1,721,014 research outputs found
Uncertainty-aware fault diagnosis for safety-related industrial systems
No full textIndustry 4.0 (I4.0) has enabled dynamic modern-day industrial environ ments through rapid automation and improved access to real-time data from complex industrial operations. The I4.0 suite of digital technologies, including the Internet of Things (IoT), data analytics, and predictive mod eling, enable intelligent industrial manufacturing systems through data driven decision-making. Further, the systematic integration of physical and virtual worlds through the cyber-physical system (CPS), a core concept of I4.0, enables the construction of expansive factories with high flexibility, adaptability, and even self-awareness. These factories are physically inter connected large-scale industrial plants requiring a higher level of process and quality management strategies to improve overall production safety and efficiency. Recently, data-driven fault diagnosis (FD) models trained on large-scale industrial process datasets using deep learning (DL) techniques have demonstrated the ability to deliver actionable insights required for intelligent process management. However, despite their potentially superior process monitoring capa bilities, DL models have limitations, including excessive data dependency, interpretability challenges, sensitivity to hyperparameters, lack of trans parency, susceptibility to adversarial attacks, and issues with imbalanced data. Furthermore, exposure to gradual changes under different operating conditions in the industrial environment significantly impacts the perfor mance of DL-based FD models. Therefore, it is crucial to design approaches that enhance the reliability of DL-based FD in dynamic industrial environ ments to guarantee the safety and efficiency of industrial systems. This thesis aims to develop techniques leveraging uncertainty estimation for data-informed decision-making under dynamic and uncertain industrial environments, highlighting the potential to enhance the safety and reliabil ity of DL-based FD applications. The proposed approaches enable the gen eration of trustworthy and interpretable fault predictions for data-driven decision-making to facilitate the deployment of DL-based FD models in safety-related industrial environments. First, this thesis proposes an uncertainty-aware ensemble combination method for an ensemble of DL-based FD models to help monitor the sta bility of industrial processes and product quality. The approach generates a continuous multivariate probability distribution as the combined model output, replacing deterministic point estimation techniques that are ineffec tive in capturing the underlying model predictive uncertainties. Next, this thesis proposes a data-driven method for generating syn thetic out-of-distribution (OOD) data based on deep generative networks. This approach leverages an in-distribution (ID) data-supporting manifold of large-scale industrial process data and a combination of strategic man ifold sampling techniques to create realistic OOD data. Generating syn thetic OOD data to augment ID data enhances the DL-based FD model ca pacity for estimating the prediction uncertainty by incorporating insights from OOD data, thereby delivering safe and reliable DL-based FD models for real-world industrial process monitoring. Finally, this thesis proposes a novel approach to enhance the training of DL-based FD systems on imbalanced datasets. The method applies logit weight vectors to the penultimate layer of a deep neural network (DNN), introducing relevant perturbations to influence the network output strate gically. In particular, the approach implements a training regime that fa cilitates the switching between logit vectors to help the classifier focus on samples from the minority classes while effectively generalizing the entire dataset. To demonstrate the effectiveness of the proposed approaches, this thesis explores the problem of monitoring the stability of industrial processes and product quality using case studies on the Steel plate faults and APS failure at Scania trucks datasets
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
On the Performance of Federated Learning Algorithms for IoT
No full textFederated Learning (FL) is a state-of-the-art technique used to build machine learning (ML) models based on distributed data sets. It enables In-Edge AI, preserves data locality, protects user data, and allows ownership. These characteristics of FL make it a suitable choice for IoT networks due to its intrinsic distributed infrastructure. However, FL presents a few unique challenges; the most noteworthy is training over largely heterogeneous data samples on IoT devices. The heterogeneity of devices and models in the complex IoT networks greatly influences the FL training process and makes traditional FL unsuitable to be directly deployed, while many recent research works claim to mitigate the negative impact of heterogeneity in FL networks, unfortunately, the effectiveness of these proposed solutions has never been studied and quantified. In this study, we thoroughly analyze the impact of heterogeneity in FL and present an overview of the practical problems exerted by the system and statistical heterogeneity. We have extensively investigated state-of-the-art algorithms focusing on their practical use over IoT networks. We have also conducted a comparative analysis of the top available federated algorithms over a heterogeneous dynamic IoT network. Our analysis shows that the existing solutions fail to effectively mitigate the problem, thus highlighting the significance of incorporating both system and statistical heterogeneity in FL system design
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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