Results 101 to 110 of about 5,652,238 (291)
Diffusion Basis Spectrum Imaging of Tissue Microstructure and Early Treatment in Multiple Sclerosis
ABSTRACT Objective Conventional MRI cannot resolve the coexisting pathological processes within multiple sclerosis tissue. Diffusion Basis Spectrum Imaging decomposes the diffusion signal into fiber, cell, and hindered ratios indexing axonal integrity, cellularity, and extracellular space, respectively.
Apisit Kaewsanit +10 more
wiley +1 more source
Analytic Moment-based Gaussian Process Filtering [PDF]
04.07.13 KB. Ok to add accepted version to Spiral, authors retain copyright.We propose an analytic moment-based filter for nonlinear stochastic dynamic systems modeled by Gaussian processes.
Huber, MF, Deisenroth, MP, Hanebeck, UD
core
dynoGP: Deep Gaussian Processes for Dynamic System Identification
This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli +3 more
wiley +1 more source
Numerical Solutions of Hamilton-Jacobi Inequalities by Constrained Gaussian Process Regression
This paper proposes numerical solutions of Hamilton-Jacobi inequalities based on constrained Gaussian process regression. While Gaussian process regression is a tool to estimate an unknown function from its input and output data conventionally, the ...
Kenji Fujimoto +2 more
doaj +1 more source
Wireless sensor networks are used in a wide range of applications, many of which require real-time transmission of the measurements. Bandwidth limitations result in limitations on the sampling frequency and number of sensors.
Jonas Beuchert +3 more
doaj +1 more source
Distributed Gaussian Processes
Copyright © 2015 by the author(s).To scale Gaussian processes (GPs) to large data sets we introduce the robust Bayesian Committee Machine (rBCM), a practical and scalable product-of-experts model for large-scale distributed GP regression. Unlike state-of-the-art sparse GP approximations, the rBCM is conceptually simple and does not rely on inducing or ...
Deisenroth, MP, Ng, JW
openaire +5 more sources
Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt +8 more
wiley +1 more source
Machine-Learning Methods for Computational Science and Engineering
The re-kindled fascination in machine learning (ML), observed over the last few decades, has also percolated into natural sciences and engineering. ML algorithms are now used in scientific computing, as well as in data-mining and processing.
Michael Frank +2 more
doaj +1 more source
A simplified thermoplastic pultrusion model is developed to predict thermal fields in glass fiber/polyethylene terephthalate (GF/PET) composites with reduced computational cost. By combining effective material homogenization, validation against literature data, and Gaussian‐process‐based optimization, the study reveals how heating limits, pulling speed,
Elder Soares +3 more
wiley +1 more source
Multi-target tracking of birds in complex low-altitude airspace based on GM_PHD filter
GM_PHD (Gaussian mixture of probability hypothesis density) cannot completely track multiple targets, such as the flying birds in the complex low-altitude airspace near the airport, due to the lack of the steps of birth detection, track extraction and ...
Tao Hong +4 more
doaj +1 more source

