Equalization Reconstruction Algorithm Based on Reference Signal Frequency Domain Block Joint for DTMB-Based Passive Radar

Channel equalization plays a pivotal role within the reconstruction phase of passive radar reference signals.In the context of reconstructing digital terrestrial multimedia broadcasting(DTMB)signals for low-slow-small(LSS)target detection,a novel frequency domain block joint equalization algorithm is presented in this article.From the DTMB signal frame structure and channel multipath transmission characteristics,this article adopts a unconventional approach where the delay and frame structure of each DTMB signal frame are reconfigured to create a circular convolution block,facilitating concurrent fast Fourier transform(FFT)calculations.Following equalization,an inverse fast Fourier transform(IFFT)-based joint output and subsequent data reordering are executed to finalize the equalization process for the DTMB signal.Simulation and measured data confirm that this algorithm outperforms conventional techniques by reducing signal errors rate and enhancing real-time processing.In passive radar LSS detection,it effectively suppresses multipath and noise through frequency domain equalization,reducing false alarms and improving the capabilities of weak target detection.

Capacitive energy storage from single pore to porous electrode identified by frequency response analysis

Rate capability,peak power,and energy density are of vital importance for the capacitive energy storage(CES)of electrochemical energy devices.The frequency response analysis(FRA)is regarded as an efficient tool in studying the CES.In the present work,a bi-scale impedance transmission line model(TLM)is firstly developed for a single pore to a porous electrode.Not only the TLM of the single pore is reparameterized but also the particle packing compactness is defined in the bi-scale.Subsequently,the CES properties are identified by FRA,focused on rate capability vs.characteristic frequency,peak power vs.equivalent series resistance,and energy density vs.low frequency limiting capacitance for a single pore to a porous electrode.Based on these relationships,the CES properties are numerically simulated and theoretically predicted for a single pore to a porous electrode in terms of intra-particle pore length,intra-particle pore diameter,inter-particle pore diameter,electrolyte conductivity,interfacial capacitance&exponent factor,electrode thickness,electrode apparent surface area,and particle packing compactness.Finally,the experimental diagnosis of four supercapacitors(SCs)with different electrode thicknesses is conducted for validating the bi-scale TLM and gaining an insight into the CES properties for a porous electrode to a single pore.The calculating results suggest,to some extent,the inter-particle pore plays a more critical role than the intra-particle pore in the CES properties such as the rate capability and the peak power density for a single pore to a porous electrode.Hence,in order to design a better porous electrode,more attention should be given to the inter-particle pore.

Correlation analysis between fault frequency and service time of underground fluid instruments

In this paper, we make a statistical analysis of the fault information of the underground fluid instruments of 12 models in China from January 2021 to May 2022 based on the Pearson correlation coefficient, and compare the fault statistics of the meteorological three-element instruments of 3 models during the study period. The results show that:(1) The numbers of faults of the underground fluid instruments of 12models with different service times are basically positively correlated with the numbers of the corresponding instruments, with good consistency. Moreover, the automatic observation instruments(8models) with more than 30 units are significantly correlated at a 0.05 significance level(95% confidence level). Even at a 0.01 significance level(99% confidence level), there are 7 models(7/8) with significant correlation.(2) The positive and negative correlations between the monthly average number of faults and the corresponding service times of the underground fluid instruments of 12 models with different service times are random, and there are 9 models(75%) with no significant correlation at a 0.05 significance level(95% confidence level), while 12 models(100%) with no significant correlation at a 0.01significance level(99% confidence level).(3) The monthly average numbers of faults of the underground fluid instruments of 12 models are basically 0.02-0.05 times/(unit·month), and the overall fault frequency is low.(4) The fault statistics results of the meteorological three-element instruments of 3 models are consistent with the characteristics of the underground fluid instruments of 12 models. In general,there is no significant correlation between the fault frequency and the service time of underground fluid instruments.(5) The results of this paper demonstrate that the service time of underground fluid instruments cannot be taken as the main reason for whether to update the instruments. Similarly, the fault frequency of the instruments cannot be taken as the main reason for the service life of the instruments in the process of formulating the service life standards of underground fluid instruments.

An operational modal analysis method in frequency and spatial domain

A frequency and spatial domain decomposition method （FSDD） for operational modal analysis （OMA） is presented in this paper, which is an extension of the complex mode indicator function （CMIF） method for experimental modal analysis （EMA）. The theoretical background of the FSDD method is clarified, Singular value decomposition is adopted to separate the signal space from the noise space. Finally, an enhanced power spectrum density （PSD） is proposed to obtain more accurate modal parameters by curve fitting in the frequency domain. Moreover, a simulation case and an application case are used to validate this method.

Time-and Frequency-Domain Analysis of Ice-Induced Displacement Responses Offshore Fixed Platforms

On the basis of ice- induced forced vibration model, ice- induced displacement responses of offshore fixed platforms are investigated in both time domain and frequency domain. The relationships of ice-induced displacement responses with ice breaking modes, ice acting directions and platform structures are analyzed and determined. The results lead to an important conclusion obtained for the first time that ice breaking frequency and the natural frequency of the first mode of the platform are the two main factors that dominate the degree of vibration. The present work provides a firm basis for both design and operation of fixed platforms against ice loading.

Modified slow-fast analysis method for slow-fast dynamical systems with two scales in frequency domain

A modified slow-fast analysis method is presented for the periodically excited non-autonomous dynamical system with an order gap between the exciting frequency and the natural frequency.By regarding the exciting term as a slow-varying parameter,a generalized autonomous fast subsystem can be defined,the equilibrium branches as well as the bifurcations of which can be employed to account for the mechanism of the bursting oscillations by combining the transformed phase portrait introduced.As an example,a typical periodically excited Hartley model is used to demonstrate the validness of the method,in which the exciting frequency is far less than the natural frequency.The equilibrium branches and their bifurcations of the fast subsystem with the variation of the slow-varying parameter are presented.Bursting oscillations for two typical cases are considered,which reveals that,fold bifurcation may cause the the trajectory to jump between different equilibrium branches,while Hopf bifurcation may cause the trajectory to oscillate around the stable limit cycle.

Analysis of OSA Syndrome from PPG Signal Using CART-PSO Classifier with Time Domain and Frequency Domain Features

Obstructive Sleep Apnea(OSA)is a respiratory syndrome that occurs due to insufficient airflow through the respiratory or respiratory arrest while sleeping and sometimes due to the reduced oxygen saturation.The aim of this paper is to analyze the respiratory signal of a person to detect the Normal Breathing Activity and the Sleep Apnea(SA)activity.In the proposed method,the time domain and frequency domain features of respiration signal obtained from the PPG device are extracted.These features are applied to the Classification and Regression Tree(CART)-Particle Swarm Optimization(PSO)classifier which classifies the signal into normal breathing signal and sleep apnea signal.The proposed method is validated to measure the performance metrics like sensitivity,specificity,accuracy and F1 score by applying time domain and frequency domain features separately.Additionally,the performance of the CART-PSO(CPSO)classification algorithm is evaluated through comparing its measures with existing classification algorithms.Concurrently,the effect of the PSO algorithm in the classifier is validated by varying the parameters of PSO.

Error Analysis in Frequency Domain for Linear Multipass Algorithms

Error analysis methods in frequency domain are developed in this paper for determining the characteristic root and transfer function errors when the linear multipass algorithms are used to solve linear differential equations. The relation between the local truncation error in time domain and the error in frequency domain is established, which is the basis for developing the error estimation methods. The error estimation methods for the digital simulation model constructed by using the Runge-Kutta algorithms and the linear multistep predictor-corrector algorithms are also given.

Analysis of Time and Frequency Domain of Auditory Brainstem Response in Normal Guinea Pigs

The auditory brainstem response (ABR) was measured for 90 cars of norml gui-nea pigs,from which the mean latency,amplitude and recognition of waves were obtain-ded.The waves were classified into six types,with wave Ⅲ as the main wave which ac-counted for 77.1% of all the waves.The frequency domain of each type was analysed bymeans of digital filtering and auto-regressive (AR)estimation.The main frequency com-position of ABR in the guinea pigs was restricted within 1600 Hz,in which there werethree peaks at AR spectrum,the mean value of the peaks being 107.33 Hz,566.67 Hzand 1076 Hz respectively.The AR spectrograms of all types waves were very similar toeach other.

Some of Advances on the Error Analysis in the Frequency Domain for a Digital Simulation Model

This paper surveys a number of recent advances in the error analysis in the frequency domain for a digital simulation model. It is emphasized to discuss the errors in characteristic roots and transfer funcnon of the digital simulation model, the frequency domain errors of the data transfers between thesimulation submodels, and some compensation methods for the errors. Some of the questions to be answered are also presented.

NEW WIND-INDUCED RESPONSES ANALYSIS METHOD OF SPATIAL STRUCTURES IN FREQUENCY DOMAIN WITH MODE COMPENSATION

Large span spatial lattice structures have many natural frequencies in a narrow frequency range, the conventional frequency domain method is difficult to contain all significant contribution modes. Through numerical examples, it is found that some high order modes are likely to be overlooked because of their higher positions of modal order, in spite of their significance to wind response. According to the contributions of modes to strain energy of system, the paper presented an efficient method to compensate the errors owing to missing out some significant high order modes. The effectiveness of the proposed method is verified through a numerical analysis of the wind responses of a spherical dome.

Goal-oriented error estimation applied to direct solution of steady-state analysis with frequency-domain finite element method

Based on the concept of the constitutive relation error along with the residuals of both the origin and the dual problems, a goal-oriented error estimation method with extended degrees of freedom is developed. It leads to the high quality locM error bounds in the problem of the direct-solution steady-state dynamic analysis with a frequency-domain finite element, which involves the enrichments with plural variable basis functions. The solution of the steady-state dynamic procedure calculates the harmonic response directly in terms of the physical degrees of freedom in the model, which uses the mass, damping, and stiffness matrices of the system. A three-dimensional finite element example is carried out to illustrate the computational procedures.

Analysis of the Frequency Domain Water-level Deconvolution Method Based on Reservoir Airgun Source Data

Based on the 2016 airgun experimental data of the Fujian Nanyi reservoir,we adopted the frequency domain water-level deconvolution method and cross-correlation time delay detection technique to study the influence of level scaling factor and the background noise level of the station on deconvolution calculation results, and analyze the effect of deconvolution on eliminating the influence of the source caused by different air-gun pressures. The results show that:( 1) When the level scaling factor is smaller,the signal to noise ratio of the waveform after the deconvolution is smaller,and when the level scaling factor is over smaller,the identification error of travel time is greater.( 2) When the SNR of the station record is higher,the recognition accuracy of travel time is higher,the influence of SNR on the reference station record is far greater than the far station,when the SNR of the far station record is more than 10,the error of travel time is within6 ms,but when the SNR of the reference station record is 30,the travel time error may reach to 20 ms.( 3) When the airgun source difference is big,the frequency domain waterlevel deconvolution method has better effect on eliminating the source influence,but the method error may be introduced when the source difference is small.

Unsupervised FIR adaptive filtering and its frequency domain analysis

An unsupervised minimum mean square error FIR adaptive filtering （UAF） algorithm is proposed to estimate the system＇ s input signal. The algorithm only uses the system＇ s output signal and noise variance without requiring knowledge of a reference signal. The frequency analysis shows that the UAF is a multi-spot bandpass filter with passing frequency determined by the system＇ s input signal. Namely, the UAF chooses the expected frequency and extremely restricts the unwanted fre- quency signal by using weight-updating scheme in time domain. However, the UAF presents the Gibbs phenomenon since the ideal filter is infinitely long which is unrealizable. The simulation and experimental results show that the UAF could effectively reduce the amplitude of the noise and im- prove the signal to noise ratio.

Mathematical Modelling of Response Amplitude Operator for Roll Motion of a Floating Body：Analysis in Frequency Domain with Numerical Validation

这份报纸在小振幅的影响下面为漂浮的身体的解开的转动运动用基于频率的分析调查反应振幅操作员(RAO ) 或转移功能的数学建模常规波浪。水动力学系数被在漂浮的身体的长度上集成用长带理论明确的表达计算。就有频率的正弦曲线波浪而言(在 0.3 rad/s 和 1.2 rad/s 之间变化的蠅) 为零前面的速度对横梁起作用到漂浮的身体，在频率领域的 RAO 的分析表情被获得。使用正规化过程和频率基于分析，组基于的分类被获得，因此管理方程为每个案例被提出。在使用第四个顺序 Runge-Kutta 方法以后，数字答案被获得，水动力学系数的相对重要性被分析。为了说明转动振幅效果，数字实验在正弦曲线的行动下面为一艘 Panamax 容器轮船被执行了有固定波浪高度的波浪。在 RAO 上的粘滞的抑制的效果被评估，模型用集中，一致性和稳定性分析被验证。这条建模途径能对为自由并且到的更高的度的模型漂浮身体动力学有用验证结果。

Modal Frequency Prediction of Chladni Patterns Using Machine Learning

The introduction of machine learning (ML) in the research domain is a new era technique. The machine learning algorithm is developed for frequency predication of patterns that are formed on the Chladni plate and focused on the application of machine learning algorithms in image processing. In the Chladni plate, nodes and antinodes are demonstrated at various excited frequencies. Sand on the plate creates specific patterns when it is excited by vibrations from a mechanical oscillator. In the experimental setup, a rectangular aluminum plate of 16 cm x 16 cm and 0.61 mm thickness was placed over the mechanical oscillator, which was driven by a sine wave signal generator. 14 Chladni patterns are obtained on a Chladni plate and validation is done with modal analysis in Ansys. For machine learning, a large number of data sets are required, as captured around 200 photos of each modal frequency and around 3000 photos with a camera of all 14 Chladni patterns for supervised learning. The current model is written in Python language and model has one convolution layer. The main modules used in this are Tensor Flow Keras, NumPy, CV2 and Maxpooling. The fed reference data is taken for 14 frequencies between 330 Hz to 3910 Hz. In the model, all the images are converted to grayscale and canny edge detected. All patterns of frequencies have an almost 80% - 99% correlation with test sample experimental data. This approach is to form a directory of Chladni patterns for future reference purpose in real-life application. A machine learning algorithm can predict the resonant frequency based on the patterns formed on the Chladni plate.

Exploring the asymmetric effect of COVID‑19 pandemic news on the cryptocurrency market:evidence from nonlinear autoregressive distributed lag approach and frequency domain causality

This paper explores the asymmetric effect of COVID-19 pandemic news,as measured by the coronavirus indices(Panic,Hype,Fake News,Sentiment,Infodemic,and Media Coverage),on the cryptocurrency market.Using daily data from January 2020 to September 2021 and the exponential generalized autoregressive conditional heter-oskedasticity model,the results revealed that both adverse and optimistic news had the same effect on Bitcoin returns,indicating fear of missing out behavior does not prevail.Furthermore,when the nonlinear autoregressive distributed lag model is esti-mated,both positive and negative shocks in pandemic indices promote Bitcoin’s daily changes;thus,Bitcoin is resistant to the SARS-CoV-2 pandemic crisis and may serve as a hedge during market turmoil.The analysis of frequency domain causality supports a unidirectional causality running from the Coronavirus Fake News Index and Sentiment Index to Bitcoin returns,whereas daily fluctuations in the Bitcoin price Granger affect the Coronavirus Panic Index and the Hype Index.These findings may have significant policy implications for investors and governments because they highlight the impor-tance of news during turbulent times.The empirical results indicate that pandemic news could significantly influence Bitcoin’s price.

Application of coupled analysis methods for prediction of blast-induced dominant vibration frequency

Blast-induced dominant vibration frequency （DVF） involves a complex, nonlinear and small sample system considering rock properties, blasting parameters and topography. In this study, a combination of grey relational analysis and dimensional analysis procedures for prediction of dominant vibration frequency are presented. Six factors are selected from extensive effect factor sequences based on grey relational analysis, and then a novel blast-induced dominant vibration frequency prediction is obtained by dimensional analysis. In addition, the prediction is simplified by sensitivity analysis with 195 experimental blast records. Validation is carried out for the proposed formula based on the site test database of the first- period blasting excavation in the Guangdong Lufeng Nuclear Power Plant （GLNPP）. The results show the proposed approach has a higher fitting degree and smaller mean error when compared with traditional predictions.