Results 101 to 110 of about 965,256 (248)
Advanced Robotics Research, EarlyView.This review maps the methods to monitor robots’ health by fusing vibration, sound, control signals, vision, force, and oil information with artificial intelligence. It identifies deep learning, transfer learning, digital twins, and physics‐informed models as key methodological pathways enabling earlier diagnosis, safer human–robot collaboration, and ...
Yuting Qiao +6 more
wiley +1 more source
Sparse Coding and Selected Applications
, 2012 Sparse coding has become a widely used framework in signal processing and pattern recognition. After a motivation of the principle of sparse coding we show the relation to Vector Quantization and Neural Gas and describe how this relation can be used to ...
Labusch, Kai +3 more
core +1 more source
Multi-modal Non-negative Sparse Coding.
, 2015 Multi-modal Non-negative Sparse Coding.
Xiang Zhang (19800) +4 more
core +1 more source
Unsupervised Feature Learning by Deep Sparse Coding [PDF]
, 2014 In this paper, we propose a new unsupervised feature learning framework, namely Deep Sparse Coding (DeepSC), that extends sparse coding to a multi-layer architecture for visual object recognition tasks.
Koray Kavukcuoglu +4 more
core +1 more source
Push the Limits: Human‐Like Multimodal Extreme Embodied Intelligence for Autonomous Racing
Advanced Robotics Research, EarlyView.Autonomous racing tests embodied intelligence under extreme control limits of speed, actuation, and decision‐making. Inspired by human multimodality, we present Apex, an embodied agent deployed on a super‐formula racing car at Yas Marina. Integrating physics‐informed perception, multimodal learning, and tasking, Apex cuts lap times by 15% and achieves ...
Yanxin Zhou +13 more
wiley +1 more source
Advanced Robotics Research, EarlyView.Sparse active‐infrared proximity sensing is coupled to an adaptive thigmotactic controller for camera‐inaccessible object retrieval. After grasping, receptor‐informed encoding transforms force, temperature, and wrist force–torque signals into spike trains for spiking neural network recognition.
Fengyi Wang, Nitish Thakor, Gordon Cheng
wiley +1 more source
Machine Learning for Green Solvents: Assessment, Selection and Substitution
Advanced Science, EarlyView.Environmental regulations have intensified demand for green solvents, but discovery is limited by Solvent Selection Guides (SSGs) that quantify solvent sustainability. Training a machine learning model on GlaxoSmithKline SSG, a database of sustainability metrics for 10,189 solvents, GreenSolventDB is developed. Integrated with Hansen solubility metrics,
Rohan Datta +4 more
wiley +1 more source
Set Prediction for Outpatient Diagnosis Coding with Sparse Mahalanobis Conformal Scoring
Big Data and Cognitive ComputingDiagnosis coding is a large-scale multi-label task in which each clinical encounter may require one or more coding labels from a large label space.
Kamonrat Tangudomkit +2 more
doaj +1 more source
Neural Fields for Highly Accelerated 2D Cine Phase Contrast MRI
Advanced Science, EarlyView.ABSTRACT 2D cine phase contrast (CPC) MRI provides quantitative information on blood velocity and flow within the human vasculature. However, data acquisition is time‐consuming, motivating the reconstruction of the velocity field from undersampled measurements to reduce scan times. In this work, neural fields are proposed as a continuous spatiotemporal
Pablo Arratia +7 more
wiley +1 more source
Learning incoherent dictionaries for sparse approximation using iterative projections and rotations [PDF]
, 2013 This work was supported by the Queen Mary University of London School Studentship, the EU FET-Open project FP7- ICT-225913-SMALL. Sparse Models, Algorithms and Learning for Large-scale data and a Leadership Fellowship from the UK Engineering and ...
Plumbley, Mark, Barchiesi, Daniele
core