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.     

论虚拟企业

网络的出现和广泛运用，以及企业间的跨越了行业间的藩篱，推动了业际化的进程；业际化与网络相结合促使企业组织形式发生质变，出现了虚拟企业。与大工业时代的企业相比较，虚拟企业具有了全新的特征、类型、经济形式和生产模式，其经济效益提升到一个新的平台。     

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

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.     

Prediction of Stellar Atmospheric Parameters using Instance-Based Machine Learning and Genetic Algorithms

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.     

STUDY ON BEIJING''''S EMERGING MOBILE COMMUNICATION INDUSTRIAL CLUSTER AND ITS POLICY IMPLICATIONS

1INTRODUCTIONClusters,definedasgeographicallyproximategroupsofinterconnectedcompaniesandassociatedinstitutionsinparticularfields,linkedbycommonalitiesandcomple-mentarities(PORTER,2000),havearousedanintenseinterestofurbanandregionalplanningresearchersandpolicymakersoverthepastdecades.Itiswidelyrecog-nizedthatclusterscanpromoteproductivityandinno-vation,developlocalcompetitiveadvantages.Popular-izedbyMichaelPORTERinhisbook“TheCompetitiveAdvantageofNationsin1990,theclusterisnotanewcon…     

海岛旅游效率空间格局演变及影响因素研究 ——以中国12个海岛县(区)为例

旅游产业是海岛地区的重要产业之一，借助 DEA 模型测度 12 个海岛县 （区） 的旅游效率，采用组合方法对海岛旅游效率的空间特征演变进行刻画，并通过 EVIEWS 软件分析海岛旅游效率的影响因素。 （1） 海岛旅游综合效率总体不高，技术效率是海岛旅游效率发展的主要驱动力，规模效率水平较低，海岛旅游发展能力未得到全面发挥。 （2） 海岛县 （区） 之间旅游效率总体差异不大，相对差异明显。海岛旅游效率空间状态演变由不稳定趋向于稳定，可能存在“贫困陷阱”和“俱乐部趋同”现象。 （3） 经济发展水平、生态环境、科技信息对海岛旅游效率的发展起到积极的推动作用，产业结构、交通条件、城镇化是海岛旅游效率发展的约束性因素。     

水下智能潜器的神经网络运动控制

本文介绍一种基于神经网络的水下智能潜器的运动控制方法，该方法通过在线学习，融控制与滤波为一体。计算机仿真与水池实验验证表明，该方法的控制与滤波性能良好，对环境的学习与适应能力强。该方法事实上可用于一般动力系统的控制。     

流形上的空间密度聚类算法研究

研究流形上的聚类分析，针对基于密度的空间聚类引入了流形概念，提出1种基于流形的密度聚类算法，该方法将流形的概念与聚类相结合，可以适用于样本为复杂分布的聚类。文中通过实例证明此算法的有效性。     

A brief catalogue of failures: Framing evaluation and learning in fisheries resource management

We propose a framework for enabling a systematic evaluation of a fisheries resource management system, which we define as a feed-back mechanism coupled to a fishery. The resource management system includes four basic functions: diagnostics, intervention, goal setting, and decision making. This model allows for the development of an evaluation framework for fisheries resource management by facilitating a typology of failures. We suggest that the potential for systemic and interdisciplinary learning will be significantly enhanced through the process of developing such a framework.