A new class of massively parallel direction splitting for the incompressible Navier–Stokes equations

We introduce in this paper a new direction splitting algorithm for solving the incompressible Navier–Stokes equations. The main originality of the method consists of using the operator (I ? ?_xx)(I ? ?_yy)(I ? ?_zz) for approximating the pressure correction instead of the Poisson operator as done in all the contemporary projection methods. The complexity of the proposed algorithm is significantly lower than that of projection methods, and it is shown the have the same stability properties as the Poisson-based pressure-correction techniques, either in standard or rotational form. The first-order (in time) version of the method is proved to have the same convergence properties as the classical first-order projection techniques. Numerical tests reveal that the second-order version of the method has the same convergence rate as its second-order projection counterpart as well. The method is suitable for parallel implementation and preliminary tests show excellent parallel performance on a distributed memory cluster of up to 1024 processors. The method has been validated on the three-dimensional lid-driven cavity flow using grids composed of up to 2 × 10⁹ points.     

Substructure based approach to finite element model updating

A substructure-based finite element model updating technique is proposed in this paper. A few eigenmodes of the independent substructures and their associated derivative matrices are assembled into a reduced eigenequation to recover the eigensolutions and eigensensitivities of the global structure. Consequently, only the concerned substructures and the reduced eigenequation are re-analyzed in the optimization process, thus reducing the computational load of the traditional model updating methods which perform on the global structure. Applications of the proposed substructure-based model updating to a frame structure and a practical bridge demonstrate that the present method is computationally effective and efficient.     

Sliding mode control of switched hybrid systems with stochastic perturbation

This paper is concerned with the sliding mode control (SMC) of a continuous-time switched stochastic system. A sufficient condition for the existence of reduced-order sliding mode dynamics is derived and an explicit parametrization of the desired sliding surface is also given. Then, a sliding mode controller is then synthesized for reaching motion. Moreover, the observer-based SMC problem is also investigated. Some sufficient conditions are established for the existence and the solvability of the desired observer and the observer-based sliding mode controller is synthesized. Finally, numerical examples are provided to illustrate the effectiveness of the proposed theory.     

Implementation issues in RFID-based anti-counterfeiting systems

RFID has emerged as a potential tool to combat product counterfeiting, which undermines the global economy hugely. Recently, a number of anti-counterfeiting approaches have been proposed for such purpose. This paper presents a track-and-trace system for RFID-based anti-counterfeiting, and addresses possible implementation issues, such as tag selection, product tagging, tag programming and locking. A packaging line for bottled products is developed and integrated with the proposed system for investigation of these issues. Experiments are conducted to determine the critical tag moving speed beyond which the tag programming rate or reliability of tagged products being transferred on a convey drops significantly. The critical tag moving speed is vital not only for determining the maximum production throughput possible, but also for setting up RFID equipment needed to ensure tagged products with erroneous tagging can be sorted out from the packaging line accordingly.     

Latent growth curve modeling for longitudinal ordinal responses with applications

In this paper, we consider the use of the latent growth curve model to analyze longitudinal ordinal categorical data that involve measurements at different time points. By operating on the assumption that the ordinal response variables at different time points are related to normally distributed underlying continuous variables, and by further modeling these underlying continuous variables for different time points with the latent growth curve model, we achieve a comprehensive and flexible model with straightforward interpretations and a variety of applications. We discuss the applications of the model in treatment comparisons and in the analysis of the covariate effects. Moreover, one prominent advantage of the model lies in its ability to address possible differences in the initial conditions for the subjects who take part in different treatments. Making use of this property, we also develop a new method to test the equivalence of two treatments that involve ordinal responses obtained at two different time points. A real data set is used to illustrate the applicability and practicality of the proposed approach.     

Estimating the error variance after a pre-test for an interval restriction on the coefficients

This paper considers the estimation of the error variance after a pre-test of an interval restriction on the coefficients. We derive the exact finite sample risks of the interval restricted and pre-test estimators of the error variance, and examine the risk properties of the estimators to model misspecification through the omission of relevant regressors. It is found that the pre-test estimator performs better than the interval restricted estimator in terms of the risk properties in a large region of the parameter space; moreover, its risk performance is more robust with respect to the degrees of model misspecification. Furthermore, we propose a bootstrap procedure for estimating the risks of the estimators, to overcome the difficulty of computing the exact risks.     

A mixture of generalized latent variable models for mixed mode and heterogeneous data

In the behavioral, biomedical, and social-psychological sciences, mixed data types such as continuous, ordinal, count, and nominal are common. Subpopulations also often exist and contribute to heterogeneity in the data. In this paper, we propose a mixture of generalized latent variable models (GLVMs) to handle mixed types of heterogeneous data. Different link functions are specified to model data of multiple types. A Bayesian approach, together with the Markov chain Monte Carlo (MCMC) method, is used to conduct the analysis. A modified DIC is used for model selection of mixture components in the GLVMs. A simulation study shows that our proposed methodology performs satisfactorily. An application of mixture GLVM to a data set from the National Longitudinal Surveys of Youth (NLSY) is presented.     

Exact Algorithms for <Emphasis Type="Italic">L</Emphasis>(2,1)-Labeling of Graphs

The notion of distance constrained graph labelings, motivated by the Frequency Assignment Problem, reads as follows: A mapping from the vertex set of a graph G=(V,E) into an interval of integers {0,…,k} is an L(2,1)-labeling of G of span k if any two adjacent vertices are mapped onto integers that are at least 2 apart, and every two vertices with a common neighbor are mapped onto distinct integers. It is known that for any fixed k≥4, deciding the existence of such a labeling is an NP-complete problem. We present exact exponential time algorithms that are faster than the naive O ^*((k+1) ⁿ ) algorithm that would try all possible mappings. The improvement is best seen in the first NP-complete case of k=4, where the running time of our algorithm is O(1.3006 ⁿ ). Furthermore we show that dynamic programming can be used to establish an O(3.8730 ⁿ ) algorithm to compute an optimal L(2,1)-labeling.     

无人机群主动式时差定位算法

在多无人机（UAV）协同定位地面固定辐射源的应用环境中,为了提高多机基于时差的定位精度,提出一种改进的基于约束总体最小二乘（CTLS）的时差定位方法和一种基于结构总体最小范数（STLN）的时差定位方法．证明了这两种方法具有相同的定位精度,但STLN方法的计算性能优于改进的CTLS方法．仿真结果表明,所提出的方法具有较高的定位精度,受观测点定位误差影响更小．