Waveform Characteristics Research Articles

Monitoring underwater volcano degassing using fiber-optic sensing

Continuous monitoring of volcanic gas emissions is crucial for understanding volcanic activity and potential eruptions. However, emissions of volcanic gases underwater are infrequently studied or quantified. This study explores the potential of Distributed Acoustic Sensing (DAS) technology to monitor underwater volcanic degassing. DAS converts fiber-optic cables into high-resolution vibration recording arrays, providing measurements at unprecedented spatio-temporal resolution. We conducted an experiment at Laacher See volcano in Germany, immersing a fiber-optic cable in the lake and interrogating it with a DAS system. We detected and analyzed numerous acoustic signals that we associated with bubble emissions in different lake areas. Three types of text-book bubbles exhibiting characteristic waveforms are all found from our detections, indicating different nucleation processes and bubble sizes. Using clustering algorithms, we classified bubble events into four distinct clusters based on their temporal and spectral characteristics. The temporal distribution of the events provided insights into the evolution of gas seepage patterns. This technology has the potential to revolutionize underwater degassing monitoring and provide valuable information for studying volcanic processes and estimating gas emissions. Furthermore, DAS can be applied to other applications, such as monitoring underwater carbon capture and storage operations or methane leaks associated with climate change.

Recognition of Tunnel Fracture Zones in Seismic Waves and Ground-Penetrating Radar Data

Fracture zones in front of tunnel faces can easily cause falling blocks and landslides during the construction process. Using seismic waves and ground-penetrating radar (GPR) data, we extracted the features of fracture zones and achieved the advanced prediction of tunnel fracture zones. The energy variation in the reflected waves propagated by seismic waves at interfaces with different impedances of contact waves was found to manifest as positive and negative reflections, and the amplitude of reflected signals within the fracture zone areas thus increased. We designed a superimposed velocity spectrum, divided the areas of variation in wave velocity, and constructed the three-dimensional spatial distribution of the tunnel fracture zones. Based on the phase change, increase in amplitude, and increase in the center-frequency characteristics of the one-dimensional time waveform of the electromagnetic waves in the fault zone area (A-scan), we located the characteristic points of the fracture zones and observed the occurrence of in-phase axis misalignment in two-dimensional scanning (B-scan). We then implemented the identification of fracture zones. This method predicted the fractured area in the rock surrounding the Liangwangshan Tunnel, and during the tunnel excavation, the fracture zones appeared in the recognition area.

Discovering optimal features for neuron-type identification from extracellular recordings.

Advancements in multichannel recordings of single-unit activity (SUA) in vivo present an opportunity to discover novel features of spatially-varying extracellularly-recorded action potentials (EAPs) that are useful for identifying neuron-types. Traditional approaches to classifying neuron-types often rely on computing EAP waveform features based on conventions of single-channel recordings and thus inherit their limitations. However, spatiotemporal EAP waveforms are the product of signals from underlying current sources being mixed within the extracellular space. We introduce a machine learning approach to demix the underlying sources of spatiotemporal EAP waveforms. Using biophysically realistic computational models, we simulate EAP waveforms and characterize them by the relative prevalence of these sources, which we use as features for identifying the neuron-types corresponding to recorded single units. These EAP sources have distinct spatial and multi-resolution temporal patterns that are robust to various sampling biases. EAP sources also are shared across many neuron-types, are predictive of gross morphological features, and expose underlying morphological domains. We then organize known neuron-types into a hierarchy of latent morpho-electrophysiological types based on differences in the source prevalences, which provides a multi-level classification scheme. We validate the robustness, accuracy, and interpretations of our demixing approach by analyzing simulated EAPs from morphologically detailed models with classification and clustering methods. This simulation-based approach provides a machine learning strategy for neuron-type identification.

Ionospheric Disturbances Generated by the 2015 Calbuco Eruption: Comparison of GITM‐R Simulations and GNSS Observations

AbstractVolcanic eruptions provide broad spectral forcing to the atmosphere and understanding the primary mechanisms that are relevant to explain the variety in waveform characteristics in the Ionosphere‐Thermosphere (IT) is still an important open question for the community. In this study, Global Navigation Satellite System (GNSS) Total Electron Content (TEC) data are analyzed and compared to simulations performed by the Global Ionosphere‐Thermosphere Model with Local Mesh Refinement (GITM‐R) for the first phase of the 2015 Calbuco eruption that occurred on 22 April. A simplified source representation and spectral acoustic‐gravity wave (AGW) propagation model are used to specify the perturbation at the lower boundary of GITM‐R at 100 km altitude. Two assumptions on the propagation structure, Direct Spherical (DS) and Ground Coupled (GC), are compared to the GNSS data and these modeling specifications show good agreement with different aspects of the observations for some waveform characteristics. Most notably, GITM‐R is able to reproduce the relative wave amplitude of AGWs as a function of radial distance from the vent, showing acoustic dominant forcing in the near field (<500 km) and gravity dominant forcing in the far‐field (>500 km). The estimated apparent phase speeds from GITM‐R simulations are consistent with observations with ∼10% difference from observation for both acoustic wave packets and a trailing gravity mode. The relevance of the simplifications made in the lower atmosphere to the simulated IT response is then discussed.

Restricted diffusion characteristics in oscillating gradient spin echo with mesoscopic phantom

In diffusion magnetic resonance imaging, oscillating gradient spin echo (OGSE) has an extremely short diffusion time if motion probing gradient (MPG) is applied to the waveform. Further, it can detect microstructural specificity. OGSE changes sensitivity to spin displacement velocity based on the MPG phase. The current study aimed to investigate the restricted diffusion characteristics of each OGSE waveform using the capillary phantom with various b-values, frequencies, and MPG phases. We performed OGSE (b-value = 300, 500, 800, 1200, 1600, and 2000 s/mm2) for the sine and cosine waveforms using the capillary phantom (6, 12, 25, 50, and 100 μm and free water) with a 9.4-T experimental magnetic resonance imaging system and a solenoid coil. We evaluated the axial and radial diffusivity (AD, RD) of each structure size. The output current of the MPG was assessed with an oscilloscope and analyzed with the gradient modulation power spectra by fast Fourier transform.In sine, the sidelobe spectrum was enhanced with increasing frequency, and the central spectrum slightly increased. The difference in RD was detected at 6 and 12 μm; however, it did not depend on the structure scale at 50 or 100 μm and free water. In cosine, the diffusion spectrum was enhanced, whereas the central spectrum decreased with increasing frequency. Both AD and RD in cosine had a frequency dependence, and AD and RD increased with a higher frequency regardless of structure size. AD and RD in either sine or cosine had no evident b-value dependence. We evaluated the OGSE-restricted diffusion characteristics. The measurements obtained diffusion information similar to the pulsed gradient spin echo. Hence, the cosine measurements indicated that a higher frequency could capture faster diffusion within the diffusion phenomena.

Paediatric norms for photopic electroretinogram testing based on a large cohort of Chinese preschool children

ObjectiveFull-field electroretinogram (ffERG) is an objective test to determine the electroretinal activities in response to light stimulation for investigating retinal physiology and diagnosing retinal diseases. This study aimed to establish...

Abstract 44: Portable Optical Blood Flow Monitor for the Detection of Large Vessel Occlusion

Introduction: Early detection of large vessel occlusion (LVO) facilitates triage to appropriate stroke centers and reduces treatment times. Prehospital stroke scales are insensitive, so we investigated the ability of the Openwater Headset, a portable optical blood flow monitor, to detect LVO in patients undergoing acute stroke evaluation. Hypothesis: We hypothesized that the Openwater Headset would detect LVO with greater sensitivity and specificity than prehospital scales. Methods: At two comprehensive stroke centers, eligible patients presented to the emergency department or were transferred from another facility for acute stroke evaluation within 24 hours of symptom onset and NIHSS ≥2. Eligible patients underwent emergent vascular imaging as per routine care. LVO was defined as occlusion of the ICA or the first/second segment of the MCA. Each patient underwent a 70-second bedside optical blood flow evaluation with the Openwater Headset (prior to endovascular therapy, if applicable). The Openwater Headset generated blood flow waveforms based on the relative change in the speckle contrast, acquired at 40 Hz. A deep neural network was trained to identify waveform features that discriminate LVO status ( Figure 1a ), withholding a validation set to test model performance. ROC analysis compared Openwater Headset performance with that of prehospital stroke scales. Results: 135 patients completed the optical evaluation a median of 6 hours after symptom onset (IQR: 3-14). The median NIHSS was 9 (IQR: 4-18). 52 patients (39%) had a LVO. The Openwater LVO stroke alert algorithm had 81% sensitivity and 80% specificity for the presence of LVO (AUC 0.85 by ROC analysis), exceeding that of prehospital stroke scales ( Figure 1b ). Conclusions: The Openwater Headset outperformed prehospital stoke scales for LVO detection in patients who underwent acute stroke evaluation, but further investigation is needed to assess performance in pre-hospital settings.

Fault mechanism and dynamic two-phase flow behavior of liquid slugging in reciprocating compressors

Liquid slugging is a fatal fault for large process compressors, leading to transient overpressure, the deformation and fracture of vital pressure-bearing parts, and even gas leakage or explosion. In the study reported here, to reveal the mechanism of overpressure formation, numerical simulations were conducted by means of the volume-of-fluid method to explore the dynamic evolution characteristics of the two-phase flow pattern. Then, high-speed photography was applied to capture the dynamic changes of the liquid boundary in the modified cylinder from different views, thus realizing the validation of the numerical model. This study reveals the significant influence of increased rotational speed on fluid flow patterns, impeding liquid discharge and exacerbating overpressure events. Additionally, changes in pressure waveform and a distinctive waveform feature were identified as effective diagnostic indicators for detecting fluid slugging. Next, a nondestructive pressure monitoring reconstruction method based on measuring bolt strain was proposed. The strain-based pressure showed good agreement with the simulated results, thereby validating its effectiveness and feasibility as an early warning indicator for liquid slugging. This study offers new perspectives on the failure mechanism of liquid slugging in reciprocating compressors by delving into the behavior of two-phase flow, with the potential to enhance the theoretical foundation of compressor condition monitoring and fault diagnosis.

Contactless Blood Pressure Measurement Via Remote Photoplethysmography With Synthetic Data Generation Using Generative Adversarial Networks.

Remote photoplethysmography (rPPG) has been used to measure vital signs such as heart rate, heart rate variability, blood pressure (BP), and blood oxygen. Recent studies adopt features developed with photoplethysmography (PPG) to achieve contactless BP measurement via rPPG. These features can be classified into two groups: time or phase differences from multiple signals, or waveform feature analysis from a single signal. Here we devise a solution to extract the time difference information from the rPPG signal captured at 30 FPS. We also propose a deep learning model architecture to estimate BP from the extracted features. To prevent overfitting and compensate for the lack of data, we leverage a multi-model design and generate synthetic data. We also use subject information related to BP to assist in model learning. For real-world usage, the subject information is replaced with values estimated from face images, with performance that is still better than the state-of-the-art. To our best knowledge, the improvements can be achieved because of: 1) the model selection with estimated subject information, 2) replacing the estimated subject information with the real one, 3) the InfoGAN assistance training (synthetic data generation), and 4) the time difference features as model input. To evaluate the performance of the proposed method, we conduct a series of experiments, including dynamic BP measurement for many single subjects and nighttime BP measurement with infrared lighting. Our approach reduces the MAE from 15.49 to 8.78 mmHg for systolic blood pressure (SBP) and 10.56 to 6.16 mmHg for diastolic blood pressure(DBP) on a self-constructed rPPG dataset. On the Taipei Veterans General Hospital(TVGH) dataset for nighttime applications, the MAE is reduced from 21.58 to 11.12 mmHg for SBP and 9.74 to 7.59 mmHg for DBP, with improvement ratios of 48.47% and 22.07% respectively.

Rat hair-follicle-associated pluripotent (HAP) stem cells can differentiate into atrial or ventricular cardiomyocytes in culture controlled by specific supplementation.

There has been only limited success to differentiate adult stem cells into cardiomyocyte subtypes. In the present study, we have successfully induced beating atrial and ventricular cardiomyocytes from rat hair-follicle-associated pluripotent (HAP) stem cells, which are adult stem cells located in the bulge area. HAP stem cells differentiated into atrial cardiomyocytes in culture with the combination of isoproterenol, activin A, bone morphogenetic protein 4 (BMP4), basic fibroblast growth factor (bFGF), and cyclosporine A (CSA). HAP stem cells differentiated into ventricular cardiomyocytes in culture with the combination of activin A, BMP4, bFGF, inhibitor of Wnt production-4 (IWP4), and vascular endothelial growth factor (VEGF). Differentiated atrial cardiomyocytes were specifically stained for anti-myosin light chain 2a (MLC2a) antibody. Ventricular cardiomyocytes were specially stained for anti-myosin light chain 2v (MLC2v) antibody. Quantitative Polymerase Chain Reaction (qPCR) showed significant expression of MLC2a in atrial cardiomyocytes and MLC2v in ventricular cardiomyocytes. Both differentiated atrial and ventricular cardiomyocytes showed characteristic waveforms in Ca2+ imaging. Differentiated atrial and ventricular cardiomyocytes formed long myocardial fibers and beat as a functional syncytium, having a structure similar to adult cardiomyocytes. The present results demonstrated that it is possible to induce cardiomyocyte subtypes, atrial and ventricular cardiomyocytes, from HAP stem cells.

Elimination of seismic characteristics of solid-filled in ultra-deep fractured-vuggy reservoirs

A large number of high-quality fractured-vuggy reservoirs (FVRs) are buried in ultra-deep (6500–9000 m) carbonate layers in the Tarim Basin, represented by the strong “beaded” reflection (SBR) on seismic profiles. This characteristic corresponds to the vast majority of high yield oil and gas wells, but there have also been a number of instances where the reservoir was filled with solid, leading to exploration failure. To avoid drilling failure, it is crucial to accurately identify fake reservoirs using geophysical methods. This paper proposes a novel method to eliminate the seismic characteristics of solid-filled in FVRs. Based on the waveform component decomposition (WCD) data, combined with drilling and logging data, an adaptive component reconstruction (ACR) can be completed to distinguish whether the reservoir is filled with solid or not, achieving the goal of eliminating the seismic characteristics of filling. Firstly, supervised WCD is performed on the target seismic data. Then, the waveform characteristics, which indicate the difference in the FVRs filled with solids, are combined accordingly with the sensitive attribute characteristics (instantaneous amplitude, heterogeneous anomalies, arc length, etc.) of each component data, and the feature templates of filled and non-filled FVRs are defined. Finally, the information entropy method is applied to optimise the component set that can distinguish the solid filled and non-filled FVRs and complete the component reconstruction. Forward modelling shows that this method can effectively distinguish the characteristics of FVRs that have been filled by solids from those that not. This method has achieved favourable results in the M block in the Tarim Basin. Successfully removed some or all the characteristic information of solid filled reservoirs confirmed by drilling from seismic data. The effectiveness of the method in identifying solid filled reservoirs has been confirmed by drilling, and the prediction rate of FVRs non-filled with solids has increased from 78% to 95%. This method can be used for reference in blocks with similar geological characteristics.

A Study of Outliers in GNSS Clock Products

Time is an extremely important element in the field of GNSS positioning. In precise positioning with a single-centimetre accuracy, satellite clock corrections are used. In this article, the longest available data set of satellite clock corrections of four GNSS systems from 2014 to 2021 was analysed. This study covers the determination of the quality (outliers number and magnitude), availability, stability, and determination of the specificity and nature of the clock correction for each satellite system. One problem with the two newest satellite systems (Galileo and BeiDou) is the lack of availability of satellite signals in the early years of the analysis. These data were available only in the later years of the period covered by the analysis, as most of the satellites have only been in orbit since 2018–2019. Interestingly, the percentage of outlying observations was highest in Galileo and lowest in BeiDou. Phase and frequency plots showed a significant number of outlying observations. On the other hand, after eliminating outlying observations, each system showed a characteristic graph waveform. The most consistent and stable satellite clock corrections are provided by the GPS and GLONASS systems. The main problems discussed in this paper are the determination of the number and magnitude of outliers in clock products of four GNSS systems (GPS, GLONASS, Galileo, Beidou) and the study on the long-term stability of GNSS clocks analysis, which covers the years 2014–2021.

Multi-objective Agent Model for XGBoost Controlled VFTO Simulator Based on Bayesian Optimization Algorithm

VFTO generated from GIS may cause interference and damage to terminal equipment on the primary and secondary sides, but experimental measures to evaluate the affected equipment and electromagnetic effects of the equipment through actual measurements are quite limited, so it is desired to build a VFTO simulator in the laboratory for experimental research and laboratory testing. Most of the current researches are conducted by building a true-type experimental platform for VFTO simulation, but this method has the disadvantages of large footprint, high cost and the scope of application can only simulate VFTO in a certain situation. Therefore, this paper establishes a broadband circuit model considering the pulse generator and the frequency domain equivalent transmission line model, and introduces the machine learning algorithm XGBoost to fit the agent model of the functional relationship between the design target and the structural parameters by establishing the sample data of the input circuit parameters and the output frequency waveform characteristics of the simulator, which is capable of dynamically adjusting the input parameters based on the waveform characteristics according to the changes of the target waveform, and ensures that a controllable waveform is obtained accurately in the laboratory the accuracy of obtaining controllable waveforms, thus realizing the output of controllable analog VFTO waveforms by changing the parameters of circuit components. Finally, the Bayesian optimization process is used for the hyper-parameter optimization of the XGBoost model, which has a fast convergence speed, and the characteristics of the output model are compared and analyzed, which verifies the feasibility and superiority of the proposed agent model as well as the multi-objective optimization method.

Deciphering the low-frequency seismic signals in the Weiyuan Shale gas field: implications for reservoir and structural heterogeneity

SUMMARY Hydraulic fracturing (HF) often stimulates the local earthquake productivity which provides a unique opportunity to characterize the crustal heterogeneities, reservoir properties and fluid injection effects. However, the velocity models acquired solely based on the arrival time records are often undermined due to the seismic network coverage and interpolation techniques. Instead, we adopt the waveform-based approach to apprehend; (1) structural heterogeneities, (2) reservoir distribution and (3) signatures of the injected fluid in the Weiyuan shale gas field. We categorize the waveforms into dominant high and low frequencies based on the qualitative inspection and frequency index analysis of the seismic waveforms. We first inspect the waveform to access the potential controlling mechanisms (source, site and path effects) at both single and multiple stations in different azimuthal orientations. As a result, we find the path effect as a dominant factor to influence the waveform characteristics, for example S-wave amplitude, and frequency. Subsequently, to localize the path effect, we conduct an in-depth examination of events within 10 km of each seismic station and classify the waveform records using their frequency indices. Notably, certain stations record a significant proportion of low-frequency waveforms (LFWs, up to 20 per cent), while others have limited occurrences (∼1 per cent) indicating suspected anomalous zones. Afterward, we identify two suspected anomalous zones based on LFWs intensity and ray tracing map. Both zones are in close proximity to fault zones and preserved reservoirs with no HF activities, where fault damage zones or the fluid-rich reservoir may contribute to our observed LFWs.

Pulsatile microvascular cerebral blood flow waveforms change with intracranial compliance and age.

Diffuse correlation spectroscopy (DCS) is an optical method to measure relative changes in cerebral blood flow (rCBF) in the microvasculature. Each heartbeat generates a pulsatile signal with distinct morphological features that we hypothesized to be related to intracranial compliance (ICC). We aim to study how three features of the pulsatile rCBF waveforms: the augmentation index (AIx), the pulsatility index, and the area under the curve, change with respect to ICC. We describe ICC as a combination of vascular compliance and extravascular compliance. Since patients with Chiari malformations (CM) () have been shown to have altered extravascular compliance, we compare the morphology of rCBF waveforms in CM patients with age-matched healthy control (). AIx measured in the supine position was significantly less in patients with CM compared to healthy controls (). Since physiologic aging also leads to changes in vessel stiffness and intravascular compliance, we evaluate how the rCBF waveform changes with respect to age and find that the AIx feature was strongly correlated with age (, , and , ). These results suggest that the AIx measured in the cerebral microvasculature using DCS may be correlated to changes in ICC.

Absorption function loss due to the history of previous ankle sprain explored by unsupervised machine learning

BackgroundAnkle sprains are common and cause persistent ankle function reduction. To biomechanically evaluate the ankle function after ankle sprains, the ground reaction force (GRF) measurement during the single-legged landing had been used. However, previous studies focused on discrete features of vertical GRF (vGRF), which largely ignored vGRF waveform features that could better identify the ankle function. PurposeTo identify how the history of ankle sprain affect the vGRF waveform during the single-legged landing with unsupervised machine learning considering the time-series information of vGRF. MethodsEighty-seven currently healthy basketball athletes (12 athletes without ankle sprain, 49 athletes with bilateral, and 26 athletes with unilateral ankle sprain more than 6 months before the test day) performed single-legged landings from a 20 centimeters (cm) high box onto the force platform. Totally 518 trials vGRF data were collected from 87 athletes of 174 ankles, including 259 ankle sprain trials (from previous sprain ankles) and 259 non-ankle sprain trials (from without sprain ankles). The first 100 milliseconds (ms) vGRF waveforms after landing were extracted. Principal component analysis (PCA) was applied to the vGRF data, selecting 8 principal components (PCs) representing 96% of the information. Based on these 8 PCs, k-means method (k = 3) clustered the 518 trials into three clusters. Chi-square test assessed significant differences (p < 0.01) in the distribution of ankle sprain and non-ankle sprain trials among clusters. FindingsThe ankle sprain trials accounted for a significantly larger percentage (63.9%) in Cluster 3, which exhibited rapidly increased impulse vGRF waveforms with larger peaks in a short time. SignificancePCA and k-means method for vGRF waveforms during single-legged landing identified that the history of previous ankle sprains caused a loss of ankle absorption ability lasting at least 6 months from an ankle sprain.

Microseismic activity characteristics and range evaluation of hydraulic fracturing in coal seam

Accurately evaluating hydraulic fracturing (HF) activity characteristics and impact range is critical to optimizing the HF scheme design. This study proposes an adaptive threshold peak detection method and a wavelet transform-support vector machine (WT-SVM) polarity analysis method based on microseismic (MS) monitoring data from a coal mine in Guizhou, China. The results show that the adaptive threshold peak detection method effectively improves the accuracy and stability of the P-wave initial arrival pickup for low signal-to-noise ratio (SNR) signals. The main frequency band of the MS signal of HF is focused on 150–300 Hz. The improved particle swarm optimization algorithm was used by introducing a random-weight strategy to determine the high-density impact areas of the HF within approximately 20 m on both sides of the fracturing borehole. Density cloud map analysis revealed a small area of the blank zone at the junction of the fracturing section, which provided a basis for fracturing parameter design and scheme optimization. The directional characteristics of the MS waveform were quantitatively described by polarity analysis, where the dip angle distribution ranged from 39° to 49°, the strike angle ranged from 61° to 74°, the slip angle was concentrated near 44°, and the stretch angle ranged from 61° to 74°. The WT-SVM proposed in this study overcame the effects of low SNR signals and the limitations of a small-area station network; 62% of the shear ruptures and 38% of the tensile ruptures of the HF were obtained. Shear cracks were the primary expression of the HF rupture type.

Algorithm Design to Identify Targets From Multipath Ghosts and Clutters for SISO IR-UWB Radar: Incorporating Waveform Features and Trajectory

Algorithm Design to Identify Targets From Multipath Ghosts and Clutters for SISO IR-UWB Radar: Incorporating Waveform Features and Trajectory

Analysis of Local Track Discontinuities and Defects in Railway Switches Based on Track-Side Accelerations.

Switches are an essential, safety-critical part of the railway infrastructure. Compared to open tracks, their complex geometry leads to increased dynamic loading on the track superstructure from passing trains, resulting in high maintenance costs. To increase efficiency, condition monitoring methods specific to railway switches are required. A common approach to track superstructure monitoring is to measure the acceleration caused by vehicle track interaction. Local interruptions in the wheel-rail contact, caused for example by local defects or track discontinuities, appear in the data as transient impact events. In this paper, such transient events are investigated in an experimental setup of a railway switch with track-side acceleration sensors, using frequency and waveform analysis. The aim is to understand if and how the origins of these impact events can be distinguished in the data of this experiment, and what the implications for condition monitoring of local track discontinuities and defects with wayside acceleration sensors are in practice. For the same experimental configuration, individual impact events are shown to be reproducible in waveform and frequency content. Nevertheless, with this track-side sensor setup, the different types of track discontinuities and defects (squats, joints, crossing) could not be clearly distinguished using characteristic frequencies or waveforms. Other factors, such as the location of impact event origin relative to the sensor, are shown to have a much stronger influence. The experimental data suggest that filtering the data to narrow frequency bands around certain natural track frequencies could be beneficial for impact event detection in practice, but differentiating between individual impact event origins requires broadband signals. A multi-sensor setup with time-synchronized acceleration sensors distributed over the switch is recommended.

Classification Algorithm for DC Power Quality Disturbances Based on SABO-BP

To better address and improve the issues related to DC power quality, this paper proposes an identification method tailored for DC power quality disturbances. First, it explores the underlying mechanisms and waveform characteristics of common DC power disturbances. By integrating the results of time–frequency analysis obtained through the S-transform, five distinct features are designed and extracted to serve as classification indicators. The SABO algorithm is subsequently employed to optimize the BP neural network, assisting in determining the optimal input weights and hidden layer thresholds. This optimization technique helps prevent the network from becoming stuck in local minima, thereby enhancing its robustness and generalization capabilities. This paper presents a simulation system for AC/DC power systems to conduct experimental verification. The system simulates various DC power quality issues and monitors abnormal waveforms. According to the designated classification index, the features of simulated disturbance signals are extracted. The SABO-BP classification prediction model is then used to automatically classify and identify the samples. The experimental results demonstrate high accuracy in classification and identification using the proposed method. In comparison to the BP neural network method, the SABO-BP method demonstrates an 8.207% improvement in accurately identifying disturbance signals. It is capable of accurately identifying direct current power quality signals, thereby assisting in the evaluation and control of power quality issues.