1,720,958 research outputs found
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
RACECAR -- The Dataset for High-Speed Autonomous Racing
Full text linkThis paper describes the first open dataset for full-scale and high-speed
autonomous racing. Multi-modal sensor data has been collected from fully
autonomous Indy race cars operating at speeds of up to 170 mph (273 kph). Six
teams who raced in the Indy Autonomous Challenge have contributed to this
dataset. The dataset spans 11 interesting racing scenarios across two race
tracks which include solo laps, multi-agent laps, overtaking situations,
high-accelerations, banked tracks, obstacle avoidance, pit entry and exit at
different speeds. The dataset contains data from 27 racing sessions across the
11 scenarios with over 6.5 hours of sensor data recorded from the track. The
data is organized and released in both ROS2 and nuScenes format. We have also
developed the ROS2-to-nuScenes conversion library to achieve this. The RACECAR
data is unique because of the high-speed environment of autonomous racing. We
present several benchmark problems on localization, object detection and
tracking (LiDAR, Radar, and Camera), and mapping using the RACECAR data to
explore issues that arise at the limits of operation of the vehicle.Comment: 9 pages, 10 figures. For links to data and reference material go to
https://github.com/linklab-uva/RACECAR_DAT
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe 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
Full text link“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
Full text linkWe 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
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Development of a LiDAR-based target detection and tracking system for the Indy Autonomous Challenge
No full textLAUREA MAGISTRALEQuesto lavoro di tesi descrive un algoritmo di tracciamento bersagli e localizzazione, basato su LiDAR, sviluppato per un'auto da corsa autonoma: è infatti stato progettato per la Dallara AV21 del Politecnico di Milano che ha vinto il terzo posto della Indy Autonomous Challenge, la prima competizione di auto da corsa a guida autonoma. La percezione dell'ambiente circostante è di cruciale importanza per i veicoli autonomi. Questa particolare applicazione di competizione richiede un'individuazione affidabile degli ostacoli, preciso tracciamento e stima della velocità, e ridondanza nella stima della localizzazione, il tutto fornito con brevi ritardi e tempi di calcolo. L'algoritmo proposto usa i dati LiDAR per fornire, per ogni ostacolo individuato, una stima del suo stato completa di velocità ed orientamento. Questo risultato viene raggiunto individuando gli ostacoli dalle differenze di altezza presenti nella Point Cloud, attraverso l'uso di una rappresentazione bidimensionale dei dati di scansione. Successivamente un filtro a media mobile viene usato per tracciare gli ostacoli stazionari, mentre un Filtro Kalman Esteso con misure virtuali fornisce una stima della dinamica completa degli ostacoli in movimento. Inoltre, un approccio innovativo alla localizzazione basata su LiDAR su un tracciato di gara, la stessa rappresentazione bidimensionale di una Point Cloud viene utilizzata per cercare primitive geometriche nei dati di scansione, con lo scopo di stimare la distanza dai muri esterni del tracciato. I risultati e le performance dell'algoritmo sono infine presentati, usando sia dati sperimentali, acquisiti durante la Indy Autonomous Challenge, che simulazioni di scenari più sfavorevoli, ottenute da un simulatore sviluppato internamente.This thesis work describes a LiDAR-based target tracking and localization algorithm developed for an autonomous racecar. It was designed for the Dallara AV21 of Politecnico di Milano, that won third place at the Indy Autonomous Challenge, the first competition of self-driving racecars. Perception of the external environment is crucial for autonomous vehicles. This particular racing application requires reliable obstacle detection, precise target tracking and speed estimation, and redundancy in the localization estimation, all of which with little computational time and delay. The proposed algorithm uses LiDAR data to provide, for each detected obstacle, an estimate of its full state, including velocity and heading. It achieves this result by detecting objects from height differences in the Point Cloud, through the use of a bidimensional representation of the scan data. A moving average filter is then used to keep track of static obstacles, while an Extended Kalman Filter with virtual measurements provides estimation of the full dynamics of moving obstacles. Furthermore, in a novel approach to LiDAR-based localization on a racetrack, the same bidimensional Point Cloud representation is used to find geometric primitives in the scan data, in order to estimate the distance from track walls. The validation of the algorithm performances is then presented, using both the experimental data, gathered during the Indy Autonomous Challenge, and simulations of worst-case scenarios, performed on an internally developed simulator
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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