地理、网络与信任:金融危机背景下的生产网络演化

网络演化是演化经济地理学研究的重要内容和新近热点之一。在当前的理论探讨中主要强调路径依赖和惯例对网络演化的强化作用,对于信任这个关键要素缺乏深入探讨。本文通过深度访谈和问卷调查相结合的方法,对比金融危机前后广东省纺织服装行业生产网络的空间结构,揭示信任与生产网络演化之间的互动机制。研究发现:信任是维系生产网络稳定性的基本要素之一,信任的强弱程度主要受合作类型(市场关系)、地理接近(空间关系)和社会文化接近根植性(社会关系)等因素影响。金融危机作为一种负面外部冲击,重构了广东省服装行业的生产网络。在这个过程中,较强信任关系的网络连结(潮汕地区)得以保留,并在危机过后进一步强化;而部分较弱信任关系的网络连结(珠三角地区)发生断裂,且在经济回暖之后被潮汕地区的网络所取代。     

深圳市道路网络结构特征的成因及其景观格局效应

本文以我国典型快速城市化地区深圳市为例,综合使用GIS技术、道路网络结构特征分析、景观格局分析和相关分析方法研究其道路网络结构特征的成因及其景观生态效应。在确定了24个独立的空间研究单元的基础上,重点分析了深圳市道路网络结构特征的相关关系、城市化水平差异对道路网络结构特征的影响和道路格局特征的景观整体及重要组分的格局效应。结果表明:城市建设用地密度的增加导致交通用地密度、节点和廊道储量增加,道路网络结构复杂程度、格局指数降低;资源条件、环境和生态保护约束是导致道路网络复杂性增加、结构发育水平下降、网络格局指数不断降低的主要原因;深圳市的道路网络格局特征对全市景观整体格局没有表现显著的约束性影响,对建设用地显示出环境保护约束和空间吸引两个方面的综合效应,对于林地则表现出生态保护约束、空间排斥和物理分割三个方面的综合效应。     

基于物联网/RS集成的环境监测研究

提出一种利用物联网的无线传感器网络技术辅助遥感解译的环境监测方法。该方法通过集成无线传感器网络技术和卫星遥感技术,充分发挥无线传感器网络获取局部地面信息翔实性和遥感技术大面积获取环境信息方便性的优势互补,为环境监测以及环境保护提供数据决策支持。文章着重探讨了基于Zigbee的无线传感器网络与远距离的GPRS结合实现远程辅助遥感解译的环境监测方法,具有广阔的应用前景。     

Gap Filling and Quality Assessment of CO2 and Water Vapour Fluxes above an Urban Area with Radial Basis Function Neural Networks

Vertical turbulent fluxes of water vapour, carbon dioxide, and sensible heat were measured from 16 August to the 28 September 2006 near the city centre of Münster in north-west Germany. In comparison to results of measurements above homogeneous ecosystem sites, the CO₂ fluxes above the urban investigation area showed more peaks and higher variances during the course of a day, probably caused by traffic and other varying, anthropogenic sources. The main goal of this study is the introduction and establishment of a new gap filling procedure using radial basis function (RBF) neural networks, which is also applicable under complex environmental conditions. We applied adapted RBF neural networks within a combined modular expert system of neural networks as an innovative approach to fill data gaps in micrometeorological flux time series. We found that RBF networks are superior to multi-layer perceptron (MLP) neural networks in the reproduction of the highly variable turbulent fluxes. In addition, we enhanced the methodology in the field of quality assessment for eddy covariance data. An RBF neural network mapping system was used to identify conditions of a turbulence regime that allows reliable quantification of turbulent fluxes through finding an acceptable minimum of the friction velocity. For the data analysed in this study, the minimum acceptable friction velocity was found to be 0.15 m s⁻¹. The obtained CO₂ fluxes, measured on a tower at 65 m a.g.l., reached average values of 12 μmol m⁻² s⁻¹ and fell to nighttime minimum values of 3 μmol m ⁻² s⁻¹. Mean daily CO₂ emissions of 21 g CO₂ m⁻²d ⁻¹ were obtained during our 6-week experiment. Hence, the city centre of Münster appeared to be a significant source of CO₂. The half-hourly average values of water vapour fluxes ranged between 0.062 and 0.989 mmol m⁻² s⁻¹and showed lower variances than the simultaneously measured fluxes of CO₂.     

Ternary forecast of heavy snowfall in the Honam area,Korea

The objective of this study is to improve the statistical modeling for the ternary forecast of heavy snowfall in the Honam area in Korea. The ternary forecast of heavy snowfall consists of one of three values, 0 for less than 50 mm, 1 for an advisory (50–150 mm), and 2 for a warning (more than 150 mm). For our study, the observed daily snow amounts and the numerical model outputs for 45 synoptic factors at 17 stations in the Honam area during 5 years (2001 to 2005) are used as observations and potential pre...     

基于MATLAB的主成分RBF神经网络降水预报模型

以前期500 hPa高度场、海温场为预报因子,采用径向基函数(RBF)神经网络与主成分分析相结合的方法,建立了广西中部5月平均降水预报模型。在5年独立样本的预测检验中,预测的平均相对误差、均方误差及平均绝对误差分别为18.12%、50.52和34.23。对比分析RBF神经网络与BP(Back Propagation)神经网络的预测结果,表明RBF神经网络预测结果更准确、精度更高。     

基于贝叶斯网络分类的遥感影像变化检测

遥感成像过程中,地面、大气等诸多要素的不确定性和波段之间的相关性等原因影响了分类精度,导致变化检测的不准确性。为了提高分类精度往往需要引入先验知识。贝叶斯网络是一种新的数据表达和推理模型,对数据没有严格的正态分布前提要求,通过动态地调整先验概率密度,能有效提高分类精度。以北京通州地区1996-05-29和2001-05-19两个时相的陆地卫星Landsat TM遥感影像为例,介绍了基于贝叶斯网络的分类算法,并在此基础上实现了两个时相遥感影像的变化检测。实验结果表明:基于贝叶斯网络分类算法的后分类比较变化检测方法是遥感影像变化检测的一种新的有效方法。     

无线传感器网络中基于时钟漂移补偿的时间同步技术

在分析和总结现有时间同步算法的特点及其存在问题的基础上,提出了一种在无线传感器网络的时间同步协议（TPSN）中同时考虑节点时钟偏移和频率漂移率的改进算法。该算法利用连续两次同步过程中所记录的时间信息来估算节点时钟的偏移和频率漂移率,并进行补偿。同传统算法相比,此算法通讯成本较低、精度较高。最后通过数学分析和仿真的方法对所提出的改进算法进行了验证。     

Neural network-based model for assessing failure potential of highway slopes in the Alishan, Taiwan Area: Pre- and post-earthquake investigation

This paper is aimed at creating an empirical model for assessing failure potential of highway slopes, with a special attention to the failure characteristics of the highway slopes in the Alishan, Taiwan area prior to, and post, the 1999 Chi-Chi, Taiwan earthquake. The basis of the study is a large database of 955 slope records from four highways in the Alishan area. Artificial neural network (ANN) is utilized to “learn” from this database. The developed ANN model is then used to study the effect of the Chi-Chi earthquake on the slope failure characteristics in the Alishan area. Significant changes in the degrees of influence of several factors (variables) are found and possible reasons for such changes are discussed. The novelty of this paper lies in the fact that the developed ANN models are used as a tool to investigate the slope failure characteristics before and after the Chi-Chi earthquake.     

Prediction of pile settlement using artificial neural networks based on standard penetration test data

In recent years artificial neural networks (ANNs) have been applied to many geotechnical engineering problems with some degree of success. With respect to the design of pile foundations, accurate prediction of pile settlement is necessary to ensure appropriate structural and serviceability performance. In this paper, an ANN model is developed for predicting pile settlement based on standard penetration test (SPT) data. Approximately 1000 data sets, obtained from the published literature, are used to develop the ANN model. In addition, the paper discusses the choice of input and internal network parameters which were examined to obtain the optimum model. Finally, the paper compares the predictions obtained by the ANN with those given by a number of traditional methods. It is demonstrated that the ANN model outperforms the traditional methods and provides accurate pile settlement predictions.