变电站室内无人机巡检可有效降低人工巡检作业强度。由于飞行精度要求高,搭载能力有限,仅依靠无人机搭载摄像头与惯性测量单元(inertial measurement unit, IMU)数据融合确定位姿无法满足精度要求,为此,提出基于变电站室内已有固定摄像...变电站室内无人机巡检可有效降低人工巡检作业强度。由于飞行精度要求高,搭载能力有限,仅依靠无人机搭载摄像头与惯性测量单元(inertial measurement unit, IMU)数据融合确定位姿无法满足精度要求,为此,提出基于变电站室内已有固定摄像头的泛在物联的多视觉-惯导融合框架,针对室内光线情况对无人机摄像头图像进行强化,并与IMU数据结合得到初步的无人机位置数据。进一步通过在无人机上布设二维码(quick response code,QR码),应用改进后的PnP(perspective-n-point)算法优化无人机位姿数据。飞行结束后在无人机机巢对IMU的累计误差进行校验。实验证明:该方法布设与维护的工作量小,相较仅依靠搭载摄像头与IMU数据融合算法,飞行精度有较大提高,可满足变电站内无人机巡检作业的需要。展开更多
准确的出行端点信息采集是保障交通规划方案有效性的重要基础。4G/5G通信技术能够连续、动态追踪个体全过程出行轨迹,为精细化出行端点采集带来了新契机。然而手机信令数据固有的不均匀时空定位特性对出行端点识别效果造成了巨大挑战,...准确的出行端点信息采集是保障交通规划方案有效性的重要基础。4G/5G通信技术能够连续、动态追踪个体全过程出行轨迹,为精细化出行端点采集带来了新契机。然而手机信令数据固有的不均匀时空定位特性对出行端点识别效果造成了巨大挑战,本文提出一种适用于手机信令不均匀时空定位轨迹的自适应出行端点识别方法。首先,构建U-DBSCAN(Uneven Positioning Density Based Spatial Clustering of Applications with Noise)算法用于不同密度数据下个体出行端点识别,该算法同步考虑信令数据时空双重不均匀约束特性,可有效弥补稀疏信令数据造成的停留点漏识别和错误识别问题;其次,基于K-平均最近邻算法建立U-DBSCAN参数自适应协同框架,实现了数据密度可调可变环境下模型参数自适应最优匹配,促进出行端点识别效果与技术普适性提升。在贵阳市开展大量同步对比实证试验,结果表明:不均匀时空定位环境下个体出行端点识别精度达90.98%,平均坐标误差为344.13 m,出行端点到达与离开时间误差均小于3 min;相较于KANN-DBSCAN(K-Average Nearest Neighbor Density Based Spatial Clustering of Applications with Noise)、ST-DBSCAN(Spatial Temporal Density Based Spatial Clustering of Applications with Noise)和DBSCAN(Density Based Spatial Clustering of Applications with Noise)等算法,准确率提升9.62%~23.45%,说明本文方法的精确性和稳定性更佳。本文能够为分析居民出行活动与需求特征,提升交通规划方案有效性提供有力支撑。展开更多
With the development of global position system（GPS）,wireless technology and location aware services,it is possible to collect a large quantity of trajectory data.In the field of data mining for moving objects,the pr...With the development of global position system（GPS）,wireless technology and location aware services,it is possible to collect a large quantity of trajectory data.In the field of data mining for moving objects,the problem of anomaly detection is a hot topic.Based on the development of anomalous trajectory detection of moving objects,this paper introduces the classical trajectory outlier detection（TRAOD） algorithm,and then proposes a density-based trajectory outlier detection（DBTOD） algorithm,which compensates the disadvantages of the TRAOD algorithm that it is unable to detect anomalous defects when the trajectory is local and dense.The results of employing the proposed algorithm to Elk1993 and Deer1995 datasets are also presented,which show the effectiveness of the algorithm.展开更多
文摘准确的出行端点信息采集是保障交通规划方案有效性的重要基础。4G/5G通信技术能够连续、动态追踪个体全过程出行轨迹,为精细化出行端点采集带来了新契机。然而手机信令数据固有的不均匀时空定位特性对出行端点识别效果造成了巨大挑战,本文提出一种适用于手机信令不均匀时空定位轨迹的自适应出行端点识别方法。首先,构建U-DBSCAN(Uneven Positioning Density Based Spatial Clustering of Applications with Noise)算法用于不同密度数据下个体出行端点识别,该算法同步考虑信令数据时空双重不均匀约束特性,可有效弥补稀疏信令数据造成的停留点漏识别和错误识别问题;其次,基于K-平均最近邻算法建立U-DBSCAN参数自适应协同框架,实现了数据密度可调可变环境下模型参数自适应最优匹配,促进出行端点识别效果与技术普适性提升。在贵阳市开展大量同步对比实证试验,结果表明:不均匀时空定位环境下个体出行端点识别精度达90.98%,平均坐标误差为344.13 m,出行端点到达与离开时间误差均小于3 min;相较于KANN-DBSCAN(K-Average Nearest Neighbor Density Based Spatial Clustering of Applications with Noise)、ST-DBSCAN(Spatial Temporal Density Based Spatial Clustering of Applications with Noise)和DBSCAN(Density Based Spatial Clustering of Applications with Noise)等算法,准确率提升9.62%~23.45%,说明本文方法的精确性和稳定性更佳。本文能够为分析居民出行活动与需求特征,提升交通规划方案有效性提供有力支撑。
基金supported by the Aeronautical Science Foundation of China(20111052010)the Jiangsu Graduates Innovation Project (CXZZ120163)+1 种基金the "333" Project of Jiangsu Provincethe Qing Lan Project of Jiangsu Province
文摘With the development of global position system（GPS）,wireless technology and location aware services,it is possible to collect a large quantity of trajectory data.In the field of data mining for moving objects,the problem of anomaly detection is a hot topic.Based on the development of anomalous trajectory detection of moving objects,this paper introduces the classical trajectory outlier detection（TRAOD） algorithm,and then proposes a density-based trajectory outlier detection（DBTOD） algorithm,which compensates the disadvantages of the TRAOD algorithm that it is unable to detect anomalous defects when the trajectory is local and dense.The results of employing the proposed algorithm to Elk1993 and Deer1995 datasets are also presented,which show the effectiveness of the algorithm.
