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Timothy J. Rogers
Researcher at University of Sheffield
Publications - 65
Citations - 759
Timothy J. Rogers is an academic researcher from University of Sheffield. The author has contributed to research in topics: Structural health monitoring & Computer science. The author has an hindex of 10, co-authored 53 publications receiving 394 citations. Previous affiliations of Timothy J. Rogers include University of Wisconsin-Madison.
Papers
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Journal ArticleDOI
Foundations of population-based SHM, Part I : homogeneous populations and forms
L.A. Bull,Paul Gardner,Julian Gosliga,Timothy J. Rogers,Nikolaos Dervilis,Elizabeth J. Cross,Evangelos Papatheou,A.E. Maguire,Carles Campos,Keith Worden +9 more
TL;DR: A framework is proposed to model a population of nominally-identical systems, such that (complete) datasets are only available from a subset of members.
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Sparse Overlapping Sets Lasso for Multitask Learning and its Application to fMRI Analysis
TL;DR: In this article, the Sparse Overlapping Sets (SOS) lasso, a convex optimization that automatically selects similar features for related learning tasks, is proposed. But it is not suitable for multi-task learning, since features useful in one task are also useful for other tasks.
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A Bayesian non-parametric clustering approach for semi-supervised Structural Health Monitoring
Timothy J. Rogers,Keith Worden,R. Fuentes,Nikolaos Dervilis,U. T. Tygesen,Elizabeth J. Cross +5 more
TL;DR: This paper presents a framework based on Bayesian non-parametric clustering, in particular Dirichlet Process (DP) mixture models, for performing SHM tasks in a semi-supervised manner, including an online feature extraction method.
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Probabilistic modelling of wind turbine power curves with application of heteroscedastic Gaussian Process regression
Timothy J. Rogers,Paul Gardner,Nikolaos Dervilis,Keith Worden,A.E. Maguire,Evangelos Papatheou,Elizabeth J. Cross +6 more
TL;DR: This work proposes the use of a heteroscedastic Gaussian Process model, which exists within a Bayesian framework which exhibits built-in protection against over-fitting and robustness to noisy measurements, and is shown to be effective on data collected from an operational wind turbine.
Journal ArticleDOI
Probabilistic active learning : an online framework for structural health monitoring
L.A. Bull,Timothy J. Rogers,Chandula T. Wickramarachchi,Elizabeth J. Cross,Keith Worden,Nikolaos Dervilis +5 more
TL;DR: A novel, probabilistic framework for the classification, investigation and labelling of data is suggested as an online strategy for Structural Health Monitoring, which allows for the definition of a multi-class classifier to aid both damage detection and identification, while using a limited number of the most informative labelled data.