浅谈错视及其纠正与地质制图

本文侧重论述了错视,错视的表现及其利用与纠正,同时举例论述了正确运用错视对提高地质制图质量的重要性。     

基于双目视觉的双臂协作教学机器人研究与设计

为弥补智能制造、机器人工程等新专业实验装置短缺、学生创新实践能力亟待提升等问题,本文设计了一种基于双目视觉的双臂协作教学机器人.双臂机械部分设计呈对称结构,底座大而稳定,可以降低制造成本及协作抓取难度;研究设计的示教动作还原算法和重投影误差最小化算法,可以提升机器人末端位姿数据采集精度,使双臂协作抓取更准确稳定.该系统不仅能够通过上位机拖动控制双臂运动完成示教编程及动作组实验,而且还能够进行基于视觉引导的图像处理及单臂抓取实验、运动分析实验、在线编程与协调抓取开放实验.该系统成本低、功能丰富、综合性强,且兼具拖动示教与开放性特点,有助于提升学生创新实践能力,非常适合在智能制造、机器人工程等专业实验教学过程中推广应用.     

FY-3A/MERSI、MODIS C5.1和C6气溶胶光学厚度产品在中国区域与地面观测站点的对比分析

准确获取气溶胶光学厚度对于气候变化研究和大气环境监测具有重要意义。通过波长插值和时空匹配方法,利用气溶胶自动观测站网（AERONET）观测的气溶胶光学厚度（AOD）对风云3A/中分辨率光谱成像仪（FY-3A/MERSI）、Terra（Aqua）/MODIS的C5.1（Collections 5.1）和C6（Collections 6）气溶胶光学厚度产品在中国区域的反演精度进行验证分析。结合一次发生在中国境内的沙尘天气与一次严重雾霾天气个例,分析上述卫星气溶胶光学厚度的分布特征。研究结果表明,（1）FY-3A/MERSI AOD的反演精度较高（R=0.887,RMSE=0.234）,其值低于AERONET的观测值（Bias=-0.293）。（2）在不同的下垫面下,各种卫星暗像元算法AOD产品反演精度有差异,植被覆盖情况越好,反演精度越高,而植被很少的地区,即亮地表甚至没有反演值。（3）MODIS C5.1深蓝算法产品能在亮地表地区反演AOD,但效果不佳。MODIS C6中的深蓝算法产品在不同下垫面的反演精度都很高（RMSE为0.096-0.127）。（4）在不同季节的对比中,各种卫星AOD产品在夏季的反演精度最差,而反演最好的季节各有不同。（5）在一次沙尘天气污染与一次严重雾霾天气个例中,中国西部与北部区域,MODIS C6深蓝算法AOD的监测效果优于其他算法AOD;MERSI AOD产品在此区域的分布不连续。总体而言,MODIS C6 AOD分布比MODIS C5.1产品连续,MODIS 3 km产品在相同区域的AOD值高于其他产品。以上结论可为卫星AOD产品在中国区域的使用提供参考。      

结合图像信息的快速点云拼接算法

现有三维激光扫描设备通常配有一个同轴相机,它可以对扫描场景进行拍摄。针对带有同轴相机的激光扫描设备,本文提出了一种结合图像信息的快速点云拼接算法。与传统拼接算法同时计算点云间的旋转和平移变换不同,本文对这两种变换分别进行求解。其中,不同扫描点云间的旋转变换是利用视觉几何知识由同轴相机在不同扫描站点下拍摄的图像直接获得,而平移变换是由本文提出的改进ICP算法得到。在改进的ICP算法中,只有平移变换的3个未知量被迭代计算,其输入是去除旋转变换后的点云。试验结果表明利用图像获得的点云旋转变换具有很高的准确性;并且由于本文算法中迭代过程只针对平移变换的3个变量进行计算,因此与需要迭代计算6个变量的传统ICP算法相比,本文算法计算复杂度大幅降低,同时更易收敛于全局最优值且收敛速度有所提高。     

Uncertainty,entropy, scaling and hydrological stochastics. 2. Time dependence of hydrological processes and time scaling / Incertitude,entropie, effet d'échelle et propriétés stochastiques hydrologiques. 2. Dépendance temporelle des processus hydrologiques et échelle temporelle

Abstract

The well-established physical and mathematical principle of maximum entropy (ME), is used to explain the distributional and autocorrelation properties of hydrological processes, including the scaling behaviour both in state and in time. In this context, maximum entropy is interpreted as maximum uncertainty. The conditions used for the maximization of entropy are as simple as possible, i.e. that hydrological processes are non-negative with specified coefficients of variation and lag-one autocorrelation. In the first part of the study, the marginal distributional properties of hydrological processes and the state scaling behaviour were investigated. This second part of the study is devoted to joint distributional properties of hydrological processes. Specifically, it investigates the time dependence structure that may result from the ME principle and shows that the time scaling behaviour (or the Hurst phenomenon) may be obtained by this principle under the additional general condition that all time scales are of equal importance for the application of the ME principle. The omnipresence of the time scaling behaviour in numerous long hydrological time series examined in the literature (one of which is used here as an example), validates the applicability of the ME principle, thus emphasizing the dominance of uncertainty in hydrological processes.     

A Modified Neural Network for Improving River Flow Prediction/Un Réseau de Neurones Modifié pour Améliorer la Prévision de L'Écoulement Fluvial

Abstract

Artificial neural network (ANN) models provide huge potential for simulating nonlinear behaviour of hydrological systems. However, the potential of ANN is yet to be fully exploited due to the problems associated with improving the model generalization performance. Generalization refers to the ability of a neural network to correctly process input data that have not been used for calibrating the neural network model. In the hydrological context, better generalization performance implies higher precision of forecasting. The primary objectives of this study are to explore new measures for improving the generalization performance of an ANN-based rainfall–runoff model, and to evaluate the applicability of the new measures. A modified neural network model (entitled goal programming (GP) neural network) for modelling the rainfall–runoff process has been developed, in which three enhancements are made as compared to the widely-used backpropagation (BP) network. The three enhancements are (a) explicit integration of hydrological prior knowledge into the neural network learning; (b) incorporation of a modified training objective function; and (c) reduction of network sensitivity to input errors. Seven watersheds across a range of climatic conditions and watershed areas in China were selected for examining the alternative networks. The results demonstrate that the GP consistently outperformed the BP both in the calibration and verification periods and three proposed measures yielded improvement of performance.     

System aspects of the EUDOXOS observatories network

The networking architecture of the EUDOXOS' robotic telescopes is presented. We have studied adopted and tested various software & hardware approaches for developing an observational facility equipped with the very high availability needed to achieve continuous operation, inherent capacity for effective multiuser support, fully robotic unattended operation, fast response to targets of opportunity and accommodation of tele‐operating instruments. Critical practical aspects and considerations of our operating implementation as well as the main points of an ongoing upgrade initiative expected to be of general interest, are discussed. (© 2006 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Coupling between weather radar rainfall data and a distributed hydrological model for real-time flood forecasting / Couplage de données pluviométriques issues de radars météorologiques et d’un modèle hydrologique distribué pour la prévision de crue en temps réel

Abstract

Abstract After the destructive flood in 1998, the Chinese government planned to build national weather radar networks and to use radar data for real-time flood forecasting. Hence, coupling of weather radar rainfall data and a hydrological (Xinanjiang) model became an important issue. The present study reports on experience in such coupling at the Shiguanhe watershed. After having corrected the radar reflectivity and the attenuation data, the weather radar rainfall was estimated and then corrected in real time using a Kalman filter. In general, the precipitation estimated from weather radar is reasonably accurate in most of the catchment investigated, after corrections as above. Compared to the results simulated by raingauge data, the simulations based on the weather radar data are of similar accuracy. Present research results show that rainfall estimated from the weather radar, the radar data correction method, the method of coupling, and the Xinanjiang model lend themselves well to application in operational real-time flood forecasting.     

深空探测巡视器视觉导航定位方法研究与地面试验场验证

地外天体表面巡视器就位探测是深空探测研究的重要手段,其自主导航定位能力是任务开展的重要前提。本文主要对视觉导航方法中的视觉同时定位与地图构建(SLAM)方案开展应用研究。介绍了当下经典的视觉SLAM框架,并以具有代表性的ORB-SLAM3算法为例开展研究;分别利用公开数据集、深空探测地面模拟场数据进行试验验证,探索视觉导航定位算法在深空探测场景下的有效性。本文研究为我国后续开展长距离连续巡视任务提供参考。