Cyber-Physical System (CPS) is a system that combines the physical world with cyber components, where event can be represented as the units for computation, communication and control. The events in CPS have two patterns: rendezvous and triggers. Rendezvous means that all events involved in an interaction are obtained. While triggers means that one of the events involved in an interaction is the event of trigger. The events algebra of triggers is provided for the complex processing of events with triggers. Meanwhile, a graphical representation is presented to symbolically represent the composition processing for $AT(ESys).$ Several examples are utilized to illustrate applications of algebra, including a formal description on feasible interactions and a snap judgment for feasible interactions.

This paper researches some problems in complex formation for multi-agents, in which two matrices are proposed to record the formation. The pattern matrix is used to describe the pattern of the formation; meanwhile, the location matrix is used to record the location of each agent. Thus, all desired positions of each agent will be obtained by geometrical relationship on the basis of two matrices above. In addition a self-adaptation flocking algorithm is proposed to control all agents to form a desired formation and avoid obstacles. The main idea is as follows: agents will form a desired formation through the method of formation control when far away from obstacles; otherwise, agents will freely fly to pass through the area of obstacles. In the simulation, three scenarios are designed to verify the effectiveness of our method. The results show that our method also can be applied in three dimensions. All agents will form a stable formation and keep the same velocity at last.

The traditional methods of action recognition are not specific for the operator, thus results are easy to be disturbed when other actions are operated in videos. The network based on mixed convolutional resnet and RPN is proposed in this paper. The rMC is tested in the data set of UCF-101 to compare with the method of R3D. The result shows that its correct rate reaches 71.07%. Meanwhile, the action recognition network is tested in our gesture and body posture data sets for specific target. The simulation achieves a good performance in which the running speed reaches 200 FPS. Finally, our model is improved by introducing the regression block and performs better, which shows the great potential of this model.