自然资源一体化政务服务系统及数据融合建设——以南京为例     

陈波  崔蓓  丁鑫 《测绘通报》2020,(12):75-78

自然资源部制定的信息化建设总体方案中，要求构建“互联网+自然资源政务服务”应用体系，实现“服务事项标准统一、整体联动、业务协同，自然资源政务服务和共享开放能力全面提高”。一体化政务服务系统作为政务服务应用体系的重点建设内容，也是落实自然资源管理“两统一”职责的重要支撑，如何整合各类数据资源、发挥系统作用，成为亟待研究的问题。本文在分析自然资源业务管理需求后，提出了一体化政务服务系统及数据融合的建设思路。  相似文献   

Developing constitutive models from EPR‐based self‐learning finite element analysis          下载免费PDF全文

Ali Nassr  Akbar Javadi  Asaad Faramarzi 《国际地质力学数值与分析法杂志》2018,42(3):401-417

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.  相似文献   

解郁安神法联合体外反搏对PCI术后气滞血瘀型胸痹患者的临床疗效观察          下载免费PDF全文

王敏  徐新利  姜光萍 《应用海洋学学报》2020,36(12):1

目的：观察解郁安神法联合体外反搏（EECP）对经皮冠状动脉介入治疗（PCI）术后气滞血瘀型胸痹患者的临床疗效。方法：将80例冠心病PCI术后气滞血瘀型胸痹患者随机分为治疗组和对照组,每组各40例。对照组采用PCI术后规范化治疗,同时联合EECP治疗;治疗组在对照组基础上加服解郁安神方治疗,随访观察7周,比较2组临床证候疗效、Zung焦虑自评量表（SAS）评分、抑郁自评量表（SDS）评分、西雅图心绞痛调查量表（SAQ）评分。结果：总有效率治疗组为82.5%（33/40）,高于对照组的60.0%（24/40）,2组比较,差异有统计学意义（P<0.05）;2组SAS、SDS、SAQ评分治疗前后组内比较及治疗后组间比较,差异均有统计学意义（P<0.05）。结论：解郁安神法联合EECP治疗可明显改善PCI术后气滞血瘀型胸痹患者的临床症状,缓解焦虑抑郁程度,提高患者生活质量,值得临床推广应用。  相似文献   

A machine learning approach for predicting computational intensity and domain decomposition in parallel geoprocessing     

Peng Yue  Fan Gao  Boyi Shangguan  Zheren Yan 《International journal of geographical information science》2020,34(11):2243-2274

ABSTRACT

High performance computing is required for fast geoprocessing of geospatial big data. Using spatial domains to represent computational intensity (CIT) and domain decomposition for parallelism are prominent strategies when designing parallel geoprocessing applications. Traditional domain decomposition is limited in evaluating the computational intensity, which often results in load imbalance and poor parallel performance. From the data science perspective, machine learning from Artificial Intelligence (AI) shows promise for better CIT evaluation. This paper proposes a machine learning approach for predicting computational intensity, followed by an optimized domain decomposition, which divides the spatial domain into balanced subdivisions based on the predicted CIT to achieve better parallel performance. The approach provides a reference framework on how various machine learning methods including feature selection and model training can be used in predicting computational intensity and optimizing parallel geoprocessing against different cases. Some comparative experiments between the approach and traditional methods were performed using the two cases, DEM generation from point clouds and spatial intersection on vector data. The results not only demonstrate the advantage of the approach, but also provide hints on how traditional GIS computation can be improved by the AI machine learning.  相似文献   

合并会计报表的电算编制方法探讨     

孙艳玲 《成都信息工程学院学报》2003,18(4):433-440

利用用友软件对如何合并会计报表进行了初步研究，并以案例的形式阐明了采用用友软件实现合并会计报表的步骤，提出了合并会计报表的编制方法。  相似文献   

青海可可西里地区综合科学考察初报     

李炳元 《山地学报》1990,(3)

本区至今还是综合科学考察的空白区。为查明基本情况,1989年夏以乘汽车路线考察方式穿行该区而作了预查,搜集了当地地质、地理、生物等基本资料。经考察发现,区内野生动物资源丰富,上新世以来火山活动普遍,湖泊众多,矿产资源可能有良好前景;所在区又为自然地域和地质构造的重要界线,对青藏高原研究具有重大科学价值。  相似文献   

Automatic Determination of Stellar Atmospheric Parameters Using Neural Networks and Instance-Based Machine Learning     

Olac Fuentes 《Experimental Astronomy》2001,12(1):21-31

In this article we show how machine learning methods can beeffectively applied to the problem of automatically predictingstellar atmospheric parameters from spectral information, a veryimportant problem in stellar astronomy. We apply feedforwardneural networks, Kohonen's self-organizing maps andlocally-weighted regression to predict the stellar atmosphericparameters effective temperature, surface gravity and metallicityfrom spectral indices. Our experimental results show that thethree methods are capable of predicting the parameters with verygood accuracy. Locally weighted regression gives slightly betterresults than the other methods using the original dataset asinput, while self-organizing maps outperform the other methods when significant amounts of noise are added. We also implemented a heterogeneous ensemble of predictors, combining the results given by the three algorithms. This ensemble yields better results than any of the three algorithms alone, using both the original and the noisy data.  相似文献   

Prediction of Stellar Atmospheric Parameters using Instance-Based Machine Learning and Genetic Algorithms   总被引：1，自引：0，他引：1  

J. Federico Ramírez  Olac Fuentes  Ravi K. Gulati 《Experimental Astronomy》2001,12(3):163-178

In this article we present a method for the automated prediction of stellar atmospheric parameters from spectral indices. This method uses a genetic algorithm (GA) for the selection of relevant spectral indices and prototypical stars and predicts their properties, using the k-nearest neighbors method (KNN). We have applied the method to predict the effective temperature, surface gravity, metallicity, luminosity class and spectral class of stars from spectral indices. Our experimental results show that the feature selection performed by the genetic algorithm reduces the running time of KNN up to 92%, and the predictive accuracy error up to 35%. This revised version was published online in July 2006 with corrections to the Cover Date.  相似文献   

Early warning research on salt-alkalization desertification in western Songnen Plain     

BIAN Jian-min  LIN Nian-feng  TANG Jie College of Environment  Resources  Jilin  University  Changchun  Jilin  China 《东北亚地学研究》2005,(Z1)

Desertification is an environmental issue in the world. The salt-alkalization desertification land area formed by both primary and secondary salt-alkalization has extended in a large scale, which has become a significant eco-environmental problem. Based on the characteristics of eco-environment and the situation of desertification in western Songnen plain, this paper reports the analyzes of its formation in background and cause. An early warning system on the salt-alkalization desertification is established and the GIS technology is used to abstract the information of desertification evaluation index. Supported by the integrated technology of the GIS and ANN, the orientation and quantitative result of desertification are gained, which is helpful to the eco-environment protection and resource development in western Songnen Plain.  相似文献   

天文光干涉与综合孔径技术的发展     

郭红锋 《紫金山天文台台刊》2003,(1)

本文简单介绍了光干涉与综合孔径技术发展的历史、国际上用于天文观测的一些著名光干涉仪的技术特点和进展、以及近年来我国开展的与光干涉 ,综合孔径技术有关的工作 ,最后简要介绍了在光学综合孔径技术领域所发展的光纤与集成光学等新技术  相似文献