A reweighted damped singular spectrum analysis method for robust seismic noise suppression

(Multichannel)Singular spectrum analysis is considered as one of the most effective methods for seismic incoherent noise suppression.It utilizes the low-rank feature of seismic signal and regards the noise suppression as a low-rank reconstruction problem.However,in some cases the seismic geophones receive some erratic disturbances and the amplitudes are dramatically larger than other receivers.The presence of this kind of noise,called erratic noise,makes singular spectrum analysis(SSA)reconstruction unstable and has undesirable effects on the final results.We robustify the low-rank reconstruction of seismic data by a reweighted damped SSA(RD-SSA)method.It incorporates the damped SSA,an improved version of SSA,into a reweighted framework.The damping operator is used to weaken the artificial disturbance introduced by the low-rank projection of both erratic and random noise.The central idea of the RD-SSA method is to iteratively approximate the observed data with the quadratic norm for the first iteration and the Tukeys bisquare norm for the rest iterations.The RD-SSA method can suppress seismic incoherent noise and keep the reconstruction process robust to the erratic disturbance.The feasibility of RD-SSA is validated via both synthetic and field data examples.

Variational Mode Decomposition-Informed Empirical Wavelet Transform for Electric Vibrator Noise Analysis

Electric vibrators find wide applications in reliability testing, waveform generation, and vibration simulation, making their noise characteristics a topic of significant interest. While Variational Mode Decomposition (VMD) and Empirical Wavelet Transform (EWT) offer valuable support for studying signal components, they also present certain limitations. This article integrates the strengths of both methods and proposes an enhanced approach that integrates VMD into the frequency band division principle of EWT. Initially, the method decomposes the signal using VMD, determining the mode count based on residuals, and subsequently employs EWT decomposition based on this information. This addresses mode aliasing issues in the original method while capitalizing on VMD’s adaptability. Feasibility was confirmed through simulation signals and ultimately applied to noise signals from vibrators. Experimental results demonstrate that the improved method not only resolves EWT frequency band division challenges but also effectively decomposes signal components compared to the VMD method.

Numerical Analysis of Flow-Induced Vibration and Noise Generation in a Variable Cross-Section Channel

Flow channels with a variable cross-section are important components of piping system and are widely used in variousfields of engineering.Using afinite element method and modal analysis theory,flow-induced noise,mode shapes,and structure-borne noise in such systems are investigated in this study.The results demonstrate that the maximum displacement and equivalent stress are located in the part with variable cross-sectional area.The aver-age excitation force on theflow channel wall increases with theflow velocity.The maximum excitation force occurs in the range of 0–20 Hz,and then it decreases gradually in the range of 20–1000 Hz.Additionally,as theflow velocity rises from 1 to 3 m/s,the overall sound pressure level associated with theflow-induced noise grows from 49.37 to 66.37 dB.Similarly,the overall sound pressure level associated with the structure-borne noise rises from 40.27 to 72.20 dB.When theflow velocity is increased,the increment of the structure-borne noise is higher than that of theflow-induced noise.

Modeling urban redevelopment:A novel approach using time-series remote sensing data and machine learning

Accurate mapping and timely monitoring of urban redevelopment are pivotal for urban studies and decisionmakers to foster sustainable urban development.Traditional mapping methods heavily depend on field surveys and subjective questionnaires,yielding less objective,reliable,and timely data.Recent advancements in Geographic Information Systems(GIS)and remote-sensing technologies have improved the identification and mapping of urban redevelopment through quantitative analysis using satellite-based observations.Nonetheless,challenges persist,particularly concerning accuracy and significant temporal delays.This study introduces a novel approach to modeling urban redevelopment,leveraging machine learning algorithms and remote-sensing data.This methodology can facilitate the accurate and timely identification of urban redevelopment activities.The study’s machine learning model can analyze time-series remote-sensing data to identify spatio-temporal and spectral patterns related to urban redevelopment.The model is thoroughly evaluated,and the results indicate that it can accurately capture the time-series patterns of urban redevelopment.This research’s findings are useful for evaluating urban demographic and economic changes,informing policymaking and urban planning,and contributing to sustainable urban development.The model can also serve as a foundation for future research on early-stage urban redevelopment detection and evaluation of the causes and impacts of urban redevelopment.

Frequency dependent polarization analysis of ambient seismic noise recorded at a broadband seismometer in the central United States

We present a new approach to polarization analysis of seismic noise recorded by three-component seismometers. It is based on statistical analysis of frequency-dependent particle motion properties determined from a large number of time windows via eigenanalysis of the 3-by-3, Hermitian, spectral covariance matrix. We applied the algorithm to continuous data recorded in 2009 by the seismic station SLM, located in central North America. A rich variety of noise sources was observed. At low frequencies （〈0.05 Hz） we observed a tilt-related signal that showed some elliptical motion in the horizontal plane. In the microseism band of 0.05-0.25 Hz, we observed Rayleigh energy arriving from the northeast, but with three distinct peaks instead of the classic single and double frequency peaks. At intermediate frequencies of 0.5-2.0 Hz, the noise was dominated by non-fundamental-mode Rayleigh energy, most likely P and Lg waves. At the highest frequencies （〉3 Hz）, Rayleigh-type energy was again dominant in the form of Rg waves created by nearby cultural activities. Analysis of the time dependence of noise power shows that a frequency range of at least 0.02-1.0 Hz （much larger than the microseism band） is sensitive to annual, meteorologically induced sources of noise.

ELECTROCHEMICAL NOISE ANALYSIS OF PURE ALUMINUM IN SODIUM CHLORIDE SOLUTION WITH WAVELET TRANSFORM TECHNIQUE

Wavelet transforms (WT) are proposed as an alternative tool to overcome the limitations of Fourier transforms (FFT) in the analysis of electrochemical noise (EN) data. The most relevant feature of this method of analysis is its capability of decomposing electrochemical noise records into different sets of wavelet coefficients, which contain information about the time scale characteristic of the associated corrosion event. In this context, the potential noise fluctuations during the free corrosion of pure aluminum in sodium chloride solution was recorded and analyzed with wavelet transform technique. The typical results showed that the EN signal is composed of distinct type of events, which can be classified according to their scales, i.e. their time constants. Meanwhile, the energy distribution plot (EDP) can be used as 'fingerprints' of EN signals and can be very useful for analyzing EN data in the future.

Simulation on a Car Interior Aerodynamic Noise Control Based on Statistical Energy Analysis

How to simulate interior aerodynamic noise accurately is an important question of a car interior noise reduction. The unsteady aerodynamic pressure on body surfaces is proved to be the key effect factor of car interior aerodynamic noise control in high frequency on high speed. In this paper, a detail statistical energy analysis （SEA） model is built. And the vibra-acoustic power inputs are loaded on the model for the valid result of car interior noise analysis. The model is the solid foundation for further optimization on car interior noise control. After the most sensitive subsystems for the power contribution to car interior noise are pointed by SEA comprehensive analysis, the sound pressure level of car interior aerodynamic noise can be reduced by improving their sound and damping characteristics. The further vehicle testing results show that it is available to improve the interior acoustic performance by using detailed SEA model, which comprised by more than 80 subsystems, with the unsteady aerodynamic pressure calculation on body surfaces and the materials improvement of sound/damping properties. It is able to acquire more than 2 dB reduction on the central frequency in the spectrum over 800 Hz. The proposed optimization method can be looked as a reference of car interior aerodynamic noise control by the detail SEA model integrated unsteady computational fluid dynamics （CFD） and sensitivity analysis of acoustic contribution.

Clustering Structure Analysis in Time-Series Data With Density-Based Clusterability Measure

Clustering is used to gain an intuition of the struc tures in the data.Most of the current clustering algorithms pro duce a clustering structure even on data that do not possess such structure.In these cases,the algorithms force a structure in the data instead of discovering one.To avoid false structures in the relations of data,a novel clusterability assessment method called density-based clusterability measure is proposed in this paper.I measures the prominence of clustering structure in the data to evaluate whether a cluster analysis could produce a meaningfu insight to the relationships in the data.This is especially useful in time-series data since visualizing the structure in time-series data is hard.The performance of the clusterability measure is evalu ated against several synthetic data sets and time-series data sets which illustrate that the density-based clusterability measure can successfully indicate clustering structure of time-series data.

Shot noise analysis on corrosion behavior of zinc alloy (ZnAl4Cul) under dry-wet cycles

The corrosion behaviors of zinc alloy （ZnAl4Cul） in 3.5% （mass fraction） NaCl, 7.3% （mass fraction） Na2SO4 and simulated acid rain solutions were investigated using electrochemical measurements. The potential noise during dry-wet cycle was monitored and analyzed by fast Fourier transform （FFT）, fast wavelet transform （FWT）, shot noise theory and stochastic theory. Cumulative probability curves of event frequency fn indicate that the corrosion events in the dry cycles are greater than those in the wet cycles. Uniform corrosion was observed in the NaCl solution compared with more localized corrosion in the Na2SO4 solution, which is evidenced by FWT and SEM. Conditional events generation rate r（t） for diffusion controlled reactions decreases with increasing the time. r（t） values for uniform corrosion and diffusion controlled process are the largest in the wet cycle in 3.5% NaCl solution. The values of r（t） for pitting corrosion in Na2SO4 solution are observed to become large during spraying periods, and r（t） for pitting corrosion has the largest value in the Na2SO4 solution. The intergranular corrosion of zinc is serious in simulated acid rain solution.

Cavity noise sensitivity analysis of tire contour design factors and application of contour optimization methodology

Cavity resonance noise of passenger car tires is generated by interacting excitation between a tire structure and the fill gas (air), and generally lies in a frequency range of 200?250 Hz. As such, this noise is strongly perceived and may be a serious source of driver annoyance. Thus, many studies regarding the cavity noise mechanism and its reduction have already been conducted. In this work, a vibro-acoustic coupled analysis was conducted between a tire structure and air cavity. Using this analysis, we can more accurately simulate the tire noise performance in the region of the cavity resonance frequency. An analysis of the effects of variation of tire contour design factors was conducted, using design-of-experiments methods. Finally, a multi-objective optimization was performed using in-house codes to reduce the cavity noise level while minimizing the loss of other performances, such as diminished ride comfort and handling caused by the variations of contour. As a result of this optimization, an optimized contour shape was derived, which satisfied the multi-objective performances.

Study of engine noise based on independent component analysis

Independent component analysis was applied to analyze the acoustic signals from diesel engine. First the basic prin-ciple of independent component analysis (ICA) was reviewed. Diesel engine acoustic signal was decomposed into several inde-pendent components (ICs); Fourier transform and continuous wavelet transform (CWT) were applied to analyze the independent components. Different noise sources of the diesel engine were separated, based on the characteristics of different component in time-frequency domain.

Noise Reduction Technique Applied to the Multichannel Analysis of Surface Waves

This paper shows the presence of noises and technique to reduce these noises during the surface wave analysis. The frequency-dependent properties of Rayleigh-type surface waves can be used for imaging and characterizing the shallow subsurface. Interference by coherent source-generated noise inhibits the reliability of shear-wave velocities determined through inversion of the phase velocities of Rayleigh waves. Among these interferences by non-planar, non-fundamental mode Rayleigh waves （noise） are body waves, scattered and non-source-generated surface waves, and highermode surface waves. For the reduction of noise, the filtering technique is implemented in this paper for the multichannel analysis of surface wave method （MASW）. With the de-noising technique during the MASW method, more robust and reliable outcome is achieved. The significance of this paper is to obtain pre-awareness about noises during surface wave analysis and take better outcomes with denoising performance in near surface soil investigations.

Stability analysis of a noise control system in a duct by using delay differential equation

The paper deals with the criteria for the closed- loop stability of a noise control system in a duct. To study the stability of the system, the model of delay differential equation is derived from the propagation of acoustic wave governed by a partial differential equation of hyperbolic type. Then, a simple feedback controller is designed, and its closed- loop stability is analyzed on the basis of the derived model of delay differential equation. The obtained criteria reveal the influence of the controller gain and the positions of a sensor and an actuator on the closed-loop stability. Finally, numerical simulations are presented to support the theoretical results.

A Hybrid Neural Network Model for Marine Dissolved Oxygen Concentrations Time-Series Forecasting Based on Multi-Factor Analysis and a Multi-Model Ensemble

Dissolved oxygen(DO)is an important indicator of aquaculture,and its accurate forecasting can effectively improve the quality of aquatic products.In this paper,a new DO hybrid forecasting model is proposed that includes three stages:multi-factor analysis,adaptive decomposition,and an optimizationbased ensemble.First,considering the complex factors affecting DO,the grey relational(GR)degree method is used to screen out the environmental factors most closely related to DO.The consideration of multiple factors makes model fusion more effective.Second,the series of DO,water temperature,salinity,and oxygen saturation are decomposed adaptively into sub-series by means of the empirical wavelet transform(EWT)method.Then,five benchmark models are utilized to forecast the sub-series of EWT decomposition.The ensemble weights of these five sub-forecasting models are calculated by particle swarm optimization and gravitational search algorithm(PSOGSA).Finally,a multi-factor ensemble model for DO is obtained by weighted allocation.The performance of the proposed model is verified by timeseries data collected by the pacific islands ocean observing system(PacIOOS)from the WQB04 station at Hilo.The evaluation indicators involved in the experiment include the Nash–Sutcliffe efficiency(NSE),Kling–Gupta efficiency(KGE),mean absolute percent error(MAPE),standard deviation of error(SDE),and coefficient of determination(R^(2)).Example analysis demonstrates that:①The proposed model can obtain excellent DO forecasting results;②the proposed model is superior to other comparison models;and③the forecasting model can be used to analyze the trend of DO and enable managers to make better management decisions.

Noise analysis for sensitivity-based structural damage detection

As vibration-based structural damage detection methods are easily affected by environmental noise, a new statistie-based noise analysis method is proposed together with the Monte Carlo technique to investigate the influence of experimental noise of modal data on sensitivity-based damage detection methods. Different from the com- monly used random perturbation technique, the proposed technique is deduced directly by Moore-Pen＂ose generalized inverse of the sensitivity matrix, which does not only make the analysis process more efficient but also can analyze the influence of noise on both frequencies and mode shapes for three commonly used sensitivity-based damage detection methods in a similar way. A one-story portal frame is adopted to evaluate the efficiency of the proposed noise analysis technique.

TIME-SERIES MODELI NG AND FAULT FORECAST STUDY ON SPECTRAL ANALYSIS OF LUBRICATING OIL

The application of ti me-series modeling and forecasting method to the spectral analysis for lubricat ing oil of mechanical equipment is discussed. The AR model is used to perform a time-series modeling and forecasting analysis for the spectral analysis data co llected from aero-engines. In the oil condition monitoring field of mechanical equipment, the use of the method of time-series analysis has rarely been report ed. As indicated in the satisfactory example, a practical method for condition m onitoring and fault forecasting of mechanical equipment has been achieved.

Time-series analysis of the characteristic pressure fluctuations in a conical fluidized bed with negative pressure

The negative pressure conical fluidized bed is widely used in the pharmaceutical industry.In this study,experiments based on the negative pressure conical fluidized bed are carried out by changing the material mass and particle size.The pressure fluctuation signals are analyzed by the time and the frequency domain methods.A method for absolutely characterizing the degree of the energy concentration at the main frequency is proposed,where the calculation is to divide the original power spectrum by the average signal power.A phenomenon where the gas velocity curve temporarily stops growing is observed when the material mass is light,and the particle size is small.The standard deviation and kurtosis both rapidly change at the minimum fluidization velocity and thus can be used to determine the flow regime,and the variation rule of the kurtosis is independent of both the material mass and particle size.In the initial fluidization stage,the dominant pressure signal comes from the material movement;with the increase in the gas velocity,the power of a 2.5 Hz signal continues to increase.A method of dividing the main frequency by the average cycle frequency can conveniently determine the fluidized state,and a novel concept called stable fluidized zone proposed in this paper can be obtained.Controlling the gas velocity within the stable fluidized zone ensures that the fluidized bed consistently remains in a stable fluidized state.

ANALYSIS OF INTERNALLY GENERATED NOISE OF BIOELECTRIC AMPLIFIERS

This paper deals with internally generated noise of bioelectric amplifiers that are usually used for processing of bioelectric events. The main purpose of this paper is to present a procedure for analysis of the effects of internal noise generated by the active circuits and to evaluate the output noise of the author's new designed bioelectric amplifier that caused by internal effects to the amplifier circuit itself in order to compare it with the noise generated by conventional amplifiers. The obtained analysis results of internally generated noise showed that the total output noise of bioelectric active circuits does not increase when some of their resistors have a larger value. This behavior is caused by the different transfer functions for the signal and the respective noise sources associated with these resistors. Moreover, the new designed bioelectric amplifier has an output noise less than that for conventional amplifiers. The obtained analysis results were also experimentally verified and the final conclusions were drawn.

The Security Analysis of Two-Step Quantum Direct Communication Protocol in Collective-Rotation Noise Channel

To analyze the security of two-step quantum direct communication protocol （QDCP） by using Einstein-Podolsky Rosen pair proposed by Deng et al. [Phys. Rev. A 68 （2003）042317] in collective-rotation noise channel, an excellent model of noise analysis is proposed. In the security analysis, the method of the entropy theory is introduced, and is compared with QDCP, an error rate point Qo（M ： （Q0, 1.0）） is given. In different noise levels, if Eve wants to obtain the same amount of information, the error rate Q is distinguishable. The larger the noise level ~ is, the larger the error rate Q is. When the noise level ~ is lower than 11%, the high error rate is 0.153 without eavesdropping. Lastly, the security of the proposed protocol is discussed. It turns out that the quantum channel will be safe when Q 〈 0.153. Similarly, if error rate Q〉 0.153 = Q0, eavesdropping information I 〉 1, which means that there exist eavesdroppers in the quantum channel, and the quantum channel will not be safe anymore.

Finite element analysis of dynamic response and structure borne noise of gearbox

A dynamic finite element method combined with finite element mixed formula for contact problem is used to analyze the dynamic characteristics of gear system. Considering the stiffness excitation, error excitation and meshing shock excitation, the dynamic finite element model is established for the entire gear system which includes gears, shafts, bearings and gearbox housing. By the software of I-DEAS, the natural frequency, normal mode, dynamic time-domain response, frequency-domain response and one-third octave velocity grade structure borne noise of gear system are studied by the method of theoretical modal analysis and dynamic response analysis. The maximum values of vibration and structure borne noise are occurred at the mesh frequency of output grade gearing.