1,720,976 research outputs found
A Machine Learning Model for Microcontrollers Enabling Low Power Indoor Positioning Systems via Visible Light Communication
No full textThis paper presents a low-power Visible Light Localisation (VLL) Artificial Intelligence (AI)-enabled system for Indoor Positioning (IP) purposes. Compared to other IP techniques, VLL offers a similar positioning accuracy, but with the extremely desirable feature of low energy consumption, an aspect of primary relevance in the framework of Wireless Sensor Networks (WSN), self-sufficient sensing systems, Industry 4.0 and Internet of Things (IoT). The proposed system is composed of three modulated optical sources (i.e. LEDs) and a photodiode receiver mounted on the target to be localised. The localisation task is performed by processing the received light intensities through Machine Learning (ML) regression models trained with a set of data gathered during a calibration phase. The regressors are designed to be executed on a low-power microcontroller present in the target, hence establishing an embedded ML paradigm also preserving reduced power consumption features. The proposed models are trained exploiting datasets with different sizes, searching for a trade-off between the training set size, i.e. the duration and complexity of the calibration phase, and the maximum tolerable root mean square error (RMSE). In both cases, some localisation tests show that a satisfactory accuracy can be reached even with a limited complexity of the calibration procedure and that the obtained results fulfil the error constraint used for model design
Optical and electrical model for vertical-mounted bifacial solar panels
No full textIn this work a novel approach to estimate the produced power and mismatch losses for vertically mounted bifacial solar panels is presented. The approach is based on the estimation of the ground reflected irradiance on the panel through the application of an analytical closed form for the view factor in a perpendicular geometry. The radiation information is then used to estimate the current-voltage characteristics at cell level for the panel through the implementation of a variation for the classic single-diode model in presence of backside current production. The proposed methodology is of simple implementation, can scale towards more complex geometries and can be used for a quick and simple assessment of the real potential peak power produced by a PV plant considering the effects of ground reflections
Enhanced Visible Light Localization Based on Machine Learning and Optimized Fingerprinting in Wireless Sensor Networks
No full textThis article presents a robust visible light localization (VLL) technique for wireless sensor networks, with 2-D indoor positioning (IP) capabilities, based on embedded machine learning (ML) running on low-cost low-power microcontrollers. The implemented VLL technique uses four optical sources (i.e., LEDs), modulated at different frequencies. In particular, the received signal strengths (RSSs) of optical signals are evaluated by a microcontroller on board the sensor nodes via fast Fourier transform (FFT). RSSs are fed to four embedded ML regressors, aiming at estimating the target position within the workspace. The four neural networks (NNs), one per each possible triplet of LEDs, are trained by exploiting a novel technique to generate the training datasets. This method, called optimized fingerprinting (OF), allows for creating arbitrarily ample datasets by performing only few measurements in the field, avoiding time-consuming steps for collecting experimental data. The NNs are devised to be accurate yet lightweight facilitating their implementation and execution by the microcontroller. Furthermore, due to the presence of four NNs, four position estimates are obtained. This redundancy is exploited to detect and effectively manage situations of total or partial shading of one light source and to enhance the positioning accuracy under normal operating conditions (i.e., no obstacles), by averaging the four positions. Test results performed in a m workspace show an overall mean accuracy of about 1 cm with standard deviation below the centimeter and maximum error around 3 cm
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
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
Information technologies for energy management in the smart factory
No full textPower supply is an urgent problem in the perspective of deploying pervasive Wireless Sensor Networks (WSNs). If, on one side, it is desirable to save grid power, on the other hand it
must be considered that, in many applications (e.g. seismic sensors installed on volcanos; weather monitoring stations for smart agriculture), the grid is unavailable,
and therefore the use of alternative power supplies – usually batteries – is mandatory. In such cases, the replacement of exhausted batteries is expensive and time-consuming, and may be
inconvenient if the installation site is hardly reachable. Thus, many energy harvesting (EH) methods, exploiting different energy sources, have been studied in order to extend as
long as possible the battery lifetime, aiming at achieving a complete energy self-sufficiency.
Among the various power sources, solar energy has driven, and is still driving, the attention of a big portion of the world marketplace of renewables. Consider that about half of the
renewable energy produced in Italy in 2021 is attributable to the solar sector. The most common material for outdoor light energy harvesting is crystalline silicon (c-Si), in its
polycrystalline (poly-Si) and monocrystalline (mono-Si) forms. Despite the fact that silicon technology is well-known and industrially standardized, a brisk research activity is still
going in order to increase at maximum the efficiency, approaching the theoretical upper limit for silicon (about 30 % under 1 sun at 25 °C). For instance, passivating
carrier-selective contacts have been studied recently to optimize the carrier transport characteristics of c-Si solar cells, leading to significant improvement in the efficiency.
In the first part of the thesis, we present a theoretical method and an experimental method for evaluating the performance of a solar module and consequently optimizing the operation
of a user device powered by the module. In particular, we propose these techniques applied to environmental pollution monitoring sensors, designed in our laboratories at the Department
of Information Engineering and Mathematics of the University of Siena, equipped with a small mono-Si solar cell. This work was presented at conference and was extended as journal
publication.
Another key aspect of energy management is to ensure a continuous and high-quality power supply to the users. The term “continuous” refers to the necessity of certain appliances of relying on
an uninterrupted power source. If, in a domestic context, the lack of electric energy can cause at worst discomfort, in other situations the consequences can be catastrophic: the lives of
the patients in a hospital can be jeopardized, or petabytes of data can be lost in a data center. The term “high-quality” is linked to the waveform delivering the electric energy.
In Italy, the standard is 50 Hz frequency, 230 Vrms between each phase and the neutral. For instance, harmonic distortion, i.e. the superposition of superior harmonics to the
fundamental 50 Hz harmonic, usually due to the presence of non-linear loads connected to the grid, can provoke additional Joule dissipation in transformers, cables and appliances,
with a subsequent temperature increment, and an abnormal current flowing in the neutral conductor. Over time, if these conditions persist, failures can occur.
In this framework, fault diagnosis assumes a central role. When dealing with power electronic circuits, and analog circuits in general, it is important to distinguish between two
classes of faults: hard faults and soft (or parametric) faults. Hard faults are typically linked to the breakdown of a component, usually manifesting as a short-circuit or an
open-circuit, leading to a potentially destructive failure of the system. On the other hand, a fault is called soft when it is related to one or more circuit parameters exceeding
the tolerance limits around the nominal value, due to aging, manufacturing defects or parasitic effects. As a result, the system functions outside the specifications, worsening the
performance, and possibly leading to system ruptures in the long term, if the faulty condition is not individuated and fixed.
With that being said, it is straightforward to understand that soft faults are subtler to be discovered than hard faults. In fact, in most cases, the quantities to be monitored are
not directly measurable in a simple way, e.g. without disassembling the system, and interrupting the service for a long period. As an example, consider the pre-charge electrolytic
capacitors in a UPS (Uninterruptible Power Supply) system . Electrolytic capacitors are particularly subject to aging, causing a deterioration of the capacitance value. Since to
periodically measure the state of the capacitors is impractical, the idea is to deduce the information that is needed from other circuit quantities, which can be constantly monitored,
can be easily measured with negligible impact on the system operation, or can be evaluated contextually to routine maintenance interventions. Such alternative measurements must be somehow
affected by the variations of the parameter to be monitored. Back to the example of electrolytic capacitors in UPS systems, if we hypothesize to discharge the capacitive bank and recharge
it, the steepness of the voltage curve across the capacitors should be influenced by the capacitance values.
A common approach to this kind of problems, which has become more and more popular in the last years, relies on neural networks. These techniques exploit machine learning to abstract
from the circuit topology, and to reduce the problem to a set of circuit “features” passed to a neural network, producing a certain output depending on how the neural network was trained.
Indeed, these methods are sometimes called “data driven methods”, signifying that they are totally founded on measurements collected from the system rather than on mathematical
models, simplifying considerably the design and the implementation of the fault diagnosis apparatus.
In this work of thesis, we studied the application of a neural network based soft fault detection strategy to the already mentioned pre-charge stage of a UPS system. The activity can be
divided into three phases. Initially, we designed a voltage down-scaled version of the circuit (the reason for this will be clear later) and simulated it via software to the aim of
individuating a convenient set of circuit features to be fed to the neural network classifier, and we validated the classification performance on simulated data. We selected a
Radial Basis Function (RBF) architecture as our neural network classifier. Our choice in favor of this kind of machine learning algorithm, which is relatively old and well-known in the
literature, was dictated mainly by its simplicity: the three-layer structure of a RBF network makes it lighter than the majority of the neural network
architectures currently employed, both computationally and for memory allocation requirements. As such, RBF networks represent a good compromise between performance and resource
occupation, enabling the possibility to implement the entire fault detection algorithm on embedded devices (e.g. low-cost microcontrollers). This would be a decisive move towards
automatic diagnostic systems. In addition, in this phase, the network was trained and the coefficients (or weights) of the network were determined. This set of coefficient values
was kept also for the following stages of the activity, i.e. the RBF network was never retrained. It is important to underline this last aspect: the training data set, which notoriously
has to be large, was obtained through simulations. If the training examples had to be obtained from measurements, the procedure would have been tediously long; moreover, it would have
been unfeasible to retrain the network in case of mistakes, or in case of any modifications one wanted to introduce (add a new fault class, change the set of features, adjust the network
parameters). The uncertainty arising from the use of simulated data can be mitigated by running a sufficient number of simulations, finely varying the circuit parameters to cover the
feature space.
The second step consisted in the realization of the voltage down-scaled version of the circuit to perform actual measurements on a physical system, to confirm or disconfirm the results
of the simulations, and consequently decide if it was necessary to take a step back and reevaluate the choice of the features and retrain the network. The purpose of this method is to
create a circuit which would present the same functionalities of the original one, but can be managed even in laboratories which do not have the required authorizations to work directly
with the mains voltage, making it possible, for instance, to collaborate remotely with companies active in the field of power electronics, without the need for testing the real products.
In this way, more comprehensive studies can be conducted separately, without interfering with the normal production chain of the company. This means that, with zero impact on the company
activities, a complete prototype of the fault classification system can be realized, ready to be installed on a real machine for final field tests.
Finally, we implemented the neural network on a commercial microcontroller board (in particular, a NUCLEO-H7A3ZI produced by STMicroelectronics). The classification performance of the
network running on the microcontroller was estimated and compared with the results obtained with the network working on a classic PC, using actual measurements executed on the down-scaled
circuit
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
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