Results 211 to 220 of about 37,745 (280)
EPSO-based rigid robotic arm for obstacle avoidance object grasping. [PDF]
Xu T, Han X, Liu H, Li Y.
europepmc +1 more source
Data‐Driven High‐Throughput Volume Fraction Estimation From X‐Ray Diffraction Patterns
Long exposure times and the need for manual evaluation limit the use of X‐ray diffraction in high‐throughput applications. This study presents a data‐driven approach addressing both issues. HiVE (a method for High‐throughput Volume fraction Estimation) performs composition estimation for high‐noise XRD patterns produced using polychromatic emission ...
Hawo H. Höfer +6 more
wiley +1 more source
High-Precision Coal Mine Microseismic P-Wave Arrival Picking via Physics-Constrained Deep Learning. [PDF]
Qin K, Deng Z, Li X, Lian Z, Ye J.
europepmc +1 more source
This article implements a unified human digital twin framework that integrates cutting edge actuation, sensing, simulation, and bidirectional feedback capability. The approach includes integrating multimodal sensing, AI, and biomechanical simulation into one compact system.
Tajbeed Ahmed Chowdhury +4 more
wiley +1 more source
Comparison of Upper Body Joint and Hand Motions in Eating Solid Foods With Chopsticks and Semisolid Foods With a Spoon in Healthy Males and Females: Observational Study. [PDF]
Nakatake J +4 more
europepmc +1 more source
The Interoperability Challenge in DFT Workflows Across Implementations
Interoperability and cross‐validation remain major challenges in the computational materials science. In this work, we introduce a common input/output standard that enables internal translation across multiple workflow managers—AiiDA, PerQueue, Pipeline Pilot, and SimStack—while producing results in a unified schema.
Simon K. Steensen +13 more
wiley +1 more source
Automatic detection of arrival time for noisy microseismic data using a transformed difference between multiwindow energy ratios method. [PDF]
Zhang Z +7 more
europepmc +1 more source
The authors develop a deep learning model for real‐time tracking of wound progression. The deep learning framework maps the nonlinear evolution of a time series of images to a latent space, where they learn a linear representation of the dynamics. The linear model is interpretable and suitable for applications in feedback control.
Fan Lu +11 more
wiley +1 more source

