Results 191 to 200 of about 5,207,763 (284)
Data‐driven emulation of flow‐dependent background‐error variance in variational data assimilation
Quarterly Journal of the Royal Meteorological Society, EarlyView.A machine‐learning approach is developed to emulate flow‐dependent background‐error variance within a variational data assimilation framework. The method reproduces uncertainty information that would normally require a 50‐member ensemble using only five members, while maintaining comparable observation fit and forecast performance.
Wei Pan +3 more
wiley +1 more source
Quarterly Journal of the Royal Meteorological Society, EarlyView.Numerical weather prediction over Indonesia is particularly challenging, because convection evolves rapidly and is modulated strongly by land–sea interactions across the maritime continent. To constrain forecasts better, there is substantial potential in assimilating all‐sky infrared observations, provided that the associated observation‐error ...
Miranti I. Hastuti +4 more
wiley +1 more source
Not All Missing Data are Equal: Choosing the Right Imputation Method for Binary Datasets
Quality and Reliability Engineering International, EarlyView.ABSTRACT Missing binary predictors are common in reliability, quality control, and industrial decision systems, yet imputation methods are often chosen by convenience rather than evidence. We conduct a Monte Carlo study comparing mode substitution, sequential hot‐deck, missForest, MICE, and KNN with three neighbourhood sizes under MCAR, MAR, and MNAR ...
Manuel Delfino, Fabio Rapallo
wiley +1 more source
Weibull Variational Autoencoder for Remaining Useful Life Prediction
Quality and Reliability Engineering International, EarlyView.ABSTRACT Remaining useful life (RUL) prediction is a critical technology for preventing unexpected failures and reducing maintenance costs in modern industrial systems. However, traditional model‐based approaches are limited by the need for explicit mathematical modeling of degradation mechanisms, while data‐driven methods often require large‐scale ...
JunWoo Yu +4 more
wiley +1 more source
Refactoring and Optimization of Bridge Dynamic Displacement Based on Ensemble Empirical Mode Decomposition. [PDF]
Sensors (Basel), 2019 Zou Y, Chen Y, Liu P.
europepmc +1 more source
[A spike denoising method combined principal component analysis with wavelet and ensemble empirical mode decomposition]. [PDF]
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi, 2020 Zhou Y, Hu Y, Li M, Yang L, Shang Z.
europepmc +1 more source
Journal of Field Robotics, EarlyView.ABSTRACT Drone light shows (DLShows) represent a rapidly growing application of swarm robotics, creating captivating aerial displays through the synchronized flight of hundreds or thousands of unmanned aerial vehicles (UAVs) as environmentally friendly and reusable alternatives to traditional pyrotechnics.
Yunes Alqudsi
wiley +1 more source
Responsive Materials, EarlyView.Machine learning provides a unifying framework to connect structure, fluorescence properties, and applications of carbon‐based quantum dots. This review highlights how data‐driven strategies enable fluorescence regulation, reveal underlying mechanisms, and accelerate the rational design of functional carbon dots.
Liangfeng Chen +8 more
wiley +1 more source
[Research on motion impedance cardiography de-noising method based on two-step spectral ensemble empirical mode decomposition and canonical correlation analysis]. [PDF]
Sheng Wu Yi Xue Gong Cheng Xue Za ZhiXie Y, Yang D, Yu H, Xie Q.
europepmc +1 more source
Assessments as Sites for Teacher Sensemaking About Epistemic Learning Goals in Science
Science Education, EarlyView.ABSTRACT Recent science education reforms aim to broaden allowable ways of knowing and learning in science classes. Doing so requires that we attend to and work to shift messages about useful ways to know and learn (i.e., epistemological messages) embedded in course designs and enactments.
Lindsay B. Wells +2 more
wiley +1 more source