Results 51 to 60 of about 346,567 (317)
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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Suspended sediment load modeling by machine learning algorithms in low and high discharge periods (Case study: Kashkan watershed) [PDF]
مدلسازی و مدیریت آب و خاک, 2023 IntroductionSediment that moves with water is called suspended sediment, and the amount of suspended sediment material that passes through a river section in a certain period of time is called suspended load.
Nasrin Beiranvand +2 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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Modelling of Infiltration Rate in Different Soil Textures using Soft Computing Techniques in Kashkan Watershed, Lorestan Province [PDF]
پژوهشهای آبخیزداری, 2022 Infiltration is one of the most parameters of hydrology that plays a fundamental role in streamflow, groundwater recharge, subsurface flow, and surface and subsurface water quality and quantity.
Leili Soleimani +2 more
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Physical Review D, 2012 Gaussian processes provide a method for extracting cosmological information from observations without assuming a cosmological model. We carry out cosmography -- mapping the time evolution of the cosmic expansion -- in a model-independent manner using kinematic variables and a geometric probe of cosmology.
Shafieloo, Arman +2 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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Sparse On-Line Gaussian Processes [PDF]
Neural Computation, 2002 We develop an approach for sparse representations of gaussian process (GP) models (which are Bayesian types of kernel machines) in order to overcome their limitations for large data sets. The method is based on a combination of a Bayesian on-line algorithm, together with a sequential construction of a relevant subsample of the data that fully ...
Csato, Lehel, Opper, Manfred
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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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