Ricerc@Sapienza

AbstractMany nonstationary time series exhibit changes in the trend and seasonality
structure, that may be modeled by splitting the time axis into different regimes.
We propose multi-regime models where, inside each regime, the trend is linear and
seasonality is explained by a Periodic Autoregressivemodel. In addition, for achieving
parsimony, we allow season grouping, i.e. seasons may consist of one, two, or
more consecutive observations. Identification is obtained by means of a Genetic Algorithm
that minimizes an identification criterion

Many nonstationary time series exhibit changes in the trend and seasonality structure, that may be modeled by splitting the time axis into different regimes. We propose multi-regime models where, inside each regime, the trend is linear and seasonality is explained by a Periodic Autoregressive model. In addition, for achieving parsimony, we allow season grouping, i.e. seasons may consists of one, two, or more consecutive observations.

This paper proposes an extension of Periodic AutoRegressive (PAR) modelling for time series with evolving features. The
large scale of modern datasets, in fact, implies that the time span may subtend several evolving patterns of the underlying
series, affecting also seasonality. The proposed model allows several regimes in time and a possibly different PAR process
with a trend term in each regime. The means, autocorrelations and residual variances may change both with the regime and

In this paper, the viscoelastic characterization of biosamples is addressed considering a measuring technique relying on the use of a MEMS techonology-based microgripper. A proper mechanical model is developed for the coupled nonlinear dynamics of the microsystem, composed of the measuring tool and the specimen to be analyzed. The Maxwell liquid drop model and the generalized Maxwell-Wiechert model are considered for the sample, and the identification of the viscoelastic parameters is performed by implementing a genetic algorithm.

With the breakthrough of pervasive advanced networking infrastructures and paradigms such as 5G and IoT, cybersecurity became an active and crucial field in the last years. Furthermore, machine learning techniques are gaining more and more attention as prospective tools for mining of (possibly malicious) packet traces and automatic synthesis of network intrusion detection systems. In this work, we propose a modular ensemble of classifiers for spotting malicious attacks on Wi-Fi networks.

Buildings are heavily responsible for energy consumption and CO2 emissions. Increasing their energy efficiency is in the direction of the EU strategies; and this is consistent with EU Energy Performance of

Multi-agent systems recently gained a lot of attention for solving machine learning and data mining problems. Furthermore, their peculiar divide-and-conquer approach is appealing when large datasets have to be analyzed. In this paper, we propose a multi-agent classification system able to tackle large datasets where each agent independently explores a random small portion of the overall dataset, searching for meaningful clusters in proper subspaces where they are well-formed (i.e., compact and populated).

Dissimilarity spaces, along with feature reduction/ selection techniques, are among the mainstream approaches when dealing with pattern recognition problems in structured (and possibly non-metric) domains. In this work, we aim at investigating dissimilarity space representations in a biology-related application, namely protein function classification, as proteins are a seminal example of structured data given their primary and tertiary structures.

With the capillary spread of multi-energy systems such as microgrids, nanogrids, smart homes and hybrid electric vehicles, the design of a suitable Energy Management System (EMS) able to schedule the local energy flows in real time has a key role for the development of Renewable Energy Sources (RESs) and for reducing pollutant emissions. In the literature, most EMSs proposed are based on the implementation of energy systems prediction which enable to run a specific optimization algorithm.

Modeling and recognizing faults and outages in a real-world power grid is a challenging task, in line with the modern concept of Smart Grids. The availability of Smart Sensors and data networks allows to “x-ray scan” the power grid states. The present paper deals with a recognition system of fault states described by heterogeneous information in the real-world power grid managed by the ACEA company in Italy.