Updating user profile using ontology based semantic similarity
Abstractions come in the form of lumped, aggregated models, which are beneficial in being easier to simulate or to analyse.Key to the novelty of this work, the proposed abstractions are quantitative in that precise error bounds with the original model can be established.The increased relevance of renewable energy sources has modified the behaviour of the electrical grid.Some renewable energy sources affect the network in a distributed manner: whilst each unit has little influence, a large population can have a significant impact on the global network, particularly in the case of synchronised behaviour.Courses: Computer-Aided Formal Verification, Probabilistic Model Checking, Machine Learning Reinforcement Learning (RL) is a known architecture for synthesising policies for Markov Decision Processes (MDP).We work on extending this paradigm to the synthesis of ‘safe policies’, or more general of policies such that a linear time property is satisfied.
The project can benefit from a visit to Honeywell Labs (Prague). Prerequisites: Some familiarity with dynamical systems. This project will explore connections of techniques from machine learning with successful approaches from formal verification.
These factors calls for the development of proper quantitative models.
This framework provides the opportunity of employing formal methods to verify properties of the microgrid.
Among other advantages, microgrids have shown positive effects over the reliability of distribution networks.
These systems present heterogeneity and complexity coming from 1. the presence of nonlinear dynamics both over continuous and discrete variables.