基于轨迹偏移算法的居民就医时空特征与空间格局分析

居民就医时空特征与空间格局反映了医疗设施的服务能力与布局合理性.本文以厦门岛为例,采用出租车轨迹数据,探讨了居民就医的时空特征和空间格局.论文提出了基于道路中心线的研究单元划分方法;提出OD轨迹偏移算法,更精细地提取出三级医院的就医OD数据,改善传统的缓冲区分析法中精确度较低的问题;对居民就医行为进行时空特征分析;基于...     

国产磁通门磁力仪稳定性分析

国产磁通门磁力仪(GM-3磁通门磁力仪)在全国各地磁台站推广应用,两年来,该仪器在武汉地震台工作比较稳定。本文对该仪器的基线值进行了多层线性回归分析。结果表明仪器性能比较稳定。     

Efficient collection of training data for sub-pixel land cover classification using neural networks

Artificial neural networks (ANNs) are a popular class of techniques for performing soft classifications of satellite images. They have successfully been applied for estimating crop areas through sub-pixel classification of medium to low resolution images. Before a network can be used for classification and estimation, however, it has to be trained. The collection of the reference area fractions needed to train an ANN is often both time-consuming and expensive. This study focuses on strategies for decreasing the efforts needed to collect the necessary reference data, without compromising the accuracy of the resulting area estimates. Two aspects were studied: the spatial sampling scheme (i) and the possibility for reusing trained networks in multiple consecutive seasons (ii). Belgium was chosen as the study area because of the vast amount of reference data available. Time series of monthly NDVI composites for both SPOT-VGT and MODIS were used as the network inputs. The results showed that accurate regional crop area estimation (R² > 80%) is possible using only 1% of the entire area for network training, provided that the training samples used are representative for the land use variability present in the study area. Limiting the training samples to a specific subset of the population, either geographically or thematically, significantly decreased the accuracy of the estimates. The results also indicate that the use of ANNs trained with data from one season to estimate area fractions in another season is not to be recommended. The interannual variability observed in the endmembers’ spectral signatures underlines the importance of using up-to-date training samples. It can thus be concluded that the representativeness of the training samples, both regarding the spatial and the temporal aspects, is an important issue in crop area estimation using ANNs that should not easily be ignored.     

北京大灰厂试验场重力变化特征及其解释

介绍了北京大灰厂试验场（简称京西试验场）的概况，对１９９３年３月以来重力重复观测资料进行了处理。计算了各期重力观测精度，结果显示１９９３年３月～１９９５年５月京西试验场存在一定重力变化。采用给定的模型反演了八宝山断层介质的密度变化，认为断层破碎带介质密度的变化可能是引起地表重力变化的主要因素     

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.     

地面三维激光雷达点云误差分析及校正方法

分析了地面三维激光雷达点云误差来源,提出了相应的误差校正模型。通过实测数据验证,该模型能够消除系统误差,提高了点云精度。     

基于GIS技术的县域城市规划数据管理模式应用的研究——以广东省增城市为例

随着近年来GIS技术在城乡规划方面的应用日趋普及,GIS技术在空间数据采集、数据管理与分析等关键环节上的优势也逐渐显露。同时,县域城市城乡规划数据种类繁杂,数据量大,亟需先进的技术手段对其进行有效组织与管理。采用GIS技术对城乡规划数据进行动态管理和更新将给城乡规划带来极大的便利。本文以广东省增城市规划管理信息系统为案例,系统阐述GIS技术在城乡规划数据管理中的应用。