References of "Brust, Mathias 50025542"
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See detailFrom communities to protein complexes: A local community detection algorithm on PPI networks
Dilmaghani, Saharnaz; Brust, Mathias UL; Ribeiro, Carlos H. et al

in PLoS ONE (2022), 17(1), 1-17

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See detailSuSy-EnGaD: Surveillance System Enhanced by Games of Drones
Stolfi Rosso, Daniel UL; Brust, Mathias UL; Danoy, Grégoire UL et al

in Drones (2022), 6(13),

In this article, we propose SuSy-EnGaD, a surveillance system enhanced by games of drones. We propose three different approaches to optimise a swarm of UAVs for improving intruder detection, two of them ... [more ▼]

In this article, we propose SuSy-EnGaD, a surveillance system enhanced by games of drones. We propose three different approaches to optimise a swarm of UAVs for improving intruder detection, two of them featuring a multi-objective optimisation approach, while the third approach relates to the evolutionary game theory where three different strategies based on games are proposed. We test our system on four different case studies, analyse the results presented as Pareto fronts in terms of flying time and area coverage, and compare them with the single-objective optimisation results from games. Finally, an analysis of the UAVs trajectories is performed to help understand the results achieved. [less ▲]

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See detailImproving Pheromone Communication for UAV Swarm Mobility Management
Stolfi Rosso, Daniel UL; Brust, Mathias UL; Danoy, Grégoire UL et al

in ICCCI 2021: Computational Collective Intelligence (2021, July 30)

In this article we address the optimisation of pheromone communication used for the mobility management of a swarm of Unmanned Aerial Vehicles (UAVs) for surveillance applications. A genetic algorithm is ... [more ▼]

In this article we address the optimisation of pheromone communication used for the mobility management of a swarm of Unmanned Aerial Vehicles (UAVs) for surveillance applications. A genetic algorithm is proposed to optimise the exchange of pheromone maps used in the CACOC (Chaotic Ant Colony Optimisation for Coverage) mobility model which improves the vehicles’ routes in order to achieve unpredictable trajectories as well as maximise area coverage. Experiments are conducted using realistic simulations, which additionally permit to assess the impact of packet loss ratios on the performance of the surveillance system, in terms of reliability and area coverage. [less ▲]

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