Time Domain Research Articles

Thermal emitter performance of ultra-broadband solar metamaterial absorber based on metal-insulator-metal device structure

In this paper, we have designed a solar absorber with ultra-broadband absorption. The structure has seven layers with a metal-dielectric periodic film stack and a metallic copper substefficiency. The metal-dielectric periodic membrane stack and the dielectric are continuous to form a metal/dielectric composite microstructure. It has a very high absorption efficiency at 280 nm–2893 nm. The absorption efficiency in this wavelength range is 96.26% and absorption efficiency at AM 1.5 is up to 95.66% by finite difference time domain method (FDTD) simulation. The thermal radiation absorption efficiency of the structure is 93.69% at 1000K and 94.5% at 1200 K, which can also be used in the manufacture of the heat sink due to its high thermal radiation absorption efficiency. Our proposed solar absorber has good incidence angle insensitivity (0°–60°) and good polarization independence (0°–90°). These advantages enable our absorbers to be widely used for solar absorption and emission and offer a variety of design options for ideal absorbers.

Light-trapping by wave interference in intermediate-thickness silicon solar cells

The power conversion efficiency of crystalline silicon (c − Si) solar cells have witnessed a 2.1% increase over the last 25 years due to improved carrier transport. Recently, the conversion efficiency of c − Si cell has reached 27.1% but falls well below the Shockley-Queisser limit as well as the statistical ray-optics based 29.43% limit. Further improvement of conversion efficiency requires reconsideration of traditional ray-trapping strategies for sunlight absorption. Wave-interference based light-trapping in photonic crystals (PhC) provides the opportunity to break the ray-optics based 4n 2 limit and offers the possibility of conversion efficiencies beyond 29.43% in c − Si cells. Using finite difference time domain simulations of Maxwell’s equations, we demonstrate photo-current densities above the 4n 2 limit in 50 − 300µm-thick inverted pyramid silicon PhCs, with lattice constant 3.1µm. Our 150µm-thick PhC design yields a maximum achievable photo-current density (MAPD) of 45.22mA/cm 2. We consider anti-reflection coatings and surface passivation consisting of SiO 2 − SiN x − Al 2 O 3 stacks. Our design optimization shows that a 80 − 120 − 150nm stack leads to slightly better solar light trapping in photonic crystal cells with thicknesses <50µm, whereas the 80 − 40 − 20nm stack performs better for cells with thicknesses >100µm. We show that replacing SiN x with SiC may improve the MAPD for PhC cells thinner than 100µm. For a fixed lattice constant of 3.1µm, we find no significant improvement in the solar absorption for 50 and 100µm-thick cells relative to a 15µm cell. A substantial improvement in the MAPD is observed for the 150µm cell, but there is practically no improvement in the solar light absorption beyond 150µm thickness.

An adaptive continuous scanning laser Doppler vibrometry technique for measuring vibrational mode shapes of structures with holes

Abstract Continuous Scanning Laser Doppler Vibrometry (CSLDV) enables fast and full-field vibration measurement in an intact area of a structure. This paper further proposes a novel adaptive CSLDV method that can obtain the Operational Deflection Shapes (ODS) and mode shape of structures with holes. Firstly, an adaptive path planning method is applied to the surface of the tested structure, which can automatically adapt to the size and position of round and square holes, as well as the resolution requirements for the measurement. Secondly, based on the symmetry and other characteristics of the planned path, the time domain signal is processed by delay optimization and demodulation to obtain the ODS of each vibration mode. Finally, a modal test is conducted on a flat structure with a square and circle holes as an example. A validation experiment is also performed using SLDV. The results show that the mode shapes obtained from both methods are consistent and the Modal Assurance Criterion (MAC) between them are all above 0.96. The method can realize CSLDV of flat plate structures with arbitrary square and round holes, and has the advantages of high efficiency and dense measurement points, which enhances its engineering applicability.

Highly Stable CsPbBr3@MoS2 Nanostructures: Synthesis and Optoelectronic Properties Toward Implementation into Solar Cells.

Halide perovskites (HPs) have gained significant interest in the scientific and technological sectors due to their unique optical, catalytic, and electrical characteristics. However, the HPs are prone to decomposition when exposed to air, oxygen, or heat. The instability of HP materials limits their commercialization, prompting significant efforts to address and overcome these limitations. Transition metal dichalcogenides, such as MoS2, are chemically stable and are suitable for electronic, optical, and catalytic applications. Moreover, it can be used as a protective media or shell for other nanoparticles. In this study, a novel CsPbBr3@MoS2 core-shell nanostructure (CS-NS) is successfully synthesized by enveloping CsPbBr3 within a MoS2 shell for the first time. Significant stability of CS-NSs dispersed in polar solvents for extended periods is also demonstrated. Remarkably, the hybrid CS-NS exhibits an absorption of MoS2 and quenching of the HP's photoluminescence, implying potential charge or energy transfer from HPs to MoS2. Using finite difference time domain simulations, it is found that the CS-NSs can be utilized to produce efficient solar cells. The addition of a MoS2 shell enhances the performance of CS-NS-based solar cells by 220% compared to their CsPbBr3 counterparts. The innovative CS-NS represents important progress in harnessing HPs for photovoltaic and optoelectronic applications.

Evaluation of the mechanical shock testing standards for electric vehicle batteries

The safety of Li-ion batteries (LIB) has become an important issue with the continuously increased use of electric vehicles (EV) in the world. In a survivable vehicle crash, when the vehicle needs to maintain structural integrity, crash-induced shock may damage EV's LIB. Therefore, an evaluation of commonly used mechanical shock test standards for EV battery module and pack is performed in this study against the crash-induced shock signals collected from National Highway Traffic Safety Administration (NHTSA) New Car Assessment Program (NCAP) tests. Various shock analysis methods including signal characteristics in time domain, power spectral density (PSD) and shock response spectrum (SRS) in frequency domain, and the acceleration/velocity-change diagram are used for the evaluation. It is found that most peak accelerations of NCAP shock signals significantly exceed the peak accelerations specified in shock testing standards. Crash-induced shocks cannot be fully represented by the half-sine pulse adopted in shock testing standards. In both time and frequency domains, the existing shock testing standards generally underestimate the severities of the crash-induced shocks, and therefore, are non-conservative. It also shows that the correct selection of a filter for the processing of the original crash-induced shock signal is crucial for the specification of EV battery shock environment and shock response analyses. The results obtained in this research can support the development of more reliable shock testing standards for EV batteries.

An LDoS attack detection method based on FSWT time–frequency distribution

The Low-rate Denial-of-Service (LDoS) attack is a stealthy and periodic attack, which belongs to the category of DoS attacks. The LDoS attack maliciously preempts and consumes target resources, causing the targeted network performance to decline and affecting the service quality. The LDoS attack is highly destructive and difficult to detect and defend against due to its abnormal attack behavior. In this paper, we discuss the network traffic behavior in the time domain, frequency domain, and time–frequency domain, and we find that the time–frequency domain contains more detailed information than the time domain and frequency domain alone. Considering the limitations of the existing time–frequency domain transformation methods, an LDoS attack detection method based on Frequency Slice Wavelet Transformation (FSWT) is presented in this paper. The entropy, ratio of energy, contrast, and correlation are extracted from the time–frequency distribution to depict the network traffic, and a decision tree is trained to detect the LDoS attack in this paper. According to the experimental results on NS2, testbed, and the comparative experiment, we conclude that the method presented in this paper performs well.

Prediction of crack plane orientation in railway wheels for different braking conditions

In this paper the stress analysis of a railway wheel is introduced. The wheel was analysed by Finite element method (FEM) under different service conditions during the braking process, which means that the friction coefficient and the relative speed between the wheel and rail were altered, creating 22 different loading conditions. Stress and strain histories were evaluated in the time domain during rolling, and the fatigue parameter was used to find the critical plane in each condition. The orientation of this critical plane was used to predict the crack propagation direction and the possible fatigue mode of the wheel. Relying on the results a critical plane map was created to visualize the possible fracture modes of the wheel under different loading conditions.

DF-CDM: Conditional diffusion model with data fusion for structural dynamic response reconstruction

In structural health monitoring (SHM) systems, data loss inevitably occurs and reduces the applicability of SHM techniques, such as condition assessment and damage identification. The current mainstream data-driven method, generative adversarial networks (GAN), suffers from convergence difficulty, limiting the accuracy and efficiency of response reconstruction. In this study, a conditional diffusion model with data fusion (DF-CDM) is proposed for structural dynamic response reconstruction. The original unsupervised diffusion model is improved by introducing the conditional input and modifying the deep denoising neural network to achieve supervised learning. Besides, data fusion is developed to further utilize the frequency-domain information and improve the reconstruction quality of the time-domain signals. The proposed model is validated on a three-span continuous bridge. Results show that the diffusion model with data fusion achieves the highest accuracy on the test set with R2, RMSE and MAE equaling 0.821, 0.0053 and 0.0042 respectively. Compared with the state-of-the-art GAN model, the diffusion model without the adversarial modules has a more stable training process and better reconstruction performance in both time and frequency domains. The modal identification and robustness analysis further verify the effectiveness of the proposed model. The proposed diffusion model with data fusion achieves high-quality structural response reconstruction, guaranteeing the applicability and reliability of subsequent response-based structural analysis.

Diurnal and semi-diurnal effects of ocean tides on polar motion and UT1: an updated assessment

We revisit the computation of diurnal and semidiurnal effects of the ocean tide on Polar Motion (PM) and UT1 considering the principal waves of the Ocean Tidal Angular Momentum (OTAM) calculated from the FES 2014 ocean tide atlas. We take into account the recent development of polar motion theory, especially the frequency dependence of the transfer functions in (sub-)diurnal bands. The method of calculating the minor waves is also carefully studied and modified: instead of an interpolation based on neighboring waves, we propose a frequency dependent expression obtained by a linear fit to the admittances and phases of principal waves. Compared in time domain with polar motion (PM) and UT1 derived from Very Long Baseline Interferometry (VLBI) observations, our model performs as well as the recent Desai-Sibois model computed from TPXO8 tidal atlas. The impact of diurnal and semi-diurnal oscillations produced by the luni-solar tidal torque on the asymmetric mean mass distribution of the Earth are observed in the corresponding residuals, which is not the case for the older PM/UT1 model recommended in IERS Convention 2010. More generally, we conclude that tidal modelling of sub-daily terms and corresponding empirical developments are consistent to within 23 μas for PM and 3 μs for UT1. In parallel, the effects based on TPXO8 OTAM waves are recalculated according to our method, and present the best agreement with the observations. Our tables, calculated from FES 2014 or TPXO8 OTAM, can be truncated to the 48 or 50 terms with amplitudes above 1 μas for PM or 0.1 μs for UT1 to describe those Earth rotation Parameters in a way consistent with their current observation uncertainties.

Composite scattering characteristics analysis of micro-ellipsoidal periodic structure optical surface and microdefects

In order to adjust and detect micro-nano periodic structure optical surface accurately and efficiently, the problem of composite scattering between micro-ellipsoidal periodic structure optical surface and pore defects is studied use the multi-resolution time domain (MRTD) approach. A calculation model is established for the intensity distribution of composite scattering, which is modulated by the micro-ellipsoidal periodic structure optical surface and microdefects. Results are in good agreement with those obtained using CST Microwave Studio software and the finite-different time-domain (FDTD) approach, which demonstrates the effectiveness of the calculation model and method. By combining the field distribution of the micro-ellipsoidal periodic structure optical surface containing microdefects with the optical response at different wavelengths, it is necessary to study the influence of various parameters of the micro-ellipsoidal structure and microdefects on the optical system of metamaterials. The effects of the parameters such as roughness, structure of micro-ellipsoidal unit, defect sizes and buried depths on the composite scattering characteristics are analyzed numerically. The results provide technical support for the fields of functional surface design, ultrasensitive detection, scattering peak orientation and frequency selection.

Bioimpedance formalism: A new approach for accessing the health status of cell and tissues

This manuscript describes a novel methodology for studying relaxation dynamics in tissues and cells using characteristic frequency of bioimpedance spectroscopy measurements. The Bioimpedance Formalism allows for the simultaneous study of bioelectrical parameters in the frequency and time domains, providing insight into possible relaxation processes occurring in the tissue or cell of interest. Results from the Cole-Cole analysis showed no multiple relaxation processes associated with heterogeneity, with a visible age group separation in males compared with females. The study of the relaxation dynamic in the time domain revealed that the β parameter can be used to analyse the charge carriers in tissues, cells, or cancer cells, potentially leading to new diagnostic and therapeutic approaches for cancer and other diseases. Overall, this approach presents a promising area of research for gaining insights into the electrical properties of tissues and cells using bioimpedance methods.

Repetitive Impedance Learning-Based Physically Human-Robot Interactive Control.

Model-based impedance learning control can provide variable impedance regulation for robots through online impedance learning without interaction force sensing. However, the existing related results only guarantee the closed-loop control systems to be uniformly ultimately bounded (UUB) and require the human impedance profiles being periodic, iteration-dependent, or slowly varying. In this article, a repetitive impedance learning control approach is proposed for physical human-robot interaction (PHRI) in repetitive tasks. The proposed control is composed of a proportional-differential (PD) control term, an adaptive control term, and a repetitive impedance learning term. Differential adaptation with projection modification is designed for estimating robotic parameters uncertainties in the time domain, while fully saturated repetitive learning is proposed for estimating time-varying human impedance uncertainties in the iterative domain. Uniform convergence of tracking errors is guaranteed by the PD control and the use of projection and full saturation in the uncertainties estimation and is theoretically proved based on a Lyapunov-like analysis. In impedance profiles, the stiffness and damping are composed of an iteration-independent term and an iteration- dependent disturbance, which are estimated by repetitive learning and compressed by the PD control, respectively. Therefore, the developed approach can be applied to the PHRI where iteration-dependent disturbances exist in the stiffness and damping. The control effectiveness and advantages are validated by simulations on a parallel robot in a repetitive following task.

Mechanical Shock Wave Suppression Structure in Hold-Down and Release Apparatus of Spacecraft

A lightweight and compact structural solution for mechanical shock suppression in spacecraft, such as the hold-down and release apparatus of satellites, is a critical and thorny issue. In this work, a ring-shaped structure is introduced to isolate mechanical waves within cylindrical shells. The underlying mechanism is that the ring-shaped structure undergoes different forms of deformation to counteract different modes of incident waves. A subwavelength Mn-Cu alloy ring is designed as a shock wave barrier in a typical spacecraft hold-down and release apparatus structure with multimodal wave excitation. We experimentally reveal that the Mn-Cu alloy ring reduces the peak value of the acceleration frequency response by 53.4% within a wide frequency range from 1 to 10 kHz. Meanwhile, in the time domain, the instantaneous acceleration peak can be weakened by 37.9%. This work provides a new strategy for shock control of shell structures in aerospace engineering and related fields.

Non-Contact Blood Pressure Estimation From Radar Signals by a Stacked Deformable Convolution Network.

This study introduces a contactless blood pressure monitoring approach that combines conventional radar signal processing with novel deep learning architectures. During the preprocessing phase, datasets suitable for synchronization are created by integrating Kalman filtering, multiscale bandpass filters, and a periodic extraction method in the time domain. These data comprise data on chest micro variations, encapsulating a complex array of physiological and biomedical information reflective of cardiac micromotions. The Radar-based Stacked Deformable convolution Network (RSD-Net) integrates channel and spatial self attention mechanisms within a deformable convolutional framework to enhance feature extraction from radar signals. The network architecture systematically employs deformable convolutions for initial deep feature extraction from individual signals. Subsequently, continuous blood pressure estimation is conducted using self attention mechanisms on feature map from single source coupled with multi-feature map channel attention. The performance of model is corroborated via the open-source dataset procured using a non-invasive 24 GHz six-port continuous wave radar system. The dataset, encompassing readings from 30 healthy individuals subjected to diverse conditions including rest, the Valsalva maneuver, apnea, and tilt-table examinations. It serves to substantiate the validity and resilience of the proposed method in the non-contact assessment of continuous blood pressure. Evaluation metrics reveal Pearson correlation coefficients of 0.838 for systolic and 0.797 for diastolic blood pressure predictions. The Mean Error (ME) and Standard Deviation (SD) for systolic and diastolic blood pressure measurements are -0.32 ±6.14 mmHg and -0.20 ±5.50 mmHg, respectively. The ablation study assesses the contribution of different structural components of the RSD-Net, validating their significance in the overall of model performance.

Thermal propagation analysis of 2G HTS stacked wires based on a dimensional coupling method

Stacked structures of high-temperature superconducting (HTS) tapes are usually employed to enhance current-carrying capacity in cable or magnet applications, but their multilayer characteristics may result in local hot spots due to uneven cooling. Besides, the inconsistencies along the length of tapes introduced during manufacturing process and the significant transverse Lorentz forces experienced in magnet operations can lead to weak parts, bringing uncertainties to thermal stability. Finite-element methods are widely used to predict thermal propagations, but 3D models encounter challenges such as distorted mesh elements and convergence issues, particularly when combining electromagnetic and heat transfer modules for long-distance wires. In this work, we have developed a Dimensional Coupling Method (DCM) to assess the thermal impact of weak parts in stacked wires applying coupled 1D and 2D models. The 2D model analyzes the electromagnetic and heat characteristics of stacked surfaces, and provides an initial heat source for the 1D model, which evaluates thermal propagation longitudinally. Simulation results of the 1D module are then transferred back to update the 2D outcomes. Models of distinct dimensions are coupled sequentially in physical steps but simultaneously in the time domain. Our approach is verified by 3D model benchmarks and offers a computational cost reduction of approximately 60 % compared to the benchmarks, making it more suitable for applications with large Iop/Ic ratios. Specially, multi-layer stacked wires under low ratios cases are also been analyzed. What’s more, two influencing factors of heat propagation, the weak-part length and position, are also investigated.

Short‐term heart rate variability: A potential approach to frailty assessment in older adults

AbstractObjectivesThis study aimed to evaluate cardiac autonomic modulation using short‐term heart rate variability (HRV) and compare it among frailty statuses in older Indian adults.MethodsA total of 210 subjects aged 60 years and above were recruited into three groups: frail (n = 70), pre‐frail (n = 70), and non‐frail (n = 70) from the outpatient department of Geriatric Medicine at a tertiary care hospital in India. Frailty status was assessed using the Rockwood frailty index (FI) criteria. HRV was derived from a 5‐min ECG recording of standard limb leads and assessed using time domain, frequency domain, and nonlinear analysis of cardiac interval variability.ResultsThe HRV parameters indicative of parasympathetic modulation such as SDNN, SDSD, rMSSD, NN50, pNN50, absolute HF power, and SD1 were significantly lower in frail subjects compared with both pre‐frail and non‐frail subjects (P < 0.05). Absolute LF power and SD2 were also lower in frail subjects compared with pre‐frail and non‐frail subjects (P < 0.05). Measures of sympatho‐vagal balance (LF/HF and SD1/SD2 ratios) did not show statistical significance. The FI demonstrated negative correlations with all HRV parameters.ConclusionsFrail individuals exhibit decreased sympathetic and parasympathetic modulation compared with pre‐frail and non‐frail individuals, although maintaining a balanced sympatho‐vagal state. Furthermore, autonomic modulation declines progressively with increasing frailty.

Integrating self-attention mechanisms in deep learning: A novel dual-head ensemble transformer with its application to bearing fault diagnosis

In this paper, we propose a novel dual-head ensemble Transformer (DHET) algorithm for the classification of signals with time-frequency features such as bearing vibration signals. The DHET model employs a dual-input time-frequency architecture, integrating a 1D Transformer model and a 2D Vision Transformer model to capture the spatial and time-frequency features. By utilizing data from both the time and time-frequency domains, the proposed algorithm broadens its feature extraction capabilities and enhances the model’s capacity for generalization. In our DHET structure, the original Transformer model leverages self-attention mechanisms to consider relationships among signal input segmentations, which makes it effective at capturing long-range dependencies in signal data, while the Vision Transformer model takes 2D images as input and creates the image patches for embedding and each patch is linearly embedded into a flat vector and treated as a ‘token,’ then the ‘tokens’ are processed by the Transformer layers to learn global contextual representations, enabling the model to perform signal classification task. This integration notably enhances the performance and capability of the model. Our DHET is especially effective for rolling bearing fault diagnosis. The simulation results show that the proposed DHET has higher classification accuracy for bearing fault diagnosis and outperforms CNN-based methods.

Influence of nonlinear heat accumulation characteristics in laser powder bed fusion (LPBF) and its effect on the shape memory bone implant

This study investigated the complex thermal accumulation characteristics and their effects in the time and spatial domains during the preparation of complex Nitinol (NiTi) alloy structures using laser powder bed fusion (LPBF) technology. The findings reveal a pronounced size dependency phenomenon when the formed dimensions fall below 2mm. Specifically, a reduction in formed size leads to a marked surge in element loss, a heightened prevalence of internal defects, and a corresponding decline in both mechanical and functional properties. Small-sized (<0.5mm) samples have significant thermal accumulation due to their fast heating and slow cooling rates, requiring control of heat input. Medium sized (0.5-2mm) samples have significant temperature gradients as a result of their rapid heating and cooling rates, requiring control of high-temperature residence time. Large sized (> 2mm) samples necessitate the insulation measures to reduce thermal stress, owing to their sluggish heating and rapid cooling characteristics. Therefore, we developed a model to evaluate the thermal history characteristics of parameters or design schemes and compensate or eliminate heat accumulation during the machining process to balance the thermal history characteristics. This model can also be combined with a thermal field detection system to develop real-time thermal management and control modules for processing. The key to eliminating size-dependency effects is to homogenize the microstructure. A single aging heat treatment (773K for 0.5h with air cooling) can effectively homogenize the structure and eliminate size dependence.

Dual-loss nonlinear independent component estimation for augmenting explainable vibration samples of rotating machinery faults

Obtaining fault samples for diagnosing faults in rotating machinery for engineering applications can be costly. To address this challenge, fault sample augmentation methods are used to train fault diagnosis models. However, existing techniques mainly focus on comparing augmented signal loss with real ones, overlooking the underlying vibration mechanism of rotating machinery faults. Addressing this limitation, a novel approach, called dual-loss nonlinear independent component estimation (DLNICE), is proposed to enhance understanding of fault features in vibration signals. This integrates augmentation losses in both time and frequency domains to enrich fault vibration samples. DLNICE effectively utilizes limited fault samples for augmentation by estimating nonlinear independent components, capturing key fault characteristics like impulsiveness and cyclo-stationarity. Therefore, augmented fault samples become more explainable for analyzing rotating machinery faults. Experimental evaluations on bearing and gearbox vibration samples confirm the effectiveness of DLNICE. Utilizing the augmented samples leads to an average accuracy of 86.27% in bearing fault diagnosis, and that of 81.60% in gearbox fault diagnosis. The results demonstrate that DLNICE excels in augmenting high-quality vibration samples of rotating machinery faults.

Data-Driven Price Trends Prediction of Ethereum: A Hybrid Machine Learning and Signal Processing Approach

Due to the recent fluctuations in cryptocurrency prices, Ethereum has gained recognition as an investment asset. Given its volatile nature, there is a significant demand for accurate predictions to guide investment choices. This paper examines the most influential features of the daily price trends of Ethereum using a novel approach that combines the Random Forest classifier and the ReliefF method. Integrating the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Short-Time Fourier Transform (STFT) resulted in high accuracy and performance metrics for Ethereum price trend predictions. This method stands out from prior research, primarily based on time series analysis, by enhancing pattern recognition across time and frequency domains. This adaptability leads to better prediction capabilities with accuracy reaching 76.56% in a highly chaotic market such as cryptocurrency. The STFT's ability to reveal cyclical trends in Ethereum's price provides valuable insights for the ANFIS model, leading to more precise predictions and addressing a notable gap in cryptocurrency research. Hence, compared to models in literature such as Gradient Boosting, Long Short-Term Memory, Random Forest, and Extreme Gradient Boosting, the proposed model adapts to complex data patterns and captures intricate non-linear relationships, making it well-suited for cryptocurrency prediction.