Volume data mining using 3D field topology analysis

This article proposes a novel approach to automating the settings of visualization parameter values for volume data mining. To this end, we extended the conventional Reeb graph-based approach to topological modeling of 3D surfaces to capture the topological skeleton of a volumetric field. The analyzed results take the form of hyper Reeb graphs which give the basic reference structure for designing comprehensible volume visualization     

Data mining for large scale 3D seismic data analysis

The automation of the analysis of large volumes of seismic data is a data mining problem, where a large database of 3D images is searched by content, for the identification of the regions that are of most interest to the oil industry. In this paper we perform this search using the 3D orientation histogram as a texture analysis tool to represent and identify regions within the data, which are compatible with a query texture.     

下视SAR数据3维表面重建

目的 合成孔径雷达(SAR)因成像方法、几何角度等原因使得采集到的数据具有稀疏性及残缺性,如果直接用其进行建模,不能真实地还原物体。针对下视SAR数据的特点,提出一种在建模过程中能够自动修补稀疏及残缺数据的重建方法。方法 首先引入大津法对3维SAR数据进行预处理,然后将2维图像分割方法中的Chan-Vese模型推广应用到下视SAR数据的表面重建中,在初始表面及轮廓指示函数的求取过程中引入距离函数和内积函数。结果 将本文方法与等值面抽取法的重建结果进行比较,本文方法在重建的过程中能够自动修补空洞,重建出的模型表面更加光滑,能更加真实地反映原物体的特征。结论 可以将本文方法推广应用到稀疏及残缺SAR数据的建模中。     

基于数据挖掘的银行全成本分析

针对银行全成本分析的业务特点和数据挖掘各种算法的应用特征,提出了基于关联规则的分类算法在银行全成本分析系统中的分析模型.将此模型与其他机器学习分类算法进行实验比较,得出此算法在该领域的最佳效果,所挖掘出的规则得到银行工作人员的肯定.     

Mapping cotton fields using data mining and MODIS time-series

ABSTRACT

Cotton is the most important fibre culture in the world. In Brazil, cotton cultivation is concentrated in the Cerrado biome, the Brazilian savanna, and is one of the most important commodities in the country. As an annual crop, the updating frequency of the spatial distribution data of cotton fields is extremely important for crop monitoring systems. In order to provide fast and accurate information for crop monitoring, time series of remote- sensing data has been used in the development of several applications in agriculture, since the high temporal resolution of some orbital sensor allows monitoring targets with high spectral-temporal variations in the land surface. However, there are still some challenges to systematize the processing of such a large amount of data available by long time series of remote-sensing imagery. Thus, this study contributes to the construction of models to identify and separate specific crop types with similar spectral behaviour to other crops practised in the same period. The objective of this study was to develop a systematic methodology based on data mining of time series of vegetation indices (VI) to map cotton fields at the regional scale. Field reference data and time series of NDVI and EVI images, obtained from MODIS sensor products during four cropping seasons (from 2012–2013 to 2015–2016), were used to construct mapping models based on decision tree algorithms. Phenological metrics were calculated from the VI time series and used to build classification rules for mapping cotton fields. Our results demonstrate that the proposed method to map cotton fields achieve high accuracy when field data and visual interpretation of NDVI temporal profiles were used for validation (accuracy higher than 95% and 93%, respectively). Comparisons with the official statistics indicated an optimal fit, with linear correlation (r) and coefficient of determination (R²) above 0.93. Therefore, the proposed method was efficient to distinguish cotton fields from other crop types with similar spectral behaviour. In addition, this method can also be applied to other cotton-producing regions and other production seasons, by reusing the models generated through machine learning approaches.     

基于产品生命周期的质量数据分析与数据挖掘

针对传统质量管理理念约束下,离散制造企业质量管理体系条块分割,管理活动分散且对大规模质量数据分析匮乏的问题,文章从产品生命周期的角度对质量数据进行了分析整理,在进行数据挖掘之前对数据的获取、数据的预处理以及数据的转化进行了讨论,构建了ETL数据预处理模型.然后对具体的数据挖掘应用方法进行了研究,提出了产品生命周期质量数据挖掘整体体系结构,并对结构中包含的数据层、方法层、知识层、应用层进行了分析阐述.     

基于数据挖掘的自适应异常分析

正常用户行为活动是随时间变化的,一个异常分析系统要能适应这种变化更新正常行为模型,避免误报警.对增量更新算法进行了研究,使用线性回归的方法对相似度进行估计,如果实际相似度与估计值差值大于某个阈值,则产生报警;否则采用改进的滑动窗增量挖掘的方法,更新正常活动模型.并用DARPA-MIT 1999数据集验证其可行性.     

基于数据挖掘技术的证券客户分析系统

基于数据挖掘技术研究并实现了证券客户分析系统,通过对数据进行详细的分析和预处理,通过数据挖掘工具SPSS CLEMENTINE8.0中的K-平均值、C5.0算法建立模型,并运用模型预测最有潜力的客户,实际应用验证了其准确性.     

基于数据挖掘的协议分析检测模型

针对现有入侵检测系统的不足,根据入侵和正常访问模式的网络数据表现形式的不同以及特定数据分组的出现规律,提出按协议分层的入侵检测模型,并在各个协议层运用不同的数据挖掘方法抽取入侵特征,以达到提高建模的准确性、检测速度和克服人工提取入侵特征的主观性的目的。其中运用的数据挖掘算法主要有关联挖掘、序列挖掘、分类算法和聚类算法。     

Customizing 3D garments based on volumetric deformation

Improving the reusability of design results is very important for garment design industry, since designing an elegant garment is usually labor-intensive and time-consuming. In this paper, we present a new approach for customizing 3D garment models. Our approach can transfer garment models initially dressed on a reference human model onto a target human model. To achieve this goal, firstly a spatial mapping between the two human models is established with the shape constraints of cross-sections. Secondly, the space around the clothed reference human model is tetrahedralized into five tetrahedral meshes each of which either can be worked dependently with its adjacent ones or can be worked independently. The clothed reference human model is parametrically encoded in the tetrahedral meshes. Thirdly, these tetrahedral meshes are deformed by fitting the reference human model onto the target human model by using constrained volumetric graph Laplacian deformation. The updated garment models are finally decoded from the deformed tetrahedral meshes. As a result, the updated garment models are fitted onto the target human model. Experiments show that our approach performs very well and has the potential to be used in the garment design industry.     

基于三维特征提取的人体运动数据分析和检索

基于各关节点三维空间和时间关系的三维特征,提出了一种描述各关节点之间三维空间关系的时空特征,可以分别处理每一个关节点的局部时空特征。同时,三维时空特征避开与原始数据的直接接触,从而很大程度避免了维数灾难。实验结果显示,三维特征提高运动数据检索的效率和精度,可应用到运动语义智能分析等领域。     

The analysis of chaotic time-series data

This paper presents a brief survey of time-series analysis methods that are applicable to processes whose behavior can be described as low-dimensional chaos. The goal of these methods is to allow experimentalists to obtain local estimates of the dynamics directly from a set of data. These estimates are often sufficiently accurate to attempt noise reduction, prediction, and control.     

基于几何特征的三维模型变形与编辑*

在三维计算的基础上针对三维颅面模型,提出了一种基于几何特征的三维模型变形与编辑算法。该算法采用迭代过程,即每进行一次变形便可以重新计算调整后的颅面几何特征值,如果满足需要则退出编辑并保存模型;否则继续变形,直到得到满意的三维模型为止。在局部变形中,为了弥补自由变形技术在变形控制和用户交互方面的不足,对点约束局部变形算法进行了改进。实验证明,该算法实现简单,且具有良好的交互性,变形效果良好。     

A deep learning approach using graph convolutional networks for slope deformation prediction based on time-series displacement data

Neural Computing and Applications - Slope deformation prediction is crucial for early warning of slope failure, which can prevent property damage and save human life. Existing predictive models...     

基于语义参数的三维复杂模型变形方法研究

针对含有自由曲面的三维模型不易变形的问题,提出一种基于语义参数的三维复杂模型变形方法,旨在实现三维模型的快速设计。该方法利用变形控制点作为模型变形的基本元素,通过设置语义参数实现层次化变形：以变形控制点带动分组部件变形,以分组部件促进三维模型整体变形。首先,采用Hausdorff测距法和均值聚类法,将已分组的电动车的自由曲面替换成二次曲面;然后,根据变形控制点间及与部件顶点的关系,计算各分组部件内部和外部的约束条件;最后,编辑修改定义在变形控制点上的语义参数,实现模型自定义变形。以电动车三维模型验证实验,结果表明,本方法支持用户通过修改少量语义参数对模型变形,有效提升三维模型个性化设计的效率。     

一种新的数据预处理算法--NLCA

针对数据预处理的方法进行了研究,提出了基于非线性相关性分析与量化（Non-Linear Correlation Analysis,NLCA）算法。NLCA算法是一种基于在多重图中通过对多重边聚合从而达到约简的工具,它包括边聚合与点聚合。这种算法能够很好地表示实时数据全局的相关性,改进了现有使用联合概率的单一计算方法。对该算法进行了大量实际数据的验证,显示出它是一种优于现有的数据预处理方法。     

Interactive visual analysis of time-series microarray data

Estimating dynamic regulatory pathways using DNA microarray time-series can provide invaluable information about the dynamic interactions among genes and result in new methods of rational drug design. Even though several purely computational methods have been introduced for DNA pathway analysis, most of these techniques do not provide a fully interactive method to explore and analyze these dynamic interactions in detail, which is necessary to obtain a full understanding. In this paper, we present a unified modeling and visual approach focusing on visual analysis of gene regulatory pathways over time. As a preliminary step in analyzing the gene interactions, the method applies two different techniques, a clustering algorithm and an auto regressive (AR) model. This approach provides a successful prediction of the dynamic pathways involved in the biological process under study. At this level, these pure computational techniques lack the transparency required for analysis and understanding of the gene interactions. To overcome the limitations, we have designed a visual analysis method that applies several visualization techniques, including pixel-based gene representation, animation, and multi-dimensional scaling (MDS), in a new way. This visual analysis framework allows the user to quickly and thoroughly search for and find the dynamic interactions among genes, highlight interesting gene information, show the detailed annotations of the selected genes, compare regulatory behaviors for different genes, and support gene sequence analysis for the interesting genes. In order to enhance these analysis capabilities, several methods are enabled, providing a simple graph display, a pixel-based gene visualization technique, and a relation-displaying technique among gene expressions and gene regulatory pathways.     

基于3D和VIRTOOLS技术的虚拟实验的实验数据分析研究

虚拟实验生成的数据是虚拟实验的重要组成部分.在开发基于3D和VIRTOOLS技术的虚拟实验过程中,使用了两种实验数据的分析方法:一种是外部程序实验数据分析方法-VEDA,即使用JSP技术对实验数据进行分析,并将其结果以文本和图表形式返回给用户;另一种是内部程序实验数据分析方法,即直接采用VIRTOOLS的BBs编程来实现的实验数据分析.对这两种方法进行比较分析,实验表明方法VEDA具有易实现,灵活等特点.     

基于3D和VIRTOOLS技术的虚拟实验的实验据分析研究

虚拟实验生成的数据是虚拟实验的重要组成部分。在开发基于3D和VIRTOOLS技术的虚拟实验过程中,使用了两种实验数据的分析方法:一种是外部程序实验数据分析方法-VEDA,即使用JSP技术对实验数据进行分析,并将其结果以文本和图表形式返回给用户;另一种是内部程序实验数据分析方法,即直接采用VIRTOOLS的BBs编程来实现的实验数据分析。对这两种方法进行比较分析,实验表明方法VEDA具有易实现、灵活等特点。