基于引文分析法的“点-轴系统”理论研究述评

论文采用引文分析法,对陆大道院士所著《关于“点-轴”空间结构系统的形成机理分析》一文的349篇引文进行分析,从引文数量、引文作者、发文期刊、研究案例地、学科领域、研究主题、引用动机7个方面,探究“点-轴系统”理论的研究现状。结果表明：自2002年以来,“点-轴系统”理论的引文数量整体呈上升趋势,研究主题聚焦于区域经济和旅游发展;研究案例地以省级和市级中观尺度为主,多为单一型研究区,主要集中在西北丝绸之路经济带、东北辽宁经济圈、西南藏滇川渝、东部长三角四大热点区域;引文质量普遍较高,并且具有多学科交叉应用的特点,但批判性思考和创造性提升相对较少。基于此,论文从拓展研究和实践应用2个角度对“点-轴系统”理论未来发展进行了展望,以期为相关研究提供参考。     

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.     

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

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.     

城镇化与生态环境耦合的动态模拟——研究进展与展望

Urbanization and eco-environment coupling is a research hotspot.Dynamic simulation of urbanization and eco-environment coupling needs to be improved because the processes of coupling are complex and statistical methods are limited.Systems science and cross-scale coupling allow us to define the coupled urbanization and eco-environment system as an open complex giant system with multiple feedback loops.We review the current state of dynamic simulation of urbanization and eco-environment coupling and find that:(1)The use of dynamic simulation is an increasing trend,the relevant theory is being developed,and modeling processes are being improved;(2)Dynamic simulation technology has become diversified,refined,intelligent and integrated;(3)Simulation is mainly performed for three aspects of the coupling,multiple regions and multiple elements,local coupling and telecoupling,and regional synergy.However,we also found some shortcomings:(1)Basic theories are inadequately developed and insufficiently integrated;(2)The methods of unifying systems and sharing data are behind the times;(3)Coupling relations and the dynamic characteristics of the main driving elements are not fully understood or completely identified.Additionally,simulation of telecoupling does not quantify parameters and is not systemically unified,and therefore cannot be used to represent spatial synergy.In the future,we must promote communication between research networks,technology integration and data sharing to identify the processes governing change in coupled relations and in the main driving elements in urban agglomerations.Finally,we must build decision support systems to plan and ensure regional sustainable urbanization.     

试论新世纪地理信息科学的发展

本文重点分析了当前国内外地理信息科学的形成与发展趋势和地理信息科学不断发展的历程 ,提出近期发展的前沿 ,以及当前地理信息产业化面临的一些重要问题。最后提出对地理信息科学发展的意见。     

Canonical theory of perturbations using eccentric anomaly as independent variable

It is shown in this paper how to build a canonical transformation of variables, so that the eccentric anomaly becomes the new independent variable. In the case of eccentric elliptical orbits it changes the equations of motion so, that they can be integrated analytically to any order of approximation comparatively easy.