Results 201 to 210 of about 229,000 (311)
Editorial: Graph learning for brain imaging
Feng Liu +6 more
doaj +1 more source
PEARL: integrative multi-omics classification and omics feature discovery via deep graph learning. [PDF]
Zhao Q +5 more
europepmc +1 more source
Data‐Driven Bulldozer Blade Control for Autonomous Terrain Leveling
A simulation‐driven framework for autonomous bulldozer leveling is presented, combining high‐fidelity terramechanics simulation with a neural‐network‐based reduced‐order model. Gradient‐based optimization enables efficient, low‐level blade control that balances leveling quality and operation time.
Harry Zhang +5 more
wiley +1 more source
HarveST uses a heterogeneous graph learning framework to reveal spatial transcriptomics patterns. [PDF]
Feng J, Yu T, Zhang Y.
europepmc +1 more source
This work presents the MicroRoboScope, a highly integrated, compact, and portable microrobotic experimentation platform combining electromagnetic and acoustic actuation with real‐time visual feedback into a single, end‐to‐end device. The system enables closed‐loop control and tracking algorithm experimentation within an accessible and unified hardware ...
Max Sokolich +4 more
wiley +1 more source
Physics-Informed Graph Learning for Spatially Contiguous and Capacity-Constrained Hospital Service Area Delineation. [PDF]
Liu L, Wang F.
europepmc +1 more source
Intelligent Sky Guardians (InSkyGuard) is introduced as a four‐drone swarm that autonomously detects, tracks, and safely captures rogue drones using a coordinated net system. Computer vision and leader–follower control architecture enable synchronized enclosure, while integrated failsafes enhance system reliability. Validated through closed‐environment
Joshua Hastings +6 more
wiley +1 more source
Discovering proteo-transcriptomic networks via biologically informed heterogeneous graph learning. [PDF]
Duan J +14 more
europepmc +1 more source
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
scHG: A supercell framework with high-order graph learning enables scalable multi-omics analysis. [PDF]
Huang Y, Gan Y, Gong X.
europepmc +1 more source

