云南边境生态环境数据组织管理研究

生态环境数据合理有效地组织与管理,对于开展区域生态环境研究意义重大。本文首先探讨了生态环境数据组织管理的基本流程。在此基础上,指出生态环境数据分类编码与元数据的规范化,以及生态环境模型的设计是实现生态环境数据组织管理的难点所在。随后,笔者以云南边境地带生态环境数据为例,提出了生态环境数据分类编码与元数据方案。并且在深入剖析生态环境数据特征的基础上,重点研究了生态环境数据的建模方案,提出了生态环境辅助数据模型(EcoADM)。最后,简要介绍了云南边境生态环境数据库管理原型系统的建设。     

基于无人船的水质监测及控制系统设计

针对执行水质监测任务过程中固定浮标监测站单点监测存在局限性、船载观测人员取样耗时耗力等问题,本文设计了一种搭载多点、分层自动采水取样装置的智能无人船水质监测系统,可实现目标水域的多点、分层连续水质数据测量及取样。该智能无人船具备基于快速随机树(Rapid Random Tree)算法的自主避障和快速路径规划功能,解决了现有无人船技术存在的多障碍自主路径规划难等问题。同时,本设计结合了ARM9控制芯片、M5310无线传输模块,通过可视化的显示界面和远程WEB访问的功能,大大提升了科研人员及时处理特殊情况便捷性。通过实验测试及比对分析,证明本设计具有智能高效、稳定可靠等优点,能够满足职能部门对于水质监测工作的需要。     

Spatial pattern and regional types of rural settlements in Xuzhou City, Jiangsu Province, China

This paper principally focuses on the morphological differences,spatial pattern and regional types of rural settlements in Xuzhou City of Jiangsu Province in China.Using satellite images of Xuzhou City taken in 2007 and 2008 and models of exploratory spatial data analysis(ESDA) and spatial metrics,the paper conducts a quantitative analysis of the morphological pattern of rural settlements,and finds significant characteristics.First,rural settlements in Xuzhou City are significantly agglomerated in terms of their spatial distribution;meanwhile,there is significant variation in the geographical density distribution.Second,the scale of rural settlements in Xuzhou City is larger than the average in Jiangsu Province,and the histogram of the scale data is more even and more like a gamma distribution.There are a significant high-value cluster in the scale distribution,and local negative correlation between the scale and density distribution of rural settlements in Xuzhou City.Third,the morphology of rural settlements in Xuzhou City shows relative regularity with good connection and integrity,but the spatial variation of the morphology is anisotropic.Finally,according to the characteristics of density,scale,and form of rural settlements,the rural settlements of Xuzhou City are divided into three types:A high-density and point-scattered type,a low-density and cluster-like type and a mass-like and sparse type.The research findings could be used as the scientific foundation for rural planning and community rebuilding,particularly in less-developed areas.     

希尔伯特-黄变换的端点延拓

希尔伯特-黄变换(HHT)是近年来发展起来的一种新的时间序列信号分析方法.该文在对HHT深入研究与充分肯定的基础上,发展了信号的镜像闭合延拓和包络的极值延拓两种方法.通过几个典型的例子检验了两种方法,并与Huang等(1998,1999)进行了比较,得到了令人满意的结果.镜像闭合延拓法根据信号端点的分布特性,把镜子放在具有对称性的极值位置,通过镜像法把镜内信号映射成一个周期性的信号,不存在端点,从根本上避免了经验模态分解和希尔伯特变换的端点问题.极值延拓法简单易行,具有与镜像闭合法相当的效果,在处理非对称波形信号时更显其优越性.     

加权方差分析及其在地壳垂直运动中的应用

经典最小二乘准则下的方差分析要求观测值的正态随机误差之间是相互独立的，而实际遇到的测量数据经常并不满足这一条件。为了从广泛意义上更科学地研究和应用方差分析这一理论，提出了加权方差理论，导出和论证了相应的公式，并以实例讨论了它在地壳垂直运动分析中的应用。     

北京市居民地铁出行出发时间弹性时空分布特征研究

伴随城市转型进程的加快,交通需求不断膨胀,导致大城市交通拥堵日趋严重,以调节出行者的选择行为为核心要素的交通需求管理理念成为相关政策的重要理论基础,但现有研究也表明,交通需求管理对出行弹性较高的出行具有显著调节作用,而对出行弹性较低的出行调节作用并不明显。因此,加强出行弹性等居民出行行为研究日益迫切,而公交刷卡数据等新的时空数据为居民复杂出行行为的挖掘提供了新的契机。本文利用北京市2014年3月地铁刷卡数据,以出行者出发时刻的可变性来测度出发时间选择的可改变程度,对居民地铁出行出发时间选择弹性进行测度,并结合GIS空间分析技术对其时空分布特征进行分析。研究表明：① 北京市地铁出行的居民出行弹性平均值为0.521,出发时间选择弹性整体上较大,表明北京居民出发时间选择相对较为灵活;② 北京市居民地铁出行弹性存在时空差异,居民个体休息日出行弹性高于工作日,一天中高峰时段出行弹性高于非高峰时段;③ 居民出行弹性存在空间自相关,倾向于在空间上发生集聚,存在明显的冷热点区域;内城居民的出行弹性明显高于城市外围居民。     

民航飞机气象资料的应用及探讨

为了将AMDAR资料应用于常规观测预报中,对天气系统的演变特征进行更细微的监测,利用2011年5月1~3日的AMDAR资料,分析AMDAR时空分布特点;对比气象常规高空观测实际,研究AMDAR资料处理技术,得出具体算法,并结合2011年5月1~3日常规高空观测资料分析天气形势,看出常规高空观测中显示的1日20时移出盆地的高原东部至盆地上空的浅槽于1日14时就已经移出盆地了,延安伸出的槽在14时也已出现。研究结果表明,高空气压的计算可直接用等温大气中的压高方程代替;AMDAR资料应用于常规高空观测可以得到比常规高空观测更为细致的结果。     

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Arctic sea-ice motion and its relation to pressure field

Daily Arctic sea-ice motion maps during the winter seasons (December–March) from December 1988 to March 2003 derived from NSCAT, QuikSCAT, SSM/I, and AMSRE data by a wavelet analysis method have been merged with those derived from buoy data. These merged sea-ice motion data have been used to study the circulation regimes and winter-to-winter variability of Arctic sea-ice motion. The relation between sea-ice motion and the pressure field in the Arctic Ocean was also studied by applying Principal Component Analysis (PCA) to the monthly merged sea-ice motion data and the monthly pressure field data from IABP. The mean Arctic sea-ice motion map of the 15 winter seasons has two distinct features: the Beaufort Gyre and a cyclonic circulation system in the Eurasian Basin, which moves ice from the Laptev Sea to Fram Strait. The strengths and sizes of the two features change from one winter season to another. Seasons with a strong or normal Beaufort Gyre alternate with seasons with a weak or no Beaufort Gyre every one to three seasons. The principal components of the first two modes of PCA of the monthly sea-ice motion are closely correlated with their counterparts of the monthly pressure field in the Arctic Ocean. The mode-one components of these two anomalies alternate between anticyclonic and cyclonic circulation systems. The correlation between Arctic Oscillation indexes and the principal components of the first mode of PCA of the monthly Arctic sea-ice motion is low but statistically significant.