Results 181 to 190 of about 165,078 (298)
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
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
Integrating Reinforcement Learning with Dynamic Knowledge Tracing for personalized learning path optimization. [PDF]
Fu Z.
europepmc +1 more source
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]
Zhou J +4 more
europepmc +1 more source
Bio‐to‐Robot Transfer of Fish Sensorimotor Dynamics via Interpretable Model
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
Precise indoor localization using a lightweight 2D-CNN with adaptive temperature guided iterative self-knowledge distillation. [PDF]
Rizwan M +4 more
europepmc +1 more source
Deep Learning Methods for Assessing Time‐Variant Nonlinear Signatures in Clutter Echoes
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
Exploring the Role of Sleep and Physical Activity in Academic Stress, Motivation, Self-Efficacy, and Dropout Intention Among Italian University Students. [PDF]
Dagani J, Buizza C, Ghilardi A.
europepmc +1 more source
Study of the Success and Dropout in the Higher Education Policy in Europe and V4 Countries [PDF]
Stiburek, Simon, Svec, Vaclav, Vlk, Ales
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