1,720,960 research outputs found
Electrification potential of fuel-based vehicles and optimal placing of charging infrastructure: a large-scale vehicle-telematics approach
Get PDFAs 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 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 fifty thousand 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, corresponding
to real charging demand and opportunities. Eligible stops 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
Flight regimes recognition in actual operating conditions: A functional data analysis approach
No full textHelicopters need adequate monitoring to prevent dynamic failures from excessively affecting components’ health status, increase the level of safety, and reduce operative costs. Health and Usage Monitoring Systems have been developed to monitor helicopters during their lifetime in the last few decades. Recent works demonstrated that despite analyzing physical components’ behavior over time, tracking the regimes performed during each flight contributes to estimating the aircraft's health and usage status, paving the way for designing accurate prognostics algorithms. However, today, most regime recognition systems rely on data recorded during certification flights. It follows that the training regimes differ from the ones proposed in the prediction phase, which are acquired during helicopter actual operating conditions. This affects these recognition system performances. Aiming at overcoming this limitation, in this work, we proposed an unsupervised regimes recognition system capable of better handling the actual helicopter usage spectrum. In detail, we proposed a system based on an unsupervised learning paradigm, which leverages a soft-membership classification technique to account even for mixed regimes and transitions. In addition, the system represents data according to functional data analysis theory, which allows for considering the temporal relationship between samples in the classification process, often neglected in state-of-the-art approaches. The proposed system was tested on experimental data, collected by Leonardo Helicopter Division, assessing outstanding capabilities in recognizing correctly standard and mixed regimes and transients. Also, the presented results demonstrate the approach capabilities in paving the way for the definition of new regimes, more consistent with the actual helicopter usage spectrum
Optimizing Automatic Flight Condition Recognition through a Multi-Strategy Machine-Learning Based Approach
No full textFlight Condition Recognition (FCR) is essential in the usage monitoring of helicopters, as maneuver instances determine the usage spectrum, and thus the assessment of the original usage assumptions, adopted at design time for the definition of the retirement life of its components. Automated FCR capabilities, exploiting algorithms to detect the aircraft maneuvers by appropriate processing of on-board sensors measurements, allow us to reconstruct the usage spectrum, supporting the definition of improved maintenance manuals, with replacement times and inspection intervals tailored to the helicopters actual usage, thus enabling Condition-Based Maintenance (CBM) schemes. However, designing an efficient automatic FCR system is a challenging task, due to the complex machine dynamics characterizing the different flight regimes. In this work, we show how to optimize a machine-learning based approach to FCR design by exploiting a multi-strategy time-series segmentation framework, which combines two supervised learning approaches that leverage sliding windows and stacking ensembles to produce reliable estimates of the flown regimes. The approach is validated on an experimental dataset of nearly 500 labeled flights from two helicopter models, demonstrating its effectiveness in predicting the different maneuver types, and its improvement over a single-strategy approach
Real-time optimal traffic management in signal-controlled intersections: A receding-horizon approach
No full textAdequate traffic signal control strategies are essential to achieve a significant reduction of traffic congestion in urban environment. This work presents a receding-horizon approach for the optimal management of a singular signalized intersection via a computationally efficient Model Predictive Control (MPC) formulation. The control strategy aims at minimizing the overall number of vehicles in queue at the traffic lights in each road, while satisfying additional safety constraints connected to the intersection's layout. Pedestrian requests are explicitly handled and potential deadlock situations in low traffic scenarios are avoided. The presented approach is validated through a realistic microscopic traffic simulator based on SUMO, in which a real intersection layout from the Italian city of Monza has been accurately reproduced and real-world traffic profiles have been provided as input
Mining the electrification potential of fuel-based vehicles mobility patterns: A data-based approach
No full textElectric Vehicles (EVs) are quickly becoming a very important segment of the automotive industry. However, the so-called range anxiety, i.e., the fear that a vehicle has insufficient range to reach its destination, the experience anxiety, i.e.,the fear of the hassle of public charging and the high selling price are still major barriers to a widespread adoption of electric cars. In this paper, we use real-world data from vehicle telematics devices to quantitatively assess whether range anxiety is a justified threat. Specifically, we evaluate the vehicles electrification potential based on their real driving patterns, showing that a significant percentage of traditional Internal Combustion Engine (ICE) Vehicles could be effortlessly replaced by EVs, without any impact on the owners' driving habits and with the current public charging infrastructure, only ensuring an overnight recharging
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
A Multivariate Time-Series Segmentation Framework for Flight Condition Recognition
Get PDFHelicopters usage monitoring has gained significant attention in recent years, due to the safety and cost management implications. At its core there is the flight condition recognition algorithm, which enables to detect the maneuvers carried out by the aircraft through on-board sensors measurements. In this work, we propose a multivariate time-series segmentation framework, which uses supervised learning algorithms, sliding windows, and stacking ensembles to produce reliable estimates of the flown flight regimes. We validate the proposed approach on a large dataset of 460 labeled load flights from two distinct helicopter models, demonstrating its efficacy in predicting a range of 49 different maneuver types
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