576,906 research outputs found

    On the Parametrization of Flavor Mixing in the Standard Model

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    It is shown that there exist nine different ways to describe the flavor mixing, in terms of three rotation angles and one CP-violating phase, within the standard electroweak theory of six quarks. For the assignment of the complex phase there essentially exists a continuum of possibilities, if one allows the phase to appear in more than four elements of the mixing matrix. If the phase is restricted to four elements, the phase assignment is uniquely defined. If one imposes the constraint that the phase disappears in a natural way in the chiral limit in which the masses of the u and d quarks are turned off, only three of the nine parametrizations are acceptable. In particular the ``standard'' parametrization advocated by the Particle Data Group is not permitted. One parametrization, in which the CP-violating phase is restricted to the light quark sector, stands up as the most favorable description of the flavor mixing.Comment: Latex 8 page

    On distribution formulas for complex and \ell-adic polylogarithms

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    We study an \ell-adic Galois analogue of the distribution formulas for polylogarithms with special emphasis on path dependency and arithmetic behaviors. As a goal, we obtain a notion of certain universal Kummer-Heisenberg measures that enable interpolating the \ell-adic polylogarithmic distribution relations for all degrees.Comment: This article has appeared in the proceedings volume "Periods in Quantum Field Theory and Arithmetic" (J.~Burgos Gil, K.~Ebrahimi-Fard, H.~Gangl eds), [Conference proceedings ICMAT-MZV 2014] Springer Proceedings in Mathematics \& Statistics {\bf 314} (2020), pp.593--61

    Nuclear mass predictions based on Bayesian neural network approach with pairing and shell effects

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    Bayesian neural network (BNN) approach is employed to improve the nuclear mass predictions of various models. It is found that the noise error in the likelihood function plays an important role in the predictive performance of the BNN approach. By including a distribution for the noise error, an appropriate value can be found automatically in the sampling process, which optimizes the nuclear mass predictions. Furthermore, two quantities related to nuclear pairing and shell effects are added to the input layer in addition to the proton and mass numbers. As a result, the theoretical accuracies are significantly improved not only for nuclear masses but also for single-nucleon separation energies. Due to the inclusion of the shell effect, in the unknown region, the BNN approach predicts a similar shell-correction structure to that in the known region, e.g., the predictions of underestimation of nuclear mass around the magic numbers in the relativistic mean-field model. This manifests that better predictive performance can be achieved if more physical features are included in the BNN approach.Comment: 15 pages, 4 figures, and 3 table

    H∞ fuzzy control for systems with repeated scalar nonlinearities and random packet losses

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    Copyright [2009] 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.This paper is concerned with the H∞ fuzzy control problem for a class of systems with repeated scalar nonlinearities and random packet losses. A modified Takagi-Sugeno (T-S) fuzzy model is proposed in which the consequent parts are composed of a set of discrete-time state equations containing a repeated scalar nonlinearity. Such a model can describe some well-known nonlinear systems such as recurrent neural networks. The measurement transmission between the plant and controller is assumed to be imperfect and a stochastic variable satisfying the Bernoulli random binary distribution is utilized to represent the phenomenon of random packet losses. Attention is focused on the analysis and design of H∞ fuzzy controllers with the same repeated scalar nonlinearities such that the closed-loop T-S fuzzy control system is stochastically stable and preserves a guaranteed H∞ performance. Sufficient conditions are obtained for the existence of admissible controllers, and the cone complementarity linearization procedure is employed to cast the controller design problem into a sequential minimization one subject to linear matrix inequalities, which can be readily solved by using standard numerical software. Two examples are given to illustrate the effectiveness of the proposed design method

    Robust H∞ filtering for a class of nonlinear networked systems with multiple stochastic communication delays and packet dropouts

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    Copyright [2010] 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 robust H∞ filtering problem is studied for a class of uncertain nonlinear networked systems with both multiple stochastic time-varying communication delays and multiple packet dropouts. A sequence of random variables, all of which are mutually independent but obey Bernoulli distribution, are introduced to account for the randomly occurred communication delays. The packet dropout phenomenon occurs in a random way and the occurrence probability for each sensor is governed by an individual random variable satisfying a certain probabilistic distribution in the interval. The discrete-time system under consideration is also subject to parameter uncertainties, state-dependent stochastic disturbances and sector-bounded nonlinearities. We aim to design a linear full-order filter such that the estimation error converges to zero exponentially in the mean square while the disturbance rejection attenuation is constrained to a give level by means of the H∞ performance index. Intensive stochastic analysis is carried out to obtain sufficient conditions for ensuring the exponential stability as well as prescribed H∞ performance for the overall filtering error dynamics, in the presence of random delays, random dropouts, nonlinearities, and the parameter uncertainties. These conditions are characterized in terms of the feasibility of a set of linear matrix inequalities (LMIs), and then the explicit expression is given for the desired filter parameters. Simulation results are employed to demonstrate the effectiveness of the proposed filter design technique in this paper.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 Alexander von Humboldt Foundation of Germany, National Natural Science Foundation of China under Grant 60825303, 60834003, 973 Project under Grant 2009CB320600, Fok Ying Tung Education Foundation under Grant 111064, and the Youth Science Fund of Heilongjiang Province under Grant QC2009C63

    Experimental investigation of feedforward control schemes of a flexible robot manipulator system

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    This paper presents experimental investigations into the applications of feedforward control schemes for vibration control of a flexible manipulator system. Feedforward control schemes based on input shaping and filtering techniques are to be examined. A constrained planar single-link flexible manipulator is considered in this experimental work. An unshaped bang-bang torque input is used to determine the characteristic parameters of the system for design and evaluation of the input shaping control techniques. The input shapers and filtering techniques are designed based on the properties of the system. Simulation results of the response of the manipulator to the shaped and filtered inputs are presented in time and frequency domains. Performances of the shapers are examined in terms of level of vibration reduction and time response specifications. The effects of derivative order of the input shaper on the performance of the system are investigated. Finally, a comparative assessment of the control strategies is presented and discusse
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