A trajectory summarisation gener

The semantic representation of the trajectory is conducive to enrich the content oftrajectory data mining. A trajectory summarisation generation method based on themobile robot behaviour analysis was proposed to realize the abstract expression andsemantic representation of the spatio-temporal motion features of the robot and itsenvironmental interaction state. First, the behavioural semantic modelling and representationof the mobile robot are completed by modelling the sub-trajectory andcalculating the topological behaviour (TOP). Second, Chinese word segmentation andsemantic slot filling methods are used to combine with hierarchical clustering to performbasic word extraction and classification for describing trajectory sentences. Then, thedescription language frame is extracted based on the TOP, and the final trajectorysummarisation is generated. The result shows that the proposed method can semanticallyrepresent robot behaviours with different motion features and topological features,extract two verb-frameworks for describing the sentences according to their topologicalfeatures, and dynamically adjust the syntactic structure for the different topological behavioursbetween the target and the environment. The proposed method can generatesemantic information of relatively high quality for spatio-temporal data and help tounderstand the higher-order semantics of moving robot behaviour.

A mixed target estimation fusion algorithm based on Gibbs-GLMB and federated filter

Mixed targets are composed of point targets,extended targets,and group targets.The point target can produce one measurement at most,the extended target and the group target can produce multiple measurements,but the sub-goals of the group target have a certain dependency relationship.At this time,the estimated fusion of the group target is converted to the estimated fusion of sub-targets with formation motion structure,and the distance among the sub-targets is very close,which brings difficulties to the estimated fusion of mixed targets.This paper combines the adjacency matrix in graph theory to dynamically model the discernible group target and introduces the concept of deformation.Also,it uses the finite mixture model method to dynamically model the extended target.Then the Gibbs-GLMB algorithm is used to estimate the state and number of the mixed targets.A dynamic detection federated filter fusion algorithm is proposed to fuse the mixed targets state estimates.The effectiveness of the algorithm is verified in the final simulation.

H_(∞)containment control with time-varying delays and communicate noise under semi-Markov switching topologies

This article focuses on H_(∞)containment control and the communication network topologies that are driven by a semi-Markov chain.Moreover,the communication channels between agents exist time-varying delays and noise.Firstly,the authors extend the Markov switching topologies to semi-Markov switching topologies.Because the transition rate of the semi-Markov switching topology is time-varying and depends on the sojourn time,the analysis of containment control under semi-Markov switching topology becomes more challenging.Secondly,a control protocol with time-varying delays is adopted.The error function is derived by the property of graph theory,convex hull and communication noise.Hence,the problem of H_(∞)containment control is transformed into the stability problem of the semi-Markov jump system.To avoid the zero initial condition in the traditional H_(∞)control approach,a novel performance function is constructed with the initial condition considered.Finally,simulation experiments are provided to verify the effectiveness of the proposed algorithm.