60,426 research outputs found
Formalization and Validation of Safety-Critical Requirements
The validation of requirements is a fundamental step in the development
process of safety-critical systems. In safety critical applications such as
aerospace, avionics and railways, the use of formal methods is of paramount
importance both for requirements and for design validation. Nevertheless, while
for the verification of the design, many formal techniques have been conceived
and applied, the research on formal methods for requirements validation is not
yet mature. The main obstacles are that, on the one hand, the correctness of
requirements is not formally defined; on the other hand that the formalization
and the validation of the requirements usually demands a strong involvement of
domain experts. We report on a methodology and a series of techniques that we
developed for the formalization and validation of high-level requirements for
safety-critical applications. The main ingredients are a very expressive formal
language and automatic satisfiability procedures. The language combines
first-order, temporal, and hybrid logic. The satisfiability procedures are
based on model checking and satisfiability modulo theory. We applied this
technology within an industrial project to the validation of railways
requirements
Validating plans with exogenous events
We are concerned with the problem of deciding the validity of a complex plan involving interacting continuous activity. In these situations there is a need to model and reason about the continuous processes and events that arise as a consequence of the behaviour of the physical world in which the plan is expected to execute. In this paper we describe how events, which occur as the outcome of uncontrolled physical processes, can be taken into account in determining whether a plan is valid with respect to the domain model. We do not consider plan generation issues in this paper but focus instead on issues in domain modelling and plan validation
A Statistical Learning Theory Approach for Uncertain Linear and Bilinear Matrix Inequalities
In this paper, we consider the problem of minimizing a linear functional
subject to uncertain linear and bilinear matrix inequalities, which depend in a
possibly nonlinear way on a vector of uncertain parameters. Motivated by recent
results in statistical learning theory, we show that probabilistic guaranteed
solutions can be obtained by means of randomized algorithms. In particular, we
show that the Vapnik-Chervonenkis dimension (VC-dimension) of the two problems
is finite, and we compute upper bounds on it. In turn, these bounds allow us to
derive explicitly the sample complexity of these problems. Using these bounds,
in the second part of the paper, we derive a sequential scheme, based on a
sequence of optimization and validation steps. The algorithm is on the same
lines of recent schemes proposed for similar problems, but improves both in
terms of complexity and generality. The effectiveness of this approach is shown
using a linear model of a robot manipulator subject to uncertain parameters.Comment: 19 pages, 2 figures, Accepted for Publication in Automatic
Regularized linear system identification using atomic, nuclear and kernel-based norms: the role of the stability constraint
Inspired by ideas taken from the machine learning literature, new
regularization techniques have been recently introduced in linear system
identification. In particular, all the adopted estimators solve a regularized
least squares problem, differing in the nature of the penalty term assigned to
the impulse response. Popular choices include atomic and nuclear norms (applied
to Hankel matrices) as well as norms induced by the so called stable spline
kernels. In this paper, a comparative study of estimators based on these
different types of regularizers is reported. Our findings reveal that stable
spline kernels outperform approaches based on atomic and nuclear norms since
they suitably embed information on impulse response stability and smoothness.
This point is illustrated using the Bayesian interpretation of regularization.
We also design a new class of regularizers defined by "integral" versions of
stable spline/TC kernels. Under quite realistic experimental conditions, the
new estimators outperform classical prediction error methods also when the
latter are equipped with an oracle for model order selection
Model checking Quantitative Linear Time Logic
This paper considers QLtl, a quantitative analagon of Ltl and presents algorithms for model checking QLtl over quantitative versions of Kripke structures and Markov chains
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