1,296 research outputs found
The Parma Polyhedra Library: Toward a Complete Set of Numerical Abstractions for the Analysis and Verification of Hardware and Software Systems
Since its inception as a student project in 2001, initially just for the
handling (as the name implies) of convex polyhedra, the Parma Polyhedra Library
has been continuously improved and extended by joining scrupulous research on
the theoretical foundations of (possibly non-convex) numerical abstractions to
a total adherence to the best available practices in software development. Even
though it is still not fully mature and functionally complete, the Parma
Polyhedra Library already offers a combination of functionality, reliability,
usability and performance that is not matched by similar, freely available
libraries. In this paper, we present the main features of the current version
of the library, emphasizing those that distinguish it from other similar
libraries and those that are important for applications in the field of
analysis and verification of hardware and software systems.Comment: 38 pages, 2 figures, 3 listings, 3 table
Facility layout problem: Bibliometric and benchmarking analysis
Facility layout problem is related to the location of departments in a facility area, with the aim of determining the most effective configuration. Researches based on different approaches have been published in the last six decades and, to prove the effectiveness of the results obtained, several instances have been developed. This paper presents a general overview on the extant literature on facility layout problems in order to identify the main research trends and propose future research questions. Firstly, in order to give the reader an overview of the literature, a bibliometric analysis is presented. Then, a clusterization of the papers referred to the main instances reported in literature was carried out in order to create a database that can be a useful tool in the benchmarking procedure for researchers that would approach this kind of problems
An Improved Tight Closure Algorithm for Integer Octagonal Constraints
Integer octagonal constraints (a.k.a. ``Unit Two Variables Per Inequality''
or ``UTVPI integer constraints'') constitute an interesting class of
constraints for the representation and solution of integer problems in the
fields of constraint programming and formal analysis and verification of
software and hardware systems, since they couple algorithms having polynomial
complexity with a relatively good expressive power. The main algorithms
required for the manipulation of such constraints are the satisfiability check
and the computation of the inferential closure of a set of constraints. The
latter is called `tight' closure to mark the difference with the (incomplete)
closure algorithm that does not exploit the integrality of the variables. In
this paper we present and fully justify an O(n^3) algorithm to compute the
tight closure of a set of UTVPI integer constraints.Comment: 15 pages, 2 figure
An Analytic Hierarchy Process for The Evaluation of Transport Policies to Reduce Climate Change Impacts
Transport is the sector with the fastest growth of greenhouse gases emissions, both in developed and in developing countries, leading to adverse climate change impacts. As the experts disagree on the occurrence of these impacts, by applying the analytic hierarchy process (AHP), we have faced the question on how to form transport policies when the experts have different opinions and beliefs. The opinions of experts have been investigated by a means of a survey questionnaire. The results show that tax schemes aiming at promoting environmental-friendly transport mode are the best policy. This incentives public and environmental-friendly transport modes, such as car sharing and car pooling.Analytic Hierarchy Process, Transport Policies, Climate Change
Godel-type space-time metrics
A simple group theoretic derivation is given of the family of space-time
metrics with isometry group SO(2,1) X SO(2) X R first described by Godel, of
which the Godel stationary cosmological solution is the member with a
perfect-fluid stress-energy tensor. Other members of the family are shown to be
interpretable as cosmological solutions with a electrically charged perfect
fluid and a magnetic field.Comment: Heavly rewritten respect to the orginal version, corrected some typos
due to files transfer in the last submitted versio
Component-wise damage detection by neural networks and refined FEs training
Multilayer perceptrons are utilized in this work for vibration-based damage detection of multicomponent aerospace structures. A back-propagation algorithm is utilized along with Monte
Carlo simulations and advanced structural theories for training Artificial Neural Networks
(ANN’s), which are able to detect and classify local damages in structures given the natural
frequencies and the associated vibrations modes. The latter ones are feed into the network
in terms of Modal Assurance Criterion (MAC), which is a scalar representing the degree of
consistency between undamaged and damaged modal vectors. Dataset and ANN training process
is carried out by means of Carrera Unified Formulation (CUF), according to which refined finite
elements with component-wise capabilities can be implemented in a hierarchical and unified
manner. The proposed results demonstrate that CUF-trained ANNs can approximate complete
mapping of the damage distribution, even in case of low damage intensities and local defects
in localized components (stringers, spar caps, webs, etc.
Damage detection in composites by AI and high-order modelling surface-strain-displacement analysis
In the recent years, machine learning algorithms have been widely employed for structural health monitoring applications. As an example, Artificial Neu-ral Networks (ANN) could be useful in giving a precise and complete map-ping of damage distribution in a structure, including low-intensity or local-ized defects, which could be difficult to detected via traditional testing tech-niques. In this domain, Convolutional Neural Network (CNN) are employed in this work along with one-dimensional refined models based on the Carrera Unified formulation (CUF) for surface strain\displacement based damage detection in composite laminates. A layer-wise kinematic is adopted, while both an isotropic and orthotropic damage formulation is implemented. In de-tail, CUF-based finite element models have been exploited in combination with Monte Carlo simulations for the creation of a dataset of damage scenar-ios used for the training of the CNN. Therefore, the latter is fed with images of the strain or displacement field in a region of particular interest for each sample, which are subjected to the same boundary conditions. The trained ANN, given the strain\displacement mapping of an unknown structure, is therefore able to detect and classify all the damages within the structure, solving the so-called inverse problem
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