面向虚拟装配的装配建模及其重构

装配建模是进行虚拟装配的前提和基础,决定着虚拟装配实施的质量和效率。针对虚拟装配中装配建模问题,介绍一种多视图层次化的装配模型,给出了模型的信息组成和信息来源,并很好地解决了在此模型基础上的重构问题。     

Statistical process monitoring using improved PCA with optimized sensor locations

The emphasis of most PCA process monitoring approaches is mainly on procedures to perform fault detection and diagnosis given a set of sensors. Little attention is paid to the actual sensor locations to efficiently perform these tasks. In this paper, graph-based techniques are used to optimize sensor locations to ensure the observability of faults, as well as the fault resolution to a maximum possible extent. Meanwhile, an improved PCA that uses two new statistics of PVR and CVR to replace the Q index in conventional PCA is introduced. The improved PCA can efficiently detect weak process changes, and give an insight to the root cause about the process malfunction. Simulation results of a CSTR process show that the improved PCA with optimized sensor locations is superior to conventional methods in fault resolution and sensibility.     

Face representation using independent component analysis

This paper addresses the problem of face recognition using independent component analysis (ICA). More specifically, we are going to address two issues on face representation using ICA. First, as the independent components (ICs) are independent but not orthogonal, images outside a training set cannot be projected into these basis functions directly. In this paper, we propose a least-squares solution method using Householder Transformation to find a new representation. Second, we demonstrate that not all ICs are useful for recognition. Along this direction, we design and develop an IC selection algorithm to find a subset of ICs for recognition. Three public available databases, namely, MIT AI Laboratory, Yale University and Olivette Research Laboratory, are selected to evaluate the performance and the results are encouraging.     

Quality prediction and analysis for large-scale processes based on multi-level principal component modeling strategy

This paper proposed a multi-level principal component regression (PCR) modeling strategy for quality prediction and analysis of large-scale processes. Based on decomposition of the large data matrix, the first level PCR model divides the process into different sub-blocks through uncorrelated principal component directions, with a related index defined for determination of variables in each sub-block. In the second level, a PCR model is developed for local quality prediction in each sub-block. Subsequently, the third level PCR model is constructed to combine the local prediction results in different sub-blocks. For process analysis, a sub-block contribution index is defined to identify the critical-to-quality sub-blocks, based on which an inside sub-block contribution index is further defined for determination of the key variables in each sub-block. As a result, correlations between process variables and quality variables can be successfully constructed. A case study on Tennessee Eastman (TE) benchmark process is provided for performance evaluation.     

EM-GPA: Generalized Procrustes analysis with hidden variables for 3D shape modeling

Aligning shapes is essential in many computer vision problems and generalized Procrustes analysis (GPA) is one of the most popular algorithms to align shapes. However, if some of the shape data are missing, GPA cannot be applied. In this paper, we propose EM-GPA, which extends GPA to handle shapes with hidden (missing) variables by using the expectation-maximization (EM) algorithm. For example, 2D shapes can be considered as 3D shapes with missing depth information due to the projection of 3D shapes into the image plane. For a set of 2D shapes, EM-GPA finds scales, rotations and 3D shapes along with their mean and covariance matrix for 3D shape modeling. A distinctive characteristic of EM-GPA is that it does not enforce any rank constraint often appeared in other work and instead uses GPA constraints to resolve the ambiguity in finding scales, rotations, and 3D shapes. The experimental results show that EM-GPA can recover depth information accurately even when the noise level is high and there are a large number of missing variables. By using the images from the FRGC database, we show that EM-GPA can successfully align 2D shapes by taking the missing information into consideration. We also demonstrate that the 3D mean shape and its covariance matrix are accurately estimated. As an application of EM-GPA, we construct a 2D + 3D AAM (active appearance model) using the 3D shapes obtained by EM-GPA, and it gives a similar success rate in model fitting compared to the method using real 3D shapes. EM-GPA is not limited to the case of missing depth information, but it can be easily extended to more general cases.     

Feature-preserving adaptive mesh generation for molecular shape modeling and simulation

We describe a chain of algorithms for molecular surface and volumetric mesh generation. We take as inputs the centers and radii of all atoms of a molecule and the toolchain outputs both triangular and tetrahedral meshes that can be used for molecular shape modeling and simulation. Experiments on a number of molecules are demonstrated, showing that our methods possess several desirable properties: feature-preservation, local adaptivity, high quality, and smoothness (for surface meshes). We also demonstrate an example of molecular simulation using the finite element method and the meshes generated by our method. The approaches presented and their implementations are also applicable to other types of inputs such as 3D scalar volumes and triangular surface meshes with low quality, and hence can be used for generation/improvement of meshes in a broad range of applications.     

Geometric modeling of curves using weighted linear and circular segments

This paper presents a method for geometric modeling of curves. Linear and circular segments with weight functions are used for curve design. Similar weight functions are used for smoothing and interpolation. The curve can be composed of consecutive linear and circular segments with smoothing at knots. If an interpolating curve is necessary, then weighted linear and circular segments are used for blending. Smoothness of the designed curve depends on the chosen weight function. It is possible to design the curve of variable smoothness using different weight functions.     

The kernel density estimate/point distribution model (KDE-PDM) for statistical shape modeling of automotive stampings and assemblies

The ability to effectively model dimensional variation of stampings and assemblies is an important tool for manufacturers to investigate, assess and control quality levels of their products. Statistical Process Control (SPC) and Six-Sigma approaches use the assumptions of statistical independence and normally distributed data to create quality process control guidelines which are predominantly used in industry. Multivariate statistical techniques such as Principal Components Analysis (PCA) have been more recently applied to automotive body assembly analysis in order to capture the surface co-linearity present in the dimensional variation of stampings and assemblies. This paper combines the Point Distribution Model, which is based on PCA, and Kernel Density Estimation to provide a statistical shape model (the KDE–PDM) that can deal with high dimensional data sets, represent correlated variation modes, and provide accurate estimates of the underlying shape distribution. Examples from FE simulation and production case studies are presented to highlight the advantages of the KDE–PDM over two other statistical shape models: the univariate shape model, and the original PDM. The KDE–PDM's capabilities make it particularly suited to variation monitoring and diagnosis of high dimensional measurement data sets made available by optical measurement devices, and some suggestions for its implementation are also presented.     

Functional data analysis in shape analysis

Mid-level processes on images often return outputs in functional form. In this context the use of functional data analysis (FDA) in image analysis is considered. In particular, attention is focussed on shape analysis, where the use of FDA in the functional approach (contour functions) shows its superiority over other approaches, such as the landmark based approach or the set theory approach, on two different problems (principal component analysis and discriminant analysis) in a well-known database of bone outlines. Furthermore, a problem that has hardly ever been considered in the literature is dealt with: multivariate functional discrimination. A discriminant function based on independent component analysis for indicating where the differences between groups are and what their level of discrimination is, is proposed. The classification results obtained with the methodology are very promising. Finally, an analysis of hippocampal differences in Alzheimer’s disease is carried out.     

基于统计方法的普通话情感语调模型

提出了一种基于数据驱动的语调建模方法．该方法采用主成分分析 (Principal component analysis, PCA) 技术, 给出了特征语调, 统计了语音情感模式在特征语调空间中的分布规律, 经过分析得出了普通话中情感模式所对应的情感语调．针对语音产生的机理复杂、语音语调受众多因素影响的特点, 为了避免这些干扰因素的影响, 设计了相应的情感语音库．利用所设计的语音库, 进行了相关实验．实验结果表明, 利用所提出的特征语调模型不仅能够非常完美地重构出语调样本的语调, 而且具有相当的情感表达能力．     

Predicting and evaluating the post-assembly shape of thin-walled components via 3D laser digitization and FEA simulation of the assembly process

Three-dimensional (3D) laser digitization has become a critical research field and a widely used technique for product quality inspection in the manufacturing and medical industries during the last decade. One common application of this technology is to analyze whether the final shape of an assembly component fulfills the designer’s geometric specifications. This task is currently performed by digitizing the component’s surface after mounting it into its final assembly or on a special testing frame. In order to speed up this process a new computational method is proposed for inspecting the final shape of an assembly component by virtually mounting it into the assembly, without the physical assembling process taking place. The developed computational method employs laser digitization to measure the initial shape of the assembly component and then finite element analysis (FEA) to predict its post-assembly shape. First, a laser-digitized dense mesh is smoothed and decimated to make it suitable for FEA. Material properties of the component, if not available, are then determined by a calibration process, and specific displacement boundary conditions are applied to reproduce the assembly process. After FEA is executed, the quality of the simulated post-assembly shape is checked using visualization tools such as light-reflection patterns and contour plots of the distance between the computed geometry and the target computer-aided design (CAD) geometry. Moreover, the accuracy of the proposed method is validated by comparing the simulated post-assembly shape with the actual post-assembly shape measured after physically assembling the component. Experiments show that the average distance between simulated shape and actual shape varies from 0.3 mm to 0.6 mm for objects with a characteristic size of a half meter, and that the isophotes and height fields of reflection (HFR) based indices are reduced up to 50%. The proposed method can thus predict the final shape of an assembled component well without assembling it, reducing the time and the cost of product quality inspection.     

Face recognition using kernel entropy component analysis

In this letter, we have reported a new face recognition algorithm based on Renyi entropy component analysis. In the proposed model, kernel-based methodology is integrated with entropy analysis to choose the best principal component vectors that are subsequently used for pattern projection to a lower-dimensional space. Extensive experimentation on Yale and UMIST face database has been conducted to reveal the performance of the entropy based principal component analysis method and comparative analysis is made with the kernel principal component analysis method to signify the importance of selection of principal component vectors based on entropy information rather based only on magnitude of eigenvalues.     

Improved support vector classification using PCA and ICA feature space modification

An approach that unifies subspace feature selection and optimal classification is presented. Independent component analysis (ICA) and principal component analysis (PCA) provide a maximally variant or statistically independent basis for pattern recognition. A support vector classifier (SVC) provides information about the significance of each feature vector. The feature vectors and the principal and independent component bases are modified to obtain classification results which provide lower classification error and better generalization than can be obtained by the SVC on the raw data and its PCA or ICA subspace representation. The performance of the approach is demonstrated with artificial data sets and an example of face recognition from an image database.     

复杂电路虚拟维修的建模与仿真技术

分析了电路虚拟维修的特点,总结了电路虚拟维修系统中必不可少的3种模型:仿真模型、交互模型和三维模型,研究了3种模型的特点和建模方法.提出了一种层次化模型结构:功能层、应用层和显示层,并研究了各层之间的信息传递.引入多Agent系统理论建立电路虚拟维修系统中不同类型的智能Agent模型,研究了基于KQML的各Agent间通信的方式.最后根据模型的层次化结构建立了一个基于多Agent系统的电路虚拟维修训练系统.     

时滞多变量系统PCA优化建模

主元分析（PCA）在工业生产过程的产品质量控制与故障诊断等方面已得到广泛应用,然而当过程的变量间存在着未知时滞性时,必须确定数据间的对应关系,否则PCA模型将会不准．基于此,提出了PCA优化建模方法．该方法以过程变量间的时滞常数为优化变量,在分析PCA模型特点基础上,确定主成分个数和SPE统计量为综合目标函数,并建立模型约束条件,采用遗传算法求解．最后给出了仿真实例,证明了所提出方法的有效性．     

Logic-based 2D geometric modeling in a CAD environment

This paper examines the use of predicate logic for deductive construction of geometric models. The process of logical inference and its use for geometric modeling is examined. The logic-based definition of a retaining wall is presented, the implementation of a logic-based CAD system using a commercial software is briefly discussed and its use is illustrated.     

飞机复杂机构虚拟培训场景运动建模方法

针对含有飞机复杂机构的虚拟培训场景建模过程复杂、生成的场景帧率低、交互性差等问题,提出一种虚拟场景建模方法。首先,对原动件在其运动范围内进行运动状态采样;然后利用机构仿真平台进行运动学建模与解算,获取所有零件的运动状态作为机构的运动状态库,并对运动状态库进行压缩;最后,在虚拟现实引擎中将库文件与经过材质编辑的网格模型融合,采用索引、插值替代实时解算,以获取零件的位置姿态数据,生成虚拟场景。使用起落架收放机构与后缘襟翼收放机构进行实验,结果表明,该方法可以实现真实感强、交互性好的飞机复杂机构虚拟培训场景运动建模。     

Improving network security using genetic algorithm approach

With the expansion of Internet and its importance, the types and number of the attacks have also grown making intrusion detection an increasingly important technique. In this work we have realized a misuse detection system based on genetic algorithm (GA) approach. For evolving and testing new rules for intrusion detection the KDD99Cup training and testing dataset were used. To be able to process network data in real time, we have deployed principal component analysis (PCA) to extract the most important features of the data. In that way we were able to keep the high level of detection rates of attacks while speeding up the processing of the data.     

虚拟制造企业的Petri网建模/仿真

为降低Petri网建模难度并增强模型的可读性,提出一种通过消息序列表（Message Sequence Chart,MSC）模型对虚拟制造（Virtual Manufacturing,VM）企业的生产经营过程进行Petri网建模的新方法．建立冲突消解机制,给出从MSC模型到Petri网模型的转换算法,在不改变Petri模型活性、安全性和有界性等特性的基础上对其进一步优化,并对Petri网模型进行分析与评估．     

A fast pixel mapping algorithm using principal component analysis

Pixel mapping is one of the basic processes in color quantization. In this paper, we shall propose a new algorithm using principal component analysis as a faster approach to pixel mapping. Within much shorter search time, our new scheme can find the nearest color which is identical to the one found using a full search. The idea behind the proposed method is quite simple. First, we compute two principal component directions (PCDs) for the palette. Then, the projected values on PCDs are computed for each color in palette. Finally, the projected values, following the triangular inequality principle, can help us reduce the computation time for finding the nearest color. The experimental results reveal that the proposed scheme is more efficient than the previous work.