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Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

How to enhance system consensus is an important problem in the research of multi agent systems. The conventional consensus protocols usually does not consider the Hub Nodes in the topology, and the topology weight was single, which made the system easier to split. Based on the idea that key figures in the interpersonal network could promote information exchange in different communities, this paper proposed an influence network model（INM）. First, this paper proposed a Distributed Hub Node Identify algorithm（ DHNI） to distinguish between Hub Nodes and Non Hub Nodes. Distributed multi-agent system could apply DHNI. Secondly, this paper proposed a distributed Hub Node based topology reweighting algorithm （DHNTR） to quantify the influence of different nodes on their neighbors. Finally, this paper proposed an influence network-based multi-agent consensus protocol. this paper designed a common Lyapunov function and analyzed the global stability of the system. this paper proved the stability of the system in the sense of Lyapunov. Simulation results show that the protocol can enhance system consensus. [ABSTRACT FROM AUTHOR]

Because of the high infectivity of COVID-19, it is essential to detect the close contacts of patients as soon as possible to contain the outbreak of the epidemic. However, due to the level of technological development, the current methods and research on contact detection require manual participation. This paper proposes a future oriented automation method, which uses mobile agents loaded on sensing devices and edge coordinators to form a multi-agent system on the street. Based on perception, tracking and edge-computing, the contact probability between infected people and pedestrians is estimated. A series of simulations provide the comparison of parameters in application deployment. The simulation results show that the proposed street expropriation mode and edge-computing algorithm can further improve the detection rate. [ABSTRACT FROM AUTHOR]

To settle the exact consensus problem of a class of multi-agent systems subjected to actuator saturations on a finite time interval [0,7], this paper utilized the adaptive iterative learning control scheme to design the time-varying gains with fully saturated difference adaptive updating laws. By constructing a proper composite energy function, it was proved strictly that the vectors of consensus error tended to zero as the iteration number approaches to infinity, and obtained the adaptive learning consensus algorithm without dependence on eigenvalues of the communication topology matrix. Finally, the result of a given simulation example illustrates the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]