62,891 research outputs found
Robust variance-constrained filtering for a class of nonlinear stochastic systems with missing measurements
The official published version of the article can be found at the link below.This paper is concerned with the robust filtering problem for a class of nonlinear stochastic systems with missing measurements and parameter uncertainties. The missing measurements are described by a binary switching sequence satisfying a conditional probability distribution, and the nonlinearities are expressed by the statistical means. The purpose of the filtering problem is to design a filter such that, for all admissible uncertainties and possible measurements missing, the dynamics of the filtering error is exponentially mean-square stable, and the individual steady-state error variance is not more than prescribed upper bound. A sufficient condition for the exponential mean-square stability of the filtering error system is first derived and an upper bound of the state estimation error variance is then obtained. In terms of certain linear matrix inequalities (LMIs), the solvability of the addressed problem is discussed and the explicit expression of the desired filters is also parameterized. Finally, a simulation example is provided to demonstrate the effectiveness and applicability of the proposed design approach.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, the Royal Society of the UK and the Alexander von Humboldt Foundation of Germany
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Mobile robot localization using robust extended H-infinity filtering
This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2009 Institution of Mechanical Engineers.In this paper, a novel methodology is provided for accurate localization of a mobile robot using autonomous navigation based on internal and external sensors. A new robust extended H∞ filter is developed to deal with the non-linear kinematic model of the robot and the non-linear distance measurements, together with process and measurement noises. The proposed filter relies on a two-step prediction-correction structure, which is similar to a Kalman filter. Simulations are provided to demonstrate the effectiveness of the proposed method.EPSRC, the Nuffield Foundation, and the Alexander von Humboldt Foundation
Analysis, filtering, and control for Takagi-Sugeno fuzzy models in networked systems
Copyright © 2015 Sunjie Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.The fuzzy logic theory has been proven to be effective in dealing with various nonlinear systems and has a great success in industry applications. Among different kinds of models for fuzzy systems, the so-called Takagi-Sugeno (T-S) fuzzy model has been quite popular due to its convenient and simple dynamic structure as well as its capability of approximating any smooth nonlinear function to any specified accuracy within any compact set. In terms of such a model, the performance analysis and the design of controllers and filters play important roles in the research of fuzzy systems. In this paper, we aim to survey some recent advances on the T-S fuzzy control and filtering problems with various network-induced phenomena. The network-induced phenomena under consideration mainly include communication delays, packet dropouts, signal quantization, and randomly occurring uncertainties (ROUs). With such network-induced phenomena, the developments on T-S fuzzy control and filtering issues are reviewed in detail. In addition, some latest results on this topic are highlighted. In the end, conclusions are drawn and some possible future research directions are pointed out.This work was supported in part by the National Natural Science Foundation of China under Grants 61134009, 61329301, 11301118 and 61174136, the Natural Science Foundation of Jiangsu Province of China under Grant BK20130017, the Fundamental Research Funds for the Central Universities of China under Grant CUSF-DH-D-2013061, the Royal Society of the U.K., and the Alexander von Humboldt Foundation of Germany
Brueckner-Hartree-Fock and its renormalized calculations for finite nuclei
We have performed self-consistent Brueckner-Hartree-Fock (BHF) and its
renormalized theory to the structure calculations of finite nuclei. The
-matrix is calculated within the BHF basis, and the exact Pauli exclusion
operator is determined by the BHF spectrum. Self-consistent occupation
probabilities are included in the renormalized Brueckner-Hartree-Fock (RBHF).
Various systematics and convergences are studies. Good results are obtained for
the ground-state energy and radius. RBHF can give a more reasonable
single-particle spectrum and radius. We present a first benchmark calculation
with other {\it ab initio} methods using the same effective Hamiltonian. We
find that the BHF and RBHF results are in good agreement with other
methods
Phase dynamics of inductively coupled intrinsic Josephson junctions and terahertz electromagnetic radiation
The Josephson effects associated with quantum tunneling of Cooper pairs
manifest as nonlinear relations between the superconductivity phase difference
and the bias current and voltage. Many novel phenomena appear, such as Shapiro
steps in dc cuurent-voltage (IV) characteristics of a Josephson junction under
microwave shining, which can be used as a voltage standard. Inversely, the
Josephson effects provide a unique way to generate high-frequency
electromagnetic (EM) radiation by dc bias voltage. The discovery of cuprate
high-Tc superconductors accelerated the effort to develop novel source of EM
waves based on a stack of atomically dense-packed intrinsic Josephson junctions
(IJJs), since the large superconductivity gap covers the whole terahertz
frequency band. Very recently, strong and coherent terahertz radiations have
been successfully generated from a mesa structure of
single crystal which works both as the source
of energy gain and as the cavity for resonance. It is then found theoretically
that, due to huge inductive coupling of IJJs produced by the nanometer junction
separation and the large London penetration depth of order of of
the material, a novel dynamic state is stabilized in the coupled sine-Gordon
system, in which kinks in phase differences are developed responding
to the standing wave of Josephson plasma and are stacked alternatively in the
c-axis. This novel solution of the inductively coupled sine-Gordon equations
captures the important features of experimental observations. The theory
predicts an optimal radiation power larger than the one available to date by
orders of magnitude, and thus suggests the technological relevance of the
phenomena.Comment: review article (69 pages, 30 figures
Algorithms for Replica Placement in High-Availability Storage
A new model of causal failure is presented and used to solve a novel replica
placement problem in data centers. The model describes dependencies among
system components as a directed graph. A replica placement is defined as a
subset of vertices in such a graph. A criterion for optimizing replica
placements is formalized and explained. In this work, the optimization goal is
to avoid choosing placements in which a single failure event is likely to wipe
out multiple replicas. Using this criterion, a fast algorithm is given for the
scenario in which the dependency model is a tree. The main contribution of the
paper is an dynamic programming algorithm for placing
replicas on a tree with vertices. This algorithm exhibits the
interesting property that only two subproblems need to be recursively
considered at each stage. An greedy algorithm is also briefly
reported.Comment: 22 pages, 7 figures, 4 algorithm listing
Image tag completion by local learning
The problem of tag completion is to learn the missing tags of an image. In
this paper, we propose to learn a tag scoring vector for each image by local
linear learning. A local linear function is used in the neighborhood of each
image to predict the tag scoring vectors of its neighboring images. We
construct a unified objective function for the learning of both tag scoring
vectors and local linear function parame- ters. In the objective, we impose the
learned tag scoring vectors to be consistent with the known associations to the
tags of each image, and also minimize the prediction error of each local linear
function, while reducing the complexity of each local function. The objective
function is optimized by an alternate optimization strategy and gradient
descent methods in an iterative algorithm. We compare the proposed algorithm
against different state-of-the-art tag completion methods, and the results show
its advantages
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