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Determining the creep function of viscoelastic pipes is one of the challenges of modeling these pipes to calibrate or determine defects. The present research aims to determine the creep function of viscoelastic pipes using transient flow pressure in the time and frequency domains. For this purpose, the proposed method is first implemented using a numerical example. The numerical part investigated the effect of signal sample size, the number of Kelvin-Voigt (K-V) elements, repeatability, and decision variables. Then, using an experimental test, the desired methodology has been evaluated. In this research, the K-V mechanical model was used to define the creep function, and its parameters, including elastic pressure wave speed, retardation times, and creep complaint coefficients, were calibrated. The results showed that using pressure signals in both time and frequency domains provides stable results for the investigated pipeline. Examining the effect of signal size showed that the creep function can be estimated with reasonable accuracy in the time domain with a few initial cycles. Also, 14.33 dimensionless frequency for a simple reservoir-pipe-valve system can provide accurate results in the frequency domain. The results of this research can be used as a suitable pre-processing to reduce the dimensions of inputs in models based on artificial intelligence. [ABSTRACT FROM AUTHOR]

Recent developments in acoustic signal processing have seen the integration of deep learning methodologies, alongside the continued prominence of classical wave expansion-based approaches, particularly in sound field reconstruction. Physics-informed neural networks (PINNs) have emerged as a novel framework, bridging the gap between data-driven and model-based techniques for addressing physical phenomena governed by partial differential equations. This paper introduces a PINN-based approach for the recovery of arbitrary volumetric acoustic fields. The network incorporates the wave equation to impose a regularization on signal reconstruction in the time domain. This methodology enables the network to learn the physical law of sound propagation and allows for the complete characterization of the sound field based on a limited set of observations. The proposed method's efficacy is validated through experiments involving speech signals in a real-world environment, considering varying numbers of available measurements. Moreover, a comparative analysis is undertaken against state-of-the-art frequency domain and time domain reconstruction methods from existing literature, highlighting the increased accuracy across the various measurement configurations. [ABSTRACT FROM AUTHOR]

The electroencephalogram (EEG) is a cornerstone tool for the diagnosis, management, and prognosis of selected patient populations. EEGs offer significant advantages such as high temporal resolution, real-time cortical function assessment, and bedside usability. The quantitative EEG (qEEG) added the possibility of long recordings being processed in a compressive manner, making EEG revision more efficient for experienced users, and more friendly for new ones. Recent advancements in commercially available software, such as Persyst, have significantly expanded and facilitated the use of qEEGs, marking the beginning of a new era in its application. As a result, there has been a notable increase in the practical, real-world utilization of qEEGs in recent years. This paper aims to provide an overview of the current applications of qEEGs in daily neurological emergencies and ICU practice, and some elementary principles of qEEGs using Persyst software in clinical settings. This article illustrates basic qEEG patterns encountered in critical care and adopts the new terminology proposed for spectrogram reporting. [ABSTRACT FROM AUTHOR]

There is an increasing need for compact low-cost NMR apparatus that can be used on the laboratory bench and in the field. There are four main usage variants of usage: (a) time-domain apparatus, particularly for physical measurements; (b) frequency-domain apparatus, particularly for chemical analysis, (c) NMR Cryoporometry apparatus for measuring pore-size distributions; and (d) MRI apparatus for imaging. For all of these, variable temperature capability may be vital. We have developed compact low-cost apparatus targeted at these applications. We discuss a hand-held NMR Spectrometer, and three different holdable NMR magnets, with sufficiently large internal bores for the Lab-Tools compact Peltier thermo-electric cooled variable-temperature probes. Currently, the NMR Spectrometer is very suitable for (a) NMR time-domain relaxation and (c) NMR Cryoporometry. With a suitable high-homogeneity magnet, it is also appropriate for simple use (b), spectral analysis, or, with a suitable gradient set, (d) MRI. Together, the NMR Spectrometer, one of the NMR variable-temperature probes, and any of these NMR magnets make excellent NMR Cryoporometers, as demonstrated by this paper and previously published research. Equally, they make versatile general-purpose variable-temperature NMR systems for materials science. [ABSTRACT FROM AUTHOR]

This article presents a comprehensive collection of formulas and calculations for hand-crafted feature extraction of condition monitoring signals. The documented features include 123 for the time domain and 46 for the frequency domain. Furthermore, a machine learning-based methodology is presented to evaluate the performance of features in fault classification tasks using seven data sets of different rotating machines. The evaluation methodology involves using seven ranking methods to select the best ten hand-crafted features per method for each database, to be subsequently evaluated by three types of classifiers. This process is applied exhaustively by evaluation groups, combining our databases with an external benchmark. A summary table of the performance results of the classifiers is also presented, including the percentage of classification and the number of features required to achieve that value. Through graphic resources, it has been possible to show the prevalence of certain features over others, how they are associated with the database, and the order of importance assigned by the ranking methods. In the same way, finding which features have the highest appearance percentages for each database in all experiments has been possible. The results suggest that hand-crafted feature extraction is an effective technique with low computational cost and high interpretability for fault identification and diagnosis. [ABSTRACT FROM AUTHOR]

This work investigates the interaction between water waves and multiple ice shelf fragments in front of a semi-infinite ice sheet. The hydrodynamics are modelled using shallow water wave theory and the ice shelf vibration is modelled using Euler–Bernoulli beam theory. The ensuing multiple scattering problem is solved in the frequency domain using the transfer matrix method. The appropriate conservation of energy identity is derived in order to validate our numerical calculations. The transient scattering problem for incident wave packets is constructed from the frequency domain solutions. By incorporating multiple scattering, this paper extends previous models that have only considered a continuous semi-infinite ice shelf. This paper serves as a fundamental step towards developing a comprehensive model to simulate the breakup of ice shelves. [ABSTRACT FROM AUTHOR]

There is an increasing need for compact low-cost NMR apparatus that can be used on the laboratory bench and in the field. There are four main usage variants of usage: (a) time-domain apparatus, particularly for physical measurements; (b) frequency-domain apparatus, particularly for chemical analysis, (c) NMR Cryoporometry apparatus for measuring pore-size distributions; and (d) MRI apparatus for imaging. For all of these, variable temperature capability may be vital. We have developed compact low-cost apparatus targeted at these applications. We discuss a hand-held NMR Spectrometer, and three different holdable NMR magnets, with sufficiently large internal bores for the Lab-Tools compact Peltier thermo-electric cooled variable-temperature probes. Currently, the NMR Spectrometer is very suitable for (a) NMR time-domain relaxation and (c) NMR Cryoporometry. With a suitable high-homogeneity magnet, it is also appropriate for simple use (b), spectral analysis, or, with a suitable gradient set, (d) MRI. Together, the NMR Spectrometer, one of the NMR variable-temperature probes, and any of these NMR magnets make excellent NMR Cryoporometers, as demonstrated by this paper and previously published research. Equally, they make versatile general-purpose variable-temperature NMR systems for materials science.

Abstract Recent developments in acoustic signal processing have seen the integration of deep learning methodologies, alongside the continued prominence of classical wave expansion-based approaches, particularly in sound field reconstruction. Physics-informed neural networks (PINNs) have emerged as a novel framework, bridging the gap between data-driven and model-based techniques for addressing physical phenomena governed by partial differential equations. This paper introduces a PINN-based approach for the recovery of arbitrary volumetric acoustic fields. The network incorporates the wave equation to impose a regularization on signal reconstruction in the time domain. This methodology enables the network to learn the physical law of sound propagation and allows for the complete characterization of the sound field based on a limited set of observations. The proposed method’s efficacy is validated through experiments involving speech signals in a real-world environment, considering varying numbers of available measurements. Moreover, a comparative analysis is undertaken against state-of-the-art frequency domain and time domain reconstruction methods from existing literature, highlighting the increased accuracy across the various measurement configurations.

We present a novel multi-modal adaptive feature extraction algorithm considering both time-domain and frequency-domain modalities (AFETF), coupled with a Bidirectional Long Short-Term Memory (Bi-LSTM) network based on the Grey Wolf Optimizer (GWO) for early-stage weak fault diagnosis in bearings. Singular Value Decomposition (SVD) was employed for noise reduction, while Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) was utilized for signal decomposition, facilitating further signal processing. AFETF algorithm proposed in this paper was employed to extract weak fault features. The adaptive diagnostic process was further enhanced using Bi-LSTM network optimized with GWO, ensuring objectivity in the hyperparameter optimization. The proposed method was validated for datasets containing weak faults with a 0.2 mm crack and strong faults with a 0.4 mm crack, demonstrating its effectiveness in early-stage fault detection.

Abstract The accurate classification of road surface conditions plays a vital role in ensuring road safety and effective maintenance. Vibration-based techniques have shown promise in this domain, leveraging the unique vibration signatures generated by vehicles to identify different road conditions. In this study, we focus on utilizing vehicle-mounted vibration sensors to collect road surface vibrations and comparing various data representation techniques for classifying road surface conditions into four classes: normal road surface, potholes, bad road surface, and speedbumps. Our experimental results reveal that the combination of multiple data representation techniques results in higher performance, with an average accuracy of 93.4%. This suggests that the integration of deep neural networks and signal processing techniques can produce a high-level representation better suited for challenging multivariate time series classification issues.

Over the past 10 years, deep learning has enabled significant advancements in the improvement of noisy speech. In an end-to-end speech enhancement, the deep neural networks transform a noisy speech signal to a clean speech signal in the time domain directly without any conversion or estimation of mask. Recently, the U-Net-based models achieved good enhancement performance. Despite this, some of them may neglect context-related information and detailed features of input speech in case of ordinary convolution. To address the above issues, recent studies have upgraded the performance of the model by adding various network modules such as attention mechanisms, long and short-term memory (LSTM). In this work, we propose a new U-Net-based speech enhancement model using a novel lightweight and efficient Shuffle Attention (SA), Gated Recurrent Unit (GRU), residual blocks with dilated convolutions. Residual block will be followed by a multi-scale convolution block (MSCB). The proposed hybrid structure enables the temporal context aggregation in time domain. The advantage of shuffle attention mechanism is that the channel and spatial attention are carried out simultaneously for each sub-feature in order to prevent potential noises while also highlighting the proper semantic feature areas by combining the same features from all locations. MSCB is employed for extracting rich temporal features. To represent the correlation between neighboring noisy speech frames, a two Layer GRU is added in the bottleneck of U-Net. The experimental findings demonstrate that the proposed model outperformed the other existing models in terms of short-time objective intelligibility (STOI), and perceptual evaluation of the speech quality (PESQ). [ABSTRACT FROM AUTHOR]

Facial expressions are often considered the primary indicators of emotions. However, it is challenging to detect genuine emotions because they can be controlled. Many studies on emotion recognition have been conducted actively in recent years. In this study, we designed a convolutional neural network (CNN) model and proposed an algorithm that combines the analysis of bio-signals with facial expression templates to effectively predict emotional states. We utilized the EfficientNet-B0 architecture for network design and validation, known for achieving maximum performance with minimal parameters. The accuracy for emotion recognition using facial expression images alone was 74%, while the accuracy for emotion recognition combining biological signals reached 88.2%. These results demonstrate that integrating these two types of data leads to significantly improved accuracy. By combining the image and bio-signals captured in facial expressions, our model offers a more comprehensive and accurate understanding of emotional states. [ABSTRACT FROM AUTHOR]

Optical timing with rapid, seconds-to-minutes cadences with high photometric precision and gap-free long baselines is necessary for an unambiguous physical picture of accretion phenomena, and is only possible from space. Exoplanet-hunting missions like Kepler and TESS have offered an outstanding new window into detailed jet and accretion physics, but have been severely hampered by incomplete calibration and systematics treatments and, most especially, a monochromatic single wide bandpass. Advances made using Kepler and TESS survey data, when considered alongside detailed, expensive multi-color experiments done from the ground, reveal the enormous potential of a space-based multi-color optical timing mission with a high energy focus. [ABSTRACT FROM AUTHOR]

We present a novel multi-modal adaptive feature extraction algorithm considering both time-domain and frequency-domain modalities (AFETF), coupled with a Bidirectional Long Short-Term Memory (Bi-LSTM) network based on the Grey Wolf Optimizer (GWO) for early-stage weak fault diagnosis in bearings. Singular Value Decomposition (SVD) was employed for noise reduction, while Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) was utilized for signal decomposition, facilitating further signal processing. AFETF algorithm proposed in this paper was employed to extract weak fault features. The adaptive diagnostic process was further enhanced using Bi-LSTM network optimized with GWO, ensuring objectivity in the hyperparameter optimization. The proposed method was validated for datasets containing weak faults with a 0.2 mm crack and strong faults with a 0.4 mm crack, demonstrating its effectiveness in early-stage fault detection. [ABSTRACT FROM AUTHOR]

The accurate classification of road surface conditions plays a vital role in ensuring road safety and effective maintenance. Vibration-based techniques have shown promise in this domain, leveraging the unique vibration signatures generated by vehicles to identify different road conditions. In this study, we focus on utilizing vehicle-mounted vibration sensors to collect road surface vibrations and comparing various data representation techniques for classifying road surface conditions into four classes: normal road surface, potholes, bad road surface, and speedbumps. Our experimental results reveal that the combination of multiple data representation techniques results in higher performance, with an average accuracy of 93.4%. This suggests that the integration of deep neural networks and signal processing techniques can produce a high-level representation better suited for challenging multivariate time series classification issues. [ABSTRACT FROM AUTHOR]

Induction motors (IM) play a fundamental role in the industrial sector because they are robust, efficient, and low-cost machines. Changes in the environment, installation errors, or modifications to working conditions can generate faults in induction motors. The trend on IM fault detection is focused on the design techniques and sensors capable of evaluating multiple faults with various signals using non-invasive analysis. The methodology is based on processing electric current signals by applying the short-time Fourier transform (STFT). Additionally, the computation of the mean and standard deviation of infrared thermograms is proposed as main indicators. The proposed system combines both parameters by means of Support Vector Machine and k-nearest-neighbor classifiers. The development of the diagnostic system was done with digital hardware implementations using a Xilinx PYNQ Z2 card that integrates an FPGA with a microprocessor, thus taking advantage of the acquisition and processing of digital signals and images in hardware. The proposed method has proved to be effective for the classification of healthy (HLT), misalignment (MAMT), unbalance (UNB), damaged bearing (BDF), and broken rotor bar (BRB) faults with an accuracy close to 99%. [ABSTRACT FROM AUTHOR]

Bayan Obo is a well-known polymetallic deposit containing significant quantities of rare earth elements, niobium, thorium, and iron. However, the epoch in which mineralization occurred and the mineralization process are still debated due to the complex nature of its mineralization and geological evolution. Inadequate geophysical exploration has further contributed to this lack of clarity surrounding critical issues, such as the deep link between the main orebody and the eastern orebody, the form and distribution of the extensive dolomite, and the geologic structures in the area. Therefore, we implemented the time-domain controlled-source electromagnetic method (CSEM) to acquire electrical structures at depths down to 2.5 km between the Main and Eastern mines. According to the inverted resistivity structure, in conjunction with existing geological and drilling data, we classified the main lithologies and faults based on their resistivity characteristics. Overall, the mineralized carbonatite reflects high to moderately high resistivity. The mineralized carbonatite dips overall from north to south, with a maximum extension depth not exceeding 1.5 km, and its range of occurrence is controlled by nearly east–west-striking faults distributed along the bounding line between the roof and floor rocks. The Main and Eastern mines are connected at depth, but the morphology and position of the ore bodies have significantly changed due to multiple phases of tectonic activity. The electrical structure does not reveal any obvious syncline structures, further refuting the traditional view that the Bayan syncline controls ore formation. [ABSTRACT FROM AUTHOR]

The study of electroencephalographic (EEG) signals has gained popularity in recent years because they are unlikely to intentionally fake brain activity. However, the reliability of the results is still subject to various noise sources and potential inaccuracies inherent to the acquisition process. Analyzing these signals involves three main processes: feature extraction, feature selection, and classification. The present study extensively evaluates feature sets across domains and their impact on emotion recognition. Feature selection improves results across the different domains. Additionally, hybrid models combining features from various domains offer a superior performance when applying the public DEAP dataset for emotion classification using EEG signals. Time, frequency, time–frequency, and spatial domain attributes and their combinations were analyzed. The effectiveness of the input vectors for the classifiers was validated using SVM, KNN, and ANN, which are simple classification algorithms selected for their widespread use and better performance in the state of the art. The use of simple machine learning algorithms makes the findings particularly valuable for real-time emotion recognition applications where the computational resources and processing time are often limited. After the analysis stage, feature vector combinations were proposed to identify emotions in four quadrants of the valence–arousal representation space using the DEAP dataset. This research achieved a classification accuracy of 96% using hybrid features in the four domains and the ANN classifier. A lower computational cost was obtained in the frequency domain. [ABSTRACT FROM AUTHOR]

In industrial applications, rotating machines operate under real-time variable speed and load regimes. In the presence of faults, the degradation of critical components is accelerated significantly. Therefore, robust monitoring algorithms able to identify these faults become crucial. In the literature, it is hard to find comprehensive monitoring systems that include variable speed and load regimes with combined gearbox faults using electrical and vibration signals. For this purpose, a novel signal processing methodology including a geometric classification technique is proposed. This methodology is based on using different types of sensors such as current, voltage and vibration sensors with a regime normalization, which allows the grouping of different regimes belonging to the same health state. It consists of reducing dispersion between the class observations and separating other classes representing different health states including the variation in speed and load. Then, a peripheral threshold is proposed in our classifier to diagnose new health states. To verify the effectiveness of the methodology, current, voltage and vibration data from a gearbox system are collected under variable speed and load levels. [ABSTRACT FROM AUTHOR]

This study introduces an all-digital CMOS time-domain smart temperature sensor (STS) that offers a smaller circuit area and reduced complexity. In contrast to previous studies that utilized multiple delay lines or additional path selection circuits, the proposed new structure employs a single cyclic path and fewer delay lines. The functionality of high-resolution temperature sensing, pulse-shrinking time measurement, and built-in offset-error cancellation is achieved using only one cyclic delay line (CDL). The temperature-sensing delay line generates a thermal-dependent pulse width proportional to the absolute temperature (PTAT). Subsequently, a pulse-shrinking unit, implemented within the pulse-shrinking delay line, performs time-to-digital conversion by measuring the PTAT pulse width. Finally, a time-added delay line within the CDL incorporates a simple D-type Flip Flop to enable concise offset-error cancellation, thereby improving accuracy. This study further simplifies the cancellation circuitry to reduce the overall circuit area. The proposed sensor, fabricated using a TSMC 0.35 μ m CMOS process, occupies an area of 0.022 mm 2 , providing a cost-effective solution for pulse-shrinking STSs. The maximum inaccuracy after offset-error cancellation is 1. 3 ∘ C within a temperature range of 0 ∼ 80 ∘ C, with a high resolution of approximately 0. 035 ∘ C/LSB. This resolution enhancement significantly surpasses similar studies. By reducing circuit complexity, the proposed sensor successfully achieves improvements in both area and resolution. [ABSTRACT FROM AUTHOR]

A simplified model is proposed for predicting the nonlinear dynamic response of vertically loaded tapered piles in the time domain, in which the tapered pile is divided into several frustum segments and the four-spring is used for the simulation of the soil–pile interaction. The differential equations for the tapered pile are given and solved by the finite difference method. The vertical dynamic response of a typical tapered pile is investigated, and the consistency of the computational results compared with the finite element results convincingly verifies the reliability of the proposed simplified model. Then, recommended segment numbers, considering the computational efficiency and accuracy requirements for the dynamic analysis of tapered piles, are given. And parametric studies are also carried out to investigate the effect of soil and pile parameters on the nonlinear dynamic response of the tapered pile. The results show that soil nonlinearity significantly affects the vertical dynamic characteristics of the tapered pile. And the tapered pile shows better dynamic characteristics than the cylindrical pile with the same volume and pile length. In addition, the properties of the soil along the upper part of the tapered pile have a more considerable effect on the dynamic response of the tapered pile. These results help to further improve the theory of nonlinear dynamic response analysis of tapered piles and promote its widespread application in engineering practice. [ABSTRACT FROM AUTHOR]

Optical timing with rapid, seconds-to-minutes cadences with high photometric precision and gap-free long baselines is necessary for an unambiguous physical picture of accretion phenomena, and is only possible from space. Exoplanet-hunting missions like Kepler and TESS have offered an outstanding new window into detailed jet and accretion physics, but have been severely hampered by incomplete calibration and systematics treatments and, most especially, a monochromatic single wide bandpass. Advances made using Kepler and TESS survey data, when considered alongside detailed, expensive multi-color experiments done from the ground, reveal the enormous potential of a space-based multi-color optical timing mission with a high energy focus.

In this study, we apply a novel format of Maxwell’s equations in SI Units for analyzing electromagnetic fields in conical cavities, with a special focus on time-domain analysis. This unique approach aligns the dimensions of electric (E) and magnetic (H) fields as inverse meters, thereby facilitating theoretical investigations into complex geometrical field behaviors. Our primary focus is on deriving evolutionary equations for electromagnetic fields within conical structures. Additionally, this study serves as a stepping stone for future in-depth research into the mechanical properties of electromagnetic fields, particularly due to the unified dimensional approach of E and H fields. This method is expected to provide more insightful perspectives in understanding the dynamics of electromagnetic fields in conical cavities. The implications of this research extend to practical applications, notably in the design and analysis of microwave resonant cavities and conical antennas, enhancing our comprehension of electromagnetic phenomena in specialized structures within the broader scope of electrodynamics.

Rolling bearings are a common machine element found in rotary machines. The components in the rolling bearing such as the inner race, outer race, rolling element, and cage are the parts that are often damaged. Traditionally spectrum analysis is used to diagnose bearing defects. However, spectrum analysis is not effectively applied to bearings with early defects because the vibration signal generated is dominated by frequency components from other machine elements, so the frequency of bearing defects cannot be observed. This study proposes an alternative method of detecting bearing defects based on vibration signals using machine learning with a decision tree algorithm. This method is more effective than the spectrum analysis method because machine learning is based on feature extraction and pattern recognition of vibration signal data, therefore, providing classification results directly without further analysis. Vibration signals were recorded using an accelerometer mounted on a bearing housing on a test rig. Nine-time domain statistical parameters and six frequency domain statistical parameters were extracted from the vibration signal and then used as input for decision trees. The results show that the decision trees algorithm gives an accuracy of 94.4% for classifying three rolling bearing conditions using the input of 6 selected frequency domain statistical parameters.

This work investigates the interaction between water waves and multiple ice shelf fragments in front of a semi-infinite ice sheet. The hydrodynamics are modelled using shallow water wave theory and the ice shelf vibration is modelled using Euler–Bernoulli beam theory. The ensuing multiple scattering problem is solved in the frequency domain using the transfer matrix method. The appropriate conservation of energy identity is derived in order to validate our numerical calculations. The transient scattering problem for incident wave packets is constructed from the frequency domain solutions. By incorporating multiple scattering, this paper extends previous models that have only considered a continuous semi-infinite ice shelf. This paper serves as a fundamental step towards developing a comprehensive model to simulate the breakup of ice shelves.

In addition to a carbon-neutral vision being recognized worldwide, the utilization of wind energies via horizontal-axis wind turbines, especially in offshore areas, has been intensively investigated from an academic perspective. Numerical simulations play a significant role in the design and optimization of offshore wind turbines. The current review focuses on studies concerning the numerical simulations of offshore wind turbine dynamics, including the modelling of the aerodynamic and hydrodynamic conditions of the environment and the reduced-order modelling of the wind turbine dynamic responses. In detail, the functions and mechanisms of each module in the numerical simulation of the wind turbine dynamics are articulated, which in turn demonstrates its importance for the design of offshore wind turbines, and hence the development of the offshore wind industry. Based on this review, it is argued that the vertical variations in wind velocities, the blade element momentum theory, the wave dynamic models, and the reduced-order model for structural dynamics are the major concerns for the numerical simulation of wind turbines. Consequently, such directions should be emphasized in future studies. [ABSTRACT FROM AUTHOR]

Blockages in water supply systems can arise from diverse causes, leading to pollution, energy loss, and reduced system performance. Detecting and addressing these blockages is crucial for managing pressurized systems like water supply and pipe networks. Analyzing pressure signals is a common method for detecting defects, with transient pressure signals being particularly effective compared to steady-state signals. This study focuses on experimentally investigating the impact of extended blockages on pressure signal characteristics in viscoelastic pipelines within the time and frequency domains. Elastic blockages of varying lengths and diameters were used in a laboratory experimental model of a viscoelastic pipeline. The findings reveal that blockages cause changes in the pressure signal shape, resonance frequencies, and phase in different domains. These changes are more pronounced with longer and higher percentage blockages. Increasing blockage length amplifies phase shifts and wave reflections, with pressure increase delayed by damping. Higher blockage percentages induce phase shifts and amplitude reflections during transient flow. Transient flow intensity affects only amplitude, not phase, with more pronounced reflections at high intensity. Blockage location influences the distribution of phase and amplitude frequencies. Geometric changes induce phase shifts and alter amplitude frequencies, while hydraulic characteristics solely impact amplitude. [ABSTRACT FROM AUTHOR]

Facial expressions are often considered the primary indicators of emotions. However, it is challenging to detect genuine emotions because they can be controlled. Many studies on emotion recognition have been conducted actively in recent years. In this study, we designed a convolutional neural network (CNN) model and proposed an algorithm that combines the analysis of bio-signals with facial expression templates to effectively predict emotional states. We utilized the EfficientNet-B0 architecture for network design and validation, known for achieving maximum performance with minimal parameters. The accuracy for emotion recognition using facial expression images alone was 74%, while the accuracy for emotion recognition combining biological signals reached 88.2%. These results demonstrate that integrating these two types of data leads to significantly improved accuracy. By combining the image and bio-signals captured in facial expressions, our model offers a more comprehensive and accurate understanding of emotional states.

This study addresses the fault diagnosis technology of CNC electromechanical systems in mechanical engineering equipment manufacturing, and explores the fault detection methods under the influence of structural coupling to improve the accuracy and efficiency of fault diagnosis. The study first analyzes the time-domain and frequency-domain features for fault diagnosis, including quantitative and dimensionless features used to identify different types of faults. Subsequently, the study explores feature dimensionality reduction methods, including algorithms such as PCA, LLE and t-SNE, and compares the effectiveness of their application in fault diagnosis. The research focuses on proposing a lightweight deep learning fault diagnosis framework called LTCN-BLS, which combines 2-DLTCN and 1-DLTCN branches, and an ILAEN-based BLS classifier to effectively extract and fuse time-domain and time-frequency-domain features of the data. The experimental results show that the LTCN-BLS framework has high accuracy and low network complexity in fault diagnosis, and has obvious advantages in early fault monitoring, degradation assessment, and robustness compared with traditional methods.

In the present study, a novel time domain decoupling method was proposed for the multiple successive rotations of different kinds of robots. This is achieved through the utilization of instantaneous Euler angles. For a general parallel mechanism, the Plücker coordinates of the intersection line of the before and after rotation plane are determined through the reciprocal product principle of screw theory. Additionally, the angle between these two rotation planes is defined as the instantaneous Euler angle. The analysis of the general parallel mechanism was used as an example to illustrate the solution method of the instantaneous Euler angle. To investigate the intrinsic relationship between the instantaneous Euler angle and the conventional Euler angle, the mathematical mapping relationship and the difference between the instantaneous Euler angle and the two kinds of Euler angles (Z-Y-X and Z-Y-Z) were explored, respectively. Simulations of a 3-sps-s parallel mechanism and a robotic arm were employed to illustrate the superiority of the instantaneous Euler angle. The findings showed that the instantaneous Euler angle exhibited enhanced temporal consistency compared to the conventional Euler angle. Further, it is better suited for accurately describing the decoupled rotation of robotic systems. The proposed approach is also generally applicable to robot performance evaluation, mechanism design, and other relevant fields. [ABSTRACT FROM AUTHOR]

A model coupling Reynolds-averaged Navier-Stokes (RANS) method and linearized Navier-Stokes equations (LNSEs) was established in order to investigate the acoustic excitation and attenuation effect from a coupling perspective of time–space–frequency under various flow velocities and mass fractions of methane. Results show that the energy distribution of acoustic modes under high-frequency acoustic excitation is more uniform. The amplitude of the acoustic oscillation at a multiple coupling physical field is 10,000 times higher than that at simple flow field. The case when w C H 4 = 0.8 owns the largest percentage of energy conversion from fundamental to high-frequency signals, the largest percentage of transmitted waves from the combustion chamber system to outside and the strongest non-linear effect. When w C H 4 rises, the amplitude of oscillations at points and the attenuation effect of high-frequency signals along the axial are enhanced. At the case of Uin = 15 m/s, the amplitude of harmonics is reduced by 18% compared with other cases, while the proportion of the high-frequency harmonic increases, proving the non-linearity cannot be neglected in this case. As velocity rises, the energy conversion from fundamental to high-frequency signals enhances; while closer to the outlet position, the more complex the oscillation signal is. Model-shapes analysis shows that a case of w C H 4 = 0.8 owns the largest amplitude of the second harmonic at downstream of the burner, while the amplitude of the harmonics rapidly increases at Uin = 15 m/s at the end of the burner, which further indicates that the energy conversion of low-frequency signals to high-frequency signals occurs mainly in the middle and downstream regions. [ABSTRACT FROM AUTHOR]

Many optimization problems in electrical engineering consider a large number of design parameters. A sensitivity analysis identifies the design parameters with the strongest influence on the problem of interest. This paper introduces the adjoint variable method as an efficient approach to study sensitivities of nonlinear electroquasistatic problems in time domain. In contrast to the more common direct sensitivity method, the adjoint variable method has a computational cost nearly independent of the number of parameters. The method is applied to study the sensitivity of the field grading material parameters on the performance of a 320 kV cable joint specimen, which is modeled as a finite element nonlinear transient electroquasistatic problem. Special attention is paid to the treatment of quantities of interest, which are evaluated at specific points in time or space. It is shown that the method is a valuable tool to study this strongly nonlinear and highly transient technical example. [ABSTRACT FROM AUTHOR]

This study focuses on identifying damage in reinforced concrete (RC) beams using time-domain modal testing and wavelet analysis. A numerical model of an RC beam was used to generate various damage scenarios with different severities and locations. Acceleration time histories were recorded for both damaged and undamaged structures. Two damage indices, DI_MW and DI_SW, derived from the wavelet analysis, were employed to determine the location and severity of the damage. The results showed that different wavelet families and specific mother wavelets had varying effectiveness in detecting damage. The Daubechies wavelet family (db2, db6, and db9) detected damage at the center and sides of the RC beams due to good time and frequency localization. The Biorthogonal wavelet family (bior2.8 and bior3.1) provided improved time–frequency resolution. The Symlets wavelet family (sym2 and sym7) offered a balanced trade-off between time and frequency localization. The Shannon wavelet family (shan1-0.5 and shan1-0.1) exhibited good time localization, while the Frequency B-Spline wavelet family (fbsp2-1-0.1) excelled in frequency localization. Certain combinations of mother wavelets, such as shan1-0.5 with the DI_SW index, were highly effective in detecting damage. The DI_SW index outperformed DI_MW across different numerical models. Selecting appropriate wavelet analysis techniques, particularly utilizing shan1-0.5 in the DI_SW, proved effective for detecting damage in RC beams. [ABSTRACT FROM AUTHOR]

The present work applies several advanced spectral methods to the analysis of macroeconomic fluctuations in three countries of the European Union: Italy, The Netherlands, and the United Kingdom. We focus here in particular on singular-spectrum analysis (SSA), which provides valuable spatial and frequency information of multivariate data and that goes far beyond a pure analysis in the time domain. The spectral methods discussed here are well established in the geosciences and life sciences, but not yet widespread in quantitative economics. In particular, they enable one to identify and describe nonlinear trends and dominant cycles | including seasonal and interannual components that characterize the deterministic behavior of each time series. These tools have already proven their robustness in the application on short and noisy data, and we demonstrate their usefulness in the analysis of the macroeconomic indicators of these three countries. We explore several fundamental indicators of the countries' real aggregate economy in a univariate, as well as a multivariate setting. Starting with individual single-channel analysis, we are able to identify similar spectral components among the analyzed indicators. Next, we consider combinations of indicators and countries, in order to take different effects of comovements into account. Since business cycles are cross-national phenomena, which show common characteristics across countries, our aim is to uncover hidden global behavior across the European economies. Results are compared with previous findings on the U.S. indicators (Groth et al., 2012). Finally, the analysis is extended to include several indicators from the U.S. economy, in order to examine its influence on the European market.

Bayan Obo is a well-known polymetallic deposit containing significant quantities of rare earth elements, niobium, thorium, and iron. However, the epoch in which mineralization occurred and the mineralization process are still debated due to the complex nature of its mineralization and geological evolution. Inadequate geophysical exploration has further contributed to this lack of clarity surrounding critical issues, such as the deep link between the main orebody and the eastern orebody, the form and distribution of the extensive dolomite, and the geologic structures in the area. Therefore, we implemented the time-domain controlled-source electromagnetic method (CSEM) to acquire electrical structures at depths down to 2.5 km between the Main and Eastern mines. According to the inverted resistivity structure, in conjunction with existing geological and drilling data, we classified the main lithologies and faults based on their resistivity characteristics. Overall, the mineralized carbonatite reflects high to moderately high resistivity. The mineralized carbonatite dips overall from north to south, with a maximum extension depth not exceeding 1.5 km, and its range of occurrence is controlled by nearly east–west-striking faults distributed along the bounding line between the roof and floor rocks. The Main and Eastern mines are connected at depth, but the morphology and position of the ore bodies have significantly changed due to multiple phases of tectonic activity. The electrical structure does not reveal any obvious syncline structures, further refuting the traditional view that the Bayan syncline controls ore formation.

Induction motors (IM) play a fundamental role in the industrial sector because they are robust, efficient, and low-cost machines. Changes in the environment, installation errors, or modifications to working conditions can generate faults in induction motors. The trend on IM fault detection is focused on the design techniques and sensors capable of evaluating multiple faults with various signals using non-invasive analysis. The methodology is based on processing electric current signals by applying the short-time Fourier transform (STFT). Additionally, the computation of the mean and standard deviation of infrared thermograms is proposed as main indicators. The proposed system combines both parameters by means of Support Vector Machine and k-nearest-neighbor classifiers. The development of the diagnostic system was done with digital hardware implementations using a Xilinx PYNQ Z2 card that integrates an FPGA with a microprocessor, thus taking advantage of the acquisition and processing of digital signals and images in hardware. The proposed method has proved to be effective for the classification of healthy (HLT), misalignment (MAMT), unbalance (UNB), damaged bearing (BDF), and broken rotor bar (BRB) faults with an accuracy close to 99%.

The study of electroencephalographic (EEG) signals has gained popularity in recent years because they are unlikely to intentionally fake brain activity. However, the reliability of the results is still subject to various noise sources and potential inaccuracies inherent to the acquisition process. Analyzing these signals involves three main processes: feature extraction, feature selection, and classification. The present study extensively evaluates feature sets across domains and their impact on emotion recognition. Feature selection improves results across the different domains. Additionally, hybrid models combining features from various domains offer a superior performance when applying the public DEAP dataset for emotion classification using EEG signals. Time, frequency, time–frequency, and spatial domain attributes and their combinations were analyzed. The effectiveness of the input vectors for the classifiers was validated using SVM, KNN, and ANN, which are simple classification algorithms selected for their widespread use and better performance in the state of the art. The use of simple machine learning algorithms makes the findings particularly valuable for real-time emotion recognition applications where the computational resources and processing time are often limited. After the analysis stage, feature vector combinations were proposed to identify emotions in four quadrants of the valence–arousal representation space using the DEAP dataset. This research achieved a classification accuracy of 96% using hybrid features in the four domains and the ANN classifier. A lower computational cost was obtained in the frequency domain.

A simplified model is proposed for predicting the nonlinear dynamic response of vertically loaded tapered piles in the time domain, in which the tapered pile is divided into several frustum segments and the four-spring is used for the simulation of the soil–pile interaction. The differential equations for the tapered pile are given and solved by the finite difference method. The vertical dynamic response of a typical tapered pile is investigated, and the consistency of the computational results compared with the finite element results convincingly verifies the reliability of the proposed simplified model. Then, recommended segment numbers, considering the computational efficiency and accuracy requirements for the dynamic analysis of tapered piles, are given. And parametric studies are also carried out to investigate the effect of soil and pile parameters on the nonlinear dynamic response of the tapered pile. The results show that soil nonlinearity significantly affects the vertical dynamic characteristics of the tapered pile. And the tapered pile shows better dynamic characteristics than the cylindrical pile with the same volume and pile length. In addition, the properties of the soil along the upper part of the tapered pile have a more considerable effect on the dynamic response of the tapered pile. These results help to further improve the theory of nonlinear dynamic response analysis of tapered piles and promote its widespread application in engineering practice.

The aim of this paper is to present a methodology for the calculation of the R-L parameters of a model of a nonlinear hysteretic inductor. The methodology is based on the analysis of the instantaneous magnetising power calculated from the hysteresis loop of the inductor and is completely developed in the time domain. The instantaneous magnetising power is firstly separated into the oscillatory and absorbed components. Thereafter, the parameter R is calculated using the absorbed component and the parameter L using the oscillatory component. The methodology is validated through the comparison of the results for parameters R and L obtained with the proposed method and the existing method based on the Poynting theorem. The validation is demonstrated on the specific simulated cases with idealised parameters of a nonlinear circuit. Additionally, the paper presents results for the parameters R and L calculated from the hysteresis loops measured at frequencies from 1 to 300 Hz. Furthermore, the fitting functions representing the variation of these parameters with the rate of change of magnetic flux density, and the corresponding results, are presented in the paper. A discussion of all the results presented and applicability of the methodology proposed, as well as the concluding remarks, are given thereafter. [ABSTRACT FROM AUTHOR]

The advanced improvements in deep learning neural networks in the speech enhancement area have vastly improved. The performance of speech enhancement is still limited because widely used existing techniques cannot fully exploit contextual information from multiple scales. To address this issue, we propose a nested U-Net with efficient channel attention and D3Net (ECAD3MUNet) for speech enhancement. The proposed ECAD3MUNet is an encoder and decoder model with skip connections to improve information flow. In ECAD3MUNet, a novel densely connected dilated DenseNet (D3Net) block is incorporated with a multi-scale feature extraction block to explore large-scale contextual information. In this way, the benefits of local and global features can be completely leveraged to increase speech reconstruction abilities. D3Net uses revolutionary multi-dilated convolution with a variable dilation factor in a single layer to simulate many resolutions at the same time. D3Net improves the growth of a receptive field and the simultaneous modeling of multi-resolution data in a single convolution layer. D3Net addresses the aliasing problem that occurs when we naively include dilated convolution in the DenseNet model. Additionally, a novel cross-channel interaction can be implemented via the efficient channel attention (ECA) module without dimensionality reduction. In module testing, choosing an adaptable kernel size for the ECA improved network performance significantly. We incorporated the D3Net and ECA modules into the proposed model for better feature extraction and utterance-level context aggregation. The proposed ECAD3MUNet model experimental results outperform other baseline models in objective speech quality and intelligibility scores. [ABSTRACT FROM AUTHOR]

The brake system of a mine hoist is the key component and the last barrier to ensure the safe operation in a mine. Based on the vibration and the sound signals of a test brake system, this paper studied the characteristics of the signals and the relationship between both the signals and tries to propose a new fault diagnosis method for the brake system. First, the vibration and sound signals of the test set-up under different braking conditions at different brake disc rotation speeds were collected. Then, the vibration and sound signals were filtered and reduced by the wavelet packet, and the reconstructed signal eigenvalues were extracted. The results showed that the characteristics of vibration and sound signals in fault state and normal state can be drawn. It shows the combined fault diagnosis method based on vibration and sound signals can see a wide application to the fault diagnosis for mine hoist brake system. [ABSTRACT FROM AUTHOR]

This work presents an investigation of ice shelf vibration using a model based on shallow water approximation. The study focused on the effect of changes in the draft on the vibration of the ice shelf and presents both time-domain and frequency-domain results. The model used a radiation condition for energy propagation into the ice shelf. Furthermore, an energy balance relation was derived to investigate the energy flow within the system. Results show that changes in the draft can significantly impact the ice shelf's vibration and that the energy flow within the system is affected by the geometry of the ice shelf. Results are presented for the interaction of wave packets in the time domain with the ice shelf. These show that energy is reflected and transmitted by the ice shelf and that the motion of the wave packets is very different in the ice shelf than in the open water. Overall, this study provides insight into the dynamics of ice shelf vibration and highlights the importance of considering changes in the draft and using the time domain when modelling these phenomena. [ABSTRACT FROM AUTHOR]

The autonomic nervous system (ANS) is responsible for the precise regulation of tissue functions and organs and, thus, is crucial for optimal stress reactivity, adaptive responses and health in basic and challenged states (survival). The fine-tuning of central ANS activity relies on the internal central autonomic regulation system of the central autonomic network (CAN), while the peripheral activity relies mainly on the two main and interdependent peripheral ANS tracts, the sympathetic nervous system (SNS) and the parasympathetic nervous system (PNS). In disease, autonomic imbalance is associated with decreased dynamic adaptability and increased morbidity and mortality. Acute or prolonged autonomic dysregulation, as observed in stress-related disorders, affects CAN core centers, thereby altering downstream peripheral ANS function. One of the best established and most widely used non-invasive methods for the quantitative assessment of ANS activity is the computerized analysis of heart rate variability (HRV). HRV, which is determined by different methods from those used to determine the fluctuation of instantaneous heart rate (HR), has been used in many studies as a powerful index of autonomic (re)activity and an indicator of cardiac risk and ageing. Psychiatric patients regularly show altered autonomic function with increased HR, reduced HRV and blunted diurnal/circadian changes compared to the healthy state. The aim of this article is to provide basic knowledge on ANS function and (re)activity assessment and, thus, to support a much broader use of HRV as a valid, transdiagnostic and fully translational dynamic biomarker of stress system sensitivity and vulnerability to stress-related disorders in neuroscience research and clinical psychiatric practice. In particular, we review the functional levels of central and peripheral ANS control, the main neurobiophysiologic theoretical models (e.g., polyvagal theory, neurovisceral integration model), the precise autonomic influence on cardiac function and the definition and main aspects of HRV and its different measures (i.e., time, frequency and nonlinear domains). We also provide recommendations for the proper use of electrocardiogram recordings for HRV assessment in clinical and research settings and highlight pathophysiological, clinical and research implications for a better functional understanding of the neural and molecular mechanisms underlying healthy and malfunctioning brain–heart interactions in individual stress reactivity and psychiatric disorders. [ABSTRACT FROM AUTHOR]

We review the research progress on noninvasive neural regulatory systems through system design and theoretical guidance. We provide an overview of the development history of noninvasive neuromodulation technology, focusing on system design. We also discuss typical cases of neuromodulation that use modern noninvasive electrical stimulation and the main limitations associated with this technology. In addition, we propose a closed-loop system design solution of the "time domain", "space domain", and "multi-electrode combination". For theoretical guidance, this paper provides an overview of the "digital brain" development process used for noninvasive electrical-stimulation-targeted modeling and the development of "digital human" programs in various countries. We also summarize the core problems of the existing "digital brain" used for noninvasive electrical-stimulation-targeted modeling according to the existing achievements and propose segmenting the tissue. For this, the tissue parameters of a multimodal image obtained from a fresh cadaver were considered as an index. The digital projection of the multimodal image of the brain of a living individual was implemented, following which the segmented tissues could be reconstructed to obtain a "digital twin brain" model with personalized tissue structure differences. The "closed-loop system" and "personalized digital twin brain" not only enable the noninvasive electrical stimulation of neuromodulation to achieve the visualization of the results and adaptive regulation of the stimulation parameters but also enable the system to have individual differences and more accurate stimulation. [ABSTRACT FROM AUTHOR]

Electroporation is a biophysical phenomenon involving an increase in cell membrane permeability to molecules after a high-pulsed electric field is applied to the tissue. Currently, electroporation is being developed for non-thermal ablation of cardiac tissue to treat arrhythmias. Cardiomyocytes have been shown to be more affected by electroporation when oriented with their long axis parallel to the applied electric field. However, recent studies demonstrate that the preferentially affected orientation depends on the pulse parameters. To gain better insight into the influence of cell orientation on electroporation with different pulse parameters, we developed a time-dependent nonlinear numerical model where we calculated the induced transmembrane voltage and pores creation in the membrane due to electroporation. The numerical results show that the onset of electroporation is observed at lower electric field strengths for cells oriented parallel to the electric field for pulse durations ≥10 µs, and cells oriented perpendicular for pulse durations ~100 ns. For pulses of ~1 µs duration, electroporation is not very sensitive to cell orientation. Interestingly, as the electric field strength increases beyond the onset of electroporation, perpendicular cells become more affected irrespective of pulse duration. The results obtained using the developed time-dependent nonlinear model are corroborated by in vitro experimental measurements. Our study will contribute to the process of further development and optimization of pulsed-field ablation and gene therapy in cardiac treatments. [ABSTRACT FROM AUTHOR]

An assessment is made of the stress distribution and the hydrodynamic response of the preliminary structural design of the tension leg platform of a 10 MW wind turbine. The platform supporting a 10 MW turbine is modelled and analysed by the finite element method. The stress distribution of the platform is determined in still water with the turbine at above-rated conditions, and the response of the tension leg platform is estimated in the time domain. The results of the time domain analysis show reasonable agreement between the present results and the available data. To check the design stiffener dimensions, span, and spacing against stress distribution, classification societies' recommendations are used. The results of the stress distribution analysis indicate that the critical locations of the platform are the interaction of the lower columns with the upper columns and the connection of the tower of the turbine. [ABSTRACT FROM AUTHOR]

Failure of joints can lead to structural collapse. It is vital to monitor joint stiffness during operation to prevent such failures. This paper proposes a novel method for the quality assessment of structural joints using coaxial correlation in 6D space. Coaxially placed 6D sensors on either side of the joint implemented by 3D accelerometers and 3D gyroscopes with wide frequency range, automatic synchronisation between the input signal and receivers and response signal averaging are presented. The root mean square (RMS) value from the obtained signals convolution is proposed as a measure of the similarity between two signals for monitoring joint degradation. The method's effectiveness was tested on steel beam splice connection, where it was found that the RMS of convolution signals in 6D space showed a direct correlation between the calculated RMS value in X, Y, GX, and GY axes directions and the stiffness grades of the joint. The paper concludes that the nature of the RMS during the degradation of the joint may change in different axes, and wrongly chosen axes may lead to wrong conclusions regarding the state of the investigated joint, especially in the case of complex joints, so that the measurements in 6D provide higher reliability of the result interpretation. [ABSTRACT FROM AUTHOR]