LTE系统中速度估计新算法

在移动通信速度优化问题的研究中,终端速度的估计对长期演进(LTE)系统性能有重要的影响.针对传统信道自相关函数法在高速场景下应用受限的问题,提出了一种新的联合速度估计算法.上述算法在中速和低速场景下采用信道自相关函数法进行估计,在高速场景下采用频偏估计法进行估计,利用归一化接收功率标准差进行场景判断并在两种方法中切换,以提高速度估计精度.仿真结果表明,联合估计算法在低速、中速以及高速场景下均具有较高的估计精度,具有较好的应用价值.     

煤炭漏斗车装卸载过程仿真

煤炭漏斗车端、侧墙侧压力是车体静强度校核中的重要作用载荷.运用离散单元法,建立煤炭漏斗车仿真模型,对装卸载过程中车体所受的侧压力进行了仿真分析,并对车体内部的压力场和速度场的分布情况进行了分析.仿真结果表明,装载平衡后,端、侧墙侧压力沿墙高呈非线性分布;卸载过程中,侧墙侧压力呈现明显的波动变化.研究结果说明,离散单元法用于模拟和分析漏斗车车体压力及散粒体流动规律是可行的.仿真结果对漏斗车车体结构的研发具有一定的现实指导意义.     

基于GPS和加速度感应器的组合车辆定位系统研究

GPS位置信号的获取是城市智能交通系统中的一个基础问题,为解决城市中由于建筑物遮挡或信号反射造成GPS信号误差大或根本无法获取GPS信号的问题,设计一种基于GPS接收设备和采用加速度感应器进行航位推导的组合车辆定位系统。     

梯级水电枢纽群巨灾风险分析与防控研究综述

伴随超标准巨震、极端暴雨洪水、巨型滑坡等灾害频繁发生,极端载荷作用下梯级水电枢纽群的灾害风险分析与防控等问题成为当前水利工程领域的研究热点。为分析梯级水电枢纽群巨灾风险研究现状,绘制了国内外水库大坝溃坝事件的时间序列图,分析了梯级水电枢纽群的风险特征,总结评述了梯级水电枢纽群巨灾风险分析和巨灾防控研究进展,主要结论如下：1)梯级水电枢纽群巨灾风险是我国水利水电工程风险防控面临的主要问题;2)梯级水电枢纽群风险分析方面主要聚焦于梯级水库连溃概率的分析和计算,对于溃决可能产生的巨灾损失的量化研究不足,缺乏对巨灾因子及其相关影响作用下巨灾风险的评估;3)缺乏对梯级枢纽群灾害链的阻断技术研究和应急避险方案设计研究。为此提出了梯级水电枢纽群可能最大灾难(Probable Maximum Disaster, PMD)的科学内涵,考虑可能遭遇的多种致灾因子和承灾体特征,分析相互因果关系和极端荷载组合情况,初步建立了PMD评估的理论模型,为绘制梯级水电枢纽群在巨灾情景下的灾难空间外包线和估算PMD损失上限值提供科学依据,为梯级水电枢纽群巨灾风险分析和防控提供理论基础和技术支持。     

A real-time vehicle detection and a novel vehicle tracking systems for estimating and monitoring traffic flow on highways

Real-time highway traffic monitoring systems play a vital role in road traffic management, planning, and preventing frequent traffic jams, traffic rule violations, and fatal road accidents. These systems rely entirely on online traffic flow info estimated from time-dependent vehicle trajectories. Vehicle trajectories are extracted from vehicle detection and tracking data obtained by processing road-side camera images. General-purpose object detectors including Yolo, SSD, EfficientNet have been utilized extensively for real-time object detection task, but, in principle, Yolo is preferred because it provides a high frame per second (FPS) performance and robust object localization functionality. However, this algorithm’s average vehicle classification accuracy is below 57%, which is insufficient for traffic flow monitoring. This study proposes improving the vehicle classification accuracy of Yolo, and developing a novel bounding box (Bbox)-based vehicle tracking algorithm. For this purpose, a new vehicle dataset is prepared by annotating 7216 images with 123831 object patterns collected from highway videos. Nine machine learning-based classifiers and a CNN-based classifier were selected. Next, the classifiers were trained via the dataset. One out of ten classifiers with the highest accuracy was selected to combine to Yolo. This way, the classification accuracy of the Yolo-based vehicle detector was increased from 57% to 95.45%. Vehicle detector 1 (Yolo) and vehicle detector 2 (Yolo + best classifier), and the Kalman filter-based tracking as vehicle tracker 1 and the Bbox-based tracking as vehicle tracker 2 were applied to the categorical/total vehicle counting tasks on 4 highway videos. The vehicle counting results show that the vehicle counting accuracy of the developed approach (vehicle detector 2 + vehicle tracker 2) was improved by 13.25% and this method performed better than the other 3 vehicle counting systems implemented in this study.     

中继协同无人机辅助的认知无线电网络安全通信

针对中继协同无人机（UAV）辅助的无线通信网络,提出一种基于认知无线电网络（CRN）的物理层安全通信方案。利用二次发射机协同解码转发中继向目的接收机发送机密消息,将UAV用作移动干扰器发送干扰噪声,以降低窃听者的解码能力。在不影响主用户通信的前提下,通过联合优化UAV的飞行轨迹和发射功率提高系统的平均保密率,采用基于连续凸逼近的算法求解近似凸规划的保密率最大化问题。仿真结果表明,相较于优化功率和优化轨迹这两种传统方案,该方案能够进一步提高CRN系统通信的安全性。     

适用于飞行自组网的闲置时隙预约TDMA协议

在飞行自组网中,固定时隙分配时分多址接入（TDMA）协议存在闲置时隙无法成功使用的问题。通过对TDMA协议引入闲置时隙预约机制,提出一种支持业务优先级传输机制的闲置时隙预约TDMA协议。采用短帧长的方式满足协同与控制业务的低时延传输需求,并利用闲置时隙预约机制允许节点使用闲置时隙传输感知业务,从而满足感知业务的高吞吐量传输性能要求。仿真结果表明,与CF-MAC和CTMAC协议相比,该协议能够在降低传输时延的同时,有效提高信道利用率和网络吞吐量。     

基于双重权重偏差建模的无监督域适应

无监督域适应(Unsupervised Domain Adaptation,UDA)是一类新兴的机器学习范式,其通过对源域知识在无标记目标域上的迁移利用,来促进目标域模型的训练。为建模源域与目标域之间的域分布差异,最大均值差异(Maximum Mean Discrepancy,MMD)建模被广泛应用,其对UDA的性能提升起到了有效的促进作用。然而,这些方法通常忽视了领域之间对应类规模与类分布等结构信息,因为目标域与源域的数据类规模与数据分布通常并非一致。为此,文中提出了一种基于跨域类和数据样本双重加权的无监督域适应模型(Sample weighted and Class weighted based Unsupervised Domain Adaptation Network,SCUDAN)。具体而言,一方面,通过源域类层面的适应性加权来调整源域类权重,以实现源域与目标域之间的类分布对齐;另一方面,通过目标域样本层面的适应性加权来调整目标域样本权重,以实现目标域与源域类中心的对齐。此外,文中还提出了一种CEM(Classification Expectation Maximization)优化算法,以实现对SCUDAN的优化求解。最后,通过对比实验和分析,验证了所提模型和算法的有效性。     

无人机航拍图像中裸露地表的识别

电力输电线路下方或附近的无覆盖物的裸露地表,是引起输电线路事故的主要隐患之一;从无人机电力巡检航拍图像中识别裸露地表可以预防类似事故的发生;由于Mask RCNN识别无人机电力巡检航拍图像中裸露地表的精度较低,提出一种图像特征融合的方法,即人工提取HOG和LBP两种不同的图像特征,经过不同权重的融合共同表征图像中裸露地表区域的特征,再对SVM进行训练并用于识别;实验结果表明,采用该方法识别率可以达到80％以上,识别时间少于60 ms;HOG和LBP两种特征在进行融合时,当两种特征的数量级相当时,得到的识别率最高;可见,该方法在具有较高识别率的同时,具有比较好的实时性,适合于无人机机载平台对航拍图像的初筛,且训练时间较少,权重参数规模小,为无人机航拍图像中目标物的识别提供一种新思路.     

The dynamic contact area of elastomers at different velocities

The friction in tribo-systems that contain viscoelastic materials, such as elastomers, is relevant for a large number of applications. Examples include tyres, hoses, transmission and conveyor belts. To quantify the friction in these applications, one must first understand the contact behaviour of such viscoelastic materials, both in static and in dynamic situations. This work discusses an experimental study into the change of the contact area with the sliding velocity and relates the change in contact area with the mechanical properties of the elastomer. The results show that for a tribo-system containing an elastomer, there is a threshold velocity, above which the size of the contact area significantly reduces.