软土地基桥台位移病害分析及防治措施

软土地基桥台存在三方面的主要隐患:桥头与路堤具有较大的沉降差;桥台倾斜或桥台底部出现垂直裂缝;桥台向河心移动。软土层地质结构及物理地学性质、桥台形式、勘探与施工质量是影响桥台位移的主要原因。淮安市高等级公路建设的桥梁建设实践表明,应从设计施工、养护方面采取综合防治措施,使损失减到最低限度。     

Peaks of People: Using Topographic Prominence as a Method for Determining the Ranked Significance of Population Centers

This article borrows a statistical method from physical geography—topographical prominence—to suggest a new technique for measuring the relative significance or rank of population centers. Unlike raw population measures, prominence gives consideration to both the spatial intensity of concentrated population areas as well as the spatial dependence or independence of neighboring settlement clusters in relation to one another. We explain how to apply the topographic prominence calculation method to gridded population data and examine its practical utility through case studies of several U.S. states. We then discuss some ways in which parametric choices about point-to-surface transformations can result in considerably different outcomes and offer further suggestions for conceptualizing and measuring population center significance.     

西北贫困地区乡村类型识别与振兴途径研究

乡村振兴战略是新时期我国推进城乡融合发展的重要举措。贫困地区既是脱贫攻坚的主战场,又是乡村振兴的重点区域。如何科学识别村落类型,分类推进典型区域乡村振兴发展,对于破解农村发展不平衡不充分问题具有重要的现实意义。本文解析了乡村地域系统发展理论,阐释了城乡融合发展理论,剖析了村域空间结构格局演变和城乡要素结构功能优化重组;构建了西北干旱贫困地区乡村振兴村落类型识别体系,从乡村主体、产业发展、人居环境、资源禀赋4个子系统进行逐级分类判断,识别乡村主导类型,结合专家征询分析与村落特征,将乡村振兴村落类型识别为集聚提升类、三产融合类、城郊融合类、特色保护类、搬迁撤并类,结合不同乡村振兴模式提出了相应地发展路径,以期为乡村振兴提供理论依据与决策参考。     

关联演化视角下地理学区域韧性分析的新思考

区域韧性是一个区域应对发展进程中短期冲击与长期变化、积极进行自我调适与转型的能力,表征了区域的适应性、创新性与可持续性,作为新兴理念反映了地理学可持续发展与区域研究的新需求。本研究综述了区域韧性研究进展,基于关联演化视角,探索构建区域韧性认知框架,将情境分析与过程分析并重,思考并总结区域韧性分析的方法论,尝试建立区域韧性评估方程,从经济、社会、环境及制度四个维度进行区域韧性评估,将区域韧性应用于中国地理学研究,分析中国区域韧性研究的一般性与特殊性,将区域韧性研究对接中国实际问题,以期对区域韧性研究做出理论贡献,助推地理学区域发展研究及相关认知领域的跨学科讨论、知识集成与学术交流,为国家制定相关区域政策提供参考,并为国家及区域可持续发展提供科学指引。     

珠江三角洲地区活动断裂探查方法浅析

活动断裂是指晚第四纪以来活动过的断裂,它威胁到人类生命财产安全。该文通过文献调研,结合珠江三角洲基底断裂探查研究项目的实际工作,介绍了地面地质调查、浅层地震、断层气测量、钻探及连孔剖面、断裂物质测年、历史地震分析等活动断裂探查方法,以及这些方法的优缺点,并指出了目前活动断裂探查存在的一些问题,提出了建议。     

基于两种无人机航拍影像的林窗和林冠提取分析

森林冠层和林窗的结构及其时空变化是理解森林生态系统格局、动态变化过程的重要基础。在当前生物多样性监测倍受关注的契机下,如何以合适的手段准确描述林窗面积、分布等特征,并与森林固定样地监测数据有效地结合,更好地回答群落构建的理论问题,使森林群落物种多样性维持机制得到更全面的认识,是目前亟待解决的问题。以鼎湖山南亚热带常绿阔叶林20hm2固定监测样地为研究对象,基于不同遥感影像提取方法对其林窗和林冠表层数据进行提取分析。结果表明:基于监督分类的提取方法适合RGB波段航片林窗的提取,在林窗分类中,应首先确定林窗高度、边界木与最小面积,不同分类方法差异主要表现在林冠分类中,林窗分类生产者精度和用户精度表现都较为一致。无人机航拍识别率受地形因素影响较大,在地形复杂林地应按坡度分区域进行飞行以降低误差。相对于地面调查,MD4-1000无人机航片的林窗识别率为98.7%;大疆Phantom4无人机航片的林窗识别率为72.3%,影像后期处理数据量小,同样适用于森林林窗定量研究,符合生态学、林业等从业人员对大型样地林窗长期监测的要求。无人机航拍南亚热带森林物种识别难度较大,基于MD4-1000无人机搭载的高分辨率相机,在地势平缓区域优选的4 hm2样地中可识别林冠表层物种数17种,共2 706个个体。搭载高分辨率无人机在降低飞行高度的基础上可进行部分物种识别。应用无人机近地面遥感对森林固定样地进行林冠监测,可为后期群落构建研究提供数据基础,有望从新的研究角度探讨森林群落物种多样性维持机制。     

基于图像的天体搜索研究与自动化集成软件开发

天体搜索是天文数据处理流程的一个重要环节,也是以平方公里阵列射电望远镜(Square Kilometre Array, SKA)为代表的下一代射电望远镜在面向海量数据处理中的挑战之一.现今天体自动搜索算法、软件已日趋成熟并投入应用,不过在自动化、兼容性等方面仍具有提升空间.以更自动化、更适应海量数据需求的天体搜索算法研究为宗旨,以现有算法为研究基础,天体自动搜索软件系统得到设计和开发.该系统包含友好的交互式用户操作界面,具备可视化输出数据显示、兼容不同数据输入和输出并包含为实际应用服务的文件管理功能.该系统对于大天区图以及图像集,均能够很好地进行自动化处理.测试结果显示,上述方法对于天体搜索的改进有一定成效.后续将在此基础上对该集成系统做进一步的改进开发,以适应更多的需求.     

Research on Parallel Algorithms for uv-faceting Imaging

The uv-faceting imaging is one of the widely used large field of view imaging technologies, and will be adopted for the data processing of the low-frequency array in the first stage of the Square Kilometre Array (SKA1). Due to the scale of the raw data of SKA1 is unprecedentedly large, the efficiency of data processing directly using the original uv-faceting imaging will be very low. Therefore, a uv-faceting imaging algorithm based on the MPI (Message Passing Interface)+OpenMP (Open Multi-Processing) and a uv-faceting imaging algorithm based on the MPI+CUDA (Compute Unified Device Architecture) are proposed. The most time-consuming data reading and gridding in the algorithm are optimized in parallel. The verification results show that the results of the proposed two algorithms are basically consistent with that obtained by the current mainstream data processing software CASA (Common Astronomy Software Applications), which indicates that the proposed two algorithms are basically correct. Further analysis of the accuracy and total running time shows that the MPI+CUDA method is better than the MPI+OpenMP method in both the correctness rate and running speed. The performance test results show that the proposed algorithms are effective and have certain extensibility.     

Study of Star/Galaxy Classification Based on the XGBoost Algorithm

Machine learning has achieved great success in many areas today. The lifting algorithm has a strong ability to adapt to various scenarios with a high accuracy, and has played a great role in many fields. But in astronomy, the application of lifting algorithms is still rare. In response to the low classification accuracy of the dark star/galaxy source set in the Sloan Digital Sky Survey (SDSS), a new research result of machine learning, eXtreme Gradient Boosting (XGBoost), has been introduced. The complete photometric data set is obtained from the SDSS-DR7, and divided into a bright source set and a dark source set according to the star magnitude. Firstly, the ten-fold cross-validation method is used for the bright source set and the dark source set respectively, and the XGBoost algorithm is used to establish the star/galaxy classification model. Then, the grid search and other methods are used to adjust the XGBoost parameters. Finally, based on the galaxy classification accuracy and other indicators, the classification results are analyzed, by comparing with the models of function tree (FT), Adaptive boosting (Adaboost), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Stacked Denoising AutoEncoders (SDAE), and Deep Belief Nets (DBN). The experimental results show that, the XGBoost improves the classification accuracy of galaxies in the dark source classification by nearly 10% as compared to the function tree algorithm, and improves the classification accuracy of sources with the darkest magnitudes in the dark source set by nearly 5% as compared to the function tree algorithm. Compared with other traditional machine learning algorithms and deep neural networks, the XGBoost also has different degrees of improvement.     

Research on the Methods of Performance Monitoring and Evaluation for Active Hydrogen Maserstwo

Active hydrogen maser is the main frequency standard for establishing and maintaining the time scale. It has the characteristics of high short-term stability and low phase noise. At present, it plays an increasingly important role in the international atomic time (TAI) and various local time scales. Firstly, in combination with the internal state parameters of an active hydrogen maser, the correlation between the internal state parameters and the comparison data of a hydrogen maser is analyzed, and a method for the performance monitoring of a hydrogen maser is proposed. Secondly, according to the characteristics of hydrogen maser performance, a method for evaluating the performance of a hydrogen maser is given. The hydrogen maser performance includes mainly two aspects, namely the frequency stability and predictability. The performances of two types of active hydrogen masers (CH1-75 and MHM-2010) are evaluated by this method. The correlation analysis of the atomic clock state parameters and comparison data shows that the state parameter monitoring can effectively predict the variation of the hydrogen maser performance. The evaluation results of atomic clock frequency stability and predictability show that the atomic clock with a higher medium-and-long term frequency stability has a better predictability. There are two methods for the predictability evaluation, one is based on the data published by BIPM (Bureau International des Poids et Measures), and another one is based on the quadratic model. Both methods are consistent with the weights published by BIPM. Therefore, the two methods can be used as a quantitative method to evaluate the predictability of a hydrogen maser.