Laser cutting of metals has become the reference manufacturing technology in sheet metal working thanks to the flexibility and the increased productivity it offers when compared with other competitive technologies. Considering, in particular, the fusion-cutting mode, i.e., when nitrogen is used as an assisting gas, different aspects contribute to the process quality among which dross attachment plays the most important role. To cope with the related time-dependent deterioration of the process quality and to obtain an online adaptation of the process parameters for different working conditions, a closed-loop dross regulation system is needed. To realize it, a reliable, continuous, and accurate estimation of the dross is mandatory. This work focuses on this challenging problem, presenting and comparing different approaches to estimate the dross attachment based on the process emission collected by a coaxial camera. Specifically, a method which relies on the accurate analysis of the process emissions for determining an effective classification method is compared with a deep-learning approach based on convolutional neural networks. The obtained results, validated in real experimental conditions, confirm the possibility to accurately estimate the presence of significant dross attachment in real-time and open the way to the design of a closed-loop control algorithm for the real-time regulation of the dross attachment formation and consequently of the process quality.

In this paper, Linear Quadratic (LQ) optimal control concepts are applied for the active control of vibrations in helicopters. The study is based on an identified dynamic model of the rotor. The vibration effect is captured by suitably augmenting the state vector of the rotor model. Then, Kalman filtering concepts can be used to obtain a real-time estimate of the vibration, which is then fed back to form a suitable compensation signal. This design rationale is derived here starting from a rigorous problem position in an optimal control context. Among other things, this calls for a suitable definition of the performance index, of nonstandard type. The application of these ideas to a test helicopter, by means of computer simulations, shows good performances both in terms of disturbance rejection effectiveness and control effort limitation. The performance of the obtained controller is compared with the one achievable by the so called Higher Harmonic Control (HHC) approach, well known within the helicopter community.

As apprehension grows over global warming and urban pollution, battery electric vehicles (BEVs) are experiencing a rise in worldwide popularity. Yet, their market uptake has been slowed down due to high purchase prices and concerns over the limited battery range and the insufficient public charging infrastructure. This work uses a massive real-world dataset, containing the anonymized global positioning system (GPS) traces from a fleet of private vehicles, to quantitatively evaluate if range anxiety (i.e., the fear of being stranded due to EV’s limited range) is a rational concern. In particular, the fleet’s electrification potential is assessed by analyzing the driving patterns of more than 50000 vehicles over the course of an entire year. The results reveal the potential of BEVs, which could satisfy the range needs of much of the existing fuel-powered vehicle fleet with no alteration to the owners’ routines (except for overnight recharging). Furthermore, the mileage analysis is later used to pinpoint the so-called eligible stops (ESs), corresponding to real charging demand and opportunities. ESs are aggregated through clustering analysis, obtaining a ranking of potential charging station sites. Finally, we quantitatively evaluate the effects of the increasing dissemination of charging facilities on the vehicles’ EV-switch suitability.

Mobility will surely be at the core of the Smart Cities of the future. As such, it must be planned based on novel mobility models, smart enough to answer the multifaceted needs of users, while being sustainable and energy efficient. In this evolution, Electric Vehicles (EVs) will be crucial, as confirmed by the fact that many governments are already actively sustaining their spread in place of common Internal Combustion Engine (ICE) ones. Nonetheless, for their adoption to be actually widespread, one must be able of governing the mass adoption mechanisms, by designing policies that are cost-effective and successful in making the mobility transition a reality in due time. In this work, we propose a novel framework that can represent a valuable control-oriented tool to serve this ambitious goal. Our framework lays its foundation on a quantitative description of the inclination of traditional car owners towards EVs, which is retrieved by relying on data-driven insights on their mobility habits only. This information is further exploited to construct a proximity-based network, that is combined with the individual characterization into a cascade model describing the adoption dynamics. T

Assessing the driving-style from dynamic data is a well established line of research, which has tackled the description of risky behaviours, the profiling of energy-consumption habits, and the detection of different driver's characteristics from the analysis of motion data. In the last years, as smartphone ownership has become widespread, such an assessment has been increasingly relying on the measurements taken from the inertial sensors on board of the smartphone itself. This work stands in this context, and it aims to design a 4-dimensional driving-style assessment for insurance purposes. The main contribution is adding, to more common proxies of risky-driving, the dimension of smartphone usage, the detection of which is performed through an appropriate processing of smartphone-based inertial sensors, thus not relying on privacy-sensitive monitoring of phone usage behavior. Physics-based, fine-grained dynamic features are used to classify the overall riskiness of the driving-style, thus providing a comprehensive insight into the most discriminating features. The study is based on experimental data, collected over more than five thousands kilometers of varied car trips.

Brain-computer interfaces (BCIs) have revolutionized the way humans interact with machines, particularly for patients with severe motor impairments. EEG-based BCIs have limited functionality due to the restricted pool of stimuli that they can distinguish, while those elaborating event-related potentials up to now employ paradigms that require the patient’s perception of the eliciting stimulus. In this work, we propose MIRACLE: a novel BCI system that combines functional data analysis and machine-learning techniques to decode patients’ minds from the elicited potentials. MIRACLE relies on a hierarchical ensemble classifier recognizing 10 different semantic categories of imagined stimuli. We validated MIRACLE on an extensive dataset collected from 20 volunteers, with both imagined and perceived stimuli, to compare the system performance on the two. Furthermore, we quantify the importance of each EEG channel in the decision-making process of the classifier, which can help reduce the number of electrodes required for data acquisition, enhancing patients’ comfort.