Noise interference critically impairs the stability and data accuracy of sensing systems.However,current suppression strategies fail to concurrently mitigate intrinsic system noise and extrinsic environmental noise.Th...Noise interference critically impairs the stability and data accuracy of sensing systems.However,current suppression strategies fail to concurrently mitigate intrinsic system noise and extrinsic environmental noise.This study introduces a composite denoising approach to address this challenge.This method is based on the ameliorated ellipse fitting algorithm(AEFA)and adaptive successive variational mode decomposition(ASVMD).This algorithm employs AEFA to eliminate system noise tightly coupled with direct-current and alternating-current components in the interference signal,thereby obtaining a phase signal containing only environmental noise.The ASVMD technique adaptively extracts environmental noise components predominantly present in the phase signal.To achieve optimal decomposition results automatically,the permutation entropy criterion is employed to refine decomposition parameters.The correlation coefficient is utilized to differentiate effective components from noise components in the decomposition results.Experimental results indicate that the combined AEFA and ASVMD algorithm effectively suppresses both system and environmental noises.When applied to 50 Hz vibration signal processing,the proposed approach achieves a noise reduction of 17.81 dB and a phase resolution of 35.14μrad/√Hz.Given the excellent performance of the noise suppression,the proposed approach holds great application potential in high-performance interferometric sensing systems.展开更多
In this investigation,a hybrid approach integrating the IDDES turbulence model and FW-H is employed to forecast the hydroacoustic of the rim driven thruster(RDT)under non-cavitation and uniform flow conditions at vary...In this investigation,a hybrid approach integrating the IDDES turbulence model and FW-H is employed to forecast the hydroacoustic of the rim driven thruster(RDT)under non-cavitation and uniform flow conditions at varying loading conditions(J=0.3 and J=0.6).It is revealed that the quadrupole term contribution in the P-FWH method significantly affects the monopole term in the low-frequency region,while it mainly affects the dipole term in the high-frequency region.Specifically,the overall sound pressure levels(SPL)of the RDT using the P-FWH method are 2.27 dB,10.03 dB,and 16.73 dB at the receiving points from R1 to R3 under the heavy-loaded condition,while they increase by 0.67 dB at R1,and decrease by 14.93 dB at R2,and 22.20 dB at R3,for the light-loaded condition.The study also utilizes the pressure-time derivatives to visualize the numerical noise and to pinpoint the dynamics of the vortex cores,and the optimization of the grid design can significantly reduce the numerical noise.The computational accuracy of the P-FWH method can meet the noise requirements for the preliminary design of rim driven thrusters.展开更多
Quick and accurate determination of the optimal synchrophase angle is crucial for synchrophasing control of multi-propeller aircraft with low noise.This paper proposes a novel noise prediction and optimization strateg...Quick and accurate determination of the optimal synchrophase angle is crucial for synchrophasing control of multi-propeller aircraft with low noise.This paper proposes a novel noise prediction and optimization strategy,developing a continuous and accurate noise prediction model and obtaining its minimum by solving the Hessian matrix and Fourier-Frobenius matrix.Firstly,a novel propeller noise prediction method uses acoustic simulation pressure signals and improved propeller signatures theory to accurately estimate noise for all synchrophase angles and receiving points.Secondly,a novel optimization approach is proposed to solve the analytical solution of the minimum propeller noise:(A)A noise objective function is established,and use its first derivatives’zeros and Hessian matrix to determine the function minimum.(B)A novel Euler formula transform method is proposed to convert trigonometric polynomials into algebraic polynomials,changing the zeros of the former into those of the latter.(C)Utilize the Fourier-Frobenius matrix method to solve the zeros of algebraic polynomials.To assess the computation time and accuracy,a turboprop aircraft with two six-bladed propellers was analyzed using the computational fluid dynamics and acoustic analogy method,providing acoustic pressure signals at 20 receivers for noise prediction and optimization.The Durand-Kerner and Fourier-Frobenius matrix methods were compared.Results demonstrate that improved propeller signatures theory is more accurate,and the Hessian matrix+Fourier-Frobenius matrix method is faster and more precise than the Hessian matrix+Durand-Kerner method.展开更多
Domain adaptation aims to reduce the distribution gap between the training data(source domain)and the target data.This enables effective predictions even for domains not seen during training.However,most conventional ...Domain adaptation aims to reduce the distribution gap between the training data(source domain)and the target data.This enables effective predictions even for domains not seen during training.However,most conventional domain adaptation methods assume a single source domain,making them less suitable for modern deep learning settings that rely on diverse and large-scale datasets.To address this limitation,recent research has focused on Multi-Source Domain Adaptation(MSDA),which aims to learn effectively from multiple source domains.In this paper,we propose Efficient Domain Transition for Multi-source(EDTM),a novel and efficient framework designed to tackle two major challenges in existing MSDA approaches:(1)integrating knowledge across different source domains and(2)aligning label distributions between source and target domains.EDTM leverages an ensemble-based classifier expert mechanism to enhance the contribution of source domains that are more similar to the target domain.To further stabilize the learning process and improve performance,we incorporate imitation learning into the training of the target model.In addition,Maximum Classifier Discrepancy(MCD)is employed to align class-wise label distributions between the source and target domains.Experiments were conducted using Digits-Five,one of the most representative benchmark datasets for MSDA.The results show that EDTM consistently outperforms existing methods in terms of average classification accuracy.Notably,EDTM achieved significantly higher performance on target domains such as Modified National Institute of Standards and Technolog with blended background images(MNIST-M)and Street View House Numbers(SVHN)datasets,demonstrating enhanced generalization compared to baseline approaches.Furthermore,an ablation study analyzing the contribution of each loss component validated the effectiveness of the framework,highlighting the importance of each module in achieving optimal performance.展开更多
The spatial offset of bridge has a significant impact on the safety,comfort,and durability of high-speed railway(HSR)operations,so it is crucial to rapidly and effectively detect the spatial offset of operational HSR ...The spatial offset of bridge has a significant impact on the safety,comfort,and durability of high-speed railway(HSR)operations,so it is crucial to rapidly and effectively detect the spatial offset of operational HSR bridges.Drive-by monitoring of bridge uneven settlement demonstrates significant potential due to its practicality,cost-effectiveness,and efficiency.However,existing drive-by methods for detecting bridge offset have limitations such as reliance on a single data source,low detection accuracy,and the inability to identify lateral deformations of bridges.This paper proposes a novel drive-by inspection method for spatial offset of HSR bridge based on multi-source data fusion of comprehensive inspection train.Firstly,dung beetle optimizer-variational mode decomposition was employed to achieve adaptive decomposition of non-stationary dynamic signals,and explore the hidden temporal relationships in the data.Subsequently,a long short-term memory neural network was developed to achieve feature fusion of multi-source signal and accurate prediction of spatial settlement of HSR bridge.A dataset of track irregularities and CRH380A high-speed train responses was generated using a 3D train-track-bridge interaction model,and the accuracy and effectiveness of the proposed hybrid deep learning model were numerically validated.Finally,the reliability of the proposed drive-by inspection method was further validated by analyzing the actual measurement data obtained from comprehensive inspection train.The research findings indicate that the proposed approach enables rapid and accurate detection of spatial offset in HSR bridge,ensuring the long-term operational safety of HSR bridges.展开更多
Residents living near drill-and-blast tunnels often experience disturbances from blasting operations.This motivates us to investigate the characteristics of airblasts and resulting noise through on-site monitoring at ...Residents living near drill-and-blast tunnels often experience disturbances from blasting operations.This motivates us to investigate the characteristics of airblasts and resulting noise through on-site monitoring at three tunnels.The research focuses on both the temporal evolution and spatial propagation of airblasts.Temporal analysis,including peak overpressure(POp),positive duration(PD),and Fourier main frequency(MF),emphasizes the relationship between airblast characteristics,blasting delays,and rock grade.It shows that airblast bandwidths are typically in the range of 3e200 Hz,with noise levels exceeding 130 dB,which is comparable to jet engines and rocket launch.Spatial propagation analysis reveals the impact of tunnel space on airblast propagation.Although POp and PD typically decrease with distance inside the tunnel,wave superposition can cause increased overpressure and prolonged durations at far-field distances(above 60 m kg^(-1/3)).Outside the tunnel,sound radiation was influenced by azimuth and was basically determined by sound power d an often-overlooked factor.To address the anisotropic propagation of airblasts,a predictive model was proposed for external noise levels,considering variables like distance,azimuth angle,initial sound power,and wave expansion.Validated by tests,this model successfully unifies data from three studies,helping to explain and predict airblast disturbances near tunnels.展开更多
Local resonant acoustic metamaterials have broad applications in sound insulation,yet their single-configuration designs often exhibit limited and discontinuous bandgap widths,hindering full-frequency noise attenuatio...Local resonant acoustic metamaterials have broad applications in sound insulation,yet their single-configuration designs often exhibit limited and discontinuous bandgap widths,hindering full-frequency noise attenuation across the human auditory range.This study presents a double-phase fidget-spinner-shaped acoustic metamaterial(DFAM),specifically designed to achieve an ultra-broad,low-frequency continuous bandgap by means of synergistic structural optimization,enabling effective and robust control of audible noise.Based on Bloch's theorem and the finite element method,the dispersion relation of the DFAM structure is calculated and verified by the transmission loss curves.The propagation characteristics of sound waves within the structure are further analyzed for noise frequencies that fall within the passband.The influence of the geometric and physical parameters on the bandgap is investigated,and the corresponding transmission loss in the propagation direction is further calculated.A hybrid collaborative design strategy,leveraging multi-parameter optimization and bandgap complementarity,is developed to construct a metastructure with continuous bandgap coverage from 20 Hz to 1000 Hz.The resulting metastructure demonstrates exceptional broadband noise attenuation,achieving a total bandgap width of 876.3 Hz(87.63% of the target range)with the transmission loss up to-762.78 d B in a three-periodic arrangement.The simulation and experimental results for the transmission loss of the DFAM metastructure show strong agreement in the low-frequency range.This work provides a novel framework for designing ultra-wide low-frequency continuous bandgap metastructures,offering significant potential for noise mitigation in complex environments.展开更多
The Urumqi foreland thrust tectonic belt exhibits complex geological structures and strong seismicity.Imaging its shallow crustal structure is of great significance for understanding its tectonic mechanism and seismog...The Urumqi foreland thrust tectonic belt exhibits complex geological structures and strong seismicity.Imaging its shallow crustal structure is of great significance for understanding its tectonic mechanism and seismogenic environment.We obtained a high-resolution S-wave velocity model of the shallow crust at depths of 0–8 km using ambient noise tomography applied to data from a dense seismic array.Sediments are generally thinner in the southeast and thicker in the northwest,with a maximum thickness of more than 8 km.Variations in the velocity structure near the Xishan,Wanyaogou,and Yamalike faults indicate that their formation was related to differences in the physical properties on either side of the fault.In addition,the faults exhibit thrusting of the low-velocity sides towards the high-velocity sides.In the study area,earthquakes rarely occur at depths of less than 3 km and are mostly concentrated in the high-velocity zone in the southern part.Below 3 km depth,more earthquakes were observed,mainly distributed near faults or in relatively high-velocity areas in the southern part.This suggests that high-velocity structures are more prone to stress accumulation,resulting in earthquakes.At 6–8 km depth,the densely distributed earthquakes in the northwestern part of the Bogda mountains are well-aligned with the northwest-oriented low-velocity zone observed in this study,suggesting that this weak zone likely controls seismicity in this area.展开更多
With the development of technology,diffusion model-based solvers have shown significant promise in solving Combinatorial Optimization(CO)problems,particularly in tackling Non-deterministic Polynomial-time hard(NP-hard...With the development of technology,diffusion model-based solvers have shown significant promise in solving Combinatorial Optimization(CO)problems,particularly in tackling Non-deterministic Polynomial-time hard(NP-hard)problems such as the Traveling Salesman Problem(TSP).However,existing diffusion model-based solvers typically employ a fixed,uniform noise schedule(e.g.,linear or cosine annealing)across all training instances,failing to fully account for the unique characteristics of each problem instance.To address this challenge,we present GraphGuided Diffusion Solvers(GGDS),an enhanced method for improving graph-based diffusion models.GGDS leverages Graph Neural Networks(GNNs)to capture graph structural information embedded in node coordinates and adjacency matrices,dynamically adjusting the noise levels in the diffusion model.This study investigates the TSP by examining two distinct time-step noise generation strategies:cosine annealing and a Neural Network(NN)-based approach.We evaluate their performance across different problem scales,particularly after integrating graph structural information.Experimental results indicate that GGDS outperforms previous methods with average performance improvements of 18.7%,6.3%,and 88.7%on TSP-500,TSP-100,and TSP-50,respectively.Specifically,GGDS demonstrates superior performance on TSP-500 and TSP-50,while its performance on TSP-100 is either comparable to or slightly better than that of previous methods,depending on the chosen noise schedule and decoding strategy.展开更多
In this paper,an analysis-definition-processing(ADP)framework is proposed to search positive-incentive noise in continuous action iterated dilemma(CAID).We analyze the influence of communication noise on the cooperati...In this paper,an analysis-definition-processing(ADP)framework is proposed to search positive-incentive noise in continuous action iterated dilemma(CAID).We analyze the influence of communication noise on the cooperative behavior of players in the system and introduce the concept of positive-incentive noise in CAID.We design a global cost function to ensure convergence of the system can be achieved and strive to improve the final level of cooperation.An optimal CAID control method is proposed to derive the deterministic optimal learning rate in analytical form,avoiding the variability and uncertainty brought about by neural network fitting or parameter adjustment.On this basis,the convergence of the dynamic model is further analyzed by using the Lyapunov function instead of the Jacobian matrix.Additionally,an adaptive filtering mechanism is designed to dynamically ensure that only positive-incentive noise affects the system,effectively reducing the impact of negative noise and enhancing system stability.The framework is validated through simulations involving triple classical game models,including the hawk-dove game,the stag hunt game,the chicken game on networks,and a straightforward illustrative example.展开更多
While the Ordos Basin is recognized for its substantial hydrocarbon exploration prospects,its rugged loess tableland terrain has rendered seismic exploration exceptionally challenging[1-3].Persistent obstacles such as...While the Ordos Basin is recognized for its substantial hydrocarbon exploration prospects,its rugged loess tableland terrain has rendered seismic exploration exceptionally challenging[1-3].Persistent obstacles such as complex 3D survey planning,low signal-tonoise ratio raw data,inadequate near-surface velocity modeling,and imaging inaccuracy have long hindered the advancement of seismic exploration across this region.Through a problem-solving approach rooted in geological target analysis,this research systematically investigates the behavioral patterns of nodal seismometer-based high-density seismic acquisition in loess plateau.Tailored advancements in waveform enhancement and depth velocity modelling methodologies have been engineered.Field validations confirm that the optimized workflow demonstrates marked improvements in amplitude preservation and imaging resolution,offering novel insights for future reservoir characterization endeavors.展开更多
Recently,the zeroing neural network(ZNN)has demonstrated remarkable effectiveness in tackling time-varying problems,delivering robust performance across both noise-free and noisy environments.However,existing ZNN mode...Recently,the zeroing neural network(ZNN)has demonstrated remarkable effectiveness in tackling time-varying problems,delivering robust performance across both noise-free and noisy environments.However,existing ZNN models are limited in their ability to actively suppress noise,which constrains their robustness and precision in solving time-varying problems.This paper introduces a novel active noise rejection ZNN(ANR-ZNN)design that enhances noise suppression by integrating computational error dynamics and harmonic behaviour.Through rigorous theoretical analysis,we demonstrate that the proposed ANR-ZNN maintains robust convergence in computational error performance under environmental noise.As a case study,the ANR-ZNN model is specifically applied to time-varying matrix inversion.Comprehensive computer simulations and robotic experiments further validate the ANR-ZNN's effectiveness,emphasising the proposed design's superiority and potential for solving time-varying problems.展开更多
Tilt-to-length(TTL)coupling noise is a critical issue in space-based gravitational wave detection due to its complex dependence on multiple interacting factors,which complicates the identification of dominant paramete...Tilt-to-length(TTL)coupling noise is a critical issue in space-based gravitational wave detection due to its complex dependence on multiple interacting factors,which complicates the identification of dominant parameters.To address this challenge,we develop a simulation model of the Taiji scientific interferometer,generating noise datasets under multiparameter conditions.Given the uniqueness of the telescope as well as the convergence behavior of the algorithm,the analysis is structured hierarchically:(i)the telescope level and(ii)the optical bench level.A hierarchical framework combining XGBoost and SHapley Additive exPlanations(SHAP)values is employed to model the intricate relationships between parameters and TTL coupling noise,supplemented by sensitivity analysis.Our results identify pointing jitter and telescope radius as the dominant parameters at the telescope level,while the angles of the plane mirrors and beam splitters are most influential at the optical bench level.The parameter space is reduced from 86 dimensions to 14 dimensions without sacrificing model accuracy.This approach offers actionable insights for optimizing the Taiji interferometer design.展开更多
The estimation of the Number of Sources(NoS)is a significant challenge in signal processing,particularly due to the impact of colored noise on the performance of NoS estimation.This paper proposes a Multidimensional F...The estimation of the Number of Sources(NoS)is a significant challenge in signal processing,particularly due to the impact of colored noise on the performance of NoS estimation.This paper proposes a Multidimensional Feature Network(MFNet)which is designed for NoS estimation by extracting features of the sampled received signals and Sampled Covariance Matrix(SCM).The MFNet treats the raw signal and the SCM as two different types of data,and is able to achieve NoS estimation under colored noise and imperfect array.MFNet employs the Gated Recurrent Unit(GRU)to capture sequential information from the original signal data and to construct the Pseudo Covariance Matrix(PCM).Subsequently,various dimensional features,including eigenvalues and the Gerschgorin disk radius,are extracted from both the PCM and SCM,which are then jointly input into the subsequent network.An overall accuracy of 82%can be achieved after network training.The ablation experimental results demonstrate the effectiveness of multiple inputs.And simulation results demonstrate that the proposed MFNet achieves higher estimation accuracy compared to existing algorithms and exhibits greater robustness against colored noise.展开更多
To uncover the decision-making mechanisms and evolutionary dynamics of multiple stakeholders in highway noise pollution control,a three-party evolutionary game model involving the government,operators,and the public i...To uncover the decision-making mechanisms and evolutionary dynamics of multiple stakeholders in highway noise pollution control,a three-party evolutionary game model involving the government,operators,and the public is constructed.The operation period is divided into different stages for differentiated analysis.A simulation analysis was performed on the Lituo sinking section of the Beijing-Hong Kong-Macao Highway to assess the impact of variations in critical elements on the system.The results indicate that the Lituo sinking section of the Beijing-Hong Kong-Macao Highway is currently in its early stage of development,with the corresponding strategies being active regulation,excessive emissions,and supervision.When the cost of the government’s active regulation decreases from 1×10^(5) to 5×10^(4) yuan,the system converges more rapidly toward the active regulation strategy.When the cost of the operator’s excessive emissions increases from 14.08×10^(6) to 20.00×10^(6) yuan,the system drives the operator toward the standardized emission strategy.In addition,when the cost of public supervision decreases from 15×10^(4) to 5×10^(4) yuan and the compensation paid by operators to the public increases from 1.288×10^(6) to 2.576×10^(6) yuan,the system converges more quickly toward the supervision strategy.The cost of the operator’s excessive emissions serves as the core decision variable for achieving the ideal equilibrium in the three-party game involving government active regulation,operator standardized emissions,and public supervision.展开更多
Benthic habitat mapping is an emerging discipline in the international marine field in recent years,providing an effective tool for marine spatial planning,marine ecological management,and decision-making applications...Benthic habitat mapping is an emerging discipline in the international marine field in recent years,providing an effective tool for marine spatial planning,marine ecological management,and decision-making applications.Seabed sediment classification is one of the main contents of seabed habitat mapping.In response to the impact of remote sensing imaging quality and the limitations of acoustic measurement range,where a single data source does not fully reflect the substrate type,we proposed a high-precision seabed habitat sediment classification method that integrates data from multiple sources.Based on WorldView-2 multi-spectral remote sensing image data and multibeam bathymetry data,constructed a random forests(RF)classifier with optimal feature selection.A seabed sediment classification experiment integrating optical remote sensing and acoustic remote sensing data was carried out in the shallow water area of Wuzhizhou Island,Hainan,South China.Different seabed sediment types,such as sand,seagrass,and coral reefs were effectively identified,with an overall classification accuracy of 92%.Experimental results show that RF matrix optimized by fusing multi-source remote sensing data for feature selection were better than the classification results of simple combinations of data sources,which improved the accuracy of seabed sediment classification.Therefore,the method proposed in this paper can be effectively applied to high-precision seabed sediment classification and habitat mapping around islands and reefs.展开更多
Railway noise barriers are an essential piece of infrastructure for reducing noise propagation.However,these barriers experience aerodynamic loads generated by high-speed trains,leading to dynamic effects that may com...Railway noise barriers are an essential piece of infrastructure for reducing noise propagation.However,these barriers experience aerodynamic loads generated by high-speed trains,leading to dynamic effects that may compromise their fatigue capacity.The most common structural design for railway noise barriers consists of vertical configurations of posts and panels.However,there have been few dynamic analyses of steel post/wood panel noise barriers under train-induced aerodynamic loads.This study used dynamic finite element analysis to assess the dynamic behavior of such noise barriers.Analysis of a 40-m-long noise barrier model and a triangular simplified load model,the latter of which effectively represented the detailed aerodynamic load,were first used to establish the model and input of the moving load during dynamic simulation.Then,the effects of different parameters on the dynamic response of the noise barrier were evaluated,including the damping ratio,the profile of the steel post,the span length of the panel,the barrier height,and the train speed.Gray relational analysis indicated that barrier height exhibited the highest correlations with the dynamic responses,followed by train speed,post profile,span length,and damping ratio.A reduction in the natural frequency and an increase in the train speed result in a higher peak response and more pronounced fluctuations between the nose and tail waves.The dynamic amplification factor(DAF)was found to be related to both the natural frequency and train speed.A model was proposed showing that the DAF significantly increases as the square of the natural frequency decreases and the cube of the train speed rises.展开更多
The Haicheng region,Liaoning,China,likely hosts a conjugate fault system comprising the NW-trending Haichenghe fault and NE-trending secondary faults.On February 4,1975,at 19:36 CST,an earthquake of M_(S)7.3 and inten...The Haicheng region,Liaoning,China,likely hosts a conjugate fault system comprising the NW-trending Haichenghe fault and NE-trending secondary faults.On February 4,1975,at 19:36 CST,an earthquake of M_(S)7.3 and intensity(MMI)IX hit the city of Haicheng,Liaoning,China.Although deep seismic profiling was previously conducted along the Haichenghe fault,the limited horizontal resolution in the shallow part prevented the recognition of kilometer-scale anomalies.The velocity structure characteristics of the Haichenghe fault and its NE-trending conjugate faults remain unclear.Using the extended range phase shift method,the high-resolution S-wave velocity structures are obtained by deploying a long,dense linear array of 55 short-period seismometers across the fault and NE-trending conjugate faults.The array length was 32 km and inter-station spacing was approximately 600 m,facilitating the collection of approximately 22 days of continuous waveform data.Employing the Extended Range Phase Shift(ERPS)method enabled the extraction of broadband 0.2–5 s Rayleigh wave phase velocity dispersion curves.The broadband dispersion data were used for inversion of the high-resolution S-wave velocity structure to a depth of 8 km from the surface.The velocity structure characteristics and seismicity of the Haichenghe fault and NE-trending conjugate faults were analyzed and compared with nearby fault gas measurements.Results show(1)shallow S-wave velocities show a low-high-low horizontal distribution,corresponding to basin-uplift-basin topography;(2)significant velocity contrasts occur across the Haichenghe fault:its SW segment(0–17 km)exhibits high velocities consistent with Paleoproterozoic crystalline basement(Pt_(1)),while the NE segment(17–32 km)shows low velocities related to Yanshanian intrusions(γ_(5))and Quaternary sediments.NE-trending conjugate faults display sharp velocity gradients marking fracture locations,with all faults being near-vertical to~8 km depth;(3)seismicity at 1–6 km depth mainly clusters in high-velocity zones;at 6–8 km depth,it concentrates beneath the Haichenghe fault in low-velocity areas and along NE-trending faults;(4)the seismic activity characteristics and fault zone width of the Haicheng he fault reflected by velocity imaging results are basically consistent with those obtained by the fault gas measurement method.展开更多
Owing to the harsh conditions,wind turbine bearings are prone to faults,and the resulting fault information is easily submerged by strong noise disturbance,making conventional diagnosis challenging.Therefore,this stud...Owing to the harsh conditions,wind turbine bearings are prone to faults,and the resulting fault information is easily submerged by strong noise disturbance,making conventional diagnosis challenging.Therefore,this study presents an innovative bearing fault diagnosis approach predicated on Parameter⁃Optimized Symplectic Geometry Mode Decomposition(POSGMD)and Improved Convolutional Neural Network(ICNN).Firstly,assisted by the relative entropy⁃based adaptive selection of embedding dimension,a POSGMD is presented to adaptively decompose the collected bearing vibration signals into various Symplectic Geometry Components(SGC),which can solve the problem of manual selection of the embedding dimension in the raw Symplectic Geometry Mode Decomposition(SGMD).Meanwhile,the signal reconstruction on the decomposed SGC is conducted based on kurtosis⁃weighted principle to obtain the reconstructed signals.Subsequently,the Continuous Wavelet Transform(CWT)of the reconstructed signals is calculated to generate the corresponding time⁃frequency images as sample set.Finally,an ICNN is introduced for model training and automatic recognition of bearing faults.Two case studies are used to validate the presented methods efficacy.Comparing the presented method with traditional fault diagnosis methods,experimental results show that it can achieve greater identification accuracy and superior anti⁃noise resilience.This work provides a practical and effective solution for fault diagnosis in wind turbine bearings,contributing to the timely detection of faults and the reliable operation of wind turbines or other rotational machinery in industrial applications.展开更多
Sensor noise is a critical factor that degrades the performance of image processing systems.In traditional computing systems,noise correction is implemented in the digital domain,resulting in redundant latency and pow...Sensor noise is a critical factor that degrades the performance of image processing systems.In traditional computing systems,noise correction is implemented in the digital domain,resulting in redundant latency and power consumption overhead in the analog-to-digital conversion.In this work,we propose an analog-domain image correction architecture based on a proposed small-scale UNet,which implements a compact noise correction network within a one-transistor-one-memristor(1T1R)array.The statistical non-idealities of the fabricated 1T1R array(e.g.,device variability)are rigorously incorporated into the network's training and inference simulations.This correction network architecture leverages memristors for conducting multiply-accumulate operations aimed at rectifying non-uniform noise,defective pixels(stuck-at-bright/dark),and exposure mismatch.Compared to systems without correction,the proposed architecture achieves up to 50.13%improvement in recognition accuracy while demonstrating robust tolerance to memristor device-level errors.The proposed system achieves a 2.13-fold latency reduction and three orders of magnitude higher energy efficiency compared to conventional architecture.This work establishes a new paradigm for advancing the development of low-power,low-latency,and high-precision image processing systems.展开更多
文摘Noise interference critically impairs the stability and data accuracy of sensing systems.However,current suppression strategies fail to concurrently mitigate intrinsic system noise and extrinsic environmental noise.This study introduces a composite denoising approach to address this challenge.This method is based on the ameliorated ellipse fitting algorithm(AEFA)and adaptive successive variational mode decomposition(ASVMD).This algorithm employs AEFA to eliminate system noise tightly coupled with direct-current and alternating-current components in the interference signal,thereby obtaining a phase signal containing only environmental noise.The ASVMD technique adaptively extracts environmental noise components predominantly present in the phase signal.To achieve optimal decomposition results automatically,the permutation entropy criterion is employed to refine decomposition parameters.The correlation coefficient is utilized to differentiate effective components from noise components in the decomposition results.Experimental results indicate that the combined AEFA and ASVMD algorithm effectively suppresses both system and environmental noises.When applied to 50 Hz vibration signal processing,the proposed approach achieves a noise reduction of 17.81 dB and a phase resolution of 35.14μrad/√Hz.Given the excellent performance of the noise suppression,the proposed approach holds great application potential in high-performance interferometric sensing systems.
基金The National Natural Science Foundation of China(Grant No.52201376)the Natural Science Foundation of Hubei Province,China(Grant No.2023AFB683).
文摘In this investigation,a hybrid approach integrating the IDDES turbulence model and FW-H is employed to forecast the hydroacoustic of the rim driven thruster(RDT)under non-cavitation and uniform flow conditions at varying loading conditions(J=0.3 and J=0.6).It is revealed that the quadrupole term contribution in the P-FWH method significantly affects the monopole term in the low-frequency region,while it mainly affects the dipole term in the high-frequency region.Specifically,the overall sound pressure levels(SPL)of the RDT using the P-FWH method are 2.27 dB,10.03 dB,and 16.73 dB at the receiving points from R1 to R3 under the heavy-loaded condition,while they increase by 0.67 dB at R1,and decrease by 14.93 dB at R2,and 22.20 dB at R3,for the light-loaded condition.The study also utilizes the pressure-time derivatives to visualize the numerical noise and to pinpoint the dynamics of the vortex cores,and the optimization of the grid design can significantly reduce the numerical noise.The computational accuracy of the P-FWH method can meet the noise requirements for the preliminary design of rim driven thrusters.
基金supported by the National Natural Science Foundation of China(Nos.51576097,51976089)the Funding for Outstanding Doctoral Dissertation in Nanjing University of Aeronautics and Astronautics,China(No.BCXJ24-05)the Aeronautical Science Foundation of China(No.2023L060052001).
文摘Quick and accurate determination of the optimal synchrophase angle is crucial for synchrophasing control of multi-propeller aircraft with low noise.This paper proposes a novel noise prediction and optimization strategy,developing a continuous and accurate noise prediction model and obtaining its minimum by solving the Hessian matrix and Fourier-Frobenius matrix.Firstly,a novel propeller noise prediction method uses acoustic simulation pressure signals and improved propeller signatures theory to accurately estimate noise for all synchrophase angles and receiving points.Secondly,a novel optimization approach is proposed to solve the analytical solution of the minimum propeller noise:(A)A noise objective function is established,and use its first derivatives’zeros and Hessian matrix to determine the function minimum.(B)A novel Euler formula transform method is proposed to convert trigonometric polynomials into algebraic polynomials,changing the zeros of the former into those of the latter.(C)Utilize the Fourier-Frobenius matrix method to solve the zeros of algebraic polynomials.To assess the computation time and accuracy,a turboprop aircraft with two six-bladed propellers was analyzed using the computational fluid dynamics and acoustic analogy method,providing acoustic pressure signals at 20 receivers for noise prediction and optimization.The Durand-Kerner and Fourier-Frobenius matrix methods were compared.Results demonstrate that improved propeller signatures theory is more accurate,and the Hessian matrix+Fourier-Frobenius matrix method is faster and more precise than the Hessian matrix+Durand-Kerner method.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.RS-2024-00406320)the Institute of Information&Communica-tions Technology Planning&Evaluation(IITP)-Innovative Human Resource Development for Local Intellectualization Program Grant funded by the Korea government(MSIT)(IITP-2026-RS-2023-00259678).
文摘Domain adaptation aims to reduce the distribution gap between the training data(source domain)and the target data.This enables effective predictions even for domains not seen during training.However,most conventional domain adaptation methods assume a single source domain,making them less suitable for modern deep learning settings that rely on diverse and large-scale datasets.To address this limitation,recent research has focused on Multi-Source Domain Adaptation(MSDA),which aims to learn effectively from multiple source domains.In this paper,we propose Efficient Domain Transition for Multi-source(EDTM),a novel and efficient framework designed to tackle two major challenges in existing MSDA approaches:(1)integrating knowledge across different source domains and(2)aligning label distributions between source and target domains.EDTM leverages an ensemble-based classifier expert mechanism to enhance the contribution of source domains that are more similar to the target domain.To further stabilize the learning process and improve performance,we incorporate imitation learning into the training of the target model.In addition,Maximum Classifier Discrepancy(MCD)is employed to align class-wise label distributions between the source and target domains.Experiments were conducted using Digits-Five,one of the most representative benchmark datasets for MSDA.The results show that EDTM consistently outperforms existing methods in terms of average classification accuracy.Notably,EDTM achieved significantly higher performance on target domains such as Modified National Institute of Standards and Technolog with blended background images(MNIST-M)and Street View House Numbers(SVHN)datasets,demonstrating enhanced generalization compared to baseline approaches.Furthermore,an ablation study analyzing the contribution of each loss component validated the effectiveness of the framework,highlighting the importance of each module in achieving optimal performance.
基金sponsored by the National Natural Science Foundation of China(Grant No.52178100).
文摘The spatial offset of bridge has a significant impact on the safety,comfort,and durability of high-speed railway(HSR)operations,so it is crucial to rapidly and effectively detect the spatial offset of operational HSR bridges.Drive-by monitoring of bridge uneven settlement demonstrates significant potential due to its practicality,cost-effectiveness,and efficiency.However,existing drive-by methods for detecting bridge offset have limitations such as reliance on a single data source,low detection accuracy,and the inability to identify lateral deformations of bridges.This paper proposes a novel drive-by inspection method for spatial offset of HSR bridge based on multi-source data fusion of comprehensive inspection train.Firstly,dung beetle optimizer-variational mode decomposition was employed to achieve adaptive decomposition of non-stationary dynamic signals,and explore the hidden temporal relationships in the data.Subsequently,a long short-term memory neural network was developed to achieve feature fusion of multi-source signal and accurate prediction of spatial settlement of HSR bridge.A dataset of track irregularities and CRH380A high-speed train responses was generated using a 3D train-track-bridge interaction model,and the accuracy and effectiveness of the proposed hybrid deep learning model were numerically validated.Finally,the reliability of the proposed drive-by inspection method was further validated by analyzing the actual measurement data obtained from comprehensive inspection train.The research findings indicate that the proposed approach enables rapid and accurate detection of spatial offset in HSR bridge,ensuring the long-term operational safety of HSR bridges.
基金supported by the Shenzhen Stability Support Plan(Grant No.20231122095154003)National Natural Science Foundation of China(Grant Nos.51978671 and 52422807).
文摘Residents living near drill-and-blast tunnels often experience disturbances from blasting operations.This motivates us to investigate the characteristics of airblasts and resulting noise through on-site monitoring at three tunnels.The research focuses on both the temporal evolution and spatial propagation of airblasts.Temporal analysis,including peak overpressure(POp),positive duration(PD),and Fourier main frequency(MF),emphasizes the relationship between airblast characteristics,blasting delays,and rock grade.It shows that airblast bandwidths are typically in the range of 3e200 Hz,with noise levels exceeding 130 dB,which is comparable to jet engines and rocket launch.Spatial propagation analysis reveals the impact of tunnel space on airblast propagation.Although POp and PD typically decrease with distance inside the tunnel,wave superposition can cause increased overpressure and prolonged durations at far-field distances(above 60 m kg^(-1/3)).Outside the tunnel,sound radiation was influenced by azimuth and was basically determined by sound power d an often-overlooked factor.To address the anisotropic propagation of airblasts,a predictive model was proposed for external noise levels,considering variables like distance,azimuth angle,initial sound power,and wave expansion.Validated by tests,this model successfully unifies data from three studies,helping to explain and predict airblast disturbances near tunnels.
基金Project supported by the National Natural Science Foundation of China(No.12572020)the Key Project of Natural Science Foundation of Hebei Province of China(No.A2023210064)。
文摘Local resonant acoustic metamaterials have broad applications in sound insulation,yet their single-configuration designs often exhibit limited and discontinuous bandgap widths,hindering full-frequency noise attenuation across the human auditory range.This study presents a double-phase fidget-spinner-shaped acoustic metamaterial(DFAM),specifically designed to achieve an ultra-broad,low-frequency continuous bandgap by means of synergistic structural optimization,enabling effective and robust control of audible noise.Based on Bloch's theorem and the finite element method,the dispersion relation of the DFAM structure is calculated and verified by the transmission loss curves.The propagation characteristics of sound waves within the structure are further analyzed for noise frequencies that fall within the passband.The influence of the geometric and physical parameters on the bandgap is investigated,and the corresponding transmission loss in the propagation direction is further calculated.A hybrid collaborative design strategy,leveraging multi-parameter optimization and bandgap complementarity,is developed to construct a metastructure with continuous bandgap coverage from 20 Hz to 1000 Hz.The resulting metastructure demonstrates exceptional broadband noise attenuation,achieving a total bandgap width of 876.3 Hz(87.63% of the target range)with the transmission loss up to-762.78 d B in a three-periodic arrangement.The simulation and experimental results for the transmission loss of the DFAM metastructure show strong agreement in the low-frequency range.This work provides a novel framework for designing ultra-wide low-frequency continuous bandgap metastructures,offering significant potential for noise mitigation in complex environments.
基金supported by the Key Research and Development Program of the Xinjiang Uygur Autonomous Region(No.2020B03006-1)the National Natural Science Foundation of China(Nos.42304069,and 42102275).
文摘The Urumqi foreland thrust tectonic belt exhibits complex geological structures and strong seismicity.Imaging its shallow crustal structure is of great significance for understanding its tectonic mechanism and seismogenic environment.We obtained a high-resolution S-wave velocity model of the shallow crust at depths of 0–8 km using ambient noise tomography applied to data from a dense seismic array.Sediments are generally thinner in the southeast and thicker in the northwest,with a maximum thickness of more than 8 km.Variations in the velocity structure near the Xishan,Wanyaogou,and Yamalike faults indicate that their formation was related to differences in the physical properties on either side of the fault.In addition,the faults exhibit thrusting of the low-velocity sides towards the high-velocity sides.In the study area,earthquakes rarely occur at depths of less than 3 km and are mostly concentrated in the high-velocity zone in the southern part.Below 3 km depth,more earthquakes were observed,mainly distributed near faults or in relatively high-velocity areas in the southern part.This suggests that high-velocity structures are more prone to stress accumulation,resulting in earthquakes.At 6–8 km depth,the densely distributed earthquakes in the northwestern part of the Bogda mountains are well-aligned with the northwest-oriented low-velocity zone observed in this study,suggesting that this weak zone likely controls seismicity in this area.
基金supported by the National Science and Technology Council,Taiwan,under grant no.NSTC 114-2221-E-197-005-MY3.
文摘With the development of technology,diffusion model-based solvers have shown significant promise in solving Combinatorial Optimization(CO)problems,particularly in tackling Non-deterministic Polynomial-time hard(NP-hard)problems such as the Traveling Salesman Problem(TSP).However,existing diffusion model-based solvers typically employ a fixed,uniform noise schedule(e.g.,linear or cosine annealing)across all training instances,failing to fully account for the unique characteristics of each problem instance.To address this challenge,we present GraphGuided Diffusion Solvers(GGDS),an enhanced method for improving graph-based diffusion models.GGDS leverages Graph Neural Networks(GNNs)to capture graph structural information embedded in node coordinates and adjacency matrices,dynamically adjusting the noise levels in the diffusion model.This study investigates the TSP by examining two distinct time-step noise generation strategies:cosine annealing and a Neural Network(NN)-based approach.We evaluate their performance across different problem scales,particularly after integrating graph structural information.Experimental results indicate that GGDS outperforms previous methods with average performance improvements of 18.7%,6.3%,and 88.7%on TSP-500,TSP-100,and TSP-50,respectively.Specifically,GGDS demonstrates superior performance on TSP-500 and TSP-50,while its performance on TSP-100 is either comparable to or slightly better than that of previous methods,depending on the chosen noise schedule and decoding strategy.
基金supported by the National Science Fund for Distinguished Young Scholars(62025602)the National Natural Science Foundation of China(62373302,U22B2036)+2 种基金the National Key Research and Development Program of China(2024YFF0509600)the Fundamental Research Funds for the Central Universities(G2024WD0151,D5000240309)the Tencent Foundation and XPLORER PRIZE。
文摘In this paper,an analysis-definition-processing(ADP)framework is proposed to search positive-incentive noise in continuous action iterated dilemma(CAID).We analyze the influence of communication noise on the cooperative behavior of players in the system and introduce the concept of positive-incentive noise in CAID.We design a global cost function to ensure convergence of the system can be achieved and strive to improve the final level of cooperation.An optimal CAID control method is proposed to derive the deterministic optimal learning rate in analytical form,avoiding the variability and uncertainty brought about by neural network fitting or parameter adjustment.On this basis,the convergence of the dynamic model is further analyzed by using the Lyapunov function instead of the Jacobian matrix.Additionally,an adaptive filtering mechanism is designed to dynamically ensure that only positive-incentive noise affects the system,effectively reducing the impact of negative noise and enhancing system stability.The framework is validated through simulations involving triple classical game models,including the hawk-dove game,the stag hunt game,the chicken game on networks,and a straightforward illustrative example.
文摘While the Ordos Basin is recognized for its substantial hydrocarbon exploration prospects,its rugged loess tableland terrain has rendered seismic exploration exceptionally challenging[1-3].Persistent obstacles such as complex 3D survey planning,low signal-tonoise ratio raw data,inadequate near-surface velocity modeling,and imaging inaccuracy have long hindered the advancement of seismic exploration across this region.Through a problem-solving approach rooted in geological target analysis,this research systematically investigates the behavioral patterns of nodal seismometer-based high-density seismic acquisition in loess plateau.Tailored advancements in waveform enhancement and depth velocity modelling methodologies have been engineered.Field validations confirm that the optimized workflow demonstrates marked improvements in amplitude preservation and imaging resolution,offering novel insights for future reservoir characterization endeavors.
基金supported by the National Science and Technology Major Project(2022ZD0119901)the National Natural Science Foundation of China under Grant(U2141234,62463004 and U24A20260)+1 种基金the Hainan Province Science and Technology Special Fund(ZDYF2024GXJS003)the Scientific Research Fund of Hainan University(KYQD(ZR)23025).
文摘Recently,the zeroing neural network(ZNN)has demonstrated remarkable effectiveness in tackling time-varying problems,delivering robust performance across both noise-free and noisy environments.However,existing ZNN models are limited in their ability to actively suppress noise,which constrains their robustness and precision in solving time-varying problems.This paper introduces a novel active noise rejection ZNN(ANR-ZNN)design that enhances noise suppression by integrating computational error dynamics and harmonic behaviour.Through rigorous theoretical analysis,we demonstrate that the proposed ANR-ZNN maintains robust convergence in computational error performance under environmental noise.As a case study,the ANR-ZNN model is specifically applied to time-varying matrix inversion.Comprehensive computer simulations and robotic experiments further validate the ANR-ZNN's effectiveness,emphasising the proposed design's superiority and potential for solving time-varying problems.
基金Project supported by the National Key Research and Development Program of China(Grant No.2020YFC2200100)the CAS's Strategic Pioneer Program on Space Science(Grant No.XDA1502110201)。
文摘Tilt-to-length(TTL)coupling noise is a critical issue in space-based gravitational wave detection due to its complex dependence on multiple interacting factors,which complicates the identification of dominant parameters.To address this challenge,we develop a simulation model of the Taiji scientific interferometer,generating noise datasets under multiparameter conditions.Given the uniqueness of the telescope as well as the convergence behavior of the algorithm,the analysis is structured hierarchically:(i)the telescope level and(ii)the optical bench level.A hierarchical framework combining XGBoost and SHapley Additive exPlanations(SHAP)values is employed to model the intricate relationships between parameters and TTL coupling noise,supplemented by sensitivity analysis.Our results identify pointing jitter and telescope radius as the dominant parameters at the telescope level,while the angles of the plane mirrors and beam splitters are most influential at the optical bench level.The parameter space is reduced from 86 dimensions to 14 dimensions without sacrificing model accuracy.This approach offers actionable insights for optimizing the Taiji interferometer design.
基金supported by the National Natural Science Foundation of China(Nos.62171469,62071029)。
文摘The estimation of the Number of Sources(NoS)is a significant challenge in signal processing,particularly due to the impact of colored noise on the performance of NoS estimation.This paper proposes a Multidimensional Feature Network(MFNet)which is designed for NoS estimation by extracting features of the sampled received signals and Sampled Covariance Matrix(SCM).The MFNet treats the raw signal and the SCM as two different types of data,and is able to achieve NoS estimation under colored noise and imperfect array.MFNet employs the Gated Recurrent Unit(GRU)to capture sequential information from the original signal data and to construct the Pseudo Covariance Matrix(PCM).Subsequently,various dimensional features,including eigenvalues and the Gerschgorin disk radius,are extracted from both the PCM and SCM,which are then jointly input into the subsequent network.An overall accuracy of 82%can be achieved after network training.The ablation experimental results demonstrate the effectiveness of multiple inputs.And simulation results demonstrate that the proposed MFNet achieves higher estimation accuracy compared to existing algorithms and exhibits greater robustness against colored noise.
基金The Natural Science Foundation of Heilongjiang Province(No.LH2023E011)Open Fund of National Key Laboratory of Green and Long-Life Road Engineering in Extreme Environment in Changsha University of Science and Technology(No.kfj230105).
文摘To uncover the decision-making mechanisms and evolutionary dynamics of multiple stakeholders in highway noise pollution control,a three-party evolutionary game model involving the government,operators,and the public is constructed.The operation period is divided into different stages for differentiated analysis.A simulation analysis was performed on the Lituo sinking section of the Beijing-Hong Kong-Macao Highway to assess the impact of variations in critical elements on the system.The results indicate that the Lituo sinking section of the Beijing-Hong Kong-Macao Highway is currently in its early stage of development,with the corresponding strategies being active regulation,excessive emissions,and supervision.When the cost of the government’s active regulation decreases from 1×10^(5) to 5×10^(4) yuan,the system converges more rapidly toward the active regulation strategy.When the cost of the operator’s excessive emissions increases from 14.08×10^(6) to 20.00×10^(6) yuan,the system drives the operator toward the standardized emission strategy.In addition,when the cost of public supervision decreases from 15×10^(4) to 5×10^(4) yuan and the compensation paid by operators to the public increases from 1.288×10^(6) to 2.576×10^(6) yuan,the system converges more quickly toward the supervision strategy.The cost of the operator’s excessive emissions serves as the core decision variable for achieving the ideal equilibrium in the three-party game involving government active regulation,operator standardized emissions,and public supervision.
基金Supported by the National Natural Science Foundation of China(Nos.42376185,41876111)the Shandong Provincial Natural Science Foundation(No.ZR2023MD073)。
文摘Benthic habitat mapping is an emerging discipline in the international marine field in recent years,providing an effective tool for marine spatial planning,marine ecological management,and decision-making applications.Seabed sediment classification is one of the main contents of seabed habitat mapping.In response to the impact of remote sensing imaging quality and the limitations of acoustic measurement range,where a single data source does not fully reflect the substrate type,we proposed a high-precision seabed habitat sediment classification method that integrates data from multiple sources.Based on WorldView-2 multi-spectral remote sensing image data and multibeam bathymetry data,constructed a random forests(RF)classifier with optimal feature selection.A seabed sediment classification experiment integrating optical remote sensing and acoustic remote sensing data was carried out in the shallow water area of Wuzhizhou Island,Hainan,South China.Different seabed sediment types,such as sand,seagrass,and coral reefs were effectively identified,with an overall classification accuracy of 92%.Experimental results show that RF matrix optimized by fusing multi-source remote sensing data for feature selection were better than the classification results of simple combinations of data sources,which improved the accuracy of seabed sediment classification.Therefore,the method proposed in this paper can be effectively applied to high-precision seabed sediment classification and habitat mapping around islands and reefs.
基金financially supported by the Swedish Transport Administration(Trafikverket)through the“Excellence Area 4”and FOI-BBT program(Grant Nos.BBT-2019-022 and BBT-TRV 2024/132497).
文摘Railway noise barriers are an essential piece of infrastructure for reducing noise propagation.However,these barriers experience aerodynamic loads generated by high-speed trains,leading to dynamic effects that may compromise their fatigue capacity.The most common structural design for railway noise barriers consists of vertical configurations of posts and panels.However,there have been few dynamic analyses of steel post/wood panel noise barriers under train-induced aerodynamic loads.This study used dynamic finite element analysis to assess the dynamic behavior of such noise barriers.Analysis of a 40-m-long noise barrier model and a triangular simplified load model,the latter of which effectively represented the detailed aerodynamic load,were first used to establish the model and input of the moving load during dynamic simulation.Then,the effects of different parameters on the dynamic response of the noise barrier were evaluated,including the damping ratio,the profile of the steel post,the span length of the panel,the barrier height,and the train speed.Gray relational analysis indicated that barrier height exhibited the highest correlations with the dynamic responses,followed by train speed,post profile,span length,and damping ratio.A reduction in the natural frequency and an increase in the train speed result in a higher peak response and more pronounced fluctuations between the nose and tail waves.The dynamic amplification factor(DAF)was found to be related to both the natural frequency and train speed.A model was proposed showing that the DAF significantly increases as the square of the natural frequency decreases and the cube of the train speed rises.
基金supported by the Special Fund of the Institute of Geophysics,China Earthquake Administration,(No.DQJB21B34).
文摘The Haicheng region,Liaoning,China,likely hosts a conjugate fault system comprising the NW-trending Haichenghe fault and NE-trending secondary faults.On February 4,1975,at 19:36 CST,an earthquake of M_(S)7.3 and intensity(MMI)IX hit the city of Haicheng,Liaoning,China.Although deep seismic profiling was previously conducted along the Haichenghe fault,the limited horizontal resolution in the shallow part prevented the recognition of kilometer-scale anomalies.The velocity structure characteristics of the Haichenghe fault and its NE-trending conjugate faults remain unclear.Using the extended range phase shift method,the high-resolution S-wave velocity structures are obtained by deploying a long,dense linear array of 55 short-period seismometers across the fault and NE-trending conjugate faults.The array length was 32 km and inter-station spacing was approximately 600 m,facilitating the collection of approximately 22 days of continuous waveform data.Employing the Extended Range Phase Shift(ERPS)method enabled the extraction of broadband 0.2–5 s Rayleigh wave phase velocity dispersion curves.The broadband dispersion data were used for inversion of the high-resolution S-wave velocity structure to a depth of 8 km from the surface.The velocity structure characteristics and seismicity of the Haichenghe fault and NE-trending conjugate faults were analyzed and compared with nearby fault gas measurements.Results show(1)shallow S-wave velocities show a low-high-low horizontal distribution,corresponding to basin-uplift-basin topography;(2)significant velocity contrasts occur across the Haichenghe fault:its SW segment(0–17 km)exhibits high velocities consistent with Paleoproterozoic crystalline basement(Pt_(1)),while the NE segment(17–32 km)shows low velocities related to Yanshanian intrusions(γ_(5))and Quaternary sediments.NE-trending conjugate faults display sharp velocity gradients marking fracture locations,with all faults being near-vertical to~8 km depth;(3)seismicity at 1–6 km depth mainly clusters in high-velocity zones;at 6–8 km depth,it concentrates beneath the Haichenghe fault in low-velocity areas and along NE-trending faults;(4)the seismic activity characteristics and fault zone width of the Haicheng he fault reflected by velocity imaging results are basically consistent with those obtained by the fault gas measurement method.
基金Jiangsu Association for Science and Technology Youth Talent Support Project(Grant No.JSTJ-2024-031)National Natural Science Foundation of China(Grant No.52005265)Natural Science Fund for Colleges and Universities in Jiangsu Province(Grant No.20KJB460002)。
文摘Owing to the harsh conditions,wind turbine bearings are prone to faults,and the resulting fault information is easily submerged by strong noise disturbance,making conventional diagnosis challenging.Therefore,this study presents an innovative bearing fault diagnosis approach predicated on Parameter⁃Optimized Symplectic Geometry Mode Decomposition(POSGMD)and Improved Convolutional Neural Network(ICNN).Firstly,assisted by the relative entropy⁃based adaptive selection of embedding dimension,a POSGMD is presented to adaptively decompose the collected bearing vibration signals into various Symplectic Geometry Components(SGC),which can solve the problem of manual selection of the embedding dimension in the raw Symplectic Geometry Mode Decomposition(SGMD).Meanwhile,the signal reconstruction on the decomposed SGC is conducted based on kurtosis⁃weighted principle to obtain the reconstructed signals.Subsequently,the Continuous Wavelet Transform(CWT)of the reconstructed signals is calculated to generate the corresponding time⁃frequency images as sample set.Finally,an ICNN is introduced for model training and automatic recognition of bearing faults.Two case studies are used to validate the presented methods efficacy.Comparing the presented method with traditional fault diagnosis methods,experimental results show that it can achieve greater identification accuracy and superior anti⁃noise resilience.This work provides a practical and effective solution for fault diagnosis in wind turbine bearings,contributing to the timely detection of faults and the reliable operation of wind turbines or other rotational machinery in industrial applications.
基金Project supported by the National Key Research and Development Program of China(Grant No.2024YFA1208800)the National Natural Science Foundation of China(Grant Nos.62404253,62304254,U23A20322)。
文摘Sensor noise is a critical factor that degrades the performance of image processing systems.In traditional computing systems,noise correction is implemented in the digital domain,resulting in redundant latency and power consumption overhead in the analog-to-digital conversion.In this work,we propose an analog-domain image correction architecture based on a proposed small-scale UNet,which implements a compact noise correction network within a one-transistor-one-memristor(1T1R)array.The statistical non-idealities of the fabricated 1T1R array(e.g.,device variability)are rigorously incorporated into the network's training and inference simulations.This correction network architecture leverages memristors for conducting multiply-accumulate operations aimed at rectifying non-uniform noise,defective pixels(stuck-at-bright/dark),and exposure mismatch.Compared to systems without correction,the proposed architecture achieves up to 50.13%improvement in recognition accuracy while demonstrating robust tolerance to memristor device-level errors.The proposed system achieves a 2.13-fold latency reduction and three orders of magnitude higher energy efficiency compared to conventional architecture.This work establishes a new paradigm for advancing the development of low-power,low-latency,and high-precision image processing systems.
