Results 21 to 30 of about 11,061,498 (228)
Gaussian Process Time-Series Models for Structures under Operational Variability
Frontiers in Built Environment, 2017 A wide range of vibrating structures are characterized by variable structural dynamics resulting from changes in environmental and operational conditions, posing challenges in their identification and associated condition assessment. To tackle this issue,
Luis David Avendaño-Valencia +3 more
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Power Load Forecasting Method Based on MT-BSGP
Journal of Harbin University of Science and Technology, 2021 In order to forecast short-term household power load，a power load forecasting method based on multi-task Bayesian spatiotemporal Gaussian process ( MT-BSGP) is proposed.
LI Zhi-yong +5 more
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Sequentially Estimating the Approximate Conditional Mean Using Extreme Learning Machines
Entropy, 2020 This study examined the extreme learning machine (ELM) applied to the Wald test statistic for the model specification of the conditional mean, which we call the WELM testing procedure.
Lijuan Huo, Jin Seo Cho
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Demand response of residential air conditioning load based on user behavior
Zhejiang dianli, 2023 Residential side demand response is an important supplementary means to maintain the supply-demand balance of source-load in the power system. However， the uncertainty of user behavior makes it difficult to accurately control demand response.
LIU Yiping +5 more
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Recent developments in empirical dynamic modelling
Methods in Ecology and Evolution, 2023 Ecosystems are complex and sparsely observed making inference and prediction challenging. Empirical dynamic modelling (EDM) circumvents the need for a parametric model and complete observations of all system variables.
Stephan B. Munch +2 more
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Conformations of Steroid Hormones: Infrared and Vibrational Circular Dichroism Spectroscopy
Molecules, 2023 Steroid hormone molecules may exhibit very different functionalities based on the associated functional groups and their 3D arrangements in space, i.e., absolute configurations and conformations.
Yanqing Yang +6 more
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Hierarchical Gaussian process mixtures for regression [PDF]
, 2005 As a result of their good performance in practice and their desirable analytical properties, Gaussian process regression models are becoming increasingly of interest in statistics, engineering and other fields.
Titterington, D.M. +2 more
core +1 more source
Multiphase flow applications of nonintrusive reduced-order models with Gaussian process emulation
Data-Centric Engineering, 2022 Reduced-order models (ROMs) are computationally inexpensive simplifications of high-fidelity complex ones. Such models can be found in computational fluid dynamics where they can be used to predict the characteristics of multiphase flows.
Themistoklis Botsas +3 more
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The Astrophysical JournalWe investigate uncertainties in the estimation of the Hubble constant ( H _0 ) arising from Gaussian process (GP) reconstruction, demonstrating that the choice of kernel introduces systematic variations comparable to those arising from different ...
Ruchika, Purba Mukherjee, Arianna Favale
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Peripheral lysosomes recruit PLEKHG3 to focal adhesions and restrain protrusion dynamics
FEBS Letters, EarlyView.Proximity‐dependent labeling at the LAMTOR complex revealed the Rho GEF PLEKHG3 as a lysosome‐proximal protein directing the study toward the influence of lysosome positioning on actin dynamics and cell motility. We show that PLEKHG3 colocalizes with lysosomes at focal adhesion sites and observe that forced peripheral dispersion of lysosomes hinders ...
Rainer Ettelt +8 more
wiley +1 more source