Results 181 to 190 of about 3,652,664 (303)

Machine Learning‐Supported Analysis for Predicting and Visualizing Nonlinear Relationships Between Material Properties in Electroplated Chromium Layers

Advanced Engineering Materials, EarlyView.
This study applies machine learning regression to predict chromium layer thickness in decorative trivalent chromium electroplating, using 441 experiments from laboratory‐scale (1L) and pilot‐scale (14L) setups. Tree‐based models, particularly CatBoost, outperformed linear regression by capturing nonlinear parameter interactions (R2$R^2$ up to 0.77 ...
Christoph Baumer, Christopher Mai, Luca Eisentraut, Andreas Bund, Ricardo Buettner   +4 more
wiley   +1 more source

Driver state detection and recognition in conditional automated driving: A review. [PDF]

PLoS One
Wu H, Nartey TT, Wan G, Wang Y, Xu L, Zhou X.   +5 more
europepmc   +1 more source

Realising Meaningful Human Control Over Automated Driving Systems: A Multidisciplinary Approach. [PDF]

Minds Mach (Dordr), 2022
de Sio FS, Mecacci G, Calvert S, Heikoop D, Hagenzieker M, van Arem B.   +5 more
europepmc   +1 more source

A Practical Noise2Noise Denoising Pipeline for High‐Throughput Raman Spectroscopy

Advanced Engineering Materials, EarlyView.
A lightweight and reproducible denoising pipeline for high‐throughput Raman spectroscopy is introduced, based on a 1D convolutional autoencoder trained with a Noise2Noise strategy. Using only repeated short‐exposure acquisitions, the method suppresses stochastic noise without reference spectra, enabling reliable spectral reconstruction while preserving
David Martin‐Calle, Cesar Alvarez‐Llamas, Vincent Motto‐Ros, Christophe Dujardin, Jérémie Margueritat, David Rodney   +5 more
wiley   +1 more source

Physics-informed deep learning for robust trajectory prediction in automated driving. [PDF]

Sci Rep
Stockem Novo A.
europepmc   +1 more source

Tutorial on High-Definition Map Generation for Automated Driving in Urban Environments. [PDF]

Sensors (Basel), 2022
Jeong J, Yoon JY, Lee H, Darweesh H, Sung W.   +4 more
europepmc   +1 more source

New AI‐Assisted Approach for Expanding the Solution Space: Application to Lattice Structure Design

Advanced Engineering Materials, EarlyView.
This work introduces an innovative framework for designing structured materials by ex panding the design space through reparameterization of qualitative variables into continuous structural descriptors. Combined with machine‐learning‐based prediction and multi‐objective optimization, the approach enables the discovery of novel lattice architectures ...
G. H. Gahimbare, Y. Beideler, D. Chune, R. De Guio, T. Roland, H. Chibane   +5 more
wiley   +1 more source

Investigating factors influencing takeover performance in conditionally automated driving: The role of uncertainty communication

Safe transition following a Takeover Request (ToR) in Level 3 automated driving requires a high situational awareness, which is influenced by traffic complexity, non-driving-related tasks (NDRTs), and communication strategies.
Kunze, Lars, Tekkesinoglu, Sule
core   +1 more source

Test case sampling optimization for safety validation of automated driving systems. [PDF]

Nat Commun
Qian C, Xu J, Xing X, Guo F.
europepmc   +1 more source

The preference of onboard activities in a new age of automated driving. [PDF]

Eur Transp Res Rev, 2022
Hamadneh J, Esztergár-Kiss D.
europepmc   +1 more source