Results 71 to 80 of about 1,258,060 (324)

Combination of Multivariate Standard Addition Technique and Deep Kernel Learning Model for Determining Multi-Ion in Hydroponic Nutrient Solution

Sensors, 2020
Ion-selective electrodes (ISEs) have recently become the most attractive tools for the development of efficient hydroponic systems. Nevertheless, some inherent shortcomings such as signal drifts, secondary ion interferences, and effected high ionic ...
Vu Ngoc Tuan, Abdul Mateen Khattak, Hui Zhu, Wanlin Gao, Minjuan Wang   +4 more
doaj   +1 more source

EigenGP: Gaussian Process Models with Adaptive Eigenfunctions [PDF]

, 2015
Gaussian processes (GPs) provide a nonparametric representation of functions. However, classical GP inference suffers from high computational cost for big data. In this paper, we propose a new Bayesian approach, EigenGP, that learns both basis dictionary
Peng, Hao, Qi, Yuan
core  

Approximate Inference for Nonstationary Heteroscedastic Gaussian process Regression

, 2014
This paper presents a novel approach for approximate integration over the uncertainty of noise and signal variances in Gaussian process (GP) regression.
Jylänki, Pasi, Tolvanen, Ville, Vehtari, Aki   +2 more
core   +1 more source

Laser Metal Deposited Ti4822 Hollow Pipe: Experimental and Computational Modelling Study

Advanced Engineering Materials, EarlyView.
Laser metal deposition (LMD) of a crack‐free built Ti4822 alloys is challenging. This article reports outstanding characteristics of a hollow pipe that is built with LMD technology when a predicted, nontransformation substrate temperature of 800 °C is used.
Sadiq A. Raji, Monnamme Tlotleng, Samuel Skhosane, Lehlogonolo R. Kanyane, Abimbola P. I. Popoola, Sisa L. Pityana   +5 more
wiley   +1 more source

Proprioceptive Robot Collision Detection through Gaussian Process Regression

, 2019
This paper proposes a proprioceptive collision detection algorithm based on Gaussian Regression. Compared to sensor-based collision detection and other proprioceptive algorithms, the proposed approach has minimal sensing requirements, since only the ...
Alberto, Dalla Libera, Elisa, Tosello, Gianluigi, Pillonetto, Ruggero, Carli, Stefano, Ghidoni   +4 more
core   +1 more source

Design and Prototyping of Topology‐Optimized, Additively Manufactured Lightweight Components for Space Robotic Systems: A Case Study

Advanced Engineering Materials, EarlyView.
The results demonstrate a simulation‐driven workflow that applies LSB topology optimization with additive manufacturing constraints to mission‐specific load cases, integrating European Cooperation for Space Standardization compliant verification and manufacturability to develop structurally efficient rover suspension components.
Stelios K. Georgantzinos, Grigorios Kostopoulos, Eleni Papadopoulou, Stefanos Voulgaris, Alexandra Tseni, Panteleimon Bakalis, Maria N. Gkara, Chara Ch. Mitropoulou, Georgios Kazakis, Alin Munteanu, Jeremi Gancet, Nikos D. Lagaros   +11 more
wiley   +1 more source

Patterns and drivers of population trends on individual Breeding Bird Survey routes using spatially explicit models and route-level covariates

Avian Conservation and Ecology
Spatial patterns in population trends, particularly those at fine geographic scales, can help better understand the factors driving population change in North American birds.
Adam C Smith, Veronica I Aponte, Allison D Binley, Amelia R Cox, Lindsay Daly, Courtney Donkersteeg, Brandon P. M. Edwards, Willow English, Marie-Anne R Hudson, Kendall M Jefferys, Barry G Robinson, Christian Roy   +11 more
doaj   +1 more source

Sparse On-Line Gaussian Processes [PDF]

Neural Computation, 2002
We develop an approach for sparse representations of gaussian process (GP) models (which are Bayesian types of kernel machines) in order to overcome their limitations for large data sets. The method is based on a combination of a Bayesian on-line algorithm, together with a sequential construction of a relevant subsample of the data that fully ...
Csato, Lehel, Opper, Manfred
openaire   +5 more sources

Integral Representation of Generalized Grey Brownian Motion

, 2019
In this paper we investigate the representation of a class of non Gaussian processes, namely generalized grey Brownian motion, in terms of a weighted integral of a stochastic process which is a solution of a certain stochastic differential equation.
Bock, Wolfgang, da Silva, José Luís, Desmettre, Sascha   +2 more
core   +1 more source

Nitride‐Reinforced Metal Matrix Nanocomposite Powders Production by Plasma‐Assisted Thermochemical Treatment of Intermetallic Powders

Advanced Engineering Materials, EarlyView.
This study proposes a new methodology for producing metal matrix nanocomposites in the solid state. The process involves the plasma‐assisted thermochemical treatment of metallic alloy powders. As a proof‐of‐concept, TiN nanoparticles are synthesized directly on the surface of FeTi intermetallic powder particles, enabling the development of the ...
Deivison Daros Paim, Antônio Rogério de Souza, Tatiana Bendo, Aloisio Nelmo Klein, Rodrigo Perito Cardoso   +4 more
wiley   +1 more source