污水处理过程出水水质多模型在线软测量方法

针对污水处理过程出水水质参数难以在线监测的问题,提出了一种多模型在线软测量方法。该方法根据污水处理过程中实时工况数据具有聚类和迁移属性的特点,利用在线减法聚类算法将实时工况数据样本进行划分,并根据实时工况数据在样本空间中的分布,采用模糊策略将相应子空间的实时工况数据分配给不同的子模型进行学习,最后通过动态集成各子模型的输出而得到最终软测量结果。以某污水处理厂实际运行数据对污水处理过程出水水质氨氮进行预测,实验结果表明,该方法确实能够以实时工况数据为驱动自组织构建多模型软测量模型,且用该方法构建出的多模型软测量模型无论在建模精度、建模速度以及跟踪实时工况的能力等方面都有所提高。     

基于JITL的电量预测高斯软测量建模研究

为了解决工业过程受本身结构特征、外界因素等影响而存在严重的非线性和时变性等问题,本文提出了一种基于输入输出综合性相似度指标的即时学习高斯过程软测量建模方法。在该方法中,将样本数据进行归一化处理,首先利用传统的基于距离和角度的相似度指标分别对样本输入输出变量进行相似度计算,进而对相似度进行综合,最后选择出最终的相关样本集,建立高斯过程回归软测量模型,将所提基于输入输出相似度指标的即时学习高斯工程软测量模型应用于城市日用电量数据的预测。研究结果表明,所提出的软测量建模方法可以实现对日用电量数据的高精度预测且预测结果具有较小的误差。因此可表明该方法可在电量预测中具有一定的应用可靠性,可以在电力市场预测分析中得到广泛的应用。     

基于增量学习思想的改进AdaBoost 建模方法

针对软测量建模的特点以及建模过程中存在的主要问题,提出了基于 AdaBoost RT 集成学习方法的软测量建模方法,并根据 AdaBoost RT 算法固有的不足和软测量模型在线更新所面临的困难,提出了自适应修改阈值 和增添增量学习性能的改进方法.使用该建模方法对宝钢300 t LF 精炼炉建立钢水温度软测量模型,并使用实际生产数据对模型进行了检验.检验结果表明,该模型具有较好的预测精度,能够很好地实现在线更新.     

基于迁移子空间学习的偏最小二乘回归软测量方法

针对流程工业中工况改变易导致当前样本与历史样本分布失配,传统软测量模型失准的问题,考虑工业数据时序性、动态性以及存在过程漂移等特性对建模的影响,提出一种基于迁移子空间学习的偏最小二乘回归软测量方法.首先,回归框架采用非线性迭代偏最小二乘方法,对其求解映射向量的目标函数施加基于子空间重构的域适应正则项,映射过程中保证当前工况中每个样本能够被历史工况样本线性重构.在此基础上对重构矩阵施加低秩稀疏约束,保持数据结构的同时使重构矩阵具备块状结构以应对过程漂移特性.将所提出方法在1个数值案例和3个不同的多工况数据集中进行实验,并与现有域适应回归方法进行对比分析.实验表明,所提出方法能够有效提高模型在跨工况条件下的预测精度,减少工况间数据分布差异对模型性能的影响.     

基于AP的LS-SVM多模型建模算法

针对多工况对象的单模型建模中存在的回归精度差和泛化能力弱的问题,提出基于仿射传播聚类的LS-SVM多模型建模方法。该方法用仿射传播聚类算法对样本进行聚类,采用LS-SVM的方法对子类样本分别建立模型。测试样本根据相似性的测度进行归类,并用所属子类的模型进行预测输出。将该建模方法用在丙烯浓度的软测量建模实验中,结果表明该方法有较高的回归精度和较好的泛化能力。     

动态选择性集成神经网络软测量建模

集成建模方法能显著提高软测量模型的预测性能,其中选择性集成通过剔除一些性能不佳的子模型,能进一步提高整体软测量模型预测性能。针对目前选择性集成研究中因忽略了数据间的差异性而导致模型预测性能不佳的问题,提出了一种动态选择性集成神经网络软测量建模方法。首先将原始数据集分为训练集和验证集,采用bootstrap算法对训练集进行差异性扰动,建立了多个神经网络子模型;然后对每个待测样本,采用K-最近邻搜索算法从验证集中找到一个最近邻子集,用该子集评估各神经网络子模型的预测性能,为待预测样本筛选合适的神经网络子模型;最后根据各子模型的预测性能合理分配组合权重,从而建立集成模型,并实现待预测样本的预测。将该建模方法应用于聚丙烯熔融指数软测量研究中,研究结果表明:与单一神经网络、常规全集成和静态选择性集成神经网络模型相比,基于动态选择性集成神经网络的熔融指数软测量模型具有更佳的预测精度。     

基于分布共识的联邦增量迁移学习

相同生产工艺的工业过程协同建模是解决工业难测参数在线软测量的有效方法,但因生产原料、设备等因素差异,所形成的分布式数据往往呈现非独立同分布特性(Nonindependent Identically Distribution, Non-IID).同时,受生产环境变化影响,数据分布特性会随时间发生变化.因此,工业建模场景对模型的个性化配置和自主调整能力提出了更高的要求.为此,本文提出一种结构与参数并行优化的联邦增量迁移学习方法(Federated Incremental Transfer Learning, FITL).所提方法在增量式联邦学习框架下,建立了基于模型输出信息的联邦共识组织,并利用横向联邦进行组内增强;进而,面向联邦共识组织,通过最小化组间共识差异增量迁移不同共识组织信息;最后,结合组内横向增强和跨组织迁移学习,构造增量迁移下的联邦学习模型.在工业数据集和基准数据集上的实验结果表明,与现有方法相比,所提模型能更好地实现不同工况Non-IID情况下的协同建模.在过程工业回归任务和公开数据集的分类任务中,FITL能在多工况环境下相较基线方法提升9%和16%的模型预测精度.     

一种用于软测量建模的增量学习集成算法

针对软测量模型在实际应用中遇到的问题, 结合AdaBoost 集成学习思想, 提出适用于软测量回归的集成学习算法, 以提高传统软测量模型的精度. 为了克服模型更新技术对软测量实际应用的制约, 将增量学习机制加入软测量集成建模中, 使软测量模型具有在线实时更新的增量学习能力. 对浆纱过程使用新方法建立上浆率软测量模型, 并使用实际生产数据对模型进行检验, 检验结果表明, 该模型具有很好的预测精度, 并能够较好地实现在线更新.

     

基于统计回归的质量推断方法

统计回归是软测量建模的一个重要手段.本文在回顾了典型统计回归方法的基础上,采用部分最小二乘(PLS)建立高定量造纸过程的水分软测量模型.鉴于软测量模型的扰动拟合本质,提出了基于扰动估计的软测量建模策略.水分软测量模型的建立,验证了该建模策略的有效性.     

基于免疫遗传算法的动态递归模糊神经网络在发酵过程中的应用

针对软测量建模数据中过失误差及动态递归模糊神经网络的结构复杂,大量参数难以确定的情况,提出基于免疫遗传算法动态递归模糊神经网络软测量方法。利用样本间马氏距离进行样本相似程度分析,去除样本中错误数据以提高计算速度。此外应用减法聚类确定模糊规则数,以简化网络结构,同时应用免疫遗传算法优化模型参数以提高模型的精度和泛化能力。该方法应用于赖氨酸发酵过程菌体浓度的软测量,仿真结果表明,该方法具有较高的预测精度,满足现场测量要求。     

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.