CAD与CAPP集成研究回转体类零件特征识别

本文研究了回转体轴类,轴套类零件的特征识别。对这类零件常用的特征单元进行了定义,描述了这类零件的特征模型:介绍了它的识别方法和特征参数定义方法。     

基于内容的静态语义概念视频检索方法研究

本文针对互联网视频检索技术的发展,阐述了目前主流视频搜索引擎的技术现状,分析了互联网视频检索的关键技术,特别是对于视频特征的提取技术。本文的创新点是提出了一种通用的基于内容的静态语义视频检索方法,该方法可以弥补基于文本视频检索的有关不足,并且在TRECVID的视频概念检索数据的静态语义概念中得到验证,运行稳定。     

基于特征互补图像的人脸识别仿真研究

研究人脸识别精度问题。由于人脸图像中存在大量干扰信息的缺点,而造成了人脸识别正确率下降,为了解决上述问题,提出了一种基于特征互补图像快速特征融合算法。算法通过对人脸图像的位平面切片图像分析,采用位平面图像分解法,通过各种合成策略构造多幅样本图像。并突出高位平面图像,采用两种加权策略将每一幅人脸图像样本都生成"特征互补图像"。然后,直接用图像的二维典型相关分析(2DCCA)法对两种特征互补图像进行特征抽取。最后通过在ORL国际标准人脸库上进行的实验,结果表明,高位平面图像的典型相关鉴别特征提高了正确识别率,并且因为摒弃了原始人脸图像的大部分干扰信息所以具有更强的鲁棒性。     

基于卡尔曼滤波SVM图像鉴别方法研究

研究如何区分自然图像和计算机图形判别图像问题。由于人眼难以判别真伪图像,针对传统的图像鉴别方法由于参数的设置而造成识别准确率低,从而无法准确的区分自然图像和计算机图形。提出了一种卡尔曼滤波和支持向量机分类算法相结合的高性能图像真伪鉴别方法。算法首先采用卡尔曼滤波器提取图像的各个级别的分量,求解出各级分量和判别的误差值并作为特征数据,然后采用支持向量机分类算法对特征数据进行训练判别。仿真结果表明,提出的算法可以有效的判别图像的真伪,可以有效的识别出原始自然图像和合成图像,识别率高达99%以上,同时算法的稳定性较高,具有一定的实际应用价值。     

基于特征的派生式车身覆盖件冲压CAPP的研究

本文介绍了一种基于特征的派生式车身覆盖件冲压工艺ＣＡＰＰ系统。通过建立特征模型，引入成组技术，建立工艺数据库，系统的功能得以实现。该系统保证了车身开发中ＣＩＭＳ的信息集成。     

人脸识别研究综述*

人脸识别是一种重要的身份鉴别技术,具有广泛的应用前景。给出了人脸识别发展历程中的技术特点;根据人脸检测定位、面部特征提取和人脸确认识别三个关键的人脸识别过程,阐述了目前已成熟的核心技术和方法以及这些技术和方法的优缺点;展望了人脸识别未来的研究趋势。     

设备工况状态监测与故障诊断中的特征信息分析研究

文章阐述了机械设备工况状态信息检测与故障诊断系统中，对工况状态特征信息的分析方法一时域分析方法，频域分析方法，联合时一频分析方法等多种信号处理方法的特征分析特点及特殊信号、振动信号的分析和应用。     

A framework for variable content document generation with multiple actors

ContextAdvances in customization have highlighted the need for tools supporting variable content document management and generation in many domains. Current tools allow the generation of highly customized documents that are variable in both content and layout. However, most frameworks are technology-oriented, and their use requires advanced skills in implementation-related tools, which means their use by end users (i.e. document designers) is severely limited.ObjectiveStarting from past and current trends for customized document authoring, our goal is to provide a document generation alternative in which variants are specified at a high level of abstraction and content reuse can be maximized in high variability scenarios.MethodBased on our experience in Document Engineering, we identified areas in the variable content document management and generation field open to further improvement. We first classified the primary sources of variability in document composition processes and then developed a methodology, which we called DPL – based on Software Product Lines principles – to support document generation in high variability scenarios.ResultsIn order to validate the applicability of our methodology we implemented a tool – DPLfw – to carry out DPL processes. After using this in different scenarios, we compared our proposal with other state-of-the-art tools for variable content document management and generation.ConclusionThe DPLfw showed a good capacity for the automatic generation of variable content documents equal to or in some cases surpassing other currently available approaches. To the best of our knowledge, DPLfw is the only framework that combines variable content and document workflow facilities, easing the generation of variable content documents in which multiple actors play different roles.     

A heuristic biomarker selection approach based on professional tennis player ranking strategy

Extracting significant features from high-dimension and small sample size biological data is a challenging problem. Recently, Micha? Draminski proposed the Monte Carlo feature selection (MC) algorithm, which was able to search over large feature spaces and achieved better classification accuracies. However in MC the information of feature rank variations is not utilized and the ranks of features are not dynamically updated. Here, we propose a novel feature selection algorithm which integrates the ideas of the professional tennis players ranking, such as seed players and dynamic ranking, into Monte Carlo simulation. Seed players make the feature selection game more competitive and selective. The strategy of dynamic ranking ensures that it is always the current best players to take part in each competition. The proposed algorithm is tested on 8 biological datasets. Results demonstrate that the proposed method is computationally efficient, stable and has favorable performance in classification.