黑色素瘤表面不均匀性的轮廓描述与分析

为解决皮肤肿瘤表面不均匀性的度量问题,构建一种肿瘤表面灰度不均匀性的虚拟轮廓描述模型。提出虚拟轮廓不规则性和不对称性描述子,将肿瘤表面2D灰度分布的不均匀性转换为虚拟轮廓的不规则性,提取虚拟轮廓的不规则性和不对称性特征,依据Hausdorff距离进行特征选择和融合,实现基于虚拟轮廓不规则性和不对称性的肿瘤表面不均匀性分类。实验结果表明,该模型能实现数据降维,提高分类准确率。     

用C# ＋PhotoShop的XP桌面时钟的制作

主要介绍利用PhotoShop制作特制图片,在C#中利用图片制作不规则窗体,在不规则窗体上实现透明动画层及不规则窗体随意拖动等技术,制作三层式结构的XP桌面时钟的原理和技术。     

不规则时间序列距离的度量

针对时间序列距离度量的算法很多,但没有适用于不规则时序距离度量算法的现状,基于寻求全局序列点构成的边集之间的距离路径最小的思想,提出一种不规则时序距离度量的算法,并给出了事件序列生成算法和不规则时序距离度量算法的实现,最后利用UCI KDD的时间序列测试数据对算法进行了测试。测试结果证明了该不规则时序距离算法能够有效度量不规则时序的相似性。     

分拣扫描设备不规则物料分类自动控制方法研究

为了提高分拣扫描设备不规则物料的分类能力,提出基于粒子群进化寻优的分拣扫描设备不规则物料分类自动控制方法。构建不规则物料分类自动控制的图像扫描模型,采用网格分类方法对扫描的不规则物料图像大数据进行自适应分类,在邻域内采用超像素特征分解方法进行不规则物料分拣扫描图像区域融合滤波处理,进行不规则物料分拣扫描图像的颜色分量合并和模板匹配,挖掘不规则物料扫描图像的边缘轮廓特征量,根据不规则物料的边缘轮廓特征扫描结果进行大数据信息融合和分类,采用粒子群寻优算法,根据不规则物料的边缘轮廓特征量,更新粒子群中每个粒子的空间位置,进行不规则物料分拣过程中的外观匹配,实现对不规则物料分拣扫描分类的自动控制。仿真结果表明,采用该方法进行分拣扫描设备不规则物料分类自动控制的收敛性较好,分类结果准确可靠,提高了不规则物料分拣的效率。     

基于改进型PCNN的不规则图像自适应分割算法研究

针对不规则图像成分复杂、特征形状无法采用经典模型拟合等问题,提出了基于改进型脉冲耦合神经网络（PCNN）的自适应分割算法。该算法在原有PCNN模型基础上,对神经元反馈输入函数和动态阈值函数进行了修正,同时对神经元的输出采用多级输出模型,从而实现对不规则图像的分割。仿真实验表明,改进后的算法能够实现不规则图像的自适应分割,鲁棒性较好。     

.NET平台下不规则窗体的实现

本文讨论并采用一种新的简单方式在.NET平台下实现不规则窗体,窗体的换肤和透明功能,并结合了通知图标的使用。建立了在.NET平台下不规则窗体界面的程序模型。     

自动化仓储系统不规则货位优化问题研究

针对不规则货位的自动化仓储系统的特点,以提高系统效率和空间利用率为优化控制目标,研究了自动化仓储系统不规则货位优化分配策略,提出了首先对不规则的货位进行货位区优化,对每个货位区进行货位优化的数学模型,提出两级遗传算法解决货位优化问题。结果表明,该优化方法有效地提高了系统的效率,实现了密集存储,为自动化仓储系统中不规则货位的货位分配优化提供了理论依据和实践途径。     

不规则图形热区技术研究

不规则图形热区判定算法是计算机图形处理中的常用算法。基于有向三角形面积算法和凸包点集算法用于处理不规则图形热区,存在计算量大、不便编程实现等弊端。为此,根据计算机图形学及向量代数理论,提出不规则图形热区"叉积-夹角法"识别算法,该算法通过逐一分别求解被测点与不规则图形中同向相邻两点间的叉积及向量角,令向量角的符号与叉积符号同号,进而求取夹角和。当夹角和≥6.0或被测点位于不规则图形边线上,则判定被测点位于不规则图形内(即热区)。经实际项目应用表明,算法能正确、快速识别任意不规则图形热区,满足实际使用要求。     

ASP.NET中不规则表格的动态实现

不规则表格的处理是数据库管理系统中一个十分重要的问题,研究了.NET提供的Table控件在解决通用不规则表格中的一种应用方法,并详细论述了在Visual Studio.NET中实现该方法的过程。使用结果表明,该方案处理网页中不规则表格问题高效、可靠,具有良好的可扩充性和可重用性。     

基于XQUERY和XSLT的不规则XML文档的关联规则挖掘

目前,XQUERY语言实现的Apriori算法只能挖掘结构规整的XML数据,而无法对复杂不规则的XML数据进行挖掘.针对这个问题,改进Apriori算法,引入SDST的概念,使用XSL和XSLT将结构不规整的XML文档转换为SDST,使SDST作为Apriori算法的应用接口,从而实现对复杂不规则XML数据的关联规则挖掘.     

一种风吹树动模拟的新方法*

目前已有的关于树在风中运动的模拟方法存在两个缺点：计算量太大,难以实时模拟;成像粗糙,真实感不强。基于此,提出了一种新的模拟树木在风中运动的方法。本方法对树的基本模型进行抽象表示,采用材料力学知识详细分析树枝的受力及运动过程,并推导出空间位置的动态变化公式;选用向量表示风和节间,通过向量的运算定型模拟树枝因受力引起的运动。该模拟方法的时间复杂度仅为O(n)。实验表明,其运算速度快,模拟结果真实,可应用于实时模拟的虚拟场合中。     

Lossless fitness inheritance in genetic algorithms for decision trees

When genetic algorithms are used to evolve decision trees, key tree quality parameters can be recursively computed and re-used across generations of partially similar decision trees. Simply storing instance indices at leaves is sufficient for fitness to be piecewise computed in a lossless fashion. We show the derivation of the (substantial) expected speedup on two bounding case problems and trace the attractive property of lossless fitness inheritance to the divide-and-conquer nature of decision trees. The theoretical results are supported by experimental evidence.     

用遗传算法构造决策树

Ｃ４．５是一种归纳学习算法，它通过对一组事例的学习形成决策树形式的规则。由于Ｃ４．５采用的是局部探索的策略，它得到的决策树不一定是最优的。遗传算法是模拟自然进化的通用全局搜索算法。文中讨论了利用遗传算法的构造决策树的方法。     

Querying Linguistic Trees

Large databases of linguistic annotations are used for testing linguistic hypotheses and for training language processing models. These linguistic annotations are often syntactic or prosodic in nature, and have a hierarchical structure. Query languages are used to select particular structures of interest, or to project out large slices of a corpus for external analysis. Existing languages suffer from a variety of problems in the areas of expressiveness, efficiency, and naturalness for linguistic query. We describe the domain of linguistic trees and discuss the expressive requirements for a query language. Then we present a language that can express a wide range of queries over these trees, and show that the language is first-order complete over trees.     

基于分类及环境特征的树木真实感绘制

生成高度真实感的虚拟自然场景一直是图形学研究领域中的一个富有挑战性的难题.作为自然场景的重要组成部分,树木的真实感模拟也得到人们的广泛重视.树木种类繁多,形态各异,复杂的结构使其无论在造形、存储还是在绘制上都存在着相当的困难.针对不同的环境特征和不同类型的树木,根据它们的具体特点需采用不同的绘制手段.对于阔叶树,采用基于OpenGL的深度缓存阴影生成算法;对于针叶树,则采用结合光线跟踪和纹元(texel)绘制的技术;而对于距离视点比较远的树,采用的是体纹理映射(volumetrictexturemapping)的方法.实践证明,这几种绘制技术基本上可以满足各种不同类型场景的树木的绘制要求.     

Genetically optimized fuzzy decision trees.

In this study, we are concerned with genetically optimized fuzzy decision trees (G-DTs). Decision trees are fundamental architectures of machine learning, pattern recognition, and system modeling. Starting with the generic decision tree with discrete or interval-valued attributes, we develop its fuzzy set-based generalization. In this generalized structure we admit the values of the attributes that are represented by some membership functions. Such fuzzy decision trees are constructed in the setting of genetic optimization. The underlying genetic algorithm optimizes the parameters of the fuzzy sets associated with the individual nodes where they play a role of fuzzy "switches" by distributing a flow of processing completed within the tree. We discuss various forms of the fitness function that help capture the essence of the problem at hand (that could be either of classification nature when dealing with discrete outputs or regression-like when handling a continuous output variable). We quantify a nature of the generalization of the tree by studying an optimally adjusted spreads of the membership functions located at the nodes of the decision tree. A series of experiments exploiting synthetic and machine learning data is used to illustrate the performance of the G-DTs.     

Heuristic algorithms for designing self-repairing protection trees in mesh networks

Protection trees have been used in the past for restoring multicast and unicast traffic in networks in various failure scenarios. In this paper we focus on shared self-repairing trees for link protection in unicast mesh networks. Shared protection trees have been proposed as a relatively simple approach that is easy to reconfigure and could provide sub-second restoration times with sub-optimal redundancy requirement. The self-repairing nature of this class of protection trees may make them an attractive option for cases where dynamic changes in network topology or demand may occur. In this paper, we present heuristic algorithms to design a self-repairing protection tree for a given network. We study the restorability performance of shared trees and examine the limitations of such schemes in specific topologies, such as cases where long node chains exist. Using extensive simulations with thousands of randomly generated network graphs. We compare redundancy and average backup path length of shared protection trees with optimal tree designs and non-tree designs. We also apply our algorithms to the problem of designing the protection tree in a pre-designed fixed-capacity network, and study the performance of shared protection trees in this scenario under different network loads and link utilization levels.     

Dissimilarity between two skeletal trees in a context

Skeletal trees are commonly used in order to express geometric properties of the shape. Accordingly, tree-edit distance is used to compute a dissimilarity between two given shapes. We present a new tree-edit based shape matching method which uses a recent coarse skeleton representation. The coarse skeleton representation allows us to represent both shapes and shape categories in the form of depth-1 trees. Consequently, we can easily integrate the influence of the categories into shape dissimilarity measurements. The new dissimilarity measure gives a better within group versus between group separation, and it mimics the asymmetric nature of human similarity judgements.     

External Memory View-Dependent Simplification

In this paper, we propose a novel external-memory algorithm to support view-dependent simplification for datasets much larger than main memory. In the preprocessing phase, we use a new spanned sub-meshes simplification technique to build view-dependence trees I/O-efficiently, which preserves the correct edge collapsing order and thus assures the run-time image quality. We further process the resulting view-dependence trees to build the meta-node trees, which can facilitate the run-time level-of-detail rendering and is kept in disk . During run-time navigation, we keep in main memory only the portions of the meta-node trees that are necessary to render the current level of details, plus some prefetched portions that are likely to be needed in the near future. The prefetching prediction takes advantage of the nature of the run-time traversal of the meta-node trees, and is both simple and accurate. We also employ the implicit dependencies for preventing incorrect foldovers, as well as main-memory buffer management and parallel processes scheme to separate the disk accesses from the navigation operations, all in an integrated manner. The experiments show that our approach scales well with respect to the main memory size available, with encouraging preprocessing and run-time rendering speeds and without sacrificing the image quality.     

Synthesizing trees by plantons

In this paper, we present a two-level statistical model for characterizing the stochastic and specific nature of trees. At the low level, we define plantons, which are a group of similar organs, to depict tree organ details statistically. At the high level, a set of transitions between plantons is provided to describe the stochastic distribution of organs.Based on such a tree model, we propose a novel tree modeling approach, synthesizing trees by plantons, which are extracted from tree samples. All tree samples are captured from the real world. We have designed a maximum likelihood estimation algorithm to acquire the two-level statistical tree model from single samples or multi- samples. Experimental results show that our new model is capable of synthesizing new trees with similar, yet visually different shapes. Electronic supplementary material Supplementary material is available in the online version of this article at and is accessible for authorized users.