Results 121 to 130 of about 11,210,090 (249)
In the recognition of fruit leaf diseases, image recognition technology based on deep learning has received increasing attention. However, deep learning models often perform poorly in complex backgrounds, and in some cases, they even outperform ...
Xiaowei Li +5 more
doaj +1 more source
Objective High body mass index (BMI≥27 kg/m2) and aberrant gait biomechanics are primary risk factors for knee osteoarthritis (KOA), yet the specific loading patterns linked to clinical disease features remain unclear. Unsupervised machine learning (ML) was used to identify vertical ground reaction force (vGRF) waveform modes of variation and ...
Ashley N. Buck +10 more
wiley +1 more source
Meta-Exploiting Frequency Prior for Cross-Domain Few-Shot Learning
Meta-learning offers a promising avenue for few-shot learning (FSL), enabling models to glean a generalizable feature embedding through episodic training on synthetic FSL tasks in a source domain.
Wang, Peng +7 more
core
Generative AI in clinical practice: how should rheumatologists evaluate and monitor emerging tools?
Generative artificial intelligence (AI) is increasingly being used in rheumatology practice, to assist with tasks such as documentation, record summarization, and clinical decision support. Rheumatologists need a practical approach to determining what evidence and oversight each application requires.
Koushan Kouranloo +2 more
wiley +1 more source
A Q‐Learning Algorithm to Solve the Two‐Player Zero‐Sum Game Problem for Nonlinear Systems
A Q‐learning algorithm to solve the two‐player zero‐sum game problem for nonlinear systems. ABSTRACT This paper deals with the two‐player zero‐sum game problem, which is a bounded L2$$ {L}_2 $$‐gain robust control problem. Finding an analytical solution to the complex Hamilton‐Jacobi‐Issacs (HJI) equation is a challenging task.
Afreen Islam +2 more
wiley +1 more source
Modified Frequency-Domain-Based Iterative Learning Control for 2-D Discrete Systems
In the existing time-domain based iterative learning control (ILC) results for 2-D discrete systems, iteration-independent boundary states/errors do not have an impact on the complete convergence of P-type and high-order ILC laws. However, it does affect
Kai Wan
doaj +1 more source
dynoGP: Deep Gaussian Processes for Dynamic System Identification
This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli +3 more
wiley +1 more source
In this contribution, multiuser decorrelating based frequency-domain channel estimation and long range channel prediction techniques are proposed for a generalized Multicarrier DS-CDMA (MC DS-CDMA) system communicating over a fast fading and frequency ...
Yang, L.-L., Liu, W., Hanzo, L., Hu, B.
core +1 more source
A Workflow to Accelerate Microstructure‐Sensitive Fatigue Life Predictions
This study introduces a workflow to accelerate predictions of microstructure‐sensitive fatigue life. Results from frameworks with varying levels of simplification are benchmarked against published reference results. The analysis reveals a trade‐off between accuracy and model complexity, offering researchers a practical guide for selecting the optimal ...
Luca Loiodice +2 more
wiley +1 more source
A simplified thermoplastic pultrusion model is developed to predict thermal fields in glass fiber/polyethylene terephthalate (GF/PET) composites with reduced computational cost. By combining effective material homogenization, validation against literature data, and Gaussian‐process‐based optimization, the study reveals how heating limits, pulling speed,
Elder Soares +3 more
wiley +1 more source

