三维虚拟火电厂参数化模型库的实现

为提高火电厂三维虚拟建模的效率,结合火电厂设备的特点,提出基于Pro/E二次开发技术Pro/Toolkit的参数化设计流程和方法。对三维虚拟火电厂的架构进行认知,明确火电厂应用领域参数化建模的要求;以Pro/E软件为开发工具,通过分析比较其二次开发工具的优劣,选择Pro/Toolkit技术来完成开发设计;在此基础上设计三维虚拟火电厂常用设备组件的参数化模型库,使用VC++编程语言实现友好、高效的交互界面,以电力变压器为实例阐述基于Pro/toolkit二次开发技术的参数化设计过程。该方法解决了采用三维实体造型技术来进行三维建模时重复建模、工作量大、效率低下等问题,提高了建模效率,降低了建模难度,节约了建模成本。     

基于达索平台CAA 架构的混凝土连续梁桥主体结构BIM 建模工具开发

针对传统的预应力混凝土连续梁桥主体结构BIM 建模方法效率低下、数据稳定性 无法保证的问题，使用达索/CAA 二次开发语言，遵循“骨架-模板”建模思想，开发出一种预应 力混凝土连续梁桥主体结构建模工具。以用户输入窗口和Excel 表格作为数据传递媒介，充分 利用CAA 的几何建模工具、特征建模工具、物理建模工具，通过界面模块、特征模块、节段 拓扑模块对程序内部结构的合理组织，最终实现节段自定义特征的实例化和连续梁节段零件组 装。结果表明，该建模工具能够大幅度提高建模效率并保证数据稳定性，且为基于达索/CAA 二次开发的其他BIM 研究提供了基本思路和方法。     

基于Pro／E平台的工程图纸自动化生成软件的开发

本文研究了如何运用二次开发工具Pro/TOOLKIT对Pro／E进行二次开发、采用编制VC控制台程序（Console application）来开发Pro/E二次应用程序的方法，实现了轴类零件工程图纸的自动生成，并给出了实现方案和程序示例。     

基于Pro/E二次开发的辅助布线技术

针对Pro/E布线模块在线缆建模过程中无法进行干涉检查、线缆信息输出等功能缺失问题,在Pro/E平台上,对Pro/E简单布线技术、线缆干涉调整、信息管理和参数化建模技术进行研究,提出线缆干涉调整和信息输出策略。结合Visual Studio环境下的Pro/E二次开发技术,开发一个具有上述功能的辅助布线系统,验证了技术的可行性。     

基于Pro/ENGINEER直齿圆锥齿轮参数化设计及其二次开发模型库的建立

本文详细说明了在Pro/ENGINEER的CAD模块中,通过对直齿锥齿轮参数化建模的应用,解读参数化建模的过程;通过应用族表技术建立其参数化模型库,并介绍了基于Pro/Program的二次开发平台的直齿锥齿轮参数化模型库的建立。     

面向并行设计的特征识别与模型重构方法

在分析现有CAD软件以及产品建模方法不足的基础上，引入广义工程语义特征的概念，应用广义特征对象实现面向并行设计的产品广义信息模型的简化表示；提出一种自动特征识别方法，对识别对象进行分类；以截面复杂的回转体特征为例，着重表述了基于基面的特征识别算法；扼要介绍Pro／E软件中的特征结构树，提出面向Pro／E的特征信息三级提取策略与特征信息模型重构原则，并据此开发了DFX原型系统．     

基于Pro/E的远程零件参数化设计系统研究

提出了一种远程设计机械零部件的新方案.利用VC+ +对Pro/E进行二次开发,将开发的程序封装成COM组件,利用Pro/E进行参数化建模,通过ASP技术调用COM组件实现远程参数化设计.以斜齿圆柱齿轮为例说明机械零部件远程设计系统的实现过程.     

机械产品特征造型系统的研究

本文通过对机械产品特征造型设计过程的分析，以EUCLID－IS、AUTOCAD等为软件平台，利用软件二次开发技术建立机械产品特征造型系统，论述了系统建模方法、系统结构及系统功能，阐明应用系统实现机械产品特征造型设计的方法和步骤。     

循序渐进学Pro/ENGINEER 第三课Pro/ENGINEER的建模

一、建模的常用方式和方法 1.方法概述 Pro/ENGINEER中最常用的实体建模方法有以下两种:     

利用MFC二次开发Pro/E

Pro／E软件的二次开发工作逐渐得到人们的重视，但是由于其开发工具Pro/Toolkit和MFC的兼容性问题，导致开发人员无法或要通过比较烦琐的方法才能利用MFC的强大功能和丰富资源．针对这一弊端，提出了在Visual C 6．0的平台上，简便而且充分地利用MFC来进行Pro／E二次开发的方法．     

支持特征的非流形造型数据结构的研究

文章提出了一种新的数据结构,统一的表示了线框、表面和实体三种模型,及非几何特征信息,扩大传统实体造型的覆盖域,并可表示非流形模型。     

基于STEP的特征模型及重构算法

随着CAX技术的发展,特征技术得到越来越多的重视,特征技术的使用,大幅度提高了产品的设计、制造、集成等过程的效率。目前,基于特征的CAD、CAM系统是市场的主流,CAPP是完全基于特征的应用系统。但是在STEP的众多应用协议中,涉及机械设计、制造、管理的应用协议有很多,但是这些应用协议基本上没有提供对特征的支持。AP224是较早支持特征的STEP应用协议,主     

基于最大决策边界的局部在线流特征选择

现有的在线流特征选择算法通常选择一个最优的全局特征子集,并假设该子集适用于样本空间的所有区域.但是,样本空间的每个区域都使用独有的特征子集进行准确描述,这些特征子集的特征和大小可能有所不同.因此,文中提出基于最大决策边界的局部在线流特征选择算法.引入局部特征选择,在充分利用局部信息的基础上,设计基于最大决策边界的特征衡量标准,尽可能分开同类样本和不同类样本.同时,使用最大化平均决策边界、最大化决策边界和最小化冗余3种策略选择合适的特征.针对局部区域选择最优的特征子集,然后使用类相似度测量方法进行分类.在14个数据集上的实验结果和统计假设检验验证文中算法的分类有效性和稳定性.     

一种基于关联性的特征选择算法

目前在文本分类领域较常用到的特征选择算法中,仅仅考虑了特征与类别之间的关联性,而对特征与特征之间的关联性没有予以足够的重视.提出一种新的基于关联分析的特征选择算法,该方法以信息论量度为基本工具,综合考虑了计算代价以及特征评估的客观性等问题.算法在保留类别相关特征的同时识别并摒弃了冗余特征,取得了较好的约简效果.     

基于特征的CAD／CAM集成技术评述与研究

本文对特征识别，基于特征的设计和特征转换等方法进行了评述和研究，并据此提出了基于特征的ＣＡＤ／ＣＡＭ集成系统的总体设计方案。     

Stability of feature selection algorithms: a study on high-dimensional spaces

With the proliferation of extremely high-dimensional data, feature selection algorithms have become indispensable components of the learning process. Strangely, despite extensive work on the stability of learning algorithms, the stability of feature selection algorithms has been relatively neglected. This study is an attempt to fill that gap by quantifying the sensitivity of feature selection algorithms to variations in the training set. We assess the stability of feature selection algorithms based on the stability of the feature preferences that they express in the form of weights-scores, ranks, or a selected feature subset. We examine a number of measures to quantify the stability of feature preferences and propose an empirical way to estimate them. We perform a series of experiments with several feature selection algorithms on a set of proteomics datasets. The experiments allow us to explore the merits of each stability measure and create stability profiles of the feature selection algorithms. Finally, we show how stability profiles can support the choice of a feature selection algorithm. Alexandros Kalousis received the B.Sc. degree in computer science, in 1994, and the M.Sc. degree in advanced information systems, in 1997, both from the University of Athens, Greece. He received the Ph.D. degree in meta-learning for classification algorithm selection from the University of Geneva, Department of Computer Science, Geneva, in 2002. Since then he is a Senior Researcher in the same university. His research interests include relational learning with kernels and distances, stability of feature selection algorithms, and feature extraction from spectral data. Julien Prados is a Ph.D. student at the University of Geneva, Switzerland. In 1999 and 2001, he received the B.Sc. and M.Sc. degrees in computer science from the University Joseph Fourier (Grenoble, France). After a year of work in industry, he joined the Geneva Artificial Intelligence Laboratory, where he is working on bioinformatics and datamining tools for mass spectrometry data analysis. Melanie Hilario has a Ph.D. in computer science from the University of Paris VI and currently works at the University of Geneva’s Artificial Intelligence Laboratory. She has initiated and participated in several European research projects on neuro-symbolic integration, meta-learning, and biological text mining. She has served on the program committees of many conferences and workshops in machine learning, data mining, and artificial intelligence. She is currently an Associate Editor of theInternational Journal on Artificial Intelligence Toolsand a member of the Editorial Board of theIntelligent Data Analysis journal.     

Pedestrian Re-ID based on feature consistency and contrast enhancement

For robust person re-identification(Re-ID), the key is effectively learning the features of body parts and their long-distance dependence. ResNet and Transformer are respectively good at learning local dependence and long-distance dependence between region features due to their respective special structures. In order to fully integrate the advantages of the two models, we propose a novel person Re-ID framework that effectively incorporates pixel-level region features, posture-level relation features and the long-distance dependence of region features. Specifically, we design a Semantic Correction Module (SCM) that corrects pixel-level region features and posture-level relation features in a masked manner to generate discriminative fine-grained features with high pose semantics. Considering the semantic inconsistency between relation features and region features, we propose a Contrastive Association Module (CAM) to interactively enhances the long-distance correlation and local saliency of features in a self-attention way. Finally, to improve the robustness of local and global features, we construct a CAM layer to enhance the representation of features based on their potential relationships. Extensive experiment results on general and occlusion datasets demonstrate that our approach performs favorably against the state-of-the-art methods, e.g. 96% Rank-1 on Market-1501.     

An approach to identify design and manufacturing features from a data exchanged part model

Due to the large variety of CAD systems in the market, data exchange between different CAD systems is indispensable. Currently, data exchange standards such as STEP and IGES, etc. provide a unique approach for interfacing among different CAD platforms. Once the feature-based CAD model created in one CAD system is input into another via data exchange standards, many of the original features and the feature-related information may not exist any longer. The identification of the design features and their further decomposition into machining features for the downstream activities from a data exchanged part model is a bottleneck in integrated product and process design and development. In this paper, the feature panorama is succinctly articulated from the viewpoint of product design and manufacturing. To facilitate feature identification and extraction, a multiple-level feature taxonomy and hierarchy is proposed based on the characteristics of part geometry and topology entities. The relationships between the features and their geometric entities are established. A litany of algorithms for the identification of design and machining features are proposed. Besides, how to recognize the intersecting features or compound features based on the featureless chunks of geometry entities is critical and the issue is addressed in the paper. A multi-level compound feature representation and recognition approach are presented. Finally, case studies are used to illustrate the validity of the approach and algorithms proposed for the identification of the features from CAD part models in neutral format.     

一种基于几何特征的表情相似性度量方法

在表演驱动、表情克隆等人脸动画中,需要寻找最相似表情以提高动画真实感和逼真度。基于面部表情几何特征提出一种特征加权的表情相似性度量方法。首先,在主动外观模型上,利用链码描述各区域的形状特征以刻画局部表情细节,并根据区域特征点间的拓扑关系构建形变特征以反映整体表情信息。然后,采用特征加权方式对融合的几何特征进行相似性度量,并将权重的求解过程转化为加权目标函数最小化。最后,利用求解的权重以及特征加权函数度量表情间的相似性,寻找与之最相似的表情图像。在BU-3DFE数据库和FEEDTUM数据库上的实验结果表明,该方法在寻找相似表情的正确率方面明显高于现有的度量方法,并且对不同类型、不同强度的表情描述保持较好鲁棒性,尤其在嘴型、脸颊收缩、嘴开合幅度等表情细节维持较高相似度。     

基于AdaBoost的人脸特征挑选与特征融合算法研究

针对人脸识别中的特征挑选和特征融合问题进行研究。结合已有的基于AdaBoost的人脸特征挑选方法,挑选出最具分类能力的特征,并将挑选出的多类人脸特征在特征层进行融合,得到一个统一的人脸特征用于模式分类。通过在FERET人脸库上的实验表明,其识别方法具有良好的识别效果。