地理命名实体分类体系的设计与应用分析

自然语言是一种重要的空间数据来源,从自然语言中获取地理空间信息是地理信息科学的重要研究议题。完善的地理命名实体分类体系,有助于实现自然语言中地理空间信息的解析、存贮、组织、管理、分析及共享应用。现有的基础地理要素分类体系、地名分类体系和组织机构分类体系,分别侧重于不同的应用领域,只能表达自然语言中的部分地理命名实体,没有考虑时空关联特性。本文在参照大量相关标准的基础上,根据大量自然语言文本的标注结果,以地理命名实体所指代的空间位置、地理特征和属性作为分类标准,采用主分表和复分表相结合的方式,设计了地理命名实体分类体系(简称"GNEC")。采用定量和定性相结合的方法,分析了GNEC与GB/T18521-2001,GB/T13923-2006,CHG IS的地名分类体系、ADL的FTT词表之间的兼容性,并以中文文本的地理命名实体解析和地图服务为例,验证了GNEC的应用性能。多样性是自然语言中地理实体描述的重要特征,而分类体系主要实现地理命名实体的概念化操作。因此,在GNEC基础上构建本体,将成为解决这一问题的有效途径。     

面向对象的极高海拔区水体及冰川信息提取——以珠穆朗玛峰国家级自然保护区核心区为例

极高海拔地区多为河流发源、冰川发育地,由于地形起伏强烈,且野外考察验证工作困难,传统的遥感信息提取方法很难保证该地区水体及冰川的提取精度。本文基于ASTER影像,运用面向对象的图像信息自动分析方法,对珠穆朗玛峰国家级自然保护区核心区的水体及冰川信息进行了提取研究。为保证信息提取的准确度,将数字高程模型(DEM)及其衍生数据(坡度、坡向),归一化植被指数(NDVI)数据,及有助于区分水体、冰川与其他地物的相关指数(冰雪指数NDSII)及波段运算结果(b1-b3)、(b3/b4)等,分别作为一个波段叠加到原始图像中,使之成为对目标地物光谱特征的有益补充。并对不同类型的水体及冰川进行多级、多尺度分割,以满足其对分割尺度的不同要求。分割完成后,综合考虑目标地物的光谱特征、纹理特征、空间结构特征,根据各特征指数的直方图信息,设定合适的阈值,建立了各水体及冰川类型信息提取的知识规则,并结合实地调查对信息提取的精度进行验证,改进了ASTER遥感影像自动快速提取极高海拔区水体及冰川信息的实用模型。     

Zn上的k次不可约多项式与k阶Carmichael数

设n是合数,如果对一切f（x）∈Zn[x]都满足f（x）nk≡f（x）mod（n,r（x））,那么就称n是模r（x）的k阶Carmichael数,这里r（x）是Zn[x]上的k次首一不可约多项式,用Ck,r（x）表示所有这种数的集合,并且定义Ck=Ur（x）Ck,r（x）.k阶Carmichael数,当k=4时,已证明了n=pq,p,q是不同的奇素数,p2-1,q3-1均整除n4-1,则n∈C4.主要目的是将k=4时得出的结论推广到k≥4的一般情形,利用孙子定理,通过构造Zn上的首一k次不可约多项式f（x）的方法,得出：在k≥4时,设n=pq,如果k=2m,m≥2,pm-1,q2m-1-1均整除nk-1,则n∈Ck;如果k=2m＋1,m≥2,pm-1,pm＋1-1,q2m-1均整除nk-1,则n∈Ck.     

K-means算法复杂度的能量分析方法

传统的算法复杂度分析方法重点对算法的内在流程进行分析,而且对于数据挖掘时具有很大不确定性,执行时间不能准确确定.用物理学原理从能量的角度对数据挖掘中的经典聚类算法K-means进行分析,并从算法的本质上推导出其复杂度下限,推导出该算法能优化到的极限值.     

基于可变长数据分块的灾备数据恢复算法设计与实现

利用基于字节指纹极值特征的数据分块算法,设计并实现了一种灾备数据恢复方法.该方法对源数据和备份数据进行可变长数据分块,计算并比较对应数据块摘要值,确定源数据和备份数据的差异数据,采用差异数据拷贝的方法完成数据恢复.实现了对重复数据的有效检测,比完全数据拷贝节省32.79%-85.36%的网络带宽.     

基于中分辨率成像光谱仪数据的洋面云检测新方法的研究

提出了一种利用中分辨率成像光谱仪热红外窗区通道数据进行云、晴空分离的新方法。该云检测算法从热红外窗区通道亮温出发,并结合水汽廓线,运用水汽规度法计算规度因子的值,利用规度因子的数值状态实现分离云和晴空,从而达到云检测的目的。经过大量的实验,并将结果与中分辨率成像光谱仪官方云检测产品作对比分析,结果表明准确率为91％,算法具有可行性。     

基于时域导频跟踪和频域插值的正交频分复用系统信道估计方法

针对下一代无线通信长期演进系统,提出了一种基于时域导频跟踪和频域迭代离散傅里叶变换插值的改进信道估计方法,首先利用导频符号进行信道估计,然后通过导频跟踪和迭代离散傅里叶变换插值获得时域和频域非导频位置的信道估计值。仿真结果表明,基于时域导频跟踪和频域迭代离散傅里叶变换插值的估计方法比传统的方法改善约2dB误比特率性能。     

Seismic fragility of reinforced concrete girder bridges using Bayesian belief network

Infrastructure owners and operators, or governmental agencies, need rapid screening tools to prioritize detailed risk assessment and retrofit resources allocation. This paper provides one such tool, for use by highway administrations, based on Bayesian belief network (BBN) and aimed at replacing so‐called generic or typological seismic fragility functions for reinforced concrete girder bridges. Resources for detailed assessments should be allocated to bridges with highest consequence of damage, for which site hazard, bridge fragility, and traffic data are needed. The proposed BBN based model is used to quantify seismic fragility of bridges based on data that can be obtained by visual inspection and engineering drawings. Results show that the predicted fragilities are of sufficient accuracy for establishing relative ranking and prioritizing. While the actual data and seismic hazard employed to train the network (establishing conditional probability tables) refer to the Italian bridge stock, the network structure and engineering judgment can easily be adopted for bridges in different geographical locations. Copyright © 2015 John Wiley & Sons, Ltd.     

DEVELOPMENT OF EARTHQUAKE EMERGENCY DISASTER INFORMATION PRE-EVALUATION DATA BASED ON KM GRID

After an earthquake, earthquake emergency response and rescue is one of the effective ways to reduce casualties from the earthquake. Earthquake emergency disaster information is one of crucial factors to effectively guide the rescue work. However, there is a "black box effect" on the emergency disaster information acquisition after an earthquake, which means real-time earthquake disaster information is insufficient. Hazard estimates are usually used as a substitute for the real-time disaster information in the "black box" period. However, it is subject to the accuracy and speed of the estimation. The development of the km grid technology provides good prospect to solve this problem. The paper suggests to develop earthquake disaster information pre-estimation data with the support of the km grid technology. The definition and source of the pre-estimation data are introduced and its possibility in improving the estimation speed and accuracy are analyzed theoretically. Then, we elaborate the calculation model of the pre-estimation data. The framework of the model includes disaster-bearing body data, disaster-causing factors used in calculation and calculation formula. The disaster-bearing body data in km grid format are introduced, including population data in km grid format and building data in km grid format. Then the four elements of the earthquake(earthquake occurrence time, earthquake location, earthquake magnitude and focal depth)are selected as disaster-causing factors for calculation. Map algebra method is used to realize the calculation model in which calculation parameters are associated with base map in the km grid format. So the pre-estimation data are developed by python and ArcGIS, which includes building damage dataset(100 layers), death toll dataset(10 layers)and direct economic loss dataset(5 layers). Finally, the pre-estimation data based method for earthquake emergency disaster information estimation is presented. With the support of this method, two real earthquake cases are used to validate the effect of the pre-estimation data. The validation results show the pre-estimation data can not only significantly improve the speed of the estimation but also greatly improve the accuracy of the estimation. Another good result is found in the validation process that with the support of the pre-estimation data, the estimated result can display the spatial distribution of the disaster information, which will effectively aid earthquake emergency response and rescues.     

How is water availability related to the land use and morphology of an inland valley wetland in Kenya?

Small inland valley wetlands contribute substantially to the livelihoods of rural communities in East Africa. Their conversion into farmland is driven by water availability. We quantified spatial-temporal dynamics of water availability in a headwater wetland in the humid zone of Kenya. Climatic conditions, soil moisture contents, groundwater levels and discharge data were monitored. A land-use map and a digital elevation model of the valley bottom were created to relate variations in soil moisture to dominant land uses and valley morphology.Upland crops occupied about a third of the wetland area, while approximately a quarter of the wet, central part of the valley bottom was designated for flood-tolerant taro, grown either by itself or in association or in rotation with upland crops. Finally, natural vegetation was found in 3% of the mapped area, mainly in sections with nearpermanent soil saturation.The HBV rainfall-runoff model's overestimation of stream discharge during the long dry season of the hydrological year 2010/2011 can be explained by the strong seasonal impact of water abstraction on the wetland's water balance.Our study vividly demonstrates the necessity of multi-method approaches for assessing the impact of management practices on water availability in valley bottom wetlands in East Africa.