Feedback control system for blow-out preventer positioning

During the drilling of ultra-deep-water subsea petroleum wells, a blow-out preventer (BOP), a piece of safety equipment, must be assembled on the wellhead. The BOP is suspended using the drilling riser during the wellhead approach operation, and the riser's top end is connected to the floating platform rig. This article presents a feedback control system for the automatic approach of the BOP to the wellhead. Compared to state-of-the-art controls, ours does not require ancillary thrusters installed alongside the riser nor inclination sensors atop of the drilling riser. Additionally, our proposed control embeds a closed-loop dynamic positioning system, thus retaining the characteristics of the original control system and adding an extra closed-loop. This eases implementation of the BOP approach control to an existing platform. To calculate the optimal gains for the BOP controller, we assume a linear system for the riser, including only the pendulum-shape. The simulation is carried out using nonlinear models for both riser and floating platform. We assume an International Towing Tank Conference standard semi-submersible platform, coupled with a 3000-m free-hanging vertical riser for the time-domain simulation. The results show the BOP tracking to be a step-shaped input signal under current and wave loads. A discussion of the performance of feedback control under different environmental loads is also included.     

基于多源信息融合的浅海超低频声源目标探测关键技术及实现

以多传感器信息融合理论为指导,结合现代信息处理技术与数据驱动建模及科学计算技术,研究浅海超低频声源目标激发共存地震波的复合声场中超低频声波传播特性和数据驱动建模的水下目标深度识别等关键技术,并论证了技术实现方案及路径。结果表明:以复合矢量水听器、地震波监测仪等多传感器信息融合理论为指导研究前海超低频声源目标可以克服传统声场建模存在的问题,有助于浅海超低频声源目标探测及改善海洋水下声学监测手段。在提高声呐探测设备的测量准确度、精度方面具有重要的理论意义,对周边海域为浅海的我国海防具有实战价值。     

天文定位中几何精度衰减因子最小值分析

天文定位是一种重要的导航定位方法,被广泛应用于大地天文测量、天文航海等领域。该方法中观测恒星的选择会影响最终的定位精度,目前缺少针对同时测定经纬度天文定位算法中最优选星问题的研究。随着观测仪器自动化水平的提高,观测数据的获取变得更加高效,这就要求研究最优的选星方案以达到最高的定位精度。本文借鉴卫星导航中几何精度衰减因子GDOP的概念,研究了天顶距法中恒星的数量以及分布对定位精度的影响,最后通过仿真试验和实测数据验证得到结论:在天顶距观测误差的统计特性一定时,GDOP能够用来描述恒星的分布对定位结果影响的优劣,且观测的恒星方位角均匀分布时定位误差最小。考虑到不同高度的恒星天顶距大气折射改正残差不同,在实际测量中应尽量采用等天顶距且方位角均匀分布的恒星。     

多源空间数据融合的城市精细化管理

城市管理体量不断增大且管理对象关联繁多,已经成为影响城市可持续发展的重要问题。在全国智慧城市建设的大背景下,引用新技术提升城市精细化管理水平可以使城市的各管理单元协同、精确、及时地交换信息,形成智慧城市管理的配套方案,让有限的管理资源发挥出最大的使用效能,实现城市管理的最优化目标,促进经济的可持续发展。本文提出了地理信息系统、GPS、遥感、RFID等空间数据融合的技术方案,强调在空间信息和移动信息管理的基础上通过工作流和过程化管理,提高城市精细化管理的综合水平,为实现民众参与监督城市管理和智慧城市建设提供参考。     

PDR算法对地磁室内定位精度的提升研究

利用建筑物中金属结构引起的地磁场扰动可以对室内的行人目标进行定位,而且基于地磁场的定位无需布设任何额外设施,因此可以以低成本实现定位。但仅靠单一的地磁技术无法满足室内定位的精度要求。为了解决磁场数据中单点定位的模糊性问题,本文提出了一种利用粒子滤波算法将PDR与地磁相融合的室内定位方法,并开发了地磁室内导航系统,以智能手机为硬件平台构建磁力计传感器模型,建立匹配轨迹的均方误差准则并实现PDR累积误差实时校正的迭代计算。在68 m×1.8 m的试验区域内,产生的平均定位误差为1.13 m,最大定位误差为2.17 m。本文算法的定位精度比单独PDR算法提升了42%;与单一地磁指纹匹配算法相比,定位精度提高了57%。试验证明,本文提出的融合算法对提高室内定位精度具有显著的作用。     

语义因子支持的多源POI分类信息一致化处理

为了解决网络POI分类异构导致多源POI数据匹配与整合困难的问题,该文利用分类特征词提取、特征词去重与优化等关键技术,构建POI分类的语义概念格,然后通过概念格之间的语义关联关系,实现多源异构POI分类体系的映射与转换。最后通过实验验证了该文方法的有效性,并将其成功应用于省级地理信息公共服务平台(政务版)数据更新。     

Indoor localization for pedestrians with real-time capability using multi-sensor smartphones

ABSTRACT

The localization of persons or objects usually refers to a position determined in a spatial reference system. Outdoors, this is usually accomplished with Global Navigation Satellite Systems (GNSS). However, the automatic positioning of people in GNSS-free environments, especially inside of buildings (indoors) poses a huge challenge. Indoors, satellite signals are attenuated, shielded or reflected by building components (e.g. walls or ceilings). For selected applications, the automatic indoor positioning is possible based on different technologies (e.g. WiFi, RFID, or UWB). However, a standard solution is still not available. Many indoor positioning systems are only suitable for specific applications or are deployed under certain conditions, e.g. additional infrastructures or sensor technologies. Smartphones, as popular cost-effective multi-sensor systems, is a promising indoor localization platform for the mass-market and is increasingly coming into focus. Today’s devices are equipped with a variety of sensors that can be used for indoor positioning. In this contribution, an approach to smartphone-based pedestrian indoor localization is presented. The novelty of this approach refers to a holistic, real-time pedestrian localization inside of buildings based on multi-sensor smartphones and easy-to-install local positioning systems. For this purpose, the barometric altitude is estimated in order to derive the floor on which the user is located. The 2D position is determined subsequently using the principle of pedestrian dead reckoning based on user's movements extracted from the smartphone sensors. In order to minimize the strong error accumulation in the localization caused by various sensor errors, additional information is integrated into the position estimation. The building model is used to identify permissible (e.g. rooms, passageways) and impermissible (e.g. walls) building areas for the pedestrian. Several technologies contributing to higher precision and robustness are also included. For the fusion of different linear and non-linear data, an advanced algorithm based on the Sequential Monte Carlo method is presented.     

顾及局部与结构特征的稀疏多项式逻辑回归高光谱图像分类方法

稀疏多项式逻辑回归在分类中仅利用图像光谱信息,导致分类效果不太理想.本文提出了一种顾及局部与结构特征的稀疏多项式逻辑回归高光谱图像分类方法.首先利用加权均值滤波与拓展形态学多属性剖面对原始高光谱图像进行局部与结构特征提取;然后对二者进行加权平均特征级融合以获取更具唯一性的像元特征;最后由稀疏多项式逻辑回归分类器对融合结果进行分类.结果表明,本文方法能有效地提高分类精度,而且具有较强的稳健性.     

一种局部稀疏地面点云与已有DEM的融合方法

机载LiDAR采集的点云数据中会存在一些局部区域地面点稀疏的情况,利用这些稀疏地面点构建DEM时会出现“三角面片化”的问题,严重影响DEM的质量。为此,本文提出了一种局部稀疏地面点云与已有DEM的融合方法：将稀疏点云作为高精度控制点,在尽量保持原始DEM的地形形态特征的前提下,通过高斯核函数加权迭代插值算法对DEM进行高程局部改正,实现稀疏点云与DEM的一致性融合。试验分析表明,融合后的点云数据得到了较好的补充,由此构建的DEM地形形态自然,在精度上相对于融合前的稀疏地面点云有一定改善,在弱精度区域的可靠性有显著提升。