By using Richardson extrapolation and fourth-order compact finite difference scheme on different scale grids, a sixth-order solution is computed on the coarse grid. Other three techniques are applied to obtain a sixth...By using Richardson extrapolation and fourth-order compact finite difference scheme on different scale grids, a sixth-order solution is computed on the coarse grid. Other three techniques are applied to obtain a sixth-order solution on the fine grid, and thus give out three kinds of Richardson extrapolation-based sixth order compact computation methods. By carefully analyzing the truncation errors respectively on 2D Poisson equation, we compare the accuracy of these three sixth order methods theoretically. Numerical results for two test problems are discussed.展开更多
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
最小均方误差(Minimum Mean Square Error,MMSE)检测算法是大规模多输入多输出(massive MIMO)系统中能够实现接近最优检测性能的一种算法,但包含对高维矩阵的求逆运算,复杂度较高,因此不适合应用在实际工程中。针对这一问题,文章基于矩...最小均方误差(Minimum Mean Square Error,MMSE)检测算法是大规模多输入多输出(massive MIMO)系统中能够实现接近最优检测性能的一种算法,但包含对高维矩阵的求逆运算,复杂度较高,因此不适合应用在实际工程中。针对这一问题,文章基于矩阵分块思想和理查德森(Richardson,RI)算法,提出了一种预处理的理查德森(Pretreatment-Richardson,P-RI)迭代算法,该算法首先基于矩阵分块思想构造了一种新形式的线性迭代,然后用此线性迭代对理查德森算法进行预处理,有效提升了算法的收敛速度。实验结果显示,与现有的RI算法相比,该算法的检测性能更好。展开更多
文摘By using Richardson extrapolation and fourth-order compact finite difference scheme on different scale grids, a sixth-order solution is computed on the coarse grid. Other three techniques are applied to obtain a sixth-order solution on the fine grid, and thus give out three kinds of Richardson extrapolation-based sixth order compact computation methods. By carefully analyzing the truncation errors respectively on 2D Poisson equation, we compare the accuracy of these three sixth order methods theoretically. Numerical results for two test problems are discussed.
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.
文摘最小均方误差(Minimum Mean Square Error,MMSE)检测算法是大规模多输入多输出(massive MIMO)系统中能够实现接近最优检测性能的一种算法,但包含对高维矩阵的求逆运算,复杂度较高,因此不适合应用在实际工程中。针对这一问题,文章基于矩阵分块思想和理查德森(Richardson,RI)算法,提出了一种预处理的理查德森(Pretreatment-Richardson,P-RI)迭代算法,该算法首先基于矩阵分块思想构造了一种新形式的线性迭代,然后用此线性迭代对理查德森算法进行预处理,有效提升了算法的收敛速度。实验结果显示,与现有的RI算法相比,该算法的检测性能更好。
