Results 201 to 210 of about 28,026 (247)

Optisense: Computational Optimization for Strain Sensor Placement in Wearable Motion Tracking Systems

open access: yesAdvanced Intelligent Systems, EarlyView.
A computational framework for optimizing strain sensor placement in wearable motion tracking systems is presented. By combining dense strain mapping with a genetic algorithm, the method discovers counterintuitive yet highly effective configurations that reduce joint angle error by 32%.
Minu Kim   +4 more
wiley   +1 more source

Optimizing Molecular Descriptors for Reliable Adsorption Energy Prediction on Transition Metal Nanoclusters. [PDF]

open access: yesACS Omega
Pena LB   +6 more
europepmc   +1 more source

A Fully Soft Sensing Suit With Optimal Sensor Placement for Real‐Time Motion Tracking

open access: yesAdvanced Intelligent Systems, EarlyView.
A fully soft, skin‐conformable sensing suit integrating stretchable sensors, liquid metal wiring, and soft electrodes was developed using direct ink writing, with sensor placement optimized through an automated algorithmic pipeline. This system enables accurate and unobtrusive real‐time motion tracking, providing a scalable, material‐based solution to ...
Jinhyeok Oh, Joonbum Bae
wiley   +1 more source

Avaliação do conhecimento de mães e/ou responsáveis, sobre traumatismo em dentes decíduos

open access: diamond, 2006
LUCIMARA CHELES DA SILVA FRANZIN   +4 more
openalex   +2 more sources

Upsampling DINOv2 Features for Unsupervised Vision Tasks and Weakly Supervised Materials Segmentation

open access: yesAdvanced Intelligent Systems, EarlyView.
Feature from recent image foundation models (DINOv2) are useful for vision tasks (segmentation, object localization) with little or no human input. Once upsampled, they can be used for weakly supervised micrograph segmentation, achieving strong results when compared to classical features (blurs, edge detection) across a range of material systems.
Ronan Docherty   +2 more
wiley   +1 more source

Modeling and Characterization of a Self‐Sensing Soft Hydraulic Muscle

open access: yesAdvanced Intelligent Systems, EarlyView.
This article presents the self‐sensing soft hydraulic muscle (SSHM), a novel soft actuator capable of simultaneously sensing force and length without external sensors. A comprehensive model accurately predicts SSHM behavior, validated experimentally with minimal errors. Using propylene glycol enhances durability and reduces hysteresis.
Nhu An Phan   +8 more
wiley   +1 more source

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