基于DXF格式的优化数控代码自动生成方法

DXF是AutoCAD与其他软件进行数据交换的CAD通用图形文件格式。实现了一种DXF读取方式并自动生成数控加工代码。传统算法的读取结果无序,不适用于实际加工,针对这一问题根据提取结果及位置关系生成图元轮廓树,并利用最近邻插入算法对图元轮廓优化排序,得到全局优化的最短切割路径,并通过实例证实了算法的有效性,能满足实际自动编程数控系统的工作需要。     

嵌入式二维码数据无线多点传输系统设计

设计了一种基于FreeRTOS嵌入式操作系统的二维条码无线传输系统,描述了系统的软硬件实现方法和多点数据传输的链路控制机制。系统主控芯片采用通用的MCU芯片STM32,结合二维码扫描引擎和蓝牙传输模组实现多个扫描终端与基座间的数据传输。     

基于Maven的J2EE后台管理系统代码生成器

后台管理系统功能和模式相对固定,为了降低软件开发人员的重复工作量,缩短项目的开发周期,应用基于模板技术的代码生成原理,设计一款基于Maven的后台管理系统代码生成器,完成从业务模型到后台管理系统的代码生成;从业务文件定义、业务文件解析到模板合成阐述本代码生成器的内部结构。     

基于IOS平台的小型仓储管理系统设计

随着数字化、信息化技术在物流领域应用的飞速发展,各种各样的仓储管理系统日益受到关注。本文介绍的基于IOS平台的小型仓储管理系统是利用IOS设备对货物信息进行采集,通过SQLite数据库技术、ZXing库等软硬件方式构建的业务系统,可以较好地适应中小规模用户数据交互的需求。同时,系统具备造价低廉、操作便捷等优点。     

Comparing techniques for authorship attribution of source code

Attributing authorship of documents with unknown creators has been studied extensively for natural language text such as essays and literature, but less so for non‐natural languages such as computer source code. Previous attempts at attributing authorship of source code can be categorised by two attributes: the software features used for the classification, either strings of n tokens/bytes (n‐grams) or software metrics; and the classification technique that exploits those features, either information retrieval ranking or machine learning. The results of existing studies, however, are not directly comparable as all use different test beds and evaluation methodologies, making it difficult to assess which approach is superior. This paper summarises all previous techniques to source code authorship attribution, implements feature sets that are motivated by the literature, and applies information retrieval ranking methods or machine classifiers for each approach. Importantly, all approaches are tested on identical collections from varying programming languages and author types. Our conclusions are as follows: (i) ranking and machine classifier approaches are around 90% and 85% accurate, respectively, for a one‐in‐10 classification problem; (ii) the byte‐level n‐gram approach is best used with different parameters to those previously published; (iii) neural networks and support vector machines were found to be the most accurate machine classifiers of the eight evaluated; (iv) use of n‐gram features in combination with machine classifiers shows promise, but there are scalability problems that still must be overcome; and (v) approaches based on information retrieval techniques are currently more accurate than approaches based on machine learning. Copyright © 2012 John Wiley & Sons, Ltd.     

Multi-Wavelength vs. Binary Code Pattern Unwrapping in Fringe Projection Method

Phase extraction, phase unwrapping and phase to height conversion are consecutive steps within the fringe projection method for reconstruction of 3D objects. The procedure of determining discontinuities on the wrapped phase, resolving them and achieving the unwrapped phase is called phase unwrapping. A software system was developed that provides the ability to create many kinds of patterns with a verity of desirable properties suitable for different demands in fringe projection systems. In our previous work, an experimental study was reported based on a multi-wavelength unwrapping approach. In the following work, we implemented a new method called binary code pattern unwrapping that is based on time analysis. A comparison of the two unwrapping approaches in terms of resolution, quality, noise, depth accuracy, and computational cost is provided.     

口令重用行为与多维口令体系研究

互联网的迅速发展与网络服务的高度分散,促使广大网民不断注册更多的账户,并导致口令重用行为普遍化,使得用户信息面临泄露的风险。为此,基于2011年底互联网泄密门数据和大学生在线调查数据,分析了网民口令的结构特征和重用行为,并由此设计融入信息维度和分级管理思想的多维口令体系。该体系以根口令-重用码结构为基础,内容维包含多个独立的信息因子,构成口令的可记忆性主体;形式维负责形式变换,以提升口令的复杂性和安全性;时空维用于保障口令的时效性和重用性。对比量化分析结果表明,该口令体系具备良好的记忆性和便捷性,能有效抵御暴力攻击和熟人攻击。     

基于K-L散度的恶意代码模型聚类检测方法

在云计算应用环境下,由于服务系统越来越复杂,网络安全漏洞和被攻击情况急剧增加,传统的恶意代码检测技术和防护模式已无法适应云存储环境的需求。为此,通过引入高斯混合模型,建立恶意代码的分层检测机制,使用信息增益和文档频率等方法分析和提取样本数据特征值,结合K-L散度特性,提出基于K-L散度的恶意代码模型聚类检测方法。采用KDDCUP99数据集,使用Weka开源软件完成数据预处理和聚类分析。实验结果表明,在结合信息增益和文档频率进行特征分析的前提下,与贝叶斯算法相比,该方法在虚拟环境中恶意代码的平均检测时间降低16.6%,恶意代码的平均检测率提高1.05%。     

基于开发者协作关系和信息检索的需求跟踪系统

目前自动化需求跟踪的研究广泛使用文本信息检索技术。然而信息检索会遗漏一些文本不相似但是实际相关的软件制品,导致自动化跟踪的精度不高。针对上述问题,提出利用开发者协作关系来进行优化,研发了基于开发者协作关系和信息检索的需求跟踪系统。该系统在进行需求跟踪时,首先用信息检索推荐与需求文本上相似的代码,然后从代码提交日志中挖掘开发者协作关系,根据开发者协作关系再推荐相关代码,用户根据两次推荐的结果确定正确的需求代码跟踪关系。试验结果表明该系统能够找到信息检索遗漏的需求跟踪关系,能够提高自动化跟踪的准确性,节省跟踪时间。