Results 111 to 120 of about 3,137,682 (296)
AIS: Artificial Intelligent Soccer [PDF]
CRoNe2018: 4th Congress on Robotics and Neuroscience, 2019 Maximiliano Aubel +13 more
openaire +1 more source
Advanced Engineering Materials, EarlyView.Sustainability assessment requires methodologies that appropriately distinguish between additive and non‐additive material properties. A toxicity‐weighted scoring system is developed and applied that accounts for the disproportionate influence of highly toxic constituents through nonlinear weighting functions, providing more realistic estimates than ...
Seth Mehalic +2 more
wiley +1 more source
Dataset for visitations of public green spaces in Shanghai, China
Scientific DataResearch on urban green spaces remains active, with a shift towards data-driven methodologies. Leveraging mobile phone data from 10 million anonymized users in Shanghai, we identify park visitations over four months and construct a real population-level ...
Yuting Feng +8 more
doaj +1 more source
Inpainting Semantic and Depth Features to Improve Visual Place Recognition in the Wild
IEEE AccessVisual place recognition is one of the core modern computer vision tasks concerned with identifying location based on the image taken there. Modern state-of-the-art approaches heavily rely on RGB images which are largely affected by changes in the same ...
Ilia Semenkov +3 more
doaj +1 more source
Advanced Engineering Materials, EarlyView.HEA interlayers offer a versatile route for joining high‐performance structural materials. Their compositional and structural design regulates interfacial reactions, suppresses brittle IMCs, and improves metallurgical bonding. Sandwich interlayers further integrate defect healing with precipitation strengthening, enabling improved strength–ductility ...
Lin Yuan +4 more
wiley +1 more source
Injection Molding at the MIT Artificial Intelligence Lab [PDF]
, 1995 This paper describes the injection molding equipment at the MIT Artificial Intelligence Lab and how to use it. Topic covered include mold design, insert molding, safety, and material properties.MIT Artificial Intelligence ...
Binnard, Michael
core
Supporting AI Readiness Through Digital Workflows in Materials Science
Advanced Engineering Materials, EarlyView.Digitalization drives innovation in materials science by connecting data silos and turning heterogeneous processes into reusable research pipelines. Across 13 MaterialDigital projects, digital workflows reveal complementary pathways toward AI‐ready materials research, founded on structured data, persistent artifacts, executable orchestration, and ...
Marian Bruns +67 more
wiley +1 more source
On the generalization limits of quantum generative adversarial networks with pure state generators
Scientific ReportsWe investigate the capabilities of quantum generative adversarial networks (QGANs) in image generations tasks. Our analysis centers on fully quantum implementations of both the generator and discriminator.
Jasmin Frkatovic +9 more
doaj +1 more source
Artificial Intelligence (AI) is the backbone of developing smart machines that slowly but steadily replace people's roles, hence probably becoming a threat to the existence of humanity.
core +1 more source
A Multi‐Scale Machine Learning Framework for the Inverse Design of High Entropy Alloys
Advanced Engineering Materials, EarlyView.High‐entropy alloys offer vast potential for various applications, including electrocatalysis; however, their compositional complexity challenges conventional screening. We introduce an inverse‐design framework combining two neural networks to determine optimal compositions and reconstruct nanoparticle geometry from targeted properties and conventional
Mikael Takoutsin +14 more
wiley +1 more source