In order to improve the availability of regional model precipitation forecast, this project intends to use the measured heavy rainfall data of dense automatic stations to carry out historical precipitation in the high...In order to improve the availability of regional model precipitation forecast, this project intends to use the measured heavy rainfall data of dense automatic stations to carry out historical precipitation in the high resolution: the Severe Weather Automatic Nowcast System (SWAN) quantitative precipitation forecast and the High-Resolution Rapid Refresh (HRRR) regional numerical model precipitation forecast in short-term nowcasting aging. Based on the error analysis, the grid fusion technology is used to establish the measured rainfall, HRRR regional model precipitation forecast, and optical flow radar quantitative precipitation forecast (QPF) three-source fusion correction scheme, comprehensively integrate the revised forecasting effect, adjust the fusion correction parameters, establish an optimal correction plan, generate a frozen rolling update revised product based on measured dense data and short-term forecast, and put it into business operation, and perform real-time effect rolling test evaluation on the forecast product.展开更多
Printing short fibre/thermoplastic composites using the fused filament fabrication method sometimes creates a gap between print paths. In this study, the two-dimensional moving particle semi-implicit method for liquid...Printing short fibre/thermoplastic composites using the fused filament fabrication method sometimes creates a gap between print paths. In this study, the two-dimensional moving particle semi-implicit method for liquid simulation was applied to simulate the print-path fusion process. The three-dimensional movement of the nozzle was simulated using the sliding motion of the nozzle. The method was applied to the printing of short carbon fibre/polyamide-6 composites, and the simulation results were compared with those of experiments. The simulated results of the cross-sectional configuration agreed well with the experimental results. This will enable the optimization of printing process parameters thus reducing the gap between print paths.展开更多
精准的分布式光伏短期发电功率预测有助于电力系统运行与功率就地平衡。该文提出一种基于BIRCH(balanced iterative reducing and clustering using hierarchies)相似日聚类的L-Transformer(LSTM-Transformer)模型进行短期光伏功率预测...精准的分布式光伏短期发电功率预测有助于电力系统运行与功率就地平衡。该文提出一种基于BIRCH(balanced iterative reducing and clustering using hierarchies)相似日聚类的L-Transformer(LSTM-Transformer)模型进行短期光伏功率预测。首先使用BIRCH无监督聚类算法对历史数据聚类得到3种典型天气,根据聚类结果划分测试集对模型进行训练。为提高不同天气类型下的预测精度,采用双层架构的L-Transformer模型,首层通过长短期记忆网络(long short term memory,LSTM)的门控单元机制捕捉时间序列中的长期依赖关系;次层结合Transformer模型的自注意力机制聚焦于当前任务更关键的特征量,通过多注意力头与光伏数据特征量相结合生成向量,注意力头并行计算,从而高效、精确地预测短期光伏功率。实测数据验证结果表明L-Transformer模型对于不同天气类型功率预测泛化性优异、精确度高,气象数据波动大时鲁棒性强。展开更多
针对SAR图像检测船舶任务中的目标小、近岸样本目标检测困难等问题,文章提出一种名为长短路特征融合网络(Long and Short path Feature Fusion Network,LSFF-Net)的船舶检测网络。该网络通过长短路特征融合模块有效协调了大目标与小目...针对SAR图像检测船舶任务中的目标小、近岸样本目标检测困难等问题,文章提出一种名为长短路特征融合网络(Long and Short path Feature Fusion Network,LSFF-Net)的船舶检测网络。该网络通过长短路特征融合模块有效协调了大目标与小目标检测,避免小目标特征信息的丢失。网络中应用结构重参数化结构提高了模块学习能力。为了满足多尺度目标检测,加入特征金字塔网络,融合多尺度特征。为了应对近岸样本目标检测,设计数据重分配算法,提高了对近岸样本目标的检测精度。实验结果表明:在公开数据集检测时,算法的平均精度(Average Precision,AP)达到97.50%,优于主流目标检测算法。该方法为提高SAR图像中小目标和近岸样本目标检测精度提供了新的实现方案。展开更多
针对商品包装文本检测任务中弯曲密集型文本导致的错检、漏检问题,提出了一种由2个子网络组成的基于链接关系预测的文本检测框架(text detection network based on relational prediction,RPTNet)。在文本组件检测网络中,下采样采用卷...针对商品包装文本检测任务中弯曲密集型文本导致的错检、漏检问题,提出了一种由2个子网络组成的基于链接关系预测的文本检测框架(text detection network based on relational prediction,RPTNet)。在文本组件检测网络中,下采样采用卷积神经网络和自注意力并行的双分支结构提取局部和全局特征,并加入空洞特征增强模块(DFM)减少深层特征图在降维过程中信息的丢失;上采样采用特征金字塔与多级注意力融合模块(MAFM)相结合的方式进行多级特征融合以增强文本特征间的潜在联系,通过文本检测器从上采样输出的特征图中检测文本组件;在链接关系预测网络中,采用基于图卷积网络的关系推理框架预测文本组件间的深层相似度,采用双向长短时记忆网络将文本组件聚合为文本实例。为验证RRNet的检测性能,构建了一个由商品包装图片组成的文本检测数据集(text detection dataset composed of commodity packaging,CPTD1500)。实验结果表明:RPTNet不仅在公开文本数据集CTW-1500和Total-Text上取得了优异的性能,而且在CPTD1500数据集上的召回率和F值分别达到了85.4%和87.5%,均优于当前主流算法。展开更多
文摘In order to improve the availability of regional model precipitation forecast, this project intends to use the measured heavy rainfall data of dense automatic stations to carry out historical precipitation in the high resolution: the Severe Weather Automatic Nowcast System (SWAN) quantitative precipitation forecast and the High-Resolution Rapid Refresh (HRRR) regional numerical model precipitation forecast in short-term nowcasting aging. Based on the error analysis, the grid fusion technology is used to establish the measured rainfall, HRRR regional model precipitation forecast, and optical flow radar quantitative precipitation forecast (QPF) three-source fusion correction scheme, comprehensively integrate the revised forecasting effect, adjust the fusion correction parameters, establish an optimal correction plan, generate a frozen rolling update revised product based on measured dense data and short-term forecast, and put it into business operation, and perform real-time effect rolling test evaluation on the forecast product.
文摘Printing short fibre/thermoplastic composites using the fused filament fabrication method sometimes creates a gap between print paths. In this study, the two-dimensional moving particle semi-implicit method for liquid simulation was applied to simulate the print-path fusion process. The three-dimensional movement of the nozzle was simulated using the sliding motion of the nozzle. The method was applied to the printing of short carbon fibre/polyamide-6 composites, and the simulation results were compared with those of experiments. The simulated results of the cross-sectional configuration agreed well with the experimental results. This will enable the optimization of printing process parameters thus reducing the gap between print paths.
文摘精准的分布式光伏短期发电功率预测有助于电力系统运行与功率就地平衡。该文提出一种基于BIRCH(balanced iterative reducing and clustering using hierarchies)相似日聚类的L-Transformer(LSTM-Transformer)模型进行短期光伏功率预测。首先使用BIRCH无监督聚类算法对历史数据聚类得到3种典型天气,根据聚类结果划分测试集对模型进行训练。为提高不同天气类型下的预测精度,采用双层架构的L-Transformer模型,首层通过长短期记忆网络(long short term memory,LSTM)的门控单元机制捕捉时间序列中的长期依赖关系;次层结合Transformer模型的自注意力机制聚焦于当前任务更关键的特征量,通过多注意力头与光伏数据特征量相结合生成向量,注意力头并行计算,从而高效、精确地预测短期光伏功率。实测数据验证结果表明L-Transformer模型对于不同天气类型功率预测泛化性优异、精确度高,气象数据波动大时鲁棒性强。
文摘针对SAR图像检测船舶任务中的目标小、近岸样本目标检测困难等问题,文章提出一种名为长短路特征融合网络(Long and Short path Feature Fusion Network,LSFF-Net)的船舶检测网络。该网络通过长短路特征融合模块有效协调了大目标与小目标检测,避免小目标特征信息的丢失。网络中应用结构重参数化结构提高了模块学习能力。为了满足多尺度目标检测,加入特征金字塔网络,融合多尺度特征。为了应对近岸样本目标检测,设计数据重分配算法,提高了对近岸样本目标的检测精度。实验结果表明:在公开数据集检测时,算法的平均精度(Average Precision,AP)达到97.50%,优于主流目标检测算法。该方法为提高SAR图像中小目标和近岸样本目标检测精度提供了新的实现方案。
文摘针对商品包装文本检测任务中弯曲密集型文本导致的错检、漏检问题,提出了一种由2个子网络组成的基于链接关系预测的文本检测框架(text detection network based on relational prediction,RPTNet)。在文本组件检测网络中,下采样采用卷积神经网络和自注意力并行的双分支结构提取局部和全局特征,并加入空洞特征增强模块(DFM)减少深层特征图在降维过程中信息的丢失;上采样采用特征金字塔与多级注意力融合模块(MAFM)相结合的方式进行多级特征融合以增强文本特征间的潜在联系,通过文本检测器从上采样输出的特征图中检测文本组件;在链接关系预测网络中,采用基于图卷积网络的关系推理框架预测文本组件间的深层相似度,采用双向长短时记忆网络将文本组件聚合为文本实例。为验证RRNet的检测性能,构建了一个由商品包装图片组成的文本检测数据集(text detection dataset composed of commodity packaging,CPTD1500)。实验结果表明:RPTNet不仅在公开文本数据集CTW-1500和Total-Text上取得了优异的性能,而且在CPTD1500数据集上的召回率和F值分别达到了85.4%和87.5%,均优于当前主流算法。
