Soft Actor–Critic-Driven Adaptive Focusing under Obstacles

Lu, Huan; Zhu, Rongrong; Wang, Chi; Hua, Tianze; Zhang, Siqi; Chen, Tianhang

doi:10.3390/ma16041366

Cited by 5 publications

(1 citation statement)

References 42 publications

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“…E lectromagnetic (EM) metamaterial is an artificial composite composed of ordered or disordered subwavelength unit structures, with unique EM properties that natural materials do not have [1][2][3][4][5][6][7][8]. By varying the geometry of the artificial resonator, the metamaterial can exhibit characteristics such as the negative refractive index [9,10], backward Cherenkov radiation [11,12], inverse Doppler effect [13][14][15], and plate focusing [16][17][18][19]. In 2008, Landy et al first proposed the concept of metamaterial perfect absorber (MPA) [20], which showed a very high absorption rate but was very thin, and can achieve impedance matching through flexible structural design and structural parameters adjustment.…”

Section: Introductionmentioning

confidence: 99%

A Flexible Foldable Broadband Metamaterial Absorber Fabricated by Intaglio Printing Technology

Dong,

Yang,

Zhang

et al. 2024

PIER M

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Absorbing materials can absorb incident electromagnetic waves effectively and have important research value in radar fields. However, the current absorbing materials are mostly affected by the thickness and flexibility of the dielectric substrate, and they have shortcomings such as being not thin, not flexible, not folding, and not conformal with the protection target, which is not conducive to practical application. In this paper, we propose a flexible absorbing material that can be folded freely for wearable and practical engineering applications, which is composed of a conductive carbon paste ink resistance film layer, a flexible fabric dielectric substrate, and a metal backplane. When the incidence angle is less than 30 • , more than 90% absorption performance can be achieved at the operating frequency of 9.5-11.5 GHz with polarization insensitive characteristics. Simulated and experimental results prove the effectiveness of the structure. Our work provides the groundwork for the commercialization of future meta-devices such as wearable invisibility cloaks, sensors, optical filters/switchers, photodetectors, and energy converters.

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Section: Introductionmentioning

confidence: 99%

A Flexible Foldable Broadband Metamaterial Absorber Fabricated by Intaglio Printing Technology

Dong,

Yang,

Zhang

et al. 2024

PIER M

Self Cite

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Experiment-based deep learning approach for power allocation with a programmable metasurface

Zhang,

Xi,

et al. 2023

APL Machine Learning

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Metasurfaces designed with deep learning approaches have emerged as efficient tools for manipulating electromagnetic waves to achieve beam steering and power allocation objectives. However, the effects of complex environmental factors like obstacle blocking and other unavoidable scattering need to be sufficiently considered for practical applications. In this work, we employ an experiment-based deep learning approach for programmable metasurface design to control powers delivered to specific locations generally with obstacle blocking. Without prior physical knowledge of the complex system, large sets of experimental data can be efficiently collected with a programmable metasurface to train a deep neural network (DNN). The experimental data can inherently incorporate complex factors that are difficult to include if only simulation data are used for training. Moreover, the DNN can be updated by collecting new experimental data on-site to adapt to changes in the environment. Our proposed experiment-based DNN demonstrates significant potential for intelligent wireless communication, imaging, sensing, and quiet-zone control for practical applications.

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Programmable transmission metasurface scattering control under obstacles based on deep learning

Wang,

Zhao,

Yang

et al. 2024

AIP Advances

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The emergence of 5G represents a pivotal step in merging mobile communication networks with the Industrial Internet of Things. Despite the numerous advantages of 5G, the presence of unknown obstacles can adversely affect user signals. Although mitigating signal pressures can be achieved by increasing base station density, it often involves bulky equipment and high costs. To address this, we propose a deep learning-based method for controlling tunable transmissive metasurfaces and validate their scattering control capabilities in the presence of obstacles. By constructing a network model to analyze the mapping relationship between metasurface arrays and far-field scattering, rapid control of scattering characteristics is achieved. AI-driven high-performance tunable metasurfaces exhibit vast potential applications in intelligent communication, offering a universal solution for intelligent control in complex signal environments.

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Soft Actor–Critic-Driven Adaptive Focusing under Obstacles

Cited by 5 publications

References 42 publications

A Flexible Foldable Broadband Metamaterial Absorber Fabricated by Intaglio Printing Technology

A Flexible Foldable Broadband Metamaterial Absorber Fabricated by Intaglio Printing Technology

Experiment-based deep learning approach for power allocation with a programmable metasurface

Programmable transmission metasurface scattering control under obstacles based on deep learning

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