1,721,001 research outputs found
Machine Learning Algorithm for Robotic Inverse Kinematic Problem
No full textThis work proposes a numerical approach based on a machine learning method associated to neural network back-propagation algorithm to solve the inverse kinematic problem. The algorithm was tested on a redundant manipulator. Obtained numerical results exhibit acceptable accuracy and precision compatible with standard industrial applications
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
Geoinformation Technology for Analysis and Visualisation of High Spatial Resolution Greenhouse Gas Emissions Data Using a Cloud Platform
No full textThe geoinformation technology for spatial analysis and visualisation of greenhouse gas (GHG) emissions is proposed using Google Earth Engine cloud technology as a key component of interaction with high-resolution spatial data. This technology includes a website for spatial analysis and visualisation of vector data, as well as an interactive site for deeper analysis of raster data on GHG emissions. We use high-resolution vector data of emissions at the level of point, line and areal emission sources, which are converted into raster emission data. Emissions can be analysed within user-created polygons including calculation of the total, specific, maximum or average emission magnitudes. There is also the possibility to fix and select pixels containing a certain interval of emission magnitudes. Using Python’s Google Earth Engine module, we have created a website where users can clip raster data from hand-drawn polygons that can be saved on Google Drive. We have also used Python modules (Matplotlib, Pandas, Numpy) for statistical analysis of raster data and histogram construction. Geoinformation technology includes many sectors and categories of human activity included in national inventory reports on GHG emissions, such as those regarding the burning of fossil fuels for power and heat production, within the industrial, agricultural, construction, residential, institutional and waste sectors, as well reports addressing emissions caused by chemical processes. Implementation of the proposed technology is presented using high spatial resolution greenhouse gas emissions data from Poland
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Real-Time Monitoring of Road Networks for Pavement Damage Detection Based on Preprocessing and Neural Networks
Full text linkData Availability Statement: Datasets are available by link https://www.kaggle.com/datasets/dataclusterlabs/potholes-or-cracks-on-road-image-dataset, accessed on 10 October 2022. Mysak M., Yakovyna V., Shakhovska N. (2023). Pothiles and cracks on road video and image detection system (Version 1.1.1) [Computer software]. Software Heritage, https://github.com/MysakMaksym/pothole-detection.git, accessed on 14 June 2024.This paper presents a novel multi-initialization model for recognizing road surface damage, e.g. potholes and cracks, on video using convolutional neural networks (CNNs) in real-time for fast damage recognition. The model is trained by the latest Road Damage Detection dataset, which includes four types of road damage. In addition, the CNN model is updated using pseudo-labeled images from semi-learned methods to improve the performance of the pavement damage detection technique. This study describes the use of the YOLO architecture and optimizes it according to the selected parameters, demonstrating high efficiency and accuracy. The results obtained can enhance the safety and efficiency of road pavement and, hence, its traffic quality and contribute to decision-making for the maintenance and restoration of road infrastructure.National Research Foundation of Ukraine, project #2021.01/0103; British Academy Fellowship RaR\100727 Horizon Europe project ZEBAI: Innovative methodologies for the design of Zero-Emission and cost-effective Buildings enhanced by Artificial Intelligence (Grant Agreement ID: 101138678)
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