1,720,954 research outputs found
Detección y clasificación de precipitaciones en Sudamérica mediante imágenes satelitales y técnicas de aprendizaje automático
Full text linkTesis (Lic. en Física)--Universidad Nacional de Córdoba, Facultad de Matemática, Astronomía, Física y Computación, 2022.Fil: Andelsman, Federico. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía, Física y Computación; Argentina.En este trabajo se aborda el problema de la detección y clasificación de precipitaciones por intensidades a partir de información satelital y métodos provenientes del aprendizaje automático. Se ha elegido como área de estudio a una porción de Sudamérica durante el mes de enero de 2021. El estudio se centra en el análisis del producto de Estimaciones Cuantitativas de Precipitaciones (QPE del inglés), del satélite GOES-16. Se entrenó una red neuronal convolucional llamada Cloud-Net, con el fin de detectar píxeles de lluvia o clasificarlos por umbrales de intensidades, teniendo como entrada las imágenes multiespectrales de GOES-16 y como datos de etiqueta al producto QPE. A su vez, se probaron distintas funciones de pérdida para la clasificación binaria y multicategoría. En paralelo, se compararon algunos de los resultados con los del algoritmo XGBoost. Se obtuvieron buenos resultados con la red Cloud-Net, en especial para las categorías extremas de píxeles sin lluvia (99% de precisión) y de lluvia mayor a 30 mm/h (hasta 93% de precisión), pero tiene dificultades para distinguir entre algunas categorías intermedias. Al compararlo con XGBoost, Cloud-Net tiene un mejor desempeño para identificar las ubicaciones de lluvias más dispersas. Por su parte, XGBoost sólo obtiene mejores resultados con las lluvias torrenciales, distinguiéndose con una precisión del 96%. Finalmente, los dos algoritmos tienen una tendencia a subestimar respecto a las intensidades de lluvia provistas por el algoritmo QPE.The study of precipitation is one of the areas of most interest in the atmospheric sciences and with the most impact on our everyday life and on climate change projections. In this project, satellite information and Machine Learning methods are used to treat the detection and classification of precipitation. We have chosen as our area of study a portion of South America during the month of January 2021. We also focus our study on the analysis of the Quantitative Precipitation Estimation product (QPE) of the GOES-16 satellite. A convolutional neural network called Cloud-Net was trained to detect or classify pixels by rainfall intensities. The QPE product was used as label data and multispectral images of the GOES-16 satellite were used as input. Meanwhile, different loss functions were tested and the multiclass results were compared to those obtained by the XGBoost algorithm. Cloud-Net performs very well in the extreme classes of No Rain pixels (99% precision) and torrential rain over 30 mm/h (up to 93% precision), but it has difficulties distinguishing between some of the intermediate classes. When compared to XGBoost, Cloud-Net has a better performance identifying scattered rain and XGBoost only classifies torrential rain better (96% precision). Finally, both algorithms tend to underestimate rain intensities in comparison to the QPE algorithm.Fil: Andelsman, Federico. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía, Física y Computación; Argentina
Detection and classification of rainfall in South America using satellite images and machine learning techniques
No full textThe study of precipitation is one of the most intriguing areas in atmospheric sciences, with significant implications for our daily lives and climate change projections. This paper explores the estimation of rainfall trends in South American regions using convolutional neural networks (CNNs). The study focuses on the application of Cloud-Net, a CNNbased model with a format similar to an autoencoder, to obtain qualitative estimates of precipitation patterns. The employed loss functions, Categorical Cross Entropy and Categorical Focal Loss, address the challenges of classifying minority categories in unbalanced data. Regional analysis was conducted, identifying days with high rainfall intensity and the predominant intensities in 25 regions. The CNN model’s performance was compared with the XGBoost algorithm, showing excellent results for extreme rainfall categories and challenging intermediate categories. Furthermore, a comparison was made with Quantitative Precipitation Estimation (QPE) data and ground measurements from rain gauges. While the CNN model provided a valuable qualitative estimate of precipitation trends, achieving precise quantitative estimation would require an extensive data set of in-situ measurements. Overall, this research demonstrates the potential of CNNs for estimating rainfall trends and understanding precipitation patterns in South American regions. The findings offer valuable insights for further applications in meteorology and environmental studies.Fil: Andelsman, Federico. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; ArgentinaFil: Masuelli, Sergio. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; ArgentinaFil: Tamarit, Francisco. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Física Enrique Gaviola. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentin
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
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
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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