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807 results on '"De Gloria, Alessandro"'

802. Maximizing Power Transfer for Dynamic Wireless Charging Electric Vehicles

Author

Oussama Smiai, Francesco Bellotti, Vincenzo Cirimele, Riccardo Berta, Paolo Guglielmi, Alessandro De Gloria, De Gloria A., CIRIMELE, VINCENZO, Smiai, Oussama, GUGLIELMI, PAOLO, Bellotti, Francesco, Berta, Riccardo, and De Gloria, Alessandro

Subjects
Engineering, 02 engineering and technology, Industrial and Manufacturing Engineering, alignment detection, 0202 electrical engineering, electronic engineering, information engineering, Electronic engineering, Wireless, Maximum power transfer theorem, Wireless power transfer, inductive power transfer, Alignment detection, Camera, Electric Vehicles, ICT, Coupling, business.industry, 020208 electrical & electronic engineering, Electrical engineering, Centroid, Sensor fusion, dynamic IPT, Electromagnetic coil, Obstacle, 020201 artificial intelligence & image processing, business, dynamic IPT, inductive power transfer, alignment detection
Abstract

Dynamic (on the go) wireless electrical charging is a new, challenging frontier for vehicular technologies, as it would allow a dramatic increase in the feasible distance range of electrical vehicles. This paper presents a system under development for optimizing the power transfer for dynamic charging vehicles. The system relies on a coil coupling sensor and a camera. The coupling sensor measures the coupling level between the road and vehicle coils, while the camera detects the distance of the vehicle from the two lines of the lane. A sensor fusion system is needed because, on one hand, the coil sensor is highly accurate but cannot distinguish the direction of the misalignment, while the camera is less precise but can provide information about the position of the lane center, where the centroid of the transmitting coil is expected to be and complete a series of other non-magnetic information needed like the obstacle presence.

Published
2017

803. Machine Learning on Mainstream Microcontrollers †.

Author

Sakr, Fouad, Bellotti, Francesco, Berta, Riccardo, and De Gloria, Alessandro

Subjects
*MACHINE learning, *ARTIFICIAL neural networks, *SUPERVISED learning, *SUPPORT vector machines, *PERSONAL computers, *MICROCONTROLLERS
Abstract

This paper presents the Edge Learning Machine (ELM), a machine learning framework for edge devices, which manages the training phase on a desktop computer and performs inferences on microcontrollers. The framework implements, in a platform-independent C language, three supervised machine learning algorithms (Support Vector Machine (SVM) with a linear kernel, k-Nearest Neighbors (K-NN), and Decision Tree (DT)), and exploits STM X-Cube-AI to implement Artificial Neural Networks (ANNs) on STM32 Nucleo boards. We investigated the performance of these algorithms on six embedded boards and six datasets (four classifications and two regression). Our analysis—which aims to plug a gap in the literature—shows that the target platforms allow us to achieve the same performance score as a desktop machine, with a similar time latency. ANN performs better than the other algorithms in most cases, with no difference among the target devices. We observed that increasing the depth of an NN improves performance, up to a saturation level. k-NN performs similarly to ANN and, in one case, even better, but requires all the training sets to be kept in the inference phase, posing a significant memory demand, which can be afforded only by high-end edge devices. DT performance has a larger variance across datasets. In general, several factors impact performance in different ways across datasets. This highlights the importance of a framework like ELM, which is able to train and compare different algorithms. To support the developer community, ELM is released on an open-source basis. [ABSTRACT FROM AUTHOR]

Published
2020
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805. The move beyond edutainment: have we learnt our lessons from the entertainment industry?

Author

Azadegan, Aida, Baalsruf Hauge, Jannicke, Bellotti, Francesco, Berta, Ricardo, Bidarra, Rafael, Harteveld, Casper, Riedel, Johann, Stanescu, Ioana Andreea, and De Gloria, Alessandro

Subjects
ComputingMilieux_PERSONALCOMPUTING
Abstract

Serious games (SGs) have been used in the education of students and professionals for decades, but still have not reached their full potential, despite the large consensus they have gained recently. The entertainment game industry is a rapidly developing phenomenon, with a high market potential, enabled and enhanced by technological innovation. The question examined in this paper is: Did serious game designers learn from Entertainment Game (EG) designers in building a successful game? This paper presents three case study examples of games that have good learning outcomes to explore this question. This paper discusses the salient aspects and the differences between the examples and suggests how SGs could learn more from successful EGs.

Published
2013

806. Introducing the 'Serious Games Mechanics': A theoretical framework to analyse relationships between 'game' and 'pedagogical aspects' of Serious Games

Author

Suttie, Neil, Louchart, Sandy, Lim, Theo, Macvean, Andrew, Westera, Wim, Brown, Damian, Djaouti, Damien, De Gloria, Alessandro, de Freitas, Sara, and RS-Research Program Welten Onderzoeksprogramma (WO)

Subjects
learning, pedagogy, design, game, patterns, serious, GALA, mapping, serious gaming, mechanics, design framework, game mechanics
Abstract

The careful balance of education and play is essential for the development of effective Serious Games. It is necessary to develop a clear understanding of game mechanics (i.e. the tools of game-play) and how these relate to relevant educational strategies. In this paper, we raise conceptual questions regarding the nature of Serious Games and the relationship between game mechanics, pedagogy and the conceptual level at which they connect. In developing theoretical framework linking game mechanics and learning, we aim to ease the work of the Serious Game designers attempting to produce both fun and pedagogically effective Serious Games.

Published
2012

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