Abstract: The present disclosure provides a risk assessment method of water inrush in tunnels constructed in water-rich grounds. The method includes the following steps: simulating a tunnel excavation process by finite element software MIDAS GTS NX and fluid-structure interaction; according to a research method of control variables, analyzing effects of a groundwater level, an elastic modulus and advanced pipe shed grouting on the stability of surrounding rock, and improving an algorithm of a radial basis function (RBF) neural network using a Grey Relation Analysis (GRA)-based Partitioning Around Medoid (PAM) clustering algorithm to assess risks of water inrush occurring in Qingdao area.

Abstract: The present disclosure provides a multi-factor quantitative analysis method for deformation of a neighborhood tunnel. The method includes the following steps: analyzing monitoring data generated at a tunnel site; simulating collapse occurring at a shallow buried section of a tunnel; determining the degree of influence of each factor on the tunnel and a stratum; and determining quantitative influence of each factor on tunnel deformation. The present disclosure can not only provide an accurate theoretical basis for the construction of the shallow buried section of the small-distance tunnel, but also guarantee safety and cost saving during tunnel construction.

Abstract: An encoding method includes pre-encoding a current video frame in a video stream, to obtain a pre-encoded current video frame, when there is no I frame in M encoded video frames that have been encoded before the current video frame, determining whether the current video frame is a scene switching frame in which scene switching has occurred relative to a previous video frame, based on the pre-encoded current video frame and a pre-encoded previous video frame to the current video frame, M being a preset positive integer, and encoding the current video frame into an I frame when the current video frame is the scene switching frame, and encoding the current video frame into a P frame when the current video frame is not the scene switching frame.

Abstract: A video processing collaboration method includes transmitting a video processing capability request and a video analysis capability request to a terminal device, receiving video processing capability information and video analysis capability information from the terminal device based on the video processing capability request and the video analysis capability request, determining a video processing collaboration configuration based on the video processing capability information and a video analysis collaboration configuration based on the video analysis capability information, allocating an image processing task to the terminal device, to the cloud server, or to both the terminal device and the cloud server, based on the video processing collaboration configuration, and allocating an image analysis task to the terminal device, to the cloud server, or to both the terminal device and the cloud server, based on the video analysis collaboration configuration.

Abstract: A method or a system for classifying users into a plurality of categories. The system uses a first machine learning (ML) model to segment users into a first plurality of groups based in part on a first set of features, indicating relative research-skill levels of the respective users. The system uses a second ML model to segment users into a second plurality of groups based in part on a second set of features, indicating relative engagement levels of the respective users. The system then uses a third ML model to classify the plurality of users into a plurality of classes based in part on the research-skill levels and the engagement levels of the respective users, and selects and presents content to the user based in part on their classifications.

Abstract: A computing server may continuously update a set of nodes that are addable to a data tree based on past interactions of the user with one or more nodes. The computing server may track a recently interacted set of interacted nodes with which the user has interacted within a number of past interactions. The computing server may select a pool of candidate nodes based on the recently interacted set. At least one of the candidate nodes is within a domain boundary of one of the interacted nodes that is in the recently interacted set. The domain boundary may be determined by the degree of relationship. The computing server may present one or more candidate nodes in the pool as a version of the continuously updated set of nodes. The computing server may update the pool of candidate nodes as additional interactions performed by the user updates the recently interacted set.