Previous studies indicate that ENSO predictions are particularly sensitive to the initial conditions in some key areas（socalled ＂sensitive areas＂）. And yet, few studies have quantified improvements in prediction s...Previous studies indicate that ENSO predictions are particularly sensitive to the initial conditions in some key areas（socalled ＂sensitive areas＂）. And yet, few studies have quantified improvements in prediction skill in the context of an optimal observing system. In this study, the impact on prediction skill is explored using an intermediate coupled model in which errors in initial conditions formed to make ENSO predictions are removed in certain areas. Based on ideal observing system simulation experiments, the importance of various observational networks on improvement of El Ni n？o prediction skill is examined. The results indicate that the initial states in the central and eastern equatorial Pacific are important to improve El Ni n？o prediction skill effectively. When removing the initial condition errors in the central equatorial Pacific, ENSO prediction errors can be reduced by 25%. Furthermore, combinations of various subregions are considered to demonstrate the efficiency on ENSO prediction skill. Particularly, seasonally varying observational networks are suggested to improve the prediction skill more effectively. For example, in addition to observing in the central equatorial Pacific and its north throughout the year,increasing observations in the eastern equatorial Pacific during April to October is crucially important, which can improve the prediction accuracy by 62%. These results also demonstrate the effectiveness of the conditional nonlinear optimal perturbation approach on detecting sensitive areas for target observations.展开更多
In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken a...In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken as the model inputs,which brings uncertainties to LSP results.This study aims to reveal the influence rules of the different proportional random errors in conditioning factors on the LSP un-certainties,and further explore a method which can effectively reduce the random errors in conditioning factors.The original conditioning factors are firstly used to construct original factors-based LSP models,and then different random errors of 5%,10%,15% and 20%are added to these original factors for con-structing relevant errors-based LSP models.Secondly,low-pass filter-based LSP models are constructed by eliminating the random errors using low-pass filter method.Thirdly,the Ruijin County of China with 370 landslides and 16 conditioning factors are used as study case.Three typical machine learning models,i.e.multilayer perceptron(MLP),support vector machine(SVM)and random forest(RF),are selected as LSP models.Finally,the LSP uncertainties are discussed and results show that:(1)The low-pass filter can effectively reduce the random errors in conditioning factors to decrease the LSP uncertainties.(2)With the proportions of random errors increasing from 5%to 20%,the LSP uncertainty increases continuously.(3)The original factors-based models are feasible for LSP in the absence of more accurate conditioning factors.(4)The influence degrees of two uncertainty issues,machine learning models and different proportions of random errors,on the LSP modeling are large and basically the same.(5)The Shapley values effectively explain the internal mechanism of machine learning model predicting landslide sus-ceptibility.In conclusion,greater proportion of random errors in conditioning factors results in higher LSP uncertainty,and low-pass filter can effectively reduce these random errors.展开更多
With the observational wind data and the Zebiak-Cane model, the impact of Madden-Iulian Oscillation （MJO） as external forcing on El Nino-Southern Oscillation （ENSO） predictability is studied. The observational dat...With the observational wind data and the Zebiak-Cane model, the impact of Madden-Iulian Oscillation （MJO） as external forcing on El Nino-Southern Oscillation （ENSO） predictability is studied. The observational data are analyzed with Continuous Wavelet Transform （CWT） and then used to extract MJO signals, which are added into the model to get a new model. After the Conditional Nonlinear Optimal Perturbation （CNOP） method has been used, the initial errors which can evolve into maximum prediction error, model errors and their join errors are gained and then the Nifio 3 indices and spatial structures of three kinds of errors are investigated. The results mainly show that the observational MJO has little impact on the maximum prediction error of ENSO events and the initial error affects much greater than model error caused by MJO forcing. These demonstrate that the initial error might be the main error source that produces uncertainty in ENSO prediction, which could provide a theoretical foundation for the adaptive data assimilation of the ENSO forecast and contribute to the ENSO target observation.展开更多
To deal with the staircase approximation problem in the standard finite-difference time-domain（FDTD） simulation,the two-dimensional boundary condition equations（BCE） method is proposed in this paper.In the BCE met...To deal with the staircase approximation problem in the standard finite-difference time-domain（FDTD） simulation,the two-dimensional boundary condition equations（BCE） method is proposed in this paper.In the BCE method,the standard FDTD algorithm can be used as usual,and the curved surface is treated by adding the boundary condition equations.Thus,while maintaining the simplicity and computational efficiency of the standard FDTD algorithm,the BCE method can solve the staircase approximation problem.The BCE method is validated by analyzing near field and far field scattering properties of the PEC and dielectric cylinders.The results show that the BCE method can maintain a second-order accuracy by eliminating the staircase approximation errors.Moreover,the results of the BCE method show good accuracy for cylinder scattering cases with different permittivities.展开更多
文中将坐标伸缩完全匹配层CPML引入到弱无条件稳定算法HIE-FDTD中研究其吸波性能。详细推导了2维TE波模型中CPML在HIE-FDTD算法中应用的差分公式。为检验本文所提方法的吸波效能,建立了计算模型,将其与其它吸收边界条件的吸波性能进行...文中将坐标伸缩完全匹配层CPML引入到弱无条件稳定算法HIE-FDTD中研究其吸波性能。详细推导了2维TE波模型中CPML在HIE-FDTD算法中应用的差分公式。为检验本文所提方法的吸波效能,建立了计算模型,将其与其它吸收边界条件的吸波性能进行了综合比较,计算了HIE-FDTD算法选取不同条件数时的反射误差,并详细说明如何合理选取α,κmax和σmax来实现最佳相对误差。结果显示:当将本文所提方法的CPML层数设置为8时,其反射误差为-62 d B,低于传统FDTD方法的-58 d B;当选取α=0.05,κmax=10,σmax/σopt=1.3可以实现低至-83 d B的最大相对误差;在仿真中,其比传统FDTD方法也约减少48%的计算时间。展开更多
We revisit a comparison of two discriminant analysis procedures, namely the linear combination classifier of Chung and Han (2000) and the maximum likelihood estimation substitution classifier for the problem of classi...We revisit a comparison of two discriminant analysis procedures, namely the linear combination classifier of Chung and Han (2000) and the maximum likelihood estimation substitution classifier for the problem of classifying unlabeled multivariate normal observations with equal covariance matrices into one of two classes. Both classes have matching block monotone missing training data. Here, we demonstrate that for intra-class covariance structures with at least small correlation among the variables with missing data and the variables without block missing data, the maximum likelihood estimation substitution classifier outperforms the Chung and Han (2000) classifier regardless of the percent of missing observations. Specifically, we examine the differences in the estimated expected error rates for these classifiers using a Monte Carlo simulation, and we compare the two classifiers using two real data sets with monotone missing data via parametric bootstrap simulations. Our results contradict the conclusions of Chung and Han (2000) that their linear combination classifier is superior to the MLE classifier for block monotone missing multivariate normal data.展开更多
文中将CPML引入3维弱无条件稳定算法HIE-FDTD中,详细推到了CPML在3维弱无条件稳定HIE-FDTD中的差分公式。为了验证CPML在3维HIE-FDTD中的吸波性能,建立了数值计算模型,并将CPML的吸波性能同其它几种常用的吸收边界条件进行了比较。结果...文中将CPML引入3维弱无条件稳定算法HIE-FDTD中,详细推到了CPML在3维弱无条件稳定HIE-FDTD中的差分公式。为了验证CPML在3维HIE-FDTD中的吸波性能,建立了数值计算模型,并将CPML的吸波性能同其它几种常用的吸收边界条件进行了比较。结果显示,当将CPML层数设置为8时,其最大反射误差为-72 d B,远低于传统FDTD方法的反射误差。另外,当匹配层参数设置为α=0.05,可以在一个较大范围内选取κmax和σmax来实现最佳误差,从而使得在选值时易于预测反射情况。展开更多
In this paper, we study the explicit expressions of the constants in the error estimates of the lowest order mixed and nonconforming finite element methods. We start with an explicit relation between the error constan...In this paper, we study the explicit expressions of the constants in the error estimates of the lowest order mixed and nonconforming finite element methods. We start with an explicit relation between the error constant of the lowest order Raviart-Thomas interpolation error and the geometric characters of the triangle. This gives an explicit error constant of the lowest order mixed finite element method. Furthermore, similar results can be ex- tended to the nonconforming P1 scheme based on its close connection with the lowest order Raviart-Thomas method. Meanwhile, such explicit a priori error estimates can be used as computable error bounds, which are also consistent with the maximal angle condition for the optimal error estimates of mixed and nonconforming finite element methods.展开更多
To save the calculations of Jacobian,a multi-step Levenberg-Marquardt method named Shamanskii-like LM method for systems of nonlinear equations was proposed by Fa.Its convergence properties have been proved by using a...To save the calculations of Jacobian,a multi-step Levenberg-Marquardt method named Shamanskii-like LM method for systems of nonlinear equations was proposed by Fa.Its convergence properties have been proved by using a trust region technique under the local error bound condition.However,the authors wonder whether the similar convergence properties are still true with standard line searches since the direction may not be a descent direction.For this purpose,the authors present a new nonmonotone m-th order Armijo type line search to guarantee the global convergence.Under the same condition as trust region case,the convergence rate also has been shown to be m+1 by using this line search technique.Numerical experiments show the new algorithm can save much running time for the large scale problems,so it is efficient and promising.展开更多
Energy efficiency （EE） can be enhanced by retransmissions and combining in hybrid automatic repeat request （HARQ） system. However, it is difficult to optimize the transmit power of each retransmission when the acc...Energy efficiency （EE） can be enhanced by retransmissions and combining in hybrid automatic repeat request （HARQ） system. However, it is difficult to optimize the transmit power of each retransmission when the accurate retransmission number and future channel state information （CSI） cannot be obtained. This paper proposes a simple energy efficient HARQ scheme for point-to-point wireless communication. In the proposed scheme, the conditional word error rate （WER） of each retransmission is fixed and the transmit power is adapted correspondingly. Three performance metrics are analyzed including average transmission number, throughput and EE. Compared with the conventional equal power HARQ scheme, the proposed scheme can significantly improve the EE and other two metrics under the same constraint of average transmit power or average energy consumption. Furthermore, it is found that, selecting a conditional WER which is slightly smaller than the optimal one is sufficient for practical implementation.展开更多
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA19060102)the National Natural Science Foundation of China (Grant Nos. 41475101, 41690122, 41690120 and 41421005)the National Programme on Global Change and Air–Sea Interaction Interaction (Grant Nos. GASI-IPOVAI-06 and GASI-IPOVAI-01-01)
文摘Previous studies indicate that ENSO predictions are particularly sensitive to the initial conditions in some key areas（socalled ＂sensitive areas＂）. And yet, few studies have quantified improvements in prediction skill in the context of an optimal observing system. In this study, the impact on prediction skill is explored using an intermediate coupled model in which errors in initial conditions formed to make ENSO predictions are removed in certain areas. Based on ideal observing system simulation experiments, the importance of various observational networks on improvement of El Ni n？o prediction skill is examined. The results indicate that the initial states in the central and eastern equatorial Pacific are important to improve El Ni n？o prediction skill effectively. When removing the initial condition errors in the central equatorial Pacific, ENSO prediction errors can be reduced by 25%. Furthermore, combinations of various subregions are considered to demonstrate the efficiency on ENSO prediction skill. Particularly, seasonally varying observational networks are suggested to improve the prediction skill more effectively. For example, in addition to observing in the central equatorial Pacific and its north throughout the year,increasing observations in the eastern equatorial Pacific during April to October is crucially important, which can improve the prediction accuracy by 62%. These results also demonstrate the effectiveness of the conditional nonlinear optimal perturbation approach on detecting sensitive areas for target observations.
基金This work is funded by the National Natural Science Foundation of China(Grant Nos.42377164 and 52079062)the National Science Fund for Distinguished Young Scholars of China(Grant No.52222905).
文摘In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken as the model inputs,which brings uncertainties to LSP results.This study aims to reveal the influence rules of the different proportional random errors in conditioning factors on the LSP un-certainties,and further explore a method which can effectively reduce the random errors in conditioning factors.The original conditioning factors are firstly used to construct original factors-based LSP models,and then different random errors of 5%,10%,15% and 20%are added to these original factors for con-structing relevant errors-based LSP models.Secondly,low-pass filter-based LSP models are constructed by eliminating the random errors using low-pass filter method.Thirdly,the Ruijin County of China with 370 landslides and 16 conditioning factors are used as study case.Three typical machine learning models,i.e.multilayer perceptron(MLP),support vector machine(SVM)and random forest(RF),are selected as LSP models.Finally,the LSP uncertainties are discussed and results show that:(1)The low-pass filter can effectively reduce the random errors in conditioning factors to decrease the LSP uncertainties.(2)With the proportions of random errors increasing from 5%to 20%,the LSP uncertainty increases continuously.(3)The original factors-based models are feasible for LSP in the absence of more accurate conditioning factors.(4)The influence degrees of two uncertainty issues,machine learning models and different proportions of random errors,on the LSP modeling are large and basically the same.(5)The Shapley values effectively explain the internal mechanism of machine learning model predicting landslide sus-ceptibility.In conclusion,greater proportion of random errors in conditioning factors results in higher LSP uncertainty,and low-pass filter can effectively reduce these random errors.
基金The National Natural Science Foundation of China under contract No.41405062
文摘With the observational wind data and the Zebiak-Cane model, the impact of Madden-Iulian Oscillation （MJO） as external forcing on El Nino-Southern Oscillation （ENSO） predictability is studied. The observational data are analyzed with Continuous Wavelet Transform （CWT） and then used to extract MJO signals, which are added into the model to get a new model. After the Conditional Nonlinear Optimal Perturbation （CNOP） method has been used, the initial errors which can evolve into maximum prediction error, model errors and their join errors are gained and then the Nifio 3 indices and spatial structures of three kinds of errors are investigated. The results mainly show that the observational MJO has little impact on the maximum prediction error of ENSO events and the initial error affects much greater than model error caused by MJO forcing. These demonstrate that the initial error might be the main error source that produces uncertainty in ENSO prediction, which could provide a theoretical foundation for the adaptive data assimilation of the ENSO forecast and contribute to the ENSO target observation.
基金Project supported by the National Natural Science Foundation of China(Grant No.51025622)
文摘To deal with the staircase approximation problem in the standard finite-difference time-domain（FDTD） simulation,the two-dimensional boundary condition equations（BCE） method is proposed in this paper.In the BCE method,the standard FDTD algorithm can be used as usual,and the curved surface is treated by adding the boundary condition equations.Thus,while maintaining the simplicity and computational efficiency of the standard FDTD algorithm,the BCE method can solve the staircase approximation problem.The BCE method is validated by analyzing near field and far field scattering properties of the PEC and dielectric cylinders.The results show that the BCE method can maintain a second-order accuracy by eliminating the staircase approximation errors.Moreover,the results of the BCE method show good accuracy for cylinder scattering cases with different permittivities.
文摘文中将坐标伸缩完全匹配层CPML引入到弱无条件稳定算法HIE-FDTD中研究其吸波性能。详细推导了2维TE波模型中CPML在HIE-FDTD算法中应用的差分公式。为检验本文所提方法的吸波效能,建立了计算模型,将其与其它吸收边界条件的吸波性能进行了综合比较,计算了HIE-FDTD算法选取不同条件数时的反射误差,并详细说明如何合理选取α,κmax和σmax来实现最佳相对误差。结果显示:当将本文所提方法的CPML层数设置为8时,其反射误差为-62 d B,低于传统FDTD方法的-58 d B;当选取α=0.05,κmax=10,σmax/σopt=1.3可以实现低至-83 d B的最大相对误差;在仿真中,其比传统FDTD方法也约减少48%的计算时间。
文摘We revisit a comparison of two discriminant analysis procedures, namely the linear combination classifier of Chung and Han (2000) and the maximum likelihood estimation substitution classifier for the problem of classifying unlabeled multivariate normal observations with equal covariance matrices into one of two classes. Both classes have matching block monotone missing training data. Here, we demonstrate that for intra-class covariance structures with at least small correlation among the variables with missing data and the variables without block missing data, the maximum likelihood estimation substitution classifier outperforms the Chung and Han (2000) classifier regardless of the percent of missing observations. Specifically, we examine the differences in the estimated expected error rates for these classifiers using a Monte Carlo simulation, and we compare the two classifiers using two real data sets with monotone missing data via parametric bootstrap simulations. Our results contradict the conclusions of Chung and Han (2000) that their linear combination classifier is superior to the MLE classifier for block monotone missing multivariate normal data.
文摘文中将CPML引入3维弱无条件稳定算法HIE-FDTD中,详细推到了CPML在3维弱无条件稳定HIE-FDTD中的差分公式。为了验证CPML在3维HIE-FDTD中的吸波性能,建立了数值计算模型,并将CPML的吸波性能同其它几种常用的吸收边界条件进行了比较。结果显示,当将CPML层数设置为8时,其最大反射误差为-72 d B,远低于传统FDTD方法的反射误差。另外,当匹配层参数设置为α=0.05,可以在一个较大范围内选取κmax和σmax来实现最佳误差,从而使得在选值时易于预测反射情况。
基金supported by the Special Funds for Major State Basic Research Project(No.2005CB321701)
文摘In this paper, we study the explicit expressions of the constants in the error estimates of the lowest order mixed and nonconforming finite element methods. We start with an explicit relation between the error constant of the lowest order Raviart-Thomas interpolation error and the geometric characters of the triangle. This gives an explicit error constant of the lowest order mixed finite element method. Furthermore, similar results can be ex- tended to the nonconforming P1 scheme based on its close connection with the lowest order Raviart-Thomas method. Meanwhile, such explicit a priori error estimates can be used as computable error bounds, which are also consistent with the maximal angle condition for the optimal error estimates of mixed and nonconforming finite element methods.
基金supported by the Natural Science Foundation of Anhui Province under Grant No.1708085MF159the Natural Science Foundation of the Anhui Higher Education Institutions under Grant Nos.KJ2017A375+1 种基金KJ2019A0604the abroad visiting of excellent young talents in universities of Anhui province under Grant No.GXGWFX2019022。
文摘To save the calculations of Jacobian,a multi-step Levenberg-Marquardt method named Shamanskii-like LM method for systems of nonlinear equations was proposed by Fa.Its convergence properties have been proved by using a trust region technique under the local error bound condition.However,the authors wonder whether the similar convergence properties are still true with standard line searches since the direction may not be a descent direction.For this purpose,the authors present a new nonmonotone m-th order Armijo type line search to guarantee the global convergence.Under the same condition as trust region case,the convergence rate also has been shown to be m+1 by using this line search technique.Numerical experiments show the new algorithm can save much running time for the large scale problems,so it is efficient and promising.
基金supported by National Natural Science Foundation of China (61072059)
文摘Energy efficiency （EE） can be enhanced by retransmissions and combining in hybrid automatic repeat request （HARQ） system. However, it is difficult to optimize the transmit power of each retransmission when the accurate retransmission number and future channel state information （CSI） cannot be obtained. This paper proposes a simple energy efficient HARQ scheme for point-to-point wireless communication. In the proposed scheme, the conditional word error rate （WER） of each retransmission is fixed and the transmit power is adapted correspondingly. Three performance metrics are analyzed including average transmission number, throughput and EE. Compared with the conventional equal power HARQ scheme, the proposed scheme can significantly improve the EE and other two metrics under the same constraint of average transmit power or average energy consumption. Furthermore, it is found that, selecting a conditional WER which is slightly smaller than the optimal one is sufficient for practical implementation.
