Results 81 to 90 of about 35,876 (250)

Mutual Coupling Reduction in MIMO DRA through Metamaterials. [PDF]

open access: yesSensors (Basel), 2023
Khan MS   +5 more
europepmc   +1 more source

Current Status and Challenges in Data Collection for Aerospace Coatings Deposited by Plasma Spraying

open access: yesAdvanced Engineering Materials, EarlyView.
An innovative approach has been integrated into the GRENAT project to optimize plasma spraying and coating performance. Raw materials are accelerated and melted in the plasma generated by torches, creating coatings. Monitoring sensors collect process data which are combined with ex situ characterization data.
Lila Randriamananjara   +8 more
wiley   +1 more source

Symmetry‐Guided Multifunctional Acoustic System Based on Mechanically Actuated Sonic Crystals

open access: yesAdvanced Engineering Materials, EarlyView.
This study presents the design, simulation, and experimental validation of amultifunctional acoustic metamaterial based on rotationally engineered sonic crystals.By tuning cylinder orientations, controllable band gaps and six distinct functionalities—including switching, topological insulation, beam splitting, and logic operations—areachieved ...
Yuanyan Zhao   +2 more
wiley   +1 more source

Molecular Dynamics Studies of Shape Memory Polymers: From Bead–Spring Models to Atomistic Simulations

open access: yesAdvanced Engineering Materials, EarlyView.
Coarse‐grained (left) and atomistic (right) models of the shape memory polymer ESTANE ETE 75DT3 are shown schematically. The two representations bridge molecular detail and mesoscopic description. Both models capture shape memory behavior, linking segmental mobility and conformational relaxation of anisotropic chains to macroscopic recovery, and ...
Fathollah Varnik
wiley   +1 more source

Machine Learning‐Supported Analysis for Predicting and Visualizing Nonlinear Relationships Between Material Properties in Electroplated Chromium Layers

open access: yesAdvanced Engineering Materials, EarlyView.
This study applies machine learning regression to predict chromium layer thickness in decorative trivalent chromium electroplating, using 441 experiments from laboratory‐scale (1L) and pilot‐scale (14L) setups. Tree‐based models, particularly CatBoost, outperformed linear regression by capturing nonlinear parameter interactions (R2$R^2$ up to 0.77 ...
Christoph Baumer   +4 more
wiley   +1 more source

New AI‐Assisted Approach for Expanding the Solution Space: Application to Lattice Structure Design

open access: yesAdvanced Engineering Materials, EarlyView.
This work introduces an innovative framework for designing structured materials by ex panding the design space through reparameterization of qualitative variables into continuous structural descriptors. Combined with machine‐learning‐based prediction and multi‐objective optimization, the approach enables the discovery of novel lattice architectures ...
G. H. Gahimbare   +5 more
wiley   +1 more source

Sparse Reconstruction-based Direction of Arrival Estimation for MIMO Radar in the Presence of Unknown Mutual Coupling

open access: yesLeida xuebao
To improve the accuracy of Direction Of Arrival (DOA) estimation in Multiple Input Multiple Output (MIMO) radar systems under unknown mutual coupling, we propose a mutual coupling calibration and DOA estimation algorithm based on Sparse Learning via ...
Jiong XIAO, Bo TANG, Hai WANG
doaj   +1 more source

An External Calibration Method for Compensating for the Mutual Coupling Effect in Large Interferometric Aperture Synthesis Radiometers

open access: yesInternational Journal of Antennas and Propagation, 2011
This paper deals with the antenna mutual coupling effect in interferometric aperture synthesis radiometers (IASRs), which degrades the system radiometric performance.
Jian Dong   +4 more
doaj   +1 more source

Mutual coupling between stock market and cryptocurrencies. [PDF]

open access: yesHeliyon, 2023
Ahmed MY, Sarkodie SA, Leirvik T.
europepmc   +1 more source

Foundational Machine‐Learning Interatomic Potential for Simulating Chemically Complex Ni‐Based Superalloys

open access: yesAdvanced Engineering Materials, EarlyView.
We apply a foundational machine‐learning interatomic potential based on the graph atomic cluster expansion (GRACE) to simulate the commercial Ni‐based single‐crystal superalloy CMSX‐4. Hybrid Monte‐Carlo/molecular dynamics sampling resolves short‐range order in the γ phase and L12 sublattice occupancies in the γ’ phase and connects them to stacking ...
Aditya Vishwakarma   +4 more
wiley   +1 more source

Home - About - Disclaimer - Privacy