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With the increasing awareness of health, using wearable sensors to monitor individual activities and accurately estimate energy expenditure has become a current research focus. However, existing research encounters challenges including low estimation accuracy, a deficiency of frequency domain features, and difficulty in integrating time domain and frequency domain features. To address these issues, we propose an innovative framework called the Dual-Stream Fusion Network (DSFN). This framework combines the Time Domain Encoding (TDE) module, the Frequency Domain Hierarchical-Split Encoding (FDHSE) module, and a Two-Stage Feature Fusion (TSF) module. Specifically, the temporal stream of the framework employs the TDE module to capture deep temporal features that reflect the complex dynamic variations in time-series data. The frequency domain stream introduces the FDHSE module, which extracts frequency domain features using a multi-level, multi-scale approach, ensuring a comprehensive and diverse representation of frequency information. Through this dual-stream architecture, our model effectively learns both time and frequency domain features, addressing the limitations of frequency domain features observed in prior studies. Additionally, we propose the TSF module to fully integrate time and frequency domain features, effectively overcoming the challenge of fusing these two types of features. We conducted experiments on two public datasets, namely the GOTOV dataset (elderly people) and the JSI dataset (young people). Experimental results demonstrate that our method achieves excellent performance across different age groups. Compared to the baseline models, the proposed DSFN significantly improves the accuracy of human energy expenditure estimation. [ABSTRACT FROM AUTHOR]

A time domain rock mass parameter identification method considering the Rayleigh damping is proposed in this paper. Starting with a set of initial parameter values, the algorithm solves the finite-element (FE) equations updating the parameters in each iterative step until the result converges to a local optimum. Numerical FE analysis, displacement sensitivity analysis, and the truncated singular value decomposition method are integrated into the solution algorithm for parameter identification. The technique introduced is verified in numerical studies with in-situ experiments of rock mass structures. Our results show that the approach, compared with conventional methods, can reduce ambiguities caused by inadequate data and provide more accurate insights into the subsurface for rock mass quality evaluation. [ABSTRACT FROM AUTHOR]

Aim Methods Results Conclusion Current non‐invasive near‐infrared spectroscopy (NIRS) tissue oximetry suffers from suboptimal reproducibility over probe repositioning, hindering clinical threshold establishment. Time Domain‐NIRS (TD‐NIRS) offers higher precision but lacks sufficient paediatric data, preventing effective clinical application. We aimed to establish reference ranges for cerebral and mid‐upper arm (MUA) tissue haemodynamics in paediatric subjects using TD‐NIRS and explore correlations with auxological variables.TD‐NIRS measurements were conducted acquiring data from cerebral and MUA regions with the NIRSBOX tissue oximeter. Morphological and clinically relevant information were collected to explore potential correlations with TD‐NIRS derived parameters.TD‐NIRS assessment was applied in 350 children (8.4 ± 5.0 years). Precision of TD‐NIRS was demonstrated with standard deviations of 0.9% (StO2) and 4.2 μM (tHb) for frontotemporal cerebral cortex, and 0.8% (StO2) and 3.7 μM (tHb) for MUA. No user dependency was observed. The trends of values for cerebral and peripheral regions vary differently according to age and auxological parameters.This study reports resting‐state optical and haemodynamic values for a healthy paediatric population, providing a foundation for future investigations into clinically relevant deviations in these parameters. Furthermore, correlations with anthropometric and demographic values provide valuable insights for a deeper understanding of tissue haemodynamic evolution in childhood. [ABSTRACT FROM AUTHOR]

In this contribution, the time-domain harmonic state estimation is evaluated using the real-time digital simulation, Kalman filter, an optimal measurement algorithm and a physical scaled-down laboratory implementation. This methodology is implemented using MATLAB/Simulink® and runs on RT-LAB® platform in real time. The optimal placement algorithm of measurement devices is used to obtain full observability of the system. This measurement algorithm finds the optimal placement with a limited number of measurement devices, whose measurements may be contaminated with noise. The measurement model is implemented in the physical test power system. Besides, the state estimator assesses the system inputs (internal generator voltages, nonlinear electrical load variables) whose dynamics are totally unknown. The harmonic content of the waveforms is obtained through the fast Fourier transform. The estimated responses in the case studies using the RT-LAB® platform are validated through direct comparison against those obtained in the physical implementation of the electrical power system, obtaining satisfactory results. [ABSTRACT FROM AUTHOR]

The hydrodynamic performance of a high forward-speed ship in obliquely propagating waves is numerically examined to assess both free motions and wave field in comparison with a low forward-speed ship. This numerical model is based on the time-domain potential flow theory and higher-order boundary element method, where an analytical expression is completely expanded to determine the base-unsteady coupling flow imposed on the moving condition of the ship. The ship in the numerical model may possess different advancing speeds, i.e. stationary, low speed, and high speed. The role of the water depth, wave height, wave period, and incident wave angle is analyzed by means of the accurate numerical model. It is found that the resonant motions of the high forward-speed ship are triggered by comparison with the stationary one. More specifically, a higher forward speed generates a V-shaped wave region with a larger elevation, which induces stronger resonant motions corresponding to larger wave periods. The shoaling effect is adverse to the motion of the low-speed ship, but is beneficial to the resonant motion of the high-speed ship. When waves obliquely propagate toward the ship, the V-shaped wave region would be broken due to the coupling effect between roll and pitch motions. It is also demonstrated that the maximum heave motion occurs in beam seas for stationary cases but occurs in head waves for high speeds. However, the variation of the pitch motion with period is hardly affected by wave incident angles. [ABSTRACT FROM AUTHOR]

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]

Due to the importance security of sensitive speech and audio signals such as the countries president's call phone, this research work presents an efficient audio cryptosystem. Audio speech communications play main and essential role in the human daily life, covering many important fields ranging from simple personal private information up to top secret, sensitive and critical information. It is difficult to prevent the eavesdropping by unauthorized person especially 1-D audio scrambling methods, it is built by simple algorithms and has low security level. So, there is a dire need for enhancing audio cryptosystem to be more secure against different types of attacks. In this work, the enhanced audio cryptosystem is proposed, the first approach enhances multiple key audio cryptosystem utilizing the chaotic baker map through enhancing the confusion level for both encryption/decryption algorithms employing time and transform domains. Simulation experiments revealed that the proposed boosted audio cryptosystem on time and transform domains is secure and robust against different types of attacks. The boosted multiple key based audio crypto-system is performed by increasing confusion and enlarging the secret-key space. Results of the simulation experiments revealed that the proposed multiple secret key-enhanced confusion audio cryptosystem is secure and robust with respect to different types of attacks. Moreover, results for transform domains show better results than results for time domain for the three proposed boosted audio cryptosystem. Also, the third proposed audio cryptosystem DWT-based shows the best results among all other proposed audio cryptosystem. Due to the results, robustness and reliability of proposed technique are higher the existing audio cryptographic system. [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]

In this study, a method for measuring the active reflection coefficient of an active phased array antenna is proposed. Owing to the narrow array spacing in the Ka-band, the measurement must be conducted within a measurement system that includes cables. The time-domain transform and gating of a network analyzer were used to exclude cable characteristics. The circuit network of the measurement system was analyzed mathematically and measured to verify the effectiveness of the proposed method. [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]

Abstract A time domain rock mass parameter identification method considering the Rayleigh damping is proposed in this paper. Starting with a set of initial parameter values, the algorithm solves the finite-element (FE) equations updating the parameters in each iterative step until the result converges to a local optimum. Numerical FE analysis, displacement sensitivity analysis, and the truncated singular value decomposition method are integrated into the solution algorithm for parameter identification. The technique introduced is verified in numerical studies with in-situ experiments of rock mass structures. Our results show that the approach, compared with conventional methods, can reduce ambiguities caused by inadequate data and provide more accurate insights into the subsurface for rock mass quality evaluation.

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.

Dynamic impacts such as wind and earthquakes cause loss of life and economic damage. To ensure safety against these effects, various measures have been taken from past to present and solutions have been developed using different technologies. Tall buildings are more susceptible to vibrations such as wind and earthquakes. Therefore, vibration control has become an important issue in civil engineering. This study optimizes tuned mass damper inerter (TMDI) using far-fault ground motion records. This study derives the optimum parameters of TMDI using the Adaptive Harmony Search algorithm. Structure displacement and total acceleration against earthquake load are analyzed to assess the performance of the TMDI system. The effect of the inerter when connected to different floors is observed, and the results are compared to the conventional tuned mass damper (TMD). It is indicated that the case of connecting the inerter force to the 5th floor gives better results. As a result, TMD and TMDI systems reduce the displacement by 21.87% and 25.45%, respectively, and the total acceleration by 25.45% and 19.59%, respectively. These percentage reductions indicated that the structure resilience against dynamic loads can be increased using control systems. [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.

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]

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]

This paper provides the relationship between Economic Policy Uncertainty (EPU) and international tourism activities in the BRIC countries (i.e. Brazil, Russia, India and China). The researchers used continuous wavelets, wavelet coherence and wavelet phase-difference based on Granger causality analysis to investigate the relationship between EPU and tourism activities using the annual data. The findings show that there is a unidirectional causal influence of EPU on international tourism arrivals (ITA) in the short run and mid run for Brazil. In addition, there is a bi-directional causal influence of EPU on ITA in China and Russia in the long run. [ABSTRACT FROM AUTHOR]

In this study, a numerical analysis is carried out to investigate the coupled behaviour of a TLP (tension leg platform) -TAD (tender-assisted drilling) system under a series of regular waves in surge direction. The numerical model is established in the time domain considering the nonlinear characteristics of the mooring systems and the hydrodynamic interference. After the case validations, the effects of the wave periods and amplitudes on the nonlinear dynamic characteristics of the surge motions are investigated in detail. Then, the observed nonlinear dynamic characteristics are interpreted in detail through the existing theory of phase dynamics. The surge motions of the coupled system can show multiperiod nonlinear characteristics under regular waves in certain wave periods. Three forms of phase dynamic characteristics: phase locking, phase trapping, and phase drifting and slipping are observed in the surge motions of the tender-assisted drilling operation system. [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]

EET (equality equivalence test) non‑parameter method was proposed to examine people’s distributional preferences for time. Firstly, time distributional preferences were classified into nine types by utilizing the EET method, assumptions were introduced to reflect the unique features in the time domain. Then, based on the two rounds of behavioral experiments conducted in both time and money domains, decision makers’ preference types were identified by the indifference curves, and the pattern was analyzed. The results showed that: relative to money, more people are influenced by others’ payoff when exposed to time distribution problems. As people use heuristic thinking due to the infungibility of time, their own payoff resultingly converges to the payoffs of others; people tend to be more benevolent when they have greater payoff relative to their counterparts, and more malevolent when having relatively less payoff; no extreme forms of spite are detected. [ABSTRACT FROM AUTHOR]

In engineering applications, the loads acting on engineering structures are mostly distributed loads, so distributed load identification technology has always attracted much attention. The dynamic calibration technology identifies the load by establishing the relationship between the load and the response, and the traditional dynamic calibration method obtains the calibration matrix according to the finite element simulation, and the recognition results are affected by the model error. Based on the Gauss-Legendre integral, the infinite-dimensional dynamic calibration is converted into the finite-dimensional dynamic calibration, the time-domain indexing matrix is obtained through inverse Fourier transform and convolutional integration, the time-domain identification of the distributed load applied to the structure is carried out, and the effectiveness and high accuracy of the proposed method are verified by simulation. The noise is added to the response to test the noise resistance of the method, and compared with the traditional simulation calibration method. And the influence of the total number of Gaussian points on the recognition accuracy is discussed. [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]

This paper proposes a novel approach for identifying distributed dynamic loads in the time domain. Using polynomial andmodal analysis, the load is transformed intomodal space for coefficient identification. This allows the distributed dynamic load with a two-dimensional form in terms of time and space to be simultaneously identified in the form of modal force, thereby achieving dimensionality reduction. The Impulse-based Force Estimation Algorithm is proposed to identify dynamic loads in the time domain. Firstly, the algorithm establishes a recursion scheme based on convolution integral, enabling it to identify loads with a long history and rapidly changing forms over time. Secondly, the algorithm introduces moving mean and polynomial fitting to detrend, enhancing its applicability in load estimation. The aforementioned methodology successfully accomplishes the reconstruction of distributed, instead of centralized, dynamic loads on the continuum in the time domain by utilizing acceleration response. To validate the effectiveness of the method, computational and experimental verification were conducted. [ABSTRACT FROM AUTHOR]

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.

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]

Curved panels are used for ships due to complex situations that can be under external loading. So, it is very important to know about the stability related to the curved system under Low-Velocity Impact (LVI). To improve the stability of this kind of structure, graphene oxide powders (GOPs) are added to its matrix. In addition, the contact force between the impactor and shell is evaluated by implementing Hertz contact theory. So, in the current work, as a first attempt, energy absorption and low-velocity impact response of functionally graded graphene oxide powders-reinforced nanocomposite panel on the viscoelastic substrate is presented. Higher-order shear deformation theory (HSDT), which has twelve factors, is employed to determine the displacement domains. Also, the minimum potential energy technique is designated to obtain the governing equations and end conditions. Moreover, Galerkin and Newmark methods are chosen to solve the relationships. The governing equations related to the curved system under external excitation aimed at achieving the LVI and dynamic outputs are determined by the connection of Galerkin and Newmark methods. The uniqueness of this research is considering the impacts associated with HSDT and various Boundary Conditions (BCs) in addition to considering FG oxide powder reinforcement. The outputs segment displays the effects related to the BCs, the radius of the impactor, mass, and velocity, as well as the weight fraction related to the GOPs on contact force, the indentation, and the indenter velocity. [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]

This paper is devoted to establishing the resonant modal expansion of the lowfrequency part of the scattered field for acoustic bubbles embedded in elastic materials. Due to the nanobubble with damping, the Minnaert resonance can be induced at certain discrete resonant frequencies, which forms the fundamental basis of effectively constructing elastic metamaterials via the composite material theory. There are two major contributions in this work. First, we ansatz a special form of the density, approximate the incident field with a finite number of modes, and then obtain an expansion with a finite number of modes for the acoustic-elastic wave scattering in the time-harmonic regime. Second, we show that the low-frequency part of the scattered field in the time domain can be well approximated by using the modal expansion with sharp error estimates. Interestingly, we find that the 0th mode is the main contribution to reconstruct the information of the low-frequency part of the scattered field. [ABSTRACT FROM AUTHOR]