Timbre,as one of the essential elements of sound,plays an important role in determining sound properties,whereas its manipulation has been remaining challenging for passive mechanical systems due to the intrinsic disp...Timbre,as one of the essential elements of sound,plays an important role in determining sound properties,whereas its manipulation has been remaining challenging for passive mechanical systems due to the intrinsic dispersion nature of resonances.Here,we present a meta-silencer supporting intensive mode density as well as highly tunable intrinsic loss and offering a fresh pathway for designable timbre in broadband.Strong global coupling is induced by intensive mode density and delicately modulated with the guidance of the theoretical model,which efficiently suppresses the resonance dispersion and provides desirable frequency-selective wave-manipulation capacity for timbre tuning.As proof-of-concept demonstrations for our design concepts,we propose three meta-silencers with the designing targets of high-efficiency broadband sound attenuation,efficiency-controlled sound attenuation and designable timbre,respectively.The proposed meta-silencers all operate in a broadband frequency range from 500 to 3200 Hz and feature deep-subwavelength sizes around 50 mm.Our work opens up a fundamental avenue to manipulate the timbre with passive resonances-controlled acoustic metamaterials and may inspire the development of novel multifunctional devices in noise-control engineering,impedance engineering,and architectural acoustics.展开更多
The retrieval of sea surface wind speed is a key application of Global Navigation Satellite System-Reflectometry(GNSS-R).The continuous advancement of deep learning technologies has enabled the application of Convolut...The retrieval of sea surface wind speed is a key application of Global Navigation Satellite System-Reflectometry(GNSS-R).The continuous advancement of deep learning technologies has enabled the application of Convolutional Neural Network(CNN)models to retrieve sea surface wind speed from GNSS-R observables.However,the standard CNN models assign equal weight to all features,overlooking the more relevant ones,which reduces training efficiency and accuracy.To address this issue,this paper proposes a CNN model that incorporates the Squeeze-and-Excitation Network(SENet)attention mechanism,named CNN-SENet.The CNN-SENet model increases the weight for important features while suppressing the weight for less relevant ones,thereby improving accuracy and training efficiency.Results indicate that the CNN-SENet demonstrates a significant advantage in training efficiency over the standard CNN,reducing training time by nearly half.Additionally,the CNN-SENet model predicts the wind speeds in the range of 0-40 m/s with a Root Mean Square Error(RMSE)of 1.29 m/s and a coefficient of determination(R^(2))of 62.4%.It also outperforms both the standard CNN and the Geophysical Model Function(GMF),improving wind speed accuracy by 0.14 m/s and 0.62 m/s,respectively.Furthermore,the CNN-SENet model exhibits superior temporal generalization compared to the standard CNN.展开更多
Broadband sound sink/absorber via a structure with deep sub-wavelength thickness is of great and continuing interest in physics and engineering communities.An intuitive technique extensively used is to combine compone...Broadband sound sink/absorber via a structure with deep sub-wavelength thickness is of great and continuing interest in physics and engineering communities.An intuitive technique extensively used is to combine components(resonators)with quasi-perfect absorption to piece together a broad absorbing band,but the requirement of quasi-perfect absorption substantially places a very strict restriction on the impedance and thickness of the components.Here,we theoretically and experimentally demonstrate that a compact broadband acoustic sink that quasi-perfectly absorbs broadband arriving sound waves can be achieved with coherently coupled‘‘weak resonances"(resonant sound absorbing systems with low absorption peaks).Although each component exhibits rather low absorption peak alone,via manipulating the coherent coupling effect among the components,they collectively provide a remarkably improved performance over a wide frequency range with a significantly compressed thickness.To illustrate the design principle,a hybrid metasurface utilizing the coaction of parallel and cascade couplings is presented,which possesses an average absorption coefficient of 0.957 in the quasi-perfect band(a>0:9)from 870 to 3224 Hz with a thickness of only 3.9 cm.Our results open new avenues for the development of novel and highly efficient acoustic absorbers against low frequency noise,and more essentially,suggest an efficient approach towards on-demand acoustic impedance engineering in broadband.展开更多
文摘Timbre,as one of the essential elements of sound,plays an important role in determining sound properties,whereas its manipulation has been remaining challenging for passive mechanical systems due to the intrinsic dispersion nature of resonances.Here,we present a meta-silencer supporting intensive mode density as well as highly tunable intrinsic loss and offering a fresh pathway for designable timbre in broadband.Strong global coupling is induced by intensive mode density and delicately modulated with the guidance of the theoretical model,which efficiently suppresses the resonance dispersion and provides desirable frequency-selective wave-manipulation capacity for timbre tuning.As proof-of-concept demonstrations for our design concepts,we propose three meta-silencers with the designing targets of high-efficiency broadband sound attenuation,efficiency-controlled sound attenuation and designable timbre,respectively.The proposed meta-silencers all operate in a broadband frequency range from 500 to 3200 Hz and feature deep-subwavelength sizes around 50 mm.Our work opens up a fundamental avenue to manipulate the timbre with passive resonances-controlled acoustic metamaterials and may inspire the development of novel multifunctional devices in noise-control engineering,impedance engineering,and architectural acoustics.
基金supported by the National Key R&D Program of China(2021YFB3901301)the National Natural Science Foundation of China(42271420)+1 种基金the Natural Science Foundation for Young Scholars of Jiangsu Province,China(BK20220366)Jiangsu Province Department of Natural Resources Science and Technology Innovation Project(JSZRKJ202406).
文摘The retrieval of sea surface wind speed is a key application of Global Navigation Satellite System-Reflectometry(GNSS-R).The continuous advancement of deep learning technologies has enabled the application of Convolutional Neural Network(CNN)models to retrieve sea surface wind speed from GNSS-R observables.However,the standard CNN models assign equal weight to all features,overlooking the more relevant ones,which reduces training efficiency and accuracy.To address this issue,this paper proposes a CNN model that incorporates the Squeeze-and-Excitation Network(SENet)attention mechanism,named CNN-SENet.The CNN-SENet model increases the weight for important features while suppressing the weight for less relevant ones,thereby improving accuracy and training efficiency.Results indicate that the CNN-SENet demonstrates a significant advantage in training efficiency over the standard CNN,reducing training time by nearly half.Additionally,the CNN-SENet model predicts the wind speeds in the range of 0-40 m/s with a Root Mean Square Error(RMSE)of 1.29 m/s and a coefficient of determination(R^(2))of 62.4%.It also outperforms both the standard CNN and the Geophysical Model Function(GMF),improving wind speed accuracy by 0.14 m/s and 0.62 m/s,respectively.Furthermore,the CNN-SENet model exhibits superior temporal generalization compared to the standard CNN.
基金supported by the National Natural Science Foundation of China(11704284 and 11774265)the Young Elite Scientists Sponsorship by CAST(2018QNRC001)+2 种基金the Shanghai Science and Technology Committee(18JC1410900)the Shanghai Pujiang Program(17PJ1409000)the Stable Supporting Fund of Acoustic Science and Technology Laboratory.
文摘Broadband sound sink/absorber via a structure with deep sub-wavelength thickness is of great and continuing interest in physics and engineering communities.An intuitive technique extensively used is to combine components(resonators)with quasi-perfect absorption to piece together a broad absorbing band,but the requirement of quasi-perfect absorption substantially places a very strict restriction on the impedance and thickness of the components.Here,we theoretically and experimentally demonstrate that a compact broadband acoustic sink that quasi-perfectly absorbs broadband arriving sound waves can be achieved with coherently coupled‘‘weak resonances"(resonant sound absorbing systems with low absorption peaks).Although each component exhibits rather low absorption peak alone,via manipulating the coherent coupling effect among the components,they collectively provide a remarkably improved performance over a wide frequency range with a significantly compressed thickness.To illustrate the design principle,a hybrid metasurface utilizing the coaction of parallel and cascade couplings is presented,which possesses an average absorption coefficient of 0.957 in the quasi-perfect band(a>0:9)from 870 to 3224 Hz with a thickness of only 3.9 cm.Our results open new avenues for the development of novel and highly efficient acoustic absorbers against low frequency noise,and more essentially,suggest an efficient approach towards on-demand acoustic impedance engineering in broadband.
