TREOR多晶X射线衍射图指标化程序的适用性

多晶Ｘ射线衍射图指标化的ＴＲＥＯＲ程序已被普遍采用，特别是在硅酸盐矿物等材料的晶体结构研究工作中应用很多。然而，实际上它存在着一定的缺陷，有可能得出错误的结果。本文对这一程序作了详细的检验，发现即使在理想条件下它都不适用于低对称晶系的指标化。在对程序作改进以后，该程序可适用于高对称晶系多晶衍射的指标化     

弧面分度凸轮机构参数化设计与仿真

以弧面分度凸轮机构为研究对象，基于VC＋＋面向对象的方法及Pro／ENGINEER二次开发技术，建立了弧面分度凸轮参数化设计系统，该系统给出了界面友好的参数输入对话框，能够完成机构参数的自动综合和凸轮轮廓的自动生成，具有直观性、易于操作和调整，并在Pro／ENGINEER环境下进行装配、仿真与干涉检测，验证了设计的合理性。     

凸轮专用数控机床主轴的优化设计

本文以最小主轴重量及主轴前端挠度为优化目标，采用统一目标的线性组合法，引入加权因子，建立了一个统一的优化目标函数，对机床主轴进行了多目标优化设计，使优化结果更符合实际。     

直角式钢包回转台的有限元分析

在与传统直臂式钢包回转台比较的基础上，对直角式钢包回转台的倾翻力矩、变形及应力进行了分析。     

基于分度台转位装置的惯性测量单元智能检测

针对惯性测量单元(IMU)批量检测效率低、人工劳动量大的现状,设计开发出一种基于分度台转位装置的IMU智能检测系统。其采用高精度分度台转位装置为IMU提供基准角度位置,利用工控机控制分度台转位装置、程控直流电源和IMU协调有序工作,通过上位机软件智能控制程控直流电源向IMU供电,以及自动发送、采集、分析和处理检测数据,实现对IMU的智能、高效、精确检测。实验结果表明,该系统可同时自动检测4套IMU 的参数指标,并自动计算、显示和保存检测数据,且检测精度、效率和智能化程度高,人工劳动量小,性价比高,此外,软件界面上所有功能均可手动触摸操控,操作十分便捷。     

低空间复杂度的LSH算法及其在图像检索中的应用

局部敏感哈希LSH算法是有效的高维数据索引方法,如何生成哈希函数是算法的关键部分。LSH算法的哈希函数是基于p-稳态分布随机生成的,为了提高算法性能就需要增加哈希表的数量,但这会增加算法的空间复杂度。改进后的LSH算法(I-LSH)在生成哈希函数时不需要有标记的训练样本,而是仅仅利用数据点的分布信息构造投影方向。实验结果表明,在不显著降低检索性能的情况下,ILSH有效地降低了内存的使用量,适合处理大规模数据。     

基于多特征匹配的视频拷贝检测算法

针对已有的视频拷贝检测算法仅使用单一特征进行视频内容匹配,难以应对多种不同形式的拷贝变化的问题,提出一种基于多种视觉特征的视频拷贝检测算法.该算法采用级联式检测过滤框架,在提取视频帧图像的全局特征用于检测画面轻微变化的重复视频片段后,使用更精准的局部特征等来检测各种复杂变化后的拷贝内容.为在大规模数据库中实现快速检测,使用kd树型索引结构实现特征近邻检索.在标准评测数据集上的实验结果表明,文中算法对多种拷贝变化具有鲁棒性,并具有较高的检测效率.     

An efficient bit-based feature selection method

Feature selection is about finding useful (relevant) features to describe an application domain. Selecting relevant and enough features to effectively represent and index the given dataset is an important task to solve the classification and clustering problems intelligently. This task is, however, quite difficult to carry out since it usually needs a very time-consuming search to get the features desired. This paper proposes a bit-based feature selection method to find the smallest feature set to represent the indexes of a given dataset. The proposed approach originates from the bitmap indexing and rough set techniques. It consists of two-phases. In the first phase, the given dataset is transformed into a bitmap indexing matrix with some additional data information. In the second phase, a set of relevant and enough features are selected and used to represent the classification indexes of the given dataset. After the relevant and enough features are selected, they can be judged by the domain expertise and the final feature set of the given dataset is thus proposed. Finally, the experimental results on different data sets also show the efficiency and accuracy of the proposed approach.     

用于图像Hash的视觉相似度客观评价测度

由于评价图像Hash性能时,要求对两幅图像是否在感知上相似做出判断,因此针对这一需求,提出了一种衡量感知相似程度的评价测度。该测度的确定是先对图像进行低通滤波,再进行图像重叠分块;然后运用相关系数检测法计算每一对分块的相似程度,并对相似系数归一化,再分别计算若干个最小和最大的归一化相似系数的乘积;最后用最小相似系数乘积与最大相似系数乘积的比值作为感知相似性的测度。实验结果表明,该测度不仅可有效反映图像视觉质量的变化,而且能较好地区分两幅图像是否存在重要的视觉差异,其对感知相似进行评价的性能优于峰值信噪比。     

Case-Based Learning: Predictive Features in Indexing

Interest in psychological experimentation from the Artificial Intelligence community often takes the form of rigorous post-hoc evaluation of completed computer models. Through an example of our own collaborative research, we advocate a different view of how psychology and AI may be mutually relevant, and propose an integrated approach to the study of learning in humans and machines. We begin with the problem of learning appropriate indices for storing and retrieving information from memory. From a planning task perspective, the most useful indices may be those that predict potential problems and access relevant plans in memory, improving the planner's ability to predict and avoid planning failures. This predictive features hypothesis is then supported as a psychological claim, with results showing that such features offer an advantage in terms of the selectivity of reminding because they more distinctively characterize planning situations where differing plans are appropriate.We present a specific case-based model of plan execution, RUNNER, along with its indices for recognizing when to select particular plans—appropriateness conditions—and how these predictive indices serve to enhance learning. We then discuss how this predictive features claim as implemented in the RUNNER model is then tested in a second set of psychological studies. The results show that learning appropriateness conditions results in greater success in recognizing when a past plan is in fact relevant in current processing, and produces more reliable recall of the related information. This form of collaboration has resulted in a unique integration of computational and empirical efforts to create a model of case-based learning.