Time Domain Research Articles

Gaining Insights into the Interplay between Optical and Magnetic Properties in Photoexcited Coordination Compounds.

We describe early and recent advances in the fascinating field of combined magnetic and optical properties of inorganic coordination compounds and in particular of 3d-4f single molecule magnets. We cover various applied techniques which allow for the correlation of results obtained in the frequency and time domain in order to highlight the specific properties of these compounds and the future challenges towards multidimensional spectroscopic tools. An important point is to understand the details of the interplay of magnetic and optical properties through performing time-resolved studies in the presence of external fields especially magnetic ones. This will enable further exploration of this fundamental interactions i. e. the two components of electromagnetic radiation influencing optical properties.

Twins transformer: rolling bearing fault diagnosis based on cross-attention fusion of time and frequency domain features

Abstract Current self-attention based Transformer models in the field of fault diagnosis are limited to identifying correlation information within a single sequence and are unable to capture both time and frequency domain fault characteristics of the original signal. To address these limitations, this research introduces a two-channel Transformer fault diagnosis model that integrates time and frequency domain features through a cross-attention mechanism. Initially, the original time-domain fault signal is converted to the frequency domain using the Fast Fourier Transform, followed by global and local feature extraction via a Convolutional Neural Network. Next, through the self-attention mechanism on the two-channel Transformer, separate fault features associated with long distances within each sequence are modeled and then fed into the feature fusion module of the cross-attention mechanism. During the fusion process, frequency domain features serve as the query sequence Q, and time domain features as the key-value pairs K. By calculating the attention weights between Q and K, the model excavates deeper fault features of the original signal. Besides preserving the intrinsic associative information within sequences learned via the self-attention mechanism, the Twins Transformer also models the degree of association between different sequence features using the cross-attention mechanism. Finally, the proposed model's performance was validated using four different experiments on four bearing datasets, achieving average accuracy rates of 99.67%, 98.76%, 98.47%, and 99.41%. These results confirm the model's effective extraction of time and frequency domain correlation features, demonstrating fast convergence, superior performance, and high accuracy.

Graphene Plasmonic Time Crystals

The concept of photonic crystals has recently been extended to the time domain and attracted great interest. Unfortunately, realizing photonic time crystals is a challenging task due to the practical difficulty in modulating dielectric constants with large modulation depth. This problem can be resolved using graphene, the conductivity of which is tunable with significantly large contrast. Herein, graphene plasmonic time crystals, as new kinds of photonic time crystals in atomically thin 2D material, are proposed and their optical properties are investigated. Their band structures are analytically calculated and the propagations of graphene plasmons in temporal crystalline structures are numerically evaluated. Periodically driven by temporally modulating the Fermi energy, graphene plasmons exhibit in‐gap amplification and defect‐immune topological edge states, revealing the nature as plasmonic time crystals. Graphene plasmonic time crystals are realized soon after this proposal due to the possibility of modulating its conductivity with large contrast by simple electrical gating.

Multifunctional terahertz device based on plasmon-induced transparency

Enhancing light-matter interaction is crucial in optics for boosting nanophotonic device performance, which can be achieved via plasmon-induced transparency (PIT). In this study, a polarization-insensitive PIT effect at terahertz frequencies is achieved using a novel metasurface composed of a cross-shaped graphene structure surrounded by four graphene strips. The high symmetry of this metasurface ensures its insensitivity to changes in the polarization angle of incident light. The PIT effect, stemming from the coupling of graphene bright modes, was explored through finite difference time domain (FDTD) simulations and coupled mode theory (CMT) analysis. By tuning the Fermi level in graphene, we effectively modulated the PIT transparent window, achieving high-performance optical switching with a modulation depth (88.9% < MD < 98.0%) and insertion losses (0.17 dB < IL < 0.51 dB) at a carrier mobility of 2 m2/(V·s). Furthermore, the impact of graphene carrier mobility on the slow-light effect was examined, revealing that increasing the carrier mobility from 0.5 m2/(V·s) to 3 m2/(V·s) boosts the group index from 126 to 781. These findings highlight the potential for developing versatile terahertz devices, such as optical switches and slow-light apparatus.

CommanderUAP: a practical and transferable universal adversarial attacks on speech recognition models

Most of the adversarial attacks against speech recognition systems focus on specific adversarial perturbations, which are generated by adversaries for each normal example to achieve the attack. Universal adversarial perturbations (UAPs), which are independent of the examples, have recently received wide attention for their enhanced real-time applicability and expanded threat range. However, most of the UAP research concentrates on the image domain, and less on speech. In this paper, we propose a staged perturbation generation method that constructs CommanderUAP, which achieves a high success rate of universal adversarial attack against speech recognition models. Moreover, we apply some methods from model training to improve the generalization in attack and we control the imperceptibility of the perturbation in both time and frequency domains. In specific scenarios, CommanderUAP can also transfer attack some commercial speech recognition APIs.

A model-data combination driven digital twin model for few samples fault diagnosis of rolling bearings

Abstract Deep learning-based fault diagnosis methods for rolling bearings are widely utilized due to their high accuracy. However, they have limitations under conditions with few samples. To address this problem, a model-data combination driven digital twin model (MDCDT) is proposed in this work for fault diagnosis with few samples of rolling bearings. The simulation signals generated by different fault dynamic models of rolling bearings and the measured signals are mixed through MDCDT. The MDCDT generates virtual signals to bridge the gap between the simulated signals and the measured signals by combining their respective advantages. This paper also proposes image coding method based on the Markov transfer matrix (MTMIC) to convert one-dimensional vibration signals into two-dimensional images with both frequency domain information and time domain information, making it easier to extract fault features in neural network training. In the end, the developed MDCDT was evaluated using real rolling bearing data. Experiments show that the MDCDT can generate virtual data for fault diagnosis, and the fault diagnosis accuracy is significantly improved.

Design of a two-dimensional photonic crystal biosensor to identify blood cholesterol in humans

The cholesterol detection of human blood using a two-dimensional photonic crystal biosensor has been studied in this paper. The structure calculations are done using Plane Wave Expansion (PWE) and Finite Difference Time Domain (FDTD). Photonic crystals can be used to detect biological components by utilizing a two-dimensional structure and resonator nano ring. These nano rings cause waveguide and resonator coupling. The cylinders around the sensing region have investigated the improvement structure output. The quality factor and figure of merit (FOM) for this sensor are 4988 and 57.14 RIU−1, respectively, and the low detection limit (DL) is 0.001(RIU), with a sensitivity of 32 (nm/RIU).

A sequential meta-transfer (SMT) learning to combat complexities of physics-informed neural networks: Application to composites autoclave processing

Physics-Informed Neural Networks (PINNs) have gained popularity in solving nonlinear partial differential equations (PDEs) via integrating physical laws into the training of neural networks, making them superior in many scientific and engineering applications. However, conventional PINNs still fall short in accurately approximating the solution of complex systems with strong nonlinearity, especially in long temporal domains. Besides, since PINNs are designed to approximate a specific realization of a given PDE system, they lack the necessary generalizability to efficiently adapt to new system configurations. This entails computationally expensive re-training from scratch for any new change in the system. To address these shortfalls, in this work a sequential meta-transfer (SMT) learning framework is proposed, offering a unified solution for both fast training and efficient adaptation of PINNs in highly nonlinear systems with long temporal domains. Specifically, the framework decomposes PDE's time domain into smaller time segments to create “easier” PDE problems for PINNs training. Then for each time interval, a meta-learner is assigned and trained to achieve an optimal initial state for rapid adaptation to a range of related tasks. Transfer learning principles are then leveraged across time intervals to further reduce the computational cost. Through a composites autoclave processing case study, it is shown that SMT is clearly able to enhance the adaptability of PINNs while significantly reducing computational cost, by a factor of 100.

Monitoring the ultrafast buildup of Rabi oscillations in the time domain

Monitoring the ultrafast buildup of Rabi oscillations in the time domain

Research and application of Rayleigh wave imaging based on the Born–Jordan time‐frequency distribution

AbstractCurrently, the horizontal resolution of Rayleigh wave exploration is low. In this study, we propose the Born–Jordan time‐frequency distribution to analyse Rayleigh waves. The seismic signal was filtered with a wavelet transform for denoising, and the Rayleigh wave was separated in the time domain. Using the Born–Jordan time‐frequency distribution, the time waveform of each frequency comprising the Rayleigh wave from every seismic channel was obtained, and the time difference of the Rayleigh wave with the same frequency was calculated, based on which the dispersion curve between the two channels was obtained. Combined with the multichannel Rayleigh wave dispersion curve, phase velocity and frequency imaging under the seismic arrangement were obtained. Applying this method to detect abnormal geological bodies in engineering investigations showed that hard geologic bodies, such as comcrete rocks, have high velocity and frequency, whereas weak ones have low velocity and frequency. This strategy facilitated the detection of fractured zones, underground goafs and obstacles during pipe‐jacking construction near the surface.

Chatter Stability of Orthogonal Turn-Milling Process in Frequency and Discrete-Time Domains

Abstract As the industry seeks better quality and efficiency, multitasking machine tools are becoming increasingly popular owing to their ability to create complex parts in one setup. Turn-milling, a type of multi-axis machining, combines milling and turning processes to remove material through simultaneous rotations of the cutter and workpiece with the translational feed of the tool. While turn-milling can be advantageous for large parts made of hard-to-cut materials, it also offers challenges in terms of surface form errors and process stability. Because tool eccentricity and workpiece rotation lead to more complexity in process mechanics and dynamics, traditional milling stability models cannot predict the stability of turn-milling processes. This study presents a mathematical model based on process mechanics and dynamics by incorporating the unique characteristics of the orthogonal turn-milling process to avoid self-excited chatter vibrations. A novel approach was employed to model time-varying delays considering the simultaneous rotation of the tool and workpiece. Stability analysis of the system was performed in both the discrete-time and frequency domains. The effects of eccentricity and workpiece speed on stability diagrams were demonstrated and validated through experiments. The results show that the tool eccentricity and workpiece speed alter the engagement geometry and delay in the regeneration mechanism, respectively, leading to significant stability diagram alterations. The proposed approach offers a comprehensive framework for the stability of orthogonal turn-milling and guidance for the selection of process conditions to achieve stable cuts with enhanced productivity.

TiO2-BN/CNTs coating with radiative cooling and reduced friction

To date, the demand for electronic packaging materials with combined highly efficient heat dissipation and low friction attracts intensive attention. There have been hardly any reports of coatings that provide simultaneous radiative cooling, thermal conduction, and friction reduction properties. In the present work, a novel biomimetic nanocomposite coating is gown on the titanium alloy substrate. The coating consists of a TiO2 ceramic layer adorned with hemispherical protrusions and is enriched with hexagonal boron nitride (h-BN) flakes and CNTs on the surface. The optimal microstructures are determined through Finite Difference Time Domain (FDTD) simulation calculations and then prepared using plasma electrolytic oxidation (PEO). The results show that the hierarchical micro/nanostructures enable the coating to achieve an infrared emissivity of ∼ 0.9 within the 3–14 wavelength range, whilst h-BN and CNTs contribute to the thermal conduction of the TiO2-BN/CNTs ceramic coating. The advantage of the biomimetic nanocomposite coating over the bare titanium alloy is that the coating facilitates a reduction in the equilibrium temperature of a 5 W LED by around 21.5 °C. In addition, the coating exhibits outstanding friction reduction performance with a low friction coefficient (COF) of 0.15 over a sliding distance of 94.2 m, benefitted from the lubrication effect of h-BN and CNT. This work introduces a straightforward and environmentally friendly approach to fabricating bifunctional thermal regulation materials.

Linear, Nonlinear, and Distributed-Parameter Observers Used for (Renewable) Energy Processes and Systems—An Overview

Full- and reduced-order observers have been used in many engineering applications, particularly for energy systems. Applications of observers to energy systems are twofold: (1) the use of observed variables of dynamic systems for the purpose of feedback control and (2) the use of observers in their own right to observe (estimate) state variables of particular energy processes and systems. In addition to the classical Luenberger-type observers, we will review some papers on functional, fractional, and disturbance observers, as well as sliding-mode observers used for energy systems. Observers have been applied to energy systems in both continuous and discrete time domains and in both deterministic and stochastic problem formulations to observe (estimate) state variables over either finite or infinite time (steady-state) intervals. This overview paper will provide a detailed overview of observers used for linear and linearized mathematical models of energy systems and review the most important and most recent papers on the use of observers for nonlinear lumped (concentrated)-parameter systems. The emphasis will be on applications of observers to renewable energy systems, such as fuel cells, batteries, solar cells, and wind turbines. In addition, we will present recent research results on the use of observers for distributed-parameter systems and comment on their actual and potential applications in energy processes and systems. Due to the large number of papers that have been published on this topic, we will concentrate our attention mostly on papers published in high-quality journals in recent years, mostly in the past decade.

Continuous Wave Ultrasound Testing Techniques

Currently, the primary method employed for ultrasound inspection is based on sending and detecting ultrasound pulses. Typically, pulse-based methods are essentially time-domain back-reflection methods, and the time-of-flight is the main measured parameter. Recently, continuous-wave methods have been developed in the radar and the fiber-optics fields, which demonstrate significantly better spatial resolution, higher signal-to-noise ratio, and ability to detect reflecting objects at very short distances. It is proposed to use the same approach to develop continuous-wave ultrasound inspection systems. The two main differences between the proposed continuous-wave ultrasonic testing and the classical pulsed ultrasound inspection method are: (i) in the continuous-wave ultrasonic testing method, the transmitted and received signals overlap in the time domain, while in the pulsed ultrasound inspection method the transmitted and received signals are separated in the time; and (ii) in the pulsed ultrasound inspection method the most important parameter is the time delay between the transmitted and received signals, while in the continuous-wave ultrasonic testing method the most informative parameters are the frequency and phase changes in the received signal in respect to modulated transmitted signal. This allows for additional flexibility in the continuous-wave ultrasonic measurements, through control of the frequency modulation. Also, the continuous-wave ultrasonic method potentially offers improved detection range and resolution; improved signal-to-noise ratio due to better energy efficiency and easier filtering; and reduced requirements on the voltage applied to the transducer, which in turn allows the use of simplified low-cost electronics. The theoretical basis of several continuous-wave ultrasound inspection technique is explained, and their advantages and limitations are discussed. Implementation of several prototype continuous-wave ultrasound testing systems is presented. Examples of the use of the systems for non-destructive testing are described.

Towards an optimization of automatic defect detection by artificial neural network using Lamb waves

This paper presents a damage detection method based on the inverse pattern recognition technique by artificial neural network (ANN) using ultrasonic waves. Lamb waves are guided elastic waves, are widely employed in nondestructive testing thanks to their attractive properties such as their sensitivity to the small defects. In this work, finite element method was conducted by Abaqus to study Lamb modes propagation. A data collection is performed by the signals recorded from the sensor of 300 models: healthy and damaged plates excited by a tone burst signal with the frequencies: 100 kHz, 125 kHz, 150 kHz, 175 kHz, 200 kHz, and 225 kHz. The captured signals in undamaged plat are the baseline, whereas the signals measured in damaged plates are recorded for various positions of external rectangular defects. To reduce the amount of training data, only two peaks of measured signals are required to be the input of the model. Continuous wavelet transform (CWT) was adopted to calculate the key features of the signal in the time domain. The feed forward neural network is implemented using MATLAB program. The data are divided as follows: 70% for training the model, 25% for the validation, and 5% for the test. The proposed model is accurate estimating the position of the defect with an accuracy of 99.98%.

A Frequency Identification Approach for Damaged Heavy-Haul Railway Bridge with Bogie Accelerations

Bridge structural health monitoring (BSHM) is extensively employed to assess whether bridge damage exceeds maintenance limits and helps maintenance decision-making. Frequency identification is an essential part of BSHM, which utilizes the first-order modal frequency as an evaluation indicator of bridge health status. This paper proposes a method for bridge damage identification based on extracting the first-order modal frequency of the bridge from dynamic responses of vehicle bogies. Firstly, the feasibility of extracting bridge modal frequency from bogie accelerations is theoretically deduced via a vehicle–bridge interaction model. The result indicates that the bogie accelerations encompass three frequency components, namely vehicle driving frequency, vehicle pitching frequency and bridge first-order modal frequency. Then, a multi-domain conjoint analysis method incorporating the time domain, frequency domain and time–frequency domain is proposed to extract the first-order frequency of damaged bridge from bogie accelerations. By defining a damage sensitivity index of the sliding window, the time-domain sensitive boundaries of bogie acceleration to bridge damage are pinpointed. Leveraging prior knowledge of the bridge modal frequency range, the frequency-domain sensitive boundaries bogie acceleration to bridge frequency are determined. On this basis, the sensitive region of bogie acceleration to bridge frequency can be precisely localized in the Hilbert Huang transform spectrum, ultimately identifying bridge frequency through searching local maximum instantaneous energy points within this region. Finally, comprehensive experiments, considering different vehicle speeds and track spectra scenarios, are carried out to investigate the effectiveness and robustness of the proposed method. The results affirm the performance of the method to accurately identify the first-order modal frequency of the damaged bridge, further underscoring the promising prospect of on-board BSHM.

Speech preprocessing and enhancement based on joint time domain and time-frequency domain analysis.

Speech enhancement aims to make noisy speech signals clearer. Traditional time-frequency domain methods struggle to differentiate between speech and noise, leading to a risk of speech distortion. This paper introduces an approach that combines the time domain and time-frequency domain using the W-net module to suppress noise at the front end. The module is an improved version of Wave-U-Net, called TTF-W-Net. We conducted experiments using the TIMIT speech and NOISEX-92 noise datasets to evaluate the enhancement performance achieved by integrating preprocessing networks, specifically Wave-U-Net and our TTF-W-Net, into the baseline methods: Phase, FullSubNet+, and DB-AIAT. Experimental results show that TTF-W-Net outperforms the baseline Wave-U-Net by 15.7% on the PESQ metric and the effect of the network by using our preprocessing method is improved. Consequently, the TTF-W-Net preprocessing Net offers effective speech enhancement.

Research on the excitation force and vortex dynamics characteristics of pump-jet propulsor induced by shafting whirling vibration: Non-uniform blade tip clearance

The propulsion shafting whirling vibration causes non-uniform dynamic changes in the rotor tip clearance, which directly have a significant influence on the excitation force and vortex dynamic characteristics of the pump-jet propulsor. In the current study, based on improved delay detached eddy simulation, the influence of non-uniform blade tip clearance on the excitation force and vortex dynamics characteristics of the pump-jet propulsor is studied under design conditions. The results show that the application of propulsion shafting whirling vibration induces significant changes in the excitation force of the pump-jet propulsor. The rotor blades modulate the excitation forces of the stator blades and duct. The transverse and vertical excitation forces are more significant than the longitudinal excitation force. The magnitude change in the circular orbit shows a linear relationship with the excitation force magnitude. The characteristic frequency of the transverse and vertical excitation forces of each component is the shaft rotation frequency. In contrast, the characteristic frequency of the longitudinal excitation force is twice the shaft rotation frequency. In the elliptical orbit, the excitation force of each component is compressed or stretched in the time domain, and the dominant frequency is shifted in the frequency domain; there is no longer a linear relationship between the vibration magnitude change and the excitation force magnitude. Furthermore, an energy generation mechanism in the wake field of the pump-jet propulsor induces vortex frequency due to the whirling vibration of the propulsion shafting system.

Preparatory movement state enhances premovement EEG representations for brain–computer interfaces

Objective. Motor-related brain–computer interface (BCI) have a broad range of applications, with the detection of premovement intentions being a prominent use case. However, the electroencephalography (EEG) features during the premovement phase are not distinctly evident and are susceptible to attentional influences. These limitations impede the enhancement of performance in motor-based BCI. The objective of this study is to establish a premovement BCI encoding paradigm that integrates the preparatory movement state and validates its feasibility in improving the detection of movement intentions. Methods. Two button tasks were designed to induce subjects into a preparation state for two movement intentions (left and right) based on visual guidance, in contrast to spontaneous premovement. The low frequency movement-related cortical potentials (MRCPs) and high frequency event-related desynchronization (ERD) EEG data of 14 subjects were recorded. Extracted features were fused and classified using task related common spatial patterns (CSP) and CSP algorithms. Differences between prepared premovement and spontaneous premovement were compared in terms of time domain, frequency domain, and classification accuracy. Results. In the time domain, MRCPs features reveal that prepared premovement induce lower amplitude and earlier latency on both contralateral and ipsilateral motor cortex compared to spontaneous premovement, with susceptibility to the dominant hand’s influence. Frequency domain ERD features indicate that prepared premovement induce lower ERD values bilaterally, and the ERD recovery speed after button press is the fastest. By using the fusion approach, the classification accuracy increased from 78.92% for spontaneous premovement to 83.59% for prepared premovement (p < 0.05). Along with the 4.67% improvement in classification accuracy, the standard deviation decreased by 0.95. Significance. The research findings confirm that incorporating a preparatory state into premovement enhances neural representations related to movement. This encoding enhancement paradigm effectively improves the performance of motor-based BCI. Additionally, this concept has the potential to broaden the range of decodable movement intentions and related information in motor-related BCI.

The causal relationship between public debt and economic growth in G7 countries: new evidence from time and frequency domain approaches

Investigators of the public debt-economic growth nexus have yet to fully address the crucial issue of determining the direction of causality. There is an implicit assumption—or perception—that the causal relationship is mostly from public debt to economic growth. Beyond this, causal relationships may vary according to the presence of structural breaks as well as different frequency characteristics. The focus of this study is to address these issues. In this context, we comparatively investigate how structural changes and frequency characteristics affect the public debt-economic growth nexus using historical data covering the period 1870–2020 for G7 countries. Methodologically, we use Fourier Toda-Yamamoto and frequency-domain causality techniques from time and frequency-based approaches, respectively. Consistent with our expectations, we show that in the link between public debt and economic growth, they differ from or in some cases confirm each other based on the time and frequency-domain approaches. According to both approaches, in Italy and Japan, the feedback effect is valid, implying a mutual interaction between public debt and economic growth. Also, we find that this relationship is permanent. Similarly, we conclude that there is no causal relationship for France according to both approaches. For the remaining countries, however, we provide diverse evidence on both the direction of causality and the temporary/permanent nature of the causal relationship. The results on temporary or permanent causality at different frequencies offer policymakers and researchers detailed insights into an obscure aspect of the existing literature.