Zobrazeno 1 - 10
of 1 283
pro vyhledávání: '"data-driven methods"'
Publikováno v:
International Journal of Numerical Methods for Heat & Fluid Flow, 2024, Vol. 34, Issue 8, pp. 2986-3016.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/HFF-12-2023-0726
Autor:
Travis Adrian Dantzer, Branko Kerkez
Publikováno v:
Water Science and Technology, Vol 89, Iss 11, Pp 3147-3162 (2024)
Real-time and model-predictive control promises to make urban drainage systems (UDS) adaptive, coordinated, and dynamically optimal. Though early implementations are promising, existing control algorithms have drawbacks in computational expense, trus
Externí odkaz:
https://doaj.org/article/c1922cdf18ad4f2f842d8f0beda699fb
Autor:
Anders, Denis
In diesem Beitrag wird exemplarisch anhand des Verschmutzungsmechanismus von Wärmetauschern (Fouling) gezeigt wie datengetriebene Methoden zur Vorhersage des Verschmutzungsgrades und somit zu einem effizienteren Anlagenbetrieb genutzt werden können
Externí odkaz:
https://monarch.qucosa.de/id/qucosa%3A91735
https://monarch.qucosa.de/api/qucosa%3A91735/attachment/ATT-0/
https://monarch.qucosa.de/api/qucosa%3A91735/attachment/ATT-0/
Publikováno v:
Engineering Reports, Vol 6, Iss 11, Pp n/a-n/a (2024)
Abstract In the field of material informatics, artificial neural networks (ANNs) contribute to the investigation of the processing‐structure‐properties‐performance relationship of materials. This inspires us to leverage the capabilities of ANNs
Externí odkaz:
https://doaj.org/article/6777869519b4470684078d7c99b2945b
Autor:
Jiafu Niu, David Reeping
Publikováno v:
Studies in Engineering Education, Vol 5, Iss 2, Pp 150–174-150–174 (2024)
Background: Quantitative methods have been frequently used in engineering education research to investigate generalizable patterns and causal relationships in phenomena of interest to the field. With the proliferation of educational data and growing
Externí odkaz:
https://doaj.org/article/e5b890950db34813a18995a99ded0082
Publikováno v:
Frontiers in Photonics, Vol 5 (2024)
IntroductionThe moment quantities associated with the nonlinear Schrödinger equation offer important insights into the evolution dynamics of such dispersive wave partial differential equation (PDE) models. The effective dynamics of the moment quanti
Externí odkaz:
https://doaj.org/article/23b3f5a8845e4fd8a9b3ae80d79b3f74
Publikováno v:
SLAS Technology, Vol 29, Iss 5, Pp 100182- (2024)
Acute hyperglycemia is a common endocrine and metabolic disorder that seriously threatens the health and life of patients. Exploring effective diagnostic methods and treatment strategies for acute hyperglycemia to improve treatment quality and patien
Externí odkaz:
https://doaj.org/article/ad580abb936f41ff9c9027eaed305caa
Autor:
Ismail Husein, Ramaswamy Sivaraman, Sarwar Hasan Mohmmad, Forqan Ali Hussein Al-Khafaji, Sokaina Issa Kadhim, Yousof Rezakhani
Publikováno v:
Journal of Soft Computing in Civil Engineering, Vol 8, Iss 2, Pp 1-18 (2024)
This paper developed two robust data-driven models, namely gene expression programming (GEP) and multivariate adaptive regression splines (MARS), for the estimation of the slump of concrete (SL). The main feature of the proposed data-driven methods i
Externí odkaz:
https://doaj.org/article/4bd0aa74a5df40bd8961b199bbc8e129
Publikováno v:
Applied Mathematics in Science and Engineering, Vol 32, Iss 1 (2024)
ABSTRACTThe non-destructive estimation of doping concentrations in semiconductor devices is of paramount importance for many applications ranging from crystal growth to defect and inhomogeneity detection. A number of technologies (such as LBIC, EBIC
Externí odkaz:
https://doaj.org/article/498439dabe9c484abe66eabd406baeb2
Publikováno v:
Artificial Intelligence Chemistry, Vol 2, Iss 1, Pp 100045- (2024)
Synchrotron radiation technology provides high-resolution and high-sensitivity information for many fields such as material science, life science, and energy research. Synchrotron radiation data-driven methods have significantly accelerated the devel
Externí odkaz:
https://doaj.org/article/8b37534438904766ae9a3661dc0564db