基于层次分析法的装配序列评价技术

提出了一种基于层次分析法的装配序列评价方法,该方法通过计算评价子项相对权重,利用基于BP神经网络的判断矩阵调整方法对对比矩阵进行一致性检验与修正,以此获得装配序列总体评价结果。该评价方法的实现,提高了装配序列优选的自动化水平,并以小车总成虚拟装配实例对该评价方法进行了验证。     

基于Web的协同装配规划

分析协同CAD技术现状，针对装配规划工作的特点，提出网络环境下协同装配规划方法，并研究了基于Web的协同工作环境下的装配建模，装配规划任务分配以及装配序列合成等关键技术。利用VRML和Java实现一个基于Web的协同装配规划原型系统WebCAPS，该系统能为多人协同装配规划提供支持。以电动机驱动总成为示例，验证了系统的可行性。     

基于符号零压缩二叉决策图的装配可行性判定方法

为了扩大装配序列规划的求解规模并提高求解效率,提升装配自动化水平,给出了装配体联接矩阵和干涉矩阵的零压缩二叉决策图描述,建立了基于符号零压缩二叉决策图的装配操作可行性判定方法。基于该方法,可以高效地求解出一个装配体的可行装配操作。结合实例验证了基于符号零压缩二叉决策图的装配体模型和可行装配操作判定方法的正确性和可行性。     

一个基于知识的装配序列规划系统

本文提出一种基于知识的装配序列规划系统。该系统针对装配序列规划所涉及信息的纯几何描述和计算的复杂度等问题，采用领域知识推理，并结合适量几何计算的方法，求解装配序列规划问题。系统包括：任务理解，装配关系模型生成和序列规划三个组成部分。     

汽车“盖板总成(预装配)”装配专机

介绍了一种自行研制的装配专机 ,适用于汽车“盖板总成 (预装配 )”中将“调整螺钉总成”拧进“盖板总成”的装配工序。该装配专机具有自动检测和拧紧、实时数据显示、报警、打印等功能。既能确保操作精度 ,又可提高工效、减轻工人劳动强度。     

基于PETRI网的机器人装配作业建模及装配路径优化方法的研究

本文基于装配过程离散控制的思想，以扩展库所／变迁网对装配过程进行作业建模，提出了装配状态优化变迁序列的生成方法和算法．该方法有利于在不确定性存在时在任务级上实现装配过程的控制．     

虚拟环境中产品装配过程回溯方法研究

在记录虚拟装配过程的基础上，提出基于任务时间的虚拟装配过程整体回溯与基于任务对象的虚拟装配过程局部回溯方法．虚拟装配过程整体回溯以装配任务时间为回溯准则，允许将装配场景中零部件的方位与装配关系回溯到曾经存在的装配历史状态，而虚拟装配过程局部回溯，是根据装配序列中零件间装配约束与几何干涉方面存在的相关性进行装配过程的选择性回溯．文中方法在虚拟设计与装配原型系统开发中得到实现．实践表明，装配过程回溯方法有效地提高了虚拟环境中产品装配设计的效率。     

运用有向联接件知识求解装配序列规划

在分析联接件特征的基础上，提出了有向联接件的概念，建立基于联接件知识的零件表达式；运用有向联接件知识求解装配序列规划，实现了知识推理算法和几何约束推理算法的有机结合，有效地降低了装配规划的计算复杂度，保证规划所得装配序列的合理性和实用性．此外，还建立了装配体的自由空间图，可以解决非单调装配序列规划问题。     

基于规则和爆炸图的装配序列规划

提出局部爆炸图的概念，运用几何干涉规则生成各直角坐标方向的局部爆炸图；在确立各方向的合并顺序后，用合并规则生成各零件的合并约束前元集；并据此进行局部爆炸图合并求解装配序列，实现了规则推理和几何推理算法的紧密结合，有效地避免了装配序列组合爆炸，降低了装配规划的计算复杂度，保证规划所得装配序列的可行性和实用性．     

特征向量变换求解装配约束的表示方法

基于装配零件的匹配特征的解析求解方法,利用装配特征的特征向量之间转换来确定零件之间的装配方向和相互位置关系．分析了平面之间共面、角度和平行约束,圆柱面之间同轴约束和圆锥面之间的重合等约束关系;并对几种常用的装配约束关系进行归类,给出了求解旋转矩阵和平移矩阵的统一的求解方法．文中方法同样适用于装配特征与装配体全局坐标轴倾斜情况．应用实例验证了该方法的有效性．     

二次筛选法中大型稀疏矩阵规模缩减算法

利用二次筛选法分解RSA的模数时,矩阵规模对算法性能有着重要的影响,缩减矩阵的规模可以有效地缩短算法的运行时间。根据二次筛选法的原理,给出了3种缩减矩阵规模的方法,结合二次筛选中的稀疏矩阵的存储结构,提出了相应的3种缩减算法。最后实现了这3种缩减算法,并在二次筛选法分解70位十进制大数程序中进行了成功的应用,给出了实验的结果。     

A selected tour of the theory of identification matrices

A selected tour of the theory of identification matrices is offered here. We show that, among other things, shortest-path adjacency matrices are identification matrices for all simple graphs and adjacency matrices are identification matrices for all bipartite graphs. Additionally, we provide an improved proof that augmented adjacency matrices satisfying the circular 1's property are identification matrices. We also present a characterization of doubly convex bipartite graphs by identification matrices. Based on the theory of identification matrices, we describe an improved method for testing isomorphism between Γ circular arc graphs. The sequential algorithm can be implemented to run in O(n²) time and is optimal if the graphs are given as (augmented) adjacency matrices, so to speak.     

Generalized band matrices and their inverses

The idea of defining the generalized band matrices is based on the recognition that several pattern matrices and their inverses have low rank submatrices in certain domains. Theoretical considerations concerning the generalized band matrices enable us to give uniform treatment for several well known classes of matrices like band matrices, block band matrices, band matrices with low rank corrections, sparse matrices and their inverses. Making use of the new notions of information content and of compact representation of matrices, the concept of proper matrices is extended for generalized band matrices. Some reduction algorithms are presented which help to discover certain hidden structural properties of the generalized band matrices. The theoretical results are enlightened by a large number of figures illustrating numerical examples. Work supported by the Progetto Finalizzato Calcolo Parallelo e Sistemi Informatici of CNR. Visiting Professor at the University of Pisa under the support of GNIM-CNR.     

Report on test matrices for generalized inverses

This paper is a comprehensive report on test matrices for the generalized inversion of matrices. Two principles are described how to construct singular square or arbitrary rectangular test matrices and their Moore-Penrose inverses. By prescribing the singular values of the matrices or by suitably choosing the free parameters test matrices with condition numbers of any size can be obtained. We also deal with test matrices which are equal to their Moore-Penrose inverse. In addition to many advices how to construct test matrices the paper presents many test matrices explicitly, in particular singular square matrices of ordern, sets of 7×6 and 7×5 matrices of different rank, a set of 5×5 matrices which are equal to their Moore-Penrose inverse and some special test matrices known from literature. For the set of 7×6 parameter matrices also the singular values corresponding to six values of the parameter are listed. For three simple parameter matrices of order 5×4 and 6×5 even test results obtained by eight different algorithms are quoted. As “by-products” the paper contains inequalities between condition numbers of different norms, representations for unitary, orthogonal, column-orthogonal and row-orthogonal matrices, a generalization of Hadamard matrices and representations of matrices which are equal to their Moore-Penrose inverse (or their inverse). All test matrices given in this paper may also be used for testing algorithms solving linear least squares problems.     

准Toeplitz测量矩阵的有限等距性质分析*

Toeplitz测量矩阵的卷积特性使压缩感知理论在线性时不变系统辨识问题中得到广泛应用。但在信号频率较高的场合往往需要对测量结果进行欠采样以利用压缩感知理论降低系统对采样频率的要求,这导致测量模型中的测量矩阵变为由Toeplitz矩阵中等间隔抽取若干行组成的子矩阵（准Toeplitz矩阵）。为此文章讨论了准Toeplitz矩阵作为测量矩阵的可行性。首先通过理论推导证明了准Toeplitz矩阵的有限等距性质,而后在仿真中比较了使用准Toeplitz矩阵与其他测量矩阵的重构效果。结果表明,准Toeplitz满足有限等距性质,使用准Toeplitz矩阵的重构效果与其他测量矩阵相近,可以作为压缩感知测量矩阵。     

Controllability and observability covariance matrices for the analysis and order reduction of stable nonlinear systems

This paper presents a framework for nonlinear systems analysis that is based upon controllability and observability covariance matrices. These matrices are introduced in the paper and it is shown that gramians for linear systems form special cases of the covariance matrices. The covariance matrices can be transformed via a balancing-like transformation and nonlinearity measures are defined based upon these transformed covariance matrices. Subsequently, the covariance matrices are used for reduction of the nonlinear model. It is shown that the model reduction procedure reduces to balanced model truncation for linear systems for impulse inputs. Furthermore, it is also shown that several model reduction procedures that were developed by other researchers, and assumed to be independent from one another, are related. The findings are illustrated with an example.     

Parameter-dependent Lyapunov functional for systems with multiple time delays

The separation of the Lyapunov matrices and system matrices plays an important role when one use sparameter-dependent Lyapunov functional handling systems with polytopic type uncertainties. The delay-dependent robust stability problem for systems with polytopic type uncertainties is discussed by using parameter-dependent Lyapunov functional. The derivative term in the derivative of Lyapunov functional is reserved and the free weighting matrices are employed to express the relationship between the terms in the system equation such that the Lyapunov matrices are not involved in any product terms with the system matrices. In addition, the relationships between the terms in the Leibniz Newton formula are also described by some free weighting matrices and some delay-dependent stability conditions are derived. Numerical examples demonstrate that the proposed criteria are more effective than the previous results.     

A new implementation of common matrix approach using third-order tensors for face recognition

In the classical common matrix approach (CMA), the common matrix for each individual face class is obtained using basis matrices calculated by Gram–Schmidt orthogonalization of the class covariance matrix. This common matrix represents the common or invariant properties of a given face class. The CMA idea relies on the concept of basis matrices of a face class which span the, so called, difference subspace of that class. In this paper, an alternative method to obtain the basis matrices for CMA is proposed. The basis matrices are obtained using the higher order singular value decomposition (HOSVD) of a third-order tensor constructed with face images and these basis matrices are utilized in the construction of CMA. In order to exemplify the improvements in the recognition rates, face recognition experiments are carried out via the AR face database. The original face matrices, as well as 2DPCA-, 2DSVD-, and 2DFDA-based feature matrices are applied as the input matrices for the two different implementations of CMA in the experimental studies. The results indicate that the recognition rates obtained by the proposed method are slightly higher than those obtained using the basis matrices calculated by Gram–Schmidt orthogonalization.     

The division of polynomial matrices

A division of polynomial matrices is presented in this note. The polynomial matrices are written in the propers-power expansion forms. Then, the division can be accomplished by equating the coefficient matrices of the polynomial matrices. The algorithm is iterative and easy to handle.