Signal Extraction Research Articles

Integration of Laser Communication and Aiming Based on High Sensitivity Beam Position Signal Extraction (Invited)

Integration of Laser Communication and Aiming Based on High Sensitivity Beam Position Signal Extraction (Invited)

EEG Signal Filtering Method and Comparison Between Modern to Classical Filter

In neuroscience and therapeutic applications, electroencephalography (EEG) has become a crucial technique for gaining understanding of brain activity and cognitive processes. In order to improve signal quality, lower noise, and extract useful information from EEG data, preprocessing is essential. In the context of EEG signal processing, this study compares the effectiveness of three frequently used filtering methods: the median filter, average filter, and Gaussian filter.This study's main goal is to assess and compare how well these filters work to reduce the various forms of noise that may be found in EEG data, such as physiological artifacts, environmental interference, and electrode noise. The study makes use of a large dataset of EEG recordings that spans a range of experimental settings and mental states. The filtering methods are methodically applied to the unprocessed EEG data, and then the effects on signal fidelity and quality are thoroughly examined.The findings of this investigation shed important light on the advantages and disadvantages of each filtering strategy used in EEG data processing. In the end, the conclusions from this comparison study help offer criteria for choosing a suitable filtering approach based on the unique properties of EEG data and the research goals.The findings of this investigation offer important new understandings of the advantages and disadvantages of each filtering strategy used in EEG data processing. Finally, depending on the unique properties of the EEG data and the study goals, the findings from this comparison analysis help offer criteria for choosing an acceptable filtering approach.

Does corporate social responsibility influence performance persistence? A signal extraction approach with evidence from Fortune 500 companies

The study seeks to enhance the current understanding of how corporate social responsibility (CSR) and environmental, social, and governance (ESG) factors influence firm performance. We establish a theoretical framework and provide empirical data on the influence of CSR/ESG on the sustained financial performance of publicly traded Fortune 500 companies between 2018 and 2021. Our model explains the way CSR/ESG activities affect a firm's ongoing financial success by revealing valuable signals to stakeholders. We employed quantile regression analysis to assess the connection, and found that a firm's CSR/ESG initiatives have both immediate and lasting long-term effects. Our findings contribute to and expand the literature on firm performance and sustainable competitive advantage. Companies adopting CSR/ESG can enhance a firm's performance and attain a sustainable competitive advantage.

Benchmarking of Sensor Configurations and Measurement Sites for Out-of-the-Lab Photoplethysmography.

Photoplethysmography (PPG) is used for heart-rate monitoring in a variety of contexts and applications due to its versatility and simplicity. These applications, namely studies involving PPG data acquisition during day-to-day activities, require reliable and continuous measurements, which are often performed at the index finger or wrist. However, some PPG sensors are susceptible to saturation, motion artifacts, and discomfort upon their use. In this paper, an off-the-shelf PPG sensor was benchmarked and modified to improve signal saturation. Moreover, this paper explores the feasibility of using an optimized sensor in the lower limb as an alternative measurement site. Data were collected from 28 subjects with ages ranging from 18 to 59 years. To validate the sensors' performance, signal saturation and quality, wave morphology, performance of automatic systolic peak detection, and heart-rate estimation, were compared. For the upper and lower limb locations, the index finger and the first toe were used as reference locations, respectively. Lowering the amplification stage of the PPG sensor resulted in a significant reduction in signal saturation, from 18% to 0.5%. Systolic peak detection at rest using an automatic algorithm showed a sensitivity and precision of 0.99 each. The posterior wrist and upper arm showed pulse wave morphology correlations of 0.93 and 0.92, respectively. For these locations, peak detection sensitivity and precision were 0.95, 0.94 and 0.89, 0.89, respectively. Overall, the adjusted PPG sensors are a good alternative for obtaining high-quality signals at the fingertips, and for new measurement sites, the posterior pulse and the upper arm allow for high-quality signal extraction.

Extraction of atmospheric corrosion monitoring sensor signals using MSSA and corrosion progress prediction with an LSTM model

The sensitivity of corrosion signals to temperature fluctuations often presents significant challenges for signals acquired from corrosion sensors. The main objective of this study is to assess these signals collected by the atmospheric corrosion monitoring sensor based on strain measurement through the application of multivariate singular spectrum analysis (MSSA) for extracting corrosion signal data. The study results demonstrate that MSSA has consistently exhibited exceptional performance in isolating corrosion signals, particularly in scenarios where they are affected by inherent noise resulting from abrupt temperature changes, corrosion products and residual stress. Moreover, the corrosion signals extracted via MSSA were harnessed to create a long short-term memory (LSTM) model, with the aim of predicting future corrosion processes. The results of this study emphasize the effectiveness and reliability of the LSTM model in forecasting corrosion processes. This study holds a crucial role in the management of corrosion processes in materials exposed to atmospheric conditions, ensuring both structural integrity and industrial safety.

(Invited) Electrochemistry Happens at the Interface

Earth-abundant, active, selective and stable electrocatalysts are the cornerstone in our transition to defossilisation of the chemical industry, sector coupling, and sustainable energy. The catalyst surface is known to transform at the interface to the electrolyte, especially under operation conditions. Only the properties of the transformed interface coupled to the underlying layer gives rise to observed metrics like activity and stability. This complicates predictive electrocatalyst materials design – a challenge which can only be tackled by collaborative and interdisciplinary efforts, at the Interface between Chemistry, Physics, Materials Science, and Engineering. I believe this presents one of the main tasks for the young electrochemistry researchers in Europe and around the world, especially since the same challenges also arise in related technologies like Li-ion batteries.In my talk I will discuss how the old wisdom from semiconductor research “the Interface is the device” can be translated into the current challenge “Electrochemistry happens at the Interface”. We need new approaches in designing and studying these electrochemical interfaces for fundamental insights that may in a next step yield better performance in future applications. The talk will be split in two parts.Firstly, I will introduce how single crystalline surfaces can be obtained using epitaxial thin films, and discuss how these can be used to derive atomic-level structure-property relations by synergetic experimental and theoretical investigation. Such films can be fabricated with unit-cell or even atomic-layer precision and enable direct comparison to single facets typically investigated in density functional theory.Secondly, I will discuss opportunities and challenges in surface-sensitive operando characterization in a liquid medium.1 Information from the outermost surface of a catalyst can be obtained through a standing-wave approach2,3 or extraction of a surface-only signal from careful thickness-dependent studies.4 In my ERC project “Interfaces at work”, we are also developing interface-sensitive, laboratory-based operando X-ray photoelectron spectroscopy approaches based on the new XPS user facility at the University of Twente.Throughout the talk, I will refer to examples from LaNiO3 thin films, which are atomically flat both before and after application as electrocatalysts for the OER during water electrolysis. We selectively tuned the surface cationic composition in epitaxial growth. The Ni-termination is approximately twice as active for the OER as the La-termination.2 Our ex situ and operando characterization confirmed that the surface transformation pathways – and therefore the electrochemical functionality – depend on a single atomic layer at the surface. A second example will be the introduction of multi-cation compositions in so-called high entropy perovskite oxides (HEO), which can maximize the catalytic activity. The HEO LaCr0.2Mn0.2Fe0.2Co0.2Ni0.2O3-δ outperforms all of its parent compounds (single TM-site element in the LaTMO3 perovskite) by orders of magnitude.5 X-ray photoemission studies reveal a synergistic effect of simultaneous oxidation and reduction of different transition metal cations during adsorption of reaction intermediates. Rao, R. R., van den Bosch, I. C. G. & Baeumer, C. Operando X-ray characterization of interfacial charge transfer and structural rearrangements. in Reference Module in Chemistry, Molecular Sciences and Chemical Engineering 1–24 (Elsevier, 2023). doi:10.1016/B978-0-323-85669-0.00068-4.Baeumer, C. et al. Tuning electrochemically driven surface transformation in atomically flat LaNiO3 thin films for enhanced water electrolysis. Nat Mater 20, 674–682 (2021).Martins, H. P. et al. Near total reflection x-ray photoelectron spectroscopy: quantifying chemistry at solid/liquid and solid/solid interfaces. J Phys D Appl Phys 54, 464002 (2021).Baeumer, C. Operando characterization of interfacial charge transfer processes. J Appl Phys 129, 170901 (2021).Kante, M. V et al. A High-Entropy Oxide as High-Activity Electrocatalyst for Water Oxidation. ACS Nano (2023) doi:10.1021/acsnano.2c08096. Figure 1

Evaluating RGB channels in remote photoplethysmography: a comparative study with contact-based PPG.

Remote photoplethysmography (rPPG) provides a non-contact method for measuring blood volume changes. In this study, we compared rPPG signals obtained from video cameras with traditional contact-based photoplethysmography (cPPG) to assess the effectiveness of different RGB channels in cardiac signal extraction. Our objective was to determine the most effective RGB channel for detecting blood volume changes and estimating heart rate. We employed dynamic time warping, Pearson's correlation coefficient, root-mean-square error, and Beats-per-minute Difference to evaluate the performance of each RGB channel relative to cPPG. The results revealed that the green channel was superior, outperforming the blue and red channels in detecting volumetric changes and accurately estimating heart rate across various activities. We also observed that the reliability of RGB signals varied based on recording conditions and subject activity. This finding underscores the importance of understanding the performance nuances of RGB inputs, crucial for constructing rPPG signals in algorithms. Our study is significant in advancing rPPG research, offering insights that could benefit clinical applications by improving non-contact methods for blood volume assessment.

Weak signal extraction in non-stationary channel with weak measurement

An emerging challenge of integrated communication and sensing is the extraction of weak sensing signals transmitted through an unknown non-stationary channel. In this work, we propose a weak signal extraction method with weak measurement. Taking advantage of time division multiplexing, we preliminarily estimate the channel via adjustable finite impulse response filter, further suppressing the interfering signal caused by background noises via spectrum shift. By subsequently using the time-varying phase estimation method via weak measurement, the real-time detection of weak signals in the non-stationary channel is achieved. We demonstrate via theoretical analysis and confirmatory experiment that our method is able to amplify the phase shift, to suppress technical noise and to improve detection resolution limit, while proving robust against light source fluctuations, initial phase differences and detector saturation. The method hence enables weak sensing signal extraction with a low signal-to-noise ratio non-stationary channel. Furthermore, we interface our measurement method to squeezed light sources, offering the possibility of surpassing standard quantum limit.

The design of the micro-ECG amplification circuit

This essay discusses a potential method of designing a micro-wearable device that can effectively detect human heart conditions and provide real-time feedback. Compared to the traditional method of designing wearable devices, the core innovation in this new approach is the utilization of the DRL circuit for the extraction of common-mode signals. This device can filter out a significant amount of noise due to the low current and low voltage conditions. The low current and low voltage conditions under which this device operates contribute to its remarkable energy efficiency. Consequently, the device boasts a long-lasting battery, reducing the need for frequent recharges, which is often a significant inconvenience associated with wearable technology. In the meantime, the non-invasive nature of the ECG technology integrated into this wearable device ensures that wearers can go about their daily lives comfortably without causing discomfort, making it suitable for continuous heart monitoring without impeding peoples activities or affecting peoples overall comfort.

A parallel input parallel output current fed front end DC‐DC converter‐based solid state transformer utilised in grid connected mode PV applications

AbstractThe presented work operated a PIPO Current fed front end converter‐based solid‐state transformer (SST) for maximum power point tracking (MPPT) and voltage boost operation in medium power range PV applications. The proposed system employed the perturb and observe (P&O)‐based MPPT technique and upheld the ZVS and ZCS operations of all primary side switches and secondary side rectifier diodes, respectively. The proposed system introduced a new fundamental extractor titled ‘Pre‐filter double fundamental signal extractor (DFSE) based multi‐layered DFSE (p‐DFSE‐MDFSE)’ and utilised it in the grid controller for synchronisation and harmonics mitigation operation. The primary goal was to achieve a compact size, reduced weight and cheaper magnetic component‐based front‐end converter of SST on the PV array side and a fast, secure and trustworthy mitigation control of current harmonics on the utility grid side. The proposed system was inspected on the OPAL‐RT (RT‐LABv2021.3.2.307) platform for various grid anomalies, and its conduct was found to be well within the IEEE 519 norms.

Comparative Analysis of Perfusion Index and End-Tidal Carbon Dioxide in Cardiac Arrest Patients: Implications for Hemodynamic Monitoring and Resuscitation Outcomes.

During cardiopulmonary resuscitation (CPR), some parameters (e.g., intraarterial pressure measurement and end-tidal carbon dioxide (EtCO2)) indicate the quality andoutcome of resuscitation. These parameters are generally based on monitoring the hemodynamic status. Perfusion index (PI) is a calculation from the photoplethysmography (PPG) signal and displays the proportion of pulsatile to non-pulsatile light absorption or reflection in the PPG signal. It helps to evaluate cardiac output and tissue perfusion in the care of a critical patient. Its most important advantages are that it can be easily measured with a pulse oximeter probe attached to the finger (non-invasive), can be objectively repeated, can be applied quickly, and is inexpensive. Normal PI values range from 0.2% to 20%. Despite being recognized as a valuable indicator of hemodynamics, there is limited information regarding its relevance in patients experiencing cardiac arrest. Although the PI is known to be a valuable parameter to indicate hemodynamics, information about its value in cardiac arrest patients is limited. This study aims to evaluate the performance of PI and EtCO2 in predicting the return of spontaneous circulation (ROSC) among cardiac arrest patients. This was a single-center, prospective, observational clinical study including both out-of-hospital and in-hospital adult cardiac arrest patients. The study was conducted from November 1, 2018 to April 30, 2019 at the Emergency Department (ED) of the Hacettepe University Hospital, Ankara, Turkey. The EtCO2 values of the patients were recorded at the time they were intubated (t0) and every five minutes (t5, t10, t15...) during CPR. Along with these measurements, PI values were measured with the Masimo Signal Extraction Technology device (Masimo, California, United States). The study's primary outcome was PI's performance in predicting the ROSC among cardiac arrest patients. The secondary outcomes of the study were the performance of EtCO2 in predicting the ROSC among cardiac arrest patients and the association between PI and EtCO2 values. We included a total of 100 cases. The mean age of patients was 70.4 ± 13.4 years, and 65% were male. The ROSC was achieved in 29patients. There was no statistical difference in PI values between the ROSC (+) and ROSC (-) groups at any minute. However, in the ROSC (+) group, EtCO2 values were observed to be high starting from the fifth minute (t5, p=0.010; t10, p<0.001; t15, p=0.014; t20, p=0.033; t25, p=0.003, respectively). There was no correlation between the PI and EtCO2 values at 0, 5, 10, 15, 20, and 25 minutes (t0, p=0.436; t5, p=0.154; t10, p=0.557; t15, p=0.740; t20 p=0.241; t25 p=0.201, respectively). Measuring PI values during resuscitation in intubated cardiac arrest patients does not help clinicians predict the outcome. In addition, no correlation was found with EtCO2 values. However, EtCO2 values remained high in patients with the ROSC from the fifth minute onward. Further larger-scale studies are needed regarding the optimal use of PI in cardiac arrest patients.

Identification of spikes in continuous ground-based in situ time series of CO2, CH4 and CO: an extended experiment within the European ICOS Atmosphere network

Abstract. The identification of spikes (i.e., short and high variability in the measured signals due to very local emissions occurring in the proximity of a measurement site) is of interest when using continuous measurements of atmospheric greenhouse gases (GHGs) in different applications like the determination of long-term trends and/or spatial gradients, inversion experiments devoted to the top-down quantification of GHG surface–atmosphere fluxes, the characterization of local emissions, or the quality control of GHG measurements. In this work, we analyzed the results provided by two automatic spike identification methods (i.e., the standard deviation of the background (SD) and the robust extraction of baseline signal (REBS)) for a 2-year dataset of 1 min in situ observations of CO2, CH4 and CO at 10 different atmospheric sites spanning different environmental conditions (remote, continental, urban). The sensitivity of the spike detection frequency and its impact on the averaged mole fractions on method parameters was investigated. Results for both methods were compared and evaluated against manual identification by the site principal investigators (PIs). The study showed that, for CO2 and CH4, REBS identified a larger number of spikes than SD and it was less “site-sensitive” than SD. This led to a larger impact of REBS on the time-averaged values of the observed mole fractions for CO2 and CH4. Further, it could be shown that it is challenging to identify one common algorithm/configuration for all the considered sites: method-dependent and setting-dependent differences in the spike detection were observed as a function of the sites, case studies and considered atmospheric species. Neither SD nor REBS appeared to provide a perfect identification of the spike events. The REBS tendency to over-detect the spike occurrence shows limitations when adopting REBS as an operational method to perform automatic spike detection. REBS should be used only for specific sites, mostly affected by frequent very nearby local emissions. SD appeared to be more selective in identifying spike events, and the temporal variabilities in CO2, CH4 and CO were more consistent with those of the original datasets. Further activities are needed for better consolidating the fitness for purpose of the two proposed methods and to compare them with other spike detection techniques.

DarLoc: Deep learning and data-feature augmentation based robust magnetic indoor localization

Magnetic-based indoor localization has attracted considerable attention due to the pervasiveness of geomagnetic fields and is free of extra infrastructures. However, existing approaches still face heterogeneity problems caused by the type of devices, the holding attitudes, and the moving speeds of pedestrians. To cope with it, we propose a novel deep learning and data-feature augmentation based magnetic localization framework (DarLoc). First, we invoke orientation-insensitive magnetic signal extraction to remove the Direct Current (DC) component of the sequence and to eliminate the impact caused by different holding attitudes and various mobile devices. Second, we propose novel data augmentation and feature augmentation methods to extract features of speed information, thus tackling the multi-scale sequence problem caused by different moving speeds. Finally, we propose a deep multi-scale spatial–temporal learning model to extract both spatial and temporal features of the augmented sequences and to provide a robust localization for people with various moving speeds and attitudes. Extensive experiments, including a total of 189 volunteers with 4 different mobile devices and multiple moving speeds, are employed to evaluate the performance of DarLoc over 14 months. Experiment results reveal that: (1) DarLoc obtains an average localization accuracy of 0.47 m and 0.56 m under constant and variable moving speeds scenarios, respectively; (2) DarLoc obtains about 41% and 60% localization accuracy improvement compared with the state-of-the-art localization methods.

Study on match filtering based on sequency spectrum characteristics of the Walsh transform for electromagnetic flowmeter signal measurement

The signal processing techniques are crucial in applying the electromagnetic flowmeter (EMF), and its filtering mechanism directly influences the measurement accuracy. Considering heavy noise frequently contaminates EMF signals, a sequency match filtering method based on the Walsh transform for processing EMF signals is proposed for the first time. The proposed method can accurately match the signal’s sequency and realize excellent harmonic extraction by analyzing features of the sequency spectrum. For method effectiveness validation, it not only has a high signal-to-noise ratio (SNR > 75 dB) in theoretical filtering but also has a low relative indication error (e<2%) in experiments on the water flow calibration and the slurry flow measurement and a low En number (En<0.5) on performance evaluation, which indicates that the proposed method enables the efficient and accurate extraction of EMF signals.

A Density-Based Multilevel Terrain-Adaptive Noise Removal Method for ICESat-2 Photon-Counting Data.

The photon point clouds collected by the high-sensitivity single-photon detector on the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) are utilized in various applications. However, the discretely distributed noise among the signal photons greatly increases the difficulty of signal extraction, especially the edge noise adjacent to signals. To detect signal photons from vegetation coverage areas at different slopes, this paper proposes a density-based multilevel terrain-adaptive noise removal method (MTANR) that identifies noise in a coarse-to-fine strategy based on the distribution of noise photons and is evaluated with high-precision airborne LiDAR data. First, the histogram-based successive denoising method was used as a coarse denoising process to remove distant noise and part of the sparse noise, thereby increasing the fault tolerance of the subsequent steps. Second, a rotatable ellipse that adaptively corrects the direction and shape based on the slope was utilized to search for the optimal filtering direction (OFD). Based on the direction, sparse noise removal was accomplished robustly using the Otsu's method in conjunction with the ordering points to identify the clustering structure (OPTICS) and provide a nearly noise-free environment for edge searching. Finally, the edge noise was removed by near-ground edge searching, and the signal photons were better preserved by the surface lines. The proposed MTANR was validated in four typical experimental areas: two in Baishan, China, and two in Taranaki, New Zealand. A comparison was made with three other representative methods, namely differential, regressive, and Gaussian adaptive nearest neighbor (DRAGANN), used in ATL08 products, local distance statistics (LDS), and horizontal ellipse-based OPTICS. The results demonstrated that the values of the F1 score for the signal photon identification achieved by the proposed MTANR were 0.9762, 0.9857, 0.9839, and 0.9534, respectively, which were higher than those of the other methods mentioned above. In addition, the qualitative and quantitative results demonstrated that MTANR outperformed in scenes with steep slopes, abrupt terrain changes, and uneven vegetation coverage.

The Early Diagnosis of Rolling Bearings’ Faults Using Fractional Fourier Transform Information Fusion and a Lightweight Neural Network

In response to challenges associated with feature extraction and diagnostic models’ complexity in the early diagnosis of bearings’ faults, this paper presents an innovative approach for the early fault diagnosis of rolling bearings. This method combined concepts from frequency domain signal analysis with lightweight neural networks. To begin, vibration signals from rolling bearings were collected using vibration sensors, and the mean square value was utilized as an indicator for accurate early fault signal extraction. Subsequently, employing the fractional Fourier transform, the time domain signal was converted into a frequency domain signal, which provided more detailed frequency feature information. The fusion process combined amplitude frequency and phase frequency information, and was visualized as a Gram angle field map. The lightweight neural network Xception was selected as the primary fault diagnosis tool. Xception, a convolutional neural network (CNN) variant, was chosen for its lightweight design, which maintains excellent performance while significantly reducing model parameters. The experimental results demonstrated that the Xception model excelled in rolling bearing fault diagnosis, particularly when utilizing fused information datasets. This outcome underscores the advantages of combining information fusion and the Xception model to enhance the accuracy of early rolling bearing fault diagnosis, and offers a viable solution for health monitoring and fault diagnosis in industrial settings.

Deep learning algorithm using bispectrum analysis energy feature maps based on ultrasound radiofrequency signals to detect breast cancer.

Ultrasonography is an important imaging method for clinical breast cancer screening. As the original echo signals of ultrasonography, ultrasound radiofrequency (RF) signals provide abundant tissue macroscopic and microscopic information and have important development and utilization value in breast cancer detection. In this study, we proposed a deep learning method based on bispectrum analysis feature maps to process RF signals and realize breast cancer detection. The bispectrum analysis energy feature maps with frequency subdivision were first proposed and applied to breast cancer detection in this study. Our deep learning network was based on a weight sharing network framework for the input of multiple feature maps. A feature map attention module was designed for multiple feature maps input of the network to adaptively learn both feature maps and features that were conducive to classification. We also designed a similarity constraint factor, learning the similarity and difference between feature maps by cosine distance. The experiment results showed that the areas under the receiver operating characteristic curves of our proposed method in the validation set and two independent test sets for benign and malignant breast tumor classification were 0.913, 0.900, and 0.885, respectively. The performance of the model combining four ultrasound bispectrum analysis energy feature maps in breast cancer detection was superior to that of the model using an ultrasound grayscale image and the model using a single bispectrum analysis energy feature map in this study. The combination of deep learning technology and our proposed ultrasound bispectrum analysis energy feature maps effectively realized breast cancer detection and was an efficient method of feature extraction and utilization of ultrasound RF signals.

System and approach to detecting of gastric slow wave and environmental noise suppression based on optically pumped magnetometer

Gastric slow waves (SWs) are commonly used for the quantitative assessment of gastric functional disorders. Compared with surface electrogastrography, using of magnetic signals to record SWs can achieve higher-quality signal recording. In this study, we discovered that optically pumped magnetometers (OPM) based on the spin exchange relaxation-free method have comparable weak magnetic detection capabilities to superconducting quantum interference devices but without liquid helium cooling. However, owing to the inevitable interference of low-frequency environmental drift, the characteristic features of SW are obscured, greatly increasing the difficulty in detecting gastric magnetic signals. Therefore, in this study, we constructed an OPM Magnetogastrography (OPM-MGG). We proposed an adaptive filtering architecture combined with environmental drift suppression and a non-stationary signal decomposition method for extracting SW signals. Through controlled human experiments, the results demonstrated that our testing system successfully extracted SW signals in the frequency range of 2–4 cycles per minute. The extracted SW signals exhibited consistent power and time–frequency characteristics with the reported results. This study validates the feasibility of (1) using the OPM-MGG system for capturing SW signals and (2) the proposed processing strategies for identifying ultralow-frequency SW signals. In conclusion, the OPM-MGG system and the signal extraction strategies developed in this study have the potential to provide a wearable technology for bioweak magnetic field measurements, offering new opportunities for both research and clinical applications.

Research on mechanical fault diagnosis method of high voltage circuit breaker based on new measurement

Aiming at the problem that the detection of a single signal is difficult to accurately and comprehensively reflect the mechanical failure of the circuit breaker operating mechanism, the VS1 (VBM7) -12 high-voltage circuit breaker is selected as the experimental object in this paper. The coil current signal is collected by a sensor based on new measurements, and the fault diagnosis method of high-voltage circuit breaker is studied from the acquisition, preprocessing, and feature extraction of vibration signal and sound signal, combined with artificial intelligence algorithm. The research shows that the application of electro-acoustic joint features can effectively enhance the precision of fault diagnosis of high-voltage circuit breakers, and verify the feasibility and superiority of the mechanical fault diagnosis method of high-voltage circuit breakers based on new measurements.

Underwater target material classification based on feature extraction of finite element simulated signal

Feature extraction is crucial for underwater target classification and recognition, and this paper aims to propose a scheme for accurately classifying the material features of underwater targets. Finite element simulation software COMSOL is used to obtain echo signals, and then Auto Regressive (AR) coefficient, cepstrum feature, spectral peak and its frequency of Smoothing Pseudo Wigner-Ville Distribution (SPWVD) of echo signals are extracted as the input of the pattern recognition network. The classification results show that the proposed scheme is able to effectively differentiate among four types of materials: metal, brick, plastic and rubber. The scheme also shows good robustness for the target size and shape, and the proposed scheme has the potential to be applied to the practical underwater target classification.