Telecommunications service requirements: Principles for managing complexity

Telecommunications services are complex and rapidly becoming more so. If we wish to apply formal methods of requirements engineering in the telecommunications domain, then the primary obstacle we face is the sheer complexity of the behaviour to be described. This paper focuses on one kind of telecommunications system, thelong-distance telephone network (LDTN), and presents several ways of managing complexity in LDTN requirements. The alleviation of complexity comes from careful application of some general principles of requirements engineering. At the same time that these principles help us manage complexity, the productive focus on complexity enhances the motivation for these principles.     

Retrenching partial requirements into system definitions: a simple feature interaction case study

In conventional model-oriented formal refinement, the abstract model is supposed to capture all the properties of interest in the system, in an as-clutter-free-as-possible manner. Subsequently, the refinement process guides development inexorably towards a faithful implementation. However, refinement says nothing about how to obtain the abstract model in the first place. In reality developers experiment with prototype models and their refinements until a workable arrangement is discovered.Retrenchment is a formal technique intended to capture some of the informal approach to a refinable abstract model in a formal manner that will integrate with refinement. This is in order that the benefits of a formal approach can migrate further up the development hierarchy. The basic ideas of retrenchment are presented, and a simple telephone system feature interaction case study is elaborated. This illustrates not only how retrenchment can relate incompatible and partial models to a more definitive consolidated model during the development of the contracted specification, but also that the same formalism is applicable in a re-engineering context, where the subsequent evolution of a system may be partly incompatible with earlier design decisions. The case study illustrates how the natural method of composing retrenchments can give results that are too liberal in certain cases, and stronger laws of composition are derived for systems possessing suitable properties. It is shown that the methodology can encompass more ad hoc and custom-built techniques such as Zave's layered feature engineering approach to applications exhibiting a feature-oriented architecture (such as telephony).

特征形式描述及特征预编译

特征的处理是自然语言句法分析的一个重要问题。该文提出特征处理的预编译方法,详述了基于特征形式描述的特征预编译算法,并实现了一个特征预编译器。测试表明,预编译方法可以很好地增加句法分析的可扩展性和运行速度。     

基于UML状态机与B方法的高可信嵌入式软件开发

提出了一套集成UML与B方法开发高可信嵌入式软件的实用方案：以软件的UML状态机模型为起点,将其转换为B抽象模型并在B工具中验证该模型的一致性,然后遵循B模型逐步精化的开发规则,利用B方法的精化正确性验证功能,得到系统的可靠的实现模型,最后借助B工具自动生成C代码。实例分析表明,这套方法可以提高尚可信嵌入式软件的开发验证效率。给出了嵌入式软件设计中常用的UML并发状态图到B抽象模型的转换规则。     

Formal methods software engineering for the CARA system

This paper discusses the application of formal methods software engineering (FMSE) to the development of the Computer Automated Resuscitation A (CARA) medical device at Walter Reed Army Institute of Research. Because this system is potentially life critical, a high level of quality was required. A formal engineering approach to the software development activities was chosen to satisfy this need. Specifically, a technique called sequence enumeration was applied to elicit and refine requirements while deriving a formal specification. The fundamentals of the specification process that was used on the project are described along with a brief summary of the project experience in the development and testing phases. The project employed recent advances in Cleanroom software engineering methods along with older box-structured development and usage-model-based statistical testing techniques.     

机器视觉和AdaBoost的柑桔溃疡病自动检测

将计算机视觉技术应用于柑桔病害识别问题,实现了快速准确的识别柑桔溃疡病.从特征构造,特征选择和识别系统设计三方面进行了研究.在特征构造上采用了Gabor变换,边缘识别等方法得到了包括颜色、纹理及形状的综合特征;在特征选择上采用了AdaBoost算法实现;最后通过AdaBoost学习方法构造分类器并利用滑动窗口技术进行病害区域检测.实验结果证明该方法对柑桔溃疡病其识别准确率高于95%,在训练轮数较多的情况下能够接近99%的识别率,且该识别率较稳定.实验结果显示计算机自动识别效果与专家目测相当,在生产中具有一定的实用价值.     

基于数据挖掘的入侵特征选择与构造的新方法*

入侵检测问题实际上是一个分类问题, 特征选择的好坏直接决定了分类模型的性能。针对计算机安全问题是事后于计算机系统设计、没有标准的审计机制和专门的数据格式用于入侵检测分析用途的现状, 讨论了通过扩展数据挖掘基本算法来对分析数据源进行特征选择, 同时比较挖掘出来的正常模式和异常模式, 构造新的特征, 以加强入侵检测准确率和实时性。     

Object-extending class transformations

Reorganisation and evolution of class hierarchies is important for object-oriented system development and has received considerable attention in the literature. The contributions of this paper are: (1) a formal study of a set of extension relations and transformations on class hierarchies; (2) a presentation of a small set of primitive transformations which form a minimal and complete basis for the extension relations; and (3) an analysis of the impact of these transformations at the object level.The study leads to a better understanding of evolution and reuse of object-oriented software and class hierarchies. It also provides a terminology and a means of classification for design reuse. The theory presented in this paper is based on the Demeter data model, which gives a concise mathematical foundation for classes and their inheritance and part-of relationships. Parts of the theory have been implemented in the Demeter System ^TM C++, a CASE tool for object-oriented design and programming.     

角点检测技术综述

角点是图像很重要的特征,对图像图形的理解和分析有很重要的作用。对灰度图像、二值图像、边缘轮廓曲线的角点检测算法进行综述,分析了相关的算法,并对各种检测算法给出了评价。     

基于PCA方法的人脸特征提取和检测

本文介绍了PCA的原理,并利用了PCA方法完成了人脸的特征提取和人脸检测。     

基于PCA方法的人脸特征提取和检测

本文介绍了PCA的原理,并利用了PCA方法完成了人脸的特征提取和人脸检测。     

基于功能保持的特征金字塔目标检测网络

针对特征金字塔网络在多尺度与小目标检测上的问题,提出基于功能保持的特征金字塔目标检测网络.首先在主网络中选择特征图构建特征金字塔,针对不同尺度的特征图,通过功能保持融合模块自上而下地进行低损失的特征融合.功能保持融合模块有效保留高层的强语义信息,增强底层特征图对小目标的表示能力.再利用网络两个阶段的特征描述目标,提升检测精度.最后,充分利用上下文信息进一步增强对多尺度目标的判别能力.在PASCAL VOC公共数据集上的实验表明,文中网络检测效果较优.同时,通过检测效果图可看出,文中网络在目标遮挡、模糊等情况下的检测效果也较优.     

一种基于小波域特征树量化的数字水印算法*

首先使用尺度交互式特征检测算法提取图像小波域逼近子图的特征点,然后构造相应的特征树,最后对特征树所有节点进行量化以嵌入二值水印信息。水印信息嵌入到图像的特征之中,对JPEG,JPEG 2000以及高斯白噪音等多种处理具有较强的鲁棒性。     

引入通道注意力和残差学习的目标检测器

现有目标检测器特征金字塔无法充分利用不同尺度特征图的特征信息,不适用于低分辨率图像的目标和小目标的检测.针对此问题,文中提出引入通道注意力机制和残差学习块的目标检测器.首先引入通道全局注意力机制,通过网络学习特征图中不同通道特征的权重,增强有效的全局特征信息.然后采用轻量级的残差块,突出特征的微小变化,提高低分辨率图像中小目标的检测性能.最后在用于预测的浅层特征图中融合深层特征,提高小目标的检测精度.在标准测试数据集上的实验表明,文中目标检测器适用于低分辨率图像,对小目标的检测效果较优.     

图算法句法分析器自动生成

句法分析自动生成对提高自然语言处理程序开发效率有重要意义。文章提出支持复杂特征和合一文法的图算法句法分析器预自动生成方法,详述了规则描述、输出代码模式及生成过程中的主要算法,并实现了一个自动生成器。测试表明,在保证执行效率的同时,该自动生成方法可以很好地增加句法分析的可扩展性。     

AdaBoost算法在人脸检测中的应用

AdaBoost方法是目前较流行的一种图像检测方法．它是基于统计模型的检测方法。针对目前一些人脸检测方法误检率高、检测速度慢的问题,详细介绍AdaBoost算法的基本原理,并应用OpenCV程序的开发．实现检测速度快、检测率和鲁棒性高的人脸检测。     

AdaBoost算法在人脸检测中的应用

AdaBoost方法是目前较流行的一种图像检测方法,它是基于统计模型的检测方法。针对目前一些人脸检测方法误检率高、检测速度慢的问题,详细介绍AdaBoost算法的基本原理,并应用OpenCV程序的开发,实现检测速度快、检测率和鲁棒性高的人脸检测。     

实时系统软件开发过程中形式方法的作用

针对实时系统软件开发的特殊性要求,本文强调形式方法是保证实时系统软件开发正确的一种重要方法。文章首先对形式方法的含义进行了系统的介绍,然后分析了形式方法的三个分支在实时系统软件开发过程中的作用,即形式规约、定理证明、形式验证,并指出了形式方法当前主要应用的能力以及应用的局限性,最后提出了形式方法的一些主要研究方向。     

基于特征本体的文本流主题检测研究

主题检测是文本挖掘的一个重要研究方向,传统的主题检测方法以统计理论为基础,忽略了数据本身蕴含的语义,带来了偏差严重、与样本数据高度相关等缺点。针对以上缺点,面向文本流数据,提出一种基于特征本体的主题检测方法。首先构建文本特征本体;其次,将较为复杂的文本特征本体看作是由若干主题组成的连通图,然后将主题连通图分解成单边图集合;再次,将主题相似度计算问题转化为单边图贡献度和图相似度的计算问题。最后,对每一批新文本集检测是否有新主题,从而使得主题的个数随着时间的推移而增加。在科技文献和新闻语料上进行实证研究,结果发现阈值δ参数决定文本流中新主题出现的频率,且实验结果同经典主题模型基本保持一致。除此之外,同传统的方法相比,本文提出的方法能更好地支持主题的语义表示,且适用于流数据,能增量实现主题检测,在应用上具有更大的优势。