浅谈高分辨率卫星影像数据在城市污水行业的应用

随着遥感技术的发展和卫星影像分辨率的不断提高,高分辨率卫星影像广泛应用于各个行业。本文介绍了对卫星影像进行DOM(数字正射影像)制作、河涌排放口定位、污水等级监督分类及变化监测等在城市污水行业中的应用。     

Seagrass importance for a small-scale fishery in the tropics: The need for seascape management

Small-scale fisheries (SSF) in tropical seascapes (mosaics of interconnected mangroves, seagrasses and corals) are crucial for food and income. However, management is directed mostly to corals and mangroves. This research analyzes the importance of seagrasses compared to adjacent ecosystems in Chwaka Bay, Zanzibar, Tanzania. Using fish landings; the study investigated: location of fishing effort, fish production (biomass and species), and monetary benefits (aggregated value and per capita income). Seagrasses were the most visited grounds providing highest community benefits. Per capita benefits were equivalent to those from corals and mangroves. All three habitats provided income just above extreme poverty levels; however catches from seagrass appeared more stable. Seagrass are key ecosystems supporting SSF and protection and management are urgently needed. Adoption of a seascape approach considering all ecosystems underpinning SSF and the social aspects of fishing and a shift in emphasis from pure conservation to sustainable resource management would be desirable.     

基于Stacking集成学习的恒星/星系分类研究

机器学习在当今诸多领域已经取得了巨大的成功,但是机器学习的预测效果往往依赖于具体问题.集成学习通过综合多个基分类器来预测结果,因此,其适应各种场景的能力较强,分类准确率较高.基于斯隆数字巡天(Sloan Digital Sky Survey,SDSS)计划恒星/星系中最暗源星等集分类正确率低的问题,提出一种基于Stacking集成学习的恒星/星系分类算法.从SDSS-DR7(SDSS Data Release 7)中获取完整的测光数据集,并根据星等值划分为亮源星等集、暗源星等集和最暗源星等集.仅针对分类较为复杂且困难的最暗源星等集展开分类研究.首先,对最暗源星等集使用10折嵌套交叉验证,然后使用支持向量机(Support Vector Machine,SVM)、随机森林(Random Forest,RF)、XGBoost(eXtreme Gradient Boosting)等算法建立基分类器模型;使用梯度提升树(Gradient Boosting Decision Tree,GBDT)作为元分类器模型.最后,使用基于星系的分类正确率等指标,与功能树(Function Tree,FT)、SVM、RF、GBDT、XGBoost、堆叠降噪自编码(Stacked Denoising AutoEncoders,SDAE)、深度置信网络(Deep Belief Network,DBN)、深度感知决策树(Deep Perception Decision Tree,DPDT)等模型进行分类结果对比分析.实验结果表明,Stacking集成学习模型在最暗源星等集分类中要比FT算法的星系分类正确率提高了将近10%.同其他传统的机器学习算法、较强的提升算法、深度学习算法相比,Stacking集成学习模型也有较大的提升.     

基于贝叶斯模型的器测、古气候重建与气候模拟数据的融合试验

气象部门馆藏的西部最早的器测气象资料始于20世纪30年代,不能满足建立20世纪以来中国气候变化序列的需求,而古气候重建或气候模拟资料则可以延伸到器测时代以前。为了探讨长序列多源气候资料序列融合方法,采用贝叶斯方法对中国北疆地区8条树轮气温重建资料、器测资料与国际耦合模式比较计划第5阶段（CMIP5）模式模拟资料进行了融合试验。首先利用器测资料对气温代用资料进行校验与网格重建,并以此作为贝叶斯模型的先验分布,然后,用泰勒图选出了该区域气候模拟效果最佳的几个模式;最后将网格重建和气候模拟序列用贝叶斯模型进行了融合试验。结果表明,贝叶斯融合模型能有效提取各种数据来源的有用信息进行融合,融合结果的长期变化（线性）趋势更接近器测气候序列,并在一定程度上提高了序列的精度,减小了结果的不确定性;并且,融合结果能够纠正先验分布及气候模拟数据的明显偏差,为长年代气候序列重建提供了一个可行的思路。      

IMPACT OF DIABATIC PROCESSES IN AGCM ON 4-DIMENSIONAL VARIATIONAL DATA ASSIMILATION*

The impact of diabatic processes on 4-dimensional variational data assimilation (4D-Var) was studied using the 1995 version of NCEP's global spectral model with and without full physics.The adjoint was coded manually.A cost function measuring spectral errors of 6-hour forecasts to "observation" (the NCEP reanalysis data) was minimized using the L-BFGS (the limited memory quasi-Newton algorithm developed by Broyden,Fletcher,Goldfard and Shanno) for optimizing parameters and initial conditions.Minimization of the cost function constrained by an adiabatic version of the NCEP global model converged to a minimum with a significant amount of decrease in the value of the cost function.Minimization of the cost function using the diabatic model, however,failed after a few iterations due to discontinuities introduced by physical parameterizations.Examination of the convergence of the cost function in different spectral domains reveals that the large-scale flow is adjusted during the first 10 iterations,in which discontinuous diabatic parameterizations play very little role.The adjustment produced by the minimization gradually moves to relatively smaller scales between 10-20th iterations.During this transition period,discontinuities in the cost function produced by "on-off" switches in the physical parameterizations caused the cost function to stay in a shallow local minimum instead of continuously decreasing toward a deeper minimum. Next,a mixed 4D-Var scheme is tested in which large-scale flows are first adiabatically adjusted to a sufficient level,followed by a diabatic adjustment introduced after 10 to 20 iterations. The mixed 4D-Var produced a closer fit of analysis to observations,with 38% and 41% more decrease in the values of the cost function and the norm of gradient,respectively,than the standard diabatic 4D-Var,while the CPU time is reduced by 21%.The resulting optimal initial conditions improve the short-range forecast skills of 48-hour statistics.The detrimental effect of parameterization discontinuities on minimization was also reduced.     

Finding the most variable stars in the Orion Belt with the All Sky Automated Survey

We look for high‐amplitude variable young stars in the open clusters and associations of the Orion Belt. We use public data from the ASAS‐3 Photometric V ‐band Catalogue of the All Sky Automated Survey, infrared photometry from the 2MASS and IRAS catalogues, proper motions, and the Aladin sky atlas to obtain a list of the most variable stars in a survey area of side 5° centred on the bright star Alnilam (ε Ori) in the centre of the Orion Belt. We identify 32 highly variable stars, of which 16 had not been reported to vary before. They are mostly variable young stars and candidates (16) and background giants (8), but there are also field cataclysmic variables, contact binaries, and eclipsing binary candidates. Of the young stars, which typically are active Herbig Ae/Be and T Tauri stars with Hα emission and infrared flux excess, we discover four new variables and confirm the variability status of another two. Some of them belong to the well known σ Orionis cluster. Besides, six of the eight giants are new variables, and three are new periodic variables (© 2010 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

The impact of rural land management changes on soil hydraulic properties and runoff processes: results from experimental plots in upland UK

To develop an evidence base to help predict the impacts of land management change on flood generation, four experimental sites were established on improved grassland used for sheep grazing at the Pontbren catchment in upland Wales, UK. At each site, three plots were established where surface runoff was measured, supplemented by measurements of soil infiltration rates and soil and vegetation physical properties. Following baseline monitoring, treatments were applied to two of the plots: exclusion of sheep (ungrazed) and exclusion of sheep and planting with native broadleaf tree species (tree planted), with the third plot acting as a control (grazed pasture). Due to a particularly dry summer that occurred pre‐treatment, the soil hydrological responses were initially impacted by the effects of the climate on soil structure. Nevertheless, treatments did have a clear influence on soil hydrological response. On average, post‐treatment runoff volumes were reduced by 48% and 78% in ungrazed and tree‐planted plots relative to the control, although all results varied greatly over the sites. Five years following treatment application, near‐surface soil bulk density was reduced and median soil infiltration rates were 67 times greater in plots planted with trees compared to grazed pasture. The results illustrate the potential use of upland land management for ameliorating local‐scale flood generation but emphasise the need for long‐term monitoring to more clearly separate the effects of land management from those of climatic variability. Copyright © 2013 John Wiley & Sons, Ltd.     

北京大灰厂试验场重力变化特征及其解释

介绍了北京大灰厂试验场（简称京西试验场）的概况，对１９９３年３月以来重力重复观测资料进行了处理。计算了各期重力观测精度，结果显示１９９３年３月～１９９５年５月京西试验场存在一定重力变化。采用给定的模型反演了八宝山断层介质的密度变化，认为断层破碎带介质密度的变化可能是引起地表重力变化的主要因素     

gPhone重力仪网络数据采集系统中Web服务功能的设计与实现

针对gPhone重力仪网络数据采集系统,开发了一套嵌入式Web服务系统。基于uClinux平台和轻量级BOA Web服务器,利用C语言编写通用网关接口(CGI)程序,实现了远程数据下载及仪器监控等功能。     

Developing constitutive models from EPR‐based self‐learning finite element analysis

A constitutive model that captures the material behavior under a wide range of loading conditions is essential for simulating complex boundary value problems. In recent years, some attempts have been made to develop constitutive models for finite element analysis using self‐learning simulation (SelfSim). Self‐learning simulation is an inverse analysis technique that extracts material behavior from some boundary measurements (eg, load and displacement). In the heart of the self‐learning framework is a neural network which is used to train and develop a constitutive model that represents the material behavior. It is generally known that neural networks suffer from a number of drawbacks. This paper utilizes evolutionary polynomial regression (EPR) in the framework of SelfSim within an automation process which is coded in Matlab environment. EPR is a hybrid data mining technique that uses a combination of a genetic algorithm and the least square method to search for mathematical equations to represent the behavior of a system. Two strategies of material modeling have been considered in the SelfSim‐based finite element analysis. These include a total stress‐strain strategy applied to analysis of a truss structure using synthetic measurement data and an incremental stress‐strain strategy applied to simulation of triaxial tests using experimental data. The results show that effective and accurate constitutive models can be developed from the proposed EPR‐based self‐learning finite element method. The EPR‐based self‐learning FEM can provide accurate predictions to engineering problems. The main advantages of using EPR over neural network are highlighted.