1,720,976 research outputs found
Extending Asset Lifespan Through Data Augmentation-Assisted Quality Control
No full textQuality control is essential for the life cycle of components and has an impact on the costs and maintenance time of the entire production chain. In modern quality control, AI power could be unleashed if enough information for training AI models is always available. In this work, an approach based on data augmentation has been applied to oil plug quality control to mitigate the lack of images of defective parts, which hinders the training of classification models. The proposed approach has demonstrated a positive impact on quality inspection by maintaining or reducing the percentage of classification errors with respect to a baseline trained with images of real oil plugs
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
An IIoT Platform For Human-Aware Factory Digital Twins
No full textIn the context of the Industry 4.0 approach, applications and solutions supporting monitoring, simulation, optimisation and decision-making in production systems are exponentially growing. These solutions are commonly built on digital twins, i.e., comprehensive, structured and effective digital representations of the production system and its entities, whose current status is constantly updated by the plugged data sources. The arising of the Industry 5.0 paradigm and the established key role of workers in manufacturing require new Digital Twins to represent also humans. In fact, as cognitive automation becomes more and more pervasive and its behaviour unintelligible to humans, it becomes essential for improving performance and well-being, at the same time, to model humans as data-driven agents and to represent their interaction with the factory systems. Currently, a standardised solution for creating Digital Twins is missing, forcing industrial solution architects to resort to ad-hoc implementations and models. These solutions lack re-usability, scalability and extensibility, preventing the introduction of a human digital representation in existent twins, so hindering the complete shift to the new Industry 5.0 paradigm. In this paper, such limitations are faced by introducing an extensible and flexible IIoT - industrial internet of things - based platform with a twofold benefit: on the one hand, to support the creation of customised data representations of production systems and their entities including humans; on the other hand, to provide a modular infrastructure, along with its interchangeable components, for easy digital twin instantiation and ramp-up. An implementation of the platform has been tested with different applications in a laboratory setting and released as a public resource. Finally, potential future applications of the proposed digital twin are discussed, highlighting its main benefits
Impact of Collaborative Robots on Human Trust, Anxiety, and Workload: Experiment Findings
No full textThis work proposes an experiment setup and its protocols to investigate the impact of cobot’s size, speed and collaboration modes on different human factors including trust, propensity to trust, anxiety, and mental workload. The setup and the protocols supported the execution of different experiments where the 29 participants were asked to complete the Tower of Hanoi in collaboration with a cobot. The setup and the protocols provide a ready-to-use solution to expand experiments for further studies. Moreover, statistical analysis of the results shows higher cobot speeds increased trust propensity despite not significantly affecting overall trust or anxiety. Collaboration modes significantly influenced perceived workload and task performance, with the “Collaboration with Trigger” mode resulting in lower mental workload but longer task completion times. No significant differences were found in human factors concerning cobot size, indicating that variations in size do not significantly impact trust, propensity to trust, anxiety, or workload. Additionally, the collaboration mode with cobots notably affects workload perception and task performance, with specific modes reducing perceived effort but not necessarily improving task efficiency
A Framework for Human-aware Collaborative Robotics Systems Development
No full textIn the last decade, the manufacturing domain has been marked by a veritable flood of technological breakthroughs. Collaborative robotics, in particular, has enabled workstations to be shared between humans and their robotic counterparts. The ability of collaborative robots to work side-by-side with humans has opened up new possibilities for task and activity design in manufacturing. However, despite the promising outlook, actual collaboration between humans and cobots in existing applications is very limited, so the potential of the technology is only partially realised. In this paper, a framework is proposed to help practitioners and researchers in implementing human-aware collaborative robotics systems. The framework supported the development of a collaborative screwdriving application in which both the operator and the robot support and perceive each other to optimise process performance and worker well-being
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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