ABSTRACT

We propose a new text categorisation framework based on concepts lattice and cellular automata. The model is based on the mathematical properties of concept lattices. However, the complexity of generating a concept lattice and using it for text categorisation where data are huge puts a constraint to its applicability. To deal with this problem, through this paper we suggest modelling the Galois lattices by a cellular automaton. We tested the time categorisation of the proposed method on two different corpora: the results show an improvement over the standard Galois lattices.

Incidents of hydraulic or oil spills in the oceans/seas or ports occur with some regularity during the exploitation, production and transportation of petroleum products. Immediate, safe, effective and environmentally friendly measures must be adopted to reduce the impact of the oil spill on marine life. Due to the difficulty to detect and clean these areas, semi-autonomous vehicles can make a significant contribution by implementing a cooperative and coordinated response. The paper proposes a concept study of Hybrid Monitoring Detection and Cleaning System (HMDCS-UV) for a maritime region using semiautonomous unmanned vehicles. This system is based on a cooperative decision architecture for an unmanned aerial vehicle to monitor and detect dirty zones (i.e., hydraulic spills), and clean them up using a swarm of unmanned surface vehicles. The proposed solutions were implemented in a real cloud and were evaluated using different simulation scenarios. Experimental results show that the proposed HMDCS-UV can detect and reduce the level of hydraulic pollution in maritime regions with a significant gain in terms of energy consumption.

No abstract