The terrestrial time-variable gravity measurements are characterized by a high signal-to-noise ratio and sensitivity to the sources of mass change in the Earth's crust.These gravity data have many applications,suc...The terrestrial time-variable gravity measurements are characterized by a high signal-to-noise ratio and sensitivity to the sources of mass change in the Earth's crust.These gravity data have many applications,such as surface deformation,groundwater storage changes,and mass migration before and after earthquakes.Based on repeated terrestrial gravity measurements at 198 gravity stations in the Sichuan-Yunnan region(SYR)from 2015 to 2017,we determine a time series of degree 120 gravity fields using the localized spherical harmonic(Slepian)basis functions.Our results show that adopting the first 6 Slepian basis functions is sufficient for effective localized Slepian modeling in the SYR.The differences between two gravity campaigns at the same time of year show an obvious correlation with tectonic features.The degree 120 timevariable gravity models presented in this paper will benefit the study of the regional mass migration inside the crust of the SYR and supplement the existing geophysical models for the China Seismic Experimental Site.展开更多
Flowering time is an indicator of adaptation in maize and a key trait for selection in breeding.The genetic basis of flowering time in maize,especially in response to plant density,remains unclear.The objective of thi...Flowering time is an indicator of adaptation in maize and a key trait for selection in breeding.The genetic basis of flowering time in maize,especially in response to plant density,remains unclear.The objective of this study was to identify maize quantitative trait loci(QTL)associated with flowering time-related traits that are stably expressed under several plant densities and show additive effects that vary with plant density.Three hundred recombinant inbred lines(RIL)derived from a cross between Ye 478 and Qi 319,together with their parents,were planted at three plant densities(90,000,120,000,and 150,000 plants ha^(-1))in four environments.The five traits investigated were days to tasseling(DTT),days to silking(DTS),days to pollen shed(DTP),interval between anthesis and silking(ASI),and interval between tasseling and anthesis(TAI).A high-resolution bin map was used for QTL mapping.In the RIL population,the DTT,DTS,and DTP values increased with plant density,whereas the ASI and TAI values showed negligible response to plant density.A total of 72 QTL were identified for flowering time-related traits,including 15 stably expressed across environments.Maize flowering time under different densities seems to be regulated by complex pathways rather than by several major genes or an independent pathway.The effects of some stable QTL,especially qDTT8-1 and qDTT10-4,varied with plant density.Fine mapping and cloning of these QTL will shed light on the mechanism of flowering time and assist in breeding earlymaturing maize inbred lines and hybrids.展开更多
A new method for identifying nonlinear time varying systems with unknown structure is presented. The method extends the application area of basis sequence identification. The essential idea is to utilize the learning ...A new method for identifying nonlinear time varying systems with unknown structure is presented. The method extends the application area of basis sequence identification. The essential idea is to utilize the learning and nonlinear approximating ability of neural networks to model the non linearity of the system, characterize time varying dynamics of the system by the time varying parametric vector of the network, then the parametric vector of the network is approximated by a weighted sum of known basis sequences. Because of black box modeling ability of neural networks, the presented method can identify nonlinear time varying systems with unknown structure. In order to improve the real time capability of the algorithm, the neural network is trained by a simple fast learning algorithm based on local least squares presented by the authors. The effectiveness and the performance of the method are demonstrated by some simulation results.展开更多
For high-speed mobile MIMO-OFDM system,a low-complexity deep learning(DL) based timevarying channel estimation scheme is proposed.To reduce the number of estimated parameters,the basis expansion model(BEM) is employed...For high-speed mobile MIMO-OFDM system,a low-complexity deep learning(DL) based timevarying channel estimation scheme is proposed.To reduce the number of estimated parameters,the basis expansion model(BEM) is employed to model the time-varying channel,which converts the channel estimation into the estimation of the basis coefficient.Specifically,the initial basis coefficients are firstly used to train the neural network in an offline manner,and then the high-precision channel estimation can be obtained by small number of inputs.Moreover,the linear minimum mean square error(LMMSE) estimated channel is considered for the loss function in training phase,which makes the proposed method more practical.Simulation results show that the proposed method has a better performance and lower computational complexity compared with the available schemes,and it is robust to the fast time-varying channel in the high-speed mobile scenarios.展开更多
Single particles moving in a reflection-asymmetric potential are investigated by solving the Schr6dinger equation of the reflectionasymmetric Nilsson Hamiltonian with the imaginary time method in 3D lattice space and ...Single particles moving in a reflection-asymmetric potential are investigated by solving the Schr6dinger equation of the reflectionasymmetric Nilsson Hamiltonian with the imaginary time method in 3D lattice space and the harmonic oscillator basis expansion method. In the 3D lattice calculation, the l2 divergence problem is avoided by introducing a damping function, and the（l2）N term in the non-spherical case is calculated by introducing an equivalent N-independent operator. The efficiency of these numerical techniques is demonstrated by solving the spherical Nilsson Hamiltonian in 3D lattice space. The evolution of the single-particle levels in a reflection-asvmmetric ootential is obtained and discussed bv the above two numerical methods, and their consistencv is shown in the obtained single-particle energies with the differences smaller than 10-4[hω0]展开更多
Problems in unsteady aerodynamics and aeroacoustics can sometimes be formulated as integral equations,such as the boundary integral equations.Numerical discretization of integral equations in the time domain often lea...Problems in unsteady aerodynamics and aeroacoustics can sometimes be formulated as integral equations,such as the boundary integral equations.Numerical discretization of integral equations in the time domain often leads to so-called March-On-in-Time(MOT)schemes.In the literature,the temporal basis functions used in MOT schemes have been largely limited to low-order shifted Lagrange basis functions.In order to evaluate the accuracy and effectiveness of the temporal basis functions,a Fourier analysis of the temporal interpolation schemes is carried out.Based on the Fourier analysis,the spectral resolutions of various temporal basis functions are quantified.It is argued that hybrid temporal basis functions be used for interpolation of the numerical solution and its derivatives with respect to time.Stability of the proposed hybrid schemes is studied by a matrix eigenvalue method.Substantial improvement in accuracy and efficiency by using the hybrid temporal basis functions for time domain integral equations is demonstrated by numerical examples.Compared with the traditional temporal basis functions,the use of hybrid basis functions keeps numerical errors low for a larger frequency range given the same time step size.Conversely,for a given range of frequency of interest,a larger time step can be used with the hybrid temporal basis functions,resulting in an increase in computational efficiency and,at the same time,a reduction in memory requirement.展开更多
基金the National Natural Science Foundation of China(Nos.41974095,41774090,and U1939205)the Special Fund of the Institute of Geophysics,China Earthquake Administration(Nos.DQJB20X09,and DQJB21R30)The first author acknowledges support from the China Postdoctoral Science Foundation(No.2018M641424)。
文摘The terrestrial time-variable gravity measurements are characterized by a high signal-to-noise ratio and sensitivity to the sources of mass change in the Earth's crust.These gravity data have many applications,such as surface deformation,groundwater storage changes,and mass migration before and after earthquakes.Based on repeated terrestrial gravity measurements at 198 gravity stations in the Sichuan-Yunnan region(SYR)from 2015 to 2017,we determine a time series of degree 120 gravity fields using the localized spherical harmonic(Slepian)basis functions.Our results show that adopting the first 6 Slepian basis functions is sufficient for effective localized Slepian modeling in the SYR.The differences between two gravity campaigns at the same time of year show an obvious correlation with tectonic features.The degree 120 timevariable gravity models presented in this paper will benefit the study of the regional mass migration inside the crust of the SYR and supplement the existing geophysical models for the China Seismic Experimental Site.
基金This study was supported by Hebei Province Special Postdoctoral Financial Assistance(B2017003030)the Youth Innovation Fund of the Institute of Cereal and Oil Crops,Hebei Academy of Agriculture and Forestry Sciences(LYS2017001)the Hebei Financial Special Project:Construction of Talents Team for Agricultural Science Technical Innovation,and the China Agriculture Research System(CARS-02).
文摘Flowering time is an indicator of adaptation in maize and a key trait for selection in breeding.The genetic basis of flowering time in maize,especially in response to plant density,remains unclear.The objective of this study was to identify maize quantitative trait loci(QTL)associated with flowering time-related traits that are stably expressed under several plant densities and show additive effects that vary with plant density.Three hundred recombinant inbred lines(RIL)derived from a cross between Ye 478 and Qi 319,together with their parents,were planted at three plant densities(90,000,120,000,and 150,000 plants ha^(-1))in four environments.The five traits investigated were days to tasseling(DTT),days to silking(DTS),days to pollen shed(DTP),interval between anthesis and silking(ASI),and interval between tasseling and anthesis(TAI).A high-resolution bin map was used for QTL mapping.In the RIL population,the DTT,DTS,and DTP values increased with plant density,whereas the ASI and TAI values showed negligible response to plant density.A total of 72 QTL were identified for flowering time-related traits,including 15 stably expressed across environments.Maize flowering time under different densities seems to be regulated by complex pathways rather than by several major genes or an independent pathway.The effects of some stable QTL,especially qDTT8-1 and qDTT10-4,varied with plant density.Fine mapping and cloning of these QTL will shed light on the mechanism of flowering time and assist in breeding earlymaturing maize inbred lines and hybrids.
文摘A new method for identifying nonlinear time varying systems with unknown structure is presented. The method extends the application area of basis sequence identification. The essential idea is to utilize the learning and nonlinear approximating ability of neural networks to model the non linearity of the system, characterize time varying dynamics of the system by the time varying parametric vector of the network, then the parametric vector of the network is approximated by a weighted sum of known basis sequences. Because of black box modeling ability of neural networks, the presented method can identify nonlinear time varying systems with unknown structure. In order to improve the real time capability of the algorithm, the neural network is trained by a simple fast learning algorithm based on local least squares presented by the authors. The effectiveness and the performance of the method are demonstrated by some simulation results.
基金Supported by the National Science Foundation Program of Jiangsu Province (No.BK20191378)the National Science Research Project of Jiangsu Higher Education Institutions (No.18KJB510034)+2 种基金China Postdoctoral Science Fund Special Funding Project (No.2018T110530)the Key Technologies R&D Program of Jiangsu Province (No.BE2022067,BE2022067-2)Major Research Program Key Project(No.92067201)。
文摘For high-speed mobile MIMO-OFDM system,a low-complexity deep learning(DL) based timevarying channel estimation scheme is proposed.To reduce the number of estimated parameters,the basis expansion model(BEM) is employed to model the time-varying channel,which converts the channel estimation into the estimation of the basis coefficient.Specifically,the initial basis coefficients are firstly used to train the neural network in an offline manner,and then the high-precision channel estimation can be obtained by small number of inputs.Moreover,the linear minimum mean square error(LMMSE) estimated channel is considered for the loss function in training phase,which makes the proposed method more practical.Simulation results show that the proposed method has a better performance and lower computational complexity compared with the available schemes,and it is robust to the fast time-varying channel in the high-speed mobile scenarios.
基金supported by the National Basic Research Program of China (Grant No. 2013CB834400)the National Natural Science Foundation of China (Grants Nos. 11335002, 11375015, 11461141002, and 11621131001)
文摘Single particles moving in a reflection-asymmetric potential are investigated by solving the Schr6dinger equation of the reflectionasymmetric Nilsson Hamiltonian with the imaginary time method in 3D lattice space and the harmonic oscillator basis expansion method. In the 3D lattice calculation, the l2 divergence problem is avoided by introducing a damping function, and the（l2）N term in the non-spherical case is calculated by introducing an equivalent N-independent operator. The efficiency of these numerical techniques is demonstrated by solving the spherical Nilsson Hamiltonian in 3D lattice space. The evolution of the single-particle levels in a reflection-asvmmetric ootential is obtained and discussed bv the above two numerical methods, and their consistencv is shown in the obtained single-particle energies with the differences smaller than 10-4[hω0]
基金This work was supported in part by a NASA Cooperative Agreement,NNX11AI63A.
文摘Problems in unsteady aerodynamics and aeroacoustics can sometimes be formulated as integral equations,such as the boundary integral equations.Numerical discretization of integral equations in the time domain often leads to so-called March-On-in-Time(MOT)schemes.In the literature,the temporal basis functions used in MOT schemes have been largely limited to low-order shifted Lagrange basis functions.In order to evaluate the accuracy and effectiveness of the temporal basis functions,a Fourier analysis of the temporal interpolation schemes is carried out.Based on the Fourier analysis,the spectral resolutions of various temporal basis functions are quantified.It is argued that hybrid temporal basis functions be used for interpolation of the numerical solution and its derivatives with respect to time.Stability of the proposed hybrid schemes is studied by a matrix eigenvalue method.Substantial improvement in accuracy and efficiency by using the hybrid temporal basis functions for time domain integral equations is demonstrated by numerical examples.Compared with the traditional temporal basis functions,the use of hybrid basis functions keeps numerical errors low for a larger frequency range given the same time step size.Conversely,for a given range of frequency of interest,a larger time step can be used with the hybrid temporal basis functions,resulting in an increase in computational efficiency and,at the same time,a reduction in memory requirement.
