A new variable time step method,which is called the backwards calculating time step method,is presented in this paper.It allows numerical simulation of soil freezing and thawing while avoiding "phase change missi...A new variable time step method,which is called the backwards calculating time step method,is presented in this paper.It allows numerical simulation of soil freezing and thawing while avoiding "phase change missing and overflowing".A sensitive heat capacity model is introduced through which the calculation errors are analyzed.Then the equation using the self-adjusted time step is presented and solved using finite differences.Through this equation,the time needed for a space cell to reach the phase change point temperature is calculated.Using this time allows the time step to be adjusted so that errors caused by "phase change missing and overflowing" are successfully eliminated.Above all,the obvious features of this method are an accelerated rate for adjusting the time step and simplifing the computations.An actual example proves that this method can accurately calculate the temperature fields during soil freezing and thawing.It is an improvement over traditional methods and can be widely used on complicated multi-dimensional phase change problems.展开更多
The ability to predict groundwater fluxes with a minimum of effort and measurement is an important objective. Numerical modeling is one approach to obtain such a prediction. Predictions of groundwater fluxes can be us...The ability to predict groundwater fluxes with a minimum of effort and measurement is an important objective. Numerical modeling is one approach to obtain such a prediction. Predictions of groundwater fluxes can be used to determine fluxes of other materials such as salt and nutrients. In this paper an analytical model is developed to predict the flow of groundwater from mangrove forest to the creek. The model uses the geometry and hydraulic conductivity of the mangrove forest sediment, which is inundated by tidal water from day zero to day five, with the flux ranged from 0.026 to 0.007 m^3/（m^2.day） with the average error is about 10%. The solution for the groundwater flow is written in terms of an analytic series solution, based on two dimensional potential flow. The approach is basically to solve the hydraulic potential flow for steady state conditions using the Laplace equation. The advantages of this method are that it is simple but accurate, and the error in the computation can be readily calculated. The result of this model is then compared to the result of the field measurement from also day zero to day five after inundation, which ranged from 0.030 to 0.013 m3/（m2.day） with the average error is about 40%. From the above results, it is concluded that the series solution model can be used to calculate the flux of the groundwater, especially in the mangrove forest area.展开更多
The strong consistency of M estimators of the regression parameters in linear models for ρ-mixing random errors under some mild conditions is established, which is an essential improvement over the relevant results i...The strong consistency of M estimators of the regression parameters in linear models for ρ-mixing random errors under some mild conditions is established, which is an essential improvement over the relevant results in the literature on the moment conditions and mixing errors. Especially, Theorem of Wu （2005） is improved essentially on the moment conditions.展开更多
Abstract We use moderate deviations to study the signal detection problem for a diffusion model. We establish a moderate deviation principle for the log-likelihood function of the diffusion model. Then applying the mo...Abstract We use moderate deviations to study the signal detection problem for a diffusion model. We establish a moderate deviation principle for the log-likelihood function of the diffusion model. Then applying the moderate deviation estimates to hypothesis testing for signal detection problem we give a decision region such that its error probability of the second kind tends to zero with faster speed than the error probability of the first kind when the error probability of the first kind is approximated by e-ατ（T）, where α〉 0, τ（T） = o（T） and τ（T）→∞ as the observation time T goes to infinity.展开更多
LetX 1,…,X n be iid observations of a random variableX with probability density functionf(x) on the q-dimensional unit sphere Ωq in Rq+1,q ? 1. Let $f_n (x) = n^{ - 1} c(h)\sum\nolimits_{i = 1}^n {K[(1 - x'X_i )...LetX 1,…,X n be iid observations of a random variableX with probability density functionf(x) on the q-dimensional unit sphere Ωq in Rq+1,q ? 1. Let $f_n (x) = n^{ - 1} c(h)\sum\nolimits_{i = 1}^n {K[(1 - x'X_i )/h^2 ]} $ be a kernel estimator off(x). In this paper we establish a central limit theorem for integrated square error off n under some mild conditions.展开更多
基金Project 2006G1662-00 supported by the Key Science and Technology Project of Heilongjiang Province
文摘A new variable time step method,which is called the backwards calculating time step method,is presented in this paper.It allows numerical simulation of soil freezing and thawing while avoiding "phase change missing and overflowing".A sensitive heat capacity model is introduced through which the calculation errors are analyzed.Then the equation using the self-adjusted time step is presented and solved using finite differences.Through this equation,the time needed for a space cell to reach the phase change point temperature is calculated.Using this time allows the time step to be adjusted so that errors caused by "phase change missing and overflowing" are successfully eliminated.Above all,the obvious features of this method are an accelerated rate for adjusting the time step and simplifing the computations.An actual example proves that this method can accurately calculate the temperature fields during soil freezing and thawing.It is an improvement over traditional methods and can be widely used on complicated multi-dimensional phase change problems.
文摘The ability to predict groundwater fluxes with a minimum of effort and measurement is an important objective. Numerical modeling is one approach to obtain such a prediction. Predictions of groundwater fluxes can be used to determine fluxes of other materials such as salt and nutrients. In this paper an analytical model is developed to predict the flow of groundwater from mangrove forest to the creek. The model uses the geometry and hydraulic conductivity of the mangrove forest sediment, which is inundated by tidal water from day zero to day five, with the flux ranged from 0.026 to 0.007 m^3/（m^2.day） with the average error is about 10%. The solution for the groundwater flow is written in terms of an analytic series solution, based on two dimensional potential flow. The approach is basically to solve the hydraulic potential flow for steady state conditions using the Laplace equation. The advantages of this method are that it is simple but accurate, and the error in the computation can be readily calculated. The result of this model is then compared to the result of the field measurement from also day zero to day five after inundation, which ranged from 0.030 to 0.013 m3/（m2.day） with the average error is about 40%. From the above results, it is concluded that the series solution model can be used to calculate the flux of the groundwater, especially in the mangrove forest area.
基金This research is supported by the National Natural Science Foundation of China under Grant No. 11061012, the Support Program of the New Century Guangxi China Ten-hundred-thousand Talents Project under Grant No. 2005214, and the Guangxi, China Science Foundation under Grant No. 0991081.
文摘The strong consistency of M estimators of the regression parameters in linear models for ρ-mixing random errors under some mild conditions is established, which is an essential improvement over the relevant results in the literature on the moment conditions and mixing errors. Especially, Theorem of Wu （2005） is improved essentially on the moment conditions.
基金supported by National Natural Science Foundation of China (Grant Nos.10871153 and 11171262)the Specialized Research Fund for the Doctoral Program of Higher Education of China (Grant No. 200804860048)
文摘Abstract We use moderate deviations to study the signal detection problem for a diffusion model. We establish a moderate deviation principle for the log-likelihood function of the diffusion model. Then applying the moderate deviation estimates to hypothesis testing for signal detection problem we give a decision region such that its error probability of the second kind tends to zero with faster speed than the error probability of the first kind when the error probability of the first kind is approximated by e-ατ（T）, where α〉 0, τ（T） = o（T） and τ（T）→∞ as the observation time T goes to infinity.
文摘LetX 1,…,X n be iid observations of a random variableX with probability density functionf(x) on the q-dimensional unit sphere Ωq in Rq+1,q ? 1. Let $f_n (x) = n^{ - 1} c(h)\sum\nolimits_{i = 1}^n {K[(1 - x'X_i )/h^2 ]} $ be a kernel estimator off(x). In this paper we establish a central limit theorem for integrated square error off n under some mild conditions.
