Results 141 to 150 of about 110,828 (307)

Bidirectional Process Prediction in the Laser‐Induced‐Graphene Production Using Blackbox Deep Learning

Advanced Materials Technologies, EarlyView.
This study shows that a lightweight blackbox neural network provides a practical, cost‐effective solution for bidirectional process prediction in laser‐induced graphene (LIG) fabrication. Achieving high predictive performance with minimal overhead, the approach democratizes machine learning (ML) for resource‐limited environments.
Maxim Polomoshnov, Nitheesh M. Nair, Gerardo Hernández‐Sosa, Markus Reischl   +3 more
wiley   +1 more source

Curb Detection and Mapping via Robust Iterative Gaussian Process Regression

Journal of Highway and Transportation Research and Development
Curb detection and mapping are of great importance to ensure the safety and efficiency of intelligent vehicles. However, it remains challenging because shape estimation under noise and outliers is not well addressed in real traffic scenarios.
Di Wang, Si Chen, Zhenni Ma, Jiajia Shi, Fuchun Zhang   +4 more
doaj   +1 more source

Extrusion Bioprinting for Wound Healing: Innovations in Functionalized Bioinks and Bioprinting Technology

Advanced Materials Technologies, EarlyView.
Extrusion‐based bioprinting (EBB) has emerged as a versatile biofabrication platform capable of precisely depositing bioinks composed of biomaterials, cells, and bioactive agents to generate patient‐specific, biomimetic skin constructs. This paper presents a state‐of‐the‐art and forward‐looking overview of EBB for wound healing, encompassing printing ...
Hien‐Phuong Le, Anh Tuan Trong Tran, Ammara Waheed, Tran Thanh Tung, Dusan Losic   +4 more
wiley   +1 more source

tgp: An R Package for Bayesian Nonstationary, Semiparametric Nonlinear Regression and Design by Treed Gaussian Process Models

The tgp package for R is a tool for fully Bayesian nonstationary, semiparametric nonlinear regression and design by treed Gaussian processes with jumps to the limiting linear model.
Robert B. Gramacy
core   +1 more source

A Gaussian process guide for signal regression in magnetic fusion

Nuclear Fusion
Extracting reliable information from diagnostic data in tokamaks is critical for understanding, analyzing, and controlling the behavior of fusion plasmas and validating models describing that behavior.
Craig Michoski, Todd A. Oliver, David R. Hatch, Ahmed Diallo, Mike Kotschenreuther, David Eldon, Matthew Waller, Richard Groebner, Andrew Oakleigh Nelson   +8 more
doaj   +1 more source

Differentially Private Regression with Gaussian Processes.

, 2018
A major challenge for machine learning is increasing the availability of data while respecting the privacy of individuals. Here we combine the provable privacy guarantees of the differential privacy framework with the flexibility of Gaussian processes (GPs). We propose a method using GPs to provide differentially private (DP) regression.
Smith, M.T., Alvarez, M.A., Zwiessele, M., Lawrence, N.D.   +3 more
openaire   +4 more sources

Bayesian‐Optimized Dual‐Mode Luminescence‐Based Optical Thermometry Enabled by Negative Thermal Expansion in Lu2(MoO4)3:Yb–Er–Tm Phosphor

Advanced Optical Materials, EarlyView.
A Bayesian optimization framework identifies the ideal composition for Lu2(MoO4)3:Yb–Er–Tm phosphors with minimal experimental trials. By leveraging the host's negative thermal expansion, the material achieves remarkable thermal quenching compensation.
Reiko Furukawa, Takuya Hasegawa, Tong Fang, Saneyuki Ohno, Tomoyo Goto, Yasushi Sato, Ayahisa Okawa, Shu Yin   +7 more
wiley   +1 more source

Optical Manipulation of Whispering Gallery Mode Microlasers for Controlled Cellular Delivery

Advanced Optical Materials, EarlyView.
Optically trapped whispering‑gallery microlasers are precisely positioned onto non‐phagocytic mammalian cells to report local refractive‑index changes in real time. Time‑dependent wavelength shifts reveal successive stages from membrane contact to internalization.
Soraya Caixeiro, Paloma Rodríguez‐Sevilla, Kishan Dholakia, Malte C. Gather   +3 more
wiley   +1 more source

Identifying Physical Interactions in Contact‐Based Robot Manipulation for Learning from Demonstration

Advanced Robotics Research, EarlyView.
Robots can learn manipulation tasks from human demonstrations. This work proposes a versatile method to identify the physical interactions that occur in a demonstration, such as sequences of different contacts and interactions with mechanical constraints.
Alex Harm Gert‐Jan Overbeek, Herman van der Kooij, Douwe Dresscher, Mark Vlutters   +3 more
wiley   +1 more source

Teaching Functions with Gaussian Process Regression

Proceedings of the AAAI Symposium Series
Humans are remarkably adaptive instructors who adjust advice based on their estimations about a learner’s prior knowledge and current goals. Many topics that people teach, like goal-directed behaviors, causal systems, categorization, and time-series patterns, have an underlying commonality: they map inputs to outputs through an unknown function.
Maya Malaviya, Mark K. Ho
openaire   +2 more sources