drawing. There exist a variety of ways to present the same part with 3D-views. To facilitate the choice of an optimum one among them, the work divides com...A 3D-view is helpful to instantly grasp what is presented in a drawing. There exist a variety of ways to present the same part with 3D-views. To facilitate the choice of an optimum one among them, the work divides composite solid models into three categories, so as to convey the originality of design concisely and accurately by using the least ＂ engineering language＂.展开更多
在基于移动边缘计算(mobile edge computing,MEC)的第5代移动通信网络(5th generation mobile communication network,5G)中,针对无线虚拟现实(virtual reality,VR)用户在小小区间频繁切换而降低业务体验质量(quality of experience,QoE...在基于移动边缘计算(mobile edge computing,MEC)的第5代移动通信网络(5th generation mobile communication network,5G)中,针对无线虚拟现实(virtual reality,VR)用户在小小区间频繁切换而降低业务体验质量(quality of experience,QoE)的问题。考虑建立多视角3维(3-dimensional,3D)视频的主动缓存、计算和通信(caching,computing and communication,3C)资源分配数学模型,并采用深度强化学习(deep reinforcement learning,DRL)算法进行求解。将多视角3D视频的主动资源分配系统建模为联合视角选择和小基站3C资源分配的马尔科夫决策过程(Markov decision process,MDP),提出了一种基于深度确定性策略梯度(deep deterministic policy gradient,DDPG)算法来寻找有效解。仿真结果表明,与另外2种算法相比,所提算法可以为无线VR用户在小小区间移动时提供更好的业务体验。展开更多
基于BEV(bird’s eye view)多传感器融合的自动驾驶感知算法近年来取得重大进展,持续促进自动驾驶的发展。在多传感器融合感知算法研究中,多视角图像向BEV视角的转换和多模态特征融合一直是BEV感知算法的重点和难点。笔者提出MSEPE-CRN(...基于BEV(bird’s eye view)多传感器融合的自动驾驶感知算法近年来取得重大进展,持续促进自动驾驶的发展。在多传感器融合感知算法研究中,多视角图像向BEV视角的转换和多模态特征融合一直是BEV感知算法的重点和难点。笔者提出MSEPE-CRN(multi-scale feature fusion and edge and point enhancement-camera radar net),一种用于3D目标检测的相机与毫米波雷达融合感知算法,利用边缘特征和点云提高深度预测的精度,实现多视角图像向BEV特征的精确转换。同时,引入多尺度可变形大核注意力机制进行模态融合,解决因不同传感器特征差异过大导致的错位。在nuScenes开源数据集上的实验结果表明,与基准网络相比,mAP提升2.17%、NDS提升1.93%、mATE提升2.58%、mAOE提升8.08%、mAVE提升2.13%,该算法可有效提高车辆对路面上运动障碍物的感知能力,具有实用价值。展开更多
文摘A 3D-view is helpful to instantly grasp what is presented in a drawing. There exist a variety of ways to present the same part with 3D-views. To facilitate the choice of an optimum one among them, the work divides composite solid models into three categories, so as to convey the originality of design concisely and accurately by using the least ＂ engineering language＂.
文摘在基于移动边缘计算(mobile edge computing,MEC)的第5代移动通信网络(5th generation mobile communication network,5G)中,针对无线虚拟现实(virtual reality,VR)用户在小小区间频繁切换而降低业务体验质量(quality of experience,QoE)的问题。考虑建立多视角3维(3-dimensional,3D)视频的主动缓存、计算和通信(caching,computing and communication,3C)资源分配数学模型,并采用深度强化学习(deep reinforcement learning,DRL)算法进行求解。将多视角3D视频的主动资源分配系统建模为联合视角选择和小基站3C资源分配的马尔科夫决策过程(Markov decision process,MDP),提出了一种基于深度确定性策略梯度(deep deterministic policy gradient,DDPG)算法来寻找有效解。仿真结果表明,与另外2种算法相比,所提算法可以为无线VR用户在小小区间移动时提供更好的业务体验。
文摘基于BEV(bird’s eye view)多传感器融合的自动驾驶感知算法近年来取得重大进展,持续促进自动驾驶的发展。在多传感器融合感知算法研究中,多视角图像向BEV视角的转换和多模态特征融合一直是BEV感知算法的重点和难点。笔者提出MSEPE-CRN(multi-scale feature fusion and edge and point enhancement-camera radar net),一种用于3D目标检测的相机与毫米波雷达融合感知算法,利用边缘特征和点云提高深度预测的精度,实现多视角图像向BEV特征的精确转换。同时,引入多尺度可变形大核注意力机制进行模态融合,解决因不同传感器特征差异过大导致的错位。在nuScenes开源数据集上的实验结果表明,与基准网络相比,mAP提升2.17%、NDS提升1.93%、mATE提升2.58%、mAOE提升8.08%、mAVE提升2.13%,该算法可有效提高车辆对路面上运动障碍物的感知能力,具有实用价值。
