Developing crop specific area frame stratifications based on geospatial crop frequency and cultivation data layers

Area Sampling Frames （ASFs） are the basis of many statistical programs around the world. To improve the accuracy, objectivity and efficiency of crop survey estimates, an automated stratification method based on geospatial crop planting frequency and cultivation data is proposed. This paper investigates using 2008-2013 geospatial corn, soybean and wheat planting frequency data layers to create three corresponding single crop specific and one multi-crop specific South Dakota （SD） U.S. ASF stratifications. Corn, soybeans and wheat are three major crops in South Dakota. The crop specific ASF stratifications are developed based on crop frequency statistics derived at the primary sampling unit （PSU） level based on the Crop Frequency Data Layers. The SD corn, soybean and wheat mean planting frequency strata of the single crop stratifications are substratified by percent cultivation based on the 2013 Cultivation Layer. The three newly derived ASF stratifications provide more crop specific information when compared to the current National Agricultural Statistics Service （NASS） ASF based on percent cultivation alone. Further, a multi-crop stratification is developed based on the individual corn, soybean and wheat planting frequency data layers. It is observed that all four crop frequency based ASF stratifications consistently predict corn, soybean and wheat planting patterns well as verified by the 2014 Farm Service Agency （FSA） Common Land Unit （CLU） and 578 administrative data. This demonstrates that the new stratifications based on crop planting frequency and cultivation are crop type independent and applicable to all major crops. Further, these results indicate that the new crop specific ASF stratifications have great potential to improve ASF accuracy, efficiency and crop estimates.

New multi-layer data correlation algorithm for multi-passive-sensor location system

Under the scenario of dense targets in clutter, a multi-layer optimal data correlation algorithm is proposed. This algorithm eliminates a large number of false location points from the assignment process by rough correlations before we calculate the correlation cost, so it avoids the operations for the target state estimate and the calculation of the correlation cost for the false correlation sets. In the meantime, with the elimination of these points in the rough correlation, the disturbance from the false correlations in the assignment process is decreased, so the data correlation accuracy is improved correspondingly. Complexity analyses of the new multi-layer optimal algorithm and the traditional optimal assignment algorithm are given. Simulation results show that the new algorithm is feasible and effective.

Construction of a reusable data access layer based on .NET

A new design solution of data access layer for N-tier architecture is presented. It can solve the problems such as low efficiency of development and difficulties in transplantation, update and reuse. The solution utilizes the reflection technology of .NET and design pattern. A typical application of the solution demonstrates that the new solution of data access layer performs better than the current N-tier architecture. More importantly, the application suggests that the new solution of data access layer can be reused effectively.

RECONSTRUCTION OF LAYER DATA WITH DEFORMABLE B-SPLINES

A new B-spline surface reconstruction method from layer data based on deformable model is presented. An initial deformable surface, which is represented as a closed cylinder, is firstly given. The surface is subject to internal forces describing its implicit smoothness property and external forces attracting it toward the layer data points. And then finite element method is adopted to solve its energy minimization problem, which results a bicubic closed B-spline surface with C^2 continuity. The proposed method can provide a smoothness and accurate surface model directly from the layer data, without the need to fit cross-sectional curves and make them compatible. The feasibility of the proposed method is verified by the experimental results.

Thermal stability and data retention of resistive random access memory with HfOx/ZnO double layers

As an industry accepted storage scheme, hafnium oxide（HfO_x） based resistive random access memory（RRAM）should further improve its thermal stability and data retention for practical applications. We therefore fabricated RRAMs with HfO_x/ZnO double-layer as the storage medium to study their thermal stability as well as data retention. The HfO_x/ZnO double-layer is capable of reversible bipolar switching under ultralow switching current（〈 3 μA） with a Schottky emission dominant conduction for the high resistance state and a Poole–Frenkel emission governed conduction for the low resistance state. Compared with a drastically increased switching current at 120℃ for the single HfO_x layer RRAM, the HfO_x/ZnO double-layer exhibits excellent thermal stability and maintains neglectful fluctuations in switching current at high temperatures（up to 180℃）, which might be attributed to the increased Schottky barrier height to suppress current at high temperatures. Additionally, the HfO_x/ZnO double-layer exhibits 10-year data retention @85℃ that is helpful for the practical applications in RRAMs.

Modeling TCP Incast Issue in Data Center Networks and an Adaptive Application-Layer Solution

In data centers, the transmission control protocol(TCP) incast causes catastrophic goodput degradation to applications with a many-to-one traffic pattern. In this paper, we intend to tame incast at the receiver-side application. Towards this goal, we first develop an analytical model that formulates the incast probability as a function of connection variables and network environment settings. We combine the model with the optimization theory and derive some insights into minimizing the incast probability through tuning connection variables related to applications. Then,enlightened by the analytical results, we propose an adaptive application-layer solution to the TCP incast.The solution equally allocates advertised windows to concurrent connections, and dynamically adapts the number of concurrent connections to the varying conditions. Simulation results show that our solution consistently eludes incast and achieves high goodput in various scenarios including the ones with multiple bottleneck links and background TCP traffic.

Extracting multiple layers from data having graph structures

Much data such as geometric image data and drawings have graph structures. Such data are called graph structured data. In order to manage efficiently such graph structured data, we need to analyze and abstract graph structures of such data. The purpose of this paper is to find knowledge representations which indicate plural abstractions of graph structured data. Firstly, we introduce a term graph as a graph pattern having structural variables, and a substitution over term graphs which is graph rewriting system. Next, for a graph G, we define a multiple layer ( g,(θ 1,…,θ k )) of G as a pair of a term graph g and a list of k substitutions θ 1,…,θ k such that G can be obtained from g by applying substitutions θ 1,…,θ k to g. In the same way, for a set S of graphs, we also define a multiple layer for S as a pair ( D,Θ ) of a set D of term graphs and a list Θ of substitutions. Secondly, for a graph G and a set S of graphs, we present effective algorithms for extracting minimal multiple layers of G and S which give us stratifying abstractions of G and S, respectively. Finally, we report experimental results obtained by applying our algorithms to both artificial data and drawings of power plants which are real world data.

Occam Inversion of Transient Electromagnetic Data in a Layered Medium with Azimuthal Anisotropy

In recent years, the anisotropic study has become a hot topic in the field of electromagnetics. Currently, inversion technologies of transient electromagnetic sounding data are mainly based on the case of an isotropic medium. However, the actual underground electrical structure tends to be complicated and anisotropic. It is often found that the isotropic inversion technologies do not lead to good results for field transient electromagnetic sounding data. We have developed an algorithm for calculating the transient electromagnetic response in a layered medium with azimuthal anisotropy. An occam inversion algorithm has also been implemented to invert the transient electromagnetic data induced by a grounded horizontal electric dipole in a layered medium with azimuthal anisotropy. Synthetic examples demonstrate the stability and validity of the inversion algorithm. Experimental results show different data for inverting have great influence on the inversion results.

LayeredModel:一个面向室内空间的移动对象数据模型

基于室内空间的移动对象管理或称室内移动对象管理,是一个崭新而又富有挑战的研究领域.如何建立语义完备并且支持多种应用的室内移动对象模型是一个重要的基础性问题.提出了一个分层的室内移动对象数据模型:LayeredModel.该模型提出了室内空间距离的概念,并使用不同的层次关系表达室内元素、传感器以及移动对象之间的关系.从而能支持多种基于室内空间的应用,例如移动对象的跟踪、监控、导航以及室内最近邻查询等.LayeredModel模型也为后续的室内移动对象的索引、查询的研究奠定了基础.

浅谈DLSW（DatalinkswitchingPlus）在网络配置中的应用

本文描述DLSw＋及．其设计和配置DLSw＋网络。标准的DLSW关键特性和在DLSw＋中增强特性。通过了解DLSW的基本原理及其配置方法可以简化网管人员的工作步骤，提高工作效率。

Validation of ECMWF and NCEP–NCAR Reanalysis Data in Antarctica

The European Center for Medium-Range Weather Forecast （ECMWF） Re-Analysis （ERA-40） and the National Centers for Environmental Prediction-National Center for Atmospheric Research （NCEP-NCAR） ECMWF （ERA-40） and NCEP–NCAR reanalysis data were compared with Antarctic station observations, including surface-layer and upper-layer atmospheric observations, on intraseasonal and interannual timescales. At the interannual timescale, atmospheric pressure at different height levels in the ERA-40 data are in better agreement with observed pressure than that in the NCEP–NCAR reanalysis data. ERA-40 reanalysis also outperforms NCEP–NCAR reanalysis in atmospheric temperature, except in the surface layer where the biases are somewhat larger. The wind velocity fields in both datasets do not agree well with surface-and upper-layer atmospheric observations. At intraseasonal timescales, both datasets capture the observed intraseasonal variability in pressure and temperature during austral winter.

The Improvement Made by a Modified TLM in 4DVAR with a Geophysical Boundary Layer Model

The strong nonlinearity of boundary layer parameterizations in atmospheric and oceanic models can cause difficulty for tangent linear models in approximating nonlinear perturbations when the time integration grows longer. Consequently, the related 4—D variational data assimilation problems could be difficult to solve. A modified tangent linear model is built on the Mellor-Yamada turbulent closure (level 2.5) for 4-D variational data assimilation. For oceanic mixed layer model settings, the modified tangent linear model produces better finite amplitude, nonlinear perturbation than the full and simplified tangent linear models when the integration time is longer than one day. The corresponding variational data assimilation performances based on the adjoint of the modified tangent linear model are also improved compared with those adjoints of the full and simplified tangent linear models.

Baddeleyite from Large Complex Deposits: Significance for Archean-Paleozoic Plume Processes in the Arctic Region (NE Fennoscandian Shield) Based on U-Pb (ID-TIMS) and LA-ICP-MS Data

Baddeleyite is an important mineral geochronometer. It is valued in the U-Pb (ID-TIMS) geochronology more than zircon because of its magmatic origin, while zircon can be metamorphic, hydrothermal or occur as xenocrysts. Detailed mineralogical (BSE, KL, etc.) research of baddeleyite started in the Fennoscandian Shield in the 1990s. The mineral was first extracted from the Paleozoic Kovdor deposit, the second-biggest baddeleyite deposit in the world after Phalaborwa (2.1 Ga), South Africa. The mineral was successfully introduced into the U-Pb systematics. This study provides new U-Pb and LA-ICP-MS data on Archean Ti-Mgt and BIF deposits, Paleoproterozoic layered PGE intrusions with Pt-Pd and Cu-Ni reefs and Paleozoic complex deposits (baddeleyite, apatite, foscorite ores, etc.) in the NE Fennoscandian Shield. Data on concentrations of REE in baddeleyite and temperature of the U-Pb systematics closure are also provided. It is shown that baddeleyite plays an important role in the geological history of the Earth, in particular, in the break-up of supercontinents.

GIS Conceptual Data Model

In order to set up a conceptual data model that reflects the real world as accurately as possible,this paper firstly reviews and analyzes the disadvantages of previous conceptual data models used by traditional GIS in simulating geographic space,gives a new explanation to geographic space and analyzes its various essential characteristics.Finally,this paper proposes several detailed key points for designing a new type of GIS data model and gives a simple holistic GIS data model.

Structure and Temporal Variability of Mediterranean Water in Hydrological and Marine Seismic Data South of Portimao Canyon (Gulf of Cadiz), from 1999 to 2002

Hydrological and marine seismic data, collected in the Gulf of Cadiz (respectively in July 1999, 2000, 2001 and 2002, and in April 2000 and 2001) are analysed to reveal the various structures of Mediterranean Water (MW). Both the hydrological and seismic data clearly identify the MW undercurrents on the Iberian slope, detached MW eddies (meddies and a cyclone) and smaller fragments of MW (filaments and small eddies). Seismic reflectivity and synthetic reflectivity computed from hydrology, indicate that strong acoustic reflectors, associated with 8 - 64 m thick homogeneous water layers, are found above and below meddies and filaments, around the MW undercurrents, but mostly in the lower part of cyclones and below submesoscale eddies. Reflectors are also observed in the near surface layers where thermohaline contrasts are quite pronounced. The successful use of seismic data to locate submesoscale MW structures, superior to that of hydrology, is related to the improved horizontal resolution.

基于特征变量扩展的含气饱和度随机森林预测方法

采用数据驱动的方式,提出了一种基于随机森林机器学习算法训练出含气饱和度地震预测方法,并将该方法应用于中国西部复杂天然气藏中,分别对单井资料和二维地震资料进行了含气饱和度预测与分析。研究结果表明:(1)抽取井旁道纵波速度、横波速度和密度3个弹性参数叠前地震反演结果作为基本特征变量样本,引入边界合成少数类过采样技术对基本特征变量样本和对应的含气饱和度样本进行平衡化处理;利用扩展弹性阻抗结合数学变换自动生成一系列的扩展变量;再利用随机森林对特征变量进行含气饱和度预测重要性排名,并优选重要性较高的特征变量进行含气饱和度随机森林训练。(2)该方法大幅减少了特征变量提取和优选的人工工作量,且有效减少了信息冗余以及因含气饱和度样本不平衡导致的训练偏倚问题,有效增强了随机森林算法在含气饱和度地震预测方面的能力。(3)实际单井应用中预测的含气饱和度与测井解释的含气饱和度的相关系数可达0.9855;在二维地震资料应用中,该方法比基于常规未平衡化的11个弹性参数作为随机森林输入预测出的含气饱和度精度更高。

物债二分体系下非法获取区块链虚拟财产行为的入罪研究

在民刑法秩序统一的前提下,刑法财产犯罪对象之财物与民法物权对象在界定上相互对应,以直接支配为认定基础。以区块链技术构造的虚拟财产具备存在唯一、权利排他、处分独立的数字物权特征,应界定为刑法上之财物。面对数据客体法益重叠的情况,应坚持个人法益优先的法益位阶原理,在数据罪名与财产罪名间优先适用后者以形成数据犯罪处置路径。非法获取区块链虚拟财产的行为,实际上是以数字变更的形式转换财物的占有状态,此种发生于数字世界的“转移占有”行为满足盗窃罪的定性要求,应以盗窃罪定罪。

Enabling Resource Awareness in Integrated Sensor Grid Framework Using Cross Layer Scheduling Mechanism

Researches related to wireless sensor networks primarily concentrate on Routing, Location Services, Data Aggregation and Energy Calculation Methods. Due to the heterogeneity of sensor networks using the web architecture, cross layer mechanism can be implemented for integrating multiple resources. Framework for Sensor Web using the cross layer scheduling mechanisms in the grid environment is proposed in this paper. The resource discovery and the energy efficient data aggregation schemes are used to improvise the effective utilization capability in the Sensor Web. To collaborate with multiple resources environment, the grid computing concept is integrated with sensor web. Resource discovery and the scheduling schemes in the grid architecture are organized using the medium access control protocol. The various cross layer metrics proposed are Memory Awareness, Task Awareness and Energy Awareness. Based on these metrics, the parameters-Node Waiting Status, Used CPU Status, Average System Utilization, Average Utilization per Cluster, Cluster Usage per Hour and Node Energy Status are determined for the integrated heterogeneous WSN with sensor web in Grid Environment. From the comparative analysis, it is shown that sensor grid architecture with middleware framework has better resource awareness than the normal sensor network architectures.

基于多元数据的夏季鸡舍环境质量评价及其对产蛋性能的影响

蛋鸡舍环境质量直接影响蛋鸡产蛋性能。为探究夏季蛋鸡舍环境质量及其对产蛋性能的影响,研究提出基于多元数据的分析方法,首先采集鸡舍内7类关键环境因子数据,按照热环境、光环境和气体环境分组,再根据改进D-S证据理论规则进行迭代融合,得到蛋鸡舍各检测点环境质量的综合评价结果,进而分析其对产蛋性能的影响。以夏季八层层叠式蛋鸡舍为试验鸡舍开展试验。结果显示:八层层叠式蛋鸡舍下四层的环境质量和平均产蛋率的最优位置均处于鸡舍前端;平均产蛋率最差的位置处于鸡舍中端,该位置环境质量综合评价结果为一般;上四层平均产蛋率最优位置为鸡舍中端,该位置环境质量综合评价结果为适宜;平均产蛋率最差位置和环境质量最差位置均为鸡舍后端(靠近风机端)。在试验鸡舍所有检测点中,平均产蛋率高于86%的检测点,环境质量综合评价结果大都为适宜,平均产蛋率低于86%的检测点,环境质量综合评价结果为一般或差,鸡舍内各检测点环境质量综合评价结果与平均产蛋率的变化趋势高度一致。该研究为准确评价蛋鸡舍环境质量,揭示蛋鸡舍环境质量对产蛋性能的影响提供了一种行之有效的方法。

双层索引驱动的隧洞海量点云高效管理方法

针对隧洞表观性态监测,三维激光扫描获取的点云具有数据量巨大、非结构化以及狭长线状非均匀分布等特点,给隧洞点云数据处理极大的压力,也制约了隧洞工程点云监测应用的发展。为此,本文结合隧洞工程空间分布特点,提出一种基于双层索引结构的隧洞海量点云管理方法。该方法设计了一种基于Hough变换的隧洞水平中线粗提取方法,指导隧洞点云数据沿水平中线进行点云自动分段;而后利用“自下而上”的归并构建策略建立分段点云八叉树索引。在此基础上,利用非冗余的多层次细节(LOD)建模方法和内外存动态调度技术实现海量点云数据快速可视化。实验结果显示,本文方法有效提高了隧洞点云水平轴线提取效率,基于双层索引结构的隧洞点云管理在点云检索、海量点云数据可视化等方面表现出优异性能。