Selecting reliable instances based on evidence theory for transfer learning
International audience
A modeling study of ≥2 MeV electron fluxes in GEO at different prediction time scales based on LSTM and transformer netw...
Published in Journal of Space Weather and Space Climate
In this study, we develop models to predict the log10 of ≥2 MeV electron fluxes with 5-minute resolution at the geostationary orbit using the Long Short-Term Memory (LSTM) and transformer neural networks for the next 1-hour, 3-hour, 6-hour, 12-hour, and 1-day predictions. The data of the GOES-10 satellite from 2002 to 2003 are the training set, the...
A comprehensive exploration of IoT-enabled smart grid systems: power quality issues, solutions, and challenges
Published in Science and Technology for Energy Transition
The potential for Internet of Things (IoT) technology to transform energy management has led to significant interest in its incorporation into smart grid systems. This review discusses the state of IoT-powered smart grids today, focusing on applications, current technology, and power quality (PQ) issues. Key problems including harmonics, transients...
Machine learning and Bayesian optimization for performance prediction of proton-exchange membrane fuel cells
Proton-exchange membrane fuel cells (PEMFCs) are critical components of zero-emission electro-hydrogen generators. Accurate performance prediction is vital to the optimal operation management and preventive maintenance of these generators. Polarization curve remains one of the most important features representing the performance of PEMFCs in terms ...
Imputation of missing monthly rainfall data using machine learning and spatial interpolation approaches in Thale Sap Son...
Published in Environmental science and pollution research international
Missing rainfall data has been a prevalent issue and primarily interested in hydrology and meteorology. This research aimed to examine the capability of machine learning (ML) and spatial interpolation (SI) methods to estimate missing monthly rainfall data. Six ML algorithms (i.e. multiple linear regression (MLR), M5 model tree (M5), random forest (...
Feature importance in the age of explainable AI : Case study of detecting fake news & misinformation via a multi-modal f...
In recent years, fake news has become a global phenomenon due to its explosive growth and ability to leverage multimedia content to manipulate user opinions. Fake news is created by manipulating images, text, audio, and videos, particularly on social media, and the proliferation of such disinformation can trigger detrimental societal effects. False...
Inferring galaxy cluster masses from cosmic microwave background lensing with neural simulation based inference
Published in Journal of Cosmology and Astroparticle Physics
Gravitational lensing by massive galaxy clusters distorts the observed cosmic microwave background (CMB) on arcminute scales, and these distortions carry information about cluster masses. Standard approaches to extracting cluster mass constraints from the CMB cluster lensing signal are either sub-optimal, ignore important physical or observational ...
Neural simulation-based inference of the neutron star equation of state directly from telescope spectra
Published in Journal of Cosmology and Astroparticle Physics
Neutron stars provide a unique opportunity to study strongly interacting matter under extreme density conditions. The intricacies of matter inside neutron stars and their equation of state are not directly visible, but determine bulk properties, such as mass and radius, which affect the star's thermal X-ray emissions. However, the telescope spectra...
Symmetry-enforcing neural networks with applications to constitutive modeling
The use of machine learning techniques to homogenize the effective behavior of arbitrary microstructures has been shown to be not only efficient but also accurate. In a recent work, we demonstrated how to combine state-of-the-art micromechanical modeling and advanced machine learning techniques to homogenize complex microstructures exhibiting non-l...
Accelerometer-derived movement features as predictive biomarkers for muscle atrophy in neurocritical care: a prospective...
...Published in Critical care (London, England)
Physical inactivity and subsequent muscle atrophy are highly prevalent in neurocritical care and are recognized as key mechanisms underlying intensive care unit acquired weakness (ICUAW). The lack of quantifiable biomarkers for inactivity complicates the assessment of its relative importance compared to other conditions under the syndromic diagnosi...
