Search for Eccentric Black Hole Coalescences during the Third Observing Run of LIGO and Virgo

Despite the growing number of confident binary black hole coalescences observed through gravitational waves so far, the astrophysical origin of these binaries remains uncertain. Orbital eccentricity is one of the clearest tracers of binary formation channels. Identifying binary eccentricity, however, remains challenging due to the limited availability of gravitational waveforms that include effects of eccentricity. Here, we present observational results for a waveform-independent search sensitive to eccentric black hole coalescences, covering the third observing run (O3) of the LIGO and Virgo detectors. We identified no new high-significance candidates beyond those that were already identified with searches focusing on quasi-circular binaries. We determine the sensitivity of our search to high-mass (total mass $M>70$ $M_\odot$) binaries covering eccentricities up to 0.3 at 15 Hz orbital frequency, and use this to compare model predictions to search results. Assuming all detections are indeed quasi-circular, for our fiducial population model, we place an upper limit for the merger rate density of high-mass binaries with eccentricities $0 < e \leq 0.3$ at $0.33$ Gpc$^{-3}$ yr$^{-1}$ at 90\% confidence level.Comment: 24 pages, 5 figure

Human Components in Productive Systems

Rom

Simulation and design of a photovoltaic roof for automotive applications

This paper presents a software tool that has been developed to model, investigate and simulate the behavior of a photovoltaic generator to be installed on a generic non-planar surface. The innovative aspect of the tool is the possibility to consider any kind of surfaces, also curved and moving ones. The tool has been developed in the Matlab environment and allows the user to analyze any kind of surface simply starting from a CAD file. The tool is very versatile and can be used to analyze the possibility to exploit various available surfaces in order to produce photovoltaic energy in many different applications. As a practical example, the case of using solar cells on the roof of a car has been considered. Results of the simulations are provided in the paper. © 2012 IEEE

Development of an On-board unit for the monitoring and management of an electric fleet

In this paper an on-board unit for the monitoring and management of a small electric fleet is presented. The device includes a communication module and allows to provide data about the vehicles, sending information coming from various sensors to a remote server. Besides locating vehicles on a map and showing their real-time status, the device can also explore the functions of subsystems including energy modules, cells, and powertrain in Electric Vehicles (EV) by showing the instantaneous data and even trend charts. When an error occurs, the system will come with warning and maintenance suggestions; the control center may send short messages to the driver and system data are available for download for advanced engineering analysis. The system foresees a web application and data can be accessed anywhere and anytime thanks to a browser application and a database developed on purpose. The device has been initially tested on a laboratory available vehicle and it will be subsequently installed on the municipal fleet of electric vehicles and boats operating on the small island of Ventotene in Italy in order to investigate the behavior of the main components. The paper illustrates the main hardware characteristics of the system and provides some experimental results. © 2012 IEEE

Improving accuracy of electric load short-term forecasting by using MoG neural networks

Improving the prediction accuracy in electric load forecasting is an important goal to be pursued in order to optimize the management of economic and environmental resources. We propose in this paper a customized prediction approach, which relies on the chaotic behavior of the electric load time series and on the spectral characteristics of its prediction error. The proposed predictor is based on a twofold prediction scheme using Mixture of Gaussian neural networks

Neurofuzzy Approximator based on Mamdani's Model

Neurofuzzy approximators can take on numerous alternatives, as a consequence of the large body of options available for defining their basic operations. In particular, the extraction of the rules from numerical data can be conveniently based on clustering algorithms. The large number of clustering algorithms introduces a further flexibility. Neurofuzzy approximators can treat both numerical and linguistic sources. The analysis of approximator sensitivity to the previous factors is important in order to decide the best solution in actual applications. This task is carried out in the present paper by recurring to illustrative examples and exhaustive simulations. The results of the analysis are used for comparing different learning algorithms. The underlying approach to the determination of the optimal approximator architecture is constructive. This approach is not only very efficient, as suggested by learning theory, but it is also particularly suited to combat the effect of noise that can deteriorate the numerical data

Evolutionary optimization of a one-class classification system for faults recognition in smart grids

The Computational Intelligence paradigm has proven to be a useful approach when facing problems related to Smart Grids (SG). The modern SG systems are equipped with Smart Sensors scattered in the real-world power distribution lines that are able to take a fine-grained picture of the actual power grid state gathering a huge amount of heterogeneous data. Modeling and predicting general faults instances by means of processing structured patterns of faults data coming from Smart Sensors is a very challenging task. This paper deals with the problem of faults modeling and recognition on MV feeders in the real-world Smart Grid system that feeds the city of Rome, Italy. The faults recognition problem is faced by means of a One-Class classifier based on a modified k-means algorithm trained through an evolutive approach. Due to the nature of the specific data-driven problem at hand, a custom weighted dissimilarity measure designed to cope with mixed data type like numerical data, Time Series an