Matteo Intravaia - IEEE Xplore Author Profile

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In this work, the authors propose a digital noise detection technique based on an artificial intelligence signal processing method. The proposed technique is intended to be used to detect and identify acoustic interferences such as ambient noise that influence the photoacoustic signal in resonance-based photoacoustic gas sensing systems. Particularly, an unsupervised classification method based on...Show More
The problem of electrical load forecasting represents a crucial aspect in many Smart Grid applications. In Renewable Energy Communities, an effective Energy Management System, aiming at improving clean energy consumption and energy self-sufficiency, schedules the operations of the available controllable loads and energy storage systems necessarily relying on load forecasts. Obtaining accurate load...Show More
In this work, a novel strategy to determine the optimal duty cycle of a boost-type converter for battery charging applications from photovoltaic source is proposed. The optimal duty cycle is determined to keep the battery charging current constant at a fixed value. The strategy is based on a preliminary analysis of the conversion chain from the PV source to the battery load, from which a steady-st...Show More
In this paper, a technique for detecting foreign metal objects in a wireless power transfer system is presented. The proposed approach exploits a multilayer neural network with multivalued neurons. The main advantage of the proposed approach compared to the literature is the reduced number of current and voltage measurements. The procedure proposed in this work is completely based on real measurem...Show More
Decarbonisation and Nearly Zero Energy Buildings are interlinked concepts that are fundamental to addressing the goal of reducing carbon emissions and mitigate climate change. In this scenario, Building Integrated Photovoltaic systems represents a key strategy. On the other hand, PV devices in this way are dislocated in different parts of the building and this makes difficult the monitoring and se...Show More
In 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...Show More
This paper presents a novel technique based on Genetic Algorithms for the design of Inductive Wireless Power Transfer systems with LCC-S compensation. The proposed approach sizes the primary coil and selects the resonant tank components to achieve the desired output power with high transfer efficiency for any given load connected to the receiver coil. Mathematical models for self-inductance and mu...Show More
Wireless Power Transfer is growing in popularity as it represents an attractive charging method in many applications. As such, monitoring these devices to detect soft faults (which can possibly lead to destructive failures) becomes an important task to ensure continuous operation of these systems and to reduce unavailability periods. This paper proposes a study regarding soft fault diagnosis on an...Show More
This paper proposes a possible approach for object detection in Wireless Power Transfer (WPT) systems using artificial intelligence techniques. The main objective is to identify the presence of foreign objects between the primary and secondary windings by processing voltage and current measurements. To achieve this goal, different machine learning methods are proposed and compared. One of the esse...Show More
This paper proposes an innovative approach in managing prosumer batteries using a rule-based control algorithm. The main information used in this work to develop actions on a Battery Energy Storage System (BESS) are the price of energy on the day-ahead electricity market, the prosumer consumption/production forecasts and the effect of charging/discharging operations on battery degradation. In orde...Show More
This paper proposes a prognostic method capable of identifying malfunctions in electrical power transformers using their high-frequency models and specific artificial intelligence techniques. From a general point of view, this approach is based on the recognition of parametric faults by processing frequency response measurements. The starting point of the work is to develop an equivalent lumped ci...Show More
Photovoltaics represents one of the key sources of clean energy to help reduce the carbon footprint and fight climate change, enabling the so-called green energy transition. To maximize photovoltaic production in any irradiation and temperature conditions, Maximum Power Point Tracking techniques must be implemented to determine and set the working point at which the photovoltaic panel delivers the...Show More
In this paper, a strategy for the identification of models based on supercapacitors equivalent circuits is proposed. The approach is based on an innovative and efficient encoding procedure, suitable for a generic impedance network. The latter, in combination with an optimization algorithm, is used to investigate the components and topology of the best-fitting network for a given dataset. To demons...Show More
In this work, a monitoring technique for switching devices used in DC-DC converters is presented. The prognostic approach requires the processing of time domain measurements extracted from specific test points of the converter under test, with the purpose of assessing the drain-to-source resistance variations due to overheating before they lead to a total loss of functionality. The proposed monito...Show More
This paper proposes a predictive maintenance method for actuated quarter-turn valves used in oil and gas applications and carbon capture systems. The identification and classification of the degradation process allow the management of the most critical parts by adopting specific strategies. In this way, it is possible to avoid service interruptions and reduce recovery times. Therefore, the objecti...Show More
The a-priori economic and energetic design of a Renewable Energy Community (REC) requires hourly electric generation and load profiles for the community members. Energy generation profiles are easily inferable, especially in the case of photovoltaics. Energy consumption trends, on the other hand, are more unpredictable. For this reason, the reconstruction of simulated hourly load profiles becomes ...Show More
This work is focused on the realization of a low-complexity MobileNet neural network able to classify different bearing failure vibration signals. The network was designed to be deployed on an embedded microcontroller to be used in smart sensors in an IoT context, pursuing the paradigms of Embedded Artificial Intelligence and Edge Computing. Following these same concepts, data preprocessing as wel...Show More
This 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 ...Show More
This paper deals with profile thickness measurement of ferromagnetic slabs for industrial applications, by means of inductive proximity sensors. A low-cost accurate measurement system was realized, based on an embedded microcontroller and exploiting two inductive probes facing each other. Such system was designed to operate with moving plates, passing through the probe assembly mounted in a fixed ...Show More
This 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 sy...Show More
In this article, we present a smart gravimetric system for the automatic security monitoring of the accesses to public places with some entrance ticket or pass required (e.g., railway stations, subway stations, museums, exhibitions). The main objective is to spot illegal behaviors, for example, two people trying to enter using only one ticket; simultaneously, other events of interest can also be d...Show More
This paper describes the architecture and the performances of a smart robust gravimetric system for access monitoring in public places. The system is composed of a gravimetric footboard, equipped with load cells, an embedded electronics, and a VGG16 convolutional neural network based classification algorithm. The main objective is to spot irregularities, which correspond in this case to the contem...Show More
This paper describes a novel application of a tailored robust and low-cost gravimetric system for access control purposes in public spaces. The aim of the proposed device is to detect the abnormalities in the passage of people (multiple persons passing together) and, on the other hand, to recognize useful events for physical security control activities (e.g. person with stroller or trolley). The p...Show More