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The long-distance and large-scale subgrade settlement is a key issue in high-speed railway (HSR). This work focuses on the long-distance settlement threshold for the HSR subgrade. A Green's function-based hybrid dynamic model with high efficiency is established to achieve the long-distance analysis of the vehicle-track-subgrade interaction system with a wide range of subgrade settlement. Based on the analysis results, the Wavelet decomposition technique is applied to extract the low-frequency components of vehicle responses excited by the additional deformation of rails induced by the long-distance subgrade settlement. The simulation results show that the low-frequency vibrations of vehicle are mainly excited by the additional deformation of rails induced by settlement. Based on the correlation between the vehicle responses and the second derivative of additional deformation of rails, the threshold of long-distance settlement can be determined. The vehicle response and subgrade settlement signal are linked in the time-frequency domain, and the relationship between system excitation and response is clearly revealed. This paper provides a referenceable analysis framework that is convenient for engineers in the control for the amplitude of large-scale subgrade settlement in HSR. [ABSTRACT FROM AUTHOR]

The spherical mean operator has been widely studied and has seen remarkable development in many areas of harmonic analysis. In this paper, we consider the Stockwell transform related to the spherical mean operator. Since the study of time-frequency analysis is both theoretically interesting and practically useful, we will study several problems for the generalized Stockwell transform. Firstly, we explore the Shapiro uncertainty principle for this transformation. Next, we will study the boundedness and then the compactness of localization operators related to the generalized Stockwell transform, and finally we will introduce and study its scalogram. [ABSTRACT FROM AUTHOR]

In the past, diagnoses of the internal defects of ageing concrete members by sound tests have relied on the ability and experience of the inspector. However, with the development of deep neural networks, building diagnosis tools without the subjective judgements of humans is feasible. In this study, sound samples are collected from a tapping sound test on concrete slab specimens with internally embedded artificial defects. Spectrograms are generated from the sound samples and used to train a light-weighted convolutional neural network (CNN). Thereafter, the performance of the proposed CNN models at diagnosing the defects of the concrete slabs are examined and compared with convolutional autoencoder (CAE) and support vector regression (SVR). All the models performed well at distinguishing between healthy and defective concrete. Whereas noise can impair the performance of CAE and SVR, CNN is less affected, particularly when classifying sound samples with distinctive features. CNN also has other advantages: the physical area of the artificial defect can be output directly from CNN, and CNN performance is also less affected by the depth of the artificial defect and the rebar embedded in the concrete. At increased depths, the CNN is still able to identify the defect area. [ABSTRACT FROM AUTHOR]

The continuous growth of wind power technology makes condition monitoring of wind turbine components crucially important for their operational efficiency. The main shaft bearings in wind turbines have been identified as one of the most critical components in the system, especially with the ongoing increase in rotor size and weight. This increase made the 4-point suspension drivetrain more preferable. In this study, we present a novel approach for condition monitoring of the main shaft bearings in a 2 Megawatt wind turbine with 4-point suspension drivetrain using primarily acoustic emission (AE). The focus was on the analysis of time and frequency domains of the AE signal, where the dominant frequency of each AE hit was identified and plotted back in the time domain to create the so-called dominant frequency map in specific time intervals for each bearing. A comparison between the two dominant frequency maps of the two bearings gives valuable insights into the condition of the two bearings. The distinctive nature of the dominant frequency bands in the dominant frequency maps presented promising potential for this method. The presented method is straightforward and can be automated and then integrated into a planned predictive maintenance programme for this wind turbine. [ABSTRACT FROM AUTHOR]

The time–frequency analysis (TFA) method is an effective tool to separate and extract main components for non-stationary signals such as vibration signals and seismic signals, which are time-varying and affected by high noise. However, suffering from the Heisenberg uncertainty principle and cross terms of time–frequency result, conventional TFA methods usually produce vague time–frequency representations (TFRs). As a branch of the TFA method, current redistributive compressive transforms enable to generate clear TFR. However, these techniques are limited to a singular type of signal, which is not applicable to deal with complicated signals in production. In order to enhance the applicability and the time–frequency (TF) aggregation capability, this paper proposes a promoted TFA method 2D-FTSST2 based on the synchrosqueezing transform combining two-dimensional information of time and frequency domains. For an accurate IF estimate, we also define a time redistribution operator, which can describe strong time and frequency-varying signals. This algorithm not only provides a high-resolution decomposition of multicomponent signals but also enables to extract main features in noisy environments. Experiments on simulated signals and real data confirm the validity and effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes a method for harmonic time–frequency analysis and detection based on an improved multi-synchrosqueezing transform (MSST). The aim is to address the significant endpoint problem of the synchrosqueezing transform (SST) in power harmonic analysis. This approach initially employs the Burg method to estimate the parameters of the auto-regressive (AR) model for the harmonic signal. Subsequently, it conducts multiple iterative computations on the SST results of the extended harmonic signal, further compressing the time–frequency spectrum energy to obtain a more precise time–frequency spectrum of the harmonic signal. Additionally, it utilizes the robust reconstruction capability of MSST to decompose the harmonic signal and obtain a series of intrinsic mode functions (IMF) with different frequencies. Finally, the Hilbert Transform is applied to identify the harmonic parameters of each IMF component and accomplish harmonic detection. The simulation experiments and measured data results demonstrate that the proposed method outperforms the Hilbert-Huang Transform (HHT) and SST methods in achieving more accurate time–frequency analysis and detection of harmonic signals. It also reveals the time–frequency characteristics and variation patterns of power grid harmonics, making it of great significance for harmonic control. [ABSTRACT FROM AUTHOR]

This letter proposes using intermittent chaotic clocks, generated from chaotic maps, to drive cryptographic chips running the advanced encryption standard as a countermeasure against correlation power analysis (CPA) attacks. Five different chaotic maps, namely, the logistic map, the Bernoulli shift map, the Henon map, the tent map, and the Ikeda map, are used in this letter to generate chaotic clocks. The performance of these chaotic clocks is evaluated in terms of timing overhead and the resilience of the driven chip against CPA attacks. All proposed chaotic clocking schemes successfully protect the driven chip against attacks, with the clocks produced by the optimized Ikeda, Henon, and logistic maps achieving the lowest-timing overhead. These optimized maps, due to their intermittent chaotic behavior, exhibit lower-timing overhead compared to previous work. Notably, the chaotic clock generated by the optimized Ikeda map approaches the theoretical limit of timing overhead, i.e., half the execution time of a reference periodic clock. [ABSTRACT FROM AUTHOR]

Hausdorff–Young’s inequality establishes the boundedness of the Fourier transform from L p to L q spaces for 1 ≤ p ≤ 2 and q = p ′ , where p ′ denotes the Lebesgue-conjugate exponent of p. This paper extends this classical result by characterizing the L p - L q boundedness of metaplectic operators, which play a significant role in harmonic analysis. We demonstrate that metaplectic operators are bounded on Lebesgue spaces if and only if their symplectic projection is either free or lower block triangular. As a byproduct, we identify metaplectic operators that serve as homeomorphisms of L p spaces. To achieve this, we leverage a parametrization of the symplectic group by Dopico and Johnson. We use our findings to provide boundedness results within L p spaces for pseudodifferential operators with symbols in Lebesgue spaces, and quantized by means of metaplectic operators. These quantizations consists of shift-invertible metaplectic Wigner distributions, which are essential to measure local phase-space concentration of signals. Using the factorization by Dopico and Johnson, we infer a decomposition law for metaplectic operators on L 2 (R 2 d) in terms of shift-invertible metaplectic operators, establish the density of shift-invertible symplectic matrices in Sp (2 d , R) , and prove that the lack of shift-invertibility prevents metaplectic Wigner distributions to define the so-called modulation spaces M p (R d) . [ABSTRACT FROM AUTHOR]

The main crux of this paper is to introduce a new integral transform called the multidimensional Hankel-Gabor transform which generalizes the classical Gabor Fourier transform and to give some new results related to this transform as Plancherel’s, Parseval’s, inversion and Calderón’s reproducing formulas. Next, we analyse the concentration of this transform on sets of finite measure and we give uncertainty principle for orthonormal sequences. Last, using the best approximations and the theory of reproducing kernels, we study the extremal functions related to this transform and we give an integral representation, best estimates of these functions on weighted Sobolev spaces [ABSTRACT FROM AUTHOR]

The free metaplectic transformation (FMT) is an n$$ n $$‐dimensional linear canonical transform. This transform is much useful, especially in multidimensional signal processing and applications. In this paper, our aim is to achieve an efficient time‐frequency representation of higher‐dimensional nonstationary signals by introducing the novel free metaplectic wave packet transform (FM‐WPT) in L2(ℝn)$$ {L}^2\left({\mathrm{\mathbb{R}}}^n\right) $$, based on the elegant convolution structure associated with the free metaplectic transforms. The FM‐WPT preserves the properties of classical wave packet transform (WPT) in L2(ℝn)$$ {L}^2\left({\mathrm{\mathbb{R}}}^n\right) $$ and has better mathematical properties. Further, the validity of the proposed transform is demonstrated via a lucid example. The preliminary analysis encompasses the derivation of fundamental properties of the novel FM‐WPT, including boundedness, reconstruction formula, Moyal's formula, and the reproducing kernel. To extend the scope of the study, we formulate several uncertainty inequalities, including Lieb's inequality, Pitt's inequality, logarithmic inequality, Heisenberg's uncertainty inequality, and Nazarov's uncertainty inequality for the proposed transform. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering 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.)

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]

Seismic data acquired in the presence of mechanical vibrations or power facilities may be contaminated by strong interferences, significantly decreasing the data signal-to-noise ratio (S/N). Conventional methods, such as the notch filter and time-frequency transform method, are usually inadequate for suppressing non-stationary interference noises, and may distort effective signals if overprocessing. In this study, we propose a method for eliminating mechanical vibration interferences in seismic data. In our method, we extended the variational mode extraction (VME) technique to a multivariate form, called multivariate variational mode extraction (MVME), for synchronous analysis of multitrace seismic data. The interference frequencies are determined via synchrosqueezing-based time-frequency analysis of process recordings; their corresponding modes are extracted and removed from seismic data using MVME with optimal balancing factors. We used synthetic data to investigate the effectiveness of the method and the influence of tuning parameters on processing results, and then applied the method to field datasets. The results have demonstrated that, compared with the conventional methods, the proposed method could effectively suppress the mechanical vibration interferences, improve the S/Ns and enhance polarization analysis of seismic signals. [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 calculation scale of simulating wind farm dynamic radar echo is gradually growing with the increasing scale of wind farms, which can hardly meet the requirements of real-time radar echo simulation. Considering that the method of surface element division can greatly influence the result of simulation, uniform surface element division is applied to accelerate the traditional simulation algorithm based on the refined 3D scattering point model and enhance the main characteristics of the radar echo. The solution time of dynamic radar echoes from 1-8 wind turbines is calculated to test the average speed that the uniform 3D scattering point model can increase. The radar echo and Doppler spectrum of 4 wind turbines are simulated and analyzed as an example to verify the accuracy of the proposed model. The proposed model is demonstrated to accelerate the traditional 3D scattering point model simulation speed by 1.4 times without reducing much accuracy. [ABSTRACT FROM AUTHOR]

In the power industry, the proper functioning of insulators is essential for maintaining the stability of the power system. Currently, unmanned aerial vehicle inspection technology is primarily employed to identify defects in insulators. However, this method has limitations, including the potential to overlook internal defects due to camera blind spots. To address this issue, this paper proposes a method for recognizing insulator defects using acoustic signals based on a CNN-GRU network. First, this paper analyzes the causes and acoustic mechanisms of three typical insulator defects and collects their acoustic signal data. Time-frequency domain analysis is conducted to verify the feasibility of using acoustic signals for defect recognition. To more accurately characterize the acoustic signals of insulators and address the limitations of single feature parameters in information representation, this study combines traditional Mel-frequency cepstral coefficients (MFCCs) and inverse Mel-frequency cepstral coefficients (IMFCCs) to create new C-MFCC feature parameters, thereby enhancing the feature extraction algorithm, and principal component analysis is used for dimensionality reduction to prevent redundancy. Additionally, to address the limitations of traditional convolutional neural networks (CNNs), which can only extract spatial features, this paper proposes combining CNN with gated recurrent units (GRUs) to form a CNN-GRU network. This hybrid network capitalizes on the advantages of both models, enabling the extraction of both spatial and temporal features, thereby further enhancing the network's recognition rate. Finally, the proposed method is compared with other similar methods and evaluated using field-collected data to validate its superiority and feasibility. [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]

Copyright of Science & Technology of Cereals, Oils & Foods is the property of Science & Technology of Cereals, Oils & Foods 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.)

The complex field environment under conservation tillage aggravates the vibration during a planter's operation, affecting the sowing quality and fertilization depth. Studying its vibration characteristics can help to realize active vibration reduction control of planter row units. To this end, this paper took a four-row no-till planter as the research object. By establishing a field vibration model of the planter row unit, the factors affecting the vibration of the unit were clarified, and stubble height, working speed and the additional weight of the planter were used as experimental factors in carrying out field orthogonal experiments. In our experiment, we collected and analyzed vibration data on the four-row planter row units and the frame at different positions to explore the influence of various factors on the vibration characteristics of the planter. The experimental results showed that the working speed was the most important factor affecting the vibration of the planter, and the impact of stubble height and additional weight on the amplitude of the planter was more significant at low speed (1.5 m/s) than that at high speed (2.5 m/s). The difference in amplitude of each planter unit in the lateral direction was the largest, the average amplitude range of which was 1.898 m/s2. The vibration energy of each planter row unit under different working conditions was mainly concentrated in the range of 10–50 Hz. However, the three-point hitch of the planter transmitted the vibration excitation of the tractor, causing 110–120 Hz high-frequency vibration of the inner row units, while the outer row units were less affected, with the vibration energy, in the range above 100 Hz, being 2.5 dB smaller than that on the inner side. The right ground wheel transmission device was abnormal, which worked together with the excitation transmitted by the three-point hitch, making the average vibration acceleration amplitude of the planter row units on the right side in the lateral direction more than 0.522 m/s2 higher than that of the units on the left side. Therefore, different vibration reduction forces need to be applied according to the position of the planter row unit, so that the units can avoid the natural frequency of the frame (115 Hz) when vibrating. This study can provide a reference for active vibration reduction control and improvements in sowing quality for high-speed no-till planters. [ABSTRACT FROM AUTHOR]

Background: Forward prediction of atrial fibrillation (AF) termination is a challenging technical problem of increasing significance due to rising AF presentations to emergency departments worldwide. The ability to non‐invasively predict which AF episodes will terminate has important implications in terms of clinical decision‐making surrounding treatment and admission, with subsequent impacts on hospital capacity and the economic cost of AF hospitalizations. Methods and Results: MEDLINE, EMCare, CINAHL, CENTRAL, and SCOPUS were searched on 29 July 2023 for articles where an attempt to predict AF termination was made using standard surface ECG recordings. The final review included 35 articles. Signal processing techniques fit into three broad categories including machine learning (n = 14), entropy analysis (n = 12), and time–frequency/frequency analysis (n = 9). Retrospectively processed ECG data was used in all studies with no prospective validation studies. Most studies (n = 33) utilized the same ECG database, which included recordings that either terminated within 1 min or continued for over 1 h. There was no significant difference in accuracy between groups (H(2) = 0.058, p‐value = 0.971). Only one study assessed recordings earlier than several minutes preceding termination, achieving 92% accuracy using the central 10 s of paroxysmal episodes lasting up to 174. Conclusions: No studies attempted to forward predict AF termination in real‐time, representing an opportunity for novel prospective validation studies. Multiple signal processing techniques have proven accurate in predicting AF termination utilizing ECG recordings sourced from a database retrospectively. [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]

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]

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.)

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]

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]

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.)

The structural integrity of pile foundations is critical for the safety and longevity of buildings and infrastructure. Low strain impact testing is a widely used non-destructive method for assessing pile length and identifying significant defects; however, its sensitivity to minor flaws remains limited. This study aims to enhance the detection capabilities of low strain testing for minor defects by proposing an improved methodology. We conducted field tests on ten piles with small, artificially introduced flaws and complemented these tests with three-dimensional numerical simulations. Initial timedomain analyses of both field and simulated data, using low-frequency wave excitation, did not reveal distinct signal features indicative of defects. To address this limitation, we employed a set of hammers with varying weights and head materials for wave excitation, simulated with different input force pulse durations. We further applied Complex Continuous Wavelet Transform (CCWT) for time-frequency analysis of the acquired signals, which effectively identified minor defects through characteristic changes in wavelet coefficient phase angles at expected timestamps. The consistency of CCWT phase spectrum features across signals from different hammers, considering the varying sensitivities of wave excitations, facilitates the differentiation of genuine flaw-induced phase shifts from noise. The study's findings were integrated into an improved low strain pile integrity testing workflow, enhancing the method's accuracy in detecting minor flaws. [ABSTRACT FROM AUTHOR]