Buchumschlag

The problem of prediction of the transmission coefficient using neural networks with a limited quantity of data

The article discusses available approaches to predicting the transmission coefficient of metamaterials. In the paper was proposed different approaches that create the possibility of using data from various open sources, as well as the possibility of encoding complete structural information about the...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Datum:	2025
1. Verfasser:	Krysenko, Pavlo
Format:	Artikel
Sprache:	Ukrainisch
Veröffentlicht:	Kyiv National University of Construction and Architecture 2025
Schlagworte:	deep learning low pass filter convolutional neural networks metamaterials
Online Zugang:	https://es-journal.in.ua/article/view/328625
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!
Назва журналу:	Environmental safety and natural resources

Institution

Environmental safety and natural resources

Ähnliche Einträge

A prediction of the frequency of non-periodic signals based on convolutional neural networks
von: Subbotin, S. A., et al.
Veröffentlicht: (2018)

Development and research of a modified convolutional neural network for malaria cell pattern recognition
von: Федорченко, Є. М., et al.
Veröffentlicht: (2023)

Prediction of hydraulic resistance coefficient using an ensemble neural network algorithm
von: Khodnevych, Yaroslav, et al.
Veröffentlicht: (2025)

A generalized methodology of sign language recognition on video streams based on neural networks and transformers
von: Кузнєцова, Н. В., et al.
Veröffentlicht: (2023)

Recurrent neural networks for the problem of improving numerical meteorological forecasts
von: Doroshenko, А.Yu., et al.
Veröffentlicht: (2023)

Developing a semantic image model using machine learning based on convolutional neural networks
von: Andon, P.I., et al.
Veröffentlicht: (2020)

Automatic development of deep neural networks for improving numerical meteorological forecast
von: Doroshenko, А.Yu., et al.
Veröffentlicht: (2024)

APPLICATION OF DEEP LEARNING NEURAL NETWORK FOR CLASSIFICATION OF BIG DATA OF ASTRONOMIC OBSERVATIONS
von: Gorbunov, A. A., et al.
Veröffentlicht: (2017)

Convolutional neural network model and software for classification of typical pests
von: Bezliudnyi, Y.S., et al.
Veröffentlicht: (2022)

Recurrent neural network model for music generation
von: Komarskiy, O.C., et al.
Veröffentlicht: (2022)

Machine learning methods analysis in the document classification problem
von: Zhyrkova, A.P., et al.
Veröffentlicht: (2021)

Application of neural networks in OLAP-systems
von: Nabibayeva, G.Ch.
Veröffentlicht: (2024)

Software package for adaptive training of robot controllers based on neural networks
von: Vitiuk, A.Y., et al.
Veröffentlicht: (2023)

Neural and Bayesian networks in the problem of credit risk analysis
von: Kuznietsova, N. V., et al.
Veröffentlicht: (2015)

Practical content of the elements of petrophysical model of terrigenous sandstones — oil and gas reservoirs in neural networks, deep learning and regression methods
von: Vyzhva, Serhiy, et al.
Veröffentlicht: (2025)

A Convolutional Neural Network Model and Software Tool for Classifying the Presence of a Medical Mask on a Human Face
von: Hryhorenko, Y.S., et al.
Veröffentlicht: (2023)

Implementation of neural network based 2D seismic images super resolution approach
von: Noskov, Oleksii, et al.
Veröffentlicht: (2025)

Method for political propaganda detection in internet content using neural network natural language processing tools
von: Krak, Iu.V., et al.
Veröffentlicht: (2024)

APPLICATION OF AN ARTIFICIAL NEURAL NETWORK FOR PREDICTING THE THERMAL STATE OF AN AIR-SOIL HEAT EXCHANGER
von: Basok , B., et al.
Veröffentlicht: (2025)

Digital twins in intrusion detection systems based on deep learning
von: Letychevskyi, O.O., et al.
Veröffentlicht: (2025)

Development of the intelligent control system of an unmanned car
von: Dakhno, N.B., et al.
Veröffentlicht: (2024)

Adaptive maximum power point tracking using neural networks for a photovoltaic systems according grid
von: Sahraoui, H., et al.
Veröffentlicht: (2021)

Do we need a more sophisticated multilayer artificial neural network to compute roughness coefficient?
von: Khodnevych, Yaroslav V., et al.
Veröffentlicht: (2023)

New application of artificial neural network-based direct power control for permanent magnet synchronous generator
von: Akkouchi, K., et al.
Veröffentlicht: (2021)

Neural network based method of a user identification by keyboard handwriting
von: Danyliuk, I. I., et al.
Veröffentlicht: (2018)

Optimization of auto-tuning of programs using neural networks
von: Doroshenko, А.Yu., et al.
Veröffentlicht: (2018)

Neural network modeling of critical tempe-ratures for steel pitting.
von: Korniienko, O. V., et al.
Veröffentlicht: (2019)

Modelling videocard memory performance for LLM neural networks
von: Rahozin, D.V., et al.
Veröffentlicht: (2024)

Photovoltaic system faults diagnosis using discrete wavelet transform based artificial neural networks
von: Bengharbi, A. A., et al.
Veröffentlicht: (2022)

Formal verification of deep neural networks
von: Panchuk, B.O.
Veröffentlicht: (2024)

Development of a NARX neural network for a tribo-aero-electrostatic separator with rotating disk electrodes
von: Ayache, Z., et al.
Veröffentlicht: (2026)

Construction of diagnostic expert-medical system using neural networks
von: Glybovets, M.M., et al.
Veröffentlicht: (2020)

Double layer back propagation neural network based on restricted Boltzmann machines for forecasting daily particulate matter 2.5
von: Fu, Minglei, et al.
Veröffentlicht: (2017)

Application of deep learning technology for creating intellectual autonomous machines
von: Bilokon, O.S.
Veröffentlicht: (2020)

Application of neural networks modeling for interpretation of acoustic logging traces
von: Lazarenko, M., et al.
Veröffentlicht: (2015)

Neural networks’ learning process acceleration
von: Katerynych, L., et al.
Veröffentlicht: (2020)

NEURAL NETWORK MODELING IN PROBLEMS OF PREDICTION MODES OF ELECTRICAL GRIDS
von: Moroz, A. N., et al.
Veröffentlicht: (2016)

Data arrangements to train an artificial neural network within solving the tasks for calculating the Chézy roughness coefficient under uncertainty of parameters determining the hydraulic resistance to flow in river channels
von: Khodnevych, Yaroslav V., et al.
Veröffentlicht: (2022)

Neural network application to pseudorandom sequence generation simulation
von: Lesyk, V.O., et al.
Veröffentlicht: (2024)

Prediction of Flow Accelerated Corrosion of NPP Pipeline Elements by Network Simulation Method
von: Biblik, Irina V., et al.
Veröffentlicht: (2018)