Efficient graph attentional network for 3D object detection from Frustum-based LiDAR point clouds

LiDAR-based 3D object detection is important for autonomous driving scene perception, but point clouds produced by LiDAR are irregular and unstructured in nature, and cannot be adopted by the conventional Convolutional Neural Networks (CNN). Recently, Graph Convolutional Networks (GCN) has been proved as an ideal way to handle non-Euclidean structure data, as well as for point cloud processing. However, GCN involves massive computation for searching adjacent nodes, and the heavy computational cost limits its applications in processing large-scale LiDAR point cloud in autonomous driving. In this work, we adopt a frustum-based point cloud-image fusion scheme to reduce the amount of LiDAR point clouds, thus making the GCN-based large-scale LiDAR point clouds feature learning feasible. On this basis, we propose an efficient graph attentional network to accomplish the goal of 3D object detection in autonomous driving, which can learn features from raw LiDAR point cloud directly without any conversions. We evaluate the model on the public KITTI benchmark dataset, the 3D detection mAP is 63.72% on KITTI Cars, Pedestrian and Cyclists, and the inference speed achieves 7.9 fps on a single GPU, which is faster than other methods of the same type.     

Siamese visual tracking with multilayer feature fusion and corner distance IoU loss

The tracker based on the Siamese network regards tracking tasks as solving a similarity problem between the target template and search area. Using shallow networks and offline training, these trackers perform well in simple scenarios. However, due to the lack of semantic information, they have difficulty meeting the accuracy requirements of the task when faced with complex backgrounds and other challenging scenarios. In response to this problem, we propose a new model, which uses the improved ResNet-22 network to extract deep features with more semantic information. Multilayer feature fusion is used to obtain a high-quality score map to reduce the influence of interference factors in the complex background on the tracker. In addition, we propose a more powerful Corner Distance IoU (intersection over union) loss function so that the algorithm can better regression to the bounding box. In the experiments, the tracker was extensively evaluated on the object tracking benchmark data sets, OTB2013 and OTB2015, and the visual object tracking data sets, VOT2016 and VOT2017, and achieved competitive performance, proving the effectiveness of this method.     

The interlaminar shear failure characteristics of asphalt pavement coupled heating cables

Nowadays, heating cables are used as heat sources for heating pavements in practical engineering. However, there is a contradiction between the snow melting function and the interlaminar stability of heating pavement. In order to solve the contradiction, the interlaminar failure behavior of asphalt mixture coupled heating cables specimen (AMCS) was researched, through experiments and the finite element method. Under the different conditions of heating cables and rolling times, a series of direct shear tests was performed at the interface of AMCS, to compare the interlaminar stability of three different AMCS. Meanwhile, based on the bilinear cohesive zone model and coulomb friction model a 2D finite element model was established, to simulate this shear failure processes and make up for the limitations of the experiment. According to above test and simulation results, the failure mechanism and the weakest interface in AMCS were found, and the influence of the heating cable’s diameter and embedded spacing on the interlaminar shear strength were found. Then, a modified coulomb theorem model was proposed to predict the shear strength of the AMCS. This research enriches the design theory of the heating pavement and it has great significance for its structural design of heating asphalt pavement.     

g-C3N4/Pb复合材料制备及其在铅炭电池负极材料中的应用

为改善铅炭电池的析氢缺陷,提高电池循环使用寿命,以尿素为前驱体制备层状石墨相氮化碳(g-C₃N₄),并将其作为添加剂制备铅炭电池负极板,以活性炭(AC)为对照,研究了g-C₃N₄结构和添加量对铅炭电池电化学性能的影响。结果表明：g-C₃N₄的加入使析氢反应(HER)得到明显抑制,-1.5 V下1wt%gC₃N₄负极板的析氢电流仅为AC负极板的5%。交流阻抗谱图显示1wt%g-C3N4和AC负极材料阻抗(Rs)为0.19868Ω和1.749Ω。更重要的是1wt%g-C₃N₄负极板比电容比1wt%AC负极板高344%。在5 000 h高倍率部分荷电态(HRPSoC)下的电池循环寿命测试中,加入g-C₃N₄后电池寿命比加入AC提升62%。500次循环后,电池容量保持率仍有70%。g-C₃N₄     

城区内河水环境的生物治理——以池州市清溪河为例

对清溪河污染的治理,可以采用一系列理化的方法,但相比之下,利用生物治理不仅操作简便,成本低廉,而且能很好的修复水生生态系统的结构,达到良性循环的效果。驳岸的植物是一道屏障,能有效阻止和减少地表污染物进入水体,水中微生物将有机态的污染物转化为无机养份被植物吸收,植物为水中的微生物提供附着表面,而草食性鱼类能抑制植物的过渡生长,最终实现生态系统的重建与修复,使河道进入可持续发展的良性循环体系。     

夏热冬冷地区不同墙体材料节能冬季试验研究

对利用两淮矿区固体废弃物制备的夏热冬冷地区常用的三种新型墙体材料（粉煤灰蒸压加气混凝土砌块、粉煤灰混凝土空心砌块、烧结煤矸石空心砖）与传统粘土实心砖进行了冬季同环境下的能耗评价、环境评价等技术方面的对比试验研究.结果表明,新型墙体材料节能降耗指标均优于传统粘土实心砖,其中粉煤灰蒸压加气混凝土砌块相对较好.     

环 上线性码的对称重量计数器

定义了环 （其中, ）上线性码的对称重量计数器的概念,利用离散的Hadamard变换,确定了该环上线性码与其对偶码之间关于对称重量计数器的MacWilliams恒等式．最后,利用对称重量计数器,进一步建立了环 上线性码关于Hamming重量计数器和Lee重量计数器的MacWilliams恒等式．     

钢筋混凝土框架结构Pushover抗震性能分析

运用SAP2000Pushover对框架混凝土结构抗震性能进行了分析,得到了框架混凝土结构在多遇、罕遇条件下底部总剪力-顶点位移曲线、抗震性能点及层间位移角和塑性铰的分布结果,并对结果进行了分析与性能评价,基于上述分析结果,找出了结构在罕遇条件下结构的薄弱环节,且使结构满足"强柱弱梁"和"大震不倒"的抗震设防要求,可为此类结构的抗震分析与设计提供参考。     

皖西山区美丽乡村建设的现状与对策研究——以金寨县双河村为例

皖西山区因地理环境、经济产业及建设发展历程不同于省域其他地区,故美丽乡村建设有其自身的特点。本文基于对其多个乡村的整体布局、建筑风貌、空间环境、基础设施等开展的田野调查,以该地区的金寨县双河镇双河村为例,阐述了皖西山区美丽乡村建设的共性;并结合所参与的实际工程项目,着眼于技术层面,从产业、规划和建筑的角度,相应地提出了引导支撑、按需统筹、分类更新的对策,为该地区的美丽乡村建设提供一定的理论依据和实例参考。     

某超高层结构PUSHOVER抗震性能分析

结合工程实例建立MIDAS有限元模型,分别对模型进行反应谱和Pushover这两种计算方法分析,分析其在大震下和多遇地震下的抗震性能,对计算结果进行对比研究。利用结构在Pushover分析下得到的静力弹塑性曲线求取性能点,分析此时刻的塑性铰出现情况。同时,对两种情况下的楼层位移、层间位移、层间位移角等进行对比分析,说明该模型能很好的响应地震作用下的反应等。