Working condition recognition of sucker rod pumping system based on 4-segment time-frequency signature matrix and deep learning

High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an efficient diagnosis method.However,the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational effort.Additionally,different SRPSs in an oil field have various system parameters,and the same SRPS generates different DCs at different moments.Thus,there is heterogeneity in field data,which can dramatically impair the diagnostic accuracy.To solve the above problems,a working condition recognition method based on 4-segment time-frequency signature matrix(4S-TFSM)and deep learning is presented in this paper.First,the 4-segment time-frequency signature(4S-TFS)method that can reduce the computing power requirements is proposed for feature extraction of DC data.Subsequently,the 4S-TFSM is constructed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data heterogeneity.Finally,a convolutional neural network(CNN),one of the deep learning frameworks,is used to determine the functioning conditions based on the 4S-TFSM.Experiments on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN(4S-TFSM-CNN)can significantly improve the accuracy of working condition recognition with lower computational cost.To the best of our knowledge,this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS.

Design,preparation,application of advanced array structured materials and their action mechanism analyses for high performance lithium-sulfur batteries

Lithium-sulfur battery(LSB)has brought much attention and concern because of high theoretical specific capacity and energy density as one of main competitors for next-generation energy storage systems.The widely commercial application and development of LSB is mainly hindered by serious“shuttle effect”of lithium polysulfides(Li PSs),slow reaction kinetics,notorious lithium dendrites,etc.In various structures of LSB materials,array structured materials,possessing the composition of ordered micro units with the same or similar characteristics of each unit,present excellent application potential for various secondary cells due to some merits such as immobilization of active substances,high specific surface area,appropriate pore sizes,easy modification of functional material surface,accommodated huge volume change,enough facilitated transportation for electrons/lithium ions,and special functional groups strongly adsorbing Li PSs.Thus many novel array structured materials are applied to battery for tackling thorny problems mentioned above.In this review,recent progresses and developments on array structured materials applied in LSBs including preparation ways,collaborative structural designs based on array structures,and action mechanism analyses in improving electrochemical performance and safety are summarized.Meanwhile,we also have detailed discussion for array structured materials in LSBs and constructed the structure-function relationships between array structured materials and battery performances.Lastly,some directions and prospects about preparation ways,functional modifications,and practical applications of array structured materials in LSBs are generalized.We hope the review can attract more researchers'attention and bring more studying on array structured materials for other secondary batteries including LSB.

Model reduction of fractional impedance spectra for time–frequency analysis of batteries, fuel cells, and supercapacitors

Joint time–frequency analysis is an emerging method for interpreting the underlying physics in fuel cells,batteries,and supercapacitors.To increase the reliability of time–frequency analysis,a theoretical correlation between frequency-domain stationary analysis and time-domain transient analysis is urgently required.The present work formularizes a thorough model reduction of fractional impedance spectra for electrochemical energy devices involving not only the model reduction from fractional-order models to integer-order models and from high-to low-order RC circuits but also insight into the evolution of the characteristic time constants during the whole reduction process.The following work has been carried out:(i)the model-reduction theory is addressed for typical Warburg elements and RC circuits based on the continued fraction expansion theory and the response error minimization technique,respectively;(ii)the order effect on the model reduction of typical Warburg elements is quantitatively evaluated by time–frequency analysis;(iii)the results of time–frequency analysis are confirmed to be useful to determine the reduction order in terms of the kinetic information needed to be captured;and(iv)the results of time–frequency analysis are validated for the model reduction of fractional impedance spectra for lithium-ion batteries,supercapacitors,and solid oxide fuel cells.In turn,the numerical validation has demonstrated the powerful function of the joint time–frequency analysis.The thorough model reduction of fractional impedance spectra addressed in the present work not only clarifies the relationship between time-domain transient analysis and frequency-domain stationary analysis but also enhances the reliability of the joint time–frequency analysis for electrochemical energy devices.

The need to develop tailored tools for improving the quality of thematic bibliometric analyses: Evidence from papers published in Sustainability and Scientometrics

Purpose: The aim of this article is to explore up to seven parameters related to the methodological quality and reproducibility of thematic bibliometric research published in the two most productive journals in bibliometrics, Sustainability(a journal outside the discipline) and Scientometrics, the flagship journal in the field.Design/methodology/approach: The study identifies the need for developing tailored tools for improving the quality of thematic bibliometric analyses, and presents a framework that can guide the development of such tools. A total of 508 papers are analysed, 77% of Sustainability, and 23% published in Scientometrics, for the 2019-2021 period.Findings: An average of 2.6 shortcomings per paper was found for the whole sample, with an almost identical number of flaws in both journals. Sustainability has more flaws than Scientometrics in four of the seven parameters studied, while Scientometrics has more shortcomings in the remaining three variables.Research limitations: The first limitation of this work is that it is a study of two scientific journals, so the results cannot be directly extrapolated to the set of thematic bibliometric analyses published in journals from all fields.Practical implications: We propose the adoption of protocols, guidelines, and other similar tools, adapted to bibliometric practice, which could increase the thoroughness, transparency, and reproducibility of this type of research.Originality/value: These results show considerable room for improvement in terms of the adequate use and breakdown of methodological procedures in thematic bibliometric research, both in journals in the Information Science area and journals outside the discipline.

A reliability-oriented genetic algorithm-levenberg marquardt model for leak risk assessment based on time-frequency features

Since leaks in high-pressure pipelines transporting crude oil can cause severe economic losses,a reliable leak risk assessment can assist in developing an effective pipeline maintenance plan and avoiding unexpected incidents.The fast and accurate leak detection methods are essential for maintaining pipeline safety in pipeline reliability engineering.Current oil pipeline leakage signals are insufficient for feature extraction,while the training time for traditional leakage prediction models is too long.A new leak detection method is proposed based on time-frequency features and the Genetic Algorithm-Levenberg Marquardt(GA-LM)classification model for predicting the leakage status of oil pipelines.The signal that has been processed is transformed to the time and frequency domain,allowing full expression of the original signal.The traditional Back Propagation(BP)neural network is optimized by the Genetic Algorithm(GA)and Levenberg Marquardt(LM)algorithms.The results show that the recognition effect of a combined feature parameter is superior to that of a single feature parameter.The Accuracy,Precision,Recall,and F1score of the GA-LM model is 95%,93.5%,96.7%,and 95.1%,respectively,which proves that the GA-LM model has a good predictive effect and excellent stability for positive and negative samples.The proposed GA-LM model can obviously reduce training time and improve recognition efficiency.In addition,considering that a large number of samples are required for model training,a wavelet threshold method is proposed to generate sample data with higher reliability.The research results can provide an effective theoretical and technical reference for the leakage risk assessment of the actual oil pipelines.

Novel Doppler Frequency Extraction Method Based on Time-Frequency Analysis and Morphological Operation

A novel method of Doppler frequency extraction is proposed for Doppler radar scoring systems. The idea is that the time-frequency map can show how the Doppler frequency varies along the time-line, so the Doppler frequency extraction becomes curve detection in the image-view. A set of morphological operations are used to implement curve detection. And a map fusion scheme is presented to eliminate the influence of strong direct current （DC） component of echo signal during curve detection. The radar real-life data are used to illustrate the performance of the new approach. Experimental results show that the proposed method can overcome the shortcomings of piecewise-processing-based FFT method and can improve the measuring precision of miss distance.

Time-frequency analysis of Li solid-phase diffusion in spherical active particles under typical discharge modes

Li transient concentration distribution in spherical active material particles can affect the maximum power density and the safe operating regime of the electric vehicles(EVs). On one hand, the quasiexact/exact solution obtained in the time/frequency domain is time-consuming and just as a reference value for approximate solutions;on the other hand, calculation errors and application range of approximate solutions not only rely on approximate algorithms but also on discharge modes. For the purpose to track the transient dynamics for Li solid-phase diffusion in spherical active particles with a tolerable error range and for a wide applicable range, it is necessary to choose optimal approximate algorithms in terms of discharge modes and the nature of active material particles. In this study, approximation methods,such as diffusion length method, polynomial profile approximation method, Padé approximation method,pseudo steady state method, eigenfunction-based Galerkin collocation method, and separation of variables method for solving Li solid-phase diffusion in spherical active particles are compared from calculation fundamentals to algorithm implementation. Furthermore, these approximate solutions are quantitatively compared to the quasi-exact/exact solution in the time/frequency domain under typical discharge modes, i.e., start-up, slow-down, and speed-up. The results obtained from the viewpoint of time-frequency analysis offer a theoretical foundation on how to track Li transient concentration profile in spherical active particles with a high precision and for a wide application range. In turn, optimal solutions of Li solid diffusion equations for spherical active particles can improve the reliability in predicting safe operating regime and estimating maximum power for automotive batteries.

Research on Low Voltage Series Arc Fault Prediction Method Based on Multidimensional Time-Frequency Domain Characteristics

The load types in low-voltage distribution systems are diverse.Some loads have current signals that are similar to series fault arcs,making it difficult to effectively detect fault arcs during their occurrence and sustained combustion,which can easily lead to serious electrical fire accidents.To address this issue,this paper establishes a fault arc prototype experimental platform,selects multiple commonly used loads for fault arc experiments,and collects data in both normal and fault states.By analyzing waveform characteristics and selecting fault discrimination feature indicators,corresponding feature values are extracted for qualitative analysis to explore changes in timefrequency characteristics of current before and after faults.Multiple features are then selected to form a multidimensional feature vector space to effectively reduce arc misjudgments and construct a fault discrimination feature database.Based on this,a fault arc hazard prediction model is built using random forests.The model’s multiple hyperparameters are simultaneously optimized through grid search,aiming tominimize node information entropy and complete model training,thereby enhancing model robustness and generalization ability.Through experimental verification,the proposed method accurately predicts and classifies fault arcs of different load types,with an average accuracy at least 1%higher than that of the commonly used fault predictionmethods compared in the paper.

Parameterized time-frequency analysis to separate multi-radar signals

Multi-radar signal separation is a critical process in modern reconnaissance systems. However, the complicated battlefield is typically confronted with increasing electronic equipment and complex radar waveforms. The intercepted signal is difficult to separate with conventional parameters because of severe overlapping in both time and frequency domains. On the contrary, time-frequency analysis maps the 1D signal into a 2D time-frequency plane, which provides a better insight into the signal than traditional methods. Particularly, the parameterized time-frequency analysis (PTFA) shows great potential in processing such non stationary signals. Five procedures for the PTFA are proposed to separate the overlapped multi-radar signal, including initiation, instantaneous frequency estimation with PTFA, signal demodulation, signal separation with adaptive filter and signal recovery. The proposed method is verified with both simulated and real signals, which shows good performance in the application on multi-radar signal separation.

ECCM scheme against interrupted sampling repeater jammer based on time-frequency analysis

The interrupted sampling repeater jamming（ISRJ） is an effective deception jamming method for coherent radar, especially for the wideband linear frequency modulation（LFM） radar. An electronic counter-countermeasure（ECCM） scheme is proposed to remove the ISRJ-based false targets from the pulse compression result of the de-chirping radar. Through the time-frequency（TF） analysis of the radar echo signal, it can be found that the TF characteristics of the ISRJ signal are discontinuous in the pulse duration because the ISRJ jammer needs short durations to receive the radar signal. Based on the discontinuous characteristics a particular band-pass filter can be generated by two alternative approaches to retain the true target signal and suppress the ISRJ signal. The simulation results prove the validity of the proposed ECCM scheme for the ISRJ.

Mechanical stress and deformation analyses of pressurized cylindrical shells based on a higher-order modeling

In this research,mechanical stress,static strain and deformation analyses of a cylindrical pressure vessel subjected to mechanical loads are presented.The kinematic relations are developed based on higherorder sinusoidal shear deformation theory.Thickness stretching formulation is accounted for more accurate analysis.The total transverse deflection is divided into bending,shear and thickness stretching parts in which the third term is responsible for change of deflection along the thickness direction.The axisymmetric formulations are derived through principle of virtual work.A parametric study is presented to investigate variation of stress and strain components along the thickness and longitudinal directions.To explore effect of thickness stretching model on the static results,a comparison between the present results with the available results of literature is presented.As an important output,effect of micro-scale parameter is studied on the static stress and strain distribution.

NEW SAR IMAGE INTERPRETATION METHOD OF AIRCRAFT BASED ON JOINT TIME-FREQUENCY ANALYSIS

With the continuous improvement of Synthetic Aperture Radar(SAR) resolution, interpreting the small targets like aircraft in SAR images becomes possible and turn out to be a hot spot in SAR application research. However, due to the complexity of SAR imaging mechanism, interpreting targets in SAR images is a tough problem. This paper presents a new aircraft interpretation method based on the joint time-frequency analysis and multi-dimensional contrasting of basic structures. Moreover, SAR data acquisition experiment is designed for interpreting the aircraft. Analyzing the experiment data with our method, the result shows that the proposed method largely makes use of the SAR data information. The reasonable results can provide some auxiliary support for the SAR images manual interpretation.

Quasi-LFM radar waveform recognition based on fractional Fourier transform and time-frequency analysis

Recent advances in electronics have increased the complexity of radar signal modulation.The quasi-linear frequency modulation(quasi-LFM)radar waveforms(LFM,Frank code,P1−P4 code)have similar time-frequency distributions,and it is difficult to identify such signals using traditional time-frequency analysis methods.To solve this problem,this paper proposes an algorithm for automatic recognition of quasi-LFM radar waveforms based on fractional Fourier transform and time-frequency analysis.First of all,fractional Fourier transform and the Wigner-Ville distribution(WVD)are used to determine the number of main ridgelines and the tilt angle of the target component in WVD.Next,the standard deviation of the target component's width in the signal's WVD is calculated.Finally,an assembled classifier using neural network is built to recognize different waveforms by automatically combining the three features.Simulation results show that the overall recognition rate of the proposed algorithm reaches 94.17%under 0 dB.When the training data set and the test data set are mixed with noise,the recognition rate reaches 89.93%.The best recognition accuracy is achieved when the size of the training set is taken as 400.The algorithm complexity can meet the requirements of real-time recognition.

In silico prospective analysis of the medicinal plants activity on the CagA oncoprotein from Helicobacter pylori

BACKGROUND Colonization with Helicobacter pylori(H.pylori)has a strong correlation with gastric cancer,and the virulence factor CagA is implicated in carcinogenesis.Studies have been conducted using medicinal plants with the aim of eliminating the pathogen;however,the possibility of blocking H.pylori-induced cell differentiation to prevent the onset and/or progression of tumors has not been addressed.This type of study is expensive and time-consuming,requiring in vitro and/or in vivo tests,which can be solved using bioinformatics.Therefore,prospective computational analyses were conducted to assess the feasibility of interaction between phenolic compounds from medicinal plants and the CagA oncoprotein.AIM To perform a computational prospecting of the interactions between phenolic compounds from medicinal plants and the CagA oncoprotein of H.pylori.METHODS In this in silico study,the structures of the phenolic compounds(ligands)kaempferol,myricetin,quercetin,ponciretin(flavonoids),and chlorogenic acid(phenolic acid)were selected from the PubChem database.These phenolic compounds were chosen based on previous studies that suggested medicinal plants as non-drug treatments to eliminate H.pylori infection.The three-dimensional structure model of the CagA oncoprotein of H.pylori(receptor)was obtained through molecular modeling using computational tools from the I-Tasser platform,employing the threading methodology.The primary sequence of CagA was sourced from GenBank(BAK52797.1).A screening was conducted to identify binding sites in the structure of the CagA oncoprotein that could potentially interact with the ligands,utilizing the GRaSP online platform.Both the ligands and receptor were prepared for molecular docking using AutoDock Tools 4(ADT)software,and the simulations were carried out using a combination of ADT and AutoDock Vina v.1.2.0 software.Two sets of simulations were performed:One involving the central region of CagA with phenolic compounds,and another involving the carboxy-terminus region of CagA with phenolic compounds.The receptor-ligand complexes were then analyzed using PyMol and BIOVIA Discovery Studio software.RESULTS The structure model obtained for the CagA oncoprotein exhibited high quality(C-score=0.09)and was validated using parameters from the MolProbity platform.The GRaSP online platform identified 24 residues(phenylalanine and leucine)as potential binding sites on the CagA oncoprotein.Molecular docking simulations were conducted with the three-dimensional model of the CagA oncoprotein.No complexes were observed in the simulations between the carboxy-terminus region of CagA and the phenolic compounds;however,all phenolic compounds interacted with the central region of the oncoprotein.Phenolic compounds and CagA exhibited significant affinity energy(-7.9 to-9.1 kcal/mol):CagA/kaempferol formed 28 chemical bonds,CagA/myricetin formed 18 chemical bonds,CagA/quercetin formed 16 chemical bonds,CagA/ponciretin formed 13 chemical bonds,and CagA/chlorogenic acid formed 17 chemical bonds.Although none of the phenolic compounds directly bound to the amino acid residues of the K-Xn-R-X-R membrane binding motif,all of them bound to residues,mostly positively or negatively charged,located near this region.CONCLUSION In silico,the tested phenolic compounds formed stable complexes with CagA.Therefore,they could be tested in vitro and/or in vivo to validate the findings,and to assess interference in CagA/cellular target interactions and in the oncogenic differentiation of gastric cells.

Joint Time-Frequency Analysis of Seismic Signals:A Critical Review

This paper presents an evaluation of time-frequency methods for the analysis of seismic signals.Background of the present work is to describe,how the frequency content of the signal is changing in time.The theoretical basis of short time Fourier transform,Gabor transform,wavelet transform,S-transform,Wigner distribution,Wigner-Ville distribution,Pseudo Wigner-Ville distribution,Smoothed Pseudo Wigner-Ville distribution,Choi-William distribution,Born-Jordan Distribution and cone shape distribution are presented.The strengths and weaknesses of each technique are verified by applying them to a particular synthetic seismic signal and recorded real time earthquake data.

Time-Frequency Entropy Analysis of Arc Signal in Non-Stationary Submerged Arc Welding

The use of time-frequency entropy to quantitatively assess the stability of submerged arc welding process considering the distribution features of arc energy is reported in this paper. Time-frequency entropy is employed to calculate and analyze the stationary current signals, non-stationary current and voltage signals in the submerged arc welding process. It is obtained that time-frequency entropy of arc signal can be used as arc stability judgment criteria of submerged arc welding. Experimental results are provided to confirm the effectiveness of this approach.

Time-Frequency Analysis of Asymmetric Triaxial Galaxy Model Including Effect of Spherical Dark Halo Component

A method of time-frequency analysis (TFA) based on wavelets is applied to study the phase space structure of three-dimensional asymmetric triaxial galaxy enclosed by spherical dark halo component. The investigation is carried out in the presence and absence of dark halo component. Time-frequency analysis is based on the extraction of instantaneous frequency from the phase of the continuous wavelet transform. This method is comparatively fast and reliable. This method can differentiate periodic from quasi-periodic, chaotic sticky from chaotic non-sticky, ordered from chaotic and also, it can accurately determine the time interval of the resonance trapping and transitions too. Apart from that, the phenomenon of transient chaos can be explained with the help of time-frequency analysis. Comparison with the method of total angular momentum (denoted as Ltot) proposed recently is also presented.

Application of Wavelet Packet De-noising in Time-Frequency Analysis of the Local Wave Method

The local wave method is a very good time-frequency method for nonstationaryvibration signal analysis. But the interfering noise has a big influence on the accuracy oftime-frequency analysis. The wavelet packet de-noising method can eliminate the interference ofnoise and improve the signal-noise-ratio. This paper uses the local wave method to decompose thede-noising signal and perform a time-frequency analysis. We can get better characteristics. Finally,an example of wavelet packet de-noising and a local wave time-frequency spectrum application ofdiesel engine surface vibration signal is put forward.

Novel Time-frequency Analysis and Representation of EEG

A novel method of EEG time-frequency analysis and representation based on a wavelet network is presented. The wavelet network model can represent the EEG data effectively. Based on the wavelet network model, a novel time-frequency energy distribution function is obtained, which has the same time-frequency resolution as Wigner-Ville distribution and is free of cross-term interference. There is a great potential for the use of the novel time-frequency representation of nonstationary biosignal based on a wavelet network in the field of the electrophysiological signal processing and time-frequency analysis.

Analyses on Marketing Strategies of Penglai's Tourism

Although tourism marketing has made great progress in recent years,researches on it are far from enough.Current re searches on tourism marketing at home and abroad lack pertinence and mostly provide theoretical basis for tourism development within a wide range.The researches on tourism marketing of Penglai are few and far between.Based on previous studies and the status quo of tourism marketing strategy,the below statements tries to find out existing problems of Penglai's tourism,put for ward countermeasures and propose feasible marketing patterns suitable for its tourism development.It subsequently concludes that it can make contribution to the sustainable development of Penglai's tourism and provide other county-level tourism cities with reference for marketing tourism marketing strategies.