基于机器视觉高空飞机螺栓松动监测仿真

高空飞机受到自身振动、气流冲击和温度瞬变等干扰时,导致飞机螺栓结构松动、断裂或者脱落等问题.飞机螺栓结构具有复杂的动态性,传统的飞机螺丝辅助视觉松动检测方法在处理飞机螺栓复杂结构时,获取的螺丝帽和螺丝杆的相对位置过程中,受到采集环境的影响,很难得到稳定特征,导致图像监测误差,无法获取准确的螺栓松动监测结果.提出聚类RBF神经网络算法的高空飞机螺栓紧固方法.根采集检测部分螺丝视觉图像序列,并对上述图像进行离散变换处理,提取高空飞机螺栓图像的可识别分离特征.针对螺栓机器视觉图像特征误差较大的缺陷,构建特征分离目标函数,并建立聚类RBF神经网络模型,对特征进行多次校验计算,完成判断.实验结果表明,利用改进算法进行机器视觉的高空飞机螺栓松动监测,能够极大的提高监测的准确性.     

基于飞参数据的发动机碰摩故障检测模型仿真

在飞行实时状态监控过程中,强震动环境下的发动机碰摩故障检测的准确性,关系到飞行的安全.在强震动环境下,发动机转静容易发生碰摩故障,多个碰摩故障往往是同时发生的,但是,故障的振动信号之间不存在之间关联.碰摩故障特征彼此独立,故障特征数据之间缺少可直接建立的关联.传统的故障挖掘方法在发动机转静碰摩故障检测的过程中,只能 根据振动信号对这种故障进行检测,振动信号本身存在很多干扰,也很难包括所有的碰摩故障特征,导致故障检测不准确.为解决上述问题,提出采用改进关联规则挖掘的强震动环境下的发动机碰摩故障检测方法.针对飞机发动机碰摩数据进行关联聚类处理,获取样本空间的分类矩阵,并对发动机碰摩数据进行更新.根据更新结果,计算发动机碰摩数据关联概率值,得到概率决策,针对飞行器碰摩数据进行关联规则挖掘,实现发动机碰摩数据故障优化检测.实验结果表明,利用改进关联规则算法进行强震动环境下的发动机转静碰摩故障检测,极大的缩短了检测时间,降低了漏检率,提高了检测精确度.     

刀具状态在线监测与智能识别方法研究

切削加工中刀具状态是影响加工质量的关键因素,刀具的磨损直接影响工件的加工精度和表面粗糙度.选择加速度传感器监测切削加工中的振动信号,针对刀具状态变化时振动能量分布随之变化的特点,提取不同频段振动能量作为特征量,利用RBF神经网络进行聚类辨识.实验结果表明,该方法具有良好的识别效果和工程应用价值.     

云计算环境下数据弱关联挖掘模型的仿真

在对云计算环境下海量数据进行挖掘的过程中,容易出现大量冗余数据,造成数据间关联性降低,导致传统的基于模糊神经网络的云计算环境下海量数据中弱关联.由于容易陷入局部最小,无法有效完成弱关联的挖掘,提出一种采用弱聚类算法的云计算环境下海量数据中弱关联挖掘方法,通过数据的描述特征对数据特征进行分解,依据数据特征,对全部数据进行融合.基于关联决策概率将云计算环境下海量数据进行有效划分,完成所有数据特征关联概率的计算,通过弱聚类方法对属性元素进行分类,将数量型元素转换成类别型,通过弱化关联规则方法,对经聚类处理后的数据进行挖掘.实验结果表明,所提方法具有很高的高效性及有效性.     

改进型RBF神经网络的多标签算法研究

针对已有的RBF神经网络多标签算法未充分考虑多个样本标签之间的关联性,从而导致泛化性能受到一定影响的问题,研究分析了一种改进型RBF神经网络的多标签算法.该算法首先优化隐含层RBF神经网络基函数中心求取算法——k-均值聚类.采用AP聚类自动寻找k值以获得隐含层节点数目,并构造Huff man树来选取初始聚类中心以防k-均值聚类结果陷入局部最优.然后构造体现标签类之间信息的标签计数向量C,并将其与由优化k-均值聚类得到的聚类中心进行线性叠乘,进而改进RBF神经网络基函数中心,建立RBF神经网络.在公共多标签数据集emotion上的实验表明了该算法能够有效地进行多标签分类.     

基于免疫聚类的RBF神经网络研究

通过分析以往人工免疫聚类算法的不足之处,提出了一种改进的基于人工免疫聚类与RBF神经网络的混合算法.该算法由两个阶段组成:第一阶段采用人工免疫机制来确定RBF网络隐层的聚类中心的位置和数量;第二阶段建立RBF神经网络,对输入样本数据进行学习、训练,求输出层的权值矩阵W.最后以肝病病证诊断进行仿真,建立基于免疫聚类的RBF网络模型.实验结果表明:该算法用于中医病证诊断的研究是可行的和有效的.     

基于蚁群聚类的RBF NN海洋环境下武器效能评估

海洋环境复杂多变,武器装备作战效能影响要素众多并且机理复杂,效能评估问题难度加大。针对海洋环境影响下武器效能的评估问题,将蚁群聚类算法与RBF神经网络相结合,提出了基于蚁群聚类的RBF神经网络武器作战效能评估方法并建立评估模型。通过蚁群聚类算法确定RBF神经网络隐含层神经元的中心值,并且为了得到最优聚类结果,在蚁群聚类中加入了局部搜索。最后,利用样本数据对该模型进行训练,并对特定条件下武器作战效能进行评估,实验证明此评估模型具有一定的可行性和有效性。     

基于聚类算法的RBF神经网络设计综述

简要分析了径向基函数（RBF）神经网络。在此基础上,介绍了K-均值聚类算法的神经网络、C-均值聚类算法的神经网络和PAM聚类算法的神经网络三种聚类算法的RBF神经网络。展望了基于聚类的RBF神经网络设计的发展趋势。     

基于FPGA及RBF神经网络的电磁无损检测技术

电磁无损检测技术是无损检测领域的一个研究重点,针对电磁无损检测技术中的超声波处理,提出了一种基于FPFA的参数优化的RBF神经网络;首先,通过FPGA编程实现对电磁超声波信号的采集,设计了放大电路将原始的电磁超声波进行放大处理已满足RBF神经网络的需求;提出一种采用K-means聚类算法来计算RBF中径向基函数的中心和宽度的参数优化RBF算法,K-means聚类算法的初始聚类中心难以确定会导致RBF算法的参数无法优化,提出KL散度,采用数据密度分析法来计算K-means算法的聚类中心;试验表明,改进后的K-means算法的聚类误差的数量级为10~(-12),传统K-means算法的聚类误差为10~(-13),改进后的K-means算法的聚类结果更准;参数优化后的RBF神经神级网络对具有1.02 mm缺陷长度的发动机涡轮叶片的缺陷长度预测结果为0.9~1.1 mm,传统的RBF神经网络的预测结果为0.7~1.2 mm,参数优化后的RBF神经网络的预测结果更准确。     

基于改进RBF神经网络的车辆类型识别

首先利用蚁群聚类与模糊C-均值聚类相结合的方法优化径向基函数中心与宽度,然后根据样本数据的聚类结果设计RBF神经网络,最后在车辆图像预处理和特征提取的基础上将该神经网络应用于车辆类型识别.仿真结果表明改进的RBF神经网络提高了车辆识别的正确率,同时减少计算的迭代次数.     

Only connect: teaching, technology and telesis

Abstract This paper describes an approach to the design of interactive multimedia materials being developed in a European Community project. The developmental process is seen as a dialogue between technologists and teachers. This dialogue is often problematic because of the differences in training, experience and culture between them. Conditions needed for fruitful dialogue are described and the generic model for learning design used in the project is explained.     

European Community policy and the market

Abstract This paper starts with some reflections on the policy considerations and priorities which are shaping European Commission (EC) research programmes. Then it attempts to position the current projects which seek to capitalise on information and communications technologies for learning in relation to these priorities and the apparent realities of the marketplace. It concludes that while there are grounds to be optimistic about the contribution EC programmes can make to the efficiency and standard of education and training, they are still too technology driven.     

一种自适应子融合集成多分类器方法

融合集成方法已经广泛应用在模式识别领域,然而一些基分类器实时性能稳定性较差,导致多分类器融合性能差,针对上述问题本文提出了一种新的基于多分类器的子融合集成分类器系统。该方法考虑在度量层融合层次之上通过对各类基多分类器进行动态选择,票数最多的类别作为融合系统中对特征向量识别的类别,构成一种新的自适应子融合集成分类器方法。实验表明,该方法比传统的分类器以及分类融合方法识别准确率明显更高,具有更好的鲁棒性。     

Avoiding semantic and temporal gaps in developing software intensive systems

Development of software intensive systems (systems) in practice involves a series of self-contained phases for the lifecycle of a system. Semantic and temporal gaps, which occur among phases and among developer disciplines within and across phases, hinder the ongoing development of a system because of the interdependencies among phases and among disciplines. Such gaps are magnified among systems that are developed at different times by different development teams, which may limit reuse of artifacts of systems development and interoperability among the systems. This article discusses such gaps and a systems development process for avoiding them.     

Designing economic np control charts: A programmed simulation approach

This paper presents control charts models and the necessary simulation software for the location of economic values of the control parameters. The simulation program is written in FORTRAN, requires only 10K of main storage, and can run on most mini and micro computers. Two models are presented - one describes the process when it is operating at full capacity and the other when the process is operating under capacity. The models allow the product quality to deteriorate to a further level before an existing out-of-control state is detected, and they can also be used in situations where no prior knowledge exists of the out-of-control causes and the resulting proportion defectives.     

The development of robot art

Going through a few examples of robot artists who are recognized worldwide, we try to analyze the deepest meaning of what is called “robot art” and the related art field definition. We also try to highlight its well-marked borders, such as kinetic sculptures, kinetic art, cyber art, and cyberpunk. A brief excursion into the importance of the context, the message, and its semiotics is also provided, case by case, together with a few hints on the history of this discipline in the light of an artistic perspective. Therefore, the aim of this article is to try to summarize the main characteristics that might classify robot art as a unique and innovative discipline, and to track down some of the principles by which a robotic artifact can or cannot be considered an art piece in terms of social, cultural, and strictly artistic interest. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Explanation and prediction: an architecture for default and abductive reasoning

Although there are many arguments that logic is an appropriate tool for artificial intelligence, there has been a perceived problem with the monotonicity of classical logic. This paper elaborates on the idea that reasoning should be viewed as theory formation where logic tells us the consequences of our assumptions. The two activities of predicting what is expected to be true and explaining observations are considered in a simple theory formation framework. Properties of each activity are discussed, along with a number of proposals as to what should be predicted or accepted as reasonable explanations. An architecture is proposed to combine explanation and prediction into one coherent framework. Algorithms used to implement the system as well as examples from a running implementation are given.     

Three Process Perspectives: Organizations, Teams, and People

This paper provides the author's personal views and perspectives on software process improvement. Starting with his first work on technology assessment in IBM over 20 years ago, Watts Humphrey describes the process improvement work he has been directly involved in. This includes the development of the early process assessment methods, the original design of the CMM, and the introduction of the Personal Software Process (PSP)^SM and Team Software Process (TSP){^SM}. In addition to describing the original motivation for this work, the author also reviews many of the problems he and his associates encountered and why they solved them the way they did. He also comments on the outstanding issues and likely directions for future work. Finally, this work has built on the experiences and contributions of many people. Mr. Humphrey only describes work that he was personally involved in and he names many of the key contributors. However, so many people have been involved in this work that a full list of the important participants would be impractical.     

基于复小波噪声方差显著修正的SAR图像去噪

提出了一种基于复小波域统计建模与噪声方差估计显著性修正相结合的合成孔径雷达(Synthetic Aperture Radar,SAR)图像斑点噪声滤波方法。该方法首先通过对数变换将乘性噪声模型转化为加性噪声模型,然后对变换后的图像进行双树复小波变换(Dualtree Complex Wavelet Transform,DCWT),并对复数小波系数的统计分布进行建模。在此先验分布的基础上,通过运用贝叶斯估计方法从含噪系数中恢复原始系数,达到滤除噪声的目的。实验结果表明该方法在去除噪声的同时保留了图像的细节信息,取得了很好的降噪效果。     

How do children do mathematics with LOGO?

Abstract  This paper considers some results of a study designed to investigate the kinds of mathematical activity undertaken by children (aged between 8 and 11) as they learned to program in LOGO. A model of learning modes is proposed, which attempts to describe the ways in which children used and acquired understanding of the programming/mathematical concepts involved. The remainder of the paper is concerned with discussing the validity and limitations of the model, and its implications for further research and curriculum development.