基于GPS轨迹的盲用导航算法与实现

利用在Windows CE嵌入式平台下接收到的全球定位系统(GPS)轨迹数据,提出了一种适用于盲人以及视障人士的室外GPS导航算法。通过对已知GPS轨迹数据的分析,判断出轨迹上的拐点位置以及拐弯方向,在实际导航时采用通过语音播报的方式向盲人提供实时导航信息。实验测试结果表明,该算法能够有效筛选出GPS轨迹中的拐点,并为用户播报有效实用的导航信息,指导其安全准确地到达目的地。     

基于大规模船舶轨迹数据的航道边界提取方法

传统的道路数据获取方法成本高、更新慢等无法适用于海洋航道的获取，从众源轨迹数据中提取道路或航道信息具有成本低、更新快等特性，然而，由于船舶轨迹数据噪声多、数据量大、不同区域分布不均使得航道边界提取面临较大挑战。针对该问题，提出一种基于大规模船舶轨迹数据进行航道边界提取的方法。首先对大规模的船舶轨迹数据进行并行化去噪、插值、轨迹分段；然后，基于并行化及基于Geohash编码的空间聚类，将轨迹数据化简为多个方形区域的点集数据；其次，对其进行窗口划分，对传统的NiBlack方法进行扩展，提出SpatialNiBlack算法，对方形区域进行航道识别；最后，提出一种新的提取算法del-alpha-shape，基于航道识别结果获得航道边界。理论分析与实验结果表明，所提方法在最大密度值是200，最小密度值是10，窗口长和宽分别为5和5时，可同时达到86.7%的准确率和79.4%的召回率。实验结果表明，该方法可以从大规模的轨迹数据中提取有价值的航道边界，是一种有效的航道提取方法。     

基于浮动车移动轨迹的新增道路自动发现算法

为了实现电子地图数据的动态更新,以支持日新月异的交通路网的地理信息服务,提出一种基于浮动车技术的新增道路自动发现算法。系统实时计算大规模浮动车数据移动轨迹,并与现有图层进行图像配准以提取疑似新增道路集,进而通过筛选过滤算法对数据集进行数据清洗,并自动地生成包含其位置和长度等信息的疑似新增道路报表及其临时图层。实验结果表明,该算法可快捷地自动发现新增道路,新增道路最快发现时间小于5min,是解决目前交通道路图层更新滞后问题的一种有效方法。     

GPS跟踪站数据传输软件客户端程序的设计

介绍了基于Internet的GPS跟踪站数据传输的研究背景,进行了数据传输软件的需求分析和总体设计,选择了便于源代码编写的开发工具VB6.0.以Winsock为编程控件,进行了GPS跟踪站数据传输客户端程序设计.数据传输试验结果表明:在56k的Modem,ISDN和ADSL的支持下,数据传输程序均可满足GPS跟踪站对于数据传输的要求.提出了GPS跟踪站数据实时传输的设想.     

Improvement of transportation cost estimation for prefabricated construction using geo-fence-based large-scale GPS data feature extraction and support vector regression

In panelized construction, transportation is an essential process linking a manufacturing facility to a project’s jobsite using hauling equipment (e.g., trucks and trailers). Accordingly, the cost associated with transportation operations is considerable compared to a traditional stick build. Nevertheless, transportation cost estimation has often relied on a fixed-cost approach, regarding the cost as part of the overhead cost, rather than conducting detailed estimation of actual transportation operations. This is because operation-level data might be challenging to collect and analyze in practice. In this regard, the prevalent use of GPS devices for construction equipment may provide an automated means of monitoring the operations of transportation equipment, and large and detailed spatial and temporal data can be generated from multiple pieces of equipment in multiple construction projects on a daily basis or even in real time. This study thus proposes a spatial and temporal data filtering and abstracting approach to transportation cost estimation using fleet GPS data which extracts equipment activities from the GPS data and accordingly predicts the transportation demands required for an individual project. From large-scale GPS data, key operation information, such as the number of trailers and durations required (i.e., transportation demands), is extracted using a geo-fence and a rule-based equipment operation analysis algorithm. Then, the extracted transportation demand information, along with related project specifications, is used to train support vector regression (SVR) models for the purpose of predicting the transportation demand in new projects, which is in turn utilized to estimate the transportation cost using the relevant transportation unit cost of the equipment. To evaluate the performance, GPS datasets collected from 221 panelized residential projects over a period of 8 months are used to train the prediction model and are compared with actual transportation costs estimated in practice. The results show that the SVR model has an accuracy of 86% and 88% in predicting the number of trailers and the duration, respectively. For the cost estimation performance, the results reveal that the average cost difference of 57% between the fixed cost and the actual transportation cost was reduced to 14% by implementing the GPS-data-based method in various project locations and for projects of various sizes. The GPS-data-based estimation approach thus is found to provide a more accurate transportation cost estimation result for various panelized construction projects, and the method improves the understanding of large-scale spatial and temporal equipment data while increasing the utilization of the GPS data already available.     

面向无人机电力巡检的语义实体构建及航迹控制方法

航迹的合理控制是影响无人机（UAV）智能决策重要因素。考虑UAV巡检的局部观测性和任务环境的高空复杂性,以电力巡检领域知识为背景,提出面向UAV电力巡检的语义实体构建及航迹控制方法。首先,基于电力巡检领域的实体知识构建空间拓扑网络,并生成关于位置节点的语义航迹序列网络及其语义接口;然后,根据空间拓扑结构相似性度量的结果集,提出安全许可机制和基于强化学习的航迹控制策略,实现UAV电力巡检在统一的概念内涵和位置结构上的轨迹控制。实验结果表明：作为UAV巡检的实例,所提方法得到的最优策略能获得最大化的鲁棒性能;同时,该方法通过强化学习方法使目标网络的适应度稳定收敛且实体区域覆盖率高于95%,为UAV电力巡检任务决策提供了飞行依据。     

利用结构化SVM结合CNN的层次化目标检测与人体姿态估计方法

针对现有姿态估计方法不能准确提取特征参数的问题,提出了一种基于结构化支持向量机（SSVM）与卷积神经网络（CNN）的层次化模型。首先,展示了一个基于PS部件模型的SSVM如何实现为一个两层的神经网络,其中第一层是卷积层,另一层是损失增强推理层;通过将模型的结构化形式转换为模型中的一个神经网络,提出方法可以同时学习结构模型和外观模型,同时反向传播误差以学习底层的可学习参数,这些参数可从外观模型特征中提取出来;最后,将SSVM模型转换为神经网络模型,将误差反向传播到较低层,并计算确切的SSVM损失,同时通过基于次梯度的方法来学习原始SSVM。将该模型与当前较为先进的识别模型进行了对比,结果证明提出的层次化模型的识别成功率比对比方法平均高6%,具有更强的识别性能。     

KDD中规则提取的收敛网络方法及其应用

提出一种新的基于神经网络的规则提取方法.提出的网络由一个主网络及其映射网络组成,具有二次收敛过程.通过主网络的学习(第1次收敛)完成知识学习和网络构造,在此基础上构造了其网络映射,通过该映射网络的收敛过程实现规则的提取.该方法在规则提取时无须遍历解空间,从而很好地提高了搜索效率,降低了计算复杂度.同时,还提出估计规则数下限的信度差方法.模拟实验和应用实验也验证了所提出方法的有效性和正确性.