STGSA:A Novel Spatial-Temporal Graph Synchronous Aggregation Model for Traffic Prediction

The success of intelligent transportation systems relies heavily on accurate traffic prediction,in which how to model the underlying spatial-temporal information from traffic data has come under the spotlight.Most existing frameworks typically utilize separate modules for spatial and temporal correlations modeling.However,this stepwise pattern may limit the effectiveness and efficiency in spatial-temporal feature extraction and cause the overlook of important information in some steps.Furthermore,it is lacking sufficient guidance from prior information while modeling based on a given spatial adjacency graph(e.g.,deriving from the geodesic distance or approximate connectivity),and may not reflect the actual interaction between nodes.To overcome those limitations,our paper proposes a spatial-temporal graph synchronous aggregation(STGSA)model to extract the localized and long-term spatial-temporal dependencies simultaneously.Specifically,a tailored graph aggregation method in the vertex domain is designed to extract spatial and temporal features in one graph convolution process.In each STGSA block,we devise a directed temporal correlation graph to represent the localized and long-term dependencies between nodes,and the potential temporal dependence is further fine-tuned by an adaptive weighting operation.Meanwhile,we construct an elaborated spatial adjacency matrix to represent the road sensor graph by considering both physical distance and node similarity in a datadriven manner.Then,inspired by the multi-head attention mechanism which can jointly emphasize information from different r epresentation subspaces,we construct a multi-stream module based on the STGSA blocks to capture global information.It projects the embedding input repeatedly with multiple different channels.Finally,the predicted values are generated by stacking several multi-stream modules.Extensive experiments are constructed on six real-world datasets,and numerical results show that the proposed STGSA model significantly outperforms the benchmarks.

Optimization of flow uniformity control device for six-stream continuous casting tundish

For a multistream tundish,the uniformity among the streams plays a significant role in the quality of molten steel.How to analyze the uniformity quantitatively and optimize structures of a multistream tundish is an important research content for a multistream tundish.A new approach was proposed to quantify the uniformity among the streams.And a physical study and a numerical study were carried out to optimize the structure of the diversion hole based on the prototype of a six-stream tundish in a steel plant.On the basis of average residence time,the uniformity of each flow was considered fully and then the optimal structure of the diversion hole was obtained by means of the comprehensive analysis of temperature field and velocity field.The results show that the optimum structural parameters adopted for diversion holes are height of 20 mm,angle of 15°and diameter of 80 mm.