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Polycrystalline magnesium aluminate spinel (PMAS) ceramics is a kind of transparent oxide ceramic with the advantages of high hardness, high mechanical strength, thermal stability and chemical corrosion resistance. The optical transmittance and infrared absorption edge of PMAS ceramic are better than that of sapphire and ALON in the mid-infrared band with the wavelengths greater than 4.5 μm, which makes it as an ideal material for manufacturing the optics used in the fields of transparent armor protection, infrared optical window and infrared missile dome. However, the hardness of PMAS ceramics is close to that of sapphire, and the brittleness is greater than that of sapphire, which brings great challenges to ultra-precision ductile grinding. In this paper, the surface characteristics and damage mechanisms in ultra-precision grinding of PMAS ceramics were systematically researched based on AE signals monitoring. Firstly, the effects of grinding depth, feed speed and grain size on the surface characteristics were studied in detail. Secondly, the influences of grinding depth and feed speed on the damage characteristics of PMAS ceramics were researched based on energy spectrum analysis of AE signals. Thirdly, the evolution mechanisms of material removal modes and damage characteristics were revealed based on time-frequency analysis of AE signals. Then, the damage types on ductile surfaces and initiation mechanisms of damage crack were investigated. Ultimately, the crack-free ductile surfaces and surface roughness (Sa) of 3 nm were achieved through ultra-precision grinding. [ABSTRACT FROM AUTHOR]

Biological communication system for neurological disorder patients is similar to the Brain Computer Interface in a way that it facilitates the connection to the outside world in real time. The interdisciplinary field of Electroencephalogram based message depiction is gaining importance as it assists the paralysed person to communicate. In the proposed method a novel approach of feature extraction is done by Empirical Mode Decomposition on non- stationary & non-linear kind of EEG signal. EMD helps in the effective time frequency analysis by disintegrating the EEG signal in the form of six Intrinsic Mode Functions with help of the frequency components. In all nine features are extracted from the decomposed IMFs so as to predict the states or messages of the patient. The above computed features are then served to the Deep Neural Network to perform the classification. The performance of suggested method is studied through applying it to the acquired database generated by the designed hardware as well as also in real time message depiction. The maximum classification accuracy 97% for the acquired database & 85% in real time are obtained respectively by comparative analysis. The command messages generated from the proposed system helps the person suffering from neurological disorder to establish the communication link with the outside world in an efficient way. Thus, the proposed novel method shows better performance in real time message depiction purpose as related to other existing methods. [ABSTRACT FROM AUTHOR]

The research on how to effectively extract the instantaneous characteristic components of non-stationary signals continues to be both a research hotspot and a very challenging topic. In this paper, a new method of multi-component decomposition is proposed to decompose a signal into finite mono-component signals and extract their Instantaneous Amplitude (IA), Instantaneous Phase (IP), and Instantaneous Frequency (IF), which is called Sinusoidal Fitting Decomposition (SFD). The proposed method can ensure that the IA extracted from the given signal must be positive, the IP is monotonically increasing, and the signal synthesized by both IA and IP must be mono-componential and smooth. It transforms the decomposition process into a synthesis iterative process and does not rely on any dictionary or basis function space or carry out the sifting operation. In addition, the proposed method can describe the instantaneous-frequency-amplitude characteristics of the signal very well on the time-frequency plane. The results of numerical simulation and the qualitative analysis of the amount of calculation show that the proposed method is effective. [ABSTRACT FROM AUTHOR]

Distributed photovoltaic systems have encountered unprecedented opportunities for development given their environmentally friendly nature and flexible power generation characteristics. However, numerous connecting lines and taps within the distributed photovoltaic system can be subject to insulation issues, which will consequently cause direct current (DC) arc faults and severe electrical fire hazards. Moreover, the power semiconductor devices in the photovoltaic inverter can introduce common-mode noises to the DC current, resulting in unwanted tripping of the DC arc fault detector. The study proposes an arc fault detection method utilizing a deep residual shrinkage network (DRSN) to address this issue, thereby precisely detecting DC arc faults. A test platform for series arc faults in photovoltaic systems is built. The arc current data are collected for characteristic analysis in time and frequency domains to determine which bandwidth is preferred for the algorithm. The model's depth is increased by introducing residual connections, enhancing its feature extraction, and improving noise reduction capabilities. The residual shrinkage network has been enhanced to prevent a computation increase from increased network depth. Introducing a convolutional auto-encoder for data dimension reduction has decreased neural network parameters, thereby improving training speed. A prototype for detecting photovoltaic DC arc faults was constructed using Raspberry Pi 4B, validating the practical application value of the proposed method. Experimental results demonstrate that the prototype for detecting photovoltaic DC arc faults successfully fulfills the real-time detection standard of the conduction test. [ABSTRACT FROM AUTHOR]

Nonstationary fault signals collected from wind turbine planetary gearboxes and bearings often exhibit close-spaced instantaneous frequencies (IFs), or even crossed IFs, bringing challenges for existing time-frequency analysis (TFA) methods. To address the issue, a data-driven TFA technique, termed CTNet is developed. The CTNet is a novel model that combines a fully convolutional auto-encoder network with the convolutional block attention module (CBAM). In the CTNet, the encoder layer is first designed to extract coarse features of the time-frequency representation (TFR) calculated by the general linear Chirplet transform (GLCT); second, the decoder layer is combined to restore and conserve details of the key time-frequency features; third, the skip connections are designed to accelerate training by linking extracted and reconstructed features; finally, the CBAM is introduced to adaptively explore channel and spatial relationships of the TFR, focusing more on close-spaced or crossed frequency features, and effectively reconstruct the TFR. The effectiveness of the CTNet is validated by numerical signals with close-spaced or crossed IFs, and real-world signals of wind turbine planetary gearbox and bearings. Comparison analysis with state-of-the-art TFA methods shows that the CTNet has high time-frequency resolution in characterizing nonstationary signals and a much better ability to detect wind turbine faults. ● A data-driven TFA technique is proposed for fault diagnosisof wind turbine planetary gearboxes and bearings. ● The proposed CTNet is a novel model that combines a convolutional network with the attention mechanism. ● The effectiveness of the CTNet is validated through the multiple sets of simulated and real-world signals. ● The CTNet can effectively characterize signals with close-spaced or crossed IFs and detect wind turbine faults. [ABSTRACT FROM AUTHOR]

Vibration is inevitable in operation of mechanical equipment, but severe vibrations will cause serious harm to wind turbines (WTs). This paper takes the drivetrain of 8 MW WT as the object, established a multi-body electromechanical coupling model of WT and verified the accuracy of the established model through the WT drivetrain in the laboratory. Subsequently, based on this model, time-frequency domain analysis was conducted on the vibration response of the WT drivetrain under different control strategies of the generator, and the influence of the generator control strategy on the vibration of the drivetrain was studied. The result shows that the generator control strategy has a significant impact on the vibration response amplitude of the drivetrain, but has a relatively small impact on the frequency component. Meanwhile, the sliding mode controller designed in this paper has good anti-interference ability, which can make the system quickly reach dynamic stability, and effectively suppress the vibration of the main components in the drivetrain. Under the new controller's control, the transverse vibration displacement of some parts of the WT drivetrain can be reduced by up to nearly 60 %. This paper provides new ideas and new methods for the active vibration reduction control of WT drivetrain. [ABSTRACT FROM AUTHOR]

Neuronal ensemble activity entrained by local field potential (LFP) patterns underlies a variety of brain functions, including emotion, cognition, and pain perception. Recent advances in machine learning approaches may enable more effective methods for analyzing LFP patterns across multiple brain areas than conventional time-frequency analysis. In this study, we tested the performance of two machine learning algorithms, AlexNet and the Transformer models, to classify LFP patterns in eight pain-related brain regions before and during acetic acid-induced visceral pain behaviors. Over short time windows lasting several seconds, applying AlexNet to LFP power datasets, but not to raw time-series LFP traces from multiple brain areas, successfully achieved superior classification performance compared with simple LFP power analysis. Furthermore, applying the Transformer directly to the raw LFP traces achieved significantly superior classification performance than AlexNet when using LFP power datasets. These results demonstrate the utility of the Transformer in the analysis of neurophysiological signals, and pave the way for its future applications in the decoding of more complex neuronal activity patterns. [ABSTRACT FROM AUTHOR]

Impulse radio ultra-wideband (IR-UWB) radar, operating in the low-frequency band, can penetrate walls and utilize its high range resolution to recognize different human activities. Complex deep neural networks have demonstrated significant performance advantages in classifying radar spectrograms of various actions, but at the cost of a substantial computational overhead. In response, this paper proposes a lightweight model named TG2-CAFNet. First, clutter suppression and time–frequency analysis are used to obtain range–time and micro-Doppler feature maps of human activities. Then, leveraging GhostV2 convolution, a lightweight feature extraction module, TG2, suitable for radar spectrograms is constructed. Using a parallel structure, the features of the two spectrograms are extracted separately. Finally, to further explore the correlation between the two spectrograms and enhance the feature representation capabilities, an improved nonlinear fusion method called coordinate attention fusion (CAF) is proposed based on attention feature fusion (AFF). This method extends the adaptive weighting fusion of AFF to a spatial distribution, effectively capturing the subtle spatial relationships between the two radar spectrograms. Experiments showed that the proposed method achieved a high degree of model lightweightness, while also achieving a recognition accuracy of 99.1%. [ABSTRACT FROM AUTHOR]

This study presents a meticulous investigation facilitated by a bespoke high-pressure test rig. The rig stands as a cornerstone of the research endeavour, providing the capability to simulate diverse leakage scenarios under controlled conditions. With its capacity to replicate conditions akin to real-world hydraulic systems, including various levels of severity such as healthy operation, moderate leakage and severe leakage. The study delves into the intricate analysis of time-series discharge flow rate signals within this experimental framework. The study utilizes sophisticated signal processing techniques, particularly the Continuous Wavelet Transform (CWT), to observe frequency components and temporal occurrences. Despite prevalent challenges distinguishing between leakage severity levels, the CWT-based analysis provides crucial insights into the dominant low frequencies (2.3–3.3 hz) characterising all leakage conditions. An advanced computer vision-based methodology is devised to address the complexities inherent in leakage differentiation, integrating cutting-edge models such as You Only Look Once (YOLO-V7) and You Only Look Once-Neural Architecture Search (YOLO-NAS). These models are meticulously trained using annotated CWT images of flow rates corresponding to different leakage conditions. Notably, the superiority of YOLO-NAS in terms of both speed and accuracy underscores its efficacy in automated leakage detection. In summary, this comprehensive approach, underpinned by the innovative hydraulic test rig and advanced signal processing coupled with state-of-the-art computer vision techniques, presents a significant advancement in the realm of internal leakage detection in hydraulic systems. [ABSTRACT FROM AUTHOR]

[Display omitted] • The height disadvantage promotes third-party punishment (TPP). • Third-parties are influenced by negative emotions resulting from height disadvantage. • The N1, P3 components are linked to the influence of height disadvantage on TPP. • The height disadvantage also evoked more theta power, and more alpha power. The study employed event-related potential (ERP), time–frequency analysis, and functional connectivity to comprehensively explore the influence of male's relative height on third-party punishment (TPP) and its underlying neural mechanism. The results found that punishment rate and transfer amount are significantly greater when the height of the third-party is lower than that of the recipient, suggesting that male's height disadvantage promotes TPP. Neural results found that the height disadvantage induced a smaller N1. The height disadvantage also evoked greater P300 amplitude, more theta power, and more alpha power. Furthermore, a significantly stronger wPLI between the rTPJ and the posterior parietal and a significantly stronger wPLI between the DLPFC and the posterior parietal were observed when third-party was at the height disadvantage. These results imply that the height disadvantage causes negative emotions and affects the fairness consideration in the early processing stage; the third-party evaluates the blame of violators and makes an appropriate punishment decision later. Our findings indicate that anger and reputation concern caused by height disadvantage promote TPP. The current study holds significance as it underscores the psychological importance of height in males, broadens the perspective on factors influencing TPP, validates the promoting effect of personal disadvantages on prosocial behavior, enriches our understanding of indirect reciprocity theory, and extends the application of the evolution theory of Napoleon complex. [ABSTRACT FROM AUTHOR]

The rapid and accurate recognition of cavitation in centrifugal pumps has become essential for improving production efficiency and ensuring machinery longevity. To address the limitations of existing methods in terms of applicability, accuracy, and efficiency, a new method based on multi-dimensional feature fusion and convolutional gate recurrent unit (MCGN) was proposed. Experimental monitoring of cavitation of centrifugal pumps was conducted. Five signals at different water temperatures and operating frequencies were collected. Key modulating features were extracted by time-frequency analysis and principal component analysis. The multi-dimensional features are fused by one and two dimensional convolutional neural networks. The cavitation state label was used to label the sample set by cavitation number, net positive suction head, and cavitation evolution images captured by high-speed cameras. Finally, the neural network based on the convolutional gate recurrent unit was used to classify the cavitation state of the centrifugal pump. The experimental results demonstrate that the proposed method achieves recognition accuracies exceeding 98% for vibration signals, noise signals, outlet pressure pulsation signals, and torque signals. Compared with the short-time Fourier transform-autoencoder model, MCGN model can improve the recognition accuracy by 4.03%, computation efficiency by 20%, and loss by 87%. These advances underscore the potential of the method in monitoring and maintenance practices for centrifugal pumps. [ABSTRACT FROM AUTHOR]

In order to study the influence of electromagnetic (EM) effect and load change on the modal characteristics and dynamic response of gear transmission system driven by permanent magnet synchronous motor (PMSM), a electromechanical coupling dynamic model is established. The composite air gap permeability coefficient is introduced, and the formula of permanent magnet flux linkage under the influence of space harmonic and cogging effect is derived. The variation laws of gear meshing stiffness and bearing support stiffness under the influence of load change are simulated and analyzed. The natural frequency and vibration modal under different load, flux linkage and PI controller gain are revealed. On this basis, the influence of EM effect and load change on the resonance domain is analyzed through frequency sweep analysis and time domain simulation. Results indicate that the 1st order natural frequency and resonance amplitude will increase with the load, and the influence of PMSM EM excitation will gradually dominate. The increase of the flux linkage and speed loop controller gain Kpω will reduce the 1st order natural frequency. When Kpω=123, a new mode appears, and there is a modal transition between the new mode and mode 1 when Kpω=161. The research results can provide theoretical guidance for improving the transmission performance and structure optimization design of the permanent magnet gear transmission system. [ABSTRACT FROM AUTHOR]

A prime goal of psychological science is to understand how humans can flexibly adapt to rapidly changing contexts. The foundation of this cognitive flexibility rests on contextual adjustments of cognitive control, which can be tested using the list‐wide proportion congruency effect (LWPC). Blocks with mostly incongruent (MI) trials show smaller conflict interference effects compared to blocks with mostly congruent (MC) trials. A critical debate is how proactive and reactive control processes drive contextual adjustments. In this preregistered study (N = 30), we address this conundrum, by using the theta rhythm as a key neural marker for cognitive control. In a confound‐minimized Stroop paradigm with short alternating MC and MI blocks, we tested reaction times, error rates, and participants' individualized theta activity (2–7 Hz) in the scalp‐recorded electroencephalogram. An LWPC effect was found for both, reaction times and error rates. Importantly, the results provided clear evidence for reactive control processes in the theta rhythm: Theta power was higher in rare incongruent compared with congruent trials in MC blocks, but there was no such modulation in MI blocks. However, regarding proactive control, there were no differences in sustained theta power between MC and MI blocks. A complementary analysis of the alpha activity (8–14 Hz) also revealed no evidence for sustained attentional resources in MI blocks. These findings suggest that contextual adjustments rely mainly on reactive control processes in the theta rhythm. Proactive control, in the present study, may be limited to a flexible attentional shift but does not seem to require sustained theta activity. Human behavior needs to be constantly adjusted to changing contexts. The present study measured theta rhythm to investigate two sides of this ability: (1) proactive control, engaged in advance, and (2) reactive control, engaged only when needed. We conclude that mainly reactive control processes drive context adjustments. Opposed to prominent theories, proactive control does not seem to require sustained theta activation and may be established through a swift attentional shift toward relevant information. [ABSTRACT FROM AUTHOR]

In the field of autonomous driving, it is important to protect vulnerable road users (VRUs) and ensure the safety of autonomous driving effectively by improving the detection accuracy of VRUs in the driver's field of vision. However, due to the strong temporal similarity between pedestrians and cyclists, the insensitivity of the traditional least squares method to their differences results in its suboptimal classification performance. In response to this issue, this paper proposes an algorithm for classifying pedestrian and cyclist targets based on the micro-Doppler effect. Firstly, distinct from conventional time-frequency fusion methods, a preprocessing module was developed to solely perform frequency-domain fitting on radar echo data of pedestrians and cyclists in forward motion, with the purpose of generating fitting coefficients for the classification task. Herein, wavelet threshold processing, short-time Fourier transform, and periodogram methods are employed to process radar echo data. Then, for the heightened sensitivity to inter-class differences, a fractional polynomial is introduced into the extraction of micro-Doppler characteristics of VRU targets to enhance extraction precision. Subsequently, the support vector machine technique is embedded for precise feature classification. Finally, subjective comparisons, objective explanations, and ablation experiments demonstrate the superior performance of our algorithm in the field of VRU target classification. [ABSTRACT FROM AUTHOR]

High voltage electric pulse (HVEP) was used to perform concrete cracking experiments to demonstrate the feasibility of this technique for cracking hard objects. The results of the study show that HVEP fracturing technology has great potential for application. This article uses a high-speed camera to record the crack expansion process, and analyze the relationship between crack expansion speed and time. The Hilbert–Huang Transform (HHT) is used to analyze the vibration signal generated by the shock waves (SWs). The energy evolution law of SWs is elaborated by the time–frequency characteristics of the vibration. The distribution of the vibration energy is more concentrated between 0 and 100 Hz. The increase of capacitance value will produce stronger vibration peak energy and force. The presence of cracks will cause the vibration energy propagation to be blocked and the energy decay rate to be accelerated. Also, the energy will be shifted in the frequency domain due to the presence of cracks. Highlights: A high-power high voltage electric pulse fracturing equipment is designed and manufactured. The process of crack formation and expansion is recorded. Hilbert–Huang transform analysis of the time–frequency characteristics of vibration signals. Increase of capacitance enhances instantaneous energy and maximum force of discharge. Cracks cause the energy decay of shock waves to become faster. [ABSTRACT FROM AUTHOR]

In order to comprehensively evaluate the driver's vibration comfort under different vibration conditions, eighteen subjects were required to drive a tractor at different speeds on field and asphalt roads respectively in the real vehicle experiment. The sEMG signals and vibration acceleration signals of the subjects were recorded. And the time-frequency domain analysis of sEMG signals and acceleration signals were used to determine the relationship among the characteristic indexes, tractor speed and road surfaces. The relevance analysis showed that there was a significant correlation between the integral electromyography (iEMG) and median frequency (MF) of the middle scalene muscle, erector spinae muscle and gastrocnemius muscle, the RMS of weighted acceleration (aw) of the neck, waist and legs, and the subjective comfort feelings. It was proven that the tractor speed had a significant impact on human body vibration based on the ANOVA result (p < 0.05). With the increase of running speed, the time domain indexes of sEMG signals including iEMG, RMS and the vibration acceleration signals of the testing body parts increased significantly, while the amplitudes of frequency domain indexes decreased. Therefore, a quantitative regression evaluation model for the comfort of the neck, waist and legs integrating the sEMG and vibration signals was established, and its relative errors were 5.05, 4.38 and 6.12% respectively. This proposed assessment model can combine characteristics of the partial and overall vibration response of human body effectively, predict the tractor driver's vibration comfort accurately, provide a theoretical basis for the evaluation of tractor cab vibration comfort. [ABSTRACT FROM AUTHOR]

Time–frequency analysis methods represent an efficacious approach to process data derived from full-waveform acoustic logging. This research proposes a coupled time–frequency analysis method that synergistically incorporates ensemble empirical mode decomposition, fractional Fourier transform, Choi–Williams (CW) distribution and adaptive filtering. This coupled method facilitates the comprehensive analysis of full-waveform acoustic logging signals within rigid geological formations. Specifically, it is designed to explore the time–frequency characteristics of shear wave (S-wave) within the context of igneous reservoirs. The coupled time–frequency analysis method significantly ameliorates the time–frequency coupling phenomenon of full-waveform acoustic logging signal. This coupled method is applied across different wells to confirm its wide-ranging applicability and study the time–frequency characteristics response of S-wave through the information of the peak time of the S-wave, the main frequency and the amplitude of the CW distribution. [ABSTRACT FROM AUTHOR]

Medium-Voltage (MV) electrical switchgear is some of the essential equipment for ensuring the stability of electrical networks. However, detecting mechanical faults in these systems, especially in light of current operations and safety concerns, is a complex task. This paper presents a useful method that uses a one-dimensional convolution neural network (1D-CNN) to find both mechanical and non-mechanical faults in MV electrical switchgear with a high level of accuracy. The identification of mechanical faults in MV electrical switchgear requires accuracy and speed to avoid aggravation of the faults and the occurrence of risks. The proposed 1D-CNN model proved to work very well in identifying spatial features. Thus, by combining time domain and frequency domain analysis, it not only refines the classifier but also increases the fault detection probability in real-world situations. Even during real-time signal pre-processing and model validation, our model was able to predict the incurred faults for the scenarios in the time and frequency domains with 100% accuracy. The study provides an original approach that increases the efficiency of fault identification and the reliability of MV electrical switchgear at the same time. Introducing our 1D-CNN model, we advance the field of fault detection by minimizing downtime and maximizing maintenance effectiveness, in both time and frequency domain analyses. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The occurrence of downhole high-frequency torsional oscillations (HFTO) can lead to the significant damage of drilling tools and can adversely affect drilling efficiency. Therefore, establishing a reliable HFTO identification model is crucial. This paper proposes an improved whale algorithm optimization support vector machine (TSWOA-SVM) for accurate HFTO identification. Initially, the population is initialized using Fuch chaotic mapping and a reverse learning strategy to enhance population quality and accelerate the whale optimization algorithm (WOA) convergence. Subsequently, the hyperbolic tangent function is introduced to dynamically adjust the inertia weight coefficient, balancing the global search and local exploration capabilities of WOA. A simulated annealing strategy is incorporated to guide the population in accepting suboptimal solutions with a certain probability, based on the Metropolis criterion and temperature, ensuring the algorithm can escape local optima. Finally, the optimized whale optimization algorithm is applied to enhance the support vector machine, leading to the establishment of the HFTO identification model. Experimental results demonstrate that the TSWOA-SVM model significantly outperforms the genetic algorithm-SVM (GA-SVM), gray wolf algorithm-SVM (GWO-SVM), and whale optimization algorithm-SVM (WOA-SVM) models in HFTO identification, achieving a classification accuracy exceeding 97%. And the 5-fold crossover experiment showed that the TSWOA-SVM model had the highest average accuracy and the smallest accuracy variance. Overall, the non-parametric TSWOA-SVM algorithm effectively mitigates uncertainties introduced by modeling errors and enhances the accuracy and speed of HFTO identification. By integrating advanced optimization techniques, this method minimizes the influence of initial parameter values and balances global exploration with local exploitation. The findings of this study can serve as a practical guide for managing near-bit states and optimizing drilling parameters. [ABSTRACT FROM AUTHOR]

This study addresses the problem of fault detection in industrial processes by developing a time/frequency feature-driven ensemble learning method. In contrast to the current works based on time domain ensemble learning, this approach adequately integrates the critical frequency domain information. The frequency domain information can be used to effectively enhance the fault detection performance in ensemble learning. Here, the feature ensemble net (FENet) is chosen to capture the time domain feature. The power spectral density (PSD)-based frequency domain feature extraction network can capture the frequency domain features. Bayesian inference can then be used to combine the fault detection results that rely on time/frequency domain features. The simulations of the Tennessee Eastman Process (TEP) demonstrate that the proposed method significantly outperforms traditional methods. The average fault detection rate (FDR) of TEP faults 3, 5, 9, 15, 16, and 21 is 90.63%, much higher than that of 75% by FENet with one feature transformation layer, and those of about 4% by principal component analysis (PCA) and dynamic PCA (DPCA). This research provides a promising framework for more advanced and reliable fault detection in industrial applications. [ABSTRACT FROM AUTHOR]

Wave erosion of slopes can easily trigger landslides into marine environments and pose severe threats to both the ecological environment and human activities. Therefore, near-shore slope monitoring becomes crucial for preventing and alerting people to these potential disasters. To achieve a comprehensive understanding, it is imperative to conduct a detailed investigation into the dynamics of wave erosion processes acting on slopes. This research is conducted through flume tests, using a wave maker to create waves of various heights and frequencies to erode the slope models. During the tests, seismic signals, acoustic signals, and pore pressure generated by wave erosion and slope failure are recorded. Seismic and acoustic signals are analyzed, and time-frequency spectra are calculated using the Hilbert–Huang Transform to identify the erosion events and signal frequency ranges. Arias Intensity is used to assess seismic energy and explore the relationship between the amount of erosion and energy. The results show that wave height has a more decisive influence on erosion behavior and retreat than wave frequency. Rapid drawdown may potentially cause the slope to slide during cyclic swash and backwash wave action. As wave erosion changes from swash to impact, there is a significant increase in the spectral magnitude and Power Spectral Density (PSD) of both seismic and acoustic signals. An increase in pore pressure is observed due to the rise in the run-up height of waves. The amplitude of pore pressure will increase as the slope undergoes further erosion. Understanding the results of this study can aid in predicting erosion and in planning effective management strategies for slopes subject to wave action. [ABSTRACT FROM AUTHOR]

Rail corrugation, one of the most common track damage problems in metro lines, has yet to be fundamentally resolved. To study the time-frequency domain characteristics of vehicle interior vibration and noise, as well as track structure vibration after rail corrugation occurs in different track structure types, and to investigate the effect of rail corrugation on vehicles and tracks, dynamic and static tests were conducted on a metro line. These tests measured the vibration and noise response inside the vehicle and the vibration response of track components under the rail corrugation excitation. Methods such as time-domain indicator statistics and 1/3-octave spectral analysis were used to analyze track vibration response characteristics and in-vehicle vibration and noise response characteristics. The results showed that on small radius curve sections, the floating slab track produced rail corrugation with a characteristic wavelength of about 200 mm, while the overall track bed produced rail corrugation with a characteristic wavelength of about 60 mm. The root mean square (RMS) value of the rail, slab, and tunnel wall vibration acceleration in the floating slab track were 1. 8, 5. 8, and 0. 3 times those of the overall track bed, respectively. The influence of rail corrugation on track vibration was mainly reflected in the mid-to-high frequency range. Around 300~400 Hz, the vibration level of floating slab track attenuated by 66 dB from the rail to the tunnel wall, while the overall track bed attenuated by 49 dB. When the train passed through the test area, the RMS values of vertical and longitudinal vibration acceleration above the bogie and in the middle of the carriage were almost the same, while the RMS value of lateral vibration acceleration in the middle of the carriage was about twice that above the bogie. The abnormal vibration at the bogie position inside the vehicle mainly originated from the excitation of the rail corrugation, and the vehicle's vibration and noise were more intense under the excitation of shortwavelength corrugation. Therefore, rail corrugation in metro systems becomes a major component in both the vibration and noise response of tracks and vehicles after it occurs, and rails should be ground in time. The research results can provide theoretical guidance for metro maintenance. [ABSTRACT FROM AUTHOR]

Real-time monitoring systems are crucial for the comprehensive management of operations and processes, as well as for assessing the impacts of coastal infrastructures on the marine environment. These systems not only support environmental protection and data-driven decision-making but also enable the early detection of adverse events and the issuance of timely warnings for prompt responses. Although water quality is a critical parameter in this monitoring framework, there are currently limited permanent systems in place dedicated to maintaining these objectives. Even fewer systems leverage their data for research purposes, leading to a gap in the literature regarding effective processing approaches for real-time water quality data. In this context, this study presents a real-time water quality monitoring system integrated into a broader in-field laboratory installed at a coastal area off the coast of Ayia Napa, Cyprus, as well as an initial measured data set of different qualitative quantities. It proposes a holistic approach for post-processing real-time seawater quality data, employing both time and frequency domain analyses, alongside filtering techniques. The study discusses the advantages of each method and emphasizes the importance of their combined use. Utilizing data collected from a three-month operational period, the study assesses the current state of marine seawater quality and examines both temporal and cyclic variations in various seawater quality parameters. The findings reveal that the examined seawater parameters are within reasonable values, indicating that the construction and operation of a nearby marina and the necessary infrastructures (e.g., breakwater) did not affect the seawater quality in the area. Additionally, the study identifies pronounced daily cyclic responses in different seawater quality parameters, including temperature, density, pH, dissolved oxygen, and turbidity. Finally, notable correlations are observed between temperature and dissolved oxygen, temperature and conductivity, oxidation–reduction potential (ORP) and salinity, ORP and dissolved oxygen, and ORP and TDS. [ABSTRACT FROM AUTHOR]

Copyright of Railway Construction Technology is the property of Railway Construction Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A reduced-order homogenization framework is proposed, providing a macro-scale-enriched continuum model for locally resonant acoustic metamaterials operating in the subwavelength regime, for both time and frequency domain analyses. The homogenized continuum has a non-standard constitutive model, capturing a metamaterial behaviour such as negative effective bulk modulus, negative effective density and Willis coupling. A suitable reduced space is constructed based on the unit cell response in a steady-state regime and the local resonance regime. A frequency domain numerical example demonstrates the efficiency and suitability of the proposed framework. This article is part of the theme issue 'Current developments in elastic and acoustic metamaterials science (Part 2)'. [ABSTRACT FROM AUTHOR]

This study investigates whether syntactic unification occurs during online L2 sentence comprehension using time‐frequency analysis. We measured the oscillatory power changes in native English speakers and L1‐Cantonese L2‐English speakers while they were reading well‐formed English sentences, syntactically intact nonsense sentences, and random word lists. Additionally, we conducted traditional ERP analyses to test L2 speakers’ sensitivity to NP‐internal number (dis)agreement. The results show that low‐beta power significantly increased in the L2 group when reading not only well‐formed sentences but also nonsense sentences, replicating the pattern found in the L1 group. This suggests that syntactic unification occurs in L2 comprehension as reliably as in L1 comprehension. However, L2 speakers did not show increased positivity for NP‐internal number disagreement, indicating that they have not developed native‐like sensitivity to this syntactic error. The implications of these time‐frequency and ERP data for L2 sentence processing and syntactic development are discussed. [ABSTRACT FROM AUTHOR]

Launched on 22 November 2013, Swarm is the fourth in a series of pioneering Earth Explorer missions and also the European Space Agency's (ESA's) first constellation to advance our understanding of the Earth's magnetic field and the near-Earth electromagnetic environment. Swarm provides an ideal platform in the topside ionosphere for observing ultra-low-frequency (ULF) waves, as well as equatorial spread-F (ESF) events or plasma bubbles, and, thus, offers an excellent opportunity for space weather studies. For this purpose, a specialized time–frequency analysis (TFA) toolbox has been developed for deriving continuous pulsations (Pc), namely Pc1 (0.2–5 Hz) and Pc3 (22–100 mHz), as well as ionospheric plasma irregularity distribution maps. In this methodological paper, we focus on the ULF pulsation and ESF activity observed by Swarm satellites during a time interval centered around the occurrence of the 24 August 2016 Central Italy M6 earthquake. Due to the Swarm orbit's proximity to the earthquake epicenter, i.e., a few hours before the earthquake occurred, data from the mission may offer a variety of interesting observations around the time of the earthquake event. These observations could be associated with the occurrence of this geophysical event. Most notably, we observed an electron density perturbation occurring 6 h prior to the earthquake. This perturbation was detected when the satellites were flying above Italy. [ABSTRACT FROM AUTHOR]

This paper develops a new frame for non-stationary signal separation, which is a combination of wavelet transform, clustering strategy and local maximum approximation. We provide a rigorous mathematical theoretical analysis and prove that the proposed algorithm can estimate instantaneous frequencies and sub-signal modes from a blind source signal. The error bounds for instantaneous frequency estimation and sub-signal recovery are provided. Numerical experiments on synthetic and real data demonstrate the effectiveness and efficiency of the proposed algorithm. Our method based on wavelet transform can be extended to other time–frequency transforms, which provides a new perspective of time–frequency analysis tools in solving the non-stationary signal separation problem. [ABSTRACT FROM AUTHOR]

The unsteady numerical analysis method was utilized to investigate the unsteady flow structure of a contra-rotating ducted fan under crosswind conditions. The development and evolution mechanism of the lip vortex under the combined action of crosswind and rotor were studied. Two perspectives, namely the time-averaged flow field and unsteady time-frequency analysis, were employed for the examination. The results indicate that the unsteady aerodynamic forces exerted on the lip of the ducted fan are primarily influenced by four sets of frequencies, ranked in descending order of magnitude: 1 BPF (Blade Passing Frequency) of the upstream rotor, 1 BPF of the downstream rotor, the combined effect of the 1 BPF of the upstream rotor and 1 BPF of the downstream rotor, and 2 BPF of the upstream rotor. The maximum velocity occurs at the position where the inner surface of the windward side lip is inclined 40° from the freestream velocity direction, and a stable separation vortex is formed below this region. The lip separation vortex triggers the generation of blade suction side separation vortex in the upstream rotor, and its periodic formation, growth, shedding, and dissipation are the primary factors contributing to the unsteady flow. The research findings lay the groundwork for further advancements in active and passive flow control technologies under crosswind conditions. [ABSTRACT FROM AUTHOR]

Passive non-destructive evaluation tools such as acoustic emission (AE) testing and acousto-ultrasonics (AU) approach present a complex problem in damage localisation in complex and nonhomogeneous geometries. A novel AU-guided AE frequency interpretation approach is proposed in this research work which aims at overcoming this limitation. For the experimental evaluation, the damage sources from a geometrically complex clear dental aligners are tested under cyclic compression load and their origins are evaluated. Despite the rapid worldwide diffusion of the clear aligners, their mechanical behaviour is poorly investigated. In this work, the frequency characteristics of the artificially simulated stress wave, generated from different dental positions of the clear aligners, are studied using the AU approach. These frequency characteristics are then used to analyse the AE signals generated by these aligners when subjected to cyclic compressive loading. In addition, the time domain characteristics of the AE signals are studied using their Time of Arrival (ToA). The Akaike Information Criterion (AIC) is used to estimate the ToA. These frequency and time domain characteristics of the AE signals are used to estimate the local damage origin in the clear dental aligners. This will help in identifying localised damage sources during the usage period of the aligners. Experimental results revealed significant damages in the left maxillary premolar and right maxillary third molar of the aligners. [ABSTRACT FROM AUTHOR]

Highlights It is more and more difficult to identify defects in carbon fiber composite materials due to the difficulty in making defect samples and the single signal analysis method. In order to better solve the problem of defect identification in carbon fiber composite materials, this study uses ultrasonic phased array equipment to quantitatively locate and detect carbon fiber composite laminates with embedded delamination defects, so as to more intuitively and effectively display the appearance of different delamination defects. The time domain analysis of the collected ultrasonic original signal and the time‐frequency domain analysis using wavelet packet are carried out. A total of 6 eigenvalues were extracted to reflect the ultrasonic signals of different delamination defects. By using genetic algorithm to optimize BP neural network, the recognition accuracy of delamination defects of different sizes is more than 95%, and the recognition accuracy of delamination defects of different depths is 100%, so as to realize the effective intelligent recognition of delamination defects of different sizes and depths of carbon fiber composites. This study is of great significance to improve the accuracy and reliability of defect identification of carbon fiber composite materials. The ultrasonic phased array equipment is used to quantitatively locate the carbon fiber composite laminates with embedded delamination defects, so that the appearance of different defects can be displayed more intuitively and effectively. Using time domain analysis and time‐frequency domain analysis based on wavelet packet, the combination of the two can more comprehensively extract the effective features of the defect signal. The BP neural network is optimized by genetic algorithm, and the results can effectively and automatically identify different layered defects, which lays a good foundation for the rapid and accurate identification of more defects in the future. [ABSTRACT FROM AUTHOR]

Linear frequency modulation (LFM) signal is a common radar signal in modern electronic warfare, and smeared spectrum (SMSP) can generate multiple false targets, causing jamming to radar detection. The authors propose a chirplet‐based masking algorithm that can solve the problem of SMSP jamming suppression and address a more complex problem: the separation of jamming signal and multiple LFM signals from intercepted mixed signal. First, the authors obtain matched chirp rates of the source signals through the changing tendency of the Rényi entropy. Then, the ridge of each source signal is extracted from the high‐resolution chirplet transform result using an image processing‐based algorithm. Finally, the jamming and LFM signals are accurately reconstructed through the time‐frequency mask to achieve separation. Even in the extreme case where multiple source signals with close chirp rates are overlapped, the proposed slope‐matching ridge extraction method and iterative update reconstruction method can still achieve commendable signal separation effects. Extensive experimental results demonstrate that the proposed algorithm performs well under extreme conditions of low signal‐to‐noise ratio, high jamming‐to‐signal ratio, and high sea state. [ABSTRACT FROM AUTHOR]

This paper aims to explore the richer dynamics of the Lazer-McKenna suspension bridge model, which is a asymmetric system of equations that has previously shown chaotic dynamics, here the study will be made by means of fractional order differential equations where the order of the entire system is varied. For the solutions it was used integration schemes previously shown in the literature, which were implemented in the Python programming language, the dynamics were then analyzed in the time domain by means of phase-space, Poincard sections, and bifurcation diagrams, and in the frequency domain by Discrete Fourier Transform (DFT), Continuous Wavelet Transform (CWT) and Hilbert-Huang Transform (HHT). The investigated responses demonstrated a great influence of the fractional order in the system, changing its time dynamics from chaotic to periodic. [ABSTRACT FROM AUTHOR]

The study of fearful emotions has important implications for animal emotional control and development. Current effects of fearful emotions on the behavior and brain activity of animals (especially birds) are mostly biased towards behavioral and physiological phenomena, and there is a lack of studies involving dynamic analysis of the patterns of neural electrical activity within specific relevant nuclei. In order to understand the rule of change of neural signals in pigeons under the fear emotion, taking pigeons as a model animal and its stratum griseum periventriculare (SGP) nuclei as the target brain area, adopting the experimental method of gradual approach of danger to study the pattern of change of neural activities under different fear proximity, the rhythmic differences of frequency domain, time-frequency energy, and functional connectivity based on the signal of Local field potential (LFP) were analyzed in-depth. The LFP signals of 2 pigeons in the resting state and the fear looming experiment were firstly collected and the four characteristic rhythmic components, Delta, Theta, Beta and Gamma, were extracted. The autoregressive modeling method was used to calculate the difference in the percentage of power spectral density between the quiet and fearful states at each rhythm. Next, the idiosyncratic change in time-frequency energy in the fear state with change in looming distance was analyzed. A coherence-based functional brain network was also constructed, and the local network connectivity characteristics of pigeons in both quiet and fearful states were statistically analyzed using a two-factor analysis of variance (ANOVA) method. Finally, a linear fitting analysis was performed to analyze the relationship between the network topological characteristics and the looming distance. The results show that the power spectral occupancy of the Theta band and the strength of brain functional network connections are significantly increased in the fear looming state of pigeons. With the shortening of the looming distance, the time-frequency energy variations within the Theta and Beta bands show a gradual upward trend, and there is a significant negative correlation between the strength of the local network connections and the looming distance. It suggests that the Theta frequency band may be related to the representation of fearful emotions, and that the difference in neuroelectric signal characteristics between the Theta and Beta frequency bands can characterize changes in fear looming distance. It can be seen that these results can provide valuable references for further exploring the study of the neural coding mechanism of fear emotion and its neural regulation in birds. [ABSTRACT FROM AUTHOR]

We consider detecting the evolutionary oscillatory pattern of a signal when it is contaminated by nonstationary noises with complexly time-varying data generating mechanism. A high-dimensional dense progressive periodogram test is proposed to accurately detect all oscillatory frequencies. A further phase-adjusted local change point detection algorithm is applied in the frequency domain to detect the locations at which the oscillatory pattern changes. Our method is shown to be able to detect all oscillatory frequencies and the corresponding change points within an accurate range with a prescribed probability asymptotically. A Gaussian approximation scheme and an overlapping-block multiplier bootstrap methodology for sums of complex-valued high dimensional nonstationary time series without variance lower bounds are established, which could be of independent interest. This study is motivated by oscillatory frequency estimation and change point detection problems encountered in physiological time series analysis. An application to spindle detection and estimation in electroencephalogram recorded during sleep is used to illustrate the usefulness of the proposed methodology. for this article are available online including a standardized description of the materials available for reproducing the work. [ABSTRACT FROM AUTHOR]

Neurodegenerative diseases (NDDs) pose significant challenges due to their debilitating nature and limited therapeutic options. Accurate and timely diagnosis is crucial for optimizing patient care and treatment strategies. Gait analysis, utilizing wearable sensors, has shown promise in assessing motor abnormalities associated with NDDs. Research Question 1 To what extent can analyzing the interaction of both limbs in the time-frequency domain serve as a suitable methodology for accurately classifying NDDs? Research Question 2 How effective is the utilization of color-coded images, in conjunction with deep transfer learning models, for the classification of NDDs? GaitNDD database was used, comprising recordings from patients with Huntington's disease, amyotrophic lateral sclerosis, Parkinson's disease, and healthy controls. The gait signals underwent signal preparation, wavelet coherence analysis, and principal component analysis for feature enhancement. Deep transfer learning models (AlexNet, GoogLeNet, SqueezeNet) were employed for classification. Performance metrics, including accuracy, sensitivity, specificity, precision, and F1 score, were evaluated using 5-fold cross-validation. The classification performance of the models varied depending on the time window used. For 5-second gait signal segments, AlexNet achieved an accuracy of 95.91 %, while GoogLeNet and SqueezeNet achieved accuracies of 96.49 % and 92.73 %, respectively. For 10-second segments, AlexNet outperformed other models with an accuracy of 99.20 %, while GoogLeNet and SqueezeNet achieved accuracies of 96.75 % and 95.00 %, respectively. Statistical tests confirmed the significance of the extracted features, indicating their discriminative power for classification. The proposed method demonstrated superior performance compared to previous studies, offering a non-invasive and cost-effective approach for the automated diagnosis of NDDs. By analyzing the interaction between both legs during walking using wavelet coherence, and utilizing deep transfer learning models, accurate classification of NDDs was achieved. • Wearable sensors detect motor abnormalities in neurodegenerative diseases. • Limb interaction & color-coded images achieve accurate NDD classification. • A non-invasive & cost-effective approach for early diagnosis & treatment planning. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to propose a method for monitoring bearing health in the time–frequency domain, termed the Lock-in spectrum, to track the evolution of bearing faults over time and frequency. Design/methodology/approach: The Lock-in spectrum uses vibration signals captured by vibration sensors and uses a lock-in process to analyze specified frequency bands. It calculates the distribution of signal amplitudes around fault characteristic frequencies over short time intervals. Findings: Experimental results demonstrate that the Lock-in spectrum effectively captures the degradation process of bearings from fault inception to complete failure. It provides time-varying information on fault frequencies and amplitudes, enabling early detection of fault growth, even in the initial stages when fault signals are weak. Compared to the benchmark short-time Fourier transform method, the Lock-in spectrum exhibits superior expressive ability, allowing for higher-resolution, long-term monitoring of bearing condition. Originality/value: The proposed Lock-in spectrum offers a novel approach to bearing health monitoring by capturing the dynamic evolution of fault frequencies over time. It surpasses traditional methods by providing enhanced frequency resolution and early fault detection capabilities. [ABSTRACT FROM AUTHOR]

This article discusses the use of spectrograms to analyze bird vocalizations and simulate bird songs using a sliding whistle and a smartphone. Spectrograms provide information about the frequency, duration, pitch, and modulation of bird sounds. The article also presents examples of different bird vocalizations, such as trills and clicking sounds, and explains how they can be replicated using the sliding whistle. The use of spectrograms and smartphone applications allows students to explore the physics of bird songs and engage in interactive learning activities. The author, Regis Barille, has a background in electronics and optronics and has conducted extensive research in experimental physics and environmental metrology. [Extracted from the article]

To explore the association between insomnia symptoms and cognitive flexibility among undergraduates, along with its potential neural mechanisms. A total of 102 participants were divided into insomnia (n = 55) and control (n = 47) groups based on sleep status. Cognitive flexibility was assessed using the Cognitive Flexibility Inventory (CFI) and the Number-Letter Task (N-L task). EEG data were recorded during the N-L task. The insomnia group exhibited lower CFI scores and higher switch costs in reaction time and accuracy compared to the control group. ERP analysis showed differences in P2, N2, and P3 component amplitudes between the two groups, with reduced N2 amplitude in the insomnia group under repeat trials. Time-frequency analysis revealed larger theta band event related synchronization in the frontal region and smaller theta band ERS in the parietal region under switch trials in the control group; the alpha band event-related desynchronization in the parietal region under repeat trials was significantly smaller in the control group compared to switch trials. Compared to undergraduates with normal sleep, those with insomnia symptoms exhibited reduced cognitive flexibility, which may be associated with some alterations in brain electrophysiological activities. • Insomnia symptoms was associated with reduced cognitive flexibility. • Neural mechanism for above difference may due to frontal and parietal dysfunctions. • We integrated multiple measures to assess the differences in cognitive flexibility. [ABSTRACT FROM AUTHOR]

This research incorporates crude oil price series with different time scales, China's dirty energy, new energy and green bond index into a system, so as to investigate the spillover effects during some major periods. The results indicate that the spillover effects of short-term oil price fluctuations are mild, and the green bond market is the least affected. While the long-term oil price fluctuations have profound effects on the aforementioned three markets, the spillovers are especially enhanced by extreme events. Our results are verified by appropriate robustness tests. [ABSTRACT FROM AUTHOR]

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Input/output (I/O) functions of distortion-product otoacoustic emissions (DPOAEs) may contain sudden amplitude minima (notches) although they are measured in animals with a negligible reflection source. We measured DPOAEs in humans and analyzed the data by time-frequency filtering to decompose the nonlinear-distortion and coherent-reflection components of DPOAE. The presented I/O functions of the nonlinear-distortion component contain notches. We suggest that because these notches are present only in the nonlinear-distortion component, they result from destructive interference between distortion-product wavelets coming from the primary generation region. Simulations conducted with a nonlinear cochlear model showed qualitative similarities with the presented experimental results. [ABSTRACT FROM AUTHOR]