FlexMat, EarlyView.Abstract Soft robots, engineered from highly compliant materials, offer superior adaptability and safety in unstructured environments compared to their rigid counterparts. Recent advancements, fueled by bio‐inspiration and material programmability, have led to the rapid co‐evolution of their core modules: actuation, sensing, protection, energy, and ...
Qiulei Liu +3 more
wiley +1 more source
Robust Sparse Bayesian Learning-Based Off-Grid DOA Estimation Method for Vehicle Localization. [PDF]
Sensors (Basel), 2020 Ling Y, Gao H, Zhou S, Yang L, Ren F.
europepmc +1 more source
Machine Learning Approaches to Forecast the Realized Volatility of Crude Oil Prices
Journal of Forecasting, EarlyView.ABSTRACT This paper presents an evaluation of the accuracy of machine learning (ML) techniques in forecasting the realized volatility of West Texas Intermediate (WTI) crude oil prices. We compare several ML algorithms, including regularization, regression trees, random forests, and neural networks, to several heterogeneous autoregressive (HAR) models ...
Talha Omer +3 more
wiley +1 more source
Hierarchical sparse Bayesian learning with adaptive Laplacian prior for single image super-resolution. [PDF]
Sci RepQi M, Zhou Y, Hu Y, Xie C, Xu S.
europepmc +1 more source
Robust Face Recognition via Block Sparse Bayesian Learning [PDF]
, 2013 Taiyong Li, Zhilin Zhang
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Forecasting House Prices: The Role of Market Interconnectedness
Journal of Forecasting, EarlyView.ABSTRACT While the existing research uncovers interconnections between various housing markets, it largely ignores the question of whether such linkages can improve house price predictions. To address this issue, we proceed in two steps. First, we forecast disaggregated house price growth rates from Australia and China to determine whether ...
Zac Chen +3 more
wiley +1 more source
Physiological swelling imaging in human calf under stocking compression by sparse Bayesian learning implemented into electrical impedance tomography (SBL-EIT). [PDF]
Biomed Eng LettAsano K +4 more
europepmc +1 more source
Journal of Forecasting, EarlyView.ABSTRACT This paper presents a new hybrid model for predicting German electricity prices. The algorithm is based on a combination of Gaussian process regression (GPR) and support vector regression (SVR). Although GPR is a competent model for learning stochastic patterns within data and for interpolation, its performance for out‐of‐sample data is not ...
Abhinav Das +2 more
wiley +1 more source
Using DSGE and Machine Learning to Forecast Public Debt for France
Journal of Forecasting, EarlyView.ABSTRACT Forecasting public debt is essential for effective policymaking and economic stability, yet traditional approaches face challenges due to data scarcity. While machine learning (ML) has demonstrated success in financial forecasting, its application to macroeconomic forecasting remains underexplored, hindered by short historical time series and ...
Emmanouil Sofianos +4 more
wiley +1 more source
Sparse Bayesian learning for structural damage detection using expectation–maximization technique [PDF]
, 2019 Rongrong Hou +3 more
openalex +1 more source