增强现实地图研究与应用

随着数字地图技术的不断革新,产生了将虚拟信息与真实世界融合的新型地图技术,即增强现实(augmented reality,AR)地图。从可视化、定位、测绘、分析几个方面阐述了AR地图的关键技术,介绍了AR地图的空间映射原理和可视化效果,提出了结合视觉定位和惯导定位的cm级室内定位方法,实现了基于AR地图的AR导航、AR测量、AR目标采集、AR地理围栏等关键技术并将其应用于AR智慧园区与AR室内场馆。     

地图集:重构复杂非线性地理世界的“百科全书”

地图学是一门经过数千年的演进发展形成的科学,地图(地图集)是地图科学研究的主阵地,是任何时代和社会都不可或缺的,对人们的工作、学习和生活具有重要作用和意义。首先,简要论述了地图学作为一门科学的社会实践和科学实践的演进发展规律,以及地图集的特点;然后,从哲学角度分析与论述了地图是怎样重构复杂非线性地理世界的(实现由"地理世界"到"地图世界"的转变),人们是怎样利用地图进一步认识复杂非线性地理世界的(实现由"地图世界"到"地理世界"的转变),提出了基于传感器网络的由"感知的地理世界"到"重构的地理世界"再到"认知的地理世界"、指导行动并反馈信息的"双向"转变的认知模式;最后,论述了地图集作为重构复杂非线性地理世界的"百科全书"的作用,举例说明了中国改革开放以来地图集的快速发展和水平的大幅提升,并阐明人工智能时代中国地图集成果将比以往任何时候都更加兴旺发达,地图集作为人们进一步认知复杂非线性地理世界的"百科全书"的功能作用将比以往任何时候都更加强大。     

人工智能在短临降水预报中应用研究综述

短临降水预报是一项重要且具有挑战性的世界性难题.研究人员曾尝试使用各种技术预报降水,但是由于降水本身具有高度非线性、随机性和复杂性的特性,使得降水预测精确度并不高.近年来,随着人工智能技术的迅猛发展,其日渐渗透到人们生活的方方面面,气象领域也因此得益.人工神经网络能够对非线性系统进行建模,因此相比于传统方法,如数值天气预报法和光流法等,人工智能方法使得降水预报的准确率大大提高.本文介绍了传统降水预报的方法,着重总结概括了用于短临降水预报的各种最新人工智能方法,并对各研究方向进行归纳分析,为各类研究人员研究提供有益参考和借鉴.     

一次冰雹过程的对流单体识别与防雹效果分析

利用2018年6月12—13日山东鲁西北地区一次冰雹过程的常规气象资料,分析了冰雹形成的天气形势和物理量指标条件以及防雹效果。结果表明：人工防雹作业前后雷达回波指标呈明显的下降趋势,其中VIL值、回波顶高指标变化分别平均下降49.9%、37.4%,冰雹概率由作业前99.3%下降到作业后的28.7%,回波强度、回波顶高、VIL值下降最大值分别为10 dBz、7 km、48 kg·m^-2。通过对比分析两个相似对流单体的发展演变,实施防雹作业的对流单体回波强度下降7 dBz、回波顶高下降3 km、VIL值下降11 kg·m^-2、冰雹概率出现剧烈波动,防雹作业后较作业前回波强度、回波顶高、冰雹概率下降速度明显加快,而没有实施防雹作业的对流单体雷达回波综合指标在编号期间变化幅度小,进一步证实了人工防雹作业有效。     

On the First Anthropic Argument in Astrobiology

We consider the little-known anthropic argument of Fontenelledealing with the nature of cometary orbits, given a year before the publication of Newton'sPrincipia. This is particularly interesting in view of the rapid development of therecently resurgent theories of cometary catastrophism and their role in the modern astrobiologicaldebates, for instance in the ``rare Earth' hypothesis of Ward and Brownlee.     

地理空间情报预测分析的一种方法

地理空间情报是对影像和地理空间信息的发掘和分析。其中,地理空间情报预测分析是决策的基础。传统的地理空间情报预测分析大多是基于逻辑思维的定性研究,此处提出了一个地理空间情报预测分析的模型——因果关联图,同时给出了计算预测事件可信度的方法。该方法能够定量、简捷、快速、有效地实现地理空间情报预测分析。     

后注浆刚性桩复合地基的工程实践

某高层建筑物原设计为人工挖孔扩底桩基础,施工过程中由于土层软弱以及地下水影响无法扩底成桩。本文介绍了对上述工程实施的大直径刚性桩复合地基设计方案,该方案利用后注浆技术提高了人工挖孔桩单桩承载力。基桩检测和建筑物沉降观测结果表明该工程地基处理方案是成功的。     

Prediction of berm geometry using a set of laboratory tests combined with teaching–learning-based optimization and artificial bee colony algorithms

Understanding sediment movement in coastal areas is crucial in planning the stability of coastal structures, the recovery of coastal areas, and the formation of new coast. Accretion or erosion profiles form as a result of sediment movement. The characteristics of these profiles depend on the bed slope, wave conditions, and sediment properties. Here, experimental studies were performed in a wave flume with regular waves, considering different values for the wave height (H₀), wave period (T), bed slope (m), and mean sediment diameter (d₅₀). Accretion profiles developed in these experiments, and the geometric parameters of the resulting berms were determined. Teaching–learning-based optimization (TLBO) and artificial bee colony (ABC) algorithms were applied to regression functions of the data from the physical model. Dimensional and dimensionless equations were found for each parameter. These equations were compared to data from the physical model, to determine the best equation for each parameter and to evaluate the performances of the TLBO and ABC algorithms in the estimation of the berm parameters. Compared to the ABC algorithm, the TLBO algorithm provided better accuracy in estimating the berm parameters. Overall, the equations successfully determined the berm parameters.     

A 24‐h Forecast of Oxidant Concentration in Tokyo Using Neural Network and Fuzzy Learning Approach

In this study, several types of adaptive network‐based fuzzy inference system (ANFIS) with different membership functions (MFs) and artificial neural network (ANN) were employed to predict hourly photochemical oxidants that were oxidizing substances such as ozone and peroxiacetyl nitrate produced by photochemical reactions. The results indicated that ANFIS statistically outperforms ANN in terms of hourly oxidant prediction. The minimum mean absolute percentage errors (MAPEs) of 4.99% could be achieved using ANFIS with bell shaped MFs. The maximum correlation coefficient, the minimum mean square errors, and the minimum root mean square errors were 0.99, 0.15, and 0.39, respectively. ANFIS's architecture consists of both ANN and fuzzy logic including linguistic expression of MFs and if‐then rules, so it can overcome the limitations of traditional neural network and increase the prediction performance.