Typhoon Haikui (2012) occurred in the northwestern Pacific Ocean, and landfall on the east coast of China brought heavy rainfall with strong winds. Because of Typhoon Haikui, sea surface temperature (SST) cooling of 3 °C occurred on the right side of the track, mainly due to Ekman transport and upwelling. SST cooling on the left side was lower than on the right side, mainly due to the rainfall. Heavy precipitation occurred on both sides of the typhoon track; however, rainfall was higher on the left side of the typhoon track. This paper explains the dynamic process between atmospheric and oceanographic parameters and verifies the variations in chlorophyll and sea surface height data before, during, and after the typhoon. Typhoon Haikui demonstrates dynamic variations and intuitively illustrates the relationship between the ocean and atmospheric parameters.
Rainfall is essential for the humanity as well as for agriculture. Zhoushan is an Island, which experiences different kinds of rainfall events, such as due to typhoon, local convection, topographic effects, etc. To find out the variations, we have chosen Global Precipitation Climatology Project (GPCP) merged rainfall data set over Zhoushan during the period 1979-2018. Monthly, seasonal, annual and decadal variations have been checked and find out there is an increasing trend in rainfall during the study period. All the seasonal variations are illustrating an increasing trend except spring season, which showing decreasing trend. Recent decade illustrating strong increase in rainfall, this may be due to heavy rainfall events increased during decade. To find out the heavy rainfall events, we have chosen few typhoons' rainfall during its life time, which clearly demonstrating that rainfall during the typhoon period. Zhoushan is experiencing heavy rainfall when the typhoon landfall over Zhejiang province also experiencing rainfall while typhoon passing through.
Human affective behavior analysis plays a vital role in human-computer interaction (HCI) systems. In this paper, we introduce our submission to the CVPR 2023 Competition on Affective Behavior Analysis in-the-wild (ABAW). We propose a single-stage trained AU detection framework. Specifically, in order to effectively extract facial local region features related to AU detection, we use a local region perception module to effectively extract features of different AUs. Meanwhile, we use a graph neural networkbased relational learning module to capture the relationship between AUs. In addition, considering the role of the overall feature of the target face on AU detection, we also use the feature fusion module to fuse the feature information extracted by the backbone network and the AU feature information extracted by the relationship learning module. We also adopted some sampling methods, data augmentation techniques and post-processing strategies to further improve the performance of the model.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.