L1空间正系数多项式的倒数逼近

本文讨论了L^1空间函数的正系数多项式的倒数逼近的Jackson型估计问题，并证明了：如果f(χ)∈L[0,1]^1，f(χ)≥0，f(χ)不恒等号0，则存在一个次数不超过n正系数多项式qn(χ)∈Пn( )，使得||f-1/qn||L^1≤Cω(f,n^-1/2)L^1，其中Пn( )表示所有次数不超过n的正系数多项式的全体。     

ON AN INVERSE INITIAL-BOUNDARY VALUE PROBLEM FOR A ONE-DIMENSIONAL HYPERBOLIC SYSTEM OF DIFFERENTIAL EQUATIONS

1.IntroductionTheproblemstodeterminethecoefficientsindifferentialequationsfromknownfunctionaloftheirsolutionsareoftencalledinverseproblems.Amongtheseproblemsthesimplestisaboutone-dimensionalwaveequations.Thisproblemcanbediscussedinthetimedomainorinthefreq…     

Nilpotient Lie群上Herz空间及它的某些应用

§1 .　IntroductionLetGbeaconnectedandsimplyconnectednilpotentLiegroupwith(bi invariant)HaarmeasuredgandLiealgebraG .Theexponetialmapissurjectiveby [12 ],Theorem 3.6 .1.Onecanassociatedasubellipticdistance (g ,h)d′( g ;h)witheachfixedalgeraicbasisa1 ,a2 ,…ad′ofG .Foralli∈ { 1,2 ,… ,d′} ,letAi =dL(ai)denotethegeneratorsoflefttranslationsactingontheclass{ai} .Thisdistancehasthecharacterizationd′( g ;h) =sup{ ｜ψ( g) - ψ(h) ｜ :ψ∈C∞0 (G) ,∑d′i=1｜ (Aiψ) ｜2 ≤ 1} ,(see [9],…     

Heisenberg群上热核及Green核的渐近性

本文讨论Heisenberg群上热核及Green核的渐近性，给出了当群中元素靠近中心和远离中心时的渐近行为     

Complexity analysis of an interior point algorithm for the semidefinite optimization based on a kernel function with a double barrier term

In this paper, we establish the polynomial complexity of a primal-dual path-following interior point algorithm for solving semidefinite optimization(SDO) problems. The proposed algorithm is based on a new kernel function which differs from the existing kernel functions in which it has a double barrier term. With this function we define a new search direction and also a new proximity function for analyzing its complexity. We show that if q1 q2 1, the algorithm has O((q1 + 1) nq1+1/2(q1-q2)logn/ε)and O((q1 + 1)3q1-2q2+1/2(q1-q2)n~1/2 logn/ε) complexity results for large- and small-update methods, respectively.     

β−type fractional Sturm‐Liouville Coulomb operator and applied results

In this article, β‐type fractional Sturm‐Liouville Coulomb operator is considered by Hilfer fractional derivative. Fundamental spectral theory is investigated for the aforementioned problem. In this context, it is shown that the operator is self‐adjoint, eigenfunctions correspond to the distinct eigenfunctions are orthogonal, and eigenvalues are real. Furthermore, applications of this problem are given by the Adomian decomposition method and the results are shown with visual graphs.     

Heat kernels of non-symmetric jump processes with exponentially decaying jumping kernel

In this paper we study the transition densities for a large class of non-symmetric Markov processes whose jumping kernels decay exponentially or subexponentially. We obtain their upper bounds which also decay at the same rate as their jumping kernels. When the lower bounds of jumping kernels satisfy the weak upper scaling condition at zero, we also establish lower bounds for the transition densities, which are sharp.     

Nonlinear semisupervised principal component regression for soft sensor modeling and its mixture form

Compared with daily recorded process variables that can be easily obtained through the distributed control system, acquirements of key quality variables are much more difficult. As a result, for soft sensor development, we may only have a small number of output data samples and have much more input data samples. In this case, it is important to incorporate more input data samples to improve the modeling performance of the soft sensor. On the basis of the semisupervised modeling method, this paper aims to extend the linear semisupervised soft sensor to the nonlinear one, with incorporation of the kernel learning algorithm. Under the probabilistic modeling framework, a mixture form of the nonlinear semisupervised soft sensor is developed in the present work. To evaluate the performance of the developed nonlinear semisupervised soft sensor, an industrial case study is provided. Copyright © 2014 John Wiley & Sons, Ltd.     

Empirical kernel map approach to nonlinear underdetermined blind separation of sparse nonnegative dependent sources: pure component extraction from nonlinear mixture mass spectra

Nonlinear underdetermined blind separation of nonnegative dependent sources consists in decomposing a set of observed nonlinearly mixed signals into a greater number of original nonnegative and dependent component (source) signals. This hard problem is practically relevant for contemporary metabolic profiling of biological samples, where sources (a.k.a. pure components or analytes) are aimed to be extracted from mass spectra of nonlinear multicomponent mixtures. This paper presents a method for nonlinear underdetermined blind separation of nonnegative dependent sources that comply with a sparse probabilistic model, that is, sources are constrained to be sparse in support and amplitude. This model is validated on experimental pure component mass spectra. Under a sparse prior, a nonlinear problem is converted into an equivalent linear one comprised of original sources and their higher‐order, mostly second‐order, monomials. The influence of these monomials, which stand for error terms, is reduced by preprocessing a matrix of mixtures by means of robust principal component analysis and hard, soft and trimmed thresholding. Preprocessed data matrices are mapped in high‐dimensional reproducible kernel Hilbert space (RKHS) of functions by means of an empirical kernel map. Sparseness‐constrained nonnegative matrix factorizations in RKHS yield sets of separated components. They are assigned to pure components from the library using a maximal correlation criterion. The methodology is exemplified on demanding numerical and experimental examples related respectively to extraction of eight dependent components from three nonlinear mixtures and to extraction of 25 dependent analytes from nine nonlinear mixture mass spectra recorded in nonlinear chemical reaction of peptide synthesis. Copyright © 2014 John Wiley & Sons, Ltd.     

The well-posedness of a nonlocal hyperbolic model for type-I superconductors

A vectorial nonlocal linear hyperbolic problem with applications in superconductors of type-I is studied. The nonlocal term is represented by a (space) convolution with a singular kernel, which is arising in Eringen's model. The well-posedness of the problem is discussed under low regularity assumptions and the error estimates for two time-discrete schemes (based on backward Euler approximation) are established.