CEMA-LSTM:Enhancing Contextual Feature Correlation for Radar Extrapolation Using Fine-Grained Echo Datasets

Accurate precipitation nowcasting can provide great convenience to the public so they can conduct corresponding arrangements in advance to deal with the possible impact of upcoming heavy rain.Recent relevant research activities have shown their concerns on various deep learning models for radar echo extrapolation,where radar echo maps were used to predict their consequent moment,so as to recognize potential severe convective weather events.However,these approaches suffer from an inaccurate prediction of echo dynamics and unreliable depiction of echo aggregation or dissipation,due to the size limitation of convolution filter,lack of global feature,and less attention to features from previous states.To address the problems,this paper proposes a CEMA-LSTM recurrent unit,which is embedded with a Contextual Feature Correlation Enhancement Block(CEB)and a Multi-Attention Mechanism Block(MAB).The CEB enhances contextual feature correlation and supports its model to memorize significant features for near-future prediction;the MAB uses a position and channel attention mechanism to capture global features of radar echoes.Two practical radar echo datasets were used involving the FREM and CIKM 2017 datasets.Both quantification and visualization of comparative experimental results have demonstrated outperformance of the proposed CEMA-LSTMover recentmodels,e.g.,PhyDNet,MIM and PredRNN++,etc.In particular,compared with the second-rankedmodel,its average POD,FAR and CSI have been improved by 3.87%,1.65%and 1.79%,respectively on the FREM,and by 1.42%,5.60%and 3.16%,respectively on the CIKM 2017.

Extrapolation of upwelling irradiance just beneath the ocean surface from the in-water radiometric profile measurements

Accurate measurements of upwelling irradiance just beneath the ocean surface,E_(u)(λ,0^(-)),can be used to calculate ocean optical parameters,and further develop retrieval algorithms for remotely sensing water component concentrations.Due to the effects of sea surface waves,perturbation from instrument platform(ship),and instrument self-shading,E_(u)(λ,0^(-))is often difficult to be accurately measured.This study presents a procedure for extrapolating the E_(u)(λ,0^(-))from the in-water radiometric profile measurements.Using the optical profile data from 13 bands(ranging from 381 to 779 nm)measured by 45 casts in the Ligurian Sea during 2003–2009,the E_(u)(λ,0^(-))was extrapolated from in-water upwelling irradiance measurements between the initial shallow depth,Z_(0),and an optimal bottom depth,Z_(1),by three linear models(linear,2-degree polynomial,and exponential)and two nonlinear models(LOESS and spline).The accumulated errors of extrapolated E_(u)(λ,0^(-))at each wavelength for the five models were calculated.It was found that the optimal Z_(1) depth for the linear and exponential models was at the depth of80%of E_(u)(λ,Z_(0)),50%of E_(u)(λ,Z_(0))for the 2-degree polynomial model,40%of E_(u)(λ,Z_(0))for the LOESS model,and 15%of E_(u)(λ,Z_(0))for the spline model.The extrapolated E_(u)(λ,0^(-))derived from the five models was in good agreement with the calculated true E_(u)(λ,0^(-)).In all bands,the 2-degree polynomial model achieved the highest accuracy,followed by the LOESS model.In the short band of 381–559 nm,the linear and exponential models had the third-best performance,and the spline model performed worst within this range.For the red band of 619–779 nm,the accuracies of the exponential and spline models had the third highest performance,and the linear model produced lowest accuracy.Hence,the 2-degree polynomial model was an optimal procedure for extrapolation of E_(u)(λ,0^(-))from the in-water radiometric profile measurements.

Extrapolation over temporal knowledge graph via hyperbolic embedding

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.

Improved Weather Radar Echo Extrapolation Through Wind Speed Data Fusion Using a New Spatiotemporal Neural Network Model

Weather radar echo extrapolation plays a crucial role in weather forecasting.However,traditional weather radar echo extrapolation methods are not very accurate and do not make full use of historical data.Deep learning algorithms based on Recurrent Neural Networks also have the problem of accumulating errors.Moreover,it is difficult to obtain higher accuracy by relying on a single historical radar echo observation.Therefore,in this study,we constructed the Fusion GRU module,which leverages a cascade structure to effectively combine radar echo data and mean wind data.We also designed the Top Connection so that the model can capture the global spatial relationship to construct constraints on the predictions.Based on the Jiangsu Province dataset,we compared some models.The results show that our proposed model,Cascade Fusion Spatiotemporal Network(CFSN),improved the critical success index(CSI)by 10.7%over the baseline at the threshold of 30 dBZ.Ablation experiments further validated the effectiveness of our model.Similarly,the CSI of the complete CFSN was 0.004 higher than the suboptimal solution without the cross-attention module at the threshold of 30 dBZ.

A New Extrapolation Economy Cascadic Multigrid Method for Image Restoration Problems

In this paper, a new extrapolation economy cascadic multigrid method is proposed to solve the image restoration model. The new method combines the new extrapolation formula and quadratic interpolation to design a nonlinear prolongation operator, which provides more accurate initial values for the fine grid level. An edge preserving denoising operator is constructed to remove noise and preserve image edges. The local smoothing operator reduces the influence of staircase effect. The experiment results show that the new method not only improves the computational efficiency but also ensures good recovery quality.

股票市场正反馈交易的空间溢出效应——基于新冠疫情冲击的计量检验

利用空间自相关正反馈交易模型,对新冠疫情爆发前后国际股票市场的正反馈交易进行了检验.结果表明,新冠疫情爆发后国际股票市场正反馈交易显著增强并呈现空间相关特征.相关国家反危机的调控措施,虽然一度抑制了股票市场下行,但同时强化了投资者非理性的外推性预期,加剧了股票市场正反馈交易的空间溢出效应.因此,在新冠疫情不断持续的情况下,监管当局应充分考虑反危机调控措施对投资者心理账户和市场预期的影响,防范疫情冲击下股票市场正反馈交易空间溢出的风险联动效应.

循环水槽船模阻力修正方法

在循环水槽中开展船模阻力测量是船型优劣分析的常规试验之一,因此对循环水槽阻力修正方法进行研究是十分必要的。以集装箱船标模(KCS)为研究对象在循环水槽中开展模型阻力和流场测量试验,根据循环水槽自身流动特性并结合拖曳水池已有的修正方法,分析并总结适用于水槽船模阻力与阻塞效应的修正方法。由总结的修正公式与修正流程可知,循环水槽中2.5~3.5 m尺寸模型试验与大模型试验结果阻力换算偏差在1%左右,设计航速点阻力换算偏差小于1%。结果表明,有必要对循环水槽中阻力进行自由面倾斜度修正,采用Tamura公式进行阻塞效应修正能得到更为精确的阻力试验结果。

71℃试验法寿命外推结果的影响因素分析

目的研究71℃试验法用于火工品贮存寿命外推结果的准确性。方法采用对比分析的方法,对美军标和国军标中71℃试验法的寿命影响因素进行分析。结果加速模型、试验条件和外推温度取值对火工品的寿命外推结果均有影响。结论71℃试验法评估结果的准确性取决于使用场景是否正确,试验条件设置是否合理,贮存环境温度取值是否准确,并提出了71℃试验法的使用建议。

基于自注意力和门控循环神经网络的雷达回波外推算法研究

为提升现有神经网络对雷达回波序列的时、空特征提取能力,建立外推性能更优的时、空序列预测模型,开展雷达回波外推算法改进研究。基于深圳市气象局与中国香港天文台共同建立的雷达回波数据集,在数据处理层面,通过改进对雷达回波图像序列归一化的方法,提升了常用的5种时、空序列预测模型对强回波的预测水平;在模型算法层面,将两个联立的自注意力结构引入ST-LSTM结构,组成新的循环门控单元,并将这些循环门控单元进行堆叠,建立ST-SARNN模型。选用CSI和POD作为精度评价指标,进行模型对比分析得到:(1)改进的归一化方法提升了近几年内常用的5种时、空序列预测模型对强回波的预测水平。(2)加入自注意力的ST-SARNN模型对雷达回波的预测性能显著优于ConvLSTM、PredRNN和MIM等模型。改进的归一化方法能改变样本数据分布,并在一定程度上提升模型外推性能;自注意力结构能够有效挖掘雷达回波序列的时、空特征,进而改进神经网络的外推表现。

基于Extrapolation Tikhonov正则化算法的重力数据三维约束反演

通过研究重力数据三维反演解的病态性,利用基于拉格朗日插值方法的Extrapolation Tikhonov正则化方法来解决反演中解的不唯一性和不稳定性问题,该方法最大限度的减少了因正则化参数的引入而在反演结果中介入的误差,同时详细讨论了基于三种选择原则的正则化双参数的具体选择方法,模型试算结果表明,与原Tikhonov方法相比,该方法提高了反演的拟合精度.其次,为了消除核函数随深度增加而快速衰减对反演结果的影响,本文改进了前人的重力数据三维反演深度加权函数,改进后的加权函数与原函数相比能更好的识别异常体底部密度分布特征,对于埋深较深的异常体具有较好的识别效果,更好的解决了由近地面趋肤效应作用引起的密度分布不均的问题.同时,利用上下限约束函数限制每一个立方体的密度差范围,并应用于多组人工合成模型.结果表明:该反演方法能准确地获得正演模型的预设参数范围和位置.

山洪灾害雨量预警指标分析方法评述与展望

山洪灾害预警指标是山洪灾害预报预警的核心,我国现阶段主要采用经验法和水位流量反推法确定雨量预警指标,并逐步推广应用复合预警指标法和动态临界雨量法。在系统阐述国内外常用雨量预警指标分析方法及其特点基础上,针对山洪灾害预警指标分析中需考虑的降雨时空分布变化、高含砂水流、泥石流及其他不确定因素影响问题,提出今后雨量预警指标研究方向。

基于核密度估计的起重机应力谱编制方法研究

准确获得起重机金属结构的应力谱是疲劳寿命评估的前提,基于短期实测应力数据样本外推至全寿命周期内应力谱的方法是当前常用的一种方法,针对起重机疲劳寿命评估中常用的基于威布尔分布统计模型外推方法存在的一些不足,文中提出了基于核密度估计的起重机应力谱的外推流程和方法,并以岸边集装箱起重机为研究对象,利用文中给出的流程、方法外推应力循环幅值数据。外推数据统计分析结果显示:基于核密度估计方法生成的数据较好地反映了测试数据的统计特征,与基于威布尔统计模型的外推方法相比,具有不需要进行先验假设、拟合精度高的特点。

利用Extrapolation Tikhonov正则化算法进行重力梯度三维密度反演（英文）

本文利用Extrapolation Tikhonov正则化算法处理重力梯度数据三维密度反演的线性不适定问题。与Tikhonov正则化方法相比,Extrapolation Tikhonov正则化方法减小了因正则化参数的引入而带来的反演结果误差,提高了预测数据与观测数据之间的拟合精度。同时为了消除位场数据反演时位置函数快速衰减对反演结果的影响,本文提出了基于重力梯度全张量特征向量法的深度加权函数,模型试验证明了该深度加权函数能有效识别异常体密度分布特征。对澳大利亚Kauring地区实测重力梯度数据进行反演,并和已有研究成果对比分析。结果表明该反演方法能够较好的获取地下异常体的密度分布信息。

基于Power Extrapolation和Adaptive Method的网页评估新算法

Google的PageRank算法通过对超链接结构的分析,有效地提高了搜索结果的排序质量。PowerExtrapolation算法通过特征值直接求解马尔可夫超链接矩阵的主特征向量,但该算法的迭代次数与参数d的选择密切相关,而参数d的确定目前无明显规律可寻。另一方面,AdaptiveMethod通过将马尔可夫超链接矩阵稀疏化以达到节省迭代时间的目的。文章在PowerExtrapolation算法的基础上引入AdaptiveMethod,实验结果初步证明了新算法可以减少迭代运算的时间。

动车组空心车轴应力强度因子研究

为研究动车组空心车轴应力强度因子,首先分析了车轴应力强度因子的影响因素,建立了含裂纹车轴有限元模型,并使用叠加法求解不同载荷下的应力强度因子。然后采用应力外推法和位移外推法计算了应力强度因子,并将计算结果与文献形状因子公式法进行对比。最后根据应力强度因子的一般解析式和位移外推法的计算结果,使用五次多项式对形状因子函数进行拟合,通过设置不同的裂纹深度和载荷验证了形状因子函数和解析式的适用性。结果表明该方法对于求解同一载荷模式下的车轴应力强度因子具有一定的参考价值。

面向降雨预报的雷达回波预测序列外推方法

雷达回波外推方法广泛应用于降雨预报中。针对雷达回波中的预测精度不够高的问题,提出了一种基于循环神经网络的深度学习模型DIPredRNN。该模型通过引入空间和通道的双注意力机制,将长时间的时间信息和通道信息结合起来,提高了时间记忆的长期依赖;通过引入隐藏状态和输入的交互框架,保留了更多的特征,提高了时间记忆的短期依赖。该模型在HKO-7数据集和四川数据集上同经典模型以及诸多先进模型进行实验对比,该模型从外推图像、MSE、SSIM、CSI-30~50 dbz多个指标对比中都取得最佳效果。实验证明了DIPredRNN提高了雷达回波预测效果,拥有先进的性能。

STRONGLY CONVERGENT INERTIAL FORWARD-BACKWARD-FORWARD ALGORITHM WITHOUT ON-LINE RULE FOR VARIATIONAL INEQUALITIES

This paper studies a strongly convergent inertial forward-backward-forward algorithm for the variational inequality problem in Hilbert spaces.In our convergence analysis,we do not assume the on-line rule of the inertial parameters and the iterates,which have been assumed by several authors whenever a strongly convergent algorithm with an inertial extrapolation step is proposed for a variational inequality problem.Consequently,our proof arguments are different from what is obtainable in the relevant literature.Finally,we give numerical tests to confirm the theoretical analysis and show that our proposed algorithm is superior to related ones in the literature.

基于POD降维外推差分算法的热传导模型研究

针对一维热传导问题,基于奇异值分解和特征投影本征分解(proper orthogonal decomposition,POD)法通过提取特征模态,建立了一种极少自由度、较高精度的降维外推仿真模型。给出模型降维近似解分量的数学推导、算法过程以及误差分析,实现了温度场的快速计算。最后,通过数值例子,将POD计算结果与有限差分法(FDM)计算结果进行了对比。结果表明:POD方法在不同的时间步长、空间步长及定解条件下,均能捕捉到传热过程的准确信息,平均计算速度比传统有限差分法计算速度提高了200倍,有效缩短了计算机模拟时间。考核了低阶模型的准确性,并说明了低阶方程可以定性的反映原高维系统的传热特性。同时,POD所得结果与FDM结果间的最大相对误差为0.15%,满足工程计算精度要求。所提出的POD降维外推算法方案,不但扩展了POD特征空间,而且可以逐步改进数值求解步骤,弥补了POD方法的不足,验证了利用POD降维算法研究传热问题的可行性与有效性。对于实现复杂传热模型的高效准确的分析与仿真数值求解过程有一定的理论参考价值。

基于因果机制约束的强化推荐系统

利用历史数据训练强化学习推荐系统已经得到越来越多研究人员的关注,但是历史数据使得强化学习模型对状态-动作估值错误,产生数据偏差,如流行度偏差和选择偏差。造成上述问题的原因是历史数据分布与强化学习策略采集的数据分布不一致以及历史数据本身带有偏差。使用因果机制可以在约束策略采集数据分布的同时解决数据偏差的问题,提出基于因果机制约束的强化推荐系统,包含因果机制约束模块和对比策略模块。因果机制约束模块用于约束推荐策略可选择的样本空间以减少策略分布与数据分布误差,考虑随时间动态变化的物品流行度分布以缓解流行度偏差。对比策略模块通过平衡正负样本的重要性,缓解选择偏差的影响。在真实数据集Ciao和Epinions上的实验结果表明,相比深度Q网络(DQN)-r、GAIL、SOFA等,该算法具有较优的准确性和多样性,包含加入因果机制约束模块后的模型在F-measure指标上分别提高2%和3%,进一步验证了因果机制约束模块的有效性。

极值服从广义Pareto分布的扭转载荷外推方法研究

提出了一种极值样本服从广义Pareto分布(generalized pareto distribution,GPD)函数的扭转载荷时域外推方法,基于极值样本的均值超出函数,首先确定一个区间范围,并以形状参数最小的均方误差为目标,通过自抽样法确定最佳阈值,再采用极大似然估计法对GPD函数的形状参数和尺度参数进行估计,获取扭转随机载荷谱的极值样本,服从GPD分布规律。以商用车驾驶室的稳定杆为研究对象,介绍了扭转载荷采集的方法,基于所提出的扭转载荷时域外推方法进行外推研究,并分别从穿级计数、功率谱密度、雨流图和潜在伪损伤量等方面对外推前后的载荷谱进行了对比分析。结果表明:所构建的载荷外推算法对商用车驾驶室稳定杆扭转载荷外推有较好适应性。