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Transfer learning approach based on satellite image time series for the crop classification problem
(Springer, 2023)
This paper presents a transfer learning approach to the crop classification problem based on time series of images from the Sentinel-2 dataset labeled for two regions: Brittany (France) and Vojvodina (Serbia). During ...
Practical ANN prediction models for the axial capacity of square CFST columns
(Springer, 2023)
In this study, two machine-learning algorithms based on the artificial neural network
(ANN) model are proposed to estimate the ultimate compressive strength of square
concrete-filled steel tubular columns. The development ...
Improving performance of bucket-type hydrological models in high latitudes with multi-model combination methods: Can we wring water from a stone?
(Elsevier, 2024)
Multi-model combination (averaging) methods (MMCMs) are used to improve the accuracy of hydrological (precipitation-runoff) outputs in simulation or forecasting/prediction modes. In this paper, we examined if the application ...
A note on r-circulant matrices involving generalized Narayana numbers
(Journal of Mathematical Inequalities, 2023)
In order to further connect structured matrices and integer sequences, r-circulant matrices involving the generalized Narayana numbers are considered. Estimates for spectral norms bounds of such matrices are presented and ...
Inspection of RCF rail defects – Review of NDT methods
(Academic Press, 2023)
Rail inspection research is a substantive topic for the railway industry. Management of rolling contact fatigue rail defects is crucial for transport safety worldwide because uncontrolled development of these defects could ...
Future Drought Propagation through the Water-Energy-Food-Ecosystem Nexus – a Nordic Perspective
(Elsevier, 2022)
Droughts can affect a multitude of public and private sectors, with impacts developing slowly over time. While droughts are traditionally quantified in relation to the hydrological components of the water cycle that they ...
Research in computing‑intensive simulations for nature‑oriented civil‑engineering and related scientific fields, using machine learning and big data: an overview of open problems
(Springer, 2023)
This article presents a taxonomy and represents a repository of open problems in computing for numerically and logically intensive problems in a number of disciplines that have to synergize for the best performance of ...
Uni- and multivariate bias adjustment methods in Nordic catchments: Complexity and performance in a changing climate
(Elsevier, 2022)
For climate-change impact studies at the catchment scale, meteorological variables are typically extracted from ensemble simulations provided by global and regional climate models, which are then downscaled and bias-adjusted ...
A multi-fidelity wind surface pressure assessment via machine learning: A high-rise building case
(Elsevier, 2023)
Computational fluid dynamics (CFD) represents an attractive tool for estimating wind pressures and wind loads on high-rise buildings. The CFD analyses can be conducted either by low-fidelity simulations (RANS) or by ...
Experimental and numerical study of structural damping in a beam with bolted splice connection
(Thin-Walled Structures, 2023)
The objective of this research is to develop a numerical model of one widely used bolted beam splice connection
that dissipates energy through structural damping. The reference experimental setup is carefully designed ...