Brunel University London

About: Brunel University London is a education organization based out in London, United Kingdom. It is known for research contribution in the topics: Large Hadron Collider & Population. The organization has 10918 authors who have published 29515 publications receiving 893330 citations. The organization is also known as: Brunel & University of Brunel.

Robust H/sub /spl infin// filtering for stochastic time-delay systems with missing measurements

Abstract: In this paper, the robust Hinfin filtering problem is studied for stochastic uncertain discrete time-delay systems with missing measurements. The missing measurements are described by a binary switching sequence satisfying a conditional probability distribution. We aim to design filters such that, for all possible missing observations and all admissible parameter uncertainties, the filtering error system is exponentially mean-square stable, and the prescribed Hinfin performance constraint is met. In terms of certain linear matrix inequalities (LMIs), sufficient conditions for the solvability of the addressed problem are obtained. When these LMIs are feasible, an explicit expression of a desired robust Hinfin filter is also given. An optimization problem is subsequently formulated by optimizing the Hinfin filtering performances. Finally, a numerical example is provided to demonstrate the effectiveness and applicability of the proposed design approach

Justification by Works or by Faith?: Evaluating the New Public Management

Abstract: The article first defines a set of policy measures that constitute the 'New Public Management' (NPM). Evidence is cited to support the contention that the NPM has been widely adopted, with local va...

Observation of the rare B-s(0)->mu(+)mu(-) decay from the combined analysis of CMS and LHCb data

Abstract: The standard model of particle physics describes the fundamental particles and their interactions via the strong, electromagnetic and weak forces. It provides precise predictions for measurable quantities that can be tested experimentally. The probabilities, or branching fractions, of the strange B meson (B-s(0)) and the B-0 meson decaying into two oppositely charged muons (mu(+) and mu(-)) are especially interesting because of their sensitivity to theories that extend the standard model. The standard model predicts that the B-s(0)->mu(+)mu(-) and B-0 ->mu(+)mu(-) decays are very rare, with about four of the former occurring for every billion B-s(0) mesons produced, and one of the latter occurring for every ten billion B-0 mesons(1). A difference in the observed branching fractions with respect to the predictions of the standard model would provide a direction in which the standard model should be extended. Before the Large Hadron Collider (LHC) at CERN2 started operating, no evidence for either decay mode had been found. Upper limits on the branching fractions were an order of magnitude above the standard model predictions. The CMS (Compact Muon Solenoid) and LHCb(Large Hadron Collider beauty) collaborations have performed a joint analysis of the data from proton-proton collisions that they collected in 2011 at a centre-of-mass energy of seven teraelectronvolts and in 2012 at eight teraelectronvolts. Here we report the first observation of the B-s(0)->mu(+)mu(-) decay, with a statistical significance exceeding six standard deviations, and the best measurement so far of its branching fraction. Furthermore, we obtained evidence for the B-0 ->mu(+)mu(-) decay with a statistical significance of three standard deviations. Both measurements are statistically compatible with standard model predictions and allow stringent constraints to be placed on theories beyond the standard model. The LHC experiments will resume taking data in 2015, recording proton-proton collisions at a centre-of-mass energy of 13 teraelectronvolts, which will approximately double the production rates of B-s(0) and B-0 mesons and lead to further improvements in the precision of these crucial tests of the standard model.

Shift-Based Density Estimation for Pareto-Based Algorithms in Many-Objective Optimization

Abstract: It is commonly accepted that Pareto-based evolutionary multiobjective optimization (EMO) algorithms encounter difficulties in dealing with many-objective problems. In these algorithms, the ineffectiveness of the Pareto dominance relation for a high-dimensional space leads diversity maintenance mechanisms to play the leading role during the evolutionary process, while the preference of diversity maintenance mechanisms for individuals in sparse regions results in the final solutions distributed widely over the objective space but distant from the desired Pareto front. Intuitively, there are two ways to address this problem: 1) modifying the Pareto dominance relation and 2) modifying the diversity maintenance mechanism in the algorithm. In this paper, we focus on the latter and propose a shift-based density estimation (SDE) strategy. The aim of our study is to develop a general modification of density estimation in order to make Pareto-based algorithms suitable for many-objective optimization. In contrast to traditional density estimation that only involves the distribution of individuals in the population, SDE covers both the distribution and convergence information of individuals. The application of SDE in three popular Pareto-based algorithms demonstrates its usefulness in handling many-objective problems. Moreover, an extensive comparison with five state-of-the-art EMO algorithms reveals its competitiveness in balancing convergence and diversity of solutions. These findings not only show that SDE is a good alternative to tackle many-objective problems, but also present a general extension of Pareto-based algorithms in many-objective optimization.