133 research outputs found
Adaptability Checking in Multi-Level Complex Systems
A hierarchical model for multi-level adaptive systems is built on two basic
levels: a lower behavioural level B accounting for the actual behaviour of the
system and an upper structural level S describing the adaptation dynamics of
the system. The behavioural level is modelled as a state machine and the
structural level as a higher-order system whose states have associated logical
formulas (constraints) over observables of the behavioural level. S is used to
capture the global and stable features of B, by a defining set of allowed
behaviours. The adaptation semantics is such that the upper S level imposes
constraints on the lower B level, which has to adapt whenever it no longer can
satisfy them. In this context, we introduce weak and strong adaptabil- ity,
i.e. the ability of a system to adapt for some evolution paths or for all
possible evolutions, respectively. We provide a relational characterisation for
these two notions and we show that adaptability checking, i.e. deciding if a
system is weak or strong adaptable, can be reduced to a CTL model checking
problem. We apply the model and the theoretical results to the case study of
motion control of autonomous transport vehicles.Comment: 57 page, 10 figures, research papaer, submitte
A Graph Grammar for Modelling RNA Folding
We propose a new approach for modelling the process of RNA folding as a graph
transformation guided by the global value of free energy. Since the folding
process evolves towards a configuration in which the free energy is minimal,
the global behaviour resembles the one of a self-adaptive system. Each RNA
configuration is a graph and the evolution of configurations is constrained by
precise rules that can be described by a graph grammar.Comment: In Proceedings GaM 2016, arXiv:1612.0105
An Individual-based Probabilistic Model for Fish Stock Simulation
We define an individual-based probabilistic model of a sole (Solea solea)
behaviour. The individual model is given in terms of an Extended Probabilistic
Discrete Timed Automaton (EPDTA), a new formalism that is introduced in the
paper and that is shown to be interpretable as a Markov decision process. A
given EPDTA model can be probabilistically model-checked by giving a suitable
translation into syntax accepted by existing model-checkers. In order to
simulate the dynamics of a given population of soles in different environmental
scenarios, an agent-based simulation environment is defined in which each agent
implements the behaviour of the given EPDTA model. By varying the probabilities
and the characteristic functions embedded in the EPDTA model it is possible to
represent different scenarios and to tune the model itself by comparing the
results of the simulations with real data about the sole stock in the North
Adriatic sea, available from the recent project SoleMon. The simulator is
presented and made available for its adaptation to other species.Comment: In Proceedings AMCA-POP 2010, arXiv:1008.314
Timed Process Calculi: From Durationless Actions to Durational Ones
Several timed process calculi have been proposed in the literature, which mainly differ for the way in which delays are represented. In particular, a distinction is made between integrated-time calculi, in which actions are durational, and orthogonal-time calculi, in which actions are instantaneous and delays are expressed separately. To reconcile the two approaches, in a previous work an encoding from the integrated-time calculus CIPA to the orthogonal-time calculus TCCS was defined, which preserves timed bisimilarity. To complete the picture, in this paper we consider the reverse translation, by examining the modifications to the two calculi that are needed to make an encoding feasible, as well as the behavioral equivalence that is appropriate to preserve. We then introduce an encoding from modified TCCS to modified CIPA, and show that it can only preserve the weak variant of timed bisimilarity
Topological classifier for detecting the emergence of epileptic seizures
Objective
An innovative method based on topological data analysis is introduced for classifying EEG recordings of patients affected by epilepsy. We construct a topological space from a collection of EEGs signals using Persistent Homology; then, we analyse the space by Persistent entropy, a global topological feature, in order to classify healthy and epileptic signals.
Results
The performance of the resulting one-feature-based linear topological classifier is tested by analysing the Physionet dataset. The quality of classification is evaluated in terms of the Area Under Curve (AUC) of the receiver operating characteristic curve. It is shown that the linear topological classifier has an AUC equal to 97.2% while the performance of a classifier based on Sample Entropy has an AUC equal to 62.0%
Shape Calculus: A spatial calculus for 3D colliding shapes
Available at http://s1report.cs.unicam.it/6
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