1,720,954 research outputs found
Physics-Informed Machine Learning for Structural Damage Diagnosis in Aluminium Plates
Full text linkDamage diagnosis plays a crucial role in Structural Health Monitoring (SHM) by facilitating the identification, localization, and estimation of the extent of defects in structures. Lamb waves, known for their sensitivity to defects, are widely employed in SHM methods for thin-walled structures. Most of those traditional methods require extracting damage indices from Lamb wave signals. This operation involves substantial post-processing and implies that part of the diagnostic information is lost. To solve those limitations and improve the damage diagnosis accuracy, machine learning methods have recently been proposed in the literature. However, the reluctance of the industrial sector to adopt conventional black-box models due to their lack of explainability poses a challenge. This study proposes a physics-informed machine-learning approach to address the limitations of standard black-box methods. Particularly, a Physics-Informed Neural Network (PINN) is implemented to predict the density of an aluminium plate based on measurements of plate displacements caused by Lamb wave excitation. This is made possible by the implementation of a specific loss function, which leverages physical knowledge in the form of the partial differential equation governing Lamb waves. Predicting the plate density based on measured displacements eliminates the need for artificial damage indices, utilizing the density variation itself to detect and localize damage. Additionally, the outputs of the PINN, rooted in physics equations, offer enhanced explainability compared to standard black-box models. The versatility of this framework extends to predicting material properties distributions for components, and efforts will be directed towards adapting the method for composite materials, where the approach may pose additional challenges
Physics-informed neural network for damage localization using Lamb waves
Full text linkLAUREA MAGISTRALELe Lamb waves sono ampiamente utilizzate per valutare i danni strutturali a causa della loro sensibilità ai
difetti. Nonostante la facilità di eccitazione e acquisizione, spesso è necessario elaborare i segnali per ottenere
indicatori univoci, noti come indici di danneggiamento. Tradizionalmente, gli indici di danneggiamento sono
stati sviluppati utilizzando algoritmi tomografici per creare mappe di probabilità di danneggiamento, anche se
questo approccio è soggetto a limitazioni. Recentemente, l’applicazione del machine learning è stata impiegata
per migliorare l’accuratezza dei modelli che usano Lamb waves per la diagnosi dei danni. Tuttavia, molti metodi
esistenti richiedono ancora l’estrazione degli indici di danneggiamento dai segnali acquisiti, potenzialmente
portando a una perdita di informazioni diagnostiche e a una diminuzione dell’accuratezza. Un nuovo approccio
appartenente al machine learning è recentemente emerso in popolarità per la sua flessibilità e spiegabilità:
questa categoria di modelli è chiamata physics-informed neural networks. Queste reti permettono di incorporare
nell’algoritmo alcune leggi fisiche conosciute, per assicurarsi che le previsioni aderiscano alla fisica del problema.
Tuttavia, non si trovano molte applicazioni nel campo della diagnosi dei danni. In questo contesto, il presente
lavoro si propone di presentare un framework informato dalla fisica per eseguire la diagnosi dei danni utilizzando
le Lamb waves evitando l'estrazione degli indici di danneggiamento. Per valutare le prestazioni del framework proposto sono stati presi in considerazione diversi
casi studio.Lamb waves have been widely utilized for assessing structural damage
due to their sensitivity to defects. Despite their ease of excitation and acquisition,
significant processing is often necessary to derive single-valued indicators, known
as damage indices, from the acquired signals. Traditionally, damage indices have
been developed using tomographic algorithms to create damage probability maps,
though this approach is subject to limitations. Recently, machine learning has
been employed to enhance the accuracy of guided wave frameworks for damage
diagnosis. However, many existing methods still involve extracting damage indices
from the acquired signals, potentially leading to the loss of diagnostic information
and decreased accuracy. Recently, a new approach within the machine learning
field has risen in popularity for his flexibility and explainability: physics-informed
neural networks. These networks allow embedding some know physical laws in
the algorithm to make sure the predictions adhere to the physics of the problem.
However, little to no applications can be found in the field of damage diagnosis.
In this context, the present work aims to present a physics-informed framework to
perform damage diagnosis using Lamb waves avoiding damage indices extraction. Various case studies were considered
for evaluating the performance of the proposed framework
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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