Ultra-short-term wind power probabilistic forecasting based on an evolutionary non-crossing multi-output quantile regression deep neural network

J Zhu, Y He, X Yang, S Yang - Energy Conversion and Management, 2024 - Elsevier
Ultra-short-term wind power probabilistic forecasting is of significance for stable power grid
operation; however, it is still challenging due to the inherent nonlinearity and uncertainty …

An integrated complete ensemble empirical mode decomposition with adaptive noise to optimize LSTM for significant wave height forecasting

L Zhao, Z Li, J Zhang, B Teng - Journal of Marine Science and …, 2023 - mdpi.com
In recent years, wave energy has gained attention for its sustainability and cleanliness. As
one of the most important parameters of wave energy, significant wave height (SWH) is …

[HTML][HTML] From micro-to nano-and time-resolved x-ray computed tomography: Bio-based applications, synchrotron capabilities, and data-driven processing

PIC Claro, EPBS Borges, GR Schleder… - Applied Physics …, 2023 - pubs.aip.org
X-ray computed microtomography (μCT) is an innovative and nondestructive versatile
technique that has been used extensively to investigate bio-based systems in multiple …

Algorithmically-enhanced design of spintronic-based tunable true random number generator for dependable stochastic computing

A Bahador, MH Moaiyeri… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
This paper proposes a tunable true random number generator (TTRNG) based on stochastic
magnetic tunnel junction (MTJ) switching in the subcritical current regime. The proposed …

Deep Neural Networks, Cellular Automata and Petri Nets: Useful Hybrids for Smart Manufacturing

O Kaikova, V Terziyan - Procedia Computer Science, 2024 - Elsevier
In the era of Industry 4.0 and beyond, intelligent and reliable models are vital for processes
and assets. Models in smart manufacturing involve combining knowledge-based and data …

Constructing Highly Nonlinear Cryptographic Balanced Boolean Functions on Learning Capabilities of Recurrent Neural Networks

HM Waseem, MA Hafeez, S Ahmed, BD Deebak… - IEEE …, 2024 - ieeexplore.ieee.org
This study presents a novel approach to cryptographic algorithm design that harnesses the
power of recurrent neural networks. Unlike traditional mathematical-based methods, neural …

Enhanced prediction of end-point carbon content in electric arc furnaces using Bayesian optimised fully connected neural networks with early stopping

H Zhu, H Lu, Z Jiang, H Li, C Yang, Z Ni… - Ironmaking & …, 2024 - journals.sagepub.com
This study developed a Bayesian optimisation-enhanced fully connected neural network
(BO-EFCNN) model with an early stopping mechanism to predict the end-point carbon …

SMDPG: Identifying Metabolite-Disease Associations via Optimized Negative Sampling and Sparse Graph Convolutional Network

Y Huang, Q Pu, W Lan, C Huang… - IEEE Transactions on …, 2025 - ieeexplore.ieee.org
Identifying disease-associated metabolites could provide critical clues for the diagnosis and
treatment of diseases. Although computational approaches have been proposed to predict …

A novel intelligent displacement prediction model of karst tunnels

H Fu, Y Zhao, H Ding, Y Rao, T Yang, M Zhou - Scientific Reports, 2022 - nature.com
Karst is a common engineering environment in the process of tunnel construction, which
poses a serious threat to the construction and operation, and the theory on calculating the …

Spiking neural network tactile classification method with faster and more accurate membrane potential representation

J Yang, Z Yu, X Ji, Z Su, S Li… - IET Collaborative Intelligent …, 2024 - Wiley Online Library
Robot perception is an important topic in artificial intelligence field, and tactile recognition in
particular is indispensable for human–computer interaction. Efficiently classifying data …