Results 221 to 230 of about 78,150 (324)

A condition monitoring dataset based on electrical signals for a squirrel cage induction generator. [PDF]

open access: yesData Brief
Tominaga RN   +8 more
europepmc   +1 more source

Deep Learning‐Assisted Design of Mechanical Metamaterials

open access: yesAdvanced 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   +5 more
wiley   +1 more source

Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook

open access: yesAdvanced Intelligent Discovery, EarlyView.
Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang   +4 more
wiley   +1 more source

Wearable Metamaterials with Embodied Intelligence for Programmable Control of Human Limbs Tremor

open access: yesAdvanced Intelligent Systems, EarlyView.
Resulting from alternating muscle contractions, tremors can severely limit human ability to perform everyday tasks like walking or talking, due to their disruptive nature. Medication and surgery may not always effectively address tremor control. A wearable device embodying programmable smart metamaterials with adaptable intelligence to meet the demand ...
Braion Barbosa de Moura   +2 more
wiley   +1 more source

Spikoder: Dual‐Mode Graphene Neuron Circuit for Hardware Intelligence

open access: yesAdvanced Intelligent Systems, EarlyView.
Spikoder, a graphene leaky integrate‐and‐fire circuit that operates as an encoder and a neuron in a spiking neural network (SNN), is introduced. A Spikoder‐driven double‐layer SNN shows an accuracy of 97.37% for the classification of the Modified National Institute of Standards and Technology dataset, demonstrating its potential as a key building block
Kannan Udaya Mohanan   +4 more
wiley   +1 more source

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