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Winter Wheat Nitrogen Status Estimation Using UAV-Based RGB Imagery and Gaussian Processes Regression

Remote Sensing, 2020
Predicting the crop nitrogen (N) nutrition status is critical for optimizing nitrogen fertilizer application. The present study examined the ability of multiple image features derived from unmanned aerial vehicle (UAV) RGB images for winter wheat N ...
Yuanyuan Fu, Guijun Yang, Zhenhai Li, Xiaoyu Song, Zhenhong Li, Xingang Xu, Pei Wang, Chunjiang Zhao   +7 more
doaj   +4 more sources

A spectrum of physics-informed Gaussian processes for regression in engineering [PDF]

Data-Centric Engineering, 2023
Despite the growing availability of sensing and data in general, we remain unable to fully characterize many in-service engineering systems and structures from a purely data-driven approach. The vast data and resources available to capture human activity
Elizabeth J. Cross, Timothy J. Rogers, Daniel J. Pitchforth, Samuel J. Gibson, Sikai Zhang, Matthew R. Jones   +5 more
doaj   +2 more sources

Prediction of Filamentous Sludge Bulking using a State-based Gaussian Processes Regression Model. [PDF]

Sci Rep, 2016
Activated sludge process has been widely adopted to remove pollutants in wastewater treatment plants (WWTPs). However, stable operation of activated sludge process is often compromised by the occurrence of filamentous bulking. The aim of this study is to
Liu Y, Guo J, Wang Q, Huang D.
europepmc   +2 more sources

Deep Gaussian processes for regression using approximate expectation propagation [PDF]

International Conference on Machine Learning, 2016
Deep Gaussian processes (DGPs) are multi-layer hierarchical generalisations of Gaussian processes (GPs) and are formally equivalent to neural networks with multiple, infinitely wide hidden layers.
Bui, TD, Hernández-Lobato, D, Hernández-Lobato, JM, Li, Y, Turner, RE   +4 more
core   +3 more sources

Optimization of Cold Metal Transfer-Based Wire Arc Additive Manufacturing Processes Using Gaussian Process Regression

Metals, 2020
Wire and arc additive manufacturing (WAAM) is among the most promising additive manufacturing techniques for metals because it yields high productivity at low raw material costs. However, additional post-processing is required to remove redundant surface
Seung Hwan Lee
doaj   +2 more sources

Automatic Kernel Selection for Gaussian Processes Regression with Approximate Bayesian Computation and Sequential Monte Carlo

Frontiers in Built Environment, 2017
The current work introduces a novel combination of two Bayesian tools, Gaussian Processes (GPs), and the use of the Approximate Bayesian Computation (ABC) algorithm for kernel selection and parameter estimation for machine learning applications.
Anis Ben Abdessalem, Nikolaos Dervilis, David J. Wagg, Keith Worden   +3 more
doaj   +2 more sources

Analysis of medical costs and two-model prediction for patients with severe mental disorders in Gansu Province, China [PDF]

Frontiers in Public Health
BackgroundThe economic burden of severe psychiatric disorders presents a major global public health challenge, particularly in regions with underdeveloped healthcare systems.
Peiji Miao, Xiaomei Jiang, Jinjuan Li, Weimin Pan, Aixiang Xue, Juan Cao, Jingchun Fan, Jingchun Fan   +7 more
doaj   +2 more sources

Mapping leaf area index in a mixed temperate forest using Fenix airborne hyperspectral data and Gaussian processes regression

International Journal of Applied Earth Observation and Geoinformation, 2021
Machine learning algorithms, in particular, kernel-based machine learning methods such as Gaussian processes regression (GPR) have shown to be promising alternatives to traditional empirical methods for retrieving vegetation parameters from remotely ...
Rui Xie, R. Darvishzadeh, A. Skidmore, M. Heurich, Stefanie Holzwarth, T. Gara, I. Reusen   +6 more
semanticscholar   +1 more source

Nonlinear Channel Equalization based on Gaussian Processes for Regression in Fiber Link

Guangtongxin yanjiu, 2022
In order to mitigate the effect of nonlinear noise nonlinear Channel Equalizer (CE) based on Gaussian Processes for Regression (GPR) is proposed and experimentally demonstrated in an intensity modulation and direct detection fiber link.
Biao WU, Jia-hao LI, Zhao-cai ZHANG
doaj   +3 more sources

Splitting Gaussian processes for computationally-efficient regression.

PLoS ONE, 2021
Gaussian processes offer a flexible kernel method for regression. While Gaussian processes have many useful theoretical properties and have proven practically useful, they suffer from poor scaling in the number of observations.
Nick Terry, Youngjun Choe
doaj   +1 more source