变风量空调系统恒温解耦控制优化

在室内恒温控制问题的研究中,变风量空调系统的房间送风量、冷冻水流量和风机转速三个输入变量与房间温度、送风温度和静压三个输出变量之间存在着不同程度的耦合关系,每个房间的温度控制会受到不同程度的干扰,严重时会影响到整个系统的稳定性.为解决上述问题,根据变风量空调系统的房间、表冷器、风机等各个子系统模型,通过寻找一个合适的开环传递函数矩阵,实现对系统的解耦控制,通过使解耦矩阵的对角元素为1,得到简化的解耦矩阵.比通常利用对角化方法和状态反馈矩阵方法直接求得的解耦矩阵要简单.运行空调实验结果表明控制回路之间干扰不明显,解耦控制效果良好.     

基于话者无关模型的说话人转换方法

提出一种基于话者无关模型的说话人转换方法.考虑到音素信息共同存在于所有说话人的语音中,假设存在一个可以用高斯混合模型来描述的话者无关空间,且可用分段线性变换来描述该空间到各说话人相关空间之间的映射关系.在一个多说话人的数据库上,用话者自适应训练算法来训练模型,并在转换阶段使用源目标说话人空间到话者无关空间的变换关系来构造源与目标之间的特征变换关系,快速、灵活的构造说话人转换系统.通过主观测听实验来验证该算法相对于传统的基于话者相关模型方法的优点.     

基于人造物体直线段结构特征的不变性识别

传统的用假设验证法进行三维物体识别的方法需要通过一组非线性方程组求解从模型到场景的坐标系变换,具有非常高的复杂度.文中提出了一种基于能够表明物体几何构造的直线段特征的人造物体识别方法,将假设验证法中对于全局坐标系变换的求解分散在各个平面单应性变换的求解中,降低了求解的复杂度.该方法首先利用几何不变量预匹配特征点,进而假设并求出场景和模型平面之间的单应矩阵,随后通过模型与场景之间直线段特征匹配的结果进行验证.实验证明,该方法能够快速准确地识别含有较多共面直线段特征的人造物体.     

时滞不确定随机系统基于参数依赖Lyapunov函数的稳定条件

针对一类具有凸多面体参数不确定性的时滞随机系统, 研究了其鲁棒稳定性问题. 通过引入适当的加权矩阵变量来寻找Leibniz-Newton公式各项之间的关系, 从而直接地处理系统中的时滞状态项, 避免了常规的应用Leibniz-Newton公式来进行模型变换的间接方法所带来的较大保守性. 采用参数依赖Lyapunov函数方法, 推导了此类系统鲁棒稳定的时滞相关的充分条件. 本文所得条件为线性矩阵不等式形式, 便于借助于内点算法进行求解. 仿真实例证明了本文所提出的稳定条件具有较低的保守性.     

基于等价类划分的配置求解与解释计算

基于约束的配置模型中会有一些变量之间不存在任何直接或间接的约束关系,这样的变量之间进行约束传播不会互相影响取值.基于配置问题的这一特点,提出了一种等价类划分的思想,用于构造产品模型时的预处理技术,可以有效地将原问题划分为若干子问题,证明了这些子问题可以分别处理.分别采用两种回溯策略对求解效率进行了测试,结果表明能够有效地提高求解效率.最后,等价类划分方法与计算解释的QUICKXPLAIN算法集成计算冲突解释,测试结果表明,经过等价类划分后,同样可以有效地提高计算解释的效率.     

全相关核偏最小二乘故障诊断方法及在抽油机上应用

针对油田抽油机生产数据存在强非线性和强耦合性, 导致故障诊断困难的问题, 本文提出一种全相关动态 核偏最小二乘(FCDKPLS)故障诊断方法. 首先, 构建抽油机生产数据自回归模型, 反映数据变量间的动态特性; 其 次, 分析了KPLS算法中输出变量与输入变量残差子空间的相关性, 为此, 在输出模型上构建一个辅助矩阵, 从而表 征输入变量与输出变量的全相关性, 建立输入变量和输出变量之间更直接的联系. 最后, 将提出的全相关动态偏最 小二乘方法应用于抽油机过程故障诊断, 实验结果表明本文提出方法的有效性.     

多变量公钥密码体制扩展方案的改进

多变量公钥密码扩展方案是一种新型的多变量公钥加密算法,它通过引入Tame变换,增加冗余变量来增强原始公钥加密体制的安全性。然而聂旭云等人声称该加密方案存在安全漏洞,并且给出了针对Tame变换中对角矩阵D的具体破解方法。针对方案中存在的漏洞,作者对原始算法中的矩阵D和冗余明文进行了两处改进,并证明了经过改进后的方案不存在聂旭云等人提出的安全漏洞,从而进一步增强了原始方案的安全性。     

动态反馈解耦规范型时域结构特征分析及变换矩阵的构造

从可解耦线性多输入多输出(Multi-input multi-output, MIMO)系统的结构特性指数出发, 根据此类系统解耦后系统的可观测矩阵与基本向量矩阵的秩的关系, 提出了按照这种关系将解耦规范型划分为4大类的观点, 同时给出了一种针对各类积分型解耦系统构造相应的非奇异变换矩阵的构造方法. 分析了解耦规范型及其变换矩阵的时域结构形式, 通过一系列定理的证明,从一般意义上解释了解耦规范型的结构与变换矩阵的关系, 并通过一个数值实例验证了所提出方法的正确性及可行性.     

飞行器动力系统多变量控制结构设计研究

针对飞行器动力系统多变量控制系统结构设计问题,提出了以相关增益矩阵方法确定发动机多变量控制方案中被控参数与控制量的一一对应关系,阐明了相关增益矩阵的意义.以二输入二输出系统为例推导了相关增益矩阵的比值计算方法.并以某型三输入三输出的变循环发动机为仿真算例,计算了其各被控参数和控制量的相关关系.根据其相关关系,确定了该变循环发动机多变量控制系统结构,并分析了其相关关系对飞行器动力系统多变量控制方案确定的意义.仿真算例表明,利用相关增益矩阵方法可以定量地确定系统各控制量和被控参数的相关关系,从而可以避免传统方法仅通过经验定性确定其相关关系的缺点.     

多自由度遥操作系统控制的波变量法

为了将波变量方法用于多自由度遥操作系统,提出了一种更具一般性的多自由度遥操作系统的波变换公式,并扩展了公式参数的选择原则.首先分析了波变量法的原理,并用波变换矩阵替换波阻抗常数b得到多自由度系统的波变换公式.然后从能量的角度分析了波阻抗矩阵的选择原则,根据此选择原则扩展了波阻抗矩阵的选择方法和矩阵之间的约束关系.接着基于耗散理论,分析了扩展的波阻抗矩阵的无源性.最后以3自由度主从遥操作系统为例,分别进行了仿真实验和机器人遥操作实验,实验结果表明所提出的波变换公式能够保证多自由度遥操作系统在时延条件下的稳定性.     

电子鼻信号特征提取与传感器优化的研究

采用PEN2型电子鼻系统对芝麻油的玉米油掺假进行定性鉴别和定量预测,运用主成分分析,逐步判别分析和Fisher线性判别函数变换对原始数据进行预处理,从而降低原始数据空间的维数,并用判别分析与人工神经网络对数据进行进一步分析,考察了不同的数据预处理方法的效果.判别分析结果表明,采用Fisher线性判别函数变换所得到的十个变量判别能力最强,误判率为0.61%,仅有1个样品出现误判.在BP神经网络的定量预测中,采用逐步判别分析所筛选出的十个变量作为网络输入,所得的预测结果最为理想,绝对误差个体值的95%置信区间最小,为(-4.71%,3.38%),均方误差为4.75,预测值与实际值之间有极显著的相关性,相关系数R=0.998 08.     

Assessing the pattern between economic and digital development of countries

This paper analyzes the digital development of 110 countries and its relationship with economic development. Using factor analysis, we combined seven ICT-related variables into a single measure of digital development. This measure was then used as the dependent variable in an OLS model that allows non-linear effects, with the GDP per capita of countries as the explanatory variable. Our findings are substantive in that the correlation between economic and digital development was found to be not linear, being much stronger in poorer countries, a finding not commonly seen in the literature. As a result, future studies that focus on the relationship between economic and digital developments may benefit from our findings, by postulating this type of relationship. In our model we were able to explain 83 % of the variation in the digital development of countries, compared to just 72 % if considering only a linear relationship.     

定常线性系统不同分解下状态变量之间的关系

本文以一般补空间的概念讨论定常线性系统的分解,指出取自然基作为补空间的基,可以使系统分解的变换矩阵求逆最简单.鉴于在正交补空间定义下用交空间方法一般得不到标准分解,本文给出了一种在一般补空间定义下利用交空间进行标准分解的方法,证明了基本的不能控状态变量、基本的能观状态变量和基本的能观不能控状态变量的不变性.     

Semi-parametric nonlinear regression and transformation using functional networks

Functional networks are used to solve some nonlinear regression problems. One particular problem is how to find the optimal transformations of the response and/or the explanatory variables and obtain the best possible functional relation between the response and predictor variables. After a brief introduction to functional networks, two specific transformation models based on functional networks are proposed. Unlike in neural networks, where the selection of the network topology is arbitrary, the selection of the initial topology of a functional network is problem driven. This important feature of functional networks is illustrated for each of the two proposed models. An equivalent, but simpler network may be obtained from the initial topology using functional equations. The resultant model is then checked for uniqueness of representation. When the functions specified by the transformations are unknown in form, families of linear independent functions are used as approximations. Two different parametric criteria are used for learning these functions: the constrained least squares and the maximum canonical correlation. Model selection criteria are used to avoid the problem of overfitting. Finally, performance of the proposed method are assessed and compared to other methods using a simulation study as well as several real-life data.     

Inference of disjoint linear and nonlinear sub-domains of a nonlinear mapping

This paper investigates new ways of inferring nonlinear dependence from measured data. The existence of unique linear and nonlinear sub-spaces which are structural invariants of general nonlinear mappings is established and necessary and sufficient conditions determining these sub-spaces are derived. The importance of these invariants in an identification context is that they provide a tractable framework for minimising the dimensionality of the nonlinear modelling task. Specifically, once the linear/nonlinear sub-spaces are known, by definition the explanatory variables may be transformed to form two disjoint sub-sets spanning, respectively, the linear and nonlinear sub-spaces. The nonlinear modelling task is confined to the latter sub-set, which will typically have a smaller number of elements than the original set of explanatory variables. Constructive algorithms are proposed for inferring the linear and nonlinear sub-spaces from noisy data.     

Restricted regression estimation in measurement error models

The problem of consistent estimation of the regression coefficients when some prior information about the regression coefficients is available is considered. Such prior information is expressed in the form of exact linear restrictions. The knowledge of covariance matrix of measurement errors that is associated with explanatory variables is used to construct the consistent estimators. Some consistent estimators are suggested which satisfy the exact linear restrictions also. Their asymptotic properties are derived and analytically analyzed under a multivariate ultrastructural model with not necessarily normally distributed measurement errors. The finite sample properties of the estimators are studied through a Monte-Carlo simulation experiment.     

挖掘回归类的混合模型的可识别性

复杂海量数据往往表现为多种结构特征的混合体，回归类混合模型就是对这种混合体的一个描述．该文基于统计学的有限混合分布理论和可识别性的相关结果，针对回归变量的三种情形：(1)解释变量固定，(2)解释变量随机，(3)解释变量固定且类别参数指定，分别讨论挖掘一般回归类的混合模型的可识别性问题，并给出同族回归类混合模型可识别的相应充分条件．这些条件的一个共同特点是它们都与一类特别的解释变量集合有关，而该类集合是由同族的回归函数与回归参数唯一确定的，其元素使不同的回归参数对应回归函数的相同值．特别地，当回归函数线性时，这类集合就是解释变量空间中的超平面．     

Linearization of logical functions defined by a set of orthogonal terms. I. Theoretical aspects

Consideration was given to the linearization of logical functions defined by a set of pairwise orthogonal terms. The linearization is carried out by computing the autocorrelation functions. Proposed was a method consisting of (i) calculation of the autocorrelation function in the space of orthogonal terms, (ii) generation of the corresponding matrix of linear transformation, and (iii) the linear transformation proper of the variables in the space of orthogonal terms. Complexity of the proposed method and its effectiveness were estimated. Effectiveness was verified by a series of experiments with standard benchmarks. The distinctions of the proposed method from other existing methods of linearization were examined.     

High-speed train fault detection with unsupervised causality-based feature extraction methods

With the development of smart sensors, large amount of operating data collected from a complex system as a high-speed train providing opportunities in efficient and effective fault detection and diagnosis (FDD). The data brings also challenges in the FDD modelling process, since the various signals may be redundant, useless and noisy for the FDD modelling of a specific sub-system. The data-driven methods suffer also from the curse of dimensionality. Feature dimension reduction can reduce the dimension of the monitoring dataset and eliminate the useless information. Different from the classical methods based on the correlation among variables, recent studies have shown that causality-based methods can make the FDD model more explanatory and robust. From the adjacency matrix of the causal network diagram, three unsupervised causality-based feature extraction methods for FDD in the braking system of a high-speed train are proposed in this paper. By constructing the causal network diagram among the raw monitoring feature variables through the causal discovery algorithm, the proposed methods extract informative features based on the causal adjacency matrix or the full causal adjacency matrix proposed in this work. These methods are adopted for fault detection with real dataset collected from the braking system in a high-speed train to verify their effectiveness. The experimental results show that the proposed causality-based feature extraction methods are effective and have certain advantages in comparison with the classical correlation-based methods. Especially, the feature extraction method based on the correlation matrix constructed from full causal adjacency matrix achieves better and stable results than the benchmark methods in the experiment.