1,720,975 research outputs found
Sistemi di Machine Learning innovativi per il monitoraggio del carico elettrico
No full textNon-Intrusive Load Monitoring (NILM) is the process that allows obtaining information about the electrical loads powered by an electrical system through a single measurement performed in a single point of the system itself.
Systems based on this process provide an alternative solution to the more traditional intrusive one. NILM requires a reduced number of equipment and less occupied space, even if it presents a greater complexity in terms of processing the acquired data. In fact, this solution is much simpler, from the hardware point of view, as it requires the measurement of a voltage and a current, or often even just the current. However, the complexity shifts to the processing section, which must identify the absorption of the individual devices through the use of appropriate algorithms.
The information required from an electrical loads monitoring system may concern their status (ON/OFF) or the electrical quantities involved in their operation. This information must be made available in a more or less short time depending on the application in which the measuring system is used.
The most common application is to monitor the electricity consumption of different devices within a residential home. In this case the information must be updated on time intervals of the order of days or weeks.
Today, new NILM systems are used in numerous innovative applications in residential environments. For example, some human activity recognition (HAR) and ambient assisted living (AAL) systems are based on disaggregated appliance activity data. Innovative commercial and industrial applications are based on the NILM technique, such as to implement predictive maintenance. Energy disaggregation is also applied to manage the generation and storage of energy in smart grids. Therefore, the times in which it is necessary to have information about the state or the electrical quantities of a load are drastically reduced, down to a few seconds.
In the first part of this thesis the current state-of-the-art of NILM systems will be defined, paying particular attention to the most significant contributions. Subsequently, the applications of these systems in industrial and residential contexts will be described in detail.
In the second part, three different systems will be proposed having different characteristics both from the point of view of the electrical quantities measured, the sampling frequency and the signal processing section. More specifically, the experimental systems created, based on a microcontroller, use Machine Learning algorithms to process the signals obtained from the measurement section. For each of the proposed systems, a wide range of measurements on test systems were carried out, in order to effectively evaluate their performance in real conditions
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
A New Convolutional Neural Network-Based System for NILM Applications
Full text linkElectrical load planning and demand response programs are often based on the analysis of individual load-level measurements obtained from houses or buildings. The identification of individual appliances’ power consumption is essential, since it allows improvements, which can reduce the appliances’ power consumption. In this article, the problem of identifying the electrical loads connected to a house, starting from the total electric current measurement, is investigated. The proposed system is capable of extracting the energy demand of each individual device using a nonintrusive load monitoring (NILM) technique. An NILM algorithm based on a convolutional neural network is proposed. The proposed algorithm allows simultaneous detection and classification of events without having to perform double processing. As a result, the calculation times can be reduced. Another important advantage is that only the acquisition of current is required. The proposed measurement system is also described in this article. Measurements are conducted using a test system, which is capable of generating the electrical loads found on a typical house. The most important experimental results are also included and discussed in the article
An Embedded Deep Learning NILM System: A Year-Long Field Study in Real Houses
No full textNonintrusive load monitoring (NILM) systems are used to identify the energy consumption patterns of individual devices in an electrical system, but broadening their market availability is a significant challenge. In this article, an NILM system using edge processing is proposed, in which energy consumption data are processed directly on the device installed at the monitored facility. Specifically, it uses a sequence-to-point approach based on a convolutional neural network (CNN) implemented on an Arm Cortex-M7 microcontroller. This article also reports the results of an extensive 12-month testing phase. The NILM system was installed in two real houses in central Italy to evaluate its installation and potential application in real-world scenarios. This study presents a promising solution that enables the widespread adoption of NILM systems by reducing their implementation cost and complexity and addresses the privacy concerns associated with cloud-based data processing. The results of our real-world testing provide compelling evidence of the potential of the proposed NILM system in various applications, including smart homes, building automation, and industrial energy management
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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