Isogeometric analysis and shape optimization via boundary integral

In this paper, we present a boundary integral based approach to isogeometric analysis and shape optimization.For analysis, it uses the same basis, Non-Uniform Rational B-Spline (NURBS) basis, for both representing object boundary and for approximating physical fields in analysis via a Boundary-Integral-Equation Method (BIEM). We propose the use of boundary points corresponding to Greville abscissae as collocation points. We conducted h-, p- and k-refinement study for linear elasticity and heat conduction problems. Our numerical experiments show that collocation at Greville abscissae leads to overall better convergence and robustness. Replacing rational B-splines with the linear B-Splines as shape functions for approximating solution space in analysis does not yield significant difference in convergence.For shape optimization, it uses NURBS control points to parameterize the boundary shape. A gradient based optimization approach is adopted where analytical sensitivities of how control points affect objective and constraint functions are derived. Two 3D shape optimization examples are demonstrated.Our study finds that the boundary integral based isogeometric analysis and optimization have the following advantages: (1) the NURBS based boundary integral exhibits superior computational advantages over the usual Lagrange polynomials based BIEM on a per degree-of-freedom basis; (2) it bypasses the need for domain parameterization, a bottleneck in current NURBS based volumetric isogeometric analysis and shape optimization; (3) it offers tighter integration of CAD and analysis since both the geometric models for both analysis and optimization are the same NURBS geometry.     

A general shape context framework for object identification

A general shape context framework is proposed for object/image retrieval in occluded and cluttered environment with hundreds of models as the potential matches of an input. The approach is general since it does not require separation of input objects from complex background. It works by first extracting consistent and structurally unique local neighborhood information from inputs or models, and then voting on the optimal matches. Its performance degrades gracefully with respect to the amount of structural information that is being occluded or lost. The local neighborhood information applicable to the system can be shape, color, texture feature, etc. Currently, we employ shape information only. The mechanism of voting is based on a novel hyper cube based indexing structure, and driven by dynamic programming. The proposed concepts have been tested on database with thousands of images. Very encouraging results have been obtained.     

一种具有等级结构的无标度网络模型

无标度网络的发现,使人类对于复杂网络的认识进入了一个新的天地.为了更好地描述真实网络的主要拓扑特性,主要研究复杂网络的演化机制,提出了一种通过边的迭代方式生成一种等级网络模型的方法.在此模型的基础上对网络的统计特性进行了理论推导,并通过计算机编程仿真了它的统计特性.理论计算和数值仿真结果可知,迭代生成的网络具有等级结构,度分布服从幂律分布,幂指数在2到3之间可调,平均路径长度以网络规模呈对数形式增长和较大的聚类系数.从而有效地论证了具有等级结构的网络模型很好地符合实际网络,说明实际网络的无标度和高聚类是等级网络自组织的结果.     

MIPAS: A neural network-based multimedia information processing and analysis system

This paper presents the structure and design criteria of a neural network-based multimedia information processing and analysis system (MIPAS) which can be used to deal with more-complicated intelligence issues. According to the structure and design criteria, a software environment (SEMIPAS), which supports the implementation of multimedia information (image + speech, image + characters, speech + characters, image + speech + characters) processing and analysis applications, is implemented and introduced. Under this software environment, a multimedia information processing and analysis system called “To Know the World” is constructed. Experiments show that the multimedia information processing and analysis is much more powerful and effective than single-medium information processing and analysis.     

A Bayesian network-based approach for fault analysis

For high-value assets such as certain types of plant equipment, the total amount of resources devoted to Operation and Maintenance may substantially exceed the resources expended in acquisition and installation of the asset, because high-value assets have long useful lifetimes. Any asset failure during this useful lifetime risks large losses in income and goodwill, and decreased safety. With the continual development of information, communication, and sensor technologies, Condition-Based Maintenance (CBM) policies have gained popularity in industries. A successfully implemented CBM reduces the losses due to equipment failure by intelligently maintaining the equipment before catastrophic failures occur. However, effective CBM requires an effective fault analysis method based on gathered sensor data. In this vein, this paper proposes a Bayesian network-based fault analysis method, from which novel fault identification, inference, and sensitivity analysis methods are developed. As a case study, the fault analysis method was analyzed in a centrifugal compressor utilized in a plant.     

A network-based assessment approach for change impacts on complex product

The complex product design is a continuously changing process from customer requirements to a maturity design. During this process a change of one part will, in most cases, causes changes in other parts and even the whole product. The assessment for the impacts of such changes can support designers’ designing and help manager to manage redesigning. A complex product can be considered as a weighted network of parts, subassemblies, or subsystems. Based on the theory of weighted networks, three changeability indices (degree-changeability, reach-changeability and between-changeability) are presented. Degree-changeability is used to calculate the direct change impacts. Reach-changeability is used to assess the indirectly change impacts because of propagation. If a part influences the other parts dramatically and it is also influenced by them, this part can be predicted by between-changeability. Finally, the three changeability indices are proven to be effective for the change impact assessment through a real-world case of Roots Blowers. With the analysis, the designers can avoid changing to “expensive” parts or subsystems.     

一种基于输入训练神经网络的非线性PCA 故障诊断方法

简要讨论了线性PCA故障诊断方法存在的问题，提出一种基于输入训练神经网络的非线性PCA故障诊断方法。该方法首先利用输入训练神经网络和BP网络双网络机制，实现非线性主元的识别，并采用统计方法进行故障检测与故障分离。对CSTR的仿真研究结果表明，该方法能够克服线性PCA方法在提取过程变量的非线性特征方面存在的不足，并能够准确地进行故障检测和分离。     

基于组增长的小世界Scale-free网络模型

小世界和Scale-free是很多大规模复杂网络的重要特征,建立体现这些特征的网络模型是研究网络结构和行为的重要基础。针对现有模型不能全面地反映实际网络所具有的特征属性,提出了基于组增长的小世界Scale—free网络模型。该模型在小世界和Scale—free模型的基础上,结合了节点的本地强连接性、新增连接的度相关性和网络动态的增长方式,把小世界和Scale-free联系了起来。仿真结果表明,该模型生成的网络图同时具有小世界和Scale—free特征。     

Planar shape matching based on binary tree shape representation

In this paper we suggest a new way of representing planar two-dimensional shapes and a shape matching method which utilizes the new representation. Through merging of the neighboring boundary runs, a shape can be partitioned into a set of triangles. These triangles are inherently connected according to a binary tree structure. Here we use the binary tree with the triangles as its nodes to represent the shape. This representation is found to be insensitive to shape translation, rotation, scaling and skewing changes due to viewer's location changes (or the object's pose changes). Furthermore, the representation is of multiresolution.

In shape matching we compare the two trees representing two given shapes node by node according to the breadth-first tree traversing sequence. The comparison is done from top of the tree and moving downward, which means that we first compare the lower resolution approximations of the two shapes. If the two approximations are different, the comparison stops. Otherwise, it goes on and compares the finer details of the two shapes. Only when the two shapes are very similar, will the two corresponding trees be compared entirely. Thus, the matching algorithm utilizes the multiresolution characteristic of the tree representation and appears to be very efficient.     

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.     

基于小世界模型的WSN簇间拓扑优化方法

针对无线传感器网络节点因能量消耗、硬件故障、通信因素等导致的链路失效问题,提出一种基于复杂网络小世界模型Kleinberg的无线传感器网络簇间拓扑优化方法,该方法依据簇头节点的局部视图ViewList信息中的长链与短链构建WSN簇间拓扑.实验分析表明,利用该方法演化的无线传感器网络拓扑在节点失效概率为0.2时,网络寿命比DECDC提高25%,并具有良好的能量均衡性和较低的消耗代价.该方法构建的拓扑具有较好的容错性和较强的鲁棒性.     

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.