Marche Polytechnic University

About: Marche Polytechnic University is a education organization based out in Ancona, Italy. It is known for research contribution in the topics: Population & Prostate cancer. The organization has 5905 authors who have published 15769 publications receiving 382286 citations. The organization is also known as: Universitá Politecnica delle Marche & Universita Politecnica delle Marche.

Beyond the Effect of Particle Size: Influence of CoFe2O4 Nanoparticle Arrangements on Magnetic Properties

Abstract: This paper focuses on the magnetic properties of CoFe2O4 nanoparticles, discussing the influence of nanoparticles arrangements obtained by different synthesis methods. Using high thermal decomposition (HTD) and direct micellar (DM) routes, three samples of CoFe2O4 nanoparticles with equal primary particle size (∼5 nm) were prepared. The HTD method allows one to obtain highly crystalline primary nanoparticles coated by oleic acid organized in a self-assembling arrangement (ACoFeHTD). The DM method results to be appropriate to prepare either irregular arrangements (IACoFeDM) or spherical iso-oriented nanoporous assemblies (SACoFeDM) of primary CoFe2O4 nanocrystals. Despite the same particle size, magnetization measurements of the HTD sample show a tendency toward cubic anisotropy (Mr/Ms ≈ 0.7), while in DM samples, a uniaxial anisotropy (Mr/Ms ≈ 0.4) is observed. The comparison between IACoFeDM and SACoFeDM samples indicates that the ordering of nanocrystals at the mesoscopic scale induces an increase of th...

Unsupervised algorithms for non-intrusive load monitoring: An up-to-date overview

Abstract: Research on Smart Grids has recently focused on the energy monitoring issue, with the objective to maximize the user consumption awareness in building contexts on one hand, and to provide a detailed description of customer habits to the utilities on the other. One of the hottest topic in this field is represented by Non-Intrusive Load Monitoring (NILM): it refers to those techniques aimed at decomposing the consumption aggregated data acquired at a single point of measurement into the diverse consumption profiles of appliances operating in the electrical system under study. The focus here is on unsupervised algorithms, which are the most interesting and of practical use in real case scenarios. Indeed, these methods rely on a sustainable amount of a-priori knowledge related to the applicative context of interest, thus minimizing the user intervention to operate, and are targeted to extract all information to operate directly from the measured aggregate data. This paper reports and describes the most promising unsupervised NILM methods recently proposed in the literature, by dividing them into two main categories: load classification and source separation approaches. An overview of the public available dataset used on purpose and a comparative analysis of the algorithms performance is provided, together with a discussion of challenges and future research directions.