1,720,963 research outputs found
Switched adaptation strategies for integral sliding mode control: Theory and application
No full textIntegral sliding mode (SM) control is an interesting approach, as it can maintain the good chattering alleviation property of higher-order SMs while making the reaching phase less critical and keeping the controlled system trajectory on a suitably selected sliding manifold since the initial time instant. In order to make such a method more robust and to improve its flexibility by the adaptation of its parameters to the current system condition, in this paper, a switched strategy is proposed. Specifically, the suboptimal Second-order SM algorithm is considered as a basis in its integral formulation, and the switching strategy is designed by partitioning the so-called auxiliary system state space in a finite number of regions. The proposed method allows one to improve the transient performance by adapting the gains through these regions, thus implying an energy saving capability. The proposal is theoretically analyzed and, in order to test its performance, the control of the lateral dynamics of ground vehicles is used as a case study. Specifically, yaw-rate tracking is considered, as it is made difficult by parametric uncertainties and nonlinear effects that arise especially with large steering angles. Extensive simulation tests are carried out using standard validation maneuvers, which favorably witness the performance of the new control algorithm
Data-Driven Collaborative Intelligent System for Automatic Activities Monitoring of Wild Animals
Full text linkActivity profiling is key to understand individual behavior and group dynamics for a species. To date, individuals monitoring is directly performed by the ethologist, leading to several limitations in the quantity and quality of the results. In this work, we propose a data-driven collaborative system for automatic remote monitoring of wild animals, in a challenging environment, properly designed to satisfy the ethologist's needs. This smart system fuses sensors data to perform an intelligent behavior identification, allowing for automatic activity profiling. As a case study, we leverage a dataset collecting time-series acquired by tri-Axial accelerometer and GPS applied to 26 baboons for 35 days, to identify running, walking, sitting, standing and feeding activities. The results obtained in terms of prediction accuracy and decision-making process interpretability show that the system can overcome the hostile environment's challenges, proving to be an effective support to perform smart remote automatic profiling
Automatic stimuli classification from ERP data for augmented communication via Brain-Computer Interfaces
No full textBrain-computer interfaces (BCIs) are systems initially designed to compensate for motor disabilities affecting people whose control of the muscular system is compromised. However, recent developments open the BCIs market to a wide range of medical and non-medical applications. This raises the need for systems capable of interpreting more and more stimuli, even from different sensory domains. In this work, we design a machine-learning system able to fit both application domains accurately recognizing visual and auditory stimuli starting from the event-related potentials (ERPs) they generate. The obtained results are promising and some practical and realization aspects are discussed
Dross attachment estimation in the laser-cutting process via Convolutional Neural Networks (CNN)
Full text linkLaser cutting of metals offers the advantage of high precision and accuracy. Dross attachment, measured as the length of the re-solidified material perpendicular to the surface, has definitely the highest impact on the overall process quality. Dross attachment is commonly judged by skilled technicians that evaluate the cut quality. Process parameters are optimized to maximize the cutting speed while keeping an acceptable level of dross attachment. However, in practice, increased levels of dross may occur due to different processing conditions. In this framework, a real-time dross attachment monitoring system is desired. Within the stream of vision based monitoring systems, in this work we use high frequency images generated by a precision camera, mounted on the laser head, to capture the cutting process light emission. A CNN-based classification system is developed, where captured images are fed into the trained network with the aim of automatically recognize if a predetermined dross attachment level is exceeded. To our best knowledge, this is the first work where a CNN is used for monitoring the quality of laser cutting process via dross attachment classification
A novel crash detection algorithm for two-wheeled vehicles
No full textThis paper presents a crash detection
strategy for motorcycles, using GPS/GNSS and inertial
measurements collected by telematic e-Boxes. The
primary goal is to jointly minimize the access time
of the emergency services and to accurately store the
event dynamics for further investigation on accident’s
responsibilities. For motorcycles, unlike for cars, crash
events cannot always be detected by monitoring abnormal
longitudinal decelerations solely. Thus, this work
proposes a novel method that detects and classifies the
severity of crash-like situations. The proposed approach
follows a new paradigm that improves the detection
performance, better generalizing the motorcycle dynamics
with respect to the specific vehicle and the
driving style. The proposed approach has been tested
and validated with experimental data, covering both
motorsport and naturalistic scenarions, involving several
riders, different road conditions and vehicles
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
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