辅助模型辨识方法(1):自回归输出误差系统

研究了自回归输出误差(AR-OE)系统的辅助模型随机梯度算法、辅助模型多新息随机梯度算法、辅助模型递推最小二乘算法,自回归输出误差自回归滑动平均(AR-OEARMA)系统(即AR-Box-Jenkins系统)的辅助模型广义增广随机梯度算法、辅助模型多新息广义增广随机梯度算法、辅助模型递推广义增广最小二乘算法,以及AR-Box-Jenkins系统的基于滤波的辅助模型广义增广随机梯度算法、基于滤波的辅助模型多新息广义增广随机梯度算法、基于滤波的辅助模型递推广义增广最小二乘算法等.     

基于WGS-84椭球面的退化四叉树经纬线格网剖分

针对数字地球的发展和解决全球问题的需求,给出了一种基于WGS-84椭球面的全球离散格网剖分方法——退化四叉树经纬线格网;分析了格网剖分的特点,明确了格网单元行列定义和经纬度坐标;计算出不同剖分层次格网单元面积、边长的变化情况并与基于正球面的剖分进行了对比。结果表明该格网系统具有粒度相对均匀性、层次嵌套性、方向一致性、地理条带性和对称性、广泛数据兼容性等许多优良特征,对构建全球地理信息系统具有一定的实用价值。     

Multiscale modeling of large deformation in geomechanics

Large deformation soil behavior underpins the operation and performance for a wide range of key geotechnical structures and needs to be properly considered in their modeling, analysis, and design. The material point method (MPM) has gained increasing popularity recently over conventional numerical methods such as finite element method (FEM) in tackling large deformation problems. In this study, we present a novel hierarchical coupling scheme to integrate MPM with discrete element method (DEM) for multiscale modeling of large deformation in geomechanics. The MPM is employed to treat a typical boundary value problem that may experience large deformation, and the DEM is used to derive the nonlinear material response from small strain to finite strain required by MPM for each of its material points. The proposed coupling framework not only inherits the advantages of MPM in tackling large deformation engineering problems over the use of FEM (eg, no need for remeshing to avoid mesh distortion in FEM), but also helps avoid the need for complicated, phenomenological assumptions on constitutive material models for soil exhibiting high nonlinearity at finite strain. The proposed framework lends great convenience for us to relate rich grain-scale information and key micromechanical mechanisms to macroscopic observations of granular soils over all deformation levels, from initial small-strain stage en route to large deformation regime before failure. Several classic geomechanics examples are used to demonstrate the key features the new MPM/DEM framework can offer on large deformation simulations, including biaxial compression test, rigid footing, soil-pipe interaction, and soil column collapse.     

基于深度学习的介形类化石识别

介形类化石对地质年代的确定、古湖泊和古海洋的研究、古环境的重建以及海底石油资源的勘探等工作都具有重要意义。然而,现有识别化石颗粒的方法费时费力,准确率也有待提高。鉴于介形类化石颗粒的类别具有科、属、种的层次结构,种类数量庞大,所以本文提出了一种层次化识别方法。首先进行目标检测,实现介形类化石的定位与属类划分;之后在目标检测模块的基础上进行智能识别,使用卷积神经网络和支持向量机提取属类下更细微的种类特征,实现化石种类划分。实验结果表明,本文提出的分层次识别模型能检测出化石图像中所有化石颗粒的位置信息并对其进行分类,分类准确率可达95%,且相较于未进行分层次识别的模型,能将识别准确率提升1.8%~5.8%。     

线性参数系统的多新息辨识方法

系统有线性和非线性之分.线性系统有统一的描述形式,非线性系统因类别无数,不可能有统一描述.线性参数系统是一类特殊的非线性系统,它在参数空间上呈现线性特征,介于线性系统与非线性系统之间.针对伪线性参数系统,讨论了基于辅助模型的多新息辨识方法、基于滤波的辅助模型多新息辨识方法、基于模型分解的辅助模型多新息辨识方法、基于滤波的分解多新息辨识方法,并给出了几个典型辨识算法的计算量、计算步骤和流程图.     

道路网层次骨架控制的道路选取方法

提出了基于路网层次骨架控制的道路自动选取方法。首先,该方法以stroke为单位,利用中介中心性值对stroke进行层次结构划分;其次,采用结构特征识别完善道路层次骨架;第三,基于层次骨架间的连通关系建立逐层传递的stroke重要性计算模型;最后,通过该计算模型自上而下的逐层计算,得到stroke重要性,并据此完成道路网选取。采用3种典型道路网数据,对本方法与其他主要常规stroke选取方法进行对比,试验结果表明本方法不但对语义的依赖性极低,同时消除了中介中心性在评价道路重要性时对边缘道路的不利影响,在保持道路网整体结构和层次性上有较为明显的优势,可适用于各种形态的道路网选取。     

Nature and theoretical foundation of ecoregionalization:a case study in China

lintroductionPhysicalgeographers,ecologistSandotherresearchershavebeeninvolvedinanal}aingspahalchrenhationofvariousfactorsontheearthsurface,suchasbiome,close,andsoil,toexplainandunderstandthephysicogeographicalprocessesandtheirSPatialpatternssincetheestablislunentoftheacademicfieldsofgeogT'aphyandecology.Theynotedablythattheregional~onoffactorsonthe~~cewashelpfulnotonlytoimpellingthedevelopmentofgeographyandecology,butalsotobroadeningtheirapplicahons,andtheybeganclasssingfactorswithvallousmet…     

Mapping multiple tree species classes using a hierarchical procedure with optimized node variables and thresholds based on high spatial resolution satellite data

ABSTRACT

Tree species distribution mapping using remotely sensed data has long been an important research area. However, previous studies have rarely established a comprehensive and efficient classification procedure to obtain an accurate result. This study proposes a hierarchical classification procedure with optimized node variables and thresholds to classify tree species based on high spatial resolution satellite imagery. A classification tree structure consisting of parent and leaf nodes was designed based on user experience and visual interpretation. Spectral, textural, and topographic variables were extracted based on pre-segmented images. The random forest algorithm was used to select variables by ranking the impact of all variables. An iterating approach was used to optimize variables and thresholds in each loop by comprehensively considering the test accuracy and selected variables. The threshold range for each selected variable was determined by a statistical method considering the mean and standard deviation for two subnode types at each parent node. Classification of tree species was implemented using the optimized variables and thresholds. The results show that (1) the proposed procedure can accurately map the tree species distribution, with an overall accuracy of over 86% for both training and test stages; (2) critical variables for each class can be identified using this proposed procedure, and optimal variables of most tree plantation nodes are spectra related; (3) the overall forest classification accuracy using the proposed method is more accurate than that using the random forest (RF) and classification and regression tree (CART). The proposed approach provides results with 3.21% and 7.56% higher overall land cover classification accuracy and 4.68% and 10.28% higher overall forest classification accuracy than RF and CART, respectively.     

基于SPOT-5卫星影像的胶东地区果园信息分区分层提取方法研究

 研究了综合应用纹理、光谱及空间等信息建立SPOT-5图像分区分层自动提取果园信息模型的方法，取得了较好的分类精度。