Original Waveform Research Articles

Solitary waves in ultrafast fiber lasers: From solitons to dissipative solitons

The development of the connections between ultrafast laser dynamics and solitary waves concepts is presented, with an attempt to emphasize on important research milestones and conceptual advances. At various stages, the concepts of conventional and dissipative solitons are found to echo, oppose, or complement each other. The pivotal role of fiber lasers is highlighted, which multiplied the investigation of ultrafast laser dynamics since the 90s, and lead to the widespread usage of dissipative soliton concepts that were instrumental in understanding original dynamical regimes and related optical waveforms, such as the generation of bright solitons in the normal dispersion regime and the self-assembly of optical soliton molecules within laser cavities.

Reconstruction of gravitational waveforms of coalescing spinless binaries in EOB theory based on PM approximation

We present a study of improving the gravitational waveforms of the coalescing spinless binaries in effective-one-body theory based on the post-Minkowskian approximation. The original ‘plus’ and ‘cross’ waveform modes of the gravitational wave generated by coalescing spinless black-hole binaries are extracted from the solution of the null tetrad component of the gravitational perturbed Weyl tensor in the effective spacetime. To enhance the accuracy of the gravitational waveforms, after Damour et al, the waveforms are factored into five components which are the Minkowskian leading factor , the source , the tail effect , the phase factor , and the residual term . In the equal mass ratio case, we find that the reconstructed waveform is closer to the five post-Newtonian result than the original waveform, while the resummation method gives the better results.

Transformer Graph Convolutional Network for Relative Travel-Time Shift Prediction

Abstract Predicting surface-wave travel-time shifts is valuable for analyzing potential effects caused by changes in medium properties, station clock errors, instrument response errors, and other factors. Many current neural networks used in seismology are single-station models trained using single-station (pair) data. However, most seismic methods require knowledge of the spatial positions between multiple stations. Multiple stations contain rich interrelationships and spatial information that cannot be exploited by single-station models. We proposed a multistation neural network structure Transformer Graph Convolutional Network (TGCN) that utilizes temporal attention and spatial attention to capture spatiotemporal information for predicting relative travel-time shifts. Before that, we introduced a method that treats station pairs as nodes and constructs a graph with multiple station pairs. We collected original ambient noise waveforms from 2017 to 2019 in the Alaska region and 2010 to 2014 in the southern California region to obtain relative travel-time shift sequences of station pairs for model training and testing. To showcase the improvement of spatial information to the model, we compared TGCN with two other baseline single-station models—temporal convolutional network and long short-term memory. Our proposed method predicted travel-time values more accurately than the two baseline models, and it also exhibited slower decay in performance when predicting over larger intervals. We also found that the number of station pairs has an impact on the model. When there are a sufficient number of station pairs, the model can effectively utilize the rich spatial information and achieve higher accuracy. Our approach, which incorporates spatiotemporal information, provides outputs that are more efficient and accurate compared with the traditional single-station (pair) method that only considers temporal information, suggesting that spatial information does enhance the performance of the model.

Microseismic event waveform classification using CNN-based transfer learning models

The efficient processing of large amounts of data collected by the microseismic monitoring system (MMS), especially the rapid identification of microseismic events in explosions and noise, is essential for mine disaster prevention. Currently, this work is primarily performed by skilled technicians, which results in severe workloads and inefficiency. In this paper, CNN-based transfer learning combined with computer vision technology was used to achieve automatic recognition and classification of multi-channel microseismic signal waveforms. First, data collected by MMS was generated into 6-channel original waveforms based on events. After that, sample data sets of microseismic events, blasts, drillings, and noises were established through manual identification. These datasets were split into training sets and test sets according to a certain proportion, and transfer learning was performed on AlexNet, GoogLeNet, and ResNet50 pre-training network models, respectively. After training and tuning, optimal models were retained and compared with support vector machine classification. Results show that transfer learning models perform well on different test sets. Overall, GoogLeNet performed best, with a recognition accuracy of 99.8%. Finally, the possible effects of the number of training sets and the imbalance of different types of sample data on the accuracy and effectiveness of classification models were discussed.

The Denoising Method for Transformer Partial Discharge Based on the Whale VMD Algorithm Combined with Adaptive Filtering and Wavelet Thresholding.

Partial discharge (PD) is the primary factor causing insulation degradation in transformers. However, the collected signals of partial discharge are often contaminated with significant noise. This makes it difficult to extract the PD signal and hinders subsequent signal analysis and processing. This paper proposes a denoising method for transformer partial discharge based on the Whale VMD algorithm combined with adaptive filtering and wavelet thresholding (WVNW). First, the WOA is used to optimize the important parameters of the VMD. The selected mode components from the VMD decomposition are then subjected to preliminary denoising based on the kurtosis criterion. The reconstructed signal is further denoised using the Adaptive Filter (NLMS) algorithm to remove narrowband interference noise. Finally, the residual white noise is eliminated using the Wavelet Thresholding algorithm. In simulation experiments and practical measurements, the proposed method is compared quantitatively with previous methods, VMD-WT, and EMD-WT, based on metrics such as SNR, RMSE, NCC, and NRR. The results indicate that the WVNW method effectively suppresses noise interference and restores the original PD signal waveform with high waveform similarity while preserving a significant amount of local discharge signal features.

Adaptive Active Frequency Drift Islanding Detection for PV Inverters

The penetration level of renewable energy resources (RESs), such as photovoltaic (PV) plant and Wind plant (WP), in the power system is increasing exponentially, such plants connected with grid via inverter. This increasing rises from the attention about the undetected islanding operation. The islanding can be defined, according to IEEE std.1547, as a situation in which part of the power system becomes isolated from the rest of the system.
 Islanding detection methods (IDMs) are divided into passive and active IDM. Among active IDMs, active frequency drift (AFD) is the most IDM applied in the literatures. AFD bases on injecting a distortion waveform to the original waveform of inverter reference current, this to drift the inverter frequency during islanding event to be out of the nominal range. Due to the distortion waveform high harmonics will be injected to the system. Recently, to decrease this harmonic an improved active frequency drift (IAFD) was presented in the literature. IAFD uses a constant step change in in 2nd and 4th quarters of the inverter reference current. IAFD has lower total harmonic distortion (THD) injected to the system compared with conventional AFD.
 Since the non-detection zone (NDZ) has been considered as a performance measure for any IDM. The IAFD method did not introduce any improvement in NDZ over the AFD method. Thus, in this paper an adaptive step change is proposed to improve the performance of IAFD method, where a positive feedback of voltage frequency is used in this work to vary the distortion factor of IAFD. The adaption manner of step change enhances in increasing of injected perturbations during islanding event only. Which, it will reduce the NDZ during islanding event and decrease the injected THD in steady state operation. The proposed method has been theoretically analyzed and modeled using MATLAB/Simulink environment. As a result, the proposed method improves the performance of IAFD regard to the non-detection zone (NDZ).

Waveform design for resonance signature of fighter‐class target

AbstractThe authors investigate the feasibility of forming a composite Gaussian waveform in the high‐frequency region for jetfighter class targets with a high potential to combat the noise effect and the late‐time signature onset ambiguity when estimating the target resonance signature. The excitation waveform comprises a composite of modulated carriers with fractional bandwidth and amplitude adapted to lower specular content while enhancing the radar target resonance content in the radar return, subsequently increasing the likelihood of extracting the resonance parameters more accurately. The validation of improvement in the resonance signature requires finding the error between the excitation frequencies and the extractable resonant frequencies in the return echo and, second, the similarity or resemblance between the original echo waveform and the reconstructed waveform by the estimated resonance set. The results presented the robustness rate in instantaneous, average, and overall fashion. In conclusion, a fractional bandwidth between 0.3 and 0.4 would significantly improve the signature overall robustness with a circular then slant scattering polarisation set outperforming the horizontal and vertical directions. Also, excluding the signal's high and low energy portions further enhance the accuracy of the estimated resonance mode set.

Coastal Assessment of Sentinel-6 Altimetry Data during the Tandem Phase with Jason-3

This study presents a comparative analysis of the coastal performances of Sentinel-6 and Jason-3 altimeters during their tandem phase, considering their different processing modes. We examine the measurements available in the standard geophysical data records (GDR) and also perform dedicated reprocessing using coastal retracking algorithms applied to the original waveforms. The performances are evaluated, taking into account the quality of retrievals (outlier analysis), their precision (along-track noise analysis), potential systematic biases, and accuracy (comparison against tide gauges). The official SAR altimetry product of Sentinel-6 demonstrates improved coastal monitoring capabilities compared to Jason-3, except for the remaining issues related to significant wave height, which have already been identified. These findings highlight the significance of dedicated coastal retracking algorithms for enhancing the capabilities of both traditional, pulse-limited altimeters and more recent developments utilizing SAR altimetry.

Method of echo recognition of target in smoke environment based on residual convolutional neural network for pulsed laser detection

A recognition algorithm based on full waveform decomposition and residual convolutional neural network is proposed for the anti-interference problem of pulsed laser detection in smoky environments. As the original waveform data lack of features, a deep network - Resnet18 was used for the initial training. Considering the complexity of the algorithm, the structure, number of convolutional kernels, and sampling rate of Resnet18 were optimized, resulting in a lightweight version of Resnet6 with optimal parameters. Then, a pulsed laser detection platform under smoke environment was built. Considering the generalization of experimental data, five different scenes were designed to evaluate the algorithm. Compared to other algorithms, the proposed algorithm demonstrates superior performance in terms of low computational complexity (0.0441 MB parameters) and high recognition accuracy (94.59% ± 0.46). Finally, additional experiments were conducted on the detection platform. Frosted material was used as the detection target with different intensity of noise signals to verify the robustness.

Method to Solve Underwater Laser Weak Waves and Superimposed Waves.

With the rapid development of Lidar technology, the use of Lidar for underwater terrain detection has become feasible. There is still a challenge in the process of signal resolution: the underwater laser echo signal is different to propagating in the air, and it is easy to produce weak waves and superimposed waves. However, existing waveform decomposition methods are not effective in processing these waveform signals, and the underwater waveform signal cannot be correctly decomposed, resulting in subsequent data-processing errors. To address these issues, this study used a drone equipped with a 532 nm laser to detect a pond as the study background. This paper proposes an improved inflection point selection decomposition method to estimate the parameter. By comparing it with other decomposition methods, we found that the RMSE is 2.544 and R2 is 0.995975, which is more stable and accurate. After estimating the parameters, this study used oscillating particle swarm optimization (OPSO) and the Levenberg-Marquardt algorithm (LM) to optimize the estimated parameters; the final results show that the method in this paper is closer to the original waveform. In order to verify the processing effect of the method on complex waveform, this paper decomposes and optimizes the simulated complex waveforms; the final RMSE is 0.0016, R2 is 1, and the Gaussian component after decomposition can fully represent the original waveform. This method is better than other decomposition methods in complex waveform decomposition, especially regarding weak waves and superimposed waves.

Restoring original signals from pile-up using deep learning

Pile-up signals are frequently produced in experimental physics. They create inaccurate physics data with high uncertainties and cause multiple problems. Therefore, the correction of pile-up signals is crucially required. In this study, we implemented a deep learning method to restore the original signals from signals piled up with unwanted signals. We showed that a deep learning model could accurately reconstruct the original signal waveforms from the pile-up waveforms. By substituting the pile-up signals with the original signals predicted by the model, the energy and timing resolution of the data are notably enhanced. The model implementation significantly improved the quality of the particle identification plot and particle tracks. This method is applicable to similar problems, such as separating multiple signals or correcting pile-up signals with other types of noises and backgrounds.

Machine tool spindle vibration monitoring system based on Bluetooth wireless network and LabVIEW

The spindle system is important in the machine tool. The vibration signal generated by the spindle system in the machine tool has a great influence on the machining accuracy of the product and the abrasion and lifetime of the tool. Therefore, it is necessary to monitor the vibration signal in the machining process of the machine tool spindle in real time. The vibration sensor is used to monitor the vibration signal of the machine tool spindle in this paper. The data collected by the sensors are transmitted to the Bluetooth module by the serial port for wireless communication. The vibration signal and its frequency domain transformation of the spindle are received by LabVIEW software platform, and the original waveform is displayed, the threshold alarm and the storage of data are realized. The experimental results show that the designed system can realize the online monitoring of the vibration state of the machine tool spindle effectively.

Arrival-time detection with histogram distance for acoustic emission signals

Arrival-time picking is a fundamental step in time-of-arrival (TOA)-based localisation in current and future acoustic emission (AE), microseismic and seismic localisation systems. The accurate detection of TOA is of great importance for high localisation accuracy. This work presents a histogram distance-based method for TOA detection of elastic wave signals. The authors treat an original elastic waveform that includes the arrival as two different locally stationary segments, namely the intervals following and preceding arrival. To determine the optimal separation of these two stationary segments, ie the arrival time, histograms of these intervals are calculated and a measure of the distance between them modified from the Bhattacharyya coefficient is proposed. The performance of the proposed method is evaluated using TOA picking for AE. The method is shown to provide accurate and robust picks on AE signals under various signal-to-noise ratios. To evaluate the adaptability of the method to other TOA picking scenarios, it is applied to detect the seismic P-phase. The detection accuracy is adequate and errors are constrained to within a few seconds. Factors that influence the detection accuracy are discussed. The results suggest that the proposed method has the potential to detect TOA in various fields.

Research on Intelligent Identification of Magnetizing Inrush Current based on Empirical Modal Decomposition

The accurate identification of the converter transformer magnetizing inrush current can ensure the safety of the operation of the UHV DC transmission system. In the differential protection of the converter transformer, it is extremely hard to distinguish between the excitation inrush current generated when the transformer is closed and the Fault current due to internal fault, which leads to frequent misoperation of the transformer protection to the extent that the transformer access fails. This paper proposes a current identification model based on empirical modal decomposition (EMD) and probabilistic neural network (PNN) algorithm with particle swarm (PSO) optimization. The EMD algorithm decomposes the current waveform, and the eigenmode functions (IMFs) of the three highest correlation bars are selected. The energy, cliffs, and standard deviation of the IMFs are extracted as the original waveform eigenvectors.

Physiowise: A Physics-aware Approach to Dicrotic Notch Identification.

Dicrotic Notch (DN), one of the most significant and indicative features of the arterial blood pressure (ABP) waveform, becomes less pronounced and thus harder to identify as a matter of aging and pathological vascular stiffness. Generalizable and automatic DN identification for such edge cases is even more challenging in the presence of unexpected ABP waveform deformations that happen due to internal and external noise sources or pathological conditions that cause hemodynamic instability. We propose a physics-aware approach, named Physiowise (PW), that first employs a cardiovascular model to augment the original ABP waveform and reduce unexpected deformations, then apply a set of predefined rules on the augmented signal to find DN locations. We have tested the proposed method on in-vivo data gathered from 14 pigs under hemorrhage and sepsis study. Our result indicates 52% overall mean error improvement with 16% higher detection accuracy within the lowest permitted error range of 30ms. An additional hybrid methodology is also proposed to allow combining augmentation with any application-specific user-defined rule set.

An Efficient Compression Method for Lightning Electromagnetic Pulse Signal Based on Convolutional Neural Network and Autoencoder.

Advances in technology have facilitated the development of lightning research and data processing. The electromagnetic pulse signals emitted by lightning (LEMP) can be collected by very low frequency (VLF)/low frequency (LF) instruments in real time. The storage and transmission of the obtained data is a crucial link, and a good compression method can improve the efficiency of this process. In this paper, a lightning convolutional stack autoencoder (LCSAE) model for compressing LEMP data was designed, which converts the data into low-dimensional feature vectors through the encoder part and reconstructs the waveform through the decoder part. Finally, we investigated the compression performance of the LCSAE model for LEMP waveform data under different compression ratios. The results show that the compression performance is positively correlated with the minimum feature of the neural network extraction model. When the compressed minimum feature is 64, the average coefficient of determination R2 of the reconstructed waveform and the original waveform can reach 96.7%. It can effectively solve the problem regarding the compression of LEMP signals collected by the lightning sensor and improve the efficiency of remote data transmission.

Radar Emitter Identification with Multi-View Adaptive Fusion Network (MAFN)

Radar emitter identification (REI) aims to extract the fingerprint of an emitter and determine the individual to which it belongs. Although many methods have used deep neural networks (DNNs) for an end-to-end REI, most of them only focus on a single view of signals, such as spectrogram, bi-spectrum, signal waveforms, and so on. When the electromagnetic environment varies, the performance of DNN will be significantly degraded. In this paper, a multi-view adaptive fusion network (MAFN) is proposed by simultaneously exploring the signal waveform and ambiguity function (AF). First, the original waveform and ambiguity function of the radar signals are used separately for feature extraction. Then, a multi-scale feature-level fusion module is constructed for the fusion of multi-view features from waveforms and AF, via the Atrous Spatial Pyramid Pooling (ASPP) structure. Next, the class probability is modeled as Dirichlet distribution to perform adaptive decision-level fusion via evidence theory. Extensive experiments are conducted on two datasets, and the results show that the proposed MAFN can achieve accurate classification of radar emitters and is more robust than its counterparts.

Compressive sensing-based de-aliasing method for array electrostatic signal

PurposeThe aero-engine array electrostatic monitoring technology (AEMT) can provide more and more accurate information about the direct product of the fault, and it is a novel condition monitoring technology that is expected to solve the problem of high false alarm rate of traditional electrostatic monitoring technology. However, aliasing of the array electrostatic signals often occurs, which will greatly affect the accuracy of the information identified by using the electrostatic sensor array. The purpose of this paper is to propose special solutions to the above problems.Design/methodology/approachIn this paper, a method for de-aliasing of array electrostatic signals based on compressive sensing principle is proposed by taking advantage of the sparsity of the distribution of multiple pulse signals that originally constitute aliased signals in the time domain.FindingsThe proposed method is verified by finite element simulation experiments. The simulation experiments show that the proposed method can recover the original pulse signal with an accuracy of 96.0%; when the number of pulse signals does not exceed 5, the proposed method can recover the pulse peak with an average absolute error of less than 5.5%; and the recovered aliased signal time-domain waveform is very similar to the original aliased signal time-domain waveform, indicating that the proposed method is accurate.Originality/valueThe proposed method is one of the key technologies of AEMT.

Phase Noise Suppression for φ-OTDR With Optimized Minima Controlled Recursive Averaging Method

We propose a phase noise suppression method for phase-sensitive optical time-domain reflectometer ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varphi $ </tex-math></inline-formula> -OTDR) system. The signal-to-noise ratio (SNR) of the demodulated phase is improved by the optimized minima controlled recursive averaging (MCRA) method. First, the original phase waveform is divided into frames. Then, the minimum control is used to track the minimum power spectrum of phase noise with Gaussian distribution. The prior variance of noise is corrected by recursive averaging. The attenuation factor is determined by appropriate threshold parameters and the noise prior variance. Ultimately, the phase waveform is reconstructed in the time–frequency domain. In this way, the SNR of the reconstructed waveform is intensified remarkably. Experimental results show that the SNR of the demodulated phase is improved by 9.12–19.63 dB in the range of 20–1200 Hz. Furthermore, the enhancement of acoustic extracted from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varphi $ </tex-math></inline-formula> -OTDR could also be verified experimentally with the proposed approach.

Sea Surface Height Estimation from Improved Modified, and Decontaminated Sub-Waveform Retracking Methods over Coastal Areas

Coastal zones are challenging areas for sensing by satellite altimeters because reflected signals from non-water surfaces and from calm sea surfaces in small bays and ports inside the radar footprint lead to erroneous powers in return waveforms. Accordingly, these contaminated waveforms do not follow the so-called Brown model in conventional retracking algorithms and fail to derive qualified ranges. Consequently, the estimated water level is erroneous as well. Therefore, selecting an optimized retracker for post-processing waveforms is significantly important to achieve a qualified water level estimation. To find the optimized retracker, we employed a methodology to minimize the effect of erroneous powers on retracked range corrections. To this end, two new approaches were presented, one based on a waveform decontamination method and the other based on a waveform modification method. We considered the first meaningful sub-waveforms in the decontaminated waveforms and in the modified waveforms to be processed with a threshold retracker. To assess their performance, we also retracked the decontaminated and modified full-waveforms. The first meaningful sub-waveform and full-waveform in the original waveforms were retracked to compare the performance of the modified and decontaminated waveform retracking with the original waveform retracking. To compare the results of our sub-waveform retracking algorithms with those of external sub-waveform retracking algorithms, the (Adaptive Leading Edge Sub-waveform) ALES database was also used. In our retracking scenarios, we used the Sentinel-3A SRAL Altimeter to estimate the water levels over the study area within 10 km from the coastlines in both the Persian Gulf and the Bay of Biscay from June 2016 to October 2020. The water levels from processing L2 products were estimated as well. We evaluated our retracking scenarios and L2, as well as the ALES processing results, against the tide gauges. Our analysis showed that within 0–10 km from the coast, the first meaningful sub-waveform of the decontaminated waveforms had the best performance. We reached maximum RMS improvements in this scenario of 53% and 86% over the Persian Gulf and the Bay of Biscay, respectively, in comparison with L2 processing. Over these distances from the coast, the first sub-waveform from the original waveforms and the modified waveforms stayed in the second and third order of performance. The ALES database with an RMS ranging from 13 to 51 cm had a worse performance than all of our sub-waveform retracking scenarios.