Waveform Spectrum Research Articles

Minimizing THD Using Integrated Multilevel Maximum Power Tracking Inverters

This work proposes a new Modified Multilevel Inverter (MMLI) and provides a comprehensive comparison with Conventional Cascaded H-bridge Inverters. The MMLI features fewer switching devices compared to the conventional H-Bridge Inverter for 9-level voltages and higher. Maximum Power Point Tracking (MPPT) incorporated with a Boost converter ensures a constant output from photovoltaic (PV) arrays, which is then fed to the inverter to achieve the desired number of voltage levels. To enhance the performance and efficiency of the system, IoT technologies were integrated for real-time monitoring and control. Smart sensors and cloud-based platforms were utilized for data collection and analysis, enabling precise control of the MPPT and inverter systems. The integration of IoT resulted in significant improvements in the system's dynamic response, energy conversion efficiency, and overall reliability. The results were validated through simulations in Simulink, with outcomes presented and compared for voltage waveform and harmonic spectrum. The integration of IoT technologies provided substantial benefits, showcasing the interdisciplinary approach of this research in reducing Total Harmonic Distortion (THD) while optimizing inverter operations

Ground-Shaking Intensity Prediction for Onsite Earthquake Early Warning Using Deep Learning

Abstract Damage caused by an earthquake is highly correlated with the shaking intensity in an area. Therefore, it is beneficial to know the shaking intensity of a certain area in advance so that proper preparations and responses can be made. This article proposes a deep learning approach to predict the onsite intensity of ground shaking for a certain location at the very beginning of an earthquake, namely ground-shaking intensity prediction (GSIP). GSIP learns the features hidden in the acceleration waveform and frequency spectrum during a short initial window after the P-wave arrival. It then predicts the intensity level of the ground shaking based on the extracted features. Traditional methods determine parameters or thresholds by experience, but it is very difficult to select appropriate thresholds, and this may need careful calibration. In contrast, GSIP determines the appropriate parameters from the waveform to predict intensity levels without the need for calibration. The waveforms used for model training and validation were collected from 1991 to 2020 data from Taiwan, 2004 to 2020 data from Japan, and 2005 to 2020 data from Italy. The results show that GSIP can achieve more than 85% accuracy at predicting the intensity level, with a tolerance of one level of intensity, as well as high accuracy and recall. Recent events that occurred in Taiwan in 2021 and 2022 were used to evaluate GSIP, and the results confirm its ability to accurately predict intensity levels in different areas. In addition, GSIP is undergoing testing on the earthquake monitoring system in the Central Weather Bureau of Taiwan and has effectively provided real-time early warning for earthquakes.

Low‐Interception Waveforms: To Prevent the Recognition of Spectrum Waveform Modulation via Adversarial Examples

AbstractDeep learning is applied to many complex tasks in the field of wireless communication, such as modulation recognition of spectrum waveforms, because of its convenience and efficiency. This leads to the problem of a malicious third party using a deep learning model to easily recognize the modulation format of the transmitted waveform. Some existing works address this problem directly using the concept of adversarial examples in the computer vision field without fully considering the characteristics of the waveform transmission in the physical world. Therefore, we propose two low‐interception waveforms (LIWs) generation methods, the LIW and ULIW algorithms, which can reduce the probability of the modulation being recognized by a third party without affecting the reliable communication of the friendly party. Among them, ULIW improves LIW algorithm by simulating channel noise during training cycle, and substantially reduces the perturbation magnitude while maintaining low interception accuracy. Our LIW and ULIW exhibit significant low‐interception performance in both numerical simulations and hardware experiments.

Statistical and sensitivity analysis of ultrasound signals for effective condition monitoring of electro-motors using industrial approach

This paper researches the importance of ultrasound methodology for swiftly detecting faults in electric motors and rotating machines. The primary focus of this research is on the intricate signal processing of ultrasound signals from both faulty and fault-free electro-motors. The principal goal is to conduct a comprehensive statistical investigation into signal factors, examining the effects of defect progression on the factors associated with continuously operating faulty electro-motors. In addition to the statistical analysis, this study explores the envelope-frequency spectrum of the signal under both healthy and defective conditions, employing the envelope method alongside Hilbert transformation. The objective is to thoroughly scrutinize the dynamic changes in ultrasound waveform and envelope spectrum of defective states, considering diverse degrees of defect severity over an extended time span. Moreover, the paper meticulously tracks the trajectory of factor changes over a 40-day operational period of a defective electro-motor. Additionally, the study delves into the sensitivity of the ultrasound method to impulse-wise shocks, which are recurrently observed in ultrasound signals, leading to deviations in certain signal factors from their established healthy thresholds. In response to this challenge, this paper conducts a particular analysis of signal factor sensitivity to impulse-wise noises, identifying robust factors that serve as reliable tools for firm condition monitoring. These identified factors are then presented as invaluable contributors to ensuring the precision and reliability of condition monitoring, especially in the presence of disruptive impulse-wise noises.

Combined Color-Doppler Flow and Angio Planewave UltraSensitive™ Imaging for Analysis of Hemodynamic Characteristics of Normal Upper Limb Arteries

BackgroundTo evaluate the hemodynamic characteristics of normal upper extremity arteries from the brachial artery to the fingertip arterioles.MethodsWe analyzed the characteristics and changes in the regularities of ultrasonic parameters in the upper extremity arteries of 104 healthy volunteers using color Doppler flow imaging and Angio Planewave UltraSensitive™ imaging. The measured ultrasonic parameters included the vessel diameter, blood-flow spectrum waveform, peak systolic velocity, end-diastolic velocity, resistance index, pulsatility index, ratio of PSV to EDV, blood-flow volume, along with systolic acceleration of each BA, radial artery, superficial palmar arch artery, palmar proper digital artery, and third-grade artery arch of the fingernail bed.ResultsFrom BA to FN3AA, the diameter, PSV, RI, S/D, VFlow, and slope of the artery significantly decreased (P < 0.001), and size of the parameters significantly correlated with the anatomic position of the arteries. The blood-flow spectrum gradually changed from various waveforms to fixed monophasic waves. There was a linear relationship between the distribution of monophasic waveforms and arterial groups (R = -0.453; P < 0.001). When the blood-flow spectrum of BA and RA showed a monophasic waveform, their branch arteries showed the same. The waveforms of PPDA and FN3AA were also monophasic.ConclusionRegular changes in the blood-flow spectrum waveform and hemodynamic parameters are related to the anatomic position of the upper limb arteries. CDFI combined with Angio PLUS accurately and systematically evaluates the hemodynamic characteristics of the upper extremity arteries.

Influence of HiPIMS Pulse Widths on the Structure and Properties of Copper Films.

High-power pulse magnetron sputtering is a new type of magnetron sputtering technology that has advantages such as high peak power density and a high ionization rate compared to DC magnetron sputtering. In this paper, we report the effects of different pulse widths on the current waveform and plasma spectrum of target material sputtering, as well as the structure and properties of Cu films prepared under the same sputtering voltage and duty cycle. Extending the pulse width can make the sputtering enter the self-sputtering (SS) stage and improve the ion quantity of sputtered particles. The Cu film prepared by HiPIMS with long pulse width has higher bond strength and lower electrical resistivity compared to the Cu film prepared by short pulse width. In terms of microstructure, the Cu film prepared by HiPIMS with the long pulse width has a larger grain size and lower micro-surface roughness. When the pulse width is bigger than 200 μs, the microstructure of the Cu film changes from granular to branched. This transformation reduces the interface on the Cu film, further reducing the resistivity of the Cu film. Compared to short pulses, long pulse width HiPIMS can obtain higher quality Cu films. This result provides a new process approach for preparing high-quality Cu films using HiPIMS technology.

Research on Leakage Current Waveform Spectrum Characteristics of Artificial Pollution Composite Insulator

In order to know about the change features of leakage current during the pollution flashover process of composite insulators, the study of the pollution flashover test on composite insulator under the pollutional condition is presented in the artificial fog cabinet. The characteristics of leakage current waveforms in time-domain and frequency-domain are measured and analyzed during the test. The analysis results show that there is a strong correlation among the fundamental harmonic, the third harmonic and fifth harmonic. The characteristics of leakage current spectrum are represented by the maximum value of leakage current (Im), total harmonic distortion (THD), and phase difference of fundamental leakage current (θ). During a near-flash event, the leakage current exhibits high amplitude, low harmonic content, and small phase difference between the fundamental leakage current and voltage. The research findings provide a basis for establishing a pollution flash warning system based on leakage current characteristic quantities.

Reinforcement Learning for FDA-MIMO Radar Power Allocation in Congested Spectral Environments

This paper studies the coexistence problem of frequency diverse array multiple-input multiple-output (FDA-MIMO) radar and communication systems. The communication system and radar share the same frequency band, and the overlap of the frequency spectrum will interfere with each other's performance, which constitutes a dynamic non-cooperative coexistence problem. We model the FDA-MIMO radar interference suppression process as a Markov decision process (MDP). The interference signals received by FDA-MIMO radar are generated by the communication system, including constant interference, frequency-hopping interference, and intermittent interference. FDA-MIMO radar realizes flexible transmission waveform spectrum control through transmission power allocation. Reinforcement learning (RL) is applied to obtain the optimal power allocation strategy of FDA-MIMO radar, and it is compared with the Sense and Avoid (SSA) algorithm. The simulation results show that the FDA-MIMO radar power allocation method based on reinforcement learning can effectively suppress the communication system's interference and improve the radar's signal-to-interference-and-noise ratio (SINR) and range resolution.

Deep-learning-based renal artery stenosis diagnosis via multimodal fusion.

Diagnosing Renal artery stenosis (RAS) presents challenges. This research aimed to develop a deep learning model for the computer-aided diagnosis of RAS, utilizing multimodal fusion technology based on ultrasound scanning images, spectral waveforms, and clinical information. A total of 1485 patients received renal artery ultrasonography from Peking Union Medical College Hospital were included and their color doppler sonography (CDS) images were classified according to anatomical site and left-right orientation. The RAS diagnosis was modeled as a process involving feature extraction and multimodal fusion. Three deep learning (DL) models (ResNeSt, ResNet, and XCiT) were trained on a multimodal dataset consisted of CDS images, spectrum waveform images, and individual basic information. Predicted performance of different models were compared with senior physician and evaluated on a test dataset (N=117 patients) with renal artery angiography results. Sample sizes of training and validation datasets were 3292 and 169 respectively. On test data (N=676 samples), predicted accuracies of three DL models were more than 80% and the ResNeSt achieved the accuracy 83.49%±0.45%, precision 81.89%±3.00%, and recall 76.97%±3.7%. There was no significant difference between the accuracy of ResNeSt and ResNet (82.84%±1.52%), and the ResNeSt was higher than the XCiT (80.71%±2.23%, p<0.05). Compared to the gold standard, renal artery angiography, the accuracy of ResNest model was 78.25%±1.62%, which was inferior to the senior physician (90.09%). Besides, compared to the multimodal fusion model, the performance of single-modal model on spectrum waveform images was relatively lower. The DL multimodal fusion model shows promising results in assisting RAS diagnosis.

A novel multi-resonator honeycomb metamaterial with enhanced impact mitigation

Local resonant metamaterials show significant promise in applications involving the attenuation of impact waves. This paper introduces a honeycomb metamaterial featuring a circular distribution of multiple mass-beam resonators designed to enhance impact mitigation by broadening low frequency bandgaps. The bandgaps of unit cells with different number of mass-beam resonators (MBR) are calculated. It is found that the unit cell with circularly distributed resonators (C-MBR) has strong low frequency local resonance in all directions, thus opening a complete wide bandgap in about 500–1200Hz. Then, a method of equivalent stiffness reduction related to the corresponding mode shapes is used to research the negative effective mass density characteristic, explaining the generation of the low frequency bandgaps. Subsequently, the impact protection capacity of local resonant metamaterial is compared with that of ordinary honeycomb metamaterial through experiment and simulation. Interestingly, the maximum acceleration values under the impact load of local resonant metamaterial are about 45% lower than that of honeycomb metamaterial, and the reaction force under the blast load is about 80% reduction. Besides, by analyzing the acceleration curve waveform and transmission spectrum, the local resonant metamaterial is found to exhibit continuous short period vibration, which blocks the wave in low frequency band. Finally, the energy dissipation performance of the local resonant metamaterial explains that it converts more impact energy into the kinetic energy of the core with resonators, thereby reducing the energy transferred to the bottom panel. These results underscore the potential application of the proposed metamaterial with multi-resonators in impact mitigation and energy dissipation.

Transformation of the Shape and Spectrum of an Ultrawideband Electromagnetic Pulse in a “Gigantic” Coaxial Line Filled with Magnetized Plasma

A propagation of ultrawideband electromagnetic pulses (UWB EMPs) through magnetized plasma has been experimentally studied using a “gigantic” coaxial line, which has been developed at IAP RAS for laboratory modeling of ionospheric effects. This coaxial line is 1.4 m in diameter and 10 m in length and is installed inside the chamber of the large-scale Krot plasma device. The line can be filled with rf inductively coupled plasma, magnetized or not. It allows one to explore the propagation of UWB EMPs in plasma along a long path without refraction and divergence and obtain a physical picture of EMP transformation. Under conditions where the duration of the UWB EMP is comparable to the period of electron plasma oscillations (fp−1), the period of cyclotron rotation of electrons (fc−1), or even significantly shorter, a complex of effects of transformation of the waveform and frequency spectrum of the pulse occurs. Without ambient magnetic field, a UWB EMP is distorted due to the effects of the cutoff and frequency dispersion. In dense magnetized plasma, i.e., when fp &gt;&gt; fc, the UWB EMP breaks into two wave packets, the high-frequency one (f &gt; fp) and low-frequency one (f &lt; fc). In rare magnetized plasma (fp &lt;&lt; fc), the cyclotron absorption produces a long train of damped oscillations at a frequency close to the cyclotron frequency fc following the UWB EMP.

Determining the quality factor of sandstone using acoustic laboratory measurements

The paper presents the velocity-pressure and quality factor-pressure dependences of a sandstone sample, using the Fourier spectra of the complete waveforms of the P waves measured in laboratory. Measurements were performed on sandstone and (a reference) aluminum sample at 40 different pressures. The professional literature uses approximate methods to determine the value of the quality factor. The spectral ratio method was used to evaluate our measurements. The measured velocity and quality factor data were processed using inversion methods. The results showed that both the velocity-pressure and the quality factor pressure dependence can be well-described utilizing the double relaxation model in forward modeling.

Preliminary Result of Swarm Activities in Toba Region Using Dense Temporary Network

For the first time, an earthquake swarm occurred in 2021 in Lake Toba (Indonesia), one of the largest calderas in the world. Although the earthquakes were located in the volcanic environment, the swarm activities could be related to the tectonic activities from the Sumatra fault system. The swarm activities occurred at a very shallow depth and were felt with the intensity of II–IV MMI by local people. The objective of this research was to investigate the characteristics of the earthquake swarm in the Toba Caldera from the waveforms recorded by temporary stations in March – June 2021. A total of 26 seismic stations were deployed for two months in May and June 2021. Automatic detection of seismic waves is important because it can be used for earthquake early warning systems and speeds up analysis of origin time, P-, and S arrival times, and earthquake locations. We found characteristics of spectrum waveform from low to high frequency of a non-swarm earthquake, while high frequency seismograms were generated by the swarm earthquake. Furthermore, we successfully captured the hypocentre that was distributed in the SW – NE direction and may figure out the unknown geological system that is responsible for the swarm activities.

Identification of hydration stages: An innovative study of ultrasonic coda waves using integrated sensing element (ISE)

In this study, a piezoelectric-based integrated sensing element (ISE) is prepared and preliminary employed for monitoring the hydration process based on the dominant mechanism of acoustic impedance variation of cement and coda wave idea. The results of the signal for a period of 24 h show that three different stages (0–2 h/2–12 h/12–24 h) of the cement during hydration can be distinguished by the reflected coda wave combined with its frequency-domain component, while the first arrival waveform shows almost the same pattern due to the wave trajectory only in packing material. Moreover, the wavelet analysis of the coda wave can distinguish these three stages, in line with the results from the time-domain waveform, energy, and frequency-domain spectrum.

Nonlinear vibration characteristics of rolling bearing considering flexible cage fracture

Rolling bearing is one of the critical parts of the transmission system and has a high demand for operating conditions. Suitable operating conditions can ensure that the rolling bearing has a higher rotation accuracy, which significantly impacts the performance of the transmission system. However, under some unsuitable operating conditions, the rolling bearing in the transmission system is faced with failure and other problems. Cage failure is one of the most common bearing failures. In particular, the cage fracture analysis is the most difficult among rolling bearing failures. In order to research its nonlinear vibration characteristics under cage fracture, a nonlinear model of the rolling bearing (4Nb+4 degrees of freedom) considering cage fracture is established. The contact between bearing rings and the rollers, the circumferential interaction between the flexible cage and the rollers, and the interaction between the rollers under the cage fracture fault are considered. Subsequently, the nonlinear characteristics under cage fracture are analyzed. The nonlinear collision forces of roller and cage under cage fracture and different cage clearances are compared. The results show that the collision force between rollers and the cage is greater when fractured than when the cage is healthy. Under multiple cage fractures (the roller number is odd), more prominent frequency doubling phenomena may occur as the number of fractures increases. More shocks are reflected in the relative angular displacement and waveform. Under multiple cage fractures (the roller number is even), the number and interval of fractures can be determined through the waveform and envelope spectrum. The research in this paper can help the health monitoring and fault diagnosis of bearing cages.

Machine learning regression implementation for high-frequency seismic wave attenuation estimation in the Aswan Reservoir area, Egypt

Attenuation characteristics have been estimated to understand the effect of the heterogeneity in the tectonically active Aswan Reservoir, the southern part of Egypt using data collected by a ten-station local seismological network operating across the reservoir. The quality factor was estimated from 350 waveform spectra of P- and S-waves from 50 earthquakes. By applying a spectral ratio technique to bandpass-filtered seismograms, obtained results show variations in both P-waves attenuation (Q_alpha) and corresponding S-waves (Q_beta) as a function of frequency, according to the power law Q=Q_0 times f^n, with n ranging between 0.85 and 1.19 for P-waves and between 0.92 and 1.18 for S-waves. A supervised machine learning algorithm known as Orthogonal distance regression was utilized to fit the attenuation power law functions. Estimates of Q_alpha and Q_beta show a clear dependence on frequency. The frequency-dependent attenuation is found to be Q_alpha = (11.22 pm 2.2) times f^{(1.09 pm 0.07)} and Q_beta = (9.89 pm 1.89) times f^{(1.14 pm 0.07)} for P- and S-waves, respectively. The average ratio Q_alpha /Q_beta is higher than unity, which is commonly observed in tectonically active regions characterized by a high degree of heterogeneity of the crustal structure of the area. Final results indicate that seismic wave attenuation in the AHDR region is highly frequency-dependent. Moreover, estimated low values of Q_0 clearly highlight the heterogeneity of the AHDR with considerably high seismic activity. These findings will be useful in any future assessment of seismic hazards and the damage pattern of earthquakes.

Study of the intermediate mass ratio black hole binary merger up to 1000:1 with numerical relativity

We explicitly demonstrate that current numerical relativity techniques are able to accurately evolve black hole binaries with mass ratios of the order of 1000:1. This proof of principle is relevant for future third generation gravitational wave detectors and space mission LISA, as by purely numerical methods we would be able to accurately compute gravitational waves from the last stages of black hole mergers, as directly predicted by general relativity. We perform a sequence of simulations in the intermediate to small mass ratio regime, , with the small hole starting from rest at a proper distance . We compare these headon full numerical evolutions with the corresponding semianalytic point particle perturbative results finding an impressive agreement for the total gravitational radiated energy and linear momentum as well as for the waveform spectra. We display numerical convergence of the results and identify the minimal numerical resolutions required to accurately solve for these very low amplitude gravitational waves. This work represents a first step towards the considerable challenge of applying numerical-relativity waveforms to interpreting gravitational-wave observations by LISA and next-generation ground-based gravitational-wave detectors.

Analysis of seizure-resistance-improvement mechanism of polymer additives by acoustic emission sensing

Analyses of the spectra of acoustic emissions produced by the deformation and fracture of frictional surfaces were used to identify the mechanisms by which polymer additives in lubricating oils improve seizure resistance. Analysis of the frequency spectra of the acoustic signal waveforms permitted the evaluation of interactions between surface asperities that were undetectable from changes in the coefficient of friction. The changes in acoustic emissions explain the action of viscosity modifiers in lubricants in relaxing the contact between surface asperities, and the sensing and analysis of acoustic emissions could provide a new method for analysing the action of lubricant additives.

A rapid method for detecting and distinguishing metallo-β-lactamase-and serine carbapenemase-producing Enterobacteriales using MALDI-TOF MS.

Carbapenemase-producing Enterobacteriales (CPE) are a major health threat worldwide, and therefore the development of rapid detection methods is needed. Here, we established a method to distinguish metallo-β-lactamase and serine carbapenemases using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) with ethylenediaminetetraacetic acid (EDTA) and phenylboronic acid (PB). To assess the specificity and sensitivity of the method, 110 carbapenemase-producing and 72 carbapenemase-negative Enterobacteriales isolates were collected, among which 51 strains produced only metallo-β-lactamase, 55 strains only serine carbapenemases, and four strains both metallo-β-lactamase and serine carbapenemases. In the proposed MALDI-TOF MS method, imipenem (IPM) and the bacterial strains to be tested were mixed, EDTA and/or PB was added, and the mixture was incubated for 4 h. The carbapenemase type was confirmed by the IPM waveform spectrum before and after incubation. Based on the presence, absence, and recovery of the IPM-cyano-4-hydroxy-cinnamic acid-specific waveform peak near 479 m/z, the detection sensitivity and specificity of the method were 98.2 and 100%, respectively. Although CPE detection by MALDI-TOF MS has been studied previously, our method distinguishes between metallo-β-lactamase and serine carbapenemases, which will be very helpful for the clinical selection of antibiotics.

Multicode Signaling in a Filter Bank Multicarrier Spread Spectrum System and Its Application to HF Communications

The use of filter banks for implementing multicarrier spread spectrum systems leads to a class of effective waveforms that are highly resilient to partial-band interferers. Such waveforms can be also designed to keep the peak-to-average power ratio (PAPR) of the resulting signal at a minimum level. The use of multiple spreading gain vectors (known as multicodes), on the other hand, is an effective method for increasing the data rate in spread spectrum systems, in general. This paper presents a detailed analysis of a class of filter bank multicarrier spread spectrum (FBMC-SS) waveforms and demonstrates an effective receiver implementation of them when multicodes are applied. Application of the developed multicode waveform for communications over high-frequency (HF) skywave channels is also explored, and the benefits that it provides are studied both numerically, through computer simulations, and experimentally, by examining the receivers performance over a variety of skywave links.