Forecasting traffic flows in irregular regions with multi-graph

The prediction of regional traffic flows is important for traffic control and management in an intelligent traffic system.With the help of deep neural networks,the convolutional neural network or residual neural network,which can be applied only to regular grids,is adopted to capture the spatial dependence for flow prediction.However,the obtained regions are always irregular considering the road network and administrative boundaries;thus,dividing the city into grids is inaccurate for prediction.In this paper,we propose a new model based on multi-graph convolutional network and gated recurrent unit(MGCN-GRU)to predict traffic flows for irregular regions.Specifically,we first construct heterogeneous inter-region graphs for a city to reflect the rela-tionships among regions.In each graph,nodes represent the irregular regions and edges represent the relationship types between regions.Then,we propose a multi-graph convolutional network to fuse different inter-region graphs and additional attributes.The GRU is further used to capture the temporal dependence and to predict future traffic flows.Experimental results based on three real-world large-scale datasets(public bicycle system dataset,taxi dataset,and dockless bike-sharing dataset)show that our MGCN-GRU model outperforms a variety of existing methods.

A Novel Steganography Algorithm Based on Instance Segmentation

Information hiding tends to hide secret information in image area where is rich texture or high frequency,so as to transmit secret information to the recipient without affecting the visual quality of the image and arousing suspicion.We take advantage of the complexity of the object texture and consider that under certain circumstances,the object texture is more complex than the background of the image,so the foreground object is more suitable for steganography than the background.On the basis of instance segmentation,such as Mask R-CNN,the proposed method hides secret information into each object's region by using the masks of instance segmentation,thus realizing the information hiding of the foreground object without background.This method not only makes it more efficient for the receiver to extract information,but also proves to be more secure and robust by experiments.

Seasonal characteristics of F region in lower solar activity period at Zhongshan Station, Antarctica

Based on data of Digisonde Portable Sounder-4 (DPS-4 ) in 1995 -1997. we have analyzed the seasonal variations of F region at Zhongshan Station (69. 4°S,76. 4°E ). During the summer of Zhongshan Station, F region ionization is mainly controlled by the solar ultraviolet radiation. Similar to the phenomena in mid-latitude area, the value f0F2 is changed with local time. During equinox scasons, soft electron precipitation from the cusp/cleft region seems significant, f0F2 is changed with rnagnetic local time, and shows the magnetic noon phenomenon. In winter. the effect of the solar radiation on the F region is less than that of summer. Instead, F region is affected by particle precipitation from cusp/cleft region as well as polar plasma convection, there fore, the diurnal variation of f0F2 is more complex and shows two peaks. F region occurs all day in summer. and seldom appears at midnight in equinox.In winter, F region shows two minimums, one is at midnight and the other is at afternoon cusp. Further analysis of the F region spread indicates that in winter the aurora oval passes over the Zhongshan Station is at 1100 UT - 1500 UT.