1,720,974 research outputs found
A multi-agent 3d simulation environment for clothing industry
No full textThe clothing artefact business is facing relevant restructuring to become able to produce items with enhanced value as for quality reliability, fashion inventiveness and mass customization. The paper presents a multi-agent simulation environment developed to assess and virtually check the feasibility and performances of flexible automation solutions that can help the clothing industry to overcome the shift towards knowledge driven organizations. It addresses new options based on distributed intelligence and robotized cooperative resources including human assisted working
Predictive hebbian association of time-delayed inputs with actions in a developmental robot platform
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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
Online learning of sensorimotor interactions using a neural network with time-delayed inputs
No full textDeep Learning and Machine Learning in Robotics
No full textDeep learning has gone through massive growth in recent years. In many fields—computer vision, speech recognition, machine translation, game playing, and others—deep learning has brought unprecedented progress and become the method of choice. Will the same happen in robotics and automation? In a sense, it is already happening. Today, deep learning is often the most common keyword for work presented at major robotics conferences. At the same time, robots, as physical systems, pose unique challenges for deep learning in terms of sample efficiency and safety in real-world robot applications. With robots, data are abundant, but labels are sparse and expensive to acquire. Reinforcement learning in principle does not require data labeling but does require a significant number of iterations on real robots. Transferring the capabilities learned in simulation to real robots and collecting sufficient data for practical robot applications both present major challenges. Further, mistakes by robot learning systems are often much more costly than those by their counterparts in the virtual world. These mistakes may cause irreversible damage to robot hardware or, even worse, loss of human lives. Safety is thus paramount for robot learning systems.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Learning & Autonomous Contro
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