Results 181 to 190 of about 165,078 (298)

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

Iterative Data Curation for Machine Learning‐Based Inverse Design of Active Composite Plates for Four‐Dimensional Printing

open access: yesAdvanced Intelligent Systems, EarlyView.
A machine learning framework is developed for the inverse design of 4D‐printed active composite plates. It utilizes a forward model to predict shapes from patterns and an inverse model to suggest initial patterns for desired shapes. This framework integrates a genetic algorithm to refine the predicted patterns, ensuring higher accuracy in achieving ...
Teerapong Poltue   +4 more
wiley   +1 more source

Adaptive Autonomy in Microrobot Motion Control via Deep Reinforcement Learning and Path Planning Synergy

open access: yesAdvanced Intelligent Systems, EarlyView.
This study introduces a data‐driven framework that combines deep reinforcement learning with classical path planning to achieve adaptive microrobot navigation. By training a surrogate neural network to emulate microrobot dynamics, the approach improves learning efficiency, reduces training time, and enables robust real‐time obstacle avoidance in ...
Amar Salehi   +3 more
wiley   +1 more source

Uncertainty-aware genomic deep learning with knowledge distillation. [PDF]

open access: yesNPJ Artif Intell
Zhou J   +4 more
europepmc   +1 more source

Bio‐to‐Robot Transfer of Fish Sensorimotor Dynamics via Interpretable Model

open access: yesAdvanced Intelligent Systems, EarlyView.
This study demonstrates how a biologically interpretable model trained on real‐fish muscle activity can accurately predict the motion of a robotic fish. By linking real‐fish sensorimotor dynamics with robotic fish, the work offers a transparent, data‐efficient framework for transferring biological intelligence to bioinspired robotic systems.
Waqar Hussain Afridi   +6 more
wiley   +1 more source

Deep Learning Methods for Assessing Time‐Variant Nonlinear Signatures in Clutter Echoes

open access: yesAdvanced Intelligent Systems, EarlyView.
Motion classification from biosonar echoes in clutter presents a fundamental challenge: extracting structured information from stochastic interference. Deep learning successfully discriminates object speed and direction from bat‐inspired signals, achieving 97% accuracy with frequency‐modulated calls but only 48% with constant‐frequency tones. This work
Ibrahim Eshera   +2 more
wiley   +1 more source

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