A sensing strategy for the reverse engineering of machined parts

The reverse engineering of machined parts requires sensing an existing part and producing a design (and perhaps a manufacturing process) for it. We have developed a reverse engineering system that has proven effective with a set of machined parts. This paper describes the system, presents some results, and discusses strategy for a new system.This work was supported by ARPA under ARO grant number DAAH04-93-G-0420, DARPA grant N00014-91-J-4123, NSF grant CDA 9024721, and a University of Utah Research Committee grant. All opinions, findings, conclusions or recommendations expressed in this document are those of the authors and do not necessarily reflect the views of the sponsoring agencies.     

气固流化床中非线性特性的多尺度分析

Deterministic chaos theory offers useful quantitative tools to characterize the non-linear dynamic behavior of a fluidized bed and the developed complexity theory presents a new approach to evaluate finite sequences. In this paper,the non-linear,hydrodynamic behavior of the pressure flutuation signals in a reactor was discussed by chaos parameters and complexity measures.Coherent results were achieved by our multi-scale analysis,which further exposed the behavior in a gas-solid two-phase system.     

抗几何失真的视频Hashing算法研究

文中提出了一种基于视频灰度直方图形状的Hashing算法,能有效抵抗各种常见的几何失真和视频处理操作.算法的鲁棒性原理如下:1)由于直方图的形状与像素位置无关,故基于直方图的视频Hashing算法能有效抵抗各种常见的几何攻击;2)由于在计算Hashing前对视频帧进行了平滑预处理,故算法对加噪攻击、模糊滤波、有损压缩等处理操作有很好的鲁棒性;3)由于计算Hashing前在时间轴上进行了低通滤波预处理,故算法能抵抗帧率变化、帧丢失等时域同步攻击.实验结果表明,所提出的Hashing算法有良好的唯一性和鲁棒性能.     

基于特征线匹配的城市建筑物识别方法研究

随着手机GPS位置测定、导航以及摄像功能的普及,对于移动状态下的位置定位以及都市空间建筑物实时搜索等应用功能存在日益增长的需求。介绍了图像处理方式的城市建筑物识别方法,通过对两种图像匹配算法--SIFT和Local Search的比较分析表明,构造物轮廓线及其组合是一种相对稳定的几何特征,在匹配时受图像的仿射变换、画质变化以及镜头畸变等因素干扰较小,此外,基于轮廓的图像匹配指数还能反映摄影位置的变化。因此,对Local Search进行了改进,利用实时图的建筑物的特征轮廓线与从三维数据库中提取的建筑群特征轮廓线进行匹配,然后选择匹配指数高的记录作为识别结果。结果表明基于Local Search算法的建筑物识别技术可以很好地适应移动条件下建筑物快速识别的要求。     

基于分形编码和LIC混沌系统的图像压缩加密算法

随着数字图像在网络中的广泛应用,其在安全、传输、存储等方面的问题亟待解决。提出的算法为分形编码提供了新型安全方案,分形图像编码具有压缩比高和重构质量高的特点,而混沌的不可预测性和初值敏感性适用于图像加密,将分形编码和混沌加密有效结合可以充分发挥两者的优势。此外,通过耦合增强构造新的混沌系统,改善了种子映射复杂度低、混沌范围有限等问题,并设计了置乱扩散同时进行的加密结构来提高算法效率。实验表明,提出的算法的密钥空间大、密钥敏感性强、相邻像素相关系数和信息熵都接近理想值,能够抵抗多种常见攻击,且加密速度更快,能满足实际应用的需要。压缩性能方面,在满足重构视觉质量的同时达到了较其他方案更高的压缩比。     

Connecting the dots: Understanding the interrelated impacts of type,quality and children's awareness of design features and the mathematics content learning goals in digital math games and related learning outcomes

This study focused on an examination of how type, quality and children's awareness of design features in digital math games, along with an awareness of the mathematics goals of the game, were related to learning outcomes. We conducted a parallel conversion mixed methods study with 45 students in Grades 3 and 4 (ages 9–10). Students participated in clinical interviews using three digital math games. The results suggest a moderated mediation relationship in which the quality of the design features moderated the mediating impact of children's awareness of the game's design features, specifically when the child was aware of the mathematics content learning goal (MCLG) of the game. These findings show how important it is that design features are of high quality in a digital math game, and how this is intertwined with children's awareness of the features and the MCLG. When these variables intertwined in just the right way, the interactions between the children and the digital math game afforded mathematical learning growth.     

基于D-FNN的混沌铁磁谐振系统非线性补偿控制方法

针对电网混沌铁磁谐振系统产生的混沌现象,提出基于动态模糊神经网络的混沌铁磁谐振系统非线性补偿控制方法。该方法采用动态模糊神经网络来逼近系统的非线性部分,消除了过电压的混沌现象,将系统稳定到目标位置,实现了对系统的非线性补偿控制。Matlab仿真结果表明,基于动态模糊神经网络的非线性补偿控制方法控制结果正确,响应快速。     

CBR系统中一种实例相似度算法的改进

本文对CBR系统中实例相似度的算法进行了改进,在传统实例相似性算法的基础上加入实例属性缺失度因子和实例可复用性因子。因此在实例检索中考虑实例检索结果的精确度和实例的可复用度,进而使检索的精确度提高和修改难度降低,最终提高CBR系统的精确度和实用性。     

Hybrid function projective synchronization of chaotic systems with uncertain time-varying parameters via Fourier series expansion

In this paper, the hybrid function projective synchronization (HFPS) of different chaotic systems with uncertain periodically time-varying parameters is carried out by Fourier series expansion and adaptive bounding technique. Fourier series expansion is used to deal with uncertain periodically time-varying parameters. Adaptive bounding technique is used to compensate the bound of truncation errors. Using the Lyapunov stability theory, an adaptive control law and six parameter updating laws are constructed to make the states of two different chaotic systems asymptotically synchronized. The control strategy does not need to know the parameters thoroughly if the time-varying parameters are periodical functions. Finally, in order to verify the effectiveness of the proposed scheme, the HFPS between Lorenz system and Chen system is completed successfully by using this scheme.     

Soft computing approach to fault diagnosis of centrifugal pump

Fault detection and isolation in rotating machinery is very important from an industrial viewpoint as it can help in maintenance activities and significantly reduce the down-time of the machine, resulting in major cost savings. Traditional methods have been found to be not very accurate. Soft computing based methods are now being increasingly employed for the purpose. The proposed method is based on a genetic programming technique which is known as gene expression programming (GEP). GEP is somewhat a new member of the genetic programming family. The main objective of this paper is to compare the classification accuracy of the proposed evolutionary computing based method with other pattern classification approaches such as support vector machine (SVM), Wavelet-GEP, and proximal support vector machine (PSVM). For this purpose, six states viz., normal, bearing fault, impeller fault, seal fault, impeller and bearing fault together, cavitation are simulated on centrifugal pump. Decision tree algorithm is used to select the features. The results obtained using GEP is compared with the performance of Wavelet-GEP, support vector machine (SVM) and proximal support vector machine (PSVM) based classifiers. It is observed that both GEP and SVM equally outperform the other two classifiers (PSVM and Wavelet-GEP) considered in the present study.