1,720,991 research outputs found
DhakaAI
No full textThe capital city of Dhaka has only 7% traffic roads (compared to 25% urban standard) in presence of approximately 8 million computers a day with in 306 sq km area. The senario of Dhaka traffic is unique which poses complex new challanges in terms of automated traffic detection. To solve the problem using advances in AI-based technology and ICT solutions, we are calling for splutions to automatic Dhaka traffic detection problems on optical images. This new AI-Based Dhaka Traffic Detection Challenge aims at accessing the ability of state-of-the-art methods to detect and recognize traffic vehicles. This solution is encountered in mordern cities where multile cultures live and communicate together, where users see various scripts and languages in a way that prevents using much a priori knowledge. Alos, at the same time, the academics and researches from region who are experts in AI or interested in exploring possibilities could be brought to a networking community throug this campaign. Working together on a common problem statement can create the right synergies needed to build AI-based community in South-East Asia
Replication Data for Remote Damage Detection of Power Plants using Deep Learning based drone image analysis
No full textReplication Data for Remote Damage Detection of Power Plants using Deep Learning-based drone image analysis
Thermal Solar, Large Solar, Small Solar, Wind turbine image dataset of drone inspection with damages annotated. Some of the images being collected from the following reference.
Estefanía Alfaro-Mejía, Humberto Loaiza-Correa, Edinson Franco-Mejía, Andrés David Restrepo-Girón, Sandra Esperanza Nope-Rodríguez, Dataset for recognition of snail trails and hot spot failures in monocrystalline Si solar panels, Data in Brief, Volume 26, 2019, 104441, ISSN 2352-3409, https://doi.org/10.1016/j.dib.2019.104441.
S. Mehta, A. P. Azad, S. A. Chemmengath, V. Raykar, and S. Kalyanaraman, DeepSolarEye: Power Loss Prediction and Weakly Supervised Soiling Localization via Fully Convolutional Networks for Solar Panels," 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, NV, 2018, pp. 333-342.
Shihavuddin, A.S.M., Chen, X., Fedorov, V., Nymark Christensen, A., Andre Brogaard Riis, N., Branner, K., Bjorholm Dahl, A. and Reinhold Paulsen, R., 2019. Wind turbine surface damage detection by deep learning aided drone inspection analysis. Energies, 12(4), p.676
Outcome Based Assessment Platform (OBAP)
No full textA unified platform for making OBE based assessment for the tertiary level
DTU - Drone inspection images of wind turbine
No full textThis dataset set has temporal inspection images for the years of 2017 and 2018 of the same 'Nordtank' wind turbine at DTU wind facilities in Roskilde, Denmark
YOLO Annotated Wind Turbine Surface Damage
No full textA dataset of wind turbine surface damage composed of images from Shihavuddin & Chen's (2018) dataset split into 586x371 pixel images with YOLO format annotations for Dirt and Damage.
SHIHAVUDDIN, ASM; Chen, Xiao (2018),
“DTU - Drone inspection images of wind turbine”,
Mendeley Data,
V2,
doi: 10.17632/hd96prn3nc.
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe 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
Get PDF“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
Get PDFWe 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
DTU Wind Turbine Blade Damage Inspection Dataset using Thermography
No full textThis dataset includes blade damage inspection results using passive thermography when the blades are under cyclic loads or in operational wind turbines. The dataset is used in the development of AQUADA and AQUADA PLUS. The related papers are:[1]Chen, X., Shihavuddin, ASM., Madsen, S. H., Thomsen, K., Rasmussen, S., & Branner, K. (2021). AQUADA: Automated quantification of damages in composite wind turbine blades for LCOE reduction. Wind Energy, 24(6), 535-548. https://doi.org/10.1002/we.2587[2]Chen, X., Semenov, S., McGugan, M., Madsen, S. H., Yeniceli, S. C., Berring, P., & Branner, K. (2021). Fatigue testing of a 14.3 m composite blade embedded with artificial defects – damage growth and structural health monitoring. Composites Part A: Applied Science and Manufacturing, 140, [106189]. https://doi.org/10.1016/j.compositesa.2020.106189[3]Chen, X., Janeliukstis, R., & Sarhadi, A. (2022). Thermographic data analytics-based damage characterization in a large-scale composite structure under cyclic loading. Composite Structures, [115525]. https://doi.org/10.1016/j.compstruct.2022.115525 [4] Chen, X., Sheiati S., Shihavuddin, ASM., AQUADA PLUS: Automated Damage Inspection of Cyclic-loaded Large-scale Composite Structures using Thermal Imagery and Computer Vision. Composite Structures https://doi.org/10.1016/j.compstruct.2023.11708
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