Reconstruction of Mandibular Defect with Porous β-TCP/BMG Artificial Bone

1Introduction Reconstructivesurgeryisoneofthehottestre searchedsubjectsthatdealingwithbonedefects.Thetra ditionalmethodisimplantationoffreshautograftbonebe causeofitsnon immunoreactiveproperty.Butautologousbonewasnotabundantinsomecases.Sowefoundbeta trica…     

Formation of composites fabricated by exothermic dispersion reaction in A1-TiO2-B2O3 system

The formation of aluminum matrix composites fabricated by exothermic dispersion reaction in A1-TiO2-B2O3 system was investigated. The thermal analysis results show that the reactions are spontaneous and exothermic. The Gibbs free energy of α-Al2O3 is the lowest among all the combustion products, followed by TiB2 and Al3Ti. It is noted that when the B2O3/TiO2 mole ratio is below 1, the reaction products are composed of particle-like α-Al2O3, TiB2 and rod-like Al3Ti. The α-Al2O3 crystallites, resulting from the reaction between A1 and TiO2 or B2O3, are segregated at the grain boundaries due to a lower wettability with the matrix. SEM micrographs show that rod-like Al3Ti phase distributes uniformly in the matrix. When the BEO3/TiO2 mole ratio is around 1, the Al3Ti phase almost disappears in the composites, and the distribution of α-Al2O3 particulates is improved evidently.     

Preparation and Compressive Stress Effect of Polymer Matrix RE-Fe Giant Magnetostrictive Composite

Polymer matrix RE-Fe giant magnetostrictire composite （GMPC） was prepared using bonding and magnetic field forming technique, and magnetostriction of samples was measured for different compressive stress. The experimental results show thai there is certain compressive effect in GMPC. And the influence of compressive stress on magnetostriction of sample was investigated. It offers essential reference for application and device design of GMPC.     

Sensitivity analysis in conjunction with evidence theory representations of epistemic uncertainty

Three applications of sampling-based sensitivity analysis in conjunction with evidence theory representations for epistemic uncertainty in model inputs are described: (i) an initial exploratory analysis to assess model behavior and provide insights for additional analysis; (ii) a stepwise analysis showing the incremental effects of uncertain variables on complementary cumulative belief functions and complementary cumulative plausibility functions; and (iii) a summary analysis showing a spectrum of variance-based sensitivity analysis results that derive from probability spaces that are consistent with the evidence space under consideration.     

松辽盆地南部岩性圈闭识别技术及效果

寻找隐蔽油气藏一直是油气勘探中的难题,我国东部吉林油田的中浅层已进入高成熟勘探阶段,油气勘探已由早期的构造圈闭勘探转变为现今的以隐蔽圈闭勘探为主的阶段。本文针对松辽盆地南部的西部前缘带三维连片工区及伊通地堑岔路河断陷,综合应用构造精细解释、声波曲线重构波阻抗反演、层序地层学、地震相及沉积(微)相分析、模型正演、地震属性分析、频谱分解及三维可视化等技术手段,所提供的多口探井获得工业油气流或有良好的油气显示,取得了较好的解释效果。     

General Solution for the Dynamic Modeling of Parallel Robots

In this paper, we present a general method to calculate the inverse and direct dynamic models of parallel robots. The models are expressed in a closed form by a single equation in which all the elements needed are expressed. The solution is given in terms of the dynamic models of the legs, the dynamics of the platform and some Jacobian matrices. The proposed method is applied in this paper on two parallel robots with different structures. Categories (2), (3).     

Solving the small sample size problem in face recognition using generalized discriminant analysis

The goal of face recognition is to distinguish persons via their facial images. Each person's images form a cluster, and a new image is recognized by assigning it to the correct cluster. Since the images are very high-dimensional, it is necessary to reduce their dimension. Linear discriminant analysis (LDA) has been shown to be effective at dimension reduction while preserving the cluster structure of the data. It is classically defined as an optimization problem involving covariance matrices that represent the scatter within and between clusters. The requirement that one of these matrices be nonsingular restricts its application to datasets in which the dimension of the data does not exceed the sample size. For face recognition, however, the dimension typically exceeds the number of images in the database, resulting in what is referred to as the small sample size problem. Recently, the applicability of LDA has been extended by using the generalized singular value decomposition (GSVD) to circumvent the nonsingularity requirement, thus making LDA directly applicable to face recognition data. Our experiments confirm that LDA/GSVD solves the small sample size problem very effectively as compared with other current methods.     

A stochastic realization algorithm via block LQ decomposition in Hilbert space

A stochastic realization problem of a stationary stochastic process is re-visited, and a new stochastically balanced realization algorithm is derived in a Hilbert space generated by second-order stationary processes. The present algorithm computes a stochastically balanced realization by means of the singular value decomposition of a weighted block Hankel matrix derived by a “block LQ decomposition”. Extension to a stochastic subspace identification method explains how the proposed abstract algorithm is implemented in system identification.     

Average consensus in networks of dynamic agents with switching topologies and multiple time-varying delays

In this paper, we discuss average consensus problem in undirected networks of dynamic agents with fixed and switching topologies as well as multiple time-varying communication delays. By employing a linear matrix inequality method, we prove that all the nodes in the network achieve average consensus asymptotically for appropriate communication delays if the network topology is connected. Particularly, several feasible linear matrix inequalities are established to determine the maximal allowable upper bound of time-varying communication delays. Numerical examples are given to demonstrate the effectiveness and the sharpness of the theoretical results.     

A parallel and scalable algorithm for ADI method with pre-propagation and message vectorization

It is hard to implement the ADI method in an efficient way on distributed-memory parallel computers. We propose “P-scheme” which parallelizes a tridiagonal linear system of equations for the ADI method, but its effectiveness is limited to the cases where the problem size is large enough mainly because of the communication cost of the propagation phase of the scheme.

In order to overcome this difficulty, we propose an improved version of the P-scheme with “message vectorization” which aggregates several communication messages into one and alleviates the communication cost. Also we evaluate the effectiveness of message vectorization for the ADI method and show that the improved version of the P-scheme works well even for smaller problems and linear and super-linear speedups can be achieved for 8194 × 8194 and 16,386 × 16,386 problems, respectively.