Results 221 to 230 of about 93,376 (303)
Conditional noise generative adversarial networks with Siamese neural network for longer time series forecasting. [PDF]
Mao H, Feng X.
europepmc +1 more source
Mg–Zn composites with a thickness of 0.21 mm were fabricated using roll bonding of a kirigami‐patterned Mg alloy inlay within a Zn matrix. Thermal activation following this process led to the formation of tailored intermetallic structures, which provided the composite with enhanced flexural strength.
Yaroslav Frolov +4 more
wiley +1 more source
MoEKAN: Multi-Scale Transformer-Based Gating KAN Experts Network for Time Series Forecasting. [PDF]
Kim D, Kang J, Hwang H, Kim H.
europepmc +1 more source
A simplified thermoplastic pultrusion model is developed to predict thermal fields in glass fiber/polyethylene terephthalate (GF/PET) composites with reduced computational cost. By combining effective material homogenization, validation against literature data, and Gaussian‐process‐based optimization, the study reveals how heating limits, pulling speed,
Elder Soares +3 more
wiley +1 more source
Deep learning in time series forecasting with transformer models and RNNs. [PDF]
Dos Santos RP +2 more
europepmc +1 more source
Additive manufacturing provides precise control over the placement of continuous fibres within polymer matrices, enabling customised mechanical performance in composite components. This article explores processing strategies, mechanical testing, and modelling approaches for additive manufactured continuous fibre‐reinforced composites.
Cherian Thomas, Amir Hosein Sakhaei
wiley +1 more source
EMAT: Enhanced Multi-Aspect Attention Transformer for Financial Time Series Forecasting. [PDF]
Chen Y, Shen W, Liu H, Cao X.
europepmc +1 more source
The temperature dependence of fatigue behavior in nickel‐based superalloys is investigated through high‐resolution measurements of plastic localization. While increasing temperature reduces localization and enhances fatigue performance in René 88DT, Inconel 718 exhibits a sharp degradation at intermediate temperature due to intensified slip ...
M. Calvat +5 more
wiley +1 more source
The Improved Hybrid STD- Radial Basis Function Neural Network Approach for Time Series Forecasting Application to Tesla Stock Price Prediction. [PDF]
H Abdullah H, A Noori N, S Hamza T.
europepmc +1 more source
Do not let thermal drift and instrument artifacts deceive high‐temperature nanoindentation results. We compare classical Oliver–Pharr and automatic image recognition analyses across steels and a Ni alloy to quantify these effects. Accounting for artifacts reveals systematic softening with temperature, while Cr and Ni additions boost resistance ...
Velislava Yonkova +2 more
wiley +1 more source

