ST-Map:an Interactive Map for Discovering Spatial and Temporal Patterns in Bibliographic Data

Getting insight into the spatiotemporal distribution patterns of knowledge innovation is receiving increasing attention from policymakers and economic research organizations.Many studies use bibliometric data to analyze the popularity of certain research topics,well-adopted methodologies,influential authors,and the interrelationships among research disciplines.However,the visual exploration of the patterns of research topics with an emphasis on their spatial and temporal distribution remains challenging.This study combined a Space-Time Cube(STC)and a 3D glyph to represent the complex multivariate bibliographic data.We further implemented a visual design by developing an interactive interface.The effectiveness,understandability,and engagement of ST-Map are evaluated by seven experts in geovisualization.The results suggest that it is promising to use three-dimensional visualization to show the overview and on-demand details on a single screen.

A Data-Driven Intersection Geometry Mapping Technique to Enhance the Scalability of Trajectory-Based Traffic Signal Performance Measures

Connected vehicle (CV) trajectory data provides practitioners with opportunities to assess traffic signal performance with no investment in detection or communication infrastructure. With over 500 billion trajectory records generated each month in the United States, operations can be evaluated virtually at any of the over 400,000 traffic signals in the nation. The manual intersection mapping required to generate accurate movement-level trajectory-based performance estimations is the most time-consuming aspect of using CV data to evaluate traffic signal operations. Various studies have utilized vehicle location data to update and create maps;however, most proposed mapping techniques focus on the identification of roadway characteristics that facilitate vehicle navigation and not on the scaling of traffic signal performance measures. This paper presents a technique that uses commercial CV trajectory and open-source OpenStreetMap (OSM) data to automatically map intersection centers and approach areas of interest to estimate signal performance. OSM traffic signal tags are processed to obtain intersection centers. CV data is then used to extract intersection geometry characteristics surrounding the intersection. To demonstrate the proposed technique, intersection geometry is mapped at 500 locations from which trajectory-based traffic signal performance measures are estimated. The results are compared to those obtained from manual geometry definitions. Statistical tests found that at a 99% confidence level, upstream-focused performance estimations are strongly correlated between both methodologies. The presented technique will aid agencies in scaling traffic signal assessment as it significantly reduces the amount of manual labor required.

Space-Time Correlation Based Fast Regional Spectrum Sensing in Cognitive Radio

In this paper,a space-time correlation based fast regional spectrum sensing(RSS)scheme is proposed to reduce the time and energy consumption of traditional spatial spectrum sensing. The target region is divided into small meshes,and all meshes are clustered into highly related groups using the spatial correlation among them. In each group,some representative meshes are selected as detecting meshes(DMs)using a multi-center mesh(MCM)clustering algorithm,while other meshes(EMs)are estimated according to their correlations with DMs and the Markov modeled dependence on history by MAP principle. Thus,detecting fewer meshes saves the sensing consumption. Since two independent estimation processes may provide contradictory results,minimum entropy principle is adopted to merge the results. Tested with data acquired by radio environment mapping measurement conducted in the downtown Beijing,our scheme is capable to reduce the consumption of traditional sensing method with acceptable sensing performance.

Traffic Maps and Smartphone Trajectories to Model Air Pollution, Exposure and Health Impact

In this study, we explored to combine traffic maps and smartphone trajectories to model traffic air pollution, exposure and health impact. The approach was step-by-step modeling through the causal chain: engine emission, traffic density versus traffic velocity, traffic pollution concentration, exposure along individual trajectories, and health risk. A generic street with 100 km/h speed limit was used as an example to test the model. A single fixed-time trajectory had maximum exposure at velocity of 45 km/h at maximum pollution concentration. The street population had maximum exposure shifted to a velocity of 15 km/h due to the congestion density of vehicles. The shift is a universal effect of exposure. In this approach, nearly every modeling step of traffic pollution depended on traffic velocity. A traffic map is a super-efficient pre-processor for calculating real-time traffic pollution exposure at global scale using big data analytics.

A Design Method of Noncoherent Unitary Space-Time Codes

We generalized an constructing method of noncoherent unitary space time codes (N-USTC) over Rayleigh flat fading channels. A family of N-USTCs with T symbol peroids, M transmit and N receive antennas was constructed by the exponential mapping method based on the tangent subspace of the Grassmann manifold. This exponential mapping method can transform the coherent space time codes (C-STC) into the N-USTC on the Grassmann manifold. We infered an universal framework of constructing a C-STC that is designed by using the algebraic number theory and has full rate and full diversity (FRFD) for t symbol periods and same antennas, where M, N, T, t are general positive integer. We discussed the constraint condition that the exponential mapping has only one solution, from which we presented a approach of searching the optimum adjustive factor αopt that can generate an optimum noncoherent codeword. For different code parameters M, N, T, t and the optimum adjustive factor αopt, we gave the simulation results of the several N-USTCs.

Automatic Lane-Level Intersection Map Generation using Low-Channel Roadside LiDAR

A lane-level intersection map is a cornerstone in high-definition(HD) traffic network maps for autonomous driving and high-precision intelligent transportation systems applications such as traffic management and control, and traffic accident evaluation and prevention. Mapping an HD intersection is time-consuming, labor-intensive, and expensive with conventional methods. In this paper, we used a low-channel roadside light detection and range sensor(LiDAR) to automatically and dynamically generate a lane-level intersection, including the signal phases, geometry, layout, and lane directions. First, a mathematical model was proposed to describe the topology and detail of a lane-level intersection. Second, continuous and discontinuous traffic object trajectories were extracted to identify the signal phases and times. Third, the layout, geometry, and lane direction were identified using the convex hull detection algorithm for trajectories. Fourth, a sliding window algorithm was presented to detect the lane marking and extract the lane, and the virtual lane connecting the inbound and outbound of the intersection were generated using the vehicle trajectories within the intersection and considering the traffic rules. In the field experiment, the mean absolute estimation error is 2 s for signal phase and time identification. The lane marking identification Precision and Recall are96% and 94.12%, respectively. Compared with the satellite-based,MMS-based, and crowdsourcing-based lane mapping methods,the average lane location deviation is 0.2 m and the update period is less than one hour by the proposed method with low-channel roadside LiDAR.

Trajectory Tracking of COVID-19 Epidemic Risk Using Self-organizing Feature Map

The ongoing COVID-19 has become a worldwide pandemic with increasing confirmed cases and deaths across the globe.By July 2022,the number of cumulative confirmed cases reported to the World Health Organization(WHO)has risen to 550 million,with more than 6 million deaths in total.The analysis of its epidemic risk remains the focus of attention all over the world for a long time.The Self-organizing feature map(SOM),a vector quantization method,offers a data mapping approach to tracking the response of time series data on a well-trained map.This study aims at a trajectory tracking of COVID-19 epidemic risk in 237 countries measured by the number of new confirmed cases and deaths per day for over one year.A hybrid clustering method uses SOM and K-means to generate a risk map and then displays the trajectory of daily risk on the map.The experimental results demonstrate the promising functionality of SOM for trajectory tracking and give experts insights into the dynamic changes of COVID-19 risk.

Gravitational Space-Time Curve Generation via Accelerated Charged Particles

A force with an acceleration that is equal to multiples greater than the speed of light per unit time is exerted on a cloud of charged particles. The particles are resultantly accelerated to within an infinitesimal fraction of the speed of light. As the force or acceleration increases, the particles’ velocity asymptotically approaches but never achieves the speed of light obeying relativity. The asymptotic increase in the particles’ velocity toward the speed of light as acceleration increasingly surpasses the speed of light per unit time does not compensate for the momentum value produced on the particles at sub-light velocities. Hence, the particles’ inertial mass value must increase as acceleration increases. This increase in the particles’ inertial mass as the particles are accelerated produce a gravitational field which is believed to occur in the oscillation of quarks achieving velocities close to the speed of light. The increased inertial mass of the density of accelerated charged particles becomes the source mass (or Big “M”) in Newton’s equation for gravitational force. This implies that a space-time curve is generated by the accelerated particles. Thus, it is shown that the acceleration number (or multiple of the speed of light greater than 1 per unit of time) and the number of charged particles in the cloud density are surjectively mapped to points on a differential manifold or space-time curved surface. Two aspects of Einstein’s field equations are used to describe the correspondence between the gravitational field produced by the accelerated particles and the resultant space-time curve. The two aspects are the Schwarzchild metric and the stress energy tensor. Lastly, the possibility of producing a sufficient acceleration or electromagnetic force on the charged particles to produce a gravitational field is shown through the Lorentz force equation. Moreover, it is shown that a sufficient voltage can be generated to produce an acceleration/force on the particles that is multiples greater than the speed of light per unit time thereby generating gravity.

一种车辆GPS轨迹数据的改进地图匹配算法

文中提出一种车辆GPS轨迹数据的改进地图匹配算法.以车辆GPS轨迹数据进行隐马尔可夫模型动态地图匹配后的缓冲区内候选路径为初始群体,以遗传算法中的改进的适应度函数为基准进行半交叉迭代,选择车辆轨迹更优的匹配路径;设计了改进地图匹配方法的算法流程;利用微软亚洲团队的GeoLift轨迹数据集验证了改进方法的有效性.

基于视觉同时定位与地图构建的水下图像增强式视觉三维重建方法

针对仿生机器鱼水下作业时面临的水下图像质量偏低、水下自主定位难的问题,提出一种颜色均衡与G-B通道先验融合的水下图像增强式算法。将该算法和视觉同时定位与地图构建(SLAM)方法结合,实现了水下图像增强式的视觉三维重建。在不同水域环境下进行了水下图像处理实验、水下环境视觉三维重建实验和运动轨迹跟踪实验,结果表明该方法有效提高了水下图像综合质量,特征匹配效率提高了16.03%,真实轨迹与估计轨迹的误差平均约为7.99 mm。

一种结合光流追踪的双目视觉里程计算法

针对特征点法视觉里程计频繁计算和匹配描述子导致系统实时性能变差的问题,提出一种结合光流追踪的双目视觉里程计算法;首先进行初始化,生成初始的关键帧和地图点,随后在追踪线程中使用光流追踪特征点获取匹配关系,计算并优化相机位姿;满足生成关键帧的条件后,将当前帧设置为关键帧,提取图像的ORB特征点,并使用描述子匹配获取与上一关键帧特征点的匹配关系,三角化生成新的地图点;最后在优化线程中将新的关键帧和地图点使用滑动窗口算法进行优化,剔除冗余关键帧和地图点;在KITTI数据集上的实验结果表明,所提出的算法轨迹误差与双目模式下的ORB-SLAM3算法处于同一水平,同时实时性能有大幅度提高,追踪每一帧图像的平均时间从52 ms降至16 ms,在保证高精度的情况下运行速度大大提高,具有较高的实用价值。

基于改进型蝴蝶优化算法的机械手轨迹规划

针对传统机械手在码垛抓取过程中轨迹优化问题,提出了改进型蝴蝶优化算法对机械手轨迹进行优化,用以提高机械手抓取的效率、准确性。提出利用切比雪夫混沌映射种群初始化,对惯性权重因子进行改进,以防止算法陷入局部最优。随后将改进型蝴蝶优化算法与原蝴蝶优化算法在3个基准函数测试中进行对比,改进型蝴蝶优化算法比原算法有显著提升,随后在Matlab中对机械手第三关节进行最优轨迹运动仿真,结果表明利用改进型蝴蝶优化算法在轨迹时间最优中有良好的表现。

资源型城市生态系统服务格局演变与功能分区研究——以抚顺市为例

构建依据生态系统主导功能的分区管理策略是实现资源型城市生态转型可持续发展的重要途径。文章以资源型城市抚顺市为研究对象,基于多种生态系统服务的时空演变特征,利用自组织特征映射神经网络识别其在时空共现的组合类型(即生态系统服务簇),并依据服务簇的主导服务功能从提高协同减少权衡的角度提出差异化分区管理措施。研究结果可为未来抚顺市因地制宜实施生态管控措施提供有益参考,对促进资源型城市精细化管理实现可持续发展具有重要意义。

面向超低空物流场景的多机协同航迹规划算法

无人机产业的迅猛发展促进了低空开放,形成了国内外城市超低空物流运输的浪潮,然而,现有的航迹规划算法没有考虑空域的划分方式与运行规则,不适用于城市超低空物流场景下多无人机的协同航迹规划,桎梏了超低空物流行业的发展。针对该问题,从实际需求出发,在空域高度层架构的基础上探索适用于城市超低空物流场景的多无人机协同航迹规划方法。将原问题分解为无人机-高度层任务分配与多无人机单高度层协同航迹规划两个相互耦合的子问题,并分别运用基于知识图谱的任务分配解法与基于粒子群算法的改进人工势场法对两个子问题进行求解。仿真实验表明,该方法在求解单高度层协同航迹规划子问题中不但能够避免传统方法的固有缺陷,平均迭代次数相较于对比方法也减少了62.09%;同时,仿真结果也表明所提方法可以快速鲁棒的解决原问题,为城市超低空物流场景提供了切实可行的多机航迹规划方法。

患者旅程地图在慢性病照护中的研究进展

阐述患者旅程地图的概念、起源与发展、基本原则、应用目的、构建方法。提出患者旅程地图是医护人员、慢性病患者和家属之间信息共享的重要工具。未来应结合我国国情及相关政策针对不同类型的慢性病患者展开患者旅程地图的探索,以优化慢性病患者就医体验及长期照护质量。

基于路网复杂度分区的轨迹分段地图匹配方法

针对现有大多数地图匹配方法在城市复杂环境下难以有效平衡匹配速度和精度的问题,提出了一种基于路网复杂度分区的轨迹分段地图匹配方法。该方法包括路网分区和轨迹分段匹配两个部分。通过构建的路网复杂度分区模型将路网划分为复杂区域和非复杂区域;对复杂区域内的轨迹段采用改进的隐马尔可夫模型进行匹配,非复杂区域内的轨迹段采用基于几何拓扑的快速匹配模型进行匹配;将不同区域内匹配的轨迹段进行拼接,得到完整轨迹的匹配结果。为得到路网复杂度分区模型的最优参数,进行了11组不同参数设置的对比实验,并将最终结果与ST-matching和传统隐马尔可夫模型两种地图匹配方法匹配的结果进行对比。结果表明,在三个数据集的匹配准确率均在96%以上,比其他两种对比算法匹配时间减少了60%,在保证匹配准确率的前提下有效提升了匹配效率。

基于分组地图匹配的异常轨迹检测

为提高轨迹数据的质量,提出一种轨迹点的多尺度综合映射算法。采用分组匹配的思想,依据每组特性剪枝无效的轨迹点,加快映射轨迹点的连接速度,快速得到路网轨迹。在此基础上,为有效进行异常轨迹的检测,提出一种基于分组地图匹配的异常轨迹检测算法,研究字符串相似性搜索,构建相似轨迹序列寻找代表轨迹,基于交叉点行驶状况对轨迹点进行约减,减少轨迹间的计算量,计算轨迹的异常阈值,判定异常轨迹。在真实的出租车数据集上进行实验,验证了提出方法的执行效率和准确性。

基于Z-map理论的喷枪轨迹生成方法

喷漆机器人作为一种先进的涂装生产设备已广泛应用于产品内外表面的喷涂,由于现代工业产品外形相当复杂,如何实现涂层均匀化变得极为重要。基于Z-map理论,在自由曲面CAD模型的基础上,将自由曲面三角网格化得到多面体模型,然后利用Z-map方法实现了等距喷涂情况下喷涂节点的坐标及其法向量的提取,将喷涂节点插补优化后连接为喷枪的喷涂轨迹,从而实现了自动生成自由曲面上喷枪轨迹。最后,以一汽车CAD模型为例生成了机器人喷涂轨迹。

中枢模式发生器与足端轨迹的非线性映射

控制的仿生性和行走的稳定性是四足机器人步态研究中重要的两个方面。为了提高四足机器人运动的稳定性,本文通过Hopf振荡器搭建了CPG模型,分别实现了多种步态及步态之间的转换。比较了基于CPG的步态控制方法和轨迹规划的步态规划方法在行走上的优劣性。为了同时利用CPG控制和轨迹规划的优点,提出采用神经网络将CPG控制曲线与足端轨迹逆运动学获得的驱动曲线进行非线性映射,使得四足机器人在控制上具备仿生特性,在足端接触上具备零冲击特性。仿真和实验结果表明,采用CPG的步态生成方法和轨迹规划方法四足机器人的行走速度理论行走速度80 mm/s相近,但采用CPG的步态生成方法四足机器人侧向位移在±10 mm以内且俯仰角在±1.5°之间,而采用轨迹规划的控制方法四足机器人侧向位移在±35 mm以内且俯仰角在±4°之间,可见两种控制方式对侧向偏移和俯仰运动的表现不一致。通过实验测量可知,机器人采用walk步态行走速度为18.57 mm/s,与理论行走速度20 mm/s接近,步态转换后以trot步态行走,行走速度为76.15 mm/s,与理论行走速度80 mm/s接近,少许误差可能是装配和行走过程打滑导致的。测量其侧向偏移程度可知,其侧向的偏移量左侧在15 mm内,右侧在25 mm内,其侧向偏移量均在合适的范围内,证明所提算法的有效性。

基于改进HMM的车辆轨迹匹配方法研究

针对传统基于隐马尔可夫模型(HMM)的车辆轨迹匹配算法在复杂特殊路段无法兼备准确率与时间效率的问题,提出了基于改进HMM建模的车辆轨迹匹配方法。在候选路段的确定上通过生成网格索引提高整体检索效率,改进的HMM模型在发射概率的计算上综合航向角因素,考虑车辆速度对航向角造成的偏差影响,并设置经验因子进行调节。同时,考虑前后观测值误差过大及曲线路段等影响因素,采用单位采样间隔内的车辆实际行驶距离代替观测距离值,以保证传递概率计算的准确性。最后,利用实测数据进行试验,验证算法的性能。实验结果表明,所提方法匹配准确率约94.0%,相较于传统HMM轨迹匹配方法提高了2.8%,在提高时间效率及复杂路段的匹配准确度上也具有一定优势,单点匹配时间减少约0.9 ms,适用于交叉路口、立交桥、平行路段等复杂路况下的匹配。