Results 161 to 170 of about 412,462 (274)

Robust Federated-Learning-Based Classifier for Smart Grid Power Quality Disturbances. [PDF]

Sensors (Basel)
Alsabaan M, Elsayed A, Bondok A, Badr MM, Mahmoud M, Alshawi T, Ibrahem MI.   +6 more
europepmc   +1 more source

Research Progress and Applications of Non‐Carrier‐Injection Electroluminescence

Advanced Electronic Materials, EarlyView.
Non‐carrier‐injection electroluminescence (NCI‐EL) uses AC fields and displacement currents to trigger light from internal charge reservoirs, enabling minimalist emitters with remotely coupled terminals. This review maps shared mechanisms across organics, GaN, quantum dots, and TMDCs, compares planar, interdigital, single‐terminal, and coaxial designs,
Wei Huang, Jianbi Le, Rong Chen, Zhixian Lin, Xiongtu Zhou, Yongai Zhang, Chaoxing Wu   +6 more
wiley   +1 more source

Lightweight Quantum Authentication and Key Agreement Scheme in the Smart Grid Environment. [PDF]

Entropy (Basel)
Jiang Z, Shi RH.
europepmc   +1 more source

Degradation Pathways of Silicon‐Based Anodes in Lithium‐Ion Batteries

Advanced Energy Materials, EarlyView.
Silicon‐based anodes undergo degradation through five primary pathways: (1) mechanical and structural deterioration of the active material, (2) loss of electrode integrity and electrical contact, (3) mechanical instability of the solid electrolyte interphase (SEI), characterized by repetitive fracture and deformation, (4) chemical instability of the ...
Yoon Jeong Choi, Ji‐Youn Bae, Ga‐On Park, Seung‐Ho Yu   +3 more
wiley   +1 more source

How Beyond-5G and 6G Makes IIoT and the Smart Grid Green-A Survey. [PDF]

Sensors (Basel)
Varga P, Jászberényi ÁI, Pásztor D, Nagy B, Nasar M, Raisz D.   +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

Environment sustainability with smart grid sensor. [PDF]

Front Artif Intell
Mahadik S, Gedam M, Shah D.
europepmc   +1 more source

Deep Learning‐Assisted Design of Mechanical Metamaterials

Advanced Intelligent Discovery, EarlyView.
This review examines the role of data‐driven deep learning methodologies in advancing mechanical metamaterial design, focusing on the specific methodologies, applications, challenges, and outlooks of this field. Mechanical metamaterials (MMs), characterized by their extraordinary mechanical behaviors derived from architected microstructures, have ...
Zisheng Zong, Pu Wen, Zhiping Chai, Dunxuan Wu, Han Ding, Zhigang Wu   +5 more
wiley   +1 more source

A smart grid data sharing scheme supporting policy update and traceability. [PDF]

Sci Rep
Yang X, Yao K, Li S, Du X, Wang C.
europepmc   +1 more source

Toward Knowledge‐Guided AI for Inverse Design in Manufacturing: A Perspective on Domain, Physics, and Human–AI Synergy

Advanced Intelligent Discovery, EarlyView.
This perspective highlights how knowledge‐guided artificial intelligence can address key challenges in manufacturing inverse design, including high‐dimensional search spaces, limited data, and process constraints. It focused on three complementary pillars—expert‐guided problem definition, physics‐informed machine learning, and large language model ...
Hugon Lee, Hyeonbin Moon, Junhyeong Lee, Seunghwa Ryu   +3 more
wiley   +1 more source