基于特征点集的水印几何失真校验方法

本文提出了一种基于特征点集的数字水印几何失真校验方法。算法利用隐秘图像受攻击前后的小波分解的低频子图的特征点几何集来估计几何失真。实验证明，算法性能稳定、精度高、可校正大强度几何失真。     

基于地理区域分布的人脸几何特征研究

文中提出了利用人脸面部几何特征地理区域分布的差异性进行人脸识别的新方法。首先针对比较典型的中国华北和西南两大区域采集100幅人脸面部图像，然后采用一种自动提取算法抽取面部几何特征，再利用感知器算法对这两大区域进行面部特征分类。实验结果表明该方法是可行而有效的。     

基于灰度差分不变量的点特征匹配

本文提出了一种新的基于灰度差分不变量的点特征匹配方法。首先，利用灰度差分不变量获得点集之间的初始匹配；然后，利用初始匹配快速、稳健地估计图象之间的唯一几何约束－对极几何约束；最后，利用对极几何约束改进初始匹配。大量的实际图象实验表明，本文所提出的匹配算法有非常快的运算速度和很高的匹配正确率。     

基于模糊粗糙集、数学形态学和分形理论的医学图像分类研究

利用模糊粗糙集约简、数学形态学和分形理论，根据头发灼烧体图像的颜色、多种微量元素的含量以及它的纹理特征和几何特征，提出了一种新的基于模糊粗糙集的医学图像边界提取新方法。     

几何曲面的双边滤波多尺度表示及几何细节增强

为了有效地对几何模型进行编辑建模及特征检测等处理,提出一种利用双边滤波对曲面进行多尺度表示及几何细节增强的算法.首先利用几何双边滤波对几何模型进行多尺度的分解,获取其多尺度的表示和多尺度的细节层;然后对不同尺度的几何细节进行自适应的增强,将这些增强的细节层与基曲面层进行重组,重建出一个几何细节增强的几何模型.利用几何模型的多尺度细节抽取,还提出一种几何细节迁移的算法,将抽取出的多尺度几何细节分解映射到其他几何模型的基曲面上,获取几何细节迁移的结果.文中算法实现简单、效率高,能有效地增强几何模型的几何细节,且整个几何模型不发生变形情况、保持几何特征.最后通过多个实例验证了该算法的有效性.     

基于圆角特征简化的工序三维几何建模方法

三维实体模型是CAX集成研究中的基础,圆角特征广泛存在于零件设计模型中,为提高此类零件工序三维几何建模的准确性,提出一种基于圆角特征简化的工序几何建模方法.通过分析工序三维几何建模过程中对圆角特征几何元素的提取过程,提出构建圆角特征线段,以便准确获取三维模型上的加工特征边界;利用加工特征边界和拉伸、旋转和扫掠3种常用的几何造型方式,建立加工体积特征;通过上一道工序的三维模型与加工体积特征作布尔差运算,得到本工序的三维几何模型.最后以某零件的工序三维几何建模过程为例,采用3种方法进行工序几何建模,通过对结果的分析对比,验证了文中方法的优越性.     

一种基于局部特征的鲁棒的盲数字水印算法

提出了一种基于图像局部特征与图像几何正则性的鲁棒数字水印算法.采取的主要方法有:1)利用图像中的局部最稳定特征点生成具有几何不变性的局部特征区域;2)在局部特征分块中快速寻找最佳几何流方向,近似最佳逼近效果;3)设计了一种正交向量盲提取水印.实验结果表明该算法能获得很高的图像质量,且具有较强的抗攻击能力.     

一种基于极坐标变换的点模式匹配算法

点模式匹配是目标识别、图像配准与匹配、姿态估计等计算机视觉与模式识别应用方向的基础问题之一。提出了一种新的利用点特征进行匹配的算法,该算法根据点集的分布与点位置信息,构建了点的特征属性图,通过极坐标变换得到对数极坐标的特征图,并利用几何不变矩方法对特征图进行描述。由特征描述向量的比较,获得粗匹配结果,然后通过几何约束迭代的方法获取最终的点集匹配结果。本文贡献如下:一,构建了一种点的极坐标变换特征,并运用不变矩进行描述,使所提特征具有旋转与平移的不变性;二,提出了利用点特征与整体点集几何约束结合的匹配算法,能有效克服出格点与噪声带来的不利影响。最终实验说明了算法的有效性和鲁棒性。     

一种适用于特征造型的参数化设计方法

本文提出了一种面向特征造型的参数化设计方法，该方法对三维几何约束在初始设计阶段采用高层表示，并基于面向特征造型的高层几何约束模型与约束传播实现尺寸驱动几何，从而能够有效地支持特征设计，初始设计。     

一种新的图像不变特征研究

为了解决图像特征受灰度及几何畸变的影响，本文利用物理学相关概念对图像进行描述，定义了图像的质量，重心，转动惯量，提出了一种新的图像不变特征即归一化转动惯量（NMI）特征，对其不变性进行了分析，实验结果表明，图像的归一化转动惯量特征具有抗灰度及TRS不变性，且提取方法简单，易于实现。     

Rolling element bearing fault diagnosis using wavelet transform

This paper is focused on fault diagnosis of ball bearings having localized defects (spalls) on the various bearing components using wavelet-based feature extraction. The statistical features required for the training and testing of artificial intelligence techniques are calculated by the implementation of a wavelet based methodology developed using Minimum Shannon Entropy Criterion. Seven different base wavelets are considered for the study and Complex Morlet wavelet is selected based on minimum Shannon Entropy Criterion to extract statistical features from wavelet coefficients of raw vibration signals. In the methodology, firstly a wavelet theory based feature extraction methodology is developed that demonstrates the information of fault from the raw signals and then the potential of various artificial intelligence techniques to predict the type of defect in bearings is investigated. Three artificial intelligence techniques are used for faults classifications, out of which two are supervised machine learning techniques i.e. support vector machine, learning vector quantization and other one is an unsupervised machine learning technique i.e. self-organizing maps. The fault classification results show that the support vector machine identified the fault categories of rolling element bearing more accurately and has a better diagnosis performance as compared to the learning vector quantization and self-organizing maps.     

Reactive process control using a blackboard architecture

A software architecture to engineer complex process control applications must combine into the same paradigm efficient reactive and real-time functionalities and mechanisms to capture dynamic time-pressured intelligent behaviors, and must provide convenient high level tools to free the programmer from having to think at an unappropriate level of detail. We implement such characteristics into a blackboard framework that builds the basic abstract elements of reactive behavior and the blackboard computational model on top of low level real-time operating system functions. Under this approach, the engineer gets a powerful and flexible high level medium to map a complex system design that requires artificial intelligence techniques, like intelligent monitoring, and reactive planning and execution, with fully support for real-time programming. The paper also reviews other alternatives which have been explored in the past recent years for implementing complex reactive planning and execution systems.     

Cooperating/distributed grammar systems with hypothesis languages

Abstract

Motivated by the blackboard model of artificial intelligence we introduce the concept of context-free cooperating/distributed grammar systems with hypothesis languages. We prove that these grammar systems have the same generative power as context-sensitive grammars.     

基于物联网的人工智能图像检测系统设计与实现

物联网络的建立促使人工智能领域取得飞跃性进展。传统图像检测方法利用小波能算法进行背景与边缘噪声划分方式进行图像检测,存在低分辨率图像检测精度低、检测速度慢、缺乏图像深度分析等一系列问题。物联网人工智能发展迅速的环境下,提出基于物联网的人工智能图像检测系统设计。采用智能人工像素点特征采集技术(IAPCCT),对图像进行逐点特征提取,运用物联网丰富数据量资源与处理运算能力对采集图像像素点进行特征分析回馈,回馈信号经人工智能信号图像合成模块(AISIS),对信号做图像转换处理并输出分析结果完成图像检测。通过仿真实验测试证明,基于物联网的人工智能图像检测系统设计具有图像检测率高、识别准确度高、运行稳定、处理高效等优点。     

基于知识的,集成工具化的汽车锻件模具设计过程的仿真思想

提出了一个基于知识的，集成ＣＡＤ／ＣＡＭ模块，有限元分析模块，Ｍｏｔｉｆ界面制作模块的汽车锻件模具设计的仿真系统，此系统可推广到其它类似的模具设计过程仿真。     

基于人工智能的自然长寿人群生命信息系统建模

以中国新疆和田地区维吾尔自然长寿人群为例，探索一种基于人工智能(artificial intelligence，AI)的生命信息系统新的建模方法．在建模过程中，由于引入人工智能和数据融合技术，能够高效地提取隐藏于复杂数据中对生命起关键作用的因素，因此成功地建立了一个既非语言表达也非数学公式描述的隐式的自然长寿人群的人工智能模型．此人工智能模型不但更接近实际，而且具有判别、预见等超前功能．该模型可修改、可移植．     

An artificial intelligence planning approach to manufacturing feature recognition

Within manufacturing, features have been widely accepted as useful concepts, and in particular they are used as an interface between CAD and CAPP systems. Previous research on feature recognition focus on the issues of intersecting features and multiple interpretations, but do not address the problem of custom features representation. Representation of features is an important aspect for making feature recognition more applicable in practice. In this paper a hybrid procedural and knowledge-based approach based on artificial intelligence planning is presented, which addresses both classic feature interpretation and also feature representation problems. STEP designs are presented as case studies in order to demonstrate the effectiveness of the model.     

Global optimization of feature weights and the number of neighbors that combine in a case-based reasoning system

Abstract: Case-based reasoning (CBR) often shows significant promise for improving the effectiveness of complex and unstructured decision-making. Consequently, it has been applied to various problem-solving areas including manufacturing, finance and marketing. However, the design of appropriate case indexing and retrieval mechanisms to improve the performance of CBR is still a challenging issue. Most previous studies on improving the effectiveness of CBR have focused on the similarity function aspect or optimization of case features and their weights. However, according to some of the prior research, finding the optimal k parameter for the k-nearest neighbor is also crucial for improving the performance of the CBR system. Nonetheless, there have been few attempts to optimize the number of neighbors, especially using artificial intelligence techniques. In this study, we introduce a genetic algorithm to optimize the number of neighbors that combine, as well as the weight of each feature. The new model is applied to the real-world case of a major telecommunication company in Korea in order to build a prediction model for customer profitability level. Experimental results show that our genetic-algorithm-optimized CBR approach outperforms other artificial intelligence techniques for this multi-class classification problem.