八节点六面体有限元网格自动消隐技术

文章基于图形消隐技术,利用C语言,开发了一套有限元网格与AutoCAD之间进行图形交换的软件系统,并利用该系统将三维有限元网格数据以3D表面的数据格式生成DXF文件.通过AutoCAD直接打开该DXF文件,然后利用AutoCAD提供的消隐功能对三维有限元网格进行自动消隐,从而提高了大量数据的显示能力,并且省去了消隐的复杂编程工作.     

连续切片三维重构绘图过程的消隐技术

消隐技术是计算机三维模拟的关键。为实现对材料三维立体图形的快速绘制,本文利用连续切片截面之间相互平行的特性,通过寻找相邻截面中的关键点,分别从两边向中间绘制侧面并填色,从整体上采用自下而上的方法,实现了立体图形的消隐。该方法避免了常规消隐方法中需要建立构成物体几何元素的方程并求解诸元素之间相交、前后遮挡关系等复杂的运算过程。     

基于OpenGL三维非均匀FDTD网格图形的消隐处理*

在分析了三维非均匀FDTD网格图形性质及计算数据结构特点的基础上,提出了一种基于OpenGL深度缓冲机制的绘图方法,采用多边形深度偏移量修正法进行精细化的消隐修正,实现了网格图形的快速生成与高效消隐,大大提高了FDTD仿真及模拟计算网格图形的可视化效果,并在此基础上设计并实现了基于OpenGL的三维网格模型仿真系统.最后,以U型天线的网格图形为例,展示其可视性.     

有限元网格图形处理技术及计算结果的可视化

针对有限元分析中图形处理和计算结果的可视化问题，提出三维有限元网格图形的快速形成以及高效消隐方法．基于这种思路，将所有有限元后处理图形在单元面上实现，有效地避免了烦琐的后处理图形的形成和消隐问题；并提出了形成和绘制诸如空间等值线、矢量图形、位移示意图以及塑性区分布图形等复杂的空间的后处理图形的方法；给出了程序的算法流程和应用实例。     

二元函数的三维网格图形的绘制算法及程序实现

利用计算机三维图形学技术,可在计算机屏幕上从不同的角度显示二元函数的三维图形。文献[1]中提出了有消隐的二元函数的三维绘图算法,但其图形是条状的。文献[2]中提出了一个二元函数的三维绘图程序,其图形是网格状的,但是没有消隐。对于较复杂的二元函数,由于隐线没有被消除,此方法将造成图形的层次不清楚。本文提出了一个二元函数的三维网格图形的绘制算法,并在IBM PC/XT机上编程实现。此算法能够消除隐线,由于图形是网格状的,它的立体感较强,而且由此可忽略一些较费时的状态判别及有关的计算。对于某些复杂的函数,本文的方法比文献[1]中的方法更省时间。     

基于深度测试的平面图形消隐技术

水本文介绍了一种在ＡｕｔｏＣＡＤ二维环境下通过将不同图形设为不同的深度值,从而利用ＡｕｔｏＣＡＤ的三维功能实现平面图形消隐的方法。重点讨论了边界命令和消隐命令以及布尔差运算在装配图生成中的应用技巧。     

利用峰值线交替扫描法实现曲面消隐

科学可视化将数据和图形图像有机地结合起来，用图形图像直观地展示数据。要生成直观的三维图形，必须进行消隐。对形如z=f(x，y)函数关系的图形，由于网格结点高度z(i，j)的任意性以及规则网格纵横截面的交错性，消隐有时比较困难，需要研究曲面消隐，找出一套有效的消隐算法。在峰值线法(浮动水平线算法)消隐的基础上，提出了一种用于实现形如z=f(x，y)函数关系的图形消隐的峰值线交替扫描法，给出了实施步骤和实例。用该集成消隐方法可方便地实现矩形域上函数z=f(x，y)的数据显示。     

一种新的旋转体消隐算法

本文提出了一种新的旋转体消隐算法，详细地论述了基于该算法的任意旋转体中隐点判别的方法，最后给出了某机器人工作空间的三维消隐图形实例。     

三维图形的一种快速消隐处理方法

本文针对信号分析中信号的三维图形显示问题,阐述了基于画家算法的图形快速消隐自理与计算,给出了详细步骤及例程,并给出了一类网格图的生成方法。该方法可方便地编程实现。     

三维图形显示系统的图形消除器和象素处理器技术实现

图形消隐器和象素处理器是三维图形显示系统的重要组成部分。讨论了图形消除器和象素处理器的算法,结合具体的硬件电路提出了实现设计的思路,实践应用表明了这种设计的可行性。     

A new approach to discover interlacing data structures in high-dimensional space

The discovery of structures hidden in high-dimensional data space is of great significance for understanding and further processing of the data. Real world datasets are often composed of multiple low dimensional patterns, the interlacement of which may impede our ability to understand the distribution rule of the data. Few of the existing methods focus on the detection and extraction of the manifolds representing distinct patterns. Inspired by the nonlinear dimensionality reduction method ISOmap, in this paper we present a novel approach called Multi-Manifold Partition to identify the interlacing low dimensional patterns. The algorithm has three steps: first a neighborhood graph is built to capture the intrinsic topological structure of the input data, then the dimensional uniformity of neighboring nodes is analyzed to discover the segments of patterns, finally the segments which are possibly from the same low-dimensional structure are combined to obtain a global representation of distribution rules. Experiments on synthetic data as well as real problems are reported. The results show that this new approach to exploratory data analysis is effective and may enhance our understanding of the data distribution.     

Graph Regularized Sparse Autoencoders with Nonnegativity Constraints

Unsupervised feature learning with deep networks has been widely studied in recent years. Among these networks, deep autoencoders have shown a decent performance in discovering hidden geometric structure of the original data. Both nonnegativity and graph constraints show the effectiveness in representing intrinsic structures in the high dimensional ambient space. This paper combines the nonnegativity and graph constraints to find the original geometrical information intrinsic to high dimensional data, keeping it in a dimensionality reduced space. In the experiments, we test the proposed networks on several standard image data sets. The results demonstrate that they outperform existing methods.     

用交点属性值法处理多边形符号轮廓的裁剪

近年来一种处理隐藏线的方法，即在用计算机进行图形隐藏线处理时，为方便起见给所求的交点按其所处理位置赋从不同的属性值标志（我们称它交点属性值法）。在绘图输出时只考虑交点属性累加值及其所在位置从而决定是否输出的方法。该方法的优点是计算方法简单，因而能提高绘图速度。但该方法多用在符号或多边形内填晕线上。本文着重讨论将该方法运用在任意多边形轮廓线的裁前上。经过作者多次试验，其结果是成功的。     

Visual Exploration of High‐Dimensional Data through Subspace Analysis and Dynamic Projections

We introduce a novel interactive framework for visualizing and exploring high‐dimensional datasets based on subspace analysis and dynamic projections. We assume the high‐dimensional dataset can be represented by a mixture of low‐dimensional linear subspaces with mixed dimensions, and provide a method to reliably estimate the intrinsic dimension and linear basis of each subspace extracted from the subspace clustering. Subsequently, we use these bases to define unique 2D linear projections as viewpoints from which to visualize the data. To understand the relationships among the different projections and to discover hidden patterns, we connect these projections through dynamic projections that create smooth animated transitions between pairs of projections. We introduce the view transition graph, which provides flexible navigation among these projections to facilitate an intuitive exploration. Finally, we provide detailed comparisons with related systems, and use real‐world examples to demonstrate the novelty and usability of our proposed framework.     

基于实值RBM的深度生成网络研究

受限玻尔兹曼机（restricted Boltzmann machine,简称RBM）是一种概率无向图,传统的RBM模型假设隐藏层单元是二值的,二值单元的优势在于计算过程和采样过程相对简单,然而二值化会对基于隐藏层单元的特征提取和数据重构过程带来信息损失.因此,将RBM的可见层单元和隐藏层单元实值化并保持模型训练的有效性,是目前RBM理论研究的重点问题.为了解决这个问题,将二值单元拓展为实值单元,利用实值单元建模数据并提取特征.具体而言,在可见层单元和隐藏层单元之间增加辅助单元,然后将图正则化项引入到能量函数中,基于二值辅助单元和图正则化项,流形上的数据有更高的概率被映射为参数化的截断高斯分布;同时,远离流形的数据有更高的概率被映射为高斯噪声.由此,模型的隐层单元可以被表示为参数化截断高斯分布或高斯噪声的采样实值.该模型称为基于辅助单元的受限玻尔兹曼机（restricted Boltzmann machine with auxiliary units,简称ARBM）.在理论上分析了模型的有效性,然后构建了相应的深度模型,并通过实验验证模型在图像重构任务和图像生成任务中的有效性.     

A new dual wing harmonium model for document retrieval

A new dual wing harmonium model that integrates term frequency features and term connection features into a low dimensional semantic space without increase of computation load is proposed for the application of document retrieval. Terms and vectorized graph connectionists are extracted from the graph representation of document by employing weighted feature extraction method. We then develop a new dual wing harmonium model projecting these multiple features into low dimensional latent topics with different probability distributions assumption. Contrastive divergence algorithm is used for efficient learning and inference. We perform extensive experimental verification, and the comparative results suggest that the proposed method is accurate and computationally efficient for document retrieval.     

基于形体补偿的多面体二维消隐算法

三维形体的二维图形消隐一般被认为是不可能的。基于形体补偿的方法，提出了在没有直接给予深度信息的二维空间中对二维图形进行消隐藏面的方法。省去了物空间或象空间消隐算法中常用的深度求交与相关性测试，直接利用二维补偿环与候选可见面环之间的相关性进行布尔运算，取得了满意的效果。     

混合贝叶斯网络隐藏变量学习研究

目前,具有已知结构的隐藏变量学习主要针对具有离散变量的贝叶斯网和具有连续变量的高斯网．该文给出了具有连续和离散变量的混合贝叶斯网络隐藏变量学习方法．该方法不需要离散化连续变量,依据专业知识或贝叶斯网络道德图中Cliques的维数发现隐藏变量的位置,基于依赖结构（星形结构或先验结构）和Gibbs抽样确定隐藏变量的值,结合扩展的MDL标准和统计方法发现隐藏变量的最优维数．实验结果表明,这种方法能够有效地进行具有已知结构的混合贝叶斯网络隐藏变量学习．     

Adaptive partial graph learning and fusion for incomplete multi-view clustering

Most of existing multi-view clustering methods assume that different feature views of data are fully observed. However, it is common that only portions of data features can be obtained in many practical applications. The presence of incomplete feature views hinders the performance of the conventional multi-view clustering methods to a large extent. Recently proposed incomplete multi-view clustering methods often focus on directly learning a common representation or a consensus affinity similarity graph from available feature views while ignore the valuable information hidden in the missing views. In this study, we present a novel incomplete multi-view clustering method via adaptive partial graph learning and fusion (APGLF), which can capture the local data structure of both within-view and cross-view. Specifically, we use the available data of each view to learn a corresponding view-specific partial graph, in which the within-view local structure can be well preserved. Then we design a cross-view graph fusion term to learn a consensus complete graph for different views, which can take advantage of the complementary information hidden in the view-specific partial graphs learned from incomplete views. In addition, a rank constraint is imposed on the graph Laplacian matrix of the fused graph to better recover the optimal cluster structure of original data. Therefore, APGLF integrates within-view partial graph learning, cross-view partial graph fusion and cluster structure recovering into a unified framework. Experiments on five incomplete multi-view data sets are conducted to validate the efficacy of APGLF when compared with eight state-of-the-art methods.     

用Java3D技术开发基于因特网的CAE系统

分析了在网络环境下Java3D的图形处理优势，就开发基于互联网的三维注塑成型流动模拟系统中的三维图形定位、显示控制以及在浏览器环境下的动态模拟显示数据的刷新问题进行了探讨，这些技术已经成功地用于基于网络的注塑成型流动模拟系统中．