In order to solve the problem of the invalidation of thermal parameters andoptimal running, we present an efficient soft sensor approach based on sparse online Gaussianprocesses( GP), which is based on a combination o...In order to solve the problem of the invalidation of thermal parameters andoptimal running, we present an efficient soft sensor approach based on sparse online Gaussianprocesses( GP), which is based on a combination of a Bayesian online algorithm together with asequential construction of a relevant subsample of the data to specify the prediction of the GPmodel. By an appealing parameterization and projection techniques that use the reproducing kernelHubert space (RKHS) norm, recursions for the effective parameters and a sparse Gaussianapproximation of the posterior process are obtained. The sparse representation of Gaussian processesmakes the GP-based soft sensor practical in a large dataset and real-time application. And theproposed thermalparameter soft sensor is of importance for the economical running of the powerplant.展开更多
In this paper, a new higher order Wilson element is presented, and the convergence is proved. Then the interpolation postprocessing technique is used to obtain the global superconvergence and posterior error estimate ...In this paper, a new higher order Wilson element is presented, and the convergence is proved. Then the interpolation postprocessing technique is used to obtain the global superconvergence and posterior error estimate of higher accuracy of this new element for the Sobolev type equations.展开更多
Factorization of polynomials is one of the foundations of symbolic computation.Its applications arise in numerous branches of mathematics and other sciences.However,the present advanced programming languages such as C...Factorization of polynomials is one of the foundations of symbolic computation.Its applications arise in numerous branches of mathematics and other sciences.However,the present advanced programming languages such as C++ and J++,do not support symbolic computation directly.Hence,it leads to difficulties in applying factorization in engineering fields.In this paper,the authors present an algorithm which use numerical method to obtain exact factors of a bivariate polynomial with rational coefficients.The proposed method can be directly implemented in efficient programming language such C++ together with the GNU Multiple-Precision Library.In addition,the numerical computation part often only requires double precision and is easily parallelizable.展开更多
The standard finite elements of degree p over the rectangular meshes are applied to solve a kind of nonlinear viscoelastic wave equations with nonlinear boundary conditions, and the superclose property of the continuo...The standard finite elements of degree p over the rectangular meshes are applied to solve a kind of nonlinear viscoelastic wave equations with nonlinear boundary conditions, and the superclose property of the continuous Galerkin approximation is derived without using the nonclassical elliptic projection of the exact solution of the model problem. The global superconvergence of one order higher than the traditional error estimate is also obtained through the postprocessing technique.展开更多
文摘In order to solve the problem of the invalidation of thermal parameters andoptimal running, we present an efficient soft sensor approach based on sparse online Gaussianprocesses( GP), which is based on a combination of a Bayesian online algorithm together with asequential construction of a relevant subsample of the data to specify the prediction of the GPmodel. By an appealing parameterization and projection techniques that use the reproducing kernelHubert space (RKHS) norm, recursions for the effective parameters and a sparse Gaussianapproximation of the posterior process are obtained. The sparse representation of Gaussian processesmakes the GP-based soft sensor practical in a large dataset and real-time application. And theproposed thermalparameter soft sensor is of importance for the economical running of the powerplant.
基金the National Natural Science Foundation of China(10671184)
文摘In this paper, a new higher order Wilson element is presented, and the convergence is proved. Then the interpolation postprocessing technique is used to obtain the global superconvergence and posterior error estimate of higher accuracy of this new element for the Sobolev type equations.
基金partly supported by the National Natural Science Foundation of China under Grant Nos.91118001 and 11170153the National Key Basic Research Project of China under Grant No.2011CB302400Chongqing Science and Technology Commission Project under Grant No.cstc2013jjys40001
文摘Factorization of polynomials is one of the foundations of symbolic computation.Its applications arise in numerous branches of mathematics and other sciences.However,the present advanced programming languages such as C++ and J++,do not support symbolic computation directly.Hence,it leads to difficulties in applying factorization in engineering fields.In this paper,the authors present an algorithm which use numerical method to obtain exact factors of a bivariate polynomial with rational coefficients.The proposed method can be directly implemented in efficient programming language such C++ together with the GNU Multiple-Precision Library.In addition,the numerical computation part often only requires double precision and is easily parallelizable.
基金supported by the National Natural Science Foundation of China under Grant Nos.10671184 and 10971203
文摘The standard finite elements of degree p over the rectangular meshes are applied to solve a kind of nonlinear viscoelastic wave equations with nonlinear boundary conditions, and the superclose property of the continuous Galerkin approximation is derived without using the nonclassical elliptic projection of the exact solution of the model problem. The global superconvergence of one order higher than the traditional error estimate is also obtained through the postprocessing technique.
