基于过滤文本和社交网络的用户常驻位置预测

社交用户的文本具有地理差异性,并且社交关系密切的用户之间居住位置更近,因而文本和社交网络均可用于推断用户常驻位置。现有基于文本和社交网络的用户常驻位置预测方法对文本的位置指示性特征挖掘不充分,而用户文本中地名等位置指示信息却提供了最有用的位置信号。因此,本文提出一种基于地理命名实体识别（GER）和图卷积神经网络（GCN）的社交用户位置预测方法。首先,通过地理命名实体识别方法对用户文本进行过滤以凸显位置指示性特征;其次,基于提及关系和关注与被关注关系抽取社交网络;再次,结合社交网络和用户文本内容,采用基于图卷积神经网络的方法进行用户常驻位置预测;最后,将GER-GCN与GCN以及最新研究成果进行比较,并探究该模型的小样本学习能力及其影响因素。基于Geotext数据集和2个微博数据集的实验表明：① GER文本过滤方法可显著提升用户位置预测精度;② 在所有实验中,GER-GCN的预测精度最高,并在基准数据集GeoText上比最新研究成果提升1%~2%;③ 在最小监督的现实场景中,本文印证了GER-GCN模型的小样本学习能力,并发现社交网络质量对其小样本学习能力起到决定性作用。实验结果验证了GER-GCN方法的先进性,且该方法符合社交媒体现实场景的应用需求。     

分形理论在交通网络分布形态研究中的应用

探讨了以分形几何理论为基础的表达空间网络分布特征的几种分维数,重点描述了相似分维数的定义及其地理意义,以实例说明了相似分维数在交通网络分布特征研究中的应用,对测算结果进行了分析和评述。     

高原自动气象站雷电防护技术的应用设计方案

在多年来对自动气象站架设和安装工作的实践基础上，针对高原地理环境及天气的主要特点，对自动气象站防雷技术问题进行了研究和应用，详细介绍了高原防雷特点及应用，提出了一套高原自动气象站雷电防护设计方案。     

Removing cosmic-ray hits from CCD images in real-time mode by means of an artificial neural network

A feed-forward artificial neural network has been implemented to the problem of removing cosmic-ray hits (CRH) from CCD images. The results of a number of tests demonstrate the effectiveness of this method especially for undersampled stellar profiles. The problem of optimal and low price preparing of training data, which could enable real-time or at least fast post-processing filtering out of CRH is discussed. The training and test ensembles were composed of a number of synthetic stellar profiles involving different S/N ratios and CRH images taken from real data. Certain aspects of the network’s architecture and its training efficiency for different modes of the back-propagation procedure as well as for the pre-process normalization of data have been examined. It is shown that for training set composed of stellar images and CRH at a ratio of 1:2 recognition can reach 99% in the case of stars and 96% for CRH. To determine the extent to which the cognition power of a network trained using an ensemble of circular symmetric stellar profiles of a given radius can be generalised the test data included stellar profiles of different radii, as well as elongated profiles. The goal was to mimic temporal changes in seeing as well as such problems as image defocusing, the lack of isoplanatism and improper sideral tracking of a telescope. The experiments provided us with the conclusion that for S/N > 10 excellent classification property is maintained in cases where the change in the radius of a circular profile is up to 30%, as well as for elongated profiles where the longest dimension is almost double that of the shortest one. Moreover, the generalization capability has been investigated for test images of synthetic pairs of overlapping stars with different distances between components. Almost 99% recognition efficiency was achieved even if the separation was nearly three times the radius of the stellar profile, a case when two stars could be analyzed by appropriate software as separate objects. The example of removal of CRH from real CCD images is presented to give an idea of how an algorithm based on a neural network can work in practice. The result of such an experiment appears fully consistent with the conclusions drawn from the tests made on synthetic data.     

CMOS Detector Technology

An entry level overview of state-of-the-art CMOS detector technology is presented. Operating principles and system architecture are explained in comparison to the well-established CCD technology, followed by a discussion of important benefits of modern CMOS-based detector arrays. A number of unique CMOS features including different shutter modes and scanning concepts are described. In addition, sub-field stitching is presented as a technique for producing very large imagers. After a brief introduction to the concept of monolithic CMOS sensors, hybrid detectors technology is introduced. A comparison of noise reduction methods for CMOS hybrids is presented. The final sections review CMOS fabrication processes for monolithic and vertically integrated image sensors.     

差分式伸缩仪试验结果分析

介绍了一种双传感器组成差分结构的伸缩仪,该仪器在武汉地震台进行3个月的试验结果表明:差分式结构明显地提高了伸缩仪的抗干扰能力.     

人工神经网络在天文学中的应用

人工神经网络具有自学习、自适应、鲁棒性好、动态响应快等特点,并具有较强的非线性处理问题能力,因此在天文学中得到广泛而成功的应用．综述了人工神经网络在天文学中主要应用模型的基本原理和优缺点,阐述了人工神经网络适用于天文学的某些基本特征,着重介绍了人工神经网络在天文学中的具体应用实例,并对其发展和应用前景进行了展望．由于天文数据分布的庞杂和天文数据量的急剧增加,人工神经网络将日益显示出优越性．     

基于WoS数据的我国气象学者合作模式实证分析

基于近10年Web of Science数据库中大气科学领域论文数据,利用社会网络分析方法对大气科学研究领域的中国作者科研合作网络进行了探讨.使用UCINET软件分析网络密度、最短距离、小团体、中心性等计量指标,并选择度值较高的节点对其合作网络进行定性分析,识别出重要的领域专家和研究团队.得出的结论有：国内气象学者的合作群体相对分散,倾向于同一地域、相同院校机构的合作,合作规模和合作程度与国际相比仍有一定差距.     

基于分布律规则的风向传感器故障检测算法

风向传感器采用的格雷码编码方式使得风向传感器故障具有一定的“隐蔽性”,而传统故障检测方法需要拆卸风向传感器破坏了气象观测资料的完整性.基于概率分布从数理统计角度建立了码盘式风向传感器的测量数学模型,并在此基础上提出了基于分布律规则的风向传感器故障检测算法,通过VB/SQL/MATLAB混合编程技术实现了图示化的风向传感器故障检测程序,最后进行了故障检测试验.试验结果表明：提出的基于分布律规则的风向传感器故障检测算法能够在不拆卸风向传感器的情况下准确发现和判断出风向传感器单一或组合式故障类型.