Results 231 to 240 of about 13,817,849 (263)

Kriging models for payload distribution optimisation of freight trains

International Journal of Production Research, 2016
AbstractThis paper deals with Kriging models applied to optimise braking performances for freight trains. More precisely, it is focused on mass distribution optimisation aimed at reducing the effects of in-train forces among vehicles, e.g. compression and tensile forces, in-train emergency braking.
Arcidiacono Gabriele, BERNI, ROSSELLA, Cantone Luciano, Placidoli Pierpaolo   +3 more
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Model Reduction by PCA and Kriging

2018
info:eu-repo/semantics ...
Aversano, Gianmarco, Li, Zhiyi, Gicquel, Olivier, Parente, Alessandro   +3 more
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A kriging interpolation model for geographical flows

International Journal of Geographical Information Science, 2023
Ya Fang, Tao Pei, Ci Song, Jie Chen 0077, Xi Wang, Xiao Chen, Yaxi Liu 0002   +6 more
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Empirical Kriging models and their applications to QSAR

Journal of Chemometrics, 2007
AbstractA general Kriging model consists of two additive components: a parametric term and a stochastic error process. It is known that Kriging is an interpolating predictor and allows for a better fit to the data, but suffers from a decreasing ability to generalize to unseen data.
Hong Yin, Runze Li, Kai‐Tai Fang, Yi‐Zeng Liang   +3 more
openaire   +1 more source

Factorial kriging for multiscale modelling

2014
SYNOPSIS This paper presents a matrix formulation of factorial kriging, and its relationships with simple and ordinary kriging. Similar to other kriging methods, factorial kriging can be applied to both stationary and intrinsic stochastic processes, and is often used as a local operator.
Ma, Y. Z., Royer, J. -J., Wang, H., Wang, Y., Zhang, Tiantian   +4 more
openaire   +2 more sources

Modeling Cloud performance with Kriging

2012 34th International Conference on Software Engineering (ICSE), 2012
Alessio Gambi, Giovanni Toffetti
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On Using Kriging Models for Complex Design

Volume 5: 37th Design Automation Conference, Parts A and B, 2011
The design of most modern systems requires the tight integration of multiple disciplines. In practice, these multiple disciplines are often optimized independently, given only fixed values or targets for their interactions with other disciplines. The result is a system that may not represent the optimal system-level design.
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Modelling Rainfall Data Using a Bayesian Kriged-Kalman Model [PDF]

, 2006
A suitable model for analyzing rainfall data needs to take into account variation in both space and time. The method of kriging is a popular approach in spatial statistics which makes predictions for spatial data. Kalman filtering using dynamic models is often used to analyze temporal data.
SAHU S. K, JONA LASINIO, Giovanna, MARDIA K. V.   +2 more
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Conformal Prediction for Functional Kriging Models

2023
In this work we introduce a conformal prediction method for functional kriging. Conformal Prediction (CP) is a framework in machine learning and statistical inference that provides a principled way to quantify uncertainty and make predictions without relying on specific distributional assumptions.
Diana A., Romano E., Adzic J.
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Linear Models for Spatial Data: Kriging

1991
Just as data collected sequentially in time may be correlated, data collected at known locations in space may be correlated. For example, deposits of high-quality copper are more likely to occur near other high-quality deposits. The levels of lead contamination in the soil around a smelter are likely to be correlated.
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