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.

High-precision frequency attenuation analysis and its application

Based on seismic attenuation theory in a fluid-filled porous medium, we improve conventional methods of low-frequency shadow analysis （LFSA） and energy absorption analysis （EAA） and propose a high-precision frequency attenuation analysis technology. First, we introduce the method of three-parameter wavelet transform and the time-frequency focused criterion and develop a high-precision time-frequency analysis method based on an adaptive three-parameter wavelet transform, which has high time-frequency resolution with benefit to LFSA and can obtain a single-peaked spectrum with narrow side-lobes with benefit to EAA. Second, we correctly compute absorption coefficient by curve fitting based on the nonlinear Nelder-Mead algorithm and effectively improve EAA precision. Practical application results show that the proposed frequency attenuation analysis technology integrated with LFSA and EAA can effectively predict favorable zones of carbonate oolitic reservoir. Furthermore, reservoir prediction results based on LFSA correspond with EAA. The new technology can effectively improve reservoir prediction reliability and reduce exploration risk.

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.

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.

Adaptive Fourier Decomposition Based Time-Frequency Analysis

The attempt to represent a signal simultaneously in time and frequency domains is full of challenges. The recently proposed adaptive Fourier decomposition （AFD） offers a practical approach to solve this problem. This paper presents the principles of the AFD based time-frequency analysis in three aspects： instantaneous frequency analysis, frequency spectrum analysis, and the spectrogram analysis. An experiment is conducted and compared with the Fourier transform in convergence rate and short-time Fourier transform in time-frequency distribution. The proposed approach performs better than both the Fourier transform and short-time Fourier transform.

Integrated interpretation of dual frequency induced polarization measurement based on wavelet analysis and metal factor methods

In mineral exploration, the apparent resistivity and apparent frequency （or apparent polarizability） parameters of induced polarization method are commonly utilized to describe the induced polarization anomaly. When the target geology structure is significantly complicated, these parameters would fail to reflect the nature of the anomaly source, and wrong conclusions may be obtained. A wavelet approach and a metal factor method were used to comprehensively interpret the induced polarization anomaly of complex geologic bodies in the Adi Bladia mine. Db5 wavelet basis was used to conduct two-scale decomposition and reconstruction, which effectively suppress the noise interference of greenschist facies regional metamorphism and magma intrusion, making energy concentrated and boundary problem unobservable. On the basis of that, the ore-induced anomaly was effectively extracted by the metal factor method.

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.

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.

A Recursive Method of Time-Frequency Analysis for the Signal Processing of Flutter Test with Progression Variable Speed

Focused on the non-statlonarity and real-time analysis of signal in flutter test with progression variable speed （FTPVS）, a new method of recursive time-frequency analysis is presented. The time-varying system is tracked on-line by building a time-varying parameter model, and then the relevant parameter spectrum can be obtained. The feasibility and advantages of the method are examined by digital simulation. The results of FTPVS at low-speed wind-tunnel promise the engineering application perspective of the method.

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.

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.

Engaging in sports via the metaverse? An examination through analysis of metaverse research trends in sports

In sports, virtual spaces are sometimes utilized to enhance performance or user experience. In this study, we conducted a frequency analysis, semantic network analysis, and topic modeling using 134 abstracts obtained through keyword searches focusing on “sport(s)” in combination with “metaverse”, “augmented reality”, “virtual reality”, “lifelogging”, and “mixed reality”. First, the top 20 words were extracted through frequency analysis, and then each type of extracted word was retained to select seven words. The analysis revealed the emergence of key themes such as “user(s)”, “game(s)”, “technolog(y/ies)”, “experience(d)”, “physical”, “training”, and “video”, with variations in intensity depending on the type of metaverse. Second, the relationships between the words were reconfirmed using semantic networks based on the seven selected words. Finally, topic modeling analysis was conducted to uncover themes specific to each type of metaverse. We also found that “performance/scoring” was a prominent word across all types of metaverses. This suggests that in addition to providing enjoyment through sports, there is a high possibility that all users (both general users and athletes) utilize the metaverse to achieve positive outcomes and success. The importance of “performance/scoring” in sports may seem obvious;however, it also provides significant insights for practitioners when combined with metaverse-related keywords. Ultimately, this study has managerial implications for enhancing the performance of specialized users in the sports industry.

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.

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.

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.

DEVELOPMENT OF VIRTUAL INSTRUMENT IN CHARACTERISTIC ANALYSIS OF ROTATING MACHINERY BASED ON INSTANTANEOUS FREQUENCY ESTIMATION

Based on the recently quick-developing time-frequency analysis (TFA)technique and virtual instrument (VI) technique, a virtual instrument in characteristic analysis ofrotating machinery is researched and developed successfully. By utilizing instantaneous frequencyestimation (IFE) theoretics of TFA technique, and based on IFE of peak searching on thetime-frequency spectrum, order analysis (OA) functions is put forward and implemented, such as orderspectrum, order spectrum matrix, order tracking, order tracking filtering, and order componentextraction, etc. Unlike the home and abroad existing popular characteristic analyzers, which needkey phasing devices such as shaft encoder, phase-locked loop (PLL), phase-locked multiple frequency,tachometer, etc, to implement constant angle sampling directly or indirectly, whereas thisinstrument only uses the vibration signal of rotating machinery to carry out OA. This instrumentmakes up the shortage of these traditional instruments in analyzing the non-stationary signal ofrun-up and run-down process of rotating machinery. Therefore, it is a great breakthrough for theexisting order analyzers.

A study of clinical psychological nursing research hotspots in China and variation trends based on word frequency analysis and visualization analysis

Objective: To analyze clinical psychological nursing research hotspots in China and variation trends in order to provide reference points on the current state of development of clinical psychological nursing and future research hotspots.Method: Clinical psychological nursing research literature sourced from Wanfang Data for the three periods of 2007-2009, 2010-2012, and 2013-2015 were selected as the research sample. A bibliographic co-occurrence analysis system(BICOMB software) was used to perform keyword word frequency analysis and generate a keyword co-occurrence matrix. In addition, Ucinet software's Netdraw tool was used to create visualized network diagrams.Results: A total of 27890 articles were retrieved, and word frequency analysis revealed that the highestfrequency keywords consisted of anxiety, depression, the elderly, expectant women, coronary heart disease, diabetes, breast cancer, perioperative period, quality of life, and psychological intervention.Research hotspot analysis revealed that consistent hotspots comprised anxiety, depression, health education, and perioperative period; expectant women became a hotspot during 2010-2012, and quality of life and efficacy became hotspots during 2013-2015.Conclusions: In addition to the care process, clinical psychological nursing research hotspots in China have increasingly included the effectiveness of psychological nursing and impact on patient quality of life. In addition, research hotspots have been influenced by the incidence of illnesses and people's health consciousness.

Theoretical analysis of natural frequency of externally prestressed concrete beam based on rigidity correction

A dynamic test on externally prestressed simply supported concrete beams separately with three typical types of tendon distributions was conducted. The results show that the natural frequencies of the beams increase with the increase in the prestressing force at the tensioning stage, and the natural frequencies decrease after the cracks occur in the beams. Following the calculation formula of natural frequency of externally prestressed beam, which was reported in a literature, the natural frequencies of the experimental beams are calculated, and big errors are found between the test results and the calculated ones of natural frequency values. As a result, this paper has tried to adopt two methods to correct the rigidity parameter of the concrete beam in the formula for natural frequency calculation, and to use the corrected formula to calculate the frequencies of the experimental beams. The calculation results indicate a good consistency with the experimental ones, which verifies the feasibility of the corrected formula.

Frequency spectrum analysis on micro-seismic signal of rock bursts induced by dynamic disturbance

Blasting and breaking of hard roof are main inducing causes of rock bursts in coal mines with danger of rock burst,and it is important to find out the frequency spectrum distribution laws of these dynamic stress waves and rock burst waves for researching the mechanism of rock burst.In this paper,Fourier transform as a micro-seismic signal conversion method of amplitude-time character to amplitude-frequency character is used to analyze the frequency spectrum characters of micro-seismic signal of blasting,hard roof breaking and rock bursts induced by the dynamic disturbance in order to find out the difference and relativity of different signals.The results indicate that blasting and breaking of hard roof are high frequency signals,and the peak values of dominant frequency of the signals are single.However,the results indicate that the rock bursts induced by the dynamic disturbance are low frequency signals,and there are two obvious peak values in the amplitude-frequency curve witch shows that the signals of rock bursts are superposition of low frequency signals and high frequency signals.The research conclusions prove that dynamic disturbance is necessary condition for rock bursts,and the conclusions provide a new way to research the mechanism of rock bursts.

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.