1,720,958 research outputs found
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
Synthetic Data Generation for Bridging Sim2Real Gap in a Production Environment
Full text linkSynthetic data is being used lately for training deep neural networks in
computer vision applications such as object detection, object segmentation and
6D object pose estimation. Domain randomization hereby plays an important role
in reducing the simulation to reality gap. However, this generalization might
not be effective in specialized domains like a production environment involving
complex assemblies. Either the individual parts, trained with synthetic images,
are integrated in much larger assemblies making them indistinguishable from
their counterparts and result in false positives or are partially occluded just
enough to give rise to false negatives. Domain knowledge is vital in these
cases and if conceived effectively while generating synthetic data, can show a
considerable improvement in bridging the simulation to reality gap. This paper
focuses on synthetic data generation procedures for parts and assemblies used
in a production environment. The basic procedures for synthetic data generation
and their various combinations are evaluated and compared on images captured in
a production environment, where results show up to 15% improvement using
combinations of basic procedures. Reducing the simulation to reality gap in
this way can aid to utilize the true potential of robot assisted production
using artificial intelligence.Comment: 17 pages, 9 figures, LaTeX; typos corrected; has not been presented
in any conference or published in journa
Synthetic data generation procedures for domain-specific environments in manufacturing
No full text16681679Synthetic data is being used lately for training deep neural networks in computer vision applications such as object detection, object segmentation and 6D object pose estimation. Domain randomization hereby plays an important role in reducing the simulation to reality gap. However, this generalization might not always be effective in specialized domains like manufacturing that involve complex assemblies. Individual parts are integrated in much larger assemblies making them indistinguishable from their counterparts. Moreover, individual parts are often partially occluded in the scene. These situations give rise to wrong detections. Target domain knowledge is vital in these cases and if conceived effectively while generating synthetic data, can show a considerable improvement in bridging the simulation to reality gap. This paper validates synthetic data generation procedures through practical experimentation ensuring that experiments are both comprehensive and reproducible. After combining domain randomization and domain adaptation procedures for parts and assemblies used in manufacturing the model performance improves by up to 15% than the state-of-the-art domain randomization techniques. Reducing the simulation to reality gap in this way can unlock the true potential of robot-assisted production using artificial intelligence.25
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
Synthetic data generation procedures for domain-specific environments in manufacturing
No full textSynthetic data is being used lately for training deep neural networks in computer vision applications such as object detection, object segmentation and 6D object pose estimation. Domain randomization hereby plays an important role in reducing the simulation to reality gap. However, this generalization might not always be effective in specialized domains like manufacturing that involve complex assemblies. Individual parts are integrated in much larger assemblies making them indistinguishable from their counterparts. Moreover, individual parts are often partially occluded in the scene. These situations give rise to wrong detections. Target domain knowledge is vital in these cases and if conceived effectively while generating synthetic data, can show a considerable improvement in bridging the simulation to reality gap. This paper validates synthetic data generation procedures through practical experimentation ensuring that experiments are both comprehensive and reproducible. After combining domain randomization and domain adaptation procedures for parts and assemblies used in manufacturing the model performance improves by up to 15% than the state-of-the-art domain randomization techniques. Reducing the simulation to reality gap in this way can unlock the true potential of robot-assisted production using artificial intelligence
CARA: Mensch-Roboter- Kollaboration leichter umsetzen
No full text8893Das Engineering-Tool CARA (Computer-Aided Risk Assessment) des Fraunhofer-Instituts für Produktionstechnik und Automatisierung IPA erleichtert die Einführung von Anwendungen mit Mensch-Roboter-Kollaboration in der Industrie, indem es die Risikobeurteilung vereinfacht und teilweise automatisiert. Laut Entwicklungspartner DENSO Corporation konnte der technische Aufwand für die Risikoanalyse und Risikominderung durch CARA um mehr als 55 % reduziert werden.114
Synthetic Data Generation for Bridging Sim2Real Gap in a Production Environment
No full textSynthetic data is being used lately for training deep neural networks in computer vision applications such as object detection, object segmentation and 6D object pose estimation. Domain randomization hereby plays an important role in reducing the simulation to reality gap. However, this generalization might not be effectie in specialized domains like a production environment involving complex assemblies. Either the individual parts, trained with synthetic images, are integrated in much larger assemblies making them indistinguishable from their counterparts and result infalse positives or are partially occluded just enough to give rise to false negatives. Domain knowledge is vital in these cases and if conceived effectiely while generating synthetic data, can show a considerable improvement in bridging the simulation to reality gap. This paper focuses on synthetic data generation procedures for parts and assemblies used in a production environment.The basic procedures for synthetic data generation and their various combinations are evaluatedand compared on images captured in a production environment, where results show up to 15% improvement using combinations of basic procedures. Reducing the simulation to reality gap in this way can aid to utilize the true potential of robot assisted production using artificial intelligence
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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