The Experts below are selected from a list of 76131 Experts worldwide ranked by ideXlab platform
G Rubinacci - One of the best experts on this subject based on the ideXlab platform.
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finite element methods for the solution of 3d eddy current problems
Advances in Imaging and Electron Physics, 1997Co-Authors: R Albanese, G RubinacciAbstract:Publisher Summary This chapter presents a critical survey on the approach to the solution of the general three-dimensional eddy current problem, which originally started from the necessity of studying the transient electromagnetic phenomena in tokamaks. The mathematical models are described, including the assumptions made on the field equations and the material properties. An excursion from full Maxwell equations to the stationary and quasistationary fields is briefly presented, with focus on the magneto-quasistationary model—that is, the eddy current problem. The chapter also presents Vector and scalar potentials along with the corresponding gauge conditions. The emphasis is put on the benefits offered by the use of the Vector potentials with the classic Coulomb and Lorentz gauges in dealing with material interfaces and the unified treatment of magnetostatic and eddy current problem. Other Vector potentials such as, two-Component Vector potential are also discussed. The main features of the edge elements, whose degrees of freedom are associated with the tangential Components or the line integrals of the Vector field along the edges, are also covered. They give rise to a set of Vector shape functions, for which the continuity of the tangential Components is preserved, allowing for the discontinuity of the normal Component between adjacent elements.
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magnetostatic field computations in terms of two Component Vector potentials
International Journal for Numerical Methods in Engineering, 1990Co-Authors: R Albanese, G RubinacciAbstract:In this paper the magnetostatic problem is stated in terms of two-Component electric and magnetic Vector potentials. An associated numerical method, based on the adoption of edge elements, is proposed. This procedure overcomes the cancellation problems and the complexity of the interface conditions encountered by similar approaches in the presence of magnetic inhomogeneities and discontinuities of currents and magnetic fields.
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Magnetostatic field computations in terms of two‐Component Vector potentials
International Journal for Numerical Methods in Engineering, 1990Co-Authors: R Albanese, G RubinacciAbstract:In this paper the magnetostatic problem is stated in terms of two-Component electric and magnetic Vector potentials. An associated numerical method, based on the adoption of edge elements, is proposed. This procedure overcomes the cancellation problems and the complexity of the interface conditions encountered by similar approaches in the presence of magnetic inhomogeneities and discontinuities of currents and magnetic fields.
A P Young - One of the best experts on this subject based on the ideXlab platform.
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Defect energy of infinite-Component Vector spin glasses.
Physical review. E Statistical nonlinear and soft matter physics, 2005Co-Authors: L W Lee, A P YoungAbstract:We compute numerically the zero-temperature defect energy DeltaE of the Vector spin glass in the limit of an infinite number of spin Components m , for a range of dimensions 2< or d < or =5 . Fitting to DeltaE approximately L(theta) , where L is the system size, we obtain: theta similar to-1.54 (d=2) , theta similar to-1.04 (d=3) , theta similar to -0.67 (d=4) , and theta similar to -0.37 (d=5) . These results show that the lower critical dimension dl (the dimension where theta changes sign) is significantly higher for m=infinity than for finite m (where 2< dl
Dmitry A. Garanin - One of the best experts on this subject based on the ideXlab platform.
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Susceptibilities and correlation functions of the anisotropic spherical model
European Physical Journal B, 1997Co-Authors: Dmitry A. GaraninAbstract:The static transverse and longitudinal correlation functions (CF) of a 3-dimensional ferromagnet are calculated for the exactly solvable anisotropic spherical model (ASM) determined as the limit D → ∞ of the classical D- Component Vector model. The results are nonequivalent to those for the standard spherical model of Berlin and Kac even in the isotropic case. Whereas the transverse CF has the usual Ornstein-Zernike form for small wave Vectors, the longitudinal CF shows a nontrivial behavior in the ordered region caused by spin-wave fluctuations. In particular, in the isotropic case below T c one has S zz(k) ∝ 1/k (the result of the spin-wave theory) for k ≲ κm ∝ T c - T.
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1/D expansion for two-dimensional antiferromagnetic D-Component Vector model: magnetization and susceptibility in magnetic field
Journal of Magnetism and Magnetic Materials, 1995Co-Authors: Dmitry A. GaraninAbstract:Abstract The thermodynamics of low-dimensional classical magnets described by the D-Component Vector model is considered with the help of the 1/D expansion in the whole temperature region and for nonzero magnetic fields. For the Heisenberg model (D = 3) the theory reproduces with a good accuracy the temperature dependence of the antiferromagnetic susceptibility χAF with the maximum at T ∼ J0 and describes the singular behavior of χAF at T, H → 0: limT → 0limH → 0 χAF = (1 − 1/D)/(2J0) and limH → 0limT → 0 χAF = 1/(2J0).
R Albanese - One of the best experts on this subject based on the ideXlab platform.
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magnetostatic field computations in terms of two Component Vector potentials
International Journal for Numerical Methods in Engineering, 1990Co-Authors: R Albanese, G RubinacciAbstract:In this paper the magnetostatic problem is stated in terms of two-Component electric and magnetic Vector potentials. An associated numerical method, based on the adoption of edge elements, is proposed. This procedure overcomes the cancellation problems and the complexity of the interface conditions encountered by similar approaches in the presence of magnetic inhomogeneities and discontinuities of currents and magnetic fields.
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Magnetostatic field computations in terms of two‐Component Vector potentials
International Journal for Numerical Methods in Engineering, 1990Co-Authors: R Albanese, G RubinacciAbstract:In this paper the magnetostatic problem is stated in terms of two-Component electric and magnetic Vector potentials. An associated numerical method, based on the adoption of edge elements, is proposed. This procedure overcomes the cancellation problems and the complexity of the interface conditions encountered by similar approaches in the presence of magnetic inhomogeneities and discontinuities of currents and magnetic fields.
Petr Tichavský - One of the best experts on this subject based on the ideXlab platform.
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Dynamic Independent Component/Vector Analysis
arXiv: Signal Processing, 2020Co-Authors: Zbyněk Koldovský, Václav Kautský, Petr TichavskýAbstract:A novel extension of the popular FastICA algorithm for Independent Component Analysis is proposed in one-unit, symmetric and block-deflation variants. The methods introduced in this paper are capable of extracting/separating one or several sources from specific types of time-varying mixtures. The algorithms are derived within a unified framework so that they are applicable in the real-valued as well as complex-valued domains, and jointly to several mixtures, similar to Independent Vector Analysis. Performance analysis of the one-unit algorithm is provided; it shows its asymptotic efficiency under the given mixing and statistical models. Numerical simulations corroborate the validity of the analysis, confirm the usefulness of the algorithms in separation of moving sources, and show the superior speed of convergence and ability to separate super-Gaussian as well as sub-Gaussian signals.
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Gradient Algorithms for Complex Non-Gaussian Independent Component/Vector Extraction, Question of Convergence
IEEE Transactions on Signal Processing, 2019Co-Authors: Zbynek Koldovský, Petr TichavskýAbstract:We revise the problem of extracting one independent Component from an instantaneous linear mixture of signals. The mixing matrix is parameterized by two Vectors: one column of the mixing matrix, and one row of the demixing matrix. The separation is based on the non-Gaussianity of the source of interest, while the remaining background signals are assumed to be Gaussian. Three gradient-based estimation algorithms are derived using the maximum likelihood principle and are compared with the Natural Gradient algorithm for Independent Component Analysis and with One-Unit FastICA based on negentropy maximization. The ideas and algorithms are also generalized to the extraction of a Vector Component when the extraction proceeds jointly from a set of instantaneous mixtures. Throughout this paper, we address the problem concerning the size of the region of convergence for which the algorithms guarantee the extraction of the desired source. We show that the size is influenced by the signal-to-interference ratio on the input channels. Simulations comparing several algorithms confirm this observation. They show a different size of the region of convergence under a scenario in which the source of interest is dominant or weak. Here, our proposed modifications of the gradient methods, taking into account the dominance/weakness of the source, show improved global convergence.
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gradient algorithms for complex non gaussian independent Component Vector extraction question of convergence
IEEE Transactions on Signal Processing, 2019Co-Authors: Zbynek Koldovský, Petr TichavskýAbstract:We revise the problem of extracting one independent Component from an instantaneous linear mixture of signals. The mixing matrix is parameterized by two Vectors: one column of the mixing matrix, and one row of the demixing matrix. The separation is based on the non-Gaussianity of the source of interest, while the remaining background signals are assumed to be Gaussian. Three gradient-based estimation algorithms are derived using the maximum likelihood principle and are compared with the Natural Gradient algorithm for Independent Component Analysis and with One-Unit FastICA based on negentropy maximization. The ideas and algorithms are also generalized to the extraction of a Vector Component when the extraction proceeds jointly from a set of instantaneous mixtures. Throughout this paper, we address the problem concerning the size of the region of convergence for which the algorithms guarantee the extraction of the desired source. We show that the size is influenced by the signal-to-interference ratio on the input channels. Simulations comparing several algorithms confirm this observation. They show a different size of the region of convergence under a scenario in which the source of interest is dominant or weak. Here, our proposed modifications of the gradient methods, taking into account the dominance/weakness of the source, show improved global convergence.
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Gradient Algorithms for Complex Non-Gaussian Independent Component/Vector Extraction.
arXiv: Signal Processing, 2018Co-Authors: Zbynek Koldovský, Petr TichavskýAbstract:We address the problem of extracting one independent Component from an instantaneous linear mixture of signals. Compared to Independent Component Analysis, a novel parameterization of the mixing model is used. Our statistical model is based on the non-Gaussianity of the source of interest, while the other background signals are assumed to be Gaussian. Three gradient-based estimation algorithms are derived using the maximum likelihood principle. These ideas and algorithms are also generalized for the extraction of a Vector Component when the extraction proceeds jointly from a set of instantaneous mixtures. In simulations, we mainly focus on the size of the region of convergence for which the algorithms guarantee the extraction of the desired source. The proposed methods show superior results under various levels of initial signal-to-interference ratio, in comparison with state-of-the-art algorithms. The computational complexity of the proposed algorithms grows linearly with the number of channels.