人体数字图像中各组织影像的边缘提取算法研究

针对人体数字医学图像中各组织影像界限不明显、图像总体模糊、单个组织影像不均匀等特点,提出一种能够有效提取影像边缘以区分和观察各组织影像的方法。利用计算机图像处理技术,对组织影像进行灰度处理,进而根据不同组织影像的特征,选定相应的阈值,用算法程序对组织影像边缘进行有效提取,得到一系列效果图,通过观察分析得出实验结果。结果表明,该方法能够有效地提取出人体不同组织影像的边缘,有助于医生的临床诊断与分析。     

测定总抗坏血酸含量的植物组织膜电极的研究

报道一种能测定样品中总抗坏血酸（还原型抗坏血酸和脱氢型抗坏血酸）含量的黄瓜组织切片膜电极．介绍了测定原理、电极性能及应用可靠性等．     

特征词提取中同义处理的新方法

本文利用文本分类中文本的特点提出了一种基于模糊集的同义词处理的新方法。本方法充分考虑不同文本类型中同义(近义) 词之间的差别,在训练中自动计算不同类型文本中特征词对其对应的同义概念的隶属度,从而实现了用模糊集来定义同义概念;然后应用同义概念来提取文本中的特征值。另外,本系统还利用模糊集来处理多义词的问题。文中给出了系统的处理算法。比较试验的结果表明该方法提高了分类的正确率,效果是令人满意的。整个系统达到了较高的自动化水平和较强的可移植性。     

响应面法优化连翘总酚的超声辅助提取工艺

采用超声辅助提取连翘中总酚。以连翘总酚提取率为指标,研究了液料比、乙醇体积分数、超声功率和提取时间对总酚提取率的影响,在此基础上,利用响应面法对超声辅助提取连翘总酚工艺条件进行了优化,建立了二次回归模型,确定了最佳提取条件。结果表明,连翘中总酚的超声波辅助提取最佳工艺条件为:以体积分数55%的乙醇溶液为提取剂,液料比20 mL/g,提取时间34 min,超声功率240 W。在此条件下,连翘中总酚的平均提取率为3.72%,与二次回归模型理论预测值3.75%的相对误差为0.8%。实验结果与模型理论预测值相符。     

高分辨率遥感影像中提取复杂道路的新方法

从遥感影像中提取具有弱边缘线的道路具有相当大的难度,在综合了相位编组和动态规划两种原理的记错上,提出了针对这一问题的新方法。首先借鉴相位编组原理形成边缘线支持区并对其进行连接;然后利用动态规划方法从支持区中检测出边缘线并对这些线进行平滑;最后连接由边缘线构成的道路段,得出道路提取结果。实验表明,该方法可以较好地提取出无清晰连续边缘线的道路,对于边缘对比度较大的道路则可取得更为令人满意的结果。     

从高分辨率遥感影像中提取城市道路的新方法

在综合几种现有算法优点的基础上,提出一种新的道路提取策略。首先以角度纹理特性法分割原始影像;接着利用直线匹配原理剔除初始分割结果中的非道路地物,得到更为规则的道路条带;然后通过形态学手段获得道路中心线,并将每条中心线拆分为多段直线;结合上下文知识的马尔可夫模型被用于组织道路段的中心线,从而恢复完整道路网。实验结果表明：新方法具有良好的性能,可以从高分辨IKONOS遥感影像中提取出复杂的城市道路。     

从LiDAR数据中提取建筑物平面目标的新方法

提出一种从机载LiDAR点云数据中自动提取建筑物平面的方法。给出了基于边长约束的三角形生长算法对建筑物初始区域进行提取,针对提取出的建筑物脚点,利用自适应Mean Shift方法在特征空间中对其进行聚类分析,并提取出平面目标,最后利用Alpha-Shape算法生成建筑物平面的轮廓线。通过实验证实了方法的有效性。     

甘草不同组织的提取技术

从甘草不同组织中提取高质量的RNA，为进一步合成双链cDNA，构建甘草cDNA文库奠定基础。方法：采用几种常用方法（高盐溶液法、LiCl沉淀法、CTAB法和SDS法）提取甘草不同组织的RNA，并以1％琼脂糖凝胶电泳和A260／A280，A260／A230的值作为指标。结果：LiCl沉淀法对于甘草不同组织中的RNA具有更好的提取效果，条带清晰，完整性较好，且A260／A280和A260／A230值均接近于2．0，根部组织产量达0．220μg／mg，叶片组织产量这0．275μg／mg。结论：采用LiCl沉淀法提取甘草不同组织中的RNA具有更好的效果，适于从富含多糖的植物组织中提取RNA。     

面向彩色图像和视频的文本提取新方法

随着基于内容检索技术的发展，人们逐渐意识到包含在彩色图像和视频中的文本具有重要的语义作用，并需要采用有效的方法来提取这些文本，文中提出一个全面作用在RGB颜色空间3个分量上的彩色图像边缘检测新算子和一种利用空间频率特征，结合彩色边缘检测与基于区域图像分割方法的彩色图像和视频文本提取新算法。实验表明在彩色图像和视频的文本提取中，文中算子形成的二值边缘图像的文本区域轮廓清晰完整，有利于下一步文本区域与背景的剥离，该算法具有较高的广西区域提取准确率和较好的对于文本字符在尺寸和颜色方面的适应性。     

一种基于神经网络规则提取的新方法

提出了一种基于神经网络对信息系统进行规则提取的新方法。首先用粗糙集对信息系统进行属性约简,然后把条件属性作为输入,决策属性作为最后输出对多层神经网络进行训练。由相关定理对神经网络的运行结果做了理论分析,并以分析结果作为规则提取的重要依据。实验结果验证了新算法的有效性。新算法与几种传统算法相比规则提取的准确率有很大的提高。     

一个新的二叉树的轮廓线索树构造算法

本文给出了二叉树的轮廓线索树的一个新的构造算法 .与 Reingdd的算法相比 ,该算法简单、高效、便于分析 ,易于推广到 m-叉树的轮廓线索树的构造算法上     

促花措施对日光温室甜樱桃幼树成花及坐果的影响

日光温室中甜樱桃采用刻芽、环剥、绞缢、PP333等促花措施处理，促花效果以环剥后不包伤口、绞缢、短梢停长期低浓度PP333喷布和中长梢停长期高浓度PP333蘸尖效果较好。日光温室中甜樱桃可多次分化花芽，中后期形成的花芽坐果率明显高于前期。     

控制系统故障树自动建造的一种方法

提出一种改进的有向图法--一般有向图法.使用这一方法,不但能较好地完成简单控制系统的自动建树,而且可以较好地完成有较复杂控制结构的控制系统的自动建树,克服了传统有向图法自动建树的困难.     

基于VPRS多变量决策树优化算法

噪声数据降低了多变量决策树的生成效率和模型质量,目前主要采用针对叶节点的剪枝策略来消除噪声数据的影响,而对决策树生成过程中的噪声干扰问题却没有给予关注。为改变这种状况,将基本粗糙集（rough set,RS）理论中相对核的概念推广到变精度粗糙集（variable precision roughset,VPRS）理论中,并利用其进行决策树初始变量选择;将两个等价关系相对泛化的概念推广为两个等价关系多数包含情况下的相对泛化,并利用其进行决策树初始属性检验;进而给出一种能够有效消除噪声数据干扰的多变量决策树构造算法。最后,采用实例验证了算法的有效性。     

Trading Accuracy for Simplicity in Decision Trees

When communicating concepts, it is often convenient or even necessary to define a concept approximately. A simple, although only approximately accurate concept definition may be more useful than a completely accurate definition which involves a lot of detail. This paper addresses the problem: given a completely accurate, but complex, definition of a concept, simplify the definition, possibly at the expense of accuracy, so that the simplified definition still corresponds to the concept sufficiently well. Concepts are represented by decision trees, and the method of simplification is tree pruning. Given a decision tree that accurately specifies a concept, the problem is to find a smallest pruned tree that still represents the concept within some specified accuracy. A pruning algorithm is presented that finds an optimal solution by generating adense sequence of pruned trees, decreasing in size, such that each tree has the highest accuracy among all the possible pruned trees of the same size. An efficient implementation of the algorithm, based on dynamic programming, is presented and empirically compared with three progressive pruning algorithms using both artificial and real-world data. An interesting empirical finding is that the real-world data generally allow significantly greater simplification at equal loss of accuracy.     

波兰果树生产考察报告

概述了近20年来波兰水果产量,栽培的果树品种构成,主栽品种,以及各种水果产品的供需状况和果树科研情况。     

密文动态后继树精简索引模型研究

随着信息技术的发展,很多单位累积了海量的科研和生产数据,其中部分是涉密数据.如何从这些海量数据中安全且快速找到所需的涉密信息是用户面临的难题.密文索引是密文全文检索系统的核心模块,为保证在密文状态下进行高效的全文检索,提出了一种密文动态后继树精简索引模型.该索引模型支持密文子串查询、潜在分词查询和索引动态更新,具有较高的查全率和查准率;给出该模型密文索引的创建、检索、和索引密文更新算法,并对其安全性和效率进行分析.实验表明,与其他已有的索引模型相比,该模型具有较好的时间效率,更适合应用于密文全文检索领域.     

On Semismooth Newton’s Methods for Total Variation Minimization

In [2], Chambolle proposed an algorithm for minimizing the total variation of an image. In this short note, based on the theory on semismooth operators, we study semismooth Newton’s methods for total variation minimization. The convergence and numerical results are also presented to show the effectiveness of the proposed algorithms. The research of this author is supported in part by Hong Kong Research Grants Council Grant Nos. 7035/04P and 7035/05P, and HKBU FRGs. The research of this author is supported in part by the Research Grant Council of Hong Kong. This work was started while the author was visiting Department of Applied Mathematics, The Hong Kong Polytechnic University. The research of this author is supported in part by The Hong Kong Polytechnic University Postdoctoral Fellowship Scheme and the National Science Foundation of China (No. 60572114). Michael Ng is a Professor in the Department of Mathematics at the Hong Kong Baptist University. As an applied mathematician, Michael’s main research areas include Bioinformatics, Data Mining, Operations Research and Scientific Computing. Michael has published and edited 5 books, published more than 140 journal papers. He is the principal editor of the Journal of Computational and Applied Mathematics, and the associate editor of SIAM Journal on Scientific Computing. Liqun Qi received his B.S. in Computational Mathematics at Tsinghua University in 1968, his M.S, and Ph.D. degree in Computer Sciences at University of Wisconsin-Madison in 1981 and 1984, respectively. Professor Qi has taught in Tsinghua University, China, University of Wisconsin-Madison, USA, University of New South Wales, Australia, and The Hong Kong Polytechnic University. He is now Chair Professor of Applied Mathematics at The Hong Kong Polytechnic University. Professor Qi has published more than 140 research papers in international journals. He established the superlinear and quadratic convergence theory of the generalized Newton method, and played a principal role in the development of reformulation methods in optimization. Professor Qi’s research work has been cited by the researchers around the world. According to the authoritative citation database ISIHighlyCited.com, he is one of the world’s most highly cited 300 mathematicians during the period from 1981 to 1999. Yu-Fei Yang received the B.Sc., M.S. and Ph.D. degrees in mathematics from Hunan University, P. R. China, in 1987, 1994 and 1999, respectively. From 1999 to 2001, he stayed at the University of New South Wales, Australia as visiting fellow. From 2002 to 2005, he held research associate and postdoctoral fellowship positions at the Hong Kong Polytechnic University. He is currently professor in the College of Mathematics and Econometrics, at Hunan University, P. R. China. His research interests includes optimization theory and methods, and partial differential equations with applications to image analysis. Yu-Mei Huang received her M.Sc. in Computer science from Lanzhou University in 2000. She is now pursuing her doctoral studies in computational mathematics in Hong Kong Baptist University. Her research interests are in image processing and numerical linear algebra.