基于小波和模式匹配的变压器色谱倾斜峰辨识方法

把小波和模式匹配技术引入了电力变压器色谱倾斜峰辨识领域;该方法为:先用小波技术提取色谱曲线大致的基线,然后扣除基线,消除由于基线漂移引起的色谱峰倾斜,再用基于模式匹配的色谱峰辨识方法查找色谱峰位;对该方法进行了实验研究,结果表明:该方法能辨识由于色谱基线漂移引起的倾斜峰的峰位。     

气相色谱和神经网络在机车故障诊断中的应用

为诊断电力机车变压器的故障类型,以提高铁路系统的安全性,提出了一种基于模糊控制的BP神经网络模型.首先应用气相色谱技术,分析变压器油中特征气体含量与故障之间的对应关系,确定电力机车变压器常见的故障类型;然后,收集各种故障类型数据,作为神经网络的训练样本数据和测试数据;最后,建立改进BP神经网络的诊断模型,实现测试数据的仿证测试.诊断结果表明,该模型在电力机车变压器故障诊断中具有良好的实用前景.     

变压器油中溶解气体色谱峰识别方法的研究及应用

针对目前变压器油中溶解气体分离后得到的色谱曲线基线存在抖动、漂移、有异常点，特别是在低浓度曲线干扰大、波形不规则，易出现对气体色谱峰的误判和漏判，导致气体浓度值检测偏差大，严重影响了故障诊断的准确性等一系列问题。采用了差分阈值滤波法、粗糙惩罚法以及粒子群优化支持向量机等算法，对油气分离后的色谱曲线序列数据中的一系列色谱峰进行干扰峰识别、分类，并采用优化半峰宽法计算出气体浓度值。有效降低了色谱峰的漏判和误判率，极大提高检测气体浓度值的稳定性、准确性和精确性。     

基于动态递归神经网络的自适应PID控制

提出一种基于动态递归神经网络的自适应PID控制方案,该控制系统由神经网络辨识器和神经网络控制器组成。辨识器采用单隐层的动态递归神经网络,网络结构为2-4-1;辨识算法为动态BP算法;控制器采用两层线性结构的神经网络,输入为系统偏差及其一阶、二阶微分,因此具有增量型PID控制结构。应用该控制系统对一非线性时变系统进行仿真研究,仿真结果表明该控制方案不仅具有良好的跟踪特性,而且对系统参数变化具有较强的鲁棒性。     

修正高斯模型神经网络的色谱重叠峰解析

目的 :研究色谱重叠峰解析新方法。　方法 :构造了以修正高斯模型 (EMG)为基函数的径向基函数神经网络 (EMG-RBFNN) ,在网络学习算法中提出采用基于共享小生境技术的约束最优保留两阶段遗传算法 :结构学习和参数最速梯度下降进化 ,从而使EMG-RBFNN具有结构学习能力 ,使该神经网络能够适应组分数未知的色谱重叠峰解析。尤其是在将气相和高效液相色谱EMG模型参数τ、σ和tR 之间的近似线性关系作为可行域约束条件引入算法后 ,极大地限制了算法可行的解空间 ,减少了局部最优解出现的概率 ,提高了算法运行效率。　结果 :将此新方法分别应用于计算机仿真色谱和 45例中药材HPLC ,算法可以以较高的精度解析出仿真色谱 ,实际色谱峰解析也有很高的解析精度。　结论 :该方法非常适用于组分数未知的各种色谱 (重叠 )峰的解析     

修正高斯模型神经网络的色谱重叠峰解析

目的：研究色谱重叠峰解析新方法。方法：构造了以修正高斯模型（EMG）为基函数的径向基函数神经网络（EMG＿RBFNN）,在网络学习算法中提出采用基于共享小生境技术的约束最优保留两阶段遗传算法：结构学习和参数最速梯度下降进化,从而使EMG＿RBFNN具有结构学习能力 ,使该神经网络能够适应组分数未知的色谱重叠峰解析。尤其是在将气相和高效液相色谱EMG模型参数τ、σ和tR之间的近似线性关系作为可行域约束条件引入算法后,极大地限制了算法可行的解空间,减少了局部最优解出现的概率,提高了算法运行效率。结果：将此新方法分别应用于计算机仿真色谱和45例中药材HPLC,算法可以以较高的精度解析出仿真色谱,实际色谱峰解析也有很高的解析精度。结论：该方法非常适用于组分数未知的各种色谱（重叠）峰的解析。     

基于优化神经网络的变压器故障诊断方法研究

传统基于反向传播（BackPropagation,BP）神经网络的电力变压器故障诊断方法正判率较低,本文利用粒子群算法优化神经网络算法进而对变压器故障诊断展开研究。具体做法是在电力变压器油的气体分析过程中采用优化后的神经网络方法进行故障诊断,其中优化算法为邻域粒子群算法,将经过采集、归类、预处理后的变压器油气体相关数据输入优化后的BP神经网络,利用网络进行充分优化与训练,完成电力变压器的故障类别诊断。案例分析结果显示本文基于神经网络的变压器故障诊断方法提高了故障类别诊断的正确率。     

线性分类器与BP网络联合诊断变压器故障

油中溶解气体分析(DGA)是目前电力充油设备潜伏性故障诊断的重要手段。为了克服传统BP网络及其改进诊断算法所具有的隐层节点数多、收敛时间长的缺陷,减少算法运算量及提高变压器故障诊断的正确率,提出了一种新的诊断算法:线性分类器-BP神经网络(LC-BP)故障辨识方法。通过对变压器大量过热和放电两类典型故障数据的研究,发现其DGA故障数据的特征空间线性可分且分离度较好。基于以上特性,先用线性分类器诊断过热和放电故障,然后利用两个小型BP网络分别进行进一步诊断,得到最终诊断结果。实验结果表明,提出的LC-BP算法具有良好的分类能力,故障诊断的正确率达到94%,且网络结构简单,运算量小,从而为变压器的故障诊断提供了一条新的有效途径。     

基于改进RBF的变压器油色谱在线监测装置配置策略研究

变压器油色谱在线监测是变压器故障诊断的重要举措,也是电网变电检修的重要手段。考虑到110千伏(66千伏)电压等级油浸式变压器在线监测装置的选配对其诊断效果具有较大影响,并且其影响因素较多且复杂,提出一种基于K-means聚类和遗传算法相结合的RBF神经网络在线监测装置配置策略。从变压器内部本体特征和外部所处环境两个方面对装置选配影响因子进行筛选和分析得到最终的影响因子;对RBF模型的适用性进行分析和阐述;建立以影响因子为输入向量,以监测装置配置评分作为输出向量的基于改进RBF在线监测装置配置模型。实验结果表明,该模型能够对在线检测装置进行准确评分,提高了选配准确度,证明了该模型的有效性。与神经网络(BP)模型相比,该模型加快了网络收敛速度,能够更加有效地解决在线监测装置配置问题,为电网提供切实可行的方案。     

人工鱼群神经网络速度辨识器及应用

为实现无速度传感器直接转矩控制,有时采用神经网络转速辨识器,但前馈神经网络结构难以确定,运用BP算法时又极易陷入局部解.将人工鱼群算法和BP算法结合,采用人工鱼群算法优化神经网络的结构及网络初始权值,再利用BP算法对网络权值进行精确调节;将人工鱼群算法与BP算法相结合的AFSA BP算法,实现了人工鱼群算法的全局搜索能力与BP算法的局部寻优性能的互补结合.将所设计的神经网络转速辨识器运用到直接转矩控制系统当中.利用MAT-LAB/SIMULINk实现无速度传感器控制系统的仿真实验结果表明,该算法具有良好辨识效果.     

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。     

基于QTE的蒙古文显示机制研究与实现

蒙古语言是中国蒙古族使用的通用语言,由于蒙古文区别于其他文字的书写方式和其自身变形机制等特点,在很多通用的文字处理引擎中都不被支持。在嵌入式产品开发与应用领域中Linux加QTE已经成为流行方式。该文给出了一种在QTE环境上实现基于标准Unicode的蒙古文点阵显示和变形算法, 并自定义了支持蒙古文的QTE组件,扩展了QTE功能,为在Linux加QTE方式的嵌入式体系结构中处理蒙古文提供了一种解决方法。