What is the blockchain?
M Di Pierro - Computing in Science & Engineering, 2017 - ieeexplore.ieee.org
What Is the Blockchain? Page 1 SECTION TITLE Editors: Konrad Hinsen, hinsen@cnrs-orleans.fr
| Konstantin Läufer, laufer@cs.luc.edu 92 Computing in Science & Engineering 1521-9615/17/$33.00 …
| Konstantin Läufer, laufer@cs.luc.edu 92 Computing in Science & Engineering 1521-9615/17/$33.00 …
A survey on Hilbert-Huang transform: Evolution, challenges and solutions
Signal processing methods are essential in scientific research, and time-frequency analysis
techniques such as Fourier Transform constitute an important progress in data analysis, but …
techniques such as Fourier Transform constitute an important progress in data analysis, but …
Short-term performance degradation prediction of a commercial vehicle fuel cell system based on CNN and LSTM hybrid neural network
B Sun, X Liu, J Wang, X Wei, H Yuan, H Dai - International Journal of …, 2023 - Elsevier
Short-term performance degradation prediction is significant for fuel cell system control and
health management. This paper presents a hybrid deep learning method by combining the …
health management. This paper presents a hybrid deep learning method by combining the …
Short-term solar radiation forecasting using hybrid deep residual learning and gated LSTM recurrent network with differential covariance matrix adaptation evolution …
Develo** an accurate and robust prediction of long-term average global solar irradiation
plays a crucial role in industries such as renewable energy, agribusiness, and hydrology …
plays a crucial role in industries such as renewable energy, agribusiness, and hydrology …
Empirical mode decomposition-based time-frequency analysis of multivariate signals: The power of adaptive data analysis
This article addresses data-driven time-frequency (TF) analysis of multivariate signals, which
is achieved through the empirical mode decomposition (EMD) algorithm and its noise …
is achieved through the empirical mode decomposition (EMD) algorithm and its noise …
Final results of Borexino Phase-I on low-energy solar neutrino spectroscopy
G Bellini, J Benziger, D Bick, G Bonfini, D Bravo… - Physical Review D, 2014 - APS
Borexino has been running since May 2007 at the Laboratori Nazionali del Gran Sasso
laboratory in Italy with the primary goal of detecting solar neutrinos. The detector, a large …
laboratory in Italy with the primary goal of detecting solar neutrinos. The detector, a large …
Improved Hilbert–Huang transform with soft sifting stop** criterion and its application to fault diagnosis of wheelset bearings
Vibration signals from rotating machineries are usually of multi-component and modulated
signals. Hilbert–Huang transform (HHT), hereby referring to the combination of empirical …
signals. Hilbert–Huang transform (HHT), hereby referring to the combination of empirical …
An efficient Hilbert–Huang transform-based bearing faults detection in induction machines
This paper focuses on rolling elements bearing fault detection in induction machines based
on stator currents analysis. Specifically, it proposes to process the stator currents using the …
on stator currents analysis. Specifically, it proposes to process the stator currents using the …
A hybrid EMD-SVR model for the short-term prediction of significant wave height
WY Duan, Y Han, LM Huang, BB Zhao, MH Wang - Ocean Engineering, 2016 - Elsevier
Short-term prediction of ocean waves is critical in oceanic operation-related activities.
Statistical models have advantages in short-term wave prediction as complex phenomena …
Statistical models have advantages in short-term wave prediction as complex phenomena …
Bearing fault detection based on hybrid ensemble detector and empirical mode decomposition
Aiming at more efficient fault diagnosis, this research work presents an integrated anomaly
detection approach for seeded bearing faults. Vibration signals from normal bearings and …
detection approach for seeded bearing faults. Vibration signals from normal bearings and …