47,579 research outputs found
Pentaquark in nuclear matter and hypernuclei
We study the properties of the in nuclear matter and
hypernuclei within the quark mean-field (QMF) model, which has been
successfully used for the description of ordinary nuclei and
hypernuclei. With the assumption that the non-strange mesons couple only to the
and quarks inside baryons, a sizable attractive potential of the
in nuclear matter is achieved as a consequence of the cancellation
between the attractive scalar potential and the repulsive vector potential. We
investigate the single-particle energies in light, medium, and heavy
nuclei. More bound states are obtained in hypernuclei in comparison
with those in hypernuclei.Comment: 16 pages, 5 figure
Gain-constrained recursive filtering with stochastic nonlinearities and probabilistic sensor delays
This is the post-print of the Article. The official published version can be accessed from the link below - Copyright @ 2013 IEEE.This paper is concerned with the gain-constrained recursive filtering problem for a class of time-varying nonlinear stochastic systems with probabilistic sensor delays and correlated noises. The stochastic nonlinearities are described by statistical means that cover the multiplicative stochastic disturbances as a special case. The phenomenon of probabilistic sensor delays is modeled by introducing a diagonal matrix composed of Bernoulli distributed random variables taking values of 1 or 0, which means that the sensors may experience randomly occurring delays with individual delay characteristics. The process noise is finite-step autocorrelated. The purpose of the addressed gain-constrained filtering problem is to design a filter such that, for all probabilistic sensor delays, stochastic nonlinearities, gain constraint as well as correlated noises, the cost function concerning the filtering error is minimized at each sampling instant, where the filter gain satisfies a certain equality constraint. A new recursive filtering algorithm is developed that ensures both the local optimality and the unbiasedness of the designed filter at each sampling instant which achieving the pre-specified filter gain constraint. A simulation example is provided to illustrate the effectiveness of the proposed filter design approach.This work was supported in part by the National Natural Science Foundation of China by Grants 61273156, 61028008, 60825303, 61104125, and 11271103, National 973 Project by Grant 2009CB320600, the Fok Ying Tung Education Fund by Grant 111064, the Special Fund for the Author of National Excellent Doctoral Dissertation of China by Grant 2007B4, the State Key Laboratory of Integrated Automation for the Process Industry (Northeastern University) of China, the Engineering and Physical Sciences Research Council (EPSRC) of the U.K. by Grant GR/S27658/01, the Royal Society of the U.K., and the Alexander von Humboldt Foundation of Germany
Superfluidity of hyperons in neutron stars
We study the superfluidity of hyperons in neutron star
matter and neutron stars. We use the relativistic mean field (RMF) theory to
calculate the properties of neutron star matter. In the RMF approach, the
meson-hyperon couplings are constrained by reasonable hyperon potentials that
include the updated information from recent developments in hypernuclear
physics. To examine the pairing gap of hyperons, we employ
several interactions based on the Nijmegen models and used in
double- hypernuclei studies. It is found that the maximal pairing gap
obtained is a few tenths of a MeV. The magnitude and the density region of the
pairing gap are dependent on the interaction and the treatment
of neutron star matter. We calculate neutron star properties and find that
whether the superfluidity of hyperons exists in the core of
neutron stars mainly depends on the interaction used.Comment: 22 pages, 2 Tables, 6 Figur
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On nonlinear H∞ filtering for discrete-time stochastic systems with missing measurements
Copyright [2008] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.In this paper, the H∞ filtering problem is investigated for a general class of nonlinear discrete-time stochastic systems with missing measurements. The system under study is not only corrupted by state-dependent white noises but also disturbed by exogenous inputs. The measurement output contains randomly missing data that is modeled by a Bernoulli distributed white sequence with a known conditional probability. A filter of very general form is first designed such that the filtering process is stochastically stable and the filtering error satisfies H infin performance constraint for all admissible missing observations and nonzero exogenous disturbances under the zero-initial condition. The existence conditions of the desired filter are described in terms of a second-order nonlinear inequality. Such an inequality can be decoupled into some auxiliary ones that can be solved independently by taking special form of the Lyapunov functionals. As a consequence, a linear time-invariant filter design problem is discussed for the benefit of practical applications, and some simplified conditions are obtained. Finally, two numerical simulation examples are given to illustrate the main results of this paper
Nonlinear analysis of dynamical complex networks
Copyright © 2013 Zidong Wang 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.Complex networks are composed of a large number of highly interconnected dynamical units and therefore exhibit very complicated dynamics. Examples of such complex networks include the Internet, that is, a network of routers or domains, the World Wide Web (WWW), that is, a network of websites, the brain, that is, a network of neurons, and an organization, that is, a network of people. Since the introduction of the small-world network principle, a great deal of research has been focused on the dependence of the asymptotic behavior of interconnected oscillatory agents on the structural properties of complex networks. It has been found out that the general structure of the interaction network may play a crucial role in the emergence of synchronization phenomena in various fields such as physics, technology, and the life sciences
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Quantized H-Infinity control for nonlinear stochastic time-delay systems with missing measurements
This is the post-print version of the Article. The official published version can be accessed from the link below - Copyright @ 2012 IEEEIn this paper, the quantized H∞ control problem is investigated for a class of nonlinear stochastic time-delay network-based systems with probabilistic data missing. A nonlinear stochastic system with state delays is employed to model the networked control systems where the measured output and the input signals are quantized by two logarithmic quantizers, respectively. Moreover, the data missing phenomena are modeled by introducing a diagonal matrix composed of Bernoulli distributed stochastic variables taking values of 1 and 0, which describes that the data from different sensors may be lost with different missing probabilities. Subsequently, a sufficient condition is first derived in virtue of the method of sector-bounded uncertainties, which guarantees that the closed-loop system is stochastically stable and the controlled output satisfies H∞ performance constraint for all nonzero exogenous disturbances under the zero-initial condition. Then, the sufficient condition is decoupled into some inequalities for the convenience of practical verification. Based on that, quantized H∞ controllers are designed successfully for some special classes of nonlinear stochastic time-delay systems by using Matlab linear matrix inequality toolbox. Finally, a numerical simulation example is exploited to show the effectiveness and applicability of the results derived.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of the U.K. under Grant GR/S27658/01, the Leverhulme Trust of the U.K., the Royal Society of the U.K., the National Natural Science Foundation of China under Grants 61028008, 61134009, 61104125, 60974030, and 61074016, and the Alexander von Humboldt Foundation of Germany
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H-infinity state estimation for discrete-time complex networks with randomly occurring sensor saturations and randomly varying sensor delays
This is the post-print of the Article. The official published version can be accessed from the link below - Copyright @ 2012 IEEEIn this paper, the state estimation problem is investigated for a class of discrete time-delay nonlinear complex networks with randomly occurring phenomena from sensor measurements. The randomly occurring phenomena include randomly occurring sensor saturations (ROSSs) and randomly varying sensor delays (RVSDs) that result typically from networked environments. A novel sensor model is proposed to describe the ROSSs and the RVSDs within a unified framework via two sets of Bernoulli-distributed white sequences with known conditional probabilities. Rather than employing the commonly used Lipschitz-type function, a more general sector-like nonlinear function is used to describe the nonlinearities existing in the network. The purpose of the addressed problem is to design a state estimator to estimate the network states through available output measurements such that, for all probabilistic sensor saturations and sensor delays, the dynamics of the estimation error is guaranteed to be exponentially mean-square stable and the effect from the exogenous disturbances to the estimation accuracy is attenuated at a given level by means of an -norm. In terms of a novel Lyapunov–Krasovskii functional and the Kronecker product, sufficient conditions are established under which the addressed state estimation problem is recast as solving a convex optimization problem via the semidefinite programming method. A simulation example is provided to show the usefulness of the proposed state estimation conditions.This work was supported in part by the Engineering and Physical Sciences
Research Council (EPSRC) of the U.K. under Grant GR/S27658/01, the Royal Society of the U.K., the National Natural Science Foundation of China under Grants 61028008, 61134009, 61104125 and 60974030, the Natural
Science Foundation of Universities in Anhui Province of China under Grant KJ2011B030, and the Alexander von Humboldt Foundation of Germany
Modification of nucleon properties in nuclear matter and finite nuclei
We present a model for the description of nuclear matter and finite nuclei,
and at the same time, for the study of medium modifications of nucleon
properties. The nucleons are described as nontopological solitons which
interact through the self-consistent exchange of scalar and vector mesons. The
model explicitly incorporates quark degrees of freedom into nuclear many-body
systems and provides satisfactory results on the nuclear properties. The
present model predicts a significant increase of the nucleon radius at normal
nuclear matter density. It is very interesting to see the nucleon properties
change from the nuclear surface to the nuclear interior.Comment: 22 pages, 10 figure
Neutron star matter in the quark-meson coupling model in strong magnetic fields
The effects of strong magnetic fields on neutron star matter are investigated
in the quark-meson coupling (QMC) model. The QMC model describes a nuclear
many-body system as nonoverlapping MIT bags in which quarks interact through
self-consistent exchange of scalar and vector mesons in the mean-field
approximation. The results of the QMC model are compared with those obtained in
a relativistic mean-field (RMF) model. It is found that quantitative
differences exist between the QMC and RMF models, while qualitative trends of
the magnetic field effects on the equation of state and composition of neutron
star matter are very similar.Comment: 16 pages, 4 figure
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