Global parameter estimation of the Cochlodinium polykrikoides model using bioassay data

Cochlodinium polykrikoides is a notoriously harmful algal species that inflicts severe damage on the aquacultures of the coastal seas of Korea and Japan. Information on their expected movement tracks and boundaries of influence is very useful and important for the effective establishment of a reduction plan. In general, the information is supported by a red-tide（a.k.a algal bloom） model. The performance of the model is highly dependent on the accuracy of parameters, which are the coefficients of functions approximating the biological growth and loss patterns of the C. polykrikoides. These parameters have been estimated using the bioassay data composed of growth-limiting factor and net growth rate value pairs. In the case of the C. polykrikoides, the parameters are different from each other in accordance with the used data because the bioassay data are sufficient compared to the other algal species. The parameters estimated by one specific dataset can be viewed as locally-optimized because they are adjusted only by that dataset. In cases where the other one data set is used, the estimation error might be considerable. In this study, the parameters are estimated by all available data sets without the use of only one specific data set and thus can be considered globally optimized. The cost function for the optimization is defined as the integrated mean squared estimation error, i.e., the difference between the values of the experimental and estimated rates. Based on quantitative error analysis, the root-mean squared errors of the global parameters show smaller values, approximately 25%–50%, than the values of the local parameters. In addition, bias is removed completely in the case of the globally estimated parameters. The parameter sets can be used as the reference default values of a red-tide model because they are optimal and representative. However, additional tuning of the parameters using the in-situ monitoring data is highly required.As opposed to the bioassay data, it is necessary because the bioassay data have limitations in terms of the in-situ coastal conditions.

Reproducing past subduction and mantle flow using high-resolution global convection models

Plate subduction drives both the internal convection and the surface geology of the solid Earth.Despite the rapid increase of computational power,it remains challenging for geodynamic models to reproduce the history of Earth-like subduction and associated mantle flow.Here,based on an adaptive approach of sequential data assimilation,we present a high-resolution global model since the mid-Mesozoic.This model incorporates the thermal structure and surface kinematics of tectonic plates based on a recent plate reconstruction to reproduce the observed subduction configuration and Earth-like convection.Introduction of temperature-and composition-dependent rheology allows for incorporation of many natural complexities,such as initiation of subduction zones,reversal of subduction polarity,and detailed plate-boundary dynamics.The resultant present-day slab geometry well matches Benioff zones and seismic tomography at depths < 1500 km,making it possible to hindcast past subduction dynamics and mantle flow.For example,the model produces a flat Farallon slab beneath North America during the Late Cretaceous to Early Cenozoic,a feature that has been geodynamically challenging to reproduce.This high-resolution model can also capture details of the 4-D evolution of slabs and the ambient mantle,such as temporally and spatially varying mantle flow associated with evolving slab geometry and buoyancy flux,as well as the formation of shallow slab tears due to subduction of young seafloors and the resulting complex mantle deformation.Such a geodynamic framework serves to further constrain uncertain plate reconstruction in the geological past,and to better understand the origin of enigmatic mantle seismic features.

Discharge Simulation in a Data-Scarce Basin Using Reanalysis and Global Precipitation Data: A Case Study of the White Volta Basin

Basins in many parts of the world are ungauged or poorly gauged, and in some cases existing measurement networks are declining. The purpose of this study was to examine the utility of reanalysis and global precipitation datasets in the river discharge simulation for a data-scarce basin. The White Volta basin of Ghana which is one of international rivers was selected as a study basin. NCEP1, NCEP2, ERA-Interim, and GPCP datasets were compared with corresponding observed precipitation data. Annual variations were not reproduced in NCEP1, NCEP2, and ERA-Interim. However, GPCP data, which is based on satellite and observed data, had good seasonal accuracy and reproduced annual variations well. Moreover, five datasets were used as input data to a hydrologic model with HYMOD, which is a water balance model, and with WTM, which is a river model;thereafter, the hydrologic model was calibrated for each datum set by a global optimization method, and river discharge were simulated. The results were evaluated by the root mean square error, relative error, and water balance error. As a result, the combination of GPCP precipitation and ERA-Interim evaporation data was the best in terms of most evaluations. The relative errors in the calibration and validation periods were 43.1% and 46.6%, respectively. Moreover, the results for the GPCP precipitation and ERA-Interim evaporation were better than those for the combination of observed precipitation and ERA-Interim evaporation. In conclusion, GPCP precipitation data and ERA-Interim evaporation data are very useful in a data-scarce basin water balance analysis.

Quantitative credibility evaluation of Global Energy Interconnection data

The development of Global Energy Interconnection(GEI)is essential for supporting a wide range of basic data resources.The Global Energy Interconnection Development and Cooperation Organization has established a comprehensive data center covering six major systems.However,methods for accurately describing and scientifically evaluating the credibility of the massive amount of GEI data remain underdeveloped.To address this lack of such methods,a GEI data credibility quantitative evaluation model is proposed here.An evaluation indicator system is established to evaluate data credibility from multiple perspectives and ensure the comprehensiveness and impartiality of evaluation results.The Cloud Model abandons the hard division of comments to ensure objectivity and accuracy in evaluation results.To evaluate the suitability of the proposed method,a case analysis is conducted,wherein the proposed method demonstrates sufficient validity and feasibility.

Influence of the modified global ocean tide model with local tides of East and South China Seas on load gravity in China and its neighbor area

By using 11 global ocean tide models and tidal gauge data obtained in the East China Sea and South China Sea, the influence of the ocean loading on gravity field in China and its neighbor area is calculated in this paper. Furthermore, the differences between the results from original global models and modified models with local tides are discussed based on above calculation. The comparison shows that the differences at the position near the sea are so large that the local tides must be taken into account in the calculation. When the global ocean tide models of CSR4.0, FES02, GOT00, NAO99 and ORI96 are chosen, the local effect for M2 is less than 0.10 × 10-8 m·s-2 over the area far away from sea. And the local effect for O1 is less than 0.05 × 10-8 m·s-2 over that area when choosing AG95 or CSR3.0 models. This numerical result demonstrates that the choice of model is a complex problem because of the inconsistent accuracy of the models over the areas of East and South China Seas.

Global Mapper软件在新建铁路预可研阶段选线中的应用

新建铁路项目前期投入较少的人力、物力,缩短项目周期是设计单位所需解决的问题,因此选线过程中快速生成多方案的地形DEM数据模型尤为重要。Global Mapper软件作为一款小型地理信息软件,应用软件专题地图制作功能,可绘制项目地理位置示意图、线路平纵断面缩图,易于操作,出图效率高。该软件支持栅格、矢量、高程数据等多种显示和处理,应用全球免费的STER数据,可以高效生产研究区域内的DEM高程模型,能够达到铁路项目预可研以及部分项目可研阶段的精度要求,具有实用参考价值。

Land Response to Atmosphere at Different Resolutions in the Common Land Model over East Asia

Towards a better understanding of hydrological interactions between the land surface and atmosphere, land surface mod- els are routinely used to simulate hydro-meteorological fluxes. However, there is a lack of observations available for model forcing, to estimate the hydro-meteorological fluxes in East Asia. In this study, Common Land Model （CLM） was used in offline-mode during the summer monsoon period of 2006 in East Asia, with different forcings from Asiaflux, Korea Land Data Assimilation System （KLDAS）, and Global Land Data Assimilation System （GLDAS）, at point and regional scales, separately. The CLM results were compared with observations from Asiaflux sites. The estimated net radiation showed good agreement, with r = 0.99 for the point scale and 0.85 for the regional scale. The estimated sensible and latent heat fluxes using Asiaflux and KLDAS data indicated reasonable agreement, with r = 0.70. The estimated soil moisture and soil temperature showed similar patterns to observations, although the estimated water fluxes using KLDAS showed larger discrepancies than those of Asiaflux because of scale mismatch. The spatial distribution of hydro-meteorological fluxes according to KLDAS for East Asia were compared to the CLM results with GLDAS, and the GLDAS provided online. The spatial distributions of CLM with KLDAS were analogous to CLM with GLDAS, and the standalone GLDAS data. The results indicate that KLDAS is a good potential source of high spatial resolution forcing data. Therefore, the KLDAS is a promising alternative product, capable of compensating for the lack of observations and low resolution grid data for East Asia.

一种解决数据异构问题的联邦学习方法

联邦学习是一种不通过中心化的数据训练就能获得机器学习模型的系统,源数据不出本地,降低了隐私泄露的风险,同时本地也获得优化训练模型。但是由于各节点之间的身份、行为、环境等不同,导致不平衡的数据分布可能引起模型在不同设备上的表现出现较大偏差,从而形成数据异构问题。针对上述问题,提出了基于节点优化的数据共享模型参数聚类算法,将聚类和数据共享同时应用到联邦学习系统中,该方法既能够有效地减少数据异构对联邦学习的影响,也加快了本地模型收敛的速度。同时,设计了一种评估全局共享模型收敛程度的方法,用于判断节点聚类的时机。最后,采用数据集EMNIST、CIFAR-10进行了实验和性能分析,验证了共享比例大小对各个节点收敛速度、准确率的影响,并进一步分析了当聚类与数据共享同时应用到联邦学习前后各个节点的准确率。实验结果表明,当引入数据共享后各节点的收敛速度以及准确率都有所提升,而当聚类与数据共享同时引入到联邦学习训练后,与FedAvg算法对比,其准确度提高10%~15%,表明了该方法针对联邦学习数据异构问题上有着良好的效果。

跨境数据流动政策偏好的全球分布:国家维度的治理模式比较研究

数据跨境流动是数字经济发展的基础,但也蕴含着很多风险与挑战。目前,世界各国在数据跨境流动领域已经形成了多种治理模式。美国为维护其数字霸权,全力推动数据“自由化”主张,日欧等发达经济体则选择推动有限的自由化与严格数据保护。很多发展中国家要求数据本地化以推动本国数字经济发展。俄罗斯具有更多的危机意识,从安全底线思维出发,推进严格的本地化与安全化数据治理模式。中国则以中国式现代化为导向,以更为灵活和敏捷的思路开展数据治理,保持发展目标与安全目标的动态平衡。以各国在跨境数据治理中的主观意愿与客观结果作标准,可以构建一个针对跨境数据流动政策的二维分析展示框架。通过对该框架的分析,能够较为清晰地展示各国跨境数据政策的偏好、特征及互动方式,勾勒出跨境数据流动政策领域以“多模式交织碰撞”为主要特征的全球图景。

云-边融合的可验证隐私保护跨域联邦学习方案

联邦学习技术的飞速发展促进不同终端用户数据协同训练梯度模型,其显著特征是训练数据集不离开本地设备,只有梯度模型在本地进行更新并共享,使边缘服务器生成全局梯度模型。然而,本地设备间的异构性会影响训练性能,且共享梯度模型更新具有隐私泄密与恶意篡改威胁。提出云-边融合的可验证隐私保护跨域联邦学习方案。在方案中,终端用户利用单掩码盲化技术保护数据隐私,利用基于向量内积的签名算法产生梯度模型的签名,边缘服务器通过盲化技术聚合隐私数据并产生去盲化聚合签名,确保全局梯度模型更新与共享过程的不可篡改性。采用多区域权重转发技术解决异构网络中设备计算资源与通信开销受限的问题。实验结果表明,该方案能够安全高效地部署在异构网络中,并在MNIST、SVHN、CIFAR-10和CIFAR-1004个基准数据集上进行系统实验仿真,与经典联邦学习方案相比,在精度相当的情况下,本文方案梯度模型收敛速度平均提高了21.6%。

一种面向工业互联网安全的边缘设备随机时间轴流量感知模型

工业互联网边缘设备的数据流量感知对生产稳定性、供应链安全性、维护和优化生产过程起着关键作用。针对工业互联网中具有隐身机制的边缘设备后台攻击和多产线底层设备集群后台的漏洞攻击等安全问题,提出一种针对边缘数据突发异常的随机时间轴流量感知模型。通过在多个工作周期中设置相同时间轴的数据流同步感知时间窗,计算不同工作周期时间窗的数据流量积量的均差比,判断设备数据流量是否出现异常;并基于该感知模型,利用不同工作周期时间窗的数据流的残差矩阵和均差比矩阵构建了面向产业集群工业互联网的全域数据流异常关联态势描述模型。实验表明,所提出的随机时间轴流量异常感知模型,能实时监测工业互联网边缘设备的后台漏洞产生的安全问题。

正弦削极修正模型的表贴式多相永磁电机模型-数据融合优化设计

为同时提高推进电机转矩密度与全转矩范围内运行效率,该文针对表贴式多相永磁直驱推进电机,提出一种从磁极局部优化到电机整体模型-数据融合优化的设计方法。首先,综合考虑定子齿槽、磁路饱和、永磁极漏磁等因素对电机气隙磁密的影响,对正弦削极下永磁体模型进行修正;其次,通过有限元计算建立数据样本,并分析筛选出与优化目标高度相关的结构尺寸作为显著变量;再次,建立多项式响应面模型,结合基于数据的改进贝叶斯算法反复迭代得到最优解,进而获得全局优化后电机整体设计方案;最后,通过仿真与实验验证磁极局部优化模型的正确性与电机模型-数据融合全局优化设计的有效性。

天津城南变电站多源异构数据的知识图谱构建

新一代电力系统的建设与发展使得变电站及其设备之间的关系复杂化,运行产生的数据也呈现出多源异构的特点。为了得到关联所有复杂设备与多元数据的全域模型,一种针对天津城南变电站数据的知识图谱构建方法在此被提出。首先对来自变电站的多源异构数据进行特征分析与知识抽取,并形成电力系统实体关系库和电力系统故障信息库。知识抽取的过程包括语句分词、关键词抽取、图像转文本等操作。其次设计出基于天津城南变电站的知识图谱结构,并构建出涵盖厂站与厂站、厂站与设备、设备与设备之间实体关系的知识图谱,形成一体化数据平台。最后简单分析了该知识图谱的应用。结果表明,变电站知识图谱可以涵盖变电站所有设备,并完成大量运行数据的存储与关联。

融合多尺度数据的机器学习算法在预测模型中的应用

随着数据收集和处理技术的飞速发展,多尺度数据在各领域的应用越来越广泛。文章探讨了融合多尺度数据的机器学习算法在预测模型中的应用,并分析了其在提高预测精度和模型鲁棒性方面的优势。通过对不同尺度数据的融合,可以捕获数据的全局和局部特征,为预测模型提供更全面的信息。

开放式虚拟地球集成共享平台GeoGlobe

如何管理覆盖全球多尺度PB级海量地理信息,构建三维虚拟地球,并进行高效传输与实时可视化以及与专业GIS的集成是我国空间信息技术面临的巨大挑战。对此,重点介绍网络三维虚拟地球中多源、多尺度、多时相海量地理信息管理、网络传输、共享集成与可视化的理论、方法与关键技术。在此基础上,研制开放式虚拟地球集成共享平台GeoGlobe,并成功应用于国家地理信息公共服务平台等项目中。

多数据库中间件的模型研究

提出一种新的多数据库中间件架构,使得异源独立分布数据的“上推/受访/集成”模型化,从而使异源独立分布数据的存取/复用/集成,能像单一关系数据库那样走向模型化,建立“DBMS的DBMS”;讨论了该模型中的几个主要关键技术,如数据的一致性、数据的复制策略、局部数据源的即插即用研究、缓冲区系统中的数据名空间。

基于XML虚拟数据库的异构数据源集成模型研究

解决企业数据源异构需要一种公共的数据源模型给用户提供统一的用户视图,XML以其所具有的自描述性、灵活性、强大的数据交换能力等优势克服了其他数据模式的缺点。结合当前数据集成方面的技术,提出了一种基于XML虚拟数据库的异构数据源集成模型,该模型很好地解决了异构数据源集成方面存在的一系列问题,并从数据模型和数据交换上阐述了该模型的可行性。剖析了该模型中的模式集成、异构数据集成视图、全局查询等。介绍了该模型在中国石油QHSE信息系统中的成功应用。

多数据库中间件中分布异构数据缓冲区系统的实现

实现了多数据库中间件中分布异构数据缓冲区,该模型能够实现分布异构数据对象的数据双向同步/异步更新机制;采用的全局数据对象存储模型和缓存管理使数据在缓冲区存储并被用户高效快捷地访问;实现了全局事务管理策略,包括局部代理的设计、全局事务的并发控制、全局事务提交协议等。

基于子元模型的全局优化与设计空间知识挖掘方法

为有效求解大资源黑盒子问题如叶轮机械设计优化等,提出了基于子元模型的全局优化与设计空间知识挖掘方法MBOE。该方法包括全局优化算法MBGO与数据挖掘两部分,其中MBGO算法需要极少的函数估值即可获得全局最优解;数据挖掘技术能有效分析变量间的相关关系,揭示最优设计性能提高的本质原因。利用MBOE,完成NASA Rotor37转子叶栅三维气动设计优化与知识挖掘,最优设计等熵效率相对参考设计提高1.74%。同时,利用MBGO算法所需要的计算量仅为进化算法的1/5。数据挖掘结果表明叶栅前缘及三维积叠参数对叶栅气动性能影响较大,最优设计由于上述叶型参数的改善有效减弱了叶栅进口激波损失,使得最优设计气动性能明显提高。由此,MBOE方法的正确性和有效性得到了验证。

GRAPES全球变分同化背景误差协方差的改进及对分析预报的影响：背景误差协方差三维结构的估计

回顾并详细推导了估计背景误差协方差统计特征的美国国家气象中心(NMC)方法及其优缺点;采用NMC方法系统地估计了新版GRAPES全球模式的背景误差方差、水平相关特征尺度和垂直相关结构,并与欧洲中心模式结果进行了比较。结果表明,目前GRAPES全球模式的背景误差方差比以前有了显著减小;水平相关特征尺度随纬度和高度有显著变化;背景误差垂直相关结构与欧洲中心模式结果非常一致,相比经验公式结果更具物理意义,同时,单点试验结果也表明,更新后的垂直相关结构产生的分析增量更合理。通过与欧洲中心模式背景误差协方差三维结构的对比,分析了不同模式间背景误差协方差的异同及GRAPES全球同化分析系统目前存在的一些不足及可能原因。为新版GRAPES全球模式的三维变分系统提供了基本的背景误差协方差的三维结构。