Results 191 to 200 of about 23,305 (264)

Random Convolutional Kernel Transform with Empirical Mode Decomposition for Classification of Insulators from Power Grid. [PDF]

Sensors (Basel)
Klaar ACR, Seman LO, Mariani VC, Coelho LDS.   +3 more
europepmc   +1 more source

Emerging Materials and Future Strategies for Solid Oxide Electrochemical Cells

Advanced Energy Materials, EarlyView.
Solid oxide electrochemical cells operate under strongly coupled electrochemical and thermodynamic conditions, where performance is constrained by interactions among crystal structure, defect chemistry, and interfacial evolution. This review, based on a structure‐defect‐property‐durability framework, reveals the roles of lattice symmetry and defect ...
Qiuchun Lu, Yufei Zhang, Han Zhao, Pooi See Lee, Daniel H.C. Chua   +4 more
wiley   +1 more source

MEMS Gyroscope Temperature Compensation Based on Improved Complete Ensemble Empirical Mode Decomposition and Optimized Extreme Learning Machine. [PDF]

Micromachines (Basel)
Zhang Z, Zhang J, Zhu X, Ren Y, Yu J, Cao H.   +5 more
europepmc   +1 more source

Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories

Advanced Energy Materials, EarlyView.
In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen, Zheyu Zhang, Kelvian T. Mularso, Bonghyun Jo, Hyun Suk Jung   +4 more
wiley   +1 more source

Improved empirical mode decomposition bagging RCSP combined with Fisher discriminant method for EEG feature extraction and classification. [PDF]

Heliyon
Chen D.
europepmc   +1 more source

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

An Optical Frequency Domain Reflectometer's (OFDR) Performance Improvement via Empirical Mode Decomposition (EMD) and Frequency Filtration for Smart Sensing. [PDF]

Sensors (Basel)
Belokrylov ME, Kambur DA, Konstantinov YA, Claude D, Barkov FL.   +4 more
europepmc   +1 more source

Empirical Mode Decomposition Complexity [PDF]

The International Conference on Electrical Engineering, 2012
openaire   +1 more source

Redesigning Interlayers for Anode‐Free Batteries: Harnessing Continuity and Metal Inventory

Advanced Energy Materials, EarlyView.
Interlayers for anode‐free batteries must provide continuous interfacial coverage while remaining thin enough to preserve the finite cathode‐derived active‐metal inventory. This Perspective couples thin‐film closure with stoichiometric active‐metal consumption to reveal practical design windows across chemistry, substrate roughness, cathode loading ...
Rosalía Cid, Lorenzo Fallarino
wiley   +1 more source