基于虚拟格网的建筑物点云轮廓线自动提取

在分析现有轮廓线提取方法不足的基础上，提出基于虚拟格网的建筑物轮廓线自动提取方法。该方法利用建筑物点云生成虚拟格网并进行二值填充；采用邻域分析方法进行边界格网的标记与追踪；为了避免边界追踪错误，设计了基于方向的单边缘格网抑制方法及基于距离的连接关系调整方法以改善提取结果质量；根据格网追踪结果，从原始建筑物点云中提取真实轮廓点以保持原始建筑物轮廓形态；采用随机抽样一致性估计及最小二乘拟合方法进行轮廓线规则化处理，实现建筑物轮廓线的自动提取。实验结果表明，该方法能快速从建筑物点云中提取轮廓线，可为建筑物轮廓线的自动提取提供一种可行的解决方案。     

超细粉体抑爆装置瞬态喷射特征参数

在敞开空间开展了容积为2 L及5 L的超细粉体抑爆装置瞬态喷射参数研究，重点介绍了5种典型装配条件下的喷射性能，并对相关特征参数进行定量分析。结果表明：抑爆剂喷射所形成的超细粉体云幕特性由气体发生器装配结构和膜片开启压力共同决定，但两者之间必须恰当配置，作用在膜片上的能量份额较大时，膜片开启状态恶化，抑爆器腔体聚压不足，抑爆剂二次喷射严重，反之膜片不能正常开启。装配优化后的I型抑爆器在触发后10 ms内腔体聚压达到峰值，膜片破裂后5 ms内抑爆剂全部喷射，喷射动态性能较好，在第1个100 ms内扩散速度达到15 m/s，在第2个100 ms内扩散速度约13 m/s，0~1 000 ms内平均扩散速度约8 m/s，扩散过程较为连续，不存在二次喷射现象，喷射距离可达到7.5 m，抑爆剂质量分布呈渐进增长，产生的超细粉体云幕特征参数优良。Ⅱ型抑爆器两种装配的喷射表明：当抑爆装置中压力上升历程及压力峰值较为接近时，抑爆剂粉体喷射特性无显著差异。上述结论的可重复性得到了数百次喷射实验的验证。     

Estimating underground mine ventilation friction factors from low density 3D data acquired by a moving LiDAR

Ventilation system analysis for underground mines has remained mostly unchanged since the Atkinson method was made popular by Mc Elroy in 1935. Data available to ventilation technicians and engineers is typically limited to the quantity of air moving through any given heading. Because computer-aided modelling, simulation, and ventilation system design tools have improved, it is now important to ensure that developed models have the most accurate information possible. This paper presents a new technique for estimating underground drift friction factors that works by processing 3 D point cloud data obtained by using a mobile Li DAR. Presented are field results that compare the proposed approach with previously published algorithms, as well as with manually acquired measurements.     

台州市"白云阁"景观建筑设计

本文系统地介绍了浙江台州白云阁的设计过程,并着重分析建筑的生成过程,尝试从不同角度探求符合当代社会特质的现代景观建筑之路。台州市“白云阁”景观建筑设计是一次有意义的探索。     

Evaluation of image-based modeling and laser scanning accuracy for emerging automated performance monitoring techniques

Accurate and rapid assessment of the as-built status on any construction site provides the opportunity to understand the current performance of a project easily and quickly. Rapid project assessment further identifies discrepancies between the as-built and as-planned progress, and facilitates decision making on the necessary remedial actions. Currently, manual visual observations and surveying are the most dominant data capturing techniques but they are time-consuming, error-prone, and infrequent, making quick and reliable decision-making difficult. Therefore, research on new approaches that allow automatic recognition of as-built performance and visualization of construction progress is essential. This paper presents and compares two methods for obtaining point cloud models for detection and visualization of as-built status for construction projects: (1) A new method of automated image-based reconstruction and modeling of the as-built project status using unordered daily construction photo collections through analysis of Structure from Motion (SfM); (2) 3D laser scanning and analysis of the as-built dense point cloud models. These approaches provide robust means for recognition of progress, productivity, and quality on a construction site. In this paper, an overview of the newly developed automated image-based reconstruction approach and exclusive features which distinct it from other image-based or conventional photogrammetric techniques is presented. Subsequently the terrestrial laser scanning approach carried out for reconstruction and comparison of as-built scenes is presented. Finally the accuracy and usability of both of these techniques for metric reconstruction, automated production of point cloud models, 3D CAD shape modeling, and as-built visualizations is evaluated and compared on eight different case studies. It is shown that for precise defect detection or alignment tasks, image-based point cloud models may not be as accurate and dense as laser scanners' point cloud models. Nonetheless image-based point cloud models provide an opportunity to extract as-built semantic information (i.e., progress, productivity, quality and safety) through the content of the images, are easy to use, and do not need add burden on the project management teams by requiring expertise for data collection or analysis. Finally image-based reconstruction automatically provides photo alignment with point cloud models and enables image-based renderings which can remarkably impact automated performance monitoring and as-built visualizations.     

基于深对流云的夜间微光传感器定标技术

NPP(National Polar-orbiting Parternership)卫星于2011年10月28日发射升空,其上搭载的VIIRS(Visible Infrared Imaging Radiometer Suite)传感器包含DNB(Day Night Band)波段,该波段采用三增益设置,可在白天、晨昏甚至夜间实现地球观测.其中,在夜间月光条件下,主要采用高增益,定标实现较为困难.以此为目的,首次提出了基于深对流云的夜间微光传感器替代定标方法.将月亮辐射模型引入到SCIATRAN中,实现了全天候的辐射传输计算,并利用该改进的辐射传输模式,通过确定深对流云光学属性、地表特性及大气廓线等的参考值,对接收大气层顶反射辐射进行敏感性试验,分析得到主要影响因子为云光学厚度COT(Cloud Optical Thickness)与有效粒子半径Re(Effective Radius),且利用固定参考值得到最大模拟误差小于5%.为了检验该替代方法的可行性,通过改进的辐射传输模式,对2012年08月~2013年01月共6个月夜间DNB数据所确定的深对流云替代目标像素进行辐射传输模拟,结果表明基于深对流云的替代定标方法效果较好,基于日变化的辐射不确定度在±10%之内,可实现对DNB夜间高增益阶段的直接辐射定标.     

Efficient 3D point clouds classification for face detection using linear programming and data mining

Most of the applications related to security and biometric rely on skin region detection such as face detection, adult 3D objects filtering, and gesture recognition. In this paper, we propose a robust method for skin detection on 3D coloured point clouds. Then, we extend this method to solve the problem of 3D face detection. To do so, we construct a weighted graph from initial coloured 3D point clouds. Then, we present a linear programming algorithm using a predictive model based on a data mining approach to classify and label graph vertices as skin and non-skin regions. Moreover, we apply some refinement rules on skin regions to confirm the presence of a face. Furthermore, we demonstrate the robustness of our method by showing and analysing some experimental results. Finally, we show that our method deals with many data that can be represented by a weighted graph such as 2D images and 3D models.     

An exploratory study for investigating the critical success factors for cloud migration in the Saudi Arabian higher education context

Saudi universities have at their disposal a huge number of low cost IT resources to aid in teaching, research and learning. By migrating to cloud services, Saudi universities will be moving data and programs from local servers to the internet, thereby providing users with the ability to access and share information at any time from multiple devices. The migration to cloud-based IT resources is not yet widespread in Saudi universities due to several challenges including security, legal policies and implementation. At present, there is lack of research and guidance for Saudi universities on how to overcome these challenges and how contextual factors can influence the successful migration to the educational clouds.This research presents a framework for the successful migration to cloud technology in the Saudi Arabian universities. In this research, a set of key critical success factors (CSFs) were identified by synthesizing components from studies concerned with the migration of cloud for higher education and factors identified from the successful implementation of WBL (Web Based Learning) and ERP (Enterprise Resource Planning) on higher education in Saudi Arabia. Based on this knowledge, the proposed framework was evaluated via expert review and a survey by IT specialists from the Saudi universities. The initial CSFs were updated based on the expert reviews and the results were analysed. Based on the findings at this stage, additional CSFs were added to the framework as suggested by the experts. Subsequently, in order to confirm the reviewed CSFs, additional investigation via a structured online questionnaire was conducted and the outcome was analysed via one-sample t-test with the data integrity analysed via Cronbach’s alpha. The outcome indicated the majority of CSFs to be statistically significant except the Physical Location CSF. Potential future study and contributions are discussed.     

A pure proactive scheduling algorithm for multiple earth observation satellites under uncertainties of clouds

Most earth observation satellites (EOSs) are equipped with optical sensors, which cannot see through clouds. Hence, observations are significantly affected and blocked by clouds. In this work, with the inspiration of the notion of a forbidden sequence, we propose a novel assignment formulation for EOS scheduling. Considering the uncertainties of clouds, we formulate the cloud coverage for observations as stochastic events, and extend the assignment formulation to a chance constraint programming (CCP) model. To solve the problem, we suggest a sample approximation (SA) method, which transforms the CCP model into an integer linear programming (ILP) model. Subsequently, a branch and cut (B&C) algorithm based on lazy constraint generation is developed to solve the ILP model. Finally, we conduct a lot of simulation experiments to verify the effectiveness and efficiency of our proposed formulation and algorithm.     

Cloud Variations under Subtropical High Conditions

T The cloud variations under subtropical high (STH) conditions during summers over a ten-year period are studied using combined data from the International Satellite Cloud Climatology Project and the National Centers for Environmental Prediction. The results reveal that clouds mainly experience an isolated evolution in the STHs, which is designated in this study by the 1540 gpm geopotential lines at 850 hPa. In the STH domain throughout the Northern Hemisphere, the average amount of total clouds exceeds 30%. Low clouds dominate in the STH domain, contributing over 60% of total cloud amount within the Pacific subtropical high and over 40% within the Atlantic subtropical high. The prevalence of low clouds in above regions is determined by the circulation pattern around 150°-180°E and 850 hPa, which suppresses both the upward development of the cloud tops and the water vapor divergences near the surface. Furthermore, clouds present great geographical incoherence within the STH domain. In the eastern STHs, the amount of middle and low clouds increases to peak in the early morning and decreases to a trough in the afternoon, while the amount of high clouds remains stable throughout the day. Conversely, in the western STHs, the diurnal amplitude of low and middle clouds is less than three, while high clouds dramatically reach the maximum in the afternoon and drop to the minimum in the evening. Among the nine cloud categories, stratocumulus clouds with greater optical thickness account for the most under STH conditions, no matter their occurrence or amount, causing more shortwave cloud radiative forcing to cool the local atmosphere and surface as a consequence.