1,721,056 research outputs found
Towards the NASA UQ Challenge 2019: Systematically forward and inverse approaches for uncertainty propagation and quantification
Get PDFThis paper is dedicated to exploring the NASA Langley Challenge on Optimization under Uncertainty by proposing a series of approaches for both forward and inverse treatment of uncertainty propagation and quantification. The primary effort is placed on the categorization of the subproblems as to be forward or inverse procedures, such that dedicated techniques are proposed for the two directions, respectively. The sensitivity analysis and reliability analysis are categorized as forward procedures, while modal calibration & uncertainty reduction, reliability-based optimization, and risk-based design are regarded as inverse procedures. For both directions, the overall approach is based on imprecise probability characterization where both aleatory and epistemic uncertainties are investigated for the inputs, and consequently, the output is described as the probability-box (P-box). Theoretic development is focused on the definition of comprehensive uncertainty quantification criteria from limited and irregular time-domain observations to extract as much as possible uncertainty information, which will be significant for the inverse procedure to refine uncertainty models. Furthermore, a decoupling approach is proposed to investigate the P-box along two directions such that the epistemic and aleatory uncertainties are decoupled, and thus a two-loop procedure is designed to propagate both epistemic and aleatory uncertainties through the systematic model. The key for successfully addressing this challenge is in obtaining on the balance among an appropriate hypothesis of the input uncertainty model, a comprehensive criterion of output uncertainty quantification, and a computational viable approach for both forward and inverse uncertainty treatment
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
Data-driven stochastic model updating and damage detection with deep generative model
No full textIs there a calibration algorithm beyond the dominant Bayesian sampling approach and sensitivity-based optimisation in model updating? Can a neural network serve not only as a surrogate model but also possess its own calibration capacity, independent of the Bayesian or optimisation framework? This work aims to address these questions by developing a unique data-driven approach for stochastic model updating and damage detection. Among a variety of models in deep learning, the class of deep generative model shares a similar objective, to estimate an unknown or intractable probability distribution from a small number of samples, with model updating. As a powerful flow-based deep generative model, a recently developed conditional Invertible Neural Network (cINN) architecture has been adopted in the task of model updating. Unlike the conventional approaches that employ the neural networks solely as a forward surrogate, the cINN-based model updating is a framework that performs as a bidirectional network where the forward training and inverse calibration are integrated into a uniform structure. The cINN consists of two parts known as the conditional network and the invertible neural network (INN). Both networks are trained jointly in the forward direction and can operate inversely to offer rapid and accurate predictions by given observation data. The application of the cINN provides a more efficient and direct manner to solve model updating problems without calculating the likelihood function in Bayesian inference. The cINN is embedded into a multilevel stochastic updating framework. Rather than directly calibrating physical parameters, this multilevel framework focuses on their statistical moments, e.g. mean and variance, referred to as hyperparameters. The hyperparameters are then utilised to determine the probability of damage (PoD), which provides a confidence level about the structural condition, facilitating stochastic damage detection. Two case studies are proposed to demonstrate the multilevel cINN-based stochastic updating and damage detection approach. The first involves a 3-degree-of-freedom spring-mass simulation model, while the second case study employs an experimental rig testcase with practical measurements, each under various damage scenarios
Mining Safety and Sustainability II
No full textSafety and sustainability are becoming ever bigger challenges for the mining industry with the increasing depth of mining. It is of great significance to reduce the disaster risk of mining accidents, enhance the safety of mining operations, and improve the efficiency and sustainability of development of mineral resource. This book provides a platform to present new research and recent advances in the safety and sustainability of mining. More specifically, Mining Safety and Sustainability presents recent theoretical and experimental studies with a focus on: safety mining, green mining, sustainable development, risk management of mines, mining methods and technologies, damage monitoring and prediction. It will be further helpful to provide theoretical support and technical support for guiding the normative, green, safe and sustainable development of the mining industry
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
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe 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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