Titlebook: Artificial Intelligence and Security; 7th International Co Xingming Sun,Xiaorui Zhang,Elisa Bertino Conference proceedings 2021 Springer Na

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Kim G. Larsen,Ulrik Nyman,Andrzej Wąsowskition and analysis are conducted according to the proposed algorithms. The results show that both planning methods can deflect the UAV flight path, however, the planning method set by tangent is more strongly biased than the planning method set by the extension line, and the final spoofing effect is better.

Tempor

Evren Ermis,Martin Schäf,Thomas Wieslection learning strategy to retain excellent individual information which accelerate the convergence speed. The improved particle swarm optimization algorithm is used to solve standard instance problems of different scales, and the effectiveness of the algorithm is verified by comparing with other methods

CON

Cornelius Diekmann,Lars Hupel,Georg Carle the LTE-U system is tested for its ability to carry services such as CBTC, CCTV/PIS, emergency text and train operation status information in urban rail transit. The test results show that LTE-U system can meet the demand of carrying urban rail transit services and is a feasible solution to the existing problems of urban rail transit.

tympanometry

Research on Spoofing Jamming of Integrated Navigation System on UAVtion and analysis are conducted according to the proposed algorithms. The results show that both planning methods can deflect the UAV flight path, however, the planning method set by tangent is more strongly biased than the planning method set by the extension line, and the final spoofing effect is better.

CARE

Application Research of Improved Particle Swarm Optimization Algorithm Used on Job Shop Scheduling Plection learning strategy to retain excellent individual information which accelerate the convergence speed. The improved particle swarm optimization algorithm is used to solve standard instance problems of different scales, and the effectiveness of the algorithm is verified by comparing with other methods

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故意

Community Detection Model Based on Graph Representation and Self-supervised Learning the mainstream community detection methods consider using neural network to establish the nonlinear model of nodes and edges to overcome the shortcomings of the traditional linear model. However, the design of such models does not fully consider the characteristics of the graph structure data, requ