Results 101 to 110 of about 31,823 (308)

Complete ensemble empirical mode decomposition hybridized with random forest and kernel ridge regression model for monthly rainfall forecasts

, 2020
Persistent risks of extreme weather events including droughts and floods due to climate change require precise and timely rainfall forecasting. Yet, the naturally occurring non-stationarity entrenched within the rainfall time series lowers the model ...
Prasad, Ramendra, Yaseen, Z., Ali, Mumtaz, Xiang, Yong   +3 more
core   +1 more source

Advancing Lithium–Oxygen Batteries: Pioneering Cathode Catalyst Innovation and Artificial Intelligence‐Driven Design Paradigms

Advanced Materials, EarlyView.
This review summarizes the principles and challenges of nonaqueous lithium‐oxygen batteries and recent advances in cathode catalysts, including carbon‐based materials, metals, oxides, sulfides, nitrides, carbides, and redox mediators. It highlights emerging design strategies and artificial intelligence‐driven approaches, emphasizing data‐assisted ...
Yuqing Yao, Chongyang Hao, Karol Viviana Mejia‐Centeno, Malik Dilshad Khan, Shang Wang, Longqiu Li, Yanhong Tian, Andreu Cabot, Qing Sun   +8 more
wiley   +1 more source

Advances in Magnesium‐Based Thermoelectrics: A Critical Review

Advanced Materials, EarlyView.
Magnesium‐based thermoelectric materials have emerged as promising candidates for low‐to‐mid‐temperature energy conversion due to their abundance, low cost, and competitive performance. This review summarizes recent advances in Mg3X2, MgAgSb, and Mg2X systems, covering transport mechanisms, fabrication strategies, stability challenges, and device ...
Li‐Min Zhang, Li Zhang, Nan‐Hai Li, Yan‐Ling Yang, Xiao‐Lei Shi, Zhi‐Gang Chen   +5 more
wiley   +1 more source

A Survey of Interlayer Interaction Models for Graphene and Other 2D Materials

Advanced Materials Interfaces, EarlyView.
Van der Waals interactions arising from electronic polarization at atomically close interfaces generate corrugated interlayer energy landscapes that govern normal and tangential tractions. This review presents an overview of quantum, atomistic, analytical, and continuum modeling approaches, highlighting their roles across length scales in capturing ...
Gourav Yadav, Shakti S. Gupta, Roger A. Sauer   +2 more
wiley   +1 more source

Fault Diagnosis of Rotating Machinery Based on an Adaptive Ensemble Empirical Mode Decomposition

Sensors, 2013
The vibration based signal processing technique is one of the principal tools for diagnosing faults of rotating machinery. Empirical mode decomposition (EMD), as a time-frequency analysis technique, has been widely used to process vibration signals of ...
Yaguo Lei, Naipeng Li, Jing Lin, Sizhe Wang   +3 more
doaj   +1 more source

Weekly soil moisture forecasting with multivariate sequential, ensemble empirical mode decomposition and Boruta-random forest hybridizer algorithm approach

, 2019
Soil moisture forecasts are vital for environmental monitoring, the health of ecological systems, hydrology, agriculture and understanding the soil characteristics. In this study, we design a new multivariate sequential predictive model that utilizes the
Deo, Ravinesh C., Prasad, Ramendra, Li, Yan, Maraseni, Tek   +3 more
core   +1 more source

Characterization of Droplet Formation in Ultrasonic Spray Coating: Influence of Ink Formulation Using Phase Doppler Anemometry and Machine Learning

Advanced Materials Technologies, EarlyView.
This study explores how machine learning models, trained on small experimental datasets obtained via Phase Doppler Anemometry (PDA), can accurately predict droplet size (D32) in ultrasonic spray coating (USSC). By capturing the influence of ink complexity (solvent, polymer, nanoparticles), power, and flow rate, the model enables precise droplet control
Pieter Verding, Danny E. P. Vanpoucke, Yunus T. Aksoy, Tobias Corthouts, Maria R. Vetrano, Wim Deferme   +5 more
wiley   +1 more source

Fault Feature Extraction for Gearboxes Using Empirical Mode Decomposition

, 2011
The paper uses empirical mode decomposition to extract the fault feature of gearboxes. Traditional techniques fail to process the non-stationary and nonlinear signals.
Chun Hong Dou
core   +1 more source

Bidirectional Process Prediction in the Laser‐Induced‐Graphene Production Using Blackbox Deep Learning

Advanced Materials Technologies, EarlyView.
This study shows that a lightweight blackbox neural network provides a practical, cost‐effective solution for bidirectional process prediction in laser‐induced graphene (LIG) fabrication. Achieving high predictive performance with minimal overhead, the approach democratizes machine learning (ML) for resource‐limited environments.
Maxim Polomoshnov, Nitheesh M. Nair, Gerardo Hernández‐Sosa, Markus Reischl   +3 more
wiley   +1 more source

Daily lake-level time series spectral analysis using EMD, VMD, EWT, and EFD

Journal of Water and Climate Change
This study investigates the dynamics of daily Urmia Lake level (ULL) changes using spectral analysis tools to discover fluctuating patterns in the ULL series.
Farhad Alizadeh, Kiyoumars Roushangar
doaj   +1 more source