Trends of Temperature Extremes in Summer and Winter during 1971–2013 in China

The diurnal temperature range(DTR) has decreased dramatically in recent decades, but it is not yet obvious whether the extreme values of DTR have also reduced. Based on the daily maximum and minimum temperature data of 653 stations in China, a set of monthly indices of warm extremes, cold extremes, and DTR extremes in summer(June, July, August) and winter(December, January, February) were studied for spatial and temporal features during the period 1971–2013. Results show that the incidence of warm extremes has been increasing in most parts of China, while the opposite trend was found in the cold extremes for summer and winter months. Both increasing and decreasing trends of monthly DTR extremes were identified in China for both seasons. For high DTR extremes, decreasing trends were identified in northern China for both seasons, but increasing trends were found only in southern China in summer, while in winter, they were found in central China. Monthly low DTR extreme indices demonstrated consistent positive trends in summer and winter, while significant increases(P < 0.05) were identified for only a few stations.

Facial expression recognition based on bidirectional gated recurrent units within deep residual network

Purpose-recent years,facial expression recognition has been widely used in human machine interaction,clinical medicine and safe driving.However,there is a limitation that conventional recurrent neural networks can only learn the time-series characteristics of expressions based on one-way propagation information.Design/methodology/approach-To solve such limitation,this paper proposes a novel model based on bidirectional gated recurrent unit networks(Bi-GRUs)with two-way propagations,and the theory of identity mapping residuals is adopted to effectively prevent the problem of gradient disappearance caused by the depth of the introduced network.Since the Inception-V3 network model for spatial feature extraction has too many parameters,it is prone to overfitting during training.This paper proposes a novel facial expression recognition model to add two reduction modules to reduce parameters,so as to obtain an Inception-W network with better generalization.Findings-Finally,the proposed model is pretrained to determine the best settings and selections.Then,the pretrained model is experimented on two facial expression data sets of CKþand Oulu-CASIA,and the recognition performance and efficiency are compared with the existing methods.The highest recognition rate is 99.6%,which shows that the method has good recognition accuracy in a certain range.Originality/value-By using the proposed model for the applications of facial expression,the high recognition accuracy and robust recognition results with lower time consumption will help to build more sophisticated applications in real world.