Alessandro Pannicelli

Learn More
This paper describes an application of fuzzy-logic and evolutionary computation to the optimization of the start-up phase of a combined cycle power plant. We modelled process experts' knowledge with fuzzy sets over the process variables in order to get the needed cost function for the Genetic Algorithm (GA) we used to obtain the optimal regulations. Due to(More)
There is epidemiological evidence for increased non-cancer mortality, primarily due to circulatory diseases after radiation exposure above 0.5 Sv. We evaluated the effects of chronic low-dose rate versus acute exposures in a murine model of spontaneous atherogenesis. Female ApoE-/- mice (60 days) were chronically irradiated for 300 days with gamma rays at(More)
The extensive use of energy generation processes presents a severe challenge to the environment and makes indispensable to focus the research on the maximization of the energy efficiency and minimization of environmental impact like NOx and CO emissions. The proposed idea describes an approach, based on an artificial life environment, for on-line(More)
In this paper we present a study on the application of fuzzy sets for the start-up optimisation of a combined cycle power plant. We fuzzyfy the output process variables and then we properly combine the resulting fuzzy sets in order to get a single value in the lattice [0,1] providing the effectiveness (zero bad, one excellent) of the given start-up(More)
We present a novel model of crowd motion and swarm behaviour, in the form of a multi-agent system. We introduce a set of behavioural rules arising from an individual's presence within a swarm of other individuals. First, the separation force causes every agent to maintain a comfortable distance from all others in the same swarm. Second, the alignment force(More)
The ideas proposed in this work are aimed to describe a novel approach based on artificial life (alife) environments for on-line adaptive optimisation of dynamical systems. The basic features of the proposed approach are: no intensive modelling (continuous learning directly from measurements) and capability to follow the system evolution (adaptation to(More)
  • 1