网格环境下的空间数据集成服务

随着网格技术在地理信息系统领域的广泛研究与应用,如何实现网格环境下的空间数据资源集成,为空间数据建立高效的共享服务机制已经成为GIS领域研究的热点问题。通过对OGC空间数据统一模型以及GIS用户在空间数据应用方面的需求分析与研究,提出了基于GMLSoAP的空间数据集成服务框架。在此基础上,详细讨论了基于GML空间数据统一模型的空间数据转换和基于SoAP的空间数据服务的建立以及网格环境下虚拟化空间数据服务应用模型,并且实现了多数据源、多类型空间数据集成应用的实验环境。     

一种信息网格主体空间模型及其应用

相对于已有的客体空间模型研究,提出一种信息网格主体EVP空间模型,从用户的全生命周期过程(包括注册、登录、使用、维护、挂起、重新接入、退出、注销等)出发,研究了用户使用网格的各个阶段,分析了该主体模型在用户全生命周期各个阶段的变化,简述了该模型在某所信息网格中的应用,试图通过该模型分析和解决信息网格中用户的分布和异构以及用户标识的全网格统一问题。     

统一用户管理系统在气象行业的应用

研究了利用LDAP技术和单点登录技术在气象行业建立统一用户管理系统的思路和方法.通过对气象业务应用系统现状进行分析,提出了一种基于LDAP目录服务和单点登录技术的统一用户管理系统解决方案,详细介绍了LDAP目录的建立、身份认证和单点登录的实现过程.同时,简要介绍了建立该系统的流程.统一用户管理系统的应用,可以有效实现气象系统网络信息资源的整合,使得用户认证和访问控制更加高效,管理更加方便.     

面向气象水文信息整合的网格模型

数据的高效获取.存储,传输和处理对气象水文信息系统至关重要,然而,管理这些存储在地理分布,异构数据源中的海量数据也是一个重大挑战.其难点在于如何处理数据源之间结构和语义异构性,如何提高分布式查询的效率、如何保证数据源中数据的安全性和查询的正确性.针对这些问题,提出了一种基于网格技术的气象水文信息整合模型.在模型中,存储管理器、查询管理器,事务管理器等组件之间交互协作以提供各种数据管理服务和保障.同时提出的类JDBC的资源统一访问接口模型,不仅可以实现异构数据库信息的整合,也可以实现异构数据库信息和特定格式的水文信息的统一整合.详细描述了各组件的功能作用和模型的工作机制.     

基于保特征调和场的交互式网格分片

网格模型分片在计算机图形学应用中具有重要意义,本文提出了一种基于网格上的调和场和图割技术的网格模型分片算法.用户可以通过划线的方式来指定网格上感兴趣的区域;算法自动构建反映该区域细节特征的调和场,进而采用图割技术,得到满足用户要求的分片结果;通过对网格分割边界的光滑处理,可有效改善锯齿型分割边界.实验结果表明,我们的算法对于特征单一或复杂的网格模型都能得到符合用户意图的分割结果.     

基于动态分区的网格水文模型分布式计算

考虑到传统的水文模型串行计算方法无法满足当前复杂的水文模拟预报需求，为提高网格水文模型的计算效率，分析网格化水文模型计算中网格汇流特点，提出基于网格汇流流向的动态数据分区方法，不同分区数据可以并行计算，基于该方法提出基于Spark的网格水文模型分布式计算模型。通过在屯溪流域使用该模型与传统模型进行水文预报计算，验证了模型的高效性。     

基于最小二乘网格的模型变形算法

针对自由变形技术难以保持模型细节的问题,提出一种基于最小二乘网格的模型变形算法.通过顶点位置约束的全局拉普拉斯光顺分解出表示模型低频信号的最小二乘网格,并求出高频信号在该网格上的编码;通过用户交互,基于均值坐标对最小二乘网格进行自由变形;根据最小二乘网格各顶点处局部标架在变形时的几何变换求出变形后的高频编码,通过解码求出变形后的网格模型.实验结果表明,该算法简单、高效且便于用户交互,有效地保持了模型的几何细节.     

网格环境下一种健壮信任评价控制模型

当前的信任模型缺乏足够的信任评价控制机制,严重影响信任模型在网格等开放计算环境中的应用。基于传统的信任评价模型,提出信任评价控制模型。该模型采用插值技术,通过服务水平估计,有效过滤和调谐用户评价,对用户提出激励处罚机制。仿真结果表明,该模型优于现有的控制模型,能够较好地解决网格实体信任评价可靠性问题,降低恶意评价对信任评价模型的影响。     

气象应用网格中改进遗传负载均衡算法研究

根据气象应用网格的特点和影响负载均衡的因素,对基于气象应用网格的负载均衡技术进行分析和探讨,提出一种气象应用网格中的负载均衡算法,实验结果表明该算法提高网格的负载能力.     

面向生物信息的网格工作流开发与运行环境

网格工作流系统通过组合网格计算结点上的资源为生物计算等领域的复杂计算任务提供了支持。为了解决已有系统中用户需要对流程语言细节有较深入了解以及操作和配置较为复杂等问题,设计和实现了面向生物信息领域的网格工作流开发与运行环境。生物信息领域的用户使用该系统,可以通过web的方式实现对生物计算模型较高抽象层次的在线流程设计、流程作业的提交、作业状态的管理以及计算结果的查看等操作。最后通过生物计算领域的基因计算任务说明了系统对生物信息学应用的支撑功能。     

基于网格的中尺度数值天气预报系统设计与实现

针对国家高性能计算环境网格计算的特点，在长沙网格点上实现了一个包括全球中期数值预报、有限区域数值预报，数值预报产品释用和五维数据可视化相配套的高分辨率中尺度数值天气预报系统。该系统的预报区域可以移动，网格可以加密，适合于区域和省一级气象中心作中尺度数值预报业务试验和尺度数值预报科学研究。介绍了该系统的组成、区域同化方案以及网格计算实现技术。     

Visualization of structured nonuniform grids

Operational forecasters and weather researchers need accurate visualization of atmospheric data from both computational models and observed data. Although these two applications share some requirements, they have different needs and goals. We've developed a visualization tool for atmospheric science researchers and research weather forecasters that allows the 3D visualization of measured radar data and rendered numerical model data to show the 3D structures as well as how the weather event would look when observed in the field. Our system lets us load the original data directly onto the graphics hardware, with the grid mapping from the rendering space to the grid space programmed on the GPU. This method is flexible enough to handle the grids important in meteorological research and enables the application of advanced visualization methods available in texture-based slicing systems. The visually accurate rendering of weather data can be useful for training weather spotters, evaluating forecasting models, training forecasters to interpret radar data, and comparing sensor data to observed weather events.     

西北电网专业气象服务系统的设计与实现

介绍了一种与电网紧密结合的专业气象服务系统,该系统在充分利用气象系统、电力系统现有软硬件资源的基础上,通过应用气象多普勒雷达、卫星云图等现代化观测手段和自动化程度很高的地面自动气象站资料以及MM5中尺度预报产品,结合有线网络传输和基于Intranet/Internet的多层分布式技术搭建西北电网专业气象服务系统,开展电网气象预警预测和气象预报服务。系统建立了实时的地面自动气象站资料数据库和预报产品数据库,实现了多种气象要素的实时监测和预报,为西北电网的安全调度运行提供了科学依据。     

基于随机森林的短期电量负荷精准预测方法

可再生能源并入电网后,电能供给量增加,短期电量负荷情况难以预测,无法制定准确的电能分配策略,由此,提出基于随机森林的短期电量负荷精准预测方法研究。深入分析短期电量负荷预测影响因素（气象、时间、电价与随机干扰因素）,选取适当的模型输入变量（历史电量负荷数据、温度数据与日类型）,结合随机森林算法构建短期电量负荷预测模型,并重复确定相似日的选取规则,采用粒子群优化算法寻找预测模型参数最佳值,将样本集输入至模型中,获得精准的短期电量负荷预测结果。实验数据显示:当输入变量数量达到一定值后,应用提出方法获得的短期电量负荷预测时延稳定在0.55s左右,短期电量负荷预测误差几乎为0,充分证实了提出方法应用性能较佳。     

A two-level parallelization method for distributed hydrological models

This paper proposes a scalable two-level parallelization method for distributed hydrological models that can use parallelizability at both the sub-basin level and the basic simulation-unit level (e.g., grid cell) simultaneously. This approach first uses the message-passing programming model to dispatch parallel tasks at the sub-basin level to different nodes with multi-core CPUs in the cluster. Each node is responsible for some of the sub-basins. Parallel tasks for each sub-basin at the basic simulation-unit level are then dispatched to multiple cores within each node using the shared-memory programming model. A grid-based distributed hydrological model was parallelized to demonstrate the performance of the proposed method, which was tested in different scenarios (e.g., different data volume, different numbers of sub-basins). Results show that the proposed two-level parallelization method had better scalability than the parallel computation at sub-basin level alone, and the parallel performance increased with data volume and the number of sub-basins.     

Forecasting building energy consumption: Adaptive long-short term memory neural networks driven by genetic algorithm

The real-world building can be regarded as a comprehensive energy engineering system; its actual energy consumption depends on complex affecting factors, including various weather data and time signature. Accurate energy consumption forecasting and effective energy system management play an essential part in improving building energy efficiency. The multi-source weather profile and energy consumption data could enable integrating data-driven models and evolutionary algorithms to achieve higher forecasting accuracy and robustness. The proposed building energy consumption forecasting system consists of three layers: data acquisition and storage layer, data pre-processing layer and data analytics layer. The core part of the data analytics layer is a hybrid genetic algorithm (GA) and long-short term memory (LSTM) neural network model for accurate and robust energy prediction. LSTM neural network is adopted to capture the interrelationship between energy consumption data and time. GA is adopted to select the optimal architecture for LSTM neural networks to improve its forecasting accuracy and robustness. The hyper-parameters for determining LSTM architecture include the number of LSTM layers, number of neurons in each LSTM layer, dropping rate of each LSTM layer and network learning rate. Meanwhile, the effects of historical weather profile and time horizon of past information are also investigated. Two real-life educational buildings are adopted to test the performance of the proposed building energy consumption forecasting system. Experiments reveal that the proposed adaptive LSTM neural network performs better than the existing feedforward neural network and LSTM-based prediction models in accuracy and robustness. It also outperforms those LSTM networks whose hyper-parameters are determined by grid search, Bayesian optimisation and PSO. Such accurate energy consumption prediction can play an essential role in various areas, including daily building energy management, decision making of facility managers, building information model designs, net-zero energy operation, climate change mitigation and circular economy.     

An on-line technique for weather forecast

A method for weather forecasting, based on application of adaptive filtering technique to a stochastic system model describing the dynamics of weather variables, is presented. The weather dynamic model is in the form of linear multiple order difference equations which include the interdependence of different weather variables as well as their correlations in terror of number of days. The identification and prediction algorithms are recursive and suitable for on-line computations. Results of real data processing for forecasting daily maximum temperature and maximum humidity based on above technique are also included.     

CASC2D分布式水文模型异构并行算法研究

针对 CASC2D 模型精细化水文模拟时面临的计算耗时长、效率低等问题,在保持产汇流算法和流域拓扑结构的基础上,采用 CPU+GPU 的异构并行算法对 CASC2D 模型程序进行重新设计和优化,模型程序中的降雨、 产流、坡面汇流和河道汇流过程均优化为并行计算,以提高 CASC2D 模型的计算效率。将优化后的 CASC2D 模型应用于前毛庄流域的洪水流量过程模拟,计算结果与原 CASC2D 模型保持一致。在栅格分辨率为 30 m,计算步长为 3 s 时,与原 CPU 串行计算方法相比,并行加速比达到 34 倍以上,并且栅格单元数据精度越高,加速比提升越明显。异构并行算法可在不降低模拟精度的条件下显著提升 CASC2D 模型的计算效率,满足实时水文预报的应用需求。     

Middleware services for P2P computing in wireless grid networks

Advances in network architecture are changing application and service delivery. The wireless industry, for example, now talks of service grids - collections of separate wireless services collated from across the network. However, with current limitations in mobile device capabilities, a scalable and efficient middleware platform is essential. The authors' new middleware architecture, called scalable inter-grid network adaptation layers (signal), integrates mobile devices with existing grid platforms to conduct peer-to-peer operations through proxy-based systems. Combining mobile P2P applications with grid technologies could ultimately give mobile devices the power of supercomputers.