dynoGP: Deep Gaussian Processes for Dynamic System Identification
This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli +3 more
wiley +1 more source
The stability criteria affecting the formation of high‐entropy alloys, particularly focusing in supersaturated solid solutions produced by mechanical alloying, are analyzed. Criteria based on Hume–Rothery rules are distinguished from those derived from thermodynamic relations. The formers are generally applicable to mechanically alloyed samples.
Javier S. Blázquez +5 more
wiley +1 more source
Gradient-Oxidized Bacterial Cellulose for Self-Sustained Soil Moisture Sensing. [PDF]
Zhou N +10 more
europepmc +1 more source
Active-reactive power coordination for DC-interconnected microgrid clusters: a small-signal stability perspective. [PDF]
Chen L, Lin Y, Xiao H.
europepmc +1 more source
Distributed electricity-carbon co-optimization of hydrogen energy storage microgrids with dynamic laplace perturbation and model predictive control. [PDF]
Li Z +5 more
europepmc +1 more source
Intelligent Wearable Rehabilitation System Based on Edge Computing. [PDF]
Chen CH, Wu YC, Chen JJ, Lin YC.
europepmc +1 more source
Multichannel, Portable and Low-Cost Source Measure Unit Platform for the Continuous Characterization of Organic Electrochemical Transistor Biosensors. [PDF]
Campanini M +7 more
europepmc +1 more source
Simultaneous acquisition of maternal and dual-fetal electrocardiography with maternal haemodynamic monitoring in anaesthetized pregnant rats. [PDF]
Pardun SP, Duff S, Ward P, Li A, Gao L.
europepmc +1 more source

