1,721,012 research outputs found
''Sviluppo e analisi di metodi per il controllo attivo delle vibrazioni, con riferimento al problema della riduzione del livello vibratorio negli elicotteri''
No full textTesi di Dottorato
Relatore: Prof. S. Bittanti, Politecnico di Milano
Correlatore: Ing. B. Lovera, Agusta Sp
Automatica. RAccolta di esercizi risolti (con appendice Matlab)
No full textQuesto libro presenta una raccolta di esercizi risolti per la preparazione all'esame di Fondamenti di Automatica o di corsi analoghi di base su questi argomenti. Il contenuto del libro deriva dall'attività didattica che gli autori svolgono da parecchi anni nell'ambito del corso di Fondamenti di Automatica per la Laurea in Ingegneria Gestionale presso il Politecnico di Milano e rispecchia pertanto il programma e gli argomenti sviluppati in tale corso e non intende coprire tutti gli aspetti delle discipline coinvolte.
I diversi argomenti sono organizzati in capitoli secondo un filo logico che va dalle basi della teoria dei sistemi all'analisi nel tempo e in frequenza di sistemi di controllo in retroazione.
L'obiettivo di questo testo è quello di essere un utile supporto nello studio di una materia che inizialmente può apparire complessa ed eccessivamente teorica ma che, una volta approfondita, può invece portare ad una nuova mentalità ingegneristica, sia nello studio che nella professione. L'Automatica rappresenta non a caso una delle materie trasversali di numerosi corsi di laurea dell'area dell'Ingegneria Industriale e dell'Informazione
Vehicle Fuel Consumption Virtual Sensing from GNSS and IMU Measurements
No full textThis paper presents a vehicle-independent, non-intrusive, and light monitoring system for accurately measuring fuel consumption in road vehicles from longitudinal speed and acceleration derived continuously in time from GNSS and IMU sensors mounted inside the vehicle. In parallel to boosting the transition to zero-carbon cars, there is an increasing interest in low-cost instruments for precise measurement of the environmental impact of the many internal combustion engine vehicles still in circulation. The main contribution of this work is the design and comparison of two innovative black-box algorithms, one based on a reduced complexity physics modeling while the other relying on a feedforward neural network for black-box fuel consumption estimation using only velocity and acceleration measurements. Based on suitable metrics, the developed algorithms outperform the state of the art best approach, both in the instantaneous and in the integral fuel consumption estimation, with errors smaller than 1% with respect to the fuel flow ground truth. The data used for model identification, testing, and experimental validation is composed of GNSS velocity and IMU acceleration measurements collected during several trips using a diesel fuel vehicle on different roads, in different seasons, and with varying numbers of passengers. Compared to built-in vehicle monitoring systems, this methodology is not customized, uses off-the-shelf sensors, and is based on two simple algorithms that have been validated offline and could be easily implemented in a real-time environment
Machine learning based car accident risk prediction for usage-based insurance
No full textThe Usage-Based Insurance paradigm, which is receiving a lot of attention in recent years, envisages computing the car policy premium based on accident risk probability, evaluated observing the past driving history and habits. However, Usage-Based Insurance strategies are usually based on simple empirical decision rules built on travelled distance. The development of intelligent systems for smart risk prediction using the stored overall driving behaviour, without the need of other insurance or socio-demographic information, is still an open challenge. This work aims at exploring a comprehensive machine learning-based approach solely based on driving-related data of private vehicles. The anonymized dataset employed in this study is provided by the telematics company UnipolTech, and contains space/time densely measured data related to trips of almost 100000 vehicles uniformly spread on the Italian territory, recorded every 2 km by on-board telematics fix devices (black boxes), from February 2018 to February 2020. An innovative feature engineering process is proposed, with the aim of uncovering novel informative quantities able to disclose complex aspects of driving behaviour. Recent and powerful learning techniques are explored to develop advanced predictive models, able to provide a reliable accident probability for each vehicle, automatically managing the critical imbalance intrinsically peculiar this kind of datasets
''On comparison and extension of control methods for narrow-band disturbance rejection''
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