Industrial-scale manufactured acoustic metamaterials for multi-bandgap sound reduction

International Journal of Mechanical Sciences, Год журнала: 2025, Номер unknown, С. 110184 - 110184

Опубликована: Март 1, 2025

Язык: Английский

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High-performance Carbonaceous Absorbers: From Heterogeneous Absorbents to Data-driven Metamaterials

Carbon, Год журнала: 2024, Номер unknown, С. 119850 - 119850

Опубликована: Ноя. 1, 2024

Язык: Английский

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Synergy between AI and Optical Metasurfaces: A Critical Overview of Recent Advances

Photonics, Год журнала: 2024, Номер 11(5), С. 442 - 442

Опубликована: Май 9, 2024

The interplay between two paradigms, artificial intelligence (AI) and optical metasurfaces, nowadays appears obvious unavoidable. AI is permeating literally all facets of human activity, from science arts to everyday life. On the other hand, metasurfaces offer diverse sophisticated multifunctionalities, many which appeared impossible only a short time ago. use for optimization general approach that has become ubiquitous. However, here we are witnessing two-way process—AI improving but some also AI. helps design, analyze utilize while ensure creation all-optical chips. This ensures positive feedback where each enhances one: this may well be revolution in making. A vast number publications already cover either first or second direction; modest includes both. an attempt make reader-friendly critical overview emerging synergy. It succinctly reviews research trends, stressing most recent findings. Then, it considers possible future developments challenges. author hopes broad interdisciplinary will useful both dedicated experts scholarly audience.

Язык: Английский

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Spatial Localisation and Sensing in Two Dimensions via Metamaterials

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Авг. 14, 2024

In this study, we introduce a two-dimensional metamaterial sensor designed to detect, locate and distinguish between different objects placed into its near field. When an object is on the surface of our metamaterial, local changes in one or more structure's meta-atoms can be detected. This interaction generally modifies inductance cell, resulting overall input impedance surface. We derive properties structure behaviour terms superposition demonstrate that observing meta-surface from single point sufficient for unambiguous localisation identification.To model these effectively identify position object, employ neural network machine learning algorithm. Our approach enables accurate all studied objects, with precision exceeding 98%. Additionally, distinct signatures allow separation them accuracy over 97%.The potential applications platform extend foreign detection arrays wireless power transfer, providing proximity many surfaces such as clothing, car bodies robotic carapaces. Furthermore, research suggests feasibility implementing touchscreen type interface requiring only waveguide connection.

Язык: Английский

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Recent Advances in Reconfigurable Electromagnetic Surfaces: Engineering Design, Full-Wave Analysis, and Large-Scale Optimization

Electromagnetic Science, Год журнала: 2024, Номер 2(3), С. 1 - 25

Опубликована: Сен. 1, 2024

Язык: Английский

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AInsectID Version 1.1: an Insect Species Identification Software Based on the Transfer Learning of Deep Convolutional Neural Networks

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Ноя. 3, 2024

ABSTRACT AInsectID Version 1.1 1 , is a GUI operable open-source insect species identification, color processing 2 and image analysis software. The software has current database of 150 insects integrates Artificial Intelligence (AI) approaches to streamline the process with focus on addressing prediction challenges posed by mimics. This paper presents methods algorithmic development, coupled rigorous machine training used enable high levels validation accuracy. Our work transfer learning prominent convolutional neural network (CNN) architectures, including VGG16, GoogLeNet, InceptionV3, MobileNetV2, ResNet50, ResNet101. Here, we employ both fine tuning hyperparameter optimization improve performance. After extensive computational experimentation, ResNet101 evidenced as being most effective CNN model, achieving accuracy 99.65%. dataset utilized for sourced from National Museum Scotland (NMS), Natural History (NHM) London open source datasets Zenodo (CERN’s Data Center), ensuring diverse comprehensive collection species.

Язык: Английский

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What lies beyond – insights into elastic microscaffolds with metamaterial properties for cell studies

Current Opinion in Biomedical Engineering, Год журнала: 2024, Номер unknown, С. 100568 - 100568

Опубликована: Ноя. 1, 2024

Язык: Английский

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Mathematical modelling and virtual design of metamaterials for reducing noise and vibration in built-up structures

Proceedings of the Nigerian Academy of Science, Год журнала: 2024, Номер 17(2), С. 61 - 82

Опубликована: Дек. 30, 2024

Noise and vibration pose significant challenges in built-up structures, affecting structural integrity occupant comfort. Traditional materials often fail to address these issues effectively across all relevant frequencies, particularly urban industrial environments. This paper presents a mathematical modeling approach virtual design framework for developing metamaterials specifically tailored mitigate noise structures. By leveraging finite element analysis, dynamic energy optimization algorithms, the study demonstrates how can create frequency-specific barriers. Comparative analyses with previous studies, performance metrics, sensitivity evaluations reveal robustness unique contributions of this approach. Validation through simulations benchmarking confirms model’s effectiveness, enhancing resilience human comfort complex Additionally, surveys natural environment. The major findings highlight effectiveness (NMs) ground attenuation, offering diverse applications proposing roadmap clean quiet environments

Язык: Английский

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Mathematical modelling and virtual design of metamaterials for reducing noise and vibration in built-up structures

V N Karazin Kharkiv National University Ser Mathematics Applied Mathematics and Mechanics, Год журнала: 2024, Номер 100, С. 19 - 47

Опубликована: Ноя. 25, 2024

Noise and vibration are pervasive challenges in built-up structures, impacting structural integrity, operational efficiency, occupant well-being. These issues particularly pronounced urban industrial settings, where traditional materials often struggle to deliver effective mitigation across the broad range of relevant frequencies. This paper introduces an integrated mathematical modeling virtual design framework for development advanced metamaterials aimed at reducing noise such complex structures. The approach combines finite element analysis, dynamic energy optimization algorithms with frequency-selective properties that create targeted barriers acoustic vibrational disturbances. study not only develops a systematic methodology designing these but also validates their efficacy through comprehensive simulations benchmarking against established solutions. results highlight advantages proposed terms adaptability, performance robustness various operating conditions. Sensitivity analyses comparative evaluations further underscore superiority addressing frequency-dependent challenges, offering significant improvements over conventional materials. A unique aspect this research is inclusion natural (NMs) as sustainable alternative mitigating ground vibrations. reviews potential NMs diverse functionalities, attenuating vibrations environments. findings emphasize versatility eco-friendliness materials, providing roadmap application achieving clean quiet framework, therefore, bridges theoretical advancements practical applications, paving way resilient solutions

Язык: Английский

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Spatial localisation and sensing in two dimensions via metasurfaces

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Окт. 15, 2024

In this study, we introduce a two-dimensional metasurface sensor designed to detect, locate and distinguish between different objects placed in its near field. When an object is on the metasurface, local changes can be detected one or more of structure's meta-atoms. This interaction generally modifies inductance meta-atom, resulting overall input impedance surface. We derive properties structure behaviour terms superposition demonstrate that observing meta-surface from single point sufficient for unambiguous localisation identification. To model these effectively identify position object, employ neural network machine learning algorithm. Our approach enables accurate all studied objects, with precision exceeding

Язык: Английский

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