石油测井三维数据场断层可视化算法研究

在总结前人关于限制Delaunay三角剖分、地质断层理论基础上,提出了一套全新的断层处理方法。本文提供的断层处理方法的最大优势在于能以统一模式处理各种类型的断层（正断层、逆断层、平移断层）,有效地解决了含逆断层、非凸包内边界地质体重构的难题。文中介绍的方法均已在Forward．NET软件平台两维、三维绘图中实践。     

Fully Automatic Visualisation of Overlapping Sets

Visualisation of taxonomies and sets has recently become an active area of research. Many application fields now require more than a strict classification of elements into a hierarchy tree. Euler diagrams, one of the most natural ways of depicting intersecting sets, may provide a solution to these problems.
In this paper, we present an approach for the automatic generation of Euler-like diagrams. This algorithm differs from previous approaches in that it has no undrawable instances of input, allowing it to be used in systems where the output is always required. We also improve the readability of Euler diagrams through the use of Bézier curves and transparent coloured textures. Our approach has been implemented using the Tulip platform. Both the source and executable program used to generate the results are freely available.     

一种从不完备数据集中发现规则的RS方法

Rough集(Rough SetS、Rs)理论被广泛应刑于数据分类问题,该文用基于RS的方法从不完备数据集中产生确定和可能的规则集,提出了一种新的规则发现算法,可以同时从不完备数据集中产生规则和估计缺失值,并指出了进一步的研究方向。     

Fitting Related Sets of Straight-line Data

Abstract

Many physical experiments give rise to sets of curves related by the requirement that, although certain of the curve parameters may vary from curve to curve, others should be the same for all of the curves. To get the “best” values of the common parameters, one would like to fit all of the curves simultaneously by the appropriate theoretical expressions. This paper deals with this problem, presenting an algorithm for its solution, in the case that the curves are straight lines with common slope and “best” fit is defined in the uniform (or minimax) sense.     

地震数据的无损压缩存储

为解决地震数据的无损压缩存储问题,研究了多种地震数据标准以及整型和浮点型数据的无损压缩算法.在此基础上,提出了地震数据的无损压缩存储方法.该方法通过分析多种地震标准的内部结构特点,有针对性地对整型和浮点型数据分别进行无损压缩.在保证了地震数据文件中各参数、标识等信息不被破坏的条件下,最大限度地减小了地震数据文件的大小.试验证明,本方法不仅对整型数据而且对于浮点型数据都可以做到无损的数据压缩与还原.     

基于CUDA的地震数据层位面自动追踪算法

层位面追踪是三维地震数据解释的一项关键操作,对于地质勘探和油藏工程有重要的指导意义。传统的层位面自动追踪算法采用的是种子点蔓延法,该方法是一个不断进行迭代计算的过程。由于三维地震数据的数据量很大,传统的基于CPU的层位面自动追踪算法的计算效率不高。文中提出了一种基于CUDA的三维层位面自动追踪算法。该算法对种子点蔓延法进行了改进,实现了层位面自动追踪的GPU并行解决方案。实验表明,与传统的基于CPU的层位面自动追踪算法相比,本算法的计算效率有明显的提升。     

Automatic Surface Reconstruction from Point Sets in Space

In this paper an algorithm is proposed that takes as input a generic set of unorganized points, sampled on a real object, and returns a closed interpolating surface. Specifically, this method generates a closed 2-manifold surface made of triangular faces, without limitations on the shape or genus of the original solid. The reconstruction method is based on generation of the Delaunay tetrahedralization of the point set, followed by a sculpturing process constrained to particular criteria. The main applications of this tool are in medical analysis and in reverse engineering areas. It is possible, for example, to reconstruct anatomical parts starting from surveys based on TACs or magnetic resonance.     

大型数据集的多维变量关联性挖掘

为了探索多维数据集中各变量之间的关系,特别是非函数关系,对数据集所在的n维立方体的各个维度进行划分,在得到的n维网格中定义自然概率密度函数,以此得到数据集在特定划分下的互信息值。对所有的划分取互信息最大值,正态化后即为所定义的特征张量的一项,取特征张量在给定最大网格数下的最大项的值定义为MIC,它度量了多维变量间的关联程度。     

燃气发电机组自动并网测控系统的设计与实现

为解决自备燃气发电机组电网与供电电网的安全并网问题,研制了一种以DSP芯片TMS320F2812为核心的发电机组自动并网测控系统.充分利用DSP芯片内部的高速A/D、CAN总线接口及其强大处理能力,使该系统具有很高的性价比.     

Simultaneous Graphic Generalization of Vector Data Sets

Manual cartographic generalization is a simultaneous process. However, most automatic approaches so far have been sequential; generalization operators are applied one at a time in a certain order. This has been the case both for model generalization (generalization of the conceptual model) and graphic generalization. Our research seeks to demonstrate that the graphic part of cartographic generalization can be formulated as an optimization problem and accordingly be solved in a single step. This paper deals with several issues regarding this optimization approach. Firstly, a set of appropriate analytical constraints for the generalization process is given, as well as rules for when to apply these constraints. In our approach, we are limited to formulating these constraints on point locations. Secondly, least-squares adjustment is proposed to find the optimal solution according to the constraints. Finally, the conjugate-gradients method is recommended for solving the normal equations. A prototype system for simultaneous graphic generalization has been implemented in C++, which communicates with a commercial map production system. Results from three tests of the prototype system are included in the paper.     

Fully Automatic Registration of Image Sets on Approximate Geometry

The photorealistic acquisition of 3D objects often requires color information from digital photography to be mapped on the acquired geometry, in order to obtain a textured 3D model. This paper presents a novel fully automatic 2D/3D global registration pipeline consisting of several stages that simultaneously register the input image set on the corresponding 3D object. The first stage exploits Structure From Motion (SFM) on the image set in order to generate a sparse point cloud. During the second stage, this point cloud is aligned to the 3D object using an extension of the 4 Point Congruent Set (4PCS) algorithm for the alignment of range maps. The extension accounts for models with different scales and unknown regions of overlap. In the last processing stage a global refinement algorithm based on mutual information optimizes the color projection of the aligned photos on the 3D object, in order to obtain high quality textures. The proposed registration pipeline is general, capable of dealing with small and big objects of any shape, and robust. We present results from six real cases, evaluating the quality of the final colors mapped onto the 3D object. A comparison with a ground truth dataset is also presented.     

面向文本数据的正则化交叉验证方法

面向文本数据建模时,交叉验证方法是特征选择及模型比较任务中的常用方法。许多研究表明,文本数据模型的性能估计对交叉验证的数据切分方式较为敏感,不合理的切分方式可能会导致不稳定的性能估计值,使得实验结果可复现性差。该文试图论证基于多次重复(m次)的2折交叉验证,通过引入对训练集、验证集分布差异的约束,所构造的正则化m×2交叉验证方法(简记为m×2 BCV)可以改善模型的性能指标的估计,适宜于模型比较。该文首先针对文本数据引入训练集与验证集分布差异的卡方度量,基于该度量构建数据切分的正则化条件,以最大化模型性能指标的信噪比为目标,给出了满足正则化条件的m×2 BCV的数据切分优化算法。最后,以自然语言处理中汉语框架语义角色标注任务为例,验证了基于m×2 BCV方法的有效性。     

Using Self-Similarity to Cluster Large Data Sets

Clustering is a widely used knowledge discovery technique. It helps uncovering structures in data that were not previously known. The clustering of large data sets has received a lot of attention in recent years, however, clustering is a still a challenging task since many published algorithms fail to do well in scaling with the size of the data set and the number of dimensions that describe the points, or in finding arbitrary shapes of clusters, or dealing effectively with the presence of noise. In this paper, we present a new clustering algorithm, based in self-similarity properties of the data sets. Self-similarity is the property of being invariant with respect to the scale used to look at the data set. While fractals are self-similar at every scale used to look at them, many data sets exhibit self-similarity over a range of scales. Self-similarity can be measured using the fractal dimension. The new algorithm which we call Fractal Clustering (FC) places points incrementally in the cluster for which the change in the fractal dimension after adding the point is the least. This is a very natural way of clustering points, since points in the same cluster have a great degree of self-similarity among them (and much less self-similarity with respect to points in other clusters). FC requires one scan of the data, is suspendable at will, providing the best answer possible at that point, and is incremental. We show via experiments that FC effectively deals with large data sets, high-dimensionality and noise and is capable of recognizing clusters of arbitrary shape.     

Scaling Kernel-Based Systems to Large Data Sets

In the form of the support vector machine and Gaussian processes, kernel-based systems are currently very popular approaches to supervised learning. Unfortunately, the computational load for training kernel-based systems increases drastically with the size of the training data set, such that these systems are not ideal candidates for applications with large data sets. Nevertheless, research in this direction is very active. In this paper, I review some of the current approaches toward scaling kernel-based systems to large data sets.     

面向相似数据集的关系数据库压缩

采用关系数据库模型进行建模,对于同一关系框架上的数据定义了相似数据集。对单个数据集,通过关系拆分对数据库模型进行规范化处理,去除了关系内部的数据冗余;对多个数据集之间的压缩提出了一种基于0-1状态标记序列的增量式无损压缩算法,压缩后的数据可以快速地完全解压缩。试验结果表明,算法可以实现对相似数据集的高效无损压缩和快速查询。     

基于不平衡类数据集分类的空间插值方法

对于不平衡类数据集的分类问题,训练分类器后,分类结果产生了向多数类偏斜的问题,少数类识别率较低.为了提高少数类的分类精度,提出了一种改进的SMOTE方法—空间插值方法,利用少数类及其k近邻构造超几何体,在超几何体内随机产生虚拟少数类样本,当其k近邻中存在多数类时,缩小构造虚拟样本的空间,加强对易错分样本的训练,降低数据集类不平衡程度,并进行有效性验证.在实际数据集上,基于多个分类器进行仿真,结果表明,空间插值法在少数类和数据集整体分类性能优化效果较好.     

Scale Space Meshing of Raw Data Point Sets

This paper develops a scale space strategy for orienting and meshing exactly and completely a raw point set. The scale space is based on the intrinsic heat equation, also called mean curvature motion (MCM). A simple iterative scheme implementing MCM directly on the raw point set is described, and a mathematical proof of its consistency with MCM is given. Points evolved by this MCM implementation can be trivially backtracked to their initial raw position. Therefore, both the orientation and mesh of the data point set obtained at a smooth scale can be transported back on the original. The gain in visual accuracy is demonstrated on archaeological objects by comparison with several state of the art meshing methods.     

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