Automatic identification of rock discontinuity sets using modified agglomerative nesting algorithm

Yan, Jianhua; Chen, Jianping; Zhan, Jiewei; Song, Shengyuan; Zhang, Yansong; Zhao, Mingyu; Liu, Yongqiang; Xu, Wanglai

doi:10.1007/s10064-022-02724-w

Cited by 8 publications

(1 citation statement)

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“…After the evaluation of similarity of swarm nodes, we employ Agglomerative Nesting (AGNES) algorithm to achieve UAV swarm clustering. AGNES algorithm is a down-top hierarchical clustering algorithm with low-complexity [36,37]. In UAV swarm scenario, the AGNES algorithm is performed under the following three steps: (i) treat each UAV node as an initial cluster; (ii) keep merging two clusters according to the SIMMAP, where the similarity among nodes is used as the evaluation value for merging clusters; (iii) terminate iteration when pre-defined condition is satisfied.…”

Section: Optimal Clustering Algorithmmentioning

confidence: 99%

Topology Duration Optimization for UAV Swarm Network under the System Performance Constraint

et al. 2023

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Network topology construction plays an important role in the application of large-scale unmanned aerial vehicle (UAV) swarm. Current researches usually perform the topology construction in terms of criteria of nodes energy consumption, transmission delay and network throughput, etc. However, another important criterion, the stability of swarm network topology, which is much critical for dynamic scenarios, has not been fully considered. In this paper, a novel topology construction method for UAV swarm network based on the criterion of topology duration is proposed. Specially, the topology construction of swarm network is formulated as an optimization problem of maximizing the topology duration while satisfying the constraints of certain network throughput, end-to-end delay, and nodes energy consumption. Then, a novel Group Trend Similarity based double-head Clustering method(GTSC) is employed to solve this problem, in which group similarity of movement, intra- and inter-cluster distance, node forwarding delay, and energy strategy are comprehensively taken into account. The proposed method is effective when used to perform the network topology construction for UAV swarm, which is verified by the simulation results. Furthermore, in comparison with representative algorithms, the proposed GTSC method exhibits better performance on topology duration, network throughput, end-to-end delay and energy consumption balance especially in a large-scale swarm scenarios.

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Section: Optimal Clustering Algorithmmentioning

confidence: 99%