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.     

论社会共同价值观

社会共同价值观是社会共同体之所以能存在和发展的凝聚力、它指导社会共同体及其成员的前进方向、它还规范和约束社会共同体成员的思想和行动以形成社会共同体的合力。社会共同价值观是在一定历史时期的特定社会共同体中各社会阶层的社会成员普遍认同并持有的价值观。社会共同价值观具有普遍性、结构性、稳定性等特征 ,具有凝聚功能、导向功能和规约功能。社会共同价值观具有理论体系的形式、文化形式、社会心理的形式等存在形式     

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.     

在社会发展的层面上认识“数字地球”

 本文讨论了“数字地球”的有关问题。指出“数字地球”是人类社会及其活动在网络空间的延伸和拓展,是信息技术带来的社会发展趋势。关于“数字地球”的讨论不应局限在空间信息技术领域,而应该在社会发展的层面上认识它     

Probing the (in)compatibilities of social theory and policy relevance in Piers Blaikie’s political ecology

One of Piers Blaikie’s most important contributions to the development of political ecology is his critique of land and resource conservation policy in the global South. In this paper I trace the development of Blaikie’s ideas about the policy relevance of political ecology, focusing particularly on the challenges posed by the introduction of poststructural social theory into the field. I begin by revisiting Blaikie’s earlier critiques of environment and development policy. This will provide the departure point to explore how his thinking on the relationship of theory and policy and of academic and development practices has evolved in subsequent writings. I have invented two personas, “early Blaikie” and “late Blaikie”, to facilitate this task. Second, I want to probe some of the challenges that late Blaikie presents for doing political ecology research, to some extent by pitting early Blaikie against late Blaikie and letting them hash it out. Third, I turn to my own and others’ research and consultation experiences as a way to examine the possibilities for reconciling theoretically driven critiques with policy relevant research.     

Church-based social capital, networks and geographical scale: Katrina evacuation, relocation, and recovery in a New Orleans Vietnamese American community

This research examines the role of social capital and networks to explain the evacuation, relocation, and recovery experiences of a Vietnamese American community in New Orleans, Louisiana in the aftermath of Hurricane Katrina. As the single largest community institution, the parish church’s complex bonding and bridging social capital and networks proved particularly critical in part because of its historically based ontological security. The process of evacuation, but especially relocation and recovery, was dependent on deploying co-ethnic social capital and networks at a variety of geographical scales. Beyond the local or community scale, extra-local, regional, and national scales of social capital and networks reproduced a spatially redefined Vietnamese American community. Part of the recovery process included constructing discursive place-based collective-action frames to successfully contest a nearby landfill that in turn engendered social capital and networks crossing ethnic boundaries to include the extra-local African American community. Engaging social capital and networks beyond the local geographical scale cultivated a Vietnamese American community with an emergent post-Katrina cultural and political identity.     

‘We are managing!’ Uncertain paths to respectable adulthoods in Accra, Ghana

Studies have shown that the life experiences and future prospects of young people in many parts of the world today are more complex and less predictable than those of past generations. This has led to a recognition of the need to reconsider the conceptual frameworks applied in youth studies. Thus far, research within geography has mainly focused on young people’s paths to adulthood in the Global North or on particularly vulnerable children’s lives in the Global South. In this paper an approach based on the ‘social navigation’ of ‘vital conjunctures’ is adopted to explore the complex ways in which young people in Ghana navigate their way through life in a city marked by economic hardship and neo-liberal reform. The stories of three young people are presented to show the variety of life conditions, the complex social positions they occupy and their various and shifting future imaginations. The actions of these young people are steered by prevailing cultural ideals and social expectations of what a desirable and respectable future entails and by the actual space of possibility in the urban space they move through. It is argued that achieving respectable adulthoods in present-day Accra is a complicated and convoluted journey involving the careful management of social relationships.     

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。