4 Contributo in Atti di Convegno (Proceeding)

4 Contributo in Atti di Convegno (Proceeding): [50361] Home page macrotipologia

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4.1 Contributo in Atti di convegno 45919
4.2 Abstract in Atti di convegno 3587
4.3 Poster 855

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Residual Dynamics in Hydrological Models: Insights from a large sample of catchments and models

Energy Sustainability Analysis of Deep Neural Network

Artificial Intelligence (AI) applications are becoming more widespread, raising significant environmental concerns due to the high energy use required to train large Deep Neural Networks (DNNs). To address this issue, we conduct a detailed quantitative analysis of the energy consumption of various models across different training steps and datasets. Our study measures energy use at each training stage with a comprehensive review of selected models. By tracking energy consumption during each t...

Efficient Dynamic Beamforming Activation for UAV-Enabled Vehicular Networks

Unmanned aerial base stations (UABSs) are a promising solution for improving coverage and capacity in vehicle-to-everything (V2X) communications, particularly in dense urban areas. However, their operation is constrained by onboard energy consumption, required for both flight and communication. Beamforming, while enhancing network performance, adds to this challenge due to its energy-intensive nature. This paper proposes a sequential and hierarchical decision-making framework for UABS operati...

Data-Driven Fault Detection of Automotive Engine Mounts Using Onboard Vibration Signals

With the increasing rate at which new technologies are developed and integrated into modern vehicles, their complexity and demands on reliability have grown. Hence, potential downtimes are directly correlated with high costs for both customers and manufacturers, making the need to ensure operational stability evident. Being the most crucial part of an automotive vehicle, the integrity of the drivetrain and its components have to be assured. Traditionally, this was achieved by increased lo...

A LIF-based Legendre Memory Unit as neuromorphic State Space Model benchmarked on a second-long spatio-temporal task

Chasing energy efficiency through biologically inspired computing has produced significant interest in neuromorphic computing as a new approach to overcome some of the limitations of conventional Machine Learning (ML) solutions. In this context, State Space Models (SSMs) are arising as a powerful tool to model the temporal evolution of a system through differential equations. Their established ability to process time-dependent information and their compact mathematical framework make them ind...

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