1,721,004 research outputs found
Night Thermal Gradient: A New Potential Tool for Earthquake Precursors Studies. An Application to the Seismic Area of L'Aquila (Central Italy)
Full text linkRelations between ground surface warming-up and earthquakes are presented by means of the analysis of thermal data detected by Meteosat satellite. The analysis has been carried out on the basis of Land Surface Temperature (LST) data, by the application of algorithms for two purposes: the reduction of the effect of cloud coverage and daily weather variability and the improvement in resolution of thermal maps. The main case study has been L'Aquila earthquake on 6th April 2009 with two different observation timeframes: one in absence of significant seismic activity (October 2008), one right before and straight after the same earthquake (January-May 2009). The detected thermal anomalies reveal the possibility to associate surface thermal phenomena to the imminent manifestation of seismic events
Dynamic control of historical buildings through interferometric radar technique: an useful approach for Structural Health Monitoring on earthquake damaged structures.” 32nd national meeting “GNGTS 2013”, Tema 3: Geofisica applicata, 19-21 November 2013, Trieste, Italy; pp. 101-106;
No full textVibration analysis of historic bell towers by means of contact and remote sensing measurements
No full textThe dynamic behaviour of two slender structures with very similar geometry has been investigated in order to evaluate the role played by the construction materials; the comparison has thus been conducted on their vibration properties as resonance frequencies, damping coefficients and mode shapes. The studied structures are two bell towers of a church which were built in two different historical times, with an interval of about one century, using different construction techniques and materials. The experimental tests were carried out by means of output-only measurements of ambient vibration using both contact and non-contact techniques. The signals have been acquired using a tri-directional tromometer or two short period seismometers, both placed in prearranged station points on the structures. Furthermore, the vibrations of the structures have also been measured with the IBIS-S microwave interferometer which is able to provide submillimetric displacements along the radar Line Of Sight without need of any contact with the surface. Therefore, the experimental dynamic response of the church-towers system has been estimated integrating both velocity and displacement data. Though the vibration of the structures had low magnitude, both surveys allowed us to identify the main linear dynamic properties of the structures. Based on these passive surveys, a linear finite element model was calibrated in order to confirm the relationship between the materials and vibration properties. The final model has been locally validated by means of in situ acoustic measurements
Identification of piecewise affine systems using a cluster refinement technique
No full textThe identification of piecewise affine (PWA) systems is a challenging mixed integer optimization problem that involves both the estimation of the dynamics associated to different modes of operation, and the partition of the state space in regions associated to said modes, the transition from one region to another corresponding to a mode switching. The challenges are mainly associated with the sample-mode assignment task, because the combinatorial complexity increases with the size of the dataset. Furthermore, some samples are consistent with more than one mode, making their classification ‘ambiguous’. The identification problem is here addressed with a two-stage iterative method, alternating between an identification phase carried out over given clusters of data associated to regions in the state space (such that each cluster is assigned to a single mode), and a refinement phase, whereby the region borders are adjusted (by reassigning samples to other clusters) to improve the model quality. Operating on data clusters (as opposed to individual samples) significantly reduces the complexity of the combinatorial mode assignment problem, and naturally avoids region outliers (isolated samples surrounded by samples assigned to a different mode). However, this approach works properly only if accompanied by a cluster refinement procedure, responsible for reshaping the mode regions and reassigning stray samples to the correct modes. The combination of these two stages is ultimately successful in determining correctly both the local models and the associated state space regions, as shown here with reference to several benchmark examples
Supervisory Control of Timed Discrete Event Systems with Logical and Timed Specifications
Full text linkA novel framework is introduced for the supervisory control (SC) of timed discrete event systems, based on Time Petri nets. The method encompasses both logical (markings to reach or avoid) and temporal specifications (arrival and departure times in specific markings). It relies on the construction of a partial forward reachability graph, of the Modified State Class Graph type, and the formulation of integer linear programming problems to establish suitable firing time intervals (FTIs) for the controllable transitions. The SC algorithm provides for each enabled controllable transition the largest FTI that guarantees that the specifications are met, irrespectively of the firing times of the uncontrollable transitions
Process noise covariance estimation via stochastic approximation
Full text linkKalman filtering for linear systems is known to provide the minimum variance estimation error, under the assumption that the model dynamics is known. While many system identification tools are available for computing the system matrices from experimental data, estimating the statistics of the output and process noises is still an open problem. Correlation-based approaches are very fast and sufficiently accurate, but there are typically restrictions on the number of noise covariance elements that can be estimated. On the other hand, maximum likelihood methods estimate all elements with high accuracy, but they are computationally expensive, and they require the use of external optimization solvers. In this paper, we propose an alternative solution, tailored for process noise covariance estimation and based on stochastic approximation and gradient-free optimization, that provides a good trade-off in terms of performance and computational load, and is also easy to implement. The effectiveness of the method as compared to the state of the art is shown on a number of recently proposed benchmark examples
A randomized two-stage iterative method for switched nonlinear systems identification
Full text linkThis paper addresses the identification of discrete time switched nonlinear systems, which are collections of discrete time nonlinear continuous systems (modes) indexed by a finite-valued variable defining the current mode. In particular, we consider the class of Switched Nonlinear AutoRegressive eXogenous (Switched NARX, or SNARX) models, where the continuous dynamics are represented by NARX models. Given a set of input–output data, the identification of a SNARX model for the underlying system involves the simultaneous identification of the mode sequence and of the NARX model associated to each mode, configuring a mixed integer non-convex optimization problem, hardly solvable in practice due to the large combinatorial complexity. In this paper, we propose a black-box iterative identification method, where each iteration is characterized by two stages. In the first stage the identification problem is addressed assuming that mode switchings can occur only at predefined time instants, while in the second one the candidate mode switching locations are refined. This strategy allows to significantly reduce the combinatorial complexity of the problem, thus allowing an efficient solution of the optimization problem. The combinatorial optimization is addressed using a randomized method, whereby the sample-mode map and the SNARX model structure are characterized by a probability distribution, which is progressively tuned via a sample-and-evaluate strategy, until convergence to a limit distribution concentrated on the best SNARX model of the system generating the observed data
Computation of K-reachable paths in Petri nets
Full text linkThe enumeration of legal transition paths leading to a target state (or set of states) is of paramount importance in the control of discrete event systems, but is hindered by the state explosion problem. A method is proposed in this paper, in the context of Petri nets, to calculate and enumerate firing count vectors for which there exists at least an admissible transition sequence leading to a given target marking. The method is based on the concept of singular complementary transition invariants proposed by Kostin and combines an integer linear programming formulation that finds the shortest minimal solution and a branching procedure that effects a partition of the solution set. The enumeration can be restricted to minimal solutions or extended to non-minimal ones. Some analytical examples are discussed in detail to show the effectiveness of the proposed approach
Optimization-based computation of bounded sequences to reach target states in DESs
No full textThe enumeration of legal transition paths leading to a target state (or set of states) is of paramount importance in the control of discrete event systems, but is hindered by the state explosion problem. A method is proposed in this paper, in the context of Petri nets, to calculate and enumerate firing count vectors for which there exists at least an admissible transition sequence leading to a given target marking. The method is shown to improve the approach based on singular complementary transition invariants proposed by Kostin and combines an integer linear programming formulation that finds the shortest minimal solution and a branching procedure that realizes a partition of the solution set. The enumeration can be restricted to minimal solutions or extended to non-minimal ones. Moreover, the approach is extended by adding a further constraint that the target transition sequences should pass by intermediate markings (in a specific order or not). Finally, source, target and via markings can be replaced by sets of markings. Some analytical examples are discussed in detail to show the effectiveness of the proposed approach
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