Homogenization-based multiscale crack modelling: From micro-diffusive damage to macro-cracks

The existence of a representative volume element (RVE) for a class of quasi-brittle materials having a random heterogeneous microstructure in tensile, shear and mixed mode loading is demonstrated by deriving traction–separation relations, which are objective with respect to RVE size. A computational homogenization based multiscale crack modelling framework, implemented in an FE² setting, for quasi-brittle solids with complex random microstructure is presented. The objectivity of the macroscopic response to the micro-sample size is shown by numerical simulations. Therefore, a homogenization scheme, which is objective with respect to macroscopic discretization and microscopic sample size, is devised. Numerical examples including a comparison with direct numerical simulation are given to demonstrate the performance of the proposed method.     

A new solution for a queueing model of a manufacturing cell with negative customers under a rotation rule

In this paper, we consider a queueing model extension for a manufacturing cell composed of a machining center and several parallel downstream production stations under a rotation rule. A queueing model is extended with the arrival processes of negative customers to capture failures of production stations, reorganization of works and disasters in the manufacturing cell. We present an exact solution for the steady-state probabilities of the proposed queueing model. The solution does not require the approximation of the infinite sum. In addition, it provides an alternative way to compute the rate matrix for the matrix-geometric method as well.     

Stability radius of implicit dynamic equations with constant coefficients on time scales

This paper deals with the stability radii of implicit dynamic equations on time scales when the structured perturbations act on both the coefficient of derivative and the right-hand side. Formulas of the stability radii are derived as a unification and generalization of some previous results. A special case where the real stability radius and the complex stability radius are equal is studied. Examples are derived to illustrate results.     

BSPNN: boosted subspace probabilistic neural network for email security

In the modern age of Internet connectivity, advanced information systems have accumulated huge volumes of data. Such fast growing, tremendous amount of data, collected and stored in large databases has far exceeded our human ability to comprehend without proper tools. There has been a great deal of research conducted to explore the potential applications of Machine Learning technologies in Security Informatics. This article studies the Network Security Detection problems in which predictive models are constructed to detect network security breaches such as spamming. Due to overwhelming volume of data, complexity and dynamics of computer networks and evolving cyber threats, current security systems suffer limited performance with low detection accuracy and high number of false alarms. To address such performance issues, a novel Machine Learning algorithm, namely Boosted Subspace Probabilistic Neural Network (BSPNN), has been proposed which combines a Radial Basis Function Neural Network with an innovative diversity-based ensemble learning framework. Extensive empirical analyses suggested that BSPNN achieved high detection accuracy with relatively small computational complexity compared with other conventional detection methods.     

Word confidence measure based on frame likelihood score

The automatic recognition of natural or close to natural speech is linked to the problem of detection of “new” or “unknown” words. These are the words or the nonverbal acoustical events that do not belong to the speech recognition system’s vocabulary. In this paper we consider a new method for the estimation of confidence score for words at the output of the recognition system based on a likelihood score of the signal frame. The method and confidence measure could be used, for example, for out-of-vocabulary (OOV) word detection and rejection. The text was submitted by the authors in English. Minh T. Nguyen received the MS degree in computer sciences from Moscow State University in 2004. In 2004 he began postgraduate studies at the Computing Centre, Russian Academy of Sciences. He is working on development and evaluation of confidence measures for speech recognition. Vladimir J. Chuchupal received the MS degree in mathematics from Moscow State Pedagogical Institute in 1976. He received his Candidate of Sciences degree from the Computing Centre, USSR Academy of Sciences, in 1984 completing his dissertation work in noisy speech enhancement methods. Since 1984 he has been with the Speech Recognition subdivision of the Computing Centre, Russian Academy of Sciences, where he works on speech recognition problems. Currently he is the head of the subdivision.     

Equilibrium analysis through separation of user and network behavior

Internet complexity makes reasoning about traffic equilibrium difficult, partly because users react to congestion. This difficulty calls for an analytic technique that is simple, yet have enough details to capture user behavior and flexibly address a broad range of issues.This paper presents such a technique. It treats traffic equilibrium as a balance between an inflow controlled by users, and an outflow controlled by the network (link capacity, congestion avoidance, etc.). This decomposition is demonstrated with a surfing session model, and validated with a traffic trace and NS2 simulations.The technique’s accessibility and breadth are illustrated through an analysis of several issues concerning the location, stability, robustness and dynamics of traffic equilibrium.     

Adaptive robust fuzzy control for path tracking of a wheeled mobile robot

This paper proposes an adaptive robust fuzzy control scheme for path tracking of a wheeled mobile robot with uncertainties. The robot dynamics including the actuator dynamics is considered in this work. The presented controller is composed of a fuzzy basis function network (FBFN) to approximate an unknown nonlinear function of the robot complete dynamics, an adaptive robust input to overcome the uncertainties, and a stabilizing control input. The stability and the convergence of the tracking errors are guaranteed using the Lyapunov stability theory. When the controller is designed, the different parameters for two actuator models in the dynamic equation are taken into account. The proposed control scheme does not require the accurate parameter values for the actuator parameters as well as the robot parameters. The validity and robustness of the proposed control scheme are demonstrated through computer simulations. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Efficient Multimedia Distribution in Source Constraint Networks

In recent years, the number of peer-to-peer (P2P) applications has increased significantly. One important problem in many P2P applications is how to efficiently disseminate data from a single source to multiple receivers on the Internet. A successful model used for analyzing this problem is a graph consisting of nodes and edges, with a capacity assigned to each edge. In some situations however, it is inconvenient to use this model. To that end, we propose to study the problem of efficient data dissemination in a source constraint network. A source constraint network is modeled as a graph in which, the capacity is associated with a node, rather than an edge. The contributions of this paper include (a) a quantitative data dissemination in any source constraint network, (b) a set of topologies suitable for data dissemination in P2P networks, and (c) an architecture and implementation of a P2P system based on the proposed optimal topologies. We will present the experimental results of our P2P system deployed on PlanetLab nodes demonstrating that our approach achieves near optimal throughput while providing scalability, low delay and bandwidth fairness among peers.     

Gene expression changes in a transgenic mouse model overexpressing human wildtype and mutant torsinA

Primary torsion dystonia is an autosomal-dominantly inherited, neurodevelopmental movement disorder caused by a GAG deletion (ΔGAG) in the DYT1 gene, encoding torsinA. This mutation is responsible for approximately 70% of cases of early-onset primary torsion dystonia. The function of wildtype torsinA is still unknown, and it is unsolved how the deletion in the DYT1 gene contributes to the development of the disease. To better understand the molecular processes involved in torsinA pathology, we used genome-wide oligonucleotide microarrays to characterize gene expression patterns in the striatum of mouse models overexpressing the human wildtype and mutant torsinA. By this approach we were able to detect gene expression changes that seem to be specific for torsinA pathology. We found an impact of torsinA, independent from genotype, on vesicle trafficking, exocytosis, and neurotransmitter release in our mouse model. In addition, we were able to identify several new pathways and processes involved in the development of the nervous system that are affected by wildtype and mutant torsinA. Furthermore, we have striking evidence from our gene expression data that glutamate receptor mediated synaptic plasticity in the striatum is the affected underlying cellular process for impaired motor learning in human ΔGAG torsinA transgenic mice.     

Unimodality,Independence Lead to NP-Hardness of Interval Probability Problems

In many real-life situations, we only have partial information about probabilities. This information is usually described by bounds on moments, on probabilities of certain events, etc. –i.e., by characteristics c(p) which are linear in terms of the unknown probabilities p _j. If we know interval bounds on some such characteristics , and we are interested in a characteristic c(p), then we can find the bounds on c(p) by solving a linear programming problem. In some situations, we also have additional conditions on the probability distribution –e.g., we may know that the two variables x ₁ and x ₂ are independent, or that the joint distribution of x ₁ and x ₂ is unimodal. We show that adding each of these conditions makes the corresponding interval probability problem NP-hard.