Signal Spectrum Research Articles

Optimized deep learning model with integrated spectrum focus transformer for pavement distress recognition and classification.

In the task of pavement distress recognition and classification, the complexity of the pavement environment, the small proportion of distresses in images, significant variation in distress scales, and the influence of features such as vehicles and traffic signs in the data make distress feature extraction challenging. This paper proposes a spectrum focus transformer (SFT) layer, which processes the signal spectrum and focuses on important frequency components. Initially, by thoroughly analyzing the frequency domain characteristics of image data, frequency value distribution information is obtained to achieve fine-tuning of different frequency components. Subsequently, frequency information and images are learned and weighted in the frequency domain, thereby enhancing the capability to capture pavement distress regions. Experiments conducted on the road pavement distress dataset revealed through heatmap analysis that distress regions received increased attention, achieving an accuracy of 97.73%. This performance demonstrates a higher accuracy compared to other models.

Single-pixel computational spectrometer based on nonlinear spectrum modulation

Computational spectroscopy with modulated light spectrum is especially well-suited for application scenarios where active illumination is needed. Most of the existing computational spectroscopy, however, uses ambient illumination and modulates the signal spectrum that is reflected or transmitted from objects, mainly because of the lack of a light source with effective spectrum modulation. Here, we present a novel computational spectrometer featuring light-source spectrum modulation achieved through polarization-induced nonlinear spectrum modulation, offering a compact and low-cost system apparatus. The modulated spectrum can evolve periodically by managing polarization-induced nonlinear phase as well as pump power, within the range from 1000 nm to 1100 nm. Combining wavelength multiplexing and compressed sensing, we can achieve a minimum spectral resolution of 0.2 nm, which is comparable to the commercial spectrometer. The utilized objects with sparse and non-sparse spectra are successfully reconstructed both in simulation and experiment, within an effective reconstruction spectral range from 1021 nm to 1077 nm.

A Computationally Efficient Method for the Diagnosis of Defects in Rolling Bearings Based on Linear Predictive Coding

Monitoring the condition of rolling bearings is a crucial task in many industries. An efficient tool for diagnosing bearing defects is necessary since they can lead to complete machine failure and significant economic losses. Traditional diagnosis solutions often rely on a complex artificial feature extraction process that is time-consuming, computationally expensive, and too complex to deploy in practice. In actual working conditions, however, the amount of labeled fault data available is relatively small, so a deep learning model with good generalization and high accuracy is difficult to train. This paper proposes a solution that uses a simple feedforward artificial neural network (NN) for classification and adopts the linear predictive coding (LPC) algorithm for feature extraction. The LPC algorithm finds several coefficients for a given signal segment containing information about the signal spectrum, which is sufficient for further classification. The LPC-NN solution was tested on the Case Western Reserve University (CWRU) and South Ural State University (SUSU) datasets. The results demonstrated that, in most cases, LPC-NN yielded an accuracy of 100%. The proposed method achieves higher diagnostic accuracy and stability to load changes than other advanced techniques, has a significantly improved time performance, and is conducive to real-time industrial fault diagnosis.

Evaluating the Effects of Biomass Burning on Severe Haze Formation in a Megacity of Sichuan Basin, Southwestern China

AbstractSevere haze pollution has long been an environmental problem, which is complicated and poorly understood in the Sichuan Basin (SCB). In this study, a field observation was carried out to investigate the factors driving haze formation in urban Chengdu, a typical megacity in the SCB. It was found that the accumulation of biomass burning organic aerosol (BBOA) played an important role in haze formation in urban Chengdu. The average mass fraction of BBOA in PM2.5 increased from ∼1% during clear days to ∼10% during severe haze episodes. A method combining backward trajectory analysis with fire spot distribution was used to evaluate the effects of regional transport of biomass burning (BB) emissions. The results showed that BBOA concentration increased by ∼3 times and PM2.5 concentration increased by ∼54% when BB emissions were transported from adjacent areas to urban Chengdu. Moreover, the parameter f60 (the ratio of the integrated signal at m/z 60 to the total signal in the organic component mass spectrum), which indicated the impacts of BB emissions, was reassessed to be 0.54% instead of the widely used value 0.3% previously. Our results uncovered the importance of BB emissions on haze formation in urban areas in the SCB and provided new insights into pollutant mitigation strategies in the region.

An Imaging Method for Marine Targets in Corner Reflector Jamming Scenario Based on Time–Frequency Analysis and Modified Clean Technique

In the corner reflector jamming scenario, the ship target and the corner reflector array have different degrees of defocusing in the synthetic aperture radar (SAR) image due to their complex motions, which is unfavorable to the subsequent target recognition. In this manuscript, we propose an imaging method for marine targets based on time–frequency analysis with the modified Clean technique. Firstly, the motion models of the ship target and the corner reflector array are established, and the characteristics of their Doppler parameter distribution are analyzed. Then, the Chirp Rate–Quadratic Chirp Rate Distribution (CR-QCRD) algorithm is utilized to estimate the Doppler parameters. To address the challenges posed by the aggregated scattering points of the ship target and the overlapping Doppler histories of the corner reflector array, the Clean technique is modified by short-time Fourier transform (STFT) filtering and amplitude–phase distortion correction using fractional Fourier transform (FrFT) filtering. This modification aims to improve the accuracy and efficiency of extracting scattering point components. Thirdly, in response to the poor universality of the traditional Clean iterative termination condition, the kurtosis of the residual signal spectrum amplitude is adopted as the new iterative termination condition. Compared with the existing imaging methods, the proposed method can adapt to the different Doppler distribution characteristics of the ship target and the corner reflector array, thus realizing better robustness in obtaining a well-focused target image. Finally, simulation experiments verify the effectiveness of the algorithm.

Fault Detection in Induction Machines Using Learning Models and Fourier Spectrum Image Analysis.

Induction motors are essential components in industry due to their efficiency and cost-effectiveness. This study presents an innovative methodology for automatic fault detection by analyzing images generated from the Fourier spectra of current signals using deep learning techniques. A new preprocessing technique incorporating a distinctive background to enhance spectral feature learning is proposed, enabling the detection of four types of faults: healthy motor coupled to a generator with a broken bar (HGB), broken rotor bar (BRB), race bearing fault (RBF), and bearing ball fault (BBF). The dataset was generated from three-phase signals of an induction motor controlled by a Direct Torque Controller under various operating conditions (20-1500 rpm with 0-100% load), resulting in 4251 images. The model, based on a Visual Geometry Group (VGG) architecture with 19 layers, achieved an overall accuracy of 98%, with specific accuracies of 99% for RAF, 100% for BRB, 100% for RBF, and 95% for BBF. A new model interpretability was assessed using explainability techniques, which allowed for the identification of specific learning patterns. This analysis introduces a new approach by demonstrating how different convolutional blocks capture particular features: the first convolutional block captures signal shape, while the second identifies background features. Additionally, distinct convolutional layers were associated with each fault type: layer 9 for RAF, layer 13 for BRB, layer 16 for RBF, and layer 14 for BBF. This methodology offers a scalable solution for predictive maintenance in induction motors, effectively combining signal processing, computer vision, and explainability techniques.

Distinguishing Majorana bound states from Andreev bound states through differential conductance and current noise spectrum

Abstract We investigate the quantum transport through a quantum dot coupled with a superconducting
(SC) nanowire. By elaborating the differential conductance and current noise spectrum, we focus on the distinct characteristics
of the topological Majorana bound states (MBSs) and 
 trivial Andereev bound states (ABSs) hosted in SC wire.
For MBSs with a topological quality factor $q=1$,
we observe the degenerate features
manifested as the zero-bias peak (ZBP) 
in differential conductance and the Rabi dips degeneracy (RDD) in noise spectrum.
In contrast, for ABSs with $q<1$,
the splitting of these degenerate features 
depends on the linewidth, arising from realistic measurement conditions.
 Furthermore, we identify the critical quality factors 
$q_{\rm c}$ and $q_{\s}$ associated with the emergences of ZBP and
 RDD, respectively.
The value of $q_{\rm c}$ is temperature-dependent, and we 
establish a suitable temperature window to ensure the visibility of single ZBP in the experiments. 
Whereas, $q_{\rm c}$ depends on the coupling strength rather temperature.
Typical values for these quality factors are
approximately $q_{\rm c}\approx 0.93$ and $q_{\s}\approx 0.99$.
Our results suggest that 
the degenerate Rabi spectrum signal 
 could serve as a hallmark for the presence of MBSs, 
 which goes beyond the scope of differential
conductance.

Synthesis, Structure and Biological Activity of 2-Methyl-5-nitro-6-phenylnicotinohydrazide-Based Hydrazones.

The synthetic availability and wide range of biological activity of hydrazides and hydrazones make them attractive subjects for investigation. In this study, we focused on synthesis of 2-methyl-5-nitro-6-phenylnicotinohydrazide-based hydrazones derived from the corresponding substituted aldehydes. The structure of the obtained compounds was studied using NMR spectroscopy and DFT calculations. After repeated recrystallization, all the synthesized compounds remained as mixtures of isomers. As a result of a detailed analysis, we found that the duplication and bifurcation of signals in the 1H NMR spectra for some atoms is a consequence of the existence of four isomers, namely Z-I, Z-II, E-I and E-II. Duplicate proton signals with a chemical shift difference of 0.1-0.2 ppm and in a ratio of about 2:1 were noticed in the experimental data. By modeling the structures of individual configurations and conformations, Gibbs free energy values were obtained, which allowed us to estimate the approximate content of rotamers for the E-isomer equal to 3:2, which coincided with experimental data. We also tested the antibacterial and antifungal activity of the synthesized compounds.

Detection of Rupture Damage Degree in Laminated Rubber Bearings Using a Piezoelectric‐Based Active Sensing Method and Hybrid Machine Learning Algorithms

Laminated rubber bearings may exhibit rupture damage due to factors such as temperature variations and seismic activity, which can reduce their isolation performance. Current detection methods, including human‐vision inspection and computer‐vision inspection, have certain limitations in accurately assessing the degree of rupture damage. This study attempts to combine the piezoelectric‐based active sensing method with a machine learning algorithm to detect rupture damage in laminated rubber bearings. A series of laminated rubber bearings with varying degrees of rupture damage were fabricated, and 1440 sets of detection signals were obtained through experiments using the active sensing method. This study proposes a hybrid machine learning algorithm that integrates a one‐dimensional convolutional neural network (1DCNN), long short–term memory (LSTM) network, Bayesian optimization (BO) algorithm, and extreme gradient boosting (XGB) algorithm. The algorithm involves using the 1DCNN and LSTM algorithms to extract the deep features from the wavelet packet energy spectra of the detection signals, and then employing the XGB algorithm optimized by the BO algorithm to construct the prediction model. The research results indicate that the proposed 1DCNN–LSTM–BO–XGB model achieved an accuracy value of 98.6% on the test set, outperforming the 1DCNN–LSTM (91.7%), 1DCNN (88.9%), LSTM (25.0%), XGB (90.3%), and SVM (66.7%) algorithms. Therefore, the combination of the active sensing method and machine learning algorithm shows promising application prospects in detecting the degree of rupture damage in laminated rubber bearings.

High-throughput calculations and machine learning modeling of 17O NMR in non-magnetic oxides.

The only NMR-active oxygen isotope, oxygen-17 (17O), serves as a sensitive probe due to its large chemical shift range, the electric field gradient at the oxygen site, and the quadrupolar interaction. Consequently, 17O solid-state NMR offers unique insights into local structures and finds significant applications in the studies of disorder, reactivity, and host-guest chemistry. Despite recent advances in sensitivity enhancement, isotopic labeling, and NMR crystallography, the application of 17O solid-state NMR is still hindered by low natural abundance, costly enrichment, and challenges in handling spectrum signals. Density functional theory calculations and machine learning techniques offer an alternative approach to mapping the local crystal structures to NMR parameters. However, the lack of high-quality data remains a challenge, despite the establishment of some datasets. In this study, we implement and execute a high-throughput workflow combining AiiDA and CASTEP to evaluate the NMR parameters. Focusing on non-magnetic oxides, we have chosen over 7100 binary, ternary, and quaternary compounds from the Materials Project database and performed calculations. Furthermore, using various descriptors for the local crystalline environments, we model the 17O NMR parameters using machine learning techniques, further enhancing our ability to predict and understand 17O NMR parameters in oxide crystals.

An Intelligent Method of Health State Division and Assessment for Gearbox Based on Acoustic Signals

ABSTRACTGearboxes are a crucial component of the transmission systems in many devices. Due to prolonged operation and high loads, it is inevitable that their condition will degrade over time. Therefore, intelligently dividing health state stages and conducting timely and effective health state assessments are essential for ensuring the safe and reliable operation of gearboxes. In response to the acoustic signals generated during the operation of gearboxes, a health state division and assessment method based on acoustic signals is proposed. Initially, fast Fourier transform (FFT) is utilized to convert the measured sound signal into a spectral signal that is relatively less disturbed by noise. Subsequently, the temporal convolutional autoencoder (TCAE) is proposed and constructed to encode and decode the spectrum signals at different moments, so that the trained encoder can be used to extract the deep features of the signals adaptively. After that, K‐Means clustering method was used to automatically divide the health state of the gearbox combined with the extracted deep features. Finally, the one‐dimensional convolutional neural networks (1DCNN) model is constructed and trained, and the deep features extracted by TCAE are input to identify the health state stage of the test sample, so as to realize the health state assessment of the gearbox. The experimental results show that, in the gearbox data set of three working conditions, the proposed method is closer to the health stage of manual calibration, which proves the rationality of the proposed intelligent method. The accuracy of the proposed health assessment method can reach 95%, 90%, and 90%, respectively, and the effect is obviously better than that of the more commonly used models at present, achieving effective health state assessment of the gearbox under non‐destructive testing conditions.

A novel noise reduction algorithm and its application in failure information extraction of bearing

The performance of rolling bearing can directly affect the reliability of a rotating machine. Due to the influence of noise and irrelevant components, the fault information is rather weak in a bearing fault, which adds to the difficulty in judgment of bearing state and identification of fault types. To solve the problem of heterogeneous feature information caused by noise jamming in rolling bearing failure, difficulty in extracting fault feature, and misdiagnosis and misjudgment, the paper has proposed a denoising algorithm according to the covariance matrix of a Hankel matrix and information entropy (Hankel-Cov-IE). First, covariance matrix of a Hankel matrix of signals is constructed, and fault signals are preliminarily denoised based on denoising capacity of the covariance matrix and its nature of combining global and local features. To address the selection of sensitive fault components, information entropy (IE) is taken advantage of to screen out the vectors containing abundant feature information from the covariance matrix. The filtered feature vectors are reconstructed in the way of equal weight and faults of rolling bearing are identified with the spectrum of signals reconstructed. The presented algorithm is compared with different typical methods. The results indicate that the presented algorithm can reduce noise more effectively and achieve extraction of bearing failure features more comprehensively and correctly, with correct judgment of fault types. The presented algorithm can correctly extract the feature frequency of bearing fault and recognize the types of faults, and is insensitive to sensor sites, speeds, and failure types of bearings.

IDENTIFICATION OF MOBILE DEVICES BY CORRELATION FEATURES OF THEIR SIGNAL SPECTRA

Context. The mass spread of Wi-Fi networks is facilitated by the simplicity of their deployment, high speed, universality, and convenience of use. The development and dissemination of these networks continue despite a number of shortcomings. One of the shortcomings is their vulnerability to various types of attacks, including those based on the forgery (imitation) of identification data. At the same time, there are physical layer characteristics, knowledge of which expands the understanding of the network’s state, can contribute to increasing the reliability of network subscriber identification, and thus prevent a number of attacks. This research is aimed at the theoretical and practical substantiation of the possibility of their application. Objective. The aim of the study is to assess the application of detailed analysis of signal spectra emitted by devices connected to wireless Wi-Fi networks for their identification. To achieve this goal, it is necessary to analyze the experimentally measured spectra of wireless devices connected to the Wi-Fi network and evaluate the possibility of using the spectrum for the identification of mobile devices. Method. This work proposes a method for processing the results of measuring the spectra of Wi-Fi device emissions by evaluating the asymmetry coefficient of the Wi-Fi device spectrum’s cross-correlation function. Mathematical modeling was used to assess the effectiveness of the method. Results. The research results show that the minimum value of the asymmetry coefficient when comparing the template with different positions of one’s own device, and large values of the asymmetry coefficient when comparing templates with foreign spectra. Therefore, this characteristic can also be used for the identification of Wi-Fi devices. Conclusions. The research results suggest the possibility of applying the proposed method for the identification of mobile devices, which will qualitatively complement existing security models with another feature for detecting unauthorized access.

The Resampling Methods Direct Sequence Spread Spectrum Signal’s Demodulator Implementation

Relevance. The direct spread spectrum signals are widely used in navigation and communication systems recently. These signals prevail in modern satellite navigation systems and are used in various communication systems with code division multiplexing in particularly. In this regard, the tasks of building direct spread spectrum signals’ demodulators have the key importance. Mach importance in the construction of demodulators is the problem chip rate variability.The purpose of the study is to propose a demodulator structure focused on solving this problem.Methods. The research is based on computer modeling methods.Decision. The paper proposes an approach to the construction of the direct spread spectrum signal’s demodulators based on modern methods of digital signal processing. It is shown that the main advantage of the proposed approach is the possibility of rebuilding the variable chip rate demodulators. Based on the results obtained, a scheme for the direct spread spectrum signals demodulator using resampling methods is proposed. Resampling, in turn, is implemented on the basis of polynomial interpolation using Lagrange polynomials. The structure of the resampler is proposed, similar to the structure of an interpolating filter with a finite impulse response. The presented simulation results show the effectiveness of the proposed approach.Novelty. It seems that the currently common methods of implementing direct spread spectrum signal in terms of delay synchronization do not sufficiently meet modern requirements. The implementation of delay synchronization schemes based on resampling is practically not discussed in well-known works. At the same time, modern methods and devices of digital signal processing make it possible to ensure an effective hardware implementation of the scheme in question. In this context, the approach proposed in the paper to the construction of demodulators seems to be very relevant.Significance. The results of the work can be used in the construction with direct spread spectrum signals’ demodulators for a wide range of communication and navigation systems. The synchronous sampling structure proposed in this paper is very promising, especially for variable chip rate demodulators.

Method for Increasing the Linearity of The Phase in The Approximation Phase On 5G Antennas Using Modified Functions of The Subtractors

The possibility of reducing the phase distortion of the signal spectra in the input paths at the stage of approximation of frequency characteristics is shown. Noticeable progress in the technology of satellite and mobile telecommunications systems, as well as in radar systems, is largely associated with the use of broadband and ultra-broadband signals. Thus, in 2012-2013, the development of Wideband High Frequency (WBHF) radio technology (hereinafter referred to as broadband HF (HF) radio communication) took place in the direction of increasing data transmission rates over HF (HF) radio channels to meet the needs of subscribers of the armed forces of NATO countries. The development of broadband HF (HF) radio communication has led to the revision of the following NATO standards and their release in a new edition: - MIL-STD-188-110C «Interoperability and operational requirements for data modems», 03.01.2012; - MIL-STD-188-141C «Functional compatibility and operational requirements for medium and high frequency radio frequency systems», 12/27/2011. The work carried out experiments to compare frequency characteristics. The changes are also related to the introduction of broadband HF (HF) radio communication technology, which defines a family of broadband modulation types for a radio channel with the necessary bandwidth of the end-to-end amplitude-frequency response (AFC) of the transceiver path from 3 kHz to 24 kHz in 3 kHz increments.

Synthesis, Antibacterial Properties and Molecular Docking of Nitrobenzoylthiourea Compounds and their Copper(II) Complex

The rise of multidrug-resistant microbial pathogens has increased the demand for highly effective antibiotics. Five nitrobenzoylthiourea ligands (1–5) with amino acid side chains and their corresponding Cu(II) complexes (6–10) were synthesised with yields ranging from 43% to 90%. The successful synthesis of ligands 1-5 were confirmed by the absence of the ν(NCS) stretching band and the presence of the ν(NH) band, indicating the complete reaction of all (NCS) with a series of amino acids as well as the appearance of two N-H signals in the 1H NMR spectra of all the synthesised ligands. On the other hand, the shift of the (C=O) carboxylic peaks in the Cu(II) complexes suggested successful coordination of ligands to the metal ion via the carboxylate group. The antibacterial activities of these compounds were tested against six bacteria: Staphylococcus aureus, Bacillus cereus, Listeria monocytogenes, Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa using the disc diffusion method. The Cu(II) complexes (6-10) exhibited enhanced antibacterial activity compared to the ligands (1-5), especially against gram-negative bacteria (E. coli, K. pneumoniae, and P. aeruginosa). For example, compound 4 showed moderate activity against K. pneumoniae with a 14 mm inhibition zone while its Cu(II) complexes, 8 recorded better inhibition against K. pneumoniae with a 16 mm inhibition zone. Molecular docking studies on all complexes (6-10) also revealed higher binding affinity with targeted proteins with binding energy between -10.4 kcal/mol to -9.0 kcal/mol, in comparison with ligand 2 and 4 with the binding energy of only -7.7 kcal/mol (against S. aureus) and -6.9 kcal/mol (against K. pneumoniae). The enhanced antibacterial activity of all complexes correlates with the higher binding affinity obtained for all complexes. Hence, this study concludes that the nitrobenzoylthiourea derivatives, and particularly their Cu(II) complexes can show potential as antibacterial agent although more thorough investigation are required to develop these compounds into useful drugs.

Efficient Screening in Obstructive Sleep Apnea Using Sequential Machine Learning Models, Questionnaires, and Pulse Oximetry Signals: Mixed Methods Study.

Obstructive sleep apnea (OSA) is a prevalent sleep disorder characterized by frequent pauses or shallow breathing during sleep. Polysomnography, the gold standard for OSA assessment, is time consuming and labor intensive, thus limiting diagnostic efficiency. This study aims to develop 2 sequential machine learning models to efficiently screen and differentiate OSA. We used 2 datasets comprising 8444 cases from the Sleep Heart Health Study (SHHS) and 1229 cases from Taipei Veterans General Hospital (TVGH). The Questionnaire Model (Model-Questionnaire) was designed to distinguish OSA from primary insomnia using demographic information and Pittsburgh Sleep Quality Index questionnaires, while the Saturation Model (Model-Saturation) categorized OSA severity based on multiple blood oxygen saturation parameters. The performance of the sequential machine learning models in screening and assessing the severity of OSA was evaluated using an independent test set derived from TVGH. The Model-Questionnaire achieved an F1-score of 0.86, incorporating demographic data and the Pittsburgh Sleep Quality Index. Model-Saturation training by the SHHS dataset displayed an F1-score of 0.82 when using the power spectrum of blood oxygen saturation signals and reached the highest F1-score of 0.85 when considering all saturation-related parameters. Model-saturation training by the TVGH dataset displayed an F1-score of 0.82. The independent test set showed stable results for Model-Questionnaire and Model-Saturation training by the TVGH dataset, but with a slightly decreased F1-score (0.78) in Model-Saturation training by the SHHS dataset. Despite reduced model accuracy across different datasets, precision remained at 0.89 for screening moderate to severe OSA. Although a composite model using multiple saturation parameters exhibits higher accuracy, optimizing this model by identifying key factors is essential. Both models demonstrated adequate at-home screening capabilities for sleep disorders, particularly for patients unsuitable for in-laboratory sleep studies.

Zircon luminescence dating revisited

Abstract. Luminescence dating plays a pivotal role in Quaternary science, yet ongoing methodological challenges persist in refining the temporal range, accuracy, and precision of luminescence methods. Our contribution revisits zircons as potential alternative dosimeters to quartz, feldspar, or calcite for routine dating applications. The essential advantage of zircons over other minerals is the time-invariant and high internal dose rate due to high radionuclide contents, dominating over the external contribution, which is more challenging to assess. Reported drawbacks are low zircon abundance, laborious sample preparation, signal instabilities, unknown optical signal resetting rates, and low signal intensities. Our present study uses modern luminescence detection equipment and analytical methods to investigate mineral separation, mineral characteristics, bleachability, signal spectra and intensities, and the potential to auto-regenerate signals. We present results for two zircon samples that are different in provenance, trace element composition, and luminescence characteristics, each of them containing a couple of hundred grains. Optically stimulated luminescence (OSL) signal resetting rates of zircon in response to simulated sunlight exposure are orders of magnitudes faster than for feldspar and slightly slower than for quartz. The recorded thermoluminescence (TL) spectra confirm previously published results with luminescence emissions in the UV/violet and red wavelength range, supplemented by narrowband emissions associated with rare earth element (REE) dopants. Storage experiments of single zircon grains for auto-regenerated measurements over 1.5 years yielded very low OSL signals. At the same time, after only 3 weeks, we measured acceptable TL signal intensities at the cost of lower bleaching rates. To date, the auto-regeneration approach seems to be a promising and accurate approach to dating zircon light exposure events, especially when combining the natural OSL with auto-regenerated TL. However, further studies are required to optimise signal intensities and establish zircons as viable targets for routine dating applications.

Discharge image reconstruction and frequency domain analysis based on event data

AbstractOptical image method has been the earliest and most used direct method for observing gas discharge. Currently, research on gas discharge monitoring based on visible light mainly relies on high‐speed cameras, but the large size, significant data storage requirements, and susceptibility to interference from complex backgrounds and lighting conditions limit their further application. Dynamic vision sensing (DVS) technology is a neuromorphic sensing technology that asynchronously measures the luminance changes at each pixel. It offers advantages such as a large dynamic range (>120 dB), high temporal resolution (up to 1 µs), and small data volume (MB level). In this study, dynamic vision sensing technology was employed to monitor both 30 mm short‐gaps and 1080 mm long‐gaps discharge processes simultaneously. This study developed the CountImage encoding method for event data and conducted image reconstruction, time‐domain analysis, and frequency‐domain characteristic analysis based on the event data. The results show that the event‐reconstructed images are highly consistent with the high‐speed camera images, and the arc development process and its path can also be clearly observed. Additionally, this study discovered a correlation between the electrical characteristics and event information during the discharge process. In the time domain, the duration of the maximum DVS event count closely matches the duration during which the voltage drops to zero during flashover. In the frequency domain, the Pearson correlation coefficient between the event stream spectrum and the voltage signal spectrum is greater than 0.95. Both the maximum number of brightening events (ONmax) and the maximum number of darkening events (OFFmax) are positively correlated with the voltage applied between the electrodes. This study demonstrates that, compared to the GB/s data rate of high‐speed cameras, this approach can record the discharge process and accurately reconstruct the discharge process, arc morphology, and discharge path at MB/s data rates, while also adapting to changes in brightness without the need for exposure adjustment. Additionally, there is a positive correlation between the frequency‐domain characteristics of the event data and the voltage characteristics. These results indicate that dynamic vision sensing holds promise as a replacement for high‐speed cameras in laboratory discharge observations and could be even effectively applied to discharge monitoring in electrical equipment in real grid.

Method of oscillation excitation for investigation of inconsistency of coating deposition on long parts

A method is proposed for studying the uniformity of electrochemical coating on the outer wall of a thin metal rod by determining the parameters of natural oscillations of this rod, which is clamped on one side. The parameters are studied by analyzing the natural frequencies of the rod. The uneven coating of the rod wall causes a change in the frequency of transverse modes and the shape of the bending line of the rod axis. The mechanical vibrations of the rod are excited by the interaction of an external magnetic field with the Ampere force arising from the current flowing through the rod. A method of adaptive tuning of the spectral components of the excitation current is developed by modifying the coefficients of a digital filter through which a noise-like signal pass. The coefficients of the digital filter are changed adaptively, based on the analysis of the signal spectrum obtained in response to the excitation in the first step. The oscillation is excited by mixing the noise-like signal and the signal received in response to the excitation in the previous step after averaging with a sliding window, the coefficients of which are obtained from the resonance characteristics of the mechanical oscillatory system. A gradual change in the ratio of the amplitude of the noise-like and stationary components of the excitation signal causes a smooth increase in the amplitude of oscillation at a frequency approaching the frequency of the rod's natural oscillations. A numerical and full-scale experiment is carried out to excite mechanical vibrations of a thin metal rod, the end of which is clamped on one side.