Dead‐beat regulation of mechanical juggling systems

In this paper, mechanical systems subject to impacts and contacts, that would be not controllable if the impacts were absent (usually called jugglers), are considered. On the basis of an algorithm taken from the literature and of a new procedure to determine a reference trajectory for such a class of systems, a fully algorithmic procedure, able to compute a control input that achieves dead–beat regulation of the “uncontrollable” subsystem just by using impacts, is given. Such a procedure exploits some tools borrowed from algebraic geometry that allow to solve parametric systems of equalities and inequalities. A practical example of application of the given procedure is reported.     

Linear and nonlinear solvers for variational phase‐field models of brittle fracture

The variational approach to fracture is effective for simulating the nucleation and propagation of complex crack patterns but is computationally demanding. The model is a strongly nonlinear non‐convex variational inequality that demands the resolution of small length scales. The current standard algorithm for its solution, alternate minimization, is robust but converges slowly and demands the solution of large, ill‐conditioned linear subproblems. In this paper, we propose several advances in the numerical solution of this model that improve its computational efficiency. We reformulate alternate minimization as a nonlinear Gauss–Seidel iteration and employ over‐relaxation to accelerate its convergence; we compose this accelerated alternate minimization with Newton's method, to further reduce the time to solution, and we formulate efficient preconditioners for the solution of the linear subproblems arising in both alternate minimization and in Newton's method. We investigate the improvements in efficiency on several examples from the literature; the new solver is five to six times faster on a majority of the test cases considered. © 2016 The Authors International Journal for Numerical Methods in Engineering Published by John Wiley & Sons Ltd.     

Clustering versus SVM for malware detection

Previous work has shown that cluster analysis can be used to effectively classify malware into meaningful families. In this research, we apply cluster analysis to the challenging problem of classifying previously unknown malware. We perform several experiments involving malware clustering. We compare our clustering results to those obtained when a support vector machine (SVM) is trained on the malware family. Using clustering, we are able to classify malware with an accuracy comparable to that of an SVM. An advantage of the clustering approach is that a new malware family can be classified before a model has been trained specifically for the family.     

A set of features to detect web security threats

The increasing growth of malicious websites and systems for distributing malware through websites is making it urgent the adoption of effective techniques for timely detection of web security threats. Current mechanisms may exhibit some limitations, mainly concerning the amount of resources required, and a low true positives rate for zero-day attacks. With this paper, we propose and validate a set of features extracted from the content and the structure of webpages, which could be used as indicators of web security threats. The features are used for building a predictor, based on five machine learning algorithms, which is applied to classify unknown web applications. The experimentation demonstrated that the proposed set of features is able to correctly classify malicious web sites with a high level of precision, corresponding to 0.84 in the best case, and recall corresponding to 0.89 in the best case. The classifiers reveal to be successful also with zero day attacks.     

Reactive blending of commercial PET and PC with freshly added catalysts

Reactive blending of commercial poly(ethylene terephthalate) (PET) and bisphenol A polycarbonate (PC), with catalysts added in the form of powder dispersed on the polymers just before melt mixing, was performed in a Brabender Plasticord 2000 apparatus at 275°C. Catalytic activity of the catalysts freshly added to polymers was found to be much higher as compared with that of the residues of the same type of catalysts remaining in PET after its synthesis. Furthermore, the catalytic activity appeared to be strongly dependent on the structure of the ligand that influences the catalyst solubility in the polymer melt. N.m.r. spectroscopy, selective degradation of PC fragments, solubility tests in methylene chloride and d.s.c. measurements made it possible to range the catalysts studied according to their catalytic activity.     

Prestressing wire breakage monitoring using sound event detection

Detecting prestressed wire breakage in concrete bridges is essential for ensuring safety and longevity and preventing catastrophic failures. This study proposes a novel approach for wire breakage detection using Mel-frequency cepstral coefficients (MFCCs) and back-propagation neural network (BPNN). Experimental data from two bridges in Italy were acquired to train and test the models. To overcome the limited availability of real-world training data, data augmentation techniques were employed to increase the data set size, enhancing the capability of the models and preventing over-fitting problems. The proposed method uses MFCCs to extract features from acoustic emission signals produced by wire breakage, which are then classified by the BPNN. The results show that the proposed method can detect and classify sound events effectively, demonstrating the promising potential of BPNN for real-time monitoring and diagnosis of bridges. The significance of this work lies in its contribution to improving bridge safety and preventing catastrophic failures. The combination of MFCCs and BPNN offers a new approach to wire breakage detection, while the use of real-world data and data augmentation techniques are significant contributions to overcoming the limited availability of training data. The proposed method has the potential to be a generalized and robust model for real-time monitoring of bridges, ultimately leading to safer and longer-lasting infrastructure.