This study proposes a novel radar echo extrapolation algorithm,OF-ConvGRU,which integrates Optical Flow(OF)and Convolutional Gated Recurrent Unit(ConvGRU)methods for improved nowcasting.Using the Standardized Radar Da...This study proposes a novel radar echo extrapolation algorithm,OF-ConvGRU,which integrates Optical Flow(OF)and Convolutional Gated Recurrent Unit(ConvGRU)methods for improved nowcasting.Using the Standardized Radar Dataset of the Guangdong-Hong Kong-Macao Greater Bay Area,the performance of OF-ConvGRU was evaluated against OF and ConvGRU methods.Threat Score(TS)and Bias Score(BIAS)were employed to assess extrapolation accuracy across various echo intensities(20-50 dBz)and weather phenomena.Results demonstrate that OF-ConvGRU significantly enhances prediction accuracy for moderate-intensity echoes(30-40 dBz),effectively combining OF s precise motion estimation with ConvGRU s nonlinear learning capabilities.However,challenges persist in low-intensity(20 dBz)and high-intensity(50 dBz)echo predictions.The study reveals distinct advantages of each method in specific contexts,highlighting the importance of multi-method approaches in operational nowcasting.OF-ConvGRU shows promise in balancing short-term accuracy with long-term stability,particularly for complex weather systems.展开更多
Extrapolation on Temporal Knowledge Graphs(TKGs)aims to predict future knowledge from a set of historical Knowledge Graphs in chronological order.The temporally adjacent facts in TKGs naturally form event sequences,ca...Extrapolation on Temporal Knowledge Graphs(TKGs)aims to predict future knowledge from a set of historical Knowledge Graphs in chronological order.The temporally adjacent facts in TKGs naturally form event sequences,called event evolution patterns,implying informative temporal dependencies between events.Recently,many extrapolation works on TKGs have been devoted to modelling these evolutional patterns,but the task is still far from resolved because most existing works simply rely on encoding these patterns into entity representations while overlooking the significant information implied by relations of evolutional patterns.However,the authors realise that the temporal dependencies inherent in the relations of these event evolution patterns may guide the follow-up event prediction to some extent.To this end,a Temporal Relational Context-based Temporal Dependencies Learning Network(TRenD)is proposed to explore the temporal context of relations for more comprehensive learning of event evolution patterns,especially those temporal dependencies caused by interactive patterns of relations.Trend incorporates a semantic context unit to capture semantic correlations between relations,and a structural context unit to learn the interaction pattern of relations.By learning the temporal contexts of relations semantically and structurally,the authors gain insights into the underlying event evolution patterns,enabling to extract comprehensive historical information for future prediction better.Experimental results on benchmark datasets demonstrate the superiority of the model.展开更多
Seismic data reconstruction can provide high-density sampling and regular input data for inversion and imaging,playing a crucial role in seismic data processing.In seismic data reconstruction,a common scenario involve...Seismic data reconstruction can provide high-density sampling and regular input data for inversion and imaging,playing a crucial role in seismic data processing.In seismic data reconstruction,a common scenario involves a significant distance between the source and the first receiver,which makes it unattainable to acquire near-offset data.A new workflow for seismic data extrapolation is proposed to address this issue,which is based on a multi-scale dynamic time warping(MS-DTW)algorithm.MS-DTW can accurately calculate the time-shift between two time series and is a robust method for predicting time-offset(t-x)domain data.Using the time-shift calculated by the MS-DTW as the basic input,predict the two-way traveltime(TWT)of other traces based on the TWT of the reference trace.Perform autoregressive polynomial fitting on TWT and extrapolate TWT based on the fitted polynomial coefficients.Extract amplitude information from the TWT curve,fit the amplitude curve,and extrapolate the amplitude using polynomial coefficients.The proposed workflow does not necessitate data conversion to other domains and does not require prior knowledge of underground geological information.It applies to both isotropic and anisotropic media.The effectiveness of the workflow was verified through synthetic data and field data.The results show that compared with the method of predictive painting based on local slope,this approach can accurately predict missing near-offset seismic signals and demonstrates good robustness to noise.展开更多
Machine learning(ML)has become a powerful tool for accelerating the design and development of new materials.Among various traditional ML algorithms,decision tree-based ensemble learning methods are frequently chosen f...Machine learning(ML)has become a powerful tool for accelerating the design and development of new materials.Among various traditional ML algorithms,decision tree-based ensemble learning methods are frequently chosen for their strong predictive capabilities.However,decision trees are limited in regression tasks to interpolating within the data range of the training set,which restricts their usefulness for designing materials with enhanced properties.Herein,we focused on predicting and optimizing the L1_(2)-phase solvus temperature(T_(L12))and density,two critical properties for multi-principal-element superalloys(MPESAs).To achieve this,we employed the piecewise symbolic regression tree(PS-Tree),which demonstrates excellent extrapolation capability.Our model successfully predicted high T_(L12)values exceeding the training data range(1242℃),with four candidate alloys achieving TL12values of 1246,1249,1254,and 1274℃.Experimental validation confirmed the accuracy of these predictions,verifying the robust extrapolative capability of the PS-Tree method.Notably,one alloy exhibited a T_(L12)of 1267℃and a density of 7.94 g cm^(-3),outperforming most MPESAs.Additionally,another alloy exhibited a compressive yield strength of 897 MPa at 750℃,with a specific yield strength at this temperature higher than that of most L1_(2)-strengthened alloys and Co/Ni-based superalloys.Moreover,the model provided generalized insights,indicating that alloys with δ_(r)>5.3 and ΔH_(mix)<-12.8 J mol^(-1)K^(-1)tend to favor higher T_(L12).展开更多
Autoregressive (AR) modeling is applied to data extrapolation of radio frequency (RF) echo signals, and Burg algorithm, which can be computed in small amount and lead to a stable prediction filter, is used to estimate...Autoregressive (AR) modeling is applied to data extrapolation of radio frequency (RF) echo signals, and Burg algorithm, which can be computed in small amount and lead to a stable prediction filter, is used to estimate the prediction parameters of AR modeling. The complex data samples are directly extrapolated to obtain the extrapolated echo data in the frequency domain. The small rotating angle data extrapolation and the large rotating angular data extrapolation are considered separately in azimuth domain. The method of data extrapolation for the small rotating angle is the same as that in frequency domain, while the amplitude samples of large rotating angle echo data are extrapolated to obtain extrapolated echo amplitude, and the complex data of large rotating angle echo samples are extrapolated to get the extrapolated echo phase respectively. The calculation results show that the extrapolated echo data obtained by the above mentioned methods are accurate.展开更多
Extending the lead time of precipitation nowcasts is vital to improvements in heavy rainfall warning, flood mitigation, and water resource management. Because the TREC vector （tracking radar echo by correlation） rep...Extending the lead time of precipitation nowcasts is vital to improvements in heavy rainfall warning, flood mitigation, and water resource management. Because the TREC vector （tracking radar echo by correlation） represents only the instantaneous trend of precipitation echo motion, the approach using derived echo motion vectors to extrapolate radar reflectivity as a rainfall forecast is not satisfactory if the lead time is beyond 30 minutes. For longer lead times, the effect of ambient winds on echo movement should be considered. In this paper, an extrapolation algorithm that extends forecast lead times up to 3 hours was developed to blend TREC vectors with model-predicted winds. The TREC vectors were derived from radar reflectivity patterns in 3 km height CAPPI （constant altitude plan position indicator） mosaics through a cross-correlation technique. The background steering winds were provided by predictions of the rapid update assimilation model CHAF （cycle of hourly assimilation and forecast）. A similarity index was designed to determine the vertical level at which model winds were applied in the extrapolation process, which occurs via a comparison between model winds and radar vectors. Based on a summer rainfall case study, it is found that the new algorithm provides a better forecast.展开更多
It is widely reported that CALPHAD is an extrapolation method when the thermodynamic properties of a multicomponent system are approximated by its subsystems.In this work the meaning of the words extrapolation and int...It is widely reported that CALPHAD is an extrapolation method when the thermodynamic properties of a multicomponent system are approximated by its subsystems.In this work the meaning of the words extrapolation and interpolation is discussed in context of the CALPHAD method.When assessing the properties in binary and ternary systems,extrapolation method is indeed often used.However,after assessment,the Gibbs energies are in fact interpolated from the lower order systems into the higher order systems in the compositional space.The metastable melting temperatures of bcc and hep in Re-W and the liquid miscibility gap in Mg-Zr system are predicted to illustrate the difference between interpolation and extrapolation.展开更多
Transient control law ensures that the aeroengine transits to the command operating state rapidly and reliably. Most of the existing approaches for transient control law design have complicated principle and arithmeti...Transient control law ensures that the aeroengine transits to the command operating state rapidly and reliably. Most of the existing approaches for transient control law design have complicated principle and arithmetic. As a result, those approaches are not convenient for application. This paper proposes an extrapolation approach based on the set-point parameters to construct the transient control law, which has a good practicability. In this approach, the transient main fuel control law for acceleration and deceleration process is designed based on the main fuel flow on steady operating state. In order to analyze the designing feature of the extrapolation approach, the simulation results of several different transient control laws designed by the same approach are compared together. The analysis indicates that the aeroengine has a good performance in the transient process and the designing feature of the extrapolation approach conforms to the elements of the turbofan aeroengine.展开更多
To enhance the resolution of parameter estimation with limited samples received by a short passive array, an iterative nonparametric algorithm for estimating the frequencies and direction-of-arrivals （DOAs） of signa...To enhance the resolution of parameter estimation with limited samples received by a short passive array, an iterative nonparametric algorithm for estimating the frequencies and direction-of-arrivals （DOAs） of signals is proposed. The cost function is constructed using 12-norm Gaussian entropy combined with an additional constraint, 12-norm constraint or linear constraint. By minimizing the cost functions in the temporal and the spatial dimensions using corresponding iteration algorithms respectively, the sparse discrete Fourier transforms （DFTs） of temporal and spatial samples are obtained to represent the extrapolated sequences with much larger sizes than the original samples. Then frequency and angle estimates are obtained by performing the traditional simple methods on the extrapolated sequences. It is shown that the proposed algorithm offers increased resolution and significantly reduced sidelobes compared with the periodogram and beamforming based methods. And it achieves high precision compared with the high-resolution method with lower computational burden. Some numerical simulations and real data processing results are presented to verify the effectiveness of the method.展开更多
An improved echo extrapolation technology( MOD-COTREC) was introduced firstly,and then two plans for lightning nowcasting based on MOD-COTREC and both isothermal radar reflectivity and MOD-COTREC were proposed based o...An improved echo extrapolation technology( MOD-COTREC) was introduced firstly,and then two plans for lightning nowcasting based on MOD-COTREC and both isothermal radar reflectivity and MOD-COTREC were proposed based on the technology. Afterwards,the two plans for lightning nowcasting were tested by a case respectively. It is concluded that during the process of lightning nowcasting singly based on MOD-COTREC,the appearance and disappearance of lightning are not considered,and only lightning position is predicted when lightning density is constant,so the prediction error is big. The plan for lightning nowcasting based on both isothermal radar reflectivity and MOD-COTREC is still at an experimental stage,and the nowcasting products of cloud-to-ground lightning based on the plan are very different from the actual density and position of cloud-to-ground lightning,so it needs to be improved further.展开更多
To realize high-resolution digital beamforming(DBF)of ultra-wideband(UWB) signals, we propose a DBF method based on Carath ′eodory representation for delay compensation and array extrapolation. Delay compensation by ...To realize high-resolution digital beamforming(DBF)of ultra-wideband(UWB) signals, we propose a DBF method based on Carath ′eodory representation for delay compensation and array extrapolation. Delay compensation by Carath ′eodory representation could achieve high interpolation accuracy while using the single channel sampling technique. Array extrapolation by Carath ′eodory representation reformulates and extends each snapshot, consequently extends the aperture of the original uniform linear array(ULA) by several times and provides a better realtime performance than the existing aperture extrapolation utilizing vector extrapolation based on the two dimensional autoregressive(2-D AR) model. The UWB linear frequency modulated(LFM) signal is used for simulation analysis. Simulation results demonstrate that the proposed method is featured by a much higher spatial resolution than traditional DBF methods and lower sidelobes than using Lagrange fractional filters.展开更多
A reduced-order extrapolation algorithm based on Crank-Nicolson least-squares mixed finite element （CNLSMFE） formulation and proper orthogonal decomposition （POD） technique for two-dimensional （2D） Sobolev equat...A reduced-order extrapolation algorithm based on Crank-Nicolson least-squares mixed finite element （CNLSMFE） formulation and proper orthogonal decomposition （POD） technique for two-dimensional （2D） Sobolev equations is established. The error estimates of the reduced-order CNLSMFE solutions and the implementation for the reduced-order extrapolation algorithm are provided. A numerical example is used to show that the results of numerical computations are consistent with theoretical conclusions. Moreover, it is shown that the reduced-order extrapolation algorithm is feasible and efficient for seeking numerical solutions to 2D Sobolev equations.展开更多
A series of SnO2‐based catalysts modified by Mn, Zr, Ti and Pb oxides with a Sn/M （M=Mn, Zr, Ti and Pb） molar ratio of 9/1 were prepared by a co‐precipitation method and used for CH4 and CO oxidation. The Mn3＋, ...A series of SnO2‐based catalysts modified by Mn, Zr, Ti and Pb oxides with a Sn/M （M=Mn, Zr, Ti and Pb） molar ratio of 9/1 were prepared by a co‐precipitation method and used for CH4 and CO oxidation. The Mn3＋, Zr4＋, Ti4＋and Pb4＋cations are incorporated into the lattice of tetragonal rutile SnO2 to form a solid solution structure. As a consequence, the surface area and thermal stability of the catalysts are improved. Moreover, the oxygen species of the modified catalysts become easier to be reduced. Therefore, the oxidation activity over the catalysts was improved, except for the one modified by Pb oxide. Manganese oxide demonstrates the best promotional effects for SnO2. Using an X‐ray diffraction extrapolation method, the lattice capacity of SnO2 for Mn2O3 was 0.135 g Mn2O3/g SnO2, which indicates that to form stable solid solution, only 21%Sn4＋cations in the lattice can be maximally replaced by Mn3＋. If the amount of Mn3＋cations is over the capacity, Mn2O3 will be formed, which is not favorable for the activity of the catalysts. The Sn rich samples with only Sn‐Mn solid solution phase show higher activity than the ones with excess Mn2O3 species.展开更多
Predicting potential facts in the future,Temporal Knowledge Graph(TKG)extrapolation remains challenging because of the deep dependence between the temporal association and semantic patterns of facts.Intuitively,facts(...Predicting potential facts in the future,Temporal Knowledge Graph(TKG)extrapolation remains challenging because of the deep dependence between the temporal association and semantic patterns of facts.Intuitively,facts(events)that happened at different timestamps have different influences on future events,which can be attributed to a hierarchy among not only facts but also relevant entities.Therefore,it is crucial to pay more attention to important entities and events when forecasting the future.However,most existing methods focus on reasoning over temporally evolving facts or mining evolutional patterns from known facts,which may be affected by the diversity and variability of the evolution,and they might fail to attach importance to facts that matter.Hyperbolic geometry was proved to be effective in capturing hierarchical patterns among data,which is considered to be a solution for modelling hierarchical relations among facts.To this end,we propose ReTIN,a novel model integrating real-time influence of historical facts for TKG reasoning based on hyperbolic geometry,which provides low-dimensional embeddings to capture latent hierarchical structures and other rich semantic patterns of the existing TKG.Considering both real-time and global features of TKG boosts the adaptation of ReTIN to the ever-changing dynamics and inherent constraints.Extensive experiments on benchmarks demonstrate the superiority of ReTIN over various baselines.The ablation study further supports the value of exploiting temporal information.展开更多
In this paper, Aitken’s extrapolation normally applied to convergent fixed point iteration is extended to extrapolate the solution of a divergent iteration. In addition, higher order Aitken extrapolation is introduce...In this paper, Aitken’s extrapolation normally applied to convergent fixed point iteration is extended to extrapolate the solution of a divergent iteration. In addition, higher order Aitken extrapolation is introduced that enables successive decomposition of high Eigen values of the iteration matrix to enable convergence. While extrapolation of a convergent fixed point iteration using a geometric series sum is a known form of Aitken acceleration, it is shown that in this paper, the same formula can be used to estimate the solution of sets of linear equations from diverging Gauss-Seidel iterations. In both convergent and divergent iterations, the ratios of differences among the consecutive values of iteration eventually form a convergent (divergent) series with a factor equal to the largest Eigen value of the iteration matrix. Higher order Aitken extrapolation is shown to eliminate the influence of dominant Eigen values of the iteration matrix in successive order until the iteration is determined by the lowest possible Eigen values. For the convergent part of the Gauss-Seidel iteration, further acceleration is made possible by coupling of the extrapolation technique with the successive over relaxation (SOR) method. Application examples from both convergent and divergent iterations have been provided. Coupling of the extrapolation with the SOR technique is also illustrated for a steady state two dimensional heat flow problem which was solved using MATLAB programming.展开更多
Based on an asymptotic expansion of finite element, an extrapolation cascadic multigrid method （EXCMG） is proposed, in which the new extrapolation and quadratic interpolation are used to provide a better initial val...Based on an asymptotic expansion of finite element, an extrapolation cascadic multigrid method （EXCMG） is proposed, in which the new extrapolation and quadratic interpolation are used to provide a better initial value on refined grid. In the case of multiple grids, both superconvergence error in H^1-norm and the optimal error in l2-norm are analyzed. The numerical experiment shows the advantage of EXCMG in comparison with CMG.展开更多
基金Scientific Research and Development Project of Hebei Meteorological Bureau(23ky08).
文摘This study proposes a novel radar echo extrapolation algorithm,OF-ConvGRU,which integrates Optical Flow(OF)and Convolutional Gated Recurrent Unit(ConvGRU)methods for improved nowcasting.Using the Standardized Radar Dataset of the Guangdong-Hong Kong-Macao Greater Bay Area,the performance of OF-ConvGRU was evaluated against OF and ConvGRU methods.Threat Score(TS)and Bias Score(BIAS)were employed to assess extrapolation accuracy across various echo intensities(20-50 dBz)and weather phenomena.Results demonstrate that OF-ConvGRU significantly enhances prediction accuracy for moderate-intensity echoes(30-40 dBz),effectively combining OF s precise motion estimation with ConvGRU s nonlinear learning capabilities.However,challenges persist in low-intensity(20 dBz)and high-intensity(50 dBz)echo predictions.The study reveals distinct advantages of each method in specific contexts,highlighting the importance of multi-method approaches in operational nowcasting.OF-ConvGRU shows promise in balancing short-term accuracy with long-term stability,particularly for complex weather systems.
基金supported in part by the National Natural Science Foundation of China(No.62302507)and the funding of Harbin Institute of Technology(Shenzhen)(No.20210035).
文摘Extrapolation on Temporal Knowledge Graphs(TKGs)aims to predict future knowledge from a set of historical Knowledge Graphs in chronological order.The temporally adjacent facts in TKGs naturally form event sequences,called event evolution patterns,implying informative temporal dependencies between events.Recently,many extrapolation works on TKGs have been devoted to modelling these evolutional patterns,but the task is still far from resolved because most existing works simply rely on encoding these patterns into entity representations while overlooking the significant information implied by relations of evolutional patterns.However,the authors realise that the temporal dependencies inherent in the relations of these event evolution patterns may guide the follow-up event prediction to some extent.To this end,a Temporal Relational Context-based Temporal Dependencies Learning Network(TRenD)is proposed to explore the temporal context of relations for more comprehensive learning of event evolution patterns,especially those temporal dependencies caused by interactive patterns of relations.Trend incorporates a semantic context unit to capture semantic correlations between relations,and a structural context unit to learn the interaction pattern of relations.By learning the temporal contexts of relations semantically and structurally,the authors gain insights into the underlying event evolution patterns,enabling to extract comprehensive historical information for future prediction better.Experimental results on benchmark datasets demonstrate the superiority of the model.
基金the National Natural Science Foundation of China(42374133)the Beijing Nova Program(2022056)for their funding of this research。
文摘Seismic data reconstruction can provide high-density sampling and regular input data for inversion and imaging,playing a crucial role in seismic data processing.In seismic data reconstruction,a common scenario involves a significant distance between the source and the first receiver,which makes it unattainable to acquire near-offset data.A new workflow for seismic data extrapolation is proposed to address this issue,which is based on a multi-scale dynamic time warping(MS-DTW)algorithm.MS-DTW can accurately calculate the time-shift between two time series and is a robust method for predicting time-offset(t-x)domain data.Using the time-shift calculated by the MS-DTW as the basic input,predict the two-way traveltime(TWT)of other traces based on the TWT of the reference trace.Perform autoregressive polynomial fitting on TWT and extrapolate TWT based on the fitted polynomial coefficients.Extract amplitude information from the TWT curve,fit the amplitude curve,and extrapolate the amplitude using polynomial coefficients.The proposed workflow does not necessitate data conversion to other domains and does not require prior knowledge of underground geological information.It applies to both isotropic and anisotropic media.The effectiveness of the workflow was verified through synthetic data and field data.The results show that compared with the method of predictive painting based on local slope,this approach can accurately predict missing near-offset seismic signals and demonstrates good robustness to noise.
基金financially supported by the National Natural Science Foundation of China(Nos.52371007 and 52301042)the National Key R&D Program of China(No.2020YFB0704503)+2 种基金Shenzhen Science and Technology Program(No.SGDX20210823104002016)Guangdong Basic and Applied Basic Research Foundation(No.2021B1515120071)Shenzhen Basic Research Project(No.JCYJ20241202123504007)
文摘Machine learning(ML)has become a powerful tool for accelerating the design and development of new materials.Among various traditional ML algorithms,decision tree-based ensemble learning methods are frequently chosen for their strong predictive capabilities.However,decision trees are limited in regression tasks to interpolating within the data range of the training set,which restricts their usefulness for designing materials with enhanced properties.Herein,we focused on predicting and optimizing the L1_(2)-phase solvus temperature(T_(L12))and density,two critical properties for multi-principal-element superalloys(MPESAs).To achieve this,we employed the piecewise symbolic regression tree(PS-Tree),which demonstrates excellent extrapolation capability.Our model successfully predicted high T_(L12)values exceeding the training data range(1242℃),with four candidate alloys achieving TL12values of 1246,1249,1254,and 1274℃.Experimental validation confirmed the accuracy of these predictions,verifying the robust extrapolative capability of the PS-Tree method.Notably,one alloy exhibited a T_(L12)of 1267℃and a density of 7.94 g cm^(-3),outperforming most MPESAs.Additionally,another alloy exhibited a compressive yield strength of 897 MPa at 750℃,with a specific yield strength at this temperature higher than that of most L1_(2)-strengthened alloys and Co/Ni-based superalloys.Moreover,the model provided generalized insights,indicating that alloys with δ_(r)>5.3 and ΔH_(mix)<-12.8 J mol^(-1)K^(-1)tend to favor higher T_(L12).
文摘Autoregressive (AR) modeling is applied to data extrapolation of radio frequency (RF) echo signals, and Burg algorithm, which can be computed in small amount and lead to a stable prediction filter, is used to estimate the prediction parameters of AR modeling. The complex data samples are directly extrapolated to obtain the extrapolated echo data in the frequency domain. The small rotating angle data extrapolation and the large rotating angular data extrapolation are considered separately in azimuth domain. The method of data extrapolation for the small rotating angle is the same as that in frequency domain, while the amplitude samples of large rotating angle echo data are extrapolated to obtain extrapolated echo amplitude, and the complex data of large rotating angle echo samples are extrapolated to get the extrapolated echo phase respectively. The calculation results show that the extrapolated echo data obtained by the above mentioned methods are accurate.
基金This study was provided by Natural Science Foundation of Guangdong Province under Grant No. 5001121the China Meteorological Administration under Grant Nos. CMATG2005Y05 and CMATG2008Z10the Guangdong Meteorological Bureau under Grant Nos. 2007A2 and GRMC2007Z03
文摘Extending the lead time of precipitation nowcasts is vital to improvements in heavy rainfall warning, flood mitigation, and water resource management. Because the TREC vector （tracking radar echo by correlation） represents only the instantaneous trend of precipitation echo motion, the approach using derived echo motion vectors to extrapolate radar reflectivity as a rainfall forecast is not satisfactory if the lead time is beyond 30 minutes. For longer lead times, the effect of ambient winds on echo movement should be considered. In this paper, an extrapolation algorithm that extends forecast lead times up to 3 hours was developed to blend TREC vectors with model-predicted winds. The TREC vectors were derived from radar reflectivity patterns in 3 km height CAPPI （constant altitude plan position indicator） mosaics through a cross-correlation technique. The background steering winds were provided by predictions of the rapid update assimilation model CHAF （cycle of hourly assimilation and forecast）. A similarity index was designed to determine the vertical level at which model winds were applied in the extrapolation process, which occurs via a comparison between model winds and radar vectors. Based on a summer rainfall case study, it is found that the new algorithm provides a better forecast.
基金financial support from the National Natural Science Foundation of China (Nos. 51671118 and 51871143)Young Elite Scientists Sponsorship Program by CAST (No. 2017QNRC001)+2 种基金the “Chenguang” project supported by Shanghai Municipal Education Commission and Shanghai Education Development Foundation (No. 17CG42)Science and Technology Committee of Shanghai (No. 16520721800)Aeronautical Science Fund ¨Integrated computational research of the additive manufacturing for ultra-high strength Ti alloys¨(No. 2017ZF25022)
文摘It is widely reported that CALPHAD is an extrapolation method when the thermodynamic properties of a multicomponent system are approximated by its subsystems.In this work the meaning of the words extrapolation and interpolation is discussed in context of the CALPHAD method.When assessing the properties in binary and ternary systems,extrapolation method is indeed often used.However,after assessment,the Gibbs energies are in fact interpolated from the lower order systems into the higher order systems in the compositional space.The metastable melting temperatures of bcc and hep in Re-W and the liquid miscibility gap in Mg-Zr system are predicted to illustrate the difference between interpolation and extrapolation.
基金support of Aeronautical Science Foundation of China (2011ZB51019)
文摘Transient control law ensures that the aeroengine transits to the command operating state rapidly and reliably. Most of the existing approaches for transient control law design have complicated principle and arithmetic. As a result, those approaches are not convenient for application. This paper proposes an extrapolation approach based on the set-point parameters to construct the transient control law, which has a good practicability. In this approach, the transient main fuel control law for acceleration and deceleration process is designed based on the main fuel flow on steady operating state. In order to analyze the designing feature of the extrapolation approach, the simulation results of several different transient control laws designed by the same approach are compared together. The analysis indicates that the aeroengine has a good performance in the transient process and the designing feature of the extrapolation approach conforms to the elements of the turbofan aeroengine.
基金supported by the Program for New Century Excellent Talents in University (NCET-06-0856)the National Natural Science Foundation of China (60772068)
文摘To enhance the resolution of parameter estimation with limited samples received by a short passive array, an iterative nonparametric algorithm for estimating the frequencies and direction-of-arrivals （DOAs） of signals is proposed. The cost function is constructed using 12-norm Gaussian entropy combined with an additional constraint, 12-norm constraint or linear constraint. By minimizing the cost functions in the temporal and the spatial dimensions using corresponding iteration algorithms respectively, the sparse discrete Fourier transforms （DFTs） of temporal and spatial samples are obtained to represent the extrapolated sequences with much larger sizes than the original samples. Then frequency and angle estimates are obtained by performing the traditional simple methods on the extrapolated sequences. It is shown that the proposed algorithm offers increased resolution and significantly reduced sidelobes compared with the periodogram and beamforming based methods. And it achieves high precision compared with the high-resolution method with lower computational burden. Some numerical simulations and real data processing results are presented to verify the effectiveness of the method.
文摘An improved echo extrapolation technology( MOD-COTREC) was introduced firstly,and then two plans for lightning nowcasting based on MOD-COTREC and both isothermal radar reflectivity and MOD-COTREC were proposed based on the technology. Afterwards,the two plans for lightning nowcasting were tested by a case respectively. It is concluded that during the process of lightning nowcasting singly based on MOD-COTREC,the appearance and disappearance of lightning are not considered,and only lightning position is predicted when lightning density is constant,so the prediction error is big. The plan for lightning nowcasting based on both isothermal radar reflectivity and MOD-COTREC is still at an experimental stage,and the nowcasting products of cloud-to-ground lightning based on the plan are very different from the actual density and position of cloud-to-ground lightning,so it needs to be improved further.
基金supported by the National Natural Science Foundation of China(61271331 61571229)
文摘To realize high-resolution digital beamforming(DBF)of ultra-wideband(UWB) signals, we propose a DBF method based on Carath ′eodory representation for delay compensation and array extrapolation. Delay compensation by Carath ′eodory representation could achieve high interpolation accuracy while using the single channel sampling technique. Array extrapolation by Carath ′eodory representation reformulates and extends each snapshot, consequently extends the aperture of the original uniform linear array(ULA) by several times and provides a better realtime performance than the existing aperture extrapolation utilizing vector extrapolation based on the two dimensional autoregressive(2-D AR) model. The UWB linear frequency modulated(LFM) signal is used for simulation analysis. Simulation results demonstrate that the proposed method is featured by a much higher spatial resolution than traditional DBF methods and lower sidelobes than using Lagrange fractional filters.
基金Supported by the National Natural Science Foundation of China(11271127)Science Research Projectof Guizhou Province Education Department(QJHKYZ[2013]207)
文摘A reduced-order extrapolation algorithm based on Crank-Nicolson least-squares mixed finite element （CNLSMFE） formulation and proper orthogonal decomposition （POD） technique for two-dimensional （2D） Sobolev equations is established. The error estimates of the reduced-order CNLSMFE solutions and the implementation for the reduced-order extrapolation algorithm are provided. A numerical example is used to show that the results of numerical computations are consistent with theoretical conclusions. Moreover, it is shown that the reduced-order extrapolation algorithm is feasible and efficient for seeking numerical solutions to 2D Sobolev equations.
基金supported by the National Natural Science Foundation of China (21263015,21567016 and 21503106)the Education Department Foundation of Jiangxi Province (KJLD14005 and GJJ150016)the Natural Science Foundation of Jiangxi Province (20142BAB213013 and 20151BBE50006),which are greatly acknowledged by the authors~~
文摘A series of SnO2‐based catalysts modified by Mn, Zr, Ti and Pb oxides with a Sn/M （M=Mn, Zr, Ti and Pb） molar ratio of 9/1 were prepared by a co‐precipitation method and used for CH4 and CO oxidation. The Mn3＋, Zr4＋, Ti4＋and Pb4＋cations are incorporated into the lattice of tetragonal rutile SnO2 to form a solid solution structure. As a consequence, the surface area and thermal stability of the catalysts are improved. Moreover, the oxygen species of the modified catalysts become easier to be reduced. Therefore, the oxidation activity over the catalysts was improved, except for the one modified by Pb oxide. Manganese oxide demonstrates the best promotional effects for SnO2. Using an X‐ray diffraction extrapolation method, the lattice capacity of SnO2 for Mn2O3 was 0.135 g Mn2O3/g SnO2, which indicates that to form stable solid solution, only 21%Sn4＋cations in the lattice can be maximally replaced by Mn3＋. If the amount of Mn3＋cations is over the capacity, Mn2O3 will be formed, which is not favorable for the activity of the catalysts. The Sn rich samples with only Sn‐Mn solid solution phase show higher activity than the ones with excess Mn2O3 species.
基金Major Key Project of Pengcheng Laboratory,Grant/Award Number:PCL2022A03。
文摘Predicting potential facts in the future,Temporal Knowledge Graph(TKG)extrapolation remains challenging because of the deep dependence between the temporal association and semantic patterns of facts.Intuitively,facts(events)that happened at different timestamps have different influences on future events,which can be attributed to a hierarchy among not only facts but also relevant entities.Therefore,it is crucial to pay more attention to important entities and events when forecasting the future.However,most existing methods focus on reasoning over temporally evolving facts or mining evolutional patterns from known facts,which may be affected by the diversity and variability of the evolution,and they might fail to attach importance to facts that matter.Hyperbolic geometry was proved to be effective in capturing hierarchical patterns among data,which is considered to be a solution for modelling hierarchical relations among facts.To this end,we propose ReTIN,a novel model integrating real-time influence of historical facts for TKG reasoning based on hyperbolic geometry,which provides low-dimensional embeddings to capture latent hierarchical structures and other rich semantic patterns of the existing TKG.Considering both real-time and global features of TKG boosts the adaptation of ReTIN to the ever-changing dynamics and inherent constraints.Extensive experiments on benchmarks demonstrate the superiority of ReTIN over various baselines.The ablation study further supports the value of exploiting temporal information.
文摘In this paper, Aitken’s extrapolation normally applied to convergent fixed point iteration is extended to extrapolate the solution of a divergent iteration. In addition, higher order Aitken extrapolation is introduced that enables successive decomposition of high Eigen values of the iteration matrix to enable convergence. While extrapolation of a convergent fixed point iteration using a geometric series sum is a known form of Aitken acceleration, it is shown that in this paper, the same formula can be used to estimate the solution of sets of linear equations from diverging Gauss-Seidel iterations. In both convergent and divergent iterations, the ratios of differences among the consecutive values of iteration eventually form a convergent (divergent) series with a factor equal to the largest Eigen value of the iteration matrix. Higher order Aitken extrapolation is shown to eliminate the influence of dominant Eigen values of the iteration matrix in successive order until the iteration is determined by the lowest possible Eigen values. For the convergent part of the Gauss-Seidel iteration, further acceleration is made possible by coupling of the extrapolation technique with the successive over relaxation (SOR) method. Application examples from both convergent and divergent iterations have been provided. Coupling of the extrapolation with the SOR technique is also illustrated for a steady state two dimensional heat flow problem which was solved using MATLAB programming.
基金Supported by National Natural Science Foundation of China (10771063)the Doctor Programme of the National Education Committee (20050542006)
文摘Based on an asymptotic expansion of finite element, an extrapolation cascadic multigrid method （EXCMG） is proposed, in which the new extrapolation and quadratic interpolation are used to provide a better initial value on refined grid. In the case of multiple grids, both superconvergence error in H^1-norm and the optimal error in l2-norm are analyzed. The numerical experiment shows the advantage of EXCMG in comparison with CMG.
