Time Domain Research Articles

Nanoscale grating-based perovskite solar cell with improved efficiency

MAPbI3 perovskite-based solar cell (PSC) has attracted much attention due to its high absorption rate. The top flat electrode degrades the behavior of PSC due to limiting the light reached to the absorber layer, which reduces the efficiency. In this study, the influence of texturing the top FTO electrode surface with a triangular saw-tooth grating on both the optical and electrical performance is reported. The interference effects are also considered in this work by modeling the PSC structure as a Fabry–Perot resonator. In this regard, the finite difference time domain method is utilized to precisely simulate the optical characteristics of the nano-structural design. Also, the optical behavior of PSC is studied at different triangular grating (TG) structures and dimensions at which the light absorption is maximized. Furthermore, the effect of absorber thickness and defect density on the optoelectronic performance is investigated. We configured the conversion efficiency (η) of the proposed PSC structure by using the bulk and Langevin recombination mechanisms. The proposed grating structure enhances the light coupling, and hence the light absorption and the generated current density are increased. For absorber thickness of 350 nm, we reported a maximum conversion efficiency (η) of 19.5% for the proposed triangular grating (TG) structure with an enhancement of 19.6% compared to the structure with a flat FTO layer. As the defect density is increased from 1012 cm−3 to 1018 cm−3, the efficiency of the optimum TG PSC is reduced from 19.5% to 10.1%, respectively. The simulation results, therefore, contribute to the understanding of the PSC-based MAPbI3 design and can be used to improve its physical behavior.

Multi-Sensor fusion and semantic map-based particle filtering for robust indoor localization

To enhance positioning accuracy and ensure long-term stability in large-scale indoor environments, this paper presents a robust particle filtering method that integrates IMU, magnetic field, Bluetooth, and semantic map data, implemented through a handheld portable device. In the particle prediction phase, based on peak candidates extracted in the time domain, the unintentional interference movement of the hand is removed through short-time Fourier transform in the frequency domain to reduce the noise of pedestrian dead reckoning. During the observation phase, we introduce a magnetic interference coefficient to model and quantify the degree of electromagnetic interference in different environments, thereby improving the accuracy of indoor magnetic headings and enhancing the alignment of particle swarms with real-world directional constraints. Furthermore, based on passable semantic information, the indoor map is segmented into distinct probability regions, allowing the particle filter to adaptively adjust the weight of the particle swarm when moving between regions, thus improving the robustness of the overall fusion system by preventing particles from entering infeasible areas. Experiments and validations were carried out in a large shopping mall and parking lot with multiple devices. Results demonstrate that the proposed method improves the average positioning accuracy by approximately 25% compared to the Bluetooth-only positioning system, with a 15% increase in cumulative distribution probability within 3 m.

Design of a High-Power Nanosecond Electromagnetic Pulse Radiation System for Verifying Spaceborne Detectors

The Spaceborne Global Lightning Location Network (SGLLN) serves the purpose of identifying transient lightning events occurring beneath the ionosphere, playing a significant role in detecting and warning of disaster weather events. To ensure the effective functioning of the wideband electromagnetic pulse detector, which is a crucial component of the SGLLN, it must be tested and verified with specific signals. However, the inherent randomness and unpredictability of lightning occurrences pose challenges to this requirement. Consequently, a high-power electromagnetic pulse radiation system with a 20 m aperture reflector is designed. This system is capable of emitting nanosecond electromagnetic pulse signals under pre-set spatial and temporal conditions, providing a controlled environment for assessing the detection capabilities of SGLLN. In the design phase, an exponentially TEM feed antenna has been designed firstly based on the principle of high-gain radiation. The feed antenna adopts a pulser-integrated design to mitigate insulation risks, and it is equipped with an asymmetric protective loading to reduce reflected energy by 85.7%. Moreover, an innovative assessment method for gain loss, based on the principle of Love’s equivalence, is proposed to quantify the impact of feed antenna on the radiation field. During the experimental phase, a specialized E-field sensor is used in the far-field experiment at a distance of 400 m. The measurements indicate that at this distance, the signal has a peak field strength of 2.2 kV/m, a rise time of 1.9 ns, and a pulse half-width of 2.5 ns. Additionally, the beamwidth in the time domain is less than 10°. At an altitude of 500 km, the spaceborne detector records a signal with a peak field strength of approximately 10 mV/m. Particularly, this signal transformed into a nonlinear frequency-modulated signal in the microsecond range across its frequency spectrum, which is consistent with the law of radio wave propagation in the ionosphere. This study offers a stable and robust radiation source for verifying spaceborne detectors and establishes an empirical foundation for investigating the impact of the ionosphere on signal propagation characteristics.

Simulation-based inference of black hole ringdowns in the time domain

Simulation-based inference of black hole ringdowns in the time domain

Dynamic buckling active control of FGM spherical shell with piezoelectric sensor and actuator layers

The mechanical snap-through phenomenon represents one of the buckling modes inherent in shallow shells, exemplifying the shell's reaction to external loads, which can potentially have detrimental effects on structural performance. This article delves into the study of this phenomenon within the context of spherical shells. Leveraging functionally graded material (FGM) in the main layer of this structure proves instrumental in enhancing the shell's performance against rapid mechanical loads. Furthermore, the strategic placement of piezoelectric layers, both symmetrically and asymmetrically, coupled with the implementation of a proportional and derivative control system, facilitates the control of the shell's response. The formulation of displacement field and electric potential is grounded in the first-order shear deformation theory (FSDT) and second-order distribution, respectively. The strong form of nonlinear dynamic and electrostatic equations is derived using Hamilton's principle. Solving these equations is achieved through the implementation of the generalized differential quadrature (GDQ) method in the spatial domain and Newmark in the time domain. Additionally, the Newton-Raphson method is employed to tackle the inherent nonlinearity of the problem. The Budiansky's criterion is employed for the assessment of the load necessary for the buckling of the shell. Following the validation of results against numerical, analytical, and laboratory outcomes, a set of parametric results is presented. These results underscore the remarkable impact of the control system in mitigating shell deformation under time-dependent mechanical loads, as well as in postponing the occurrence of the snap-through phenomenon and damping the vibrations.

Acoustic process monitoring during the laser beam welding of stainless-steel foils using an adjustable ring mode laser beam source

Electrification of the mobility sector is vital to meet the targets for reducing greenhouse gas emissions. Besides battery-based mobility solutions, polymer electrolyte membrane fuel cells (PEMFCs) are a promising technology for electrifying drive trains, especially in heavy-duty applications, such as maritime or logistics. Bipolar plates, a key component of PEMFCs, can consist of two stainless-steel foils that must be welded to be gas-tight. In order to join the two metal foils, laser beam welding is the state-of-the-art technology. Current challenges include process instabilities at higher welding speeds, such as the humping effect, which can cause weld seam imperfections. Therefore, applying sensors for laser beam welding is a promising approach to monitor the welding process. AISI 316L foils were welded within the scope of this work with various process parameters using an adjustable ring mode laser beam source. Additionally, an optical microphone was used as a process monitoring system. By applying different parameter settings and due to the introduction of artificial faults, weld seam defects, such as a burn-through or a gap, were induced. After utilizing a noise reduction algorithm for the acoustic signals, numerous features in the time and frequency domains were extracted, with which multiple machine learning algorithms were trained and compared concerning their performance. A light gradient boosting machine was identified as a suitable machine learning model for weld seam classification. Finally, hyperparameter tuning was conducted, which resulted in a cross-validation accuracy of 94.78%, depending on the quality categories considered.

All-optical AND, NAND, OR, NOR and NOT logic gates using two nested microrings in a racetrack ring resonator

Boolean logic gates are essential components for optical computing and communication systems. However, most existing methods for realizing them require complex structures, high power consumption, or multiple devices. Here, we propose a simple and compact system that can realize five Boolean logic gates, including AND, NAND, OR, NOR, and NOT, by applying different polarization modes and tuning intensities to the input signals within a SOI resonator system, formed by two nested micro rings in a racetrack ring resonator (TNMIRTR). A formula was derived for the optical transfer function of the system using the delay-line-signal method and the logic gates were simulated using the variational finite-difference time domain (varFDTD) method. The proposed structure operates by combining amplitude and polarization-conversion. TNMIRTR gate has several advantages, such as its micro-scale size, low cost, and ability to realize multiple logic gates within a single layout.

Методика обработки сигналов, позволяющая уменьшить погрешности измерений бистатических характеристик рассеяния образцов материалов

In this paper a signal processing technique for bistatic measurements of the reflection coefficient of flat material samples is presented. This technique is based on comparing the reflected signal from the object under study and the background signal in the time domain. The use of this technique makes it possible to increase the accuracy of measurements of the characteristics of bistatic scattering of objects with a small amplitude of the reflected wave. This technique allows us to offset for the measurement error caused by the inaccuracy of antenna positioning and significantly reduce the measurement error caused by the interaction of antennas with each other at large bistatic angles. The reflection coefficient of the material sample was measured. The obtained results were processed by vector subtraction, time domain and using the developed technique. It was shown that the use of this technique allows one to increase the accuracy of measuring the reflectance of the material. This technique reduces the error in determining the minimum value of the reflection coefficient and the angle at which this minimum is achieved. The most effective is to use background synthesis and time domain together.

Gravitational Wave Ringdown Analysis Using the F -statistic

After the final stage of the merger of two black holes (BHs), the ringdown signal takes an important role in providing information about the gravitational dynamics in strong field. We introduce a novel time-domain (TD) approach, predicated on the F -statistic, for ringdown analysis. This method diverges from traditional TD techniques in that its parameter space remains constant irrespective of the number of modes incorporated. This feature is achieved by reconfiguring the likelihood and analytically maximizing over the extrinsic parameters that encompass the amplitudes and reference phases of all modes. Consequently, when performing the ringdown analysis under the assumption that the ringdown signal is detected by the Einstein Telescope, the parameter estimation computation time is shortened by at most 5 orders of magnitude compared to the traditional TD method. We further establish that traditional TD methods become difficult when including multiple overtone modes due to close oscillation frequencies and damping times across different overtone modes. Encouragingly, this issue is effectively addressed by our new TD technique. The accessibility of this new TD method extends to a broad spectrum of research and offers flexibility for various topics within BH spectroscopy applicable to both current and future gravitational wave detectors.

Soil-structure interaction analysis of nuclear power plant structure using multi-step method in time-domain

Recently, several countries have experienced strong earthquakes, and there has been growing interest in the seismic design of structures. Particularly, consideration of soil-structure interaction (SSI) has become important when performing seismic response analysis for nuclear power plant (NPP) structures. A seismic analysis method that considers SSI in the time domain has been proposed in previous studies. This method is applicable to the seismic response of structures, because it can consider the influence of the SSI effect with the impedance function in the frequency domain and simultaneously considers the structural nonlinearity. However, it has only been applied to the structure comprising a single beam-stick. In this study, a modified method for applying it to more general structures is proposed, and its applicability is validated with an actual reactor containment building (RCB) structure comprising a three beam-sticks. Validation was performed by comparing the seismic responses with those of ACS SASSI. Using the proposed method, SSI analysis was performed with three soil profiles and the results were compared to investigate the effect of SSI on seismic responses, including the in-structure response spectrum (ISRS) and time history acceleration. For more realistic analysis, soil profile and earthquake input motion occurred in South Korea are adopted. Further, a multi-step method considering nonlinear behavior was also performed and compared with the linear case.

Non-coherent short-packet communications: Novel z-domain user multiplexing

In the evolution of fifth generation (5G) and beyond wireless communication systems, non-coherent (NC) short packet communication (SPC) is crucial for achieving ultra-reliable low-latency communication (URLLC). Frame design, latency, and reliability are some of the challenges associated with short-packet communication. Recently, to address these challenges, a novel modulation scheme known as modulation on conjugate-reciprocal zeros (MOCZ) has been proposed. MOCZ modulates information on conjugate reciprocal zeros in the z-domain, thereby eliminating the need for channel estimation and providing a robust solution for NC communication. However, in multi-user MOCZ (MU-MOCZ) scheme, adding guard intervals to each short packet to mitigate channel impact remains an issue, as it increases the transmission time and consequently reduces efficiency. To address the aforementioned problem, this paper introduces a novel frame design approach called z-domain user multiplexing MOCZ (ZDUM-MOCZ or ZDM-MOCZ). Unlike traditional time division multiplexing (TDM), which serves users consecutively in the time domain, this method multiplexes users in the z-domain. In this approach, each user is allocated a specific set of zeros in the z-domain, which collectively form a unique sequence in the time domain. The findings illustrate the potential for reduced latency in downlink transmission, highlighting the benefits of this novel methodology over conventional MU-MOCZ method. The proposed ZDUM-MOCZ scheme not only addresses the existing issues in the frame design of the MU-MOCZ scheme but also facilitates more efficient and reliable short packet communication in 5G and beyond wireless systems.

A complex neural network model by Hilbert Transform

The phase information of the optical wave plays a vital role in processing wave-related signals. In deep learning fields, complex-valued neural networks are put forward on the concept of complex amplitudes for full utilization of phase information. To build a complex-valued neural network, the common way is to exploit Fourier Transform of the observed signal to extract amplitude and phase information. However, this will lead to spectrum waste for a real-valued signal by introducing negative frequencies that have no physical meaning. To this end, we attempt to use Hilbert Transform as an alternative to yield a single sideband spectrum and avoid negative frequencies from interacting with positive ones. On the other hand, Fourier transform is a global analysis thus it tells nothing about the time domain. As our key insight, we further explore the usage of instantaneous frequency calculated by Hilbert Transform and propose a new method of constructing complex input from a time–frequency angle. Simple pixel-wise classification experiments are carried out on two hyperspectral datasets and MNIST dataset. Experimental results have demonstrated that Hilbert Transform with instantaneous frequency performs better by a large margin than Fourier Transform owing to the additional time information.

Maiden application of mountaineering team-based optimization algorithm optimized 1PD-PI controller for load frequency control in islanded microgrid with renewable energy sources

Load Frequency Control (LFC) is essential for maintaining the stability of Islanded Microgrids (IMGs) that rely extensively on Renewable Energy Sources (RES). This paper introduces a groundbreaking 1PD-PI (one + Proportional + Derivative-Proportional + Integral) controller, marking its inaugural use in improving LFC performance within IMGs. The creation of this advanced controller stems from the amalgamation of 1PD and PI control strategies. Furthermore, the paper presents the Mountaineering Team Based Optimization (MTBO) algorithm, a novel meta-heuristic technique introduced for the first time to effectively tackle LFC challenges. This algorithm, inspired by principles of intellectual and environmental evolution and coordinated human behavior, is utilized to optimize the controller gains. The effectiveness of the proposed methodology is rigorously evaluated within a simulated IMG environment using MATLAB/SIMULINK. This simulated IMG incorporates diverse power generation sources, including Diesel Engine Generators (DEGs), Microturbines (MTs), Fuel Cells (FCs), Energy Storage Systems (ESSs), and RES units like Wind Turbine Generators (WTGs) and Photovoltaics (PVs). This paper employs the Integral Time Multiplied by the Squared Error (ITSE) and Integral of Time Multiplied By Absolute Error (ITAE) indicators as the primary performance metrics, conventionally used to mitigate frequency deviations. To achieve optimal controller parameter tuning, a weighted composite objective function is formulated. This function incorporates multiple components: modified objective functions related to both ITSE and ITAE, along with a term addressing overshoot and settling time. Each component is assigned an appropriate weighting factor to prioritize specific performance aspects. By employing distinct objective functions for different aspects of control performance, the derivation of optimized controller gains is facilitated. The efficacy and contribution of the proposed methodology are rigorously demonstrated within the context of RES-based IMGs, featuring a comparative analysis with well-known optimization algorithms, including Particle Swarm Optimization (PSO) and the Whale Optimization Algorithm (WOA). These algorithms are used to optimize the 1PD-PI controller, resulting in three control schemes: 1PD-PI/MTBO, 1PD-PI/WOA, and 1PD-PI/PSO. The effectiveness of these control schemes is evaluated under various loading conditions, incorporating parametric uncertainties and nonlinear factors of physical constraints. Three case studies, presented in eight scenarios (I-VIII), are utilized to comprehensively assess the efficiency, robustness, and sensitivity of the proposed approach. This analysis extends beyond the time domain, considering the stability evaluation of the proposed control scheme. Simulation results unequivocally establish the superior performance of the MTBO algorithm-optimized 1PD-PI controller compared to its counterparts. This superiority is evident in terms of minimized settling time, reduced peak undershoot and overshoot, and enhanced error-integrating performance characteristics within the system responses. Improvements are observed in both the high range and within the 80–90% range for criteria such as overshoot, undershoot, and the numerical values of the objective functions. This paper underscores the practicality and effectiveness of the 1PD-PI/MTBO control scheme, offering valuable insights into the management of frequency disturbances in RES-based IMGs.

Generalised eigenfunction expansion and singularity expansion methods for canonical time-domain wave scattering problems

The generalised eigenfunction expansion method (GEM) and the singularity expansion method (SEM) are applied to solve the canonical problem of wave scattering on an infinite stretched string in the time domain. The GEM, which is shown to be equivalent to d’Alembert’s formula when no scatterer is present, is also derived in the case of a point-mass scatterer coupled to a spring. The discrete GEM, which generalises the discrete Fourier transform, is shown to reduce to matrix multiplication. The SEM, which is derived from the Fourier transform and the residue theorem, is also applied to solve the problem of scattering by the mass–spring system. The GEM and SEM are also used to solve the problem of wave scattering by a mass positioned a fixed distance from an anchor point, which supports more complicated resonant behavior.

Defect classification and quantification method based on AC magnetic flux leakage time domain signal characteristics

Pipelines play a crucial role in industries such as petroleum and nuclear power, where non-destructive testing is essential. Magnetic flux leakage testing methods are widely used for pipeline inspection due to their ability to detect both internal and external defects. However, accurately classifying and evaluating the size of such defects poses challenges due to the complex coupling relationship between ferromagnetic materials and defect magnetic fields. This paper proposes a method for defect classification and quantification based on the time domain characteristics of AC magnetic flux leakage signals. Firstly, the paper explores the shielding effects caused by transient high magnetic permeability within the material on signals from internal defects. It analyzes the differences in signals between internal and external defects. Then, based on the nonlinear attributes of defect signals, the paper proposes a defect classification method based on derivatives analysis of the windowed time-domain signal. Moreover, the study finds that rising time and zero-crossing time can be used to assess the depth of internal and external defects separately, which can decouple the width and depth. Finally, experimental validation confirms the effectiveness of defects classification and quantification. This paper provides a feasible method for evaluating the defect of ferromagnetic materials.

Impact of slouching on heart rate variability and cardiovascular health among medical students

Background: Medical students frequently adopt poor postures such as slouching potentially impacting health. Heart rate variability (HRV) serves as an indicator of autonomic nervous system function, correlated with cardiovascular health. Aims and Objectives: The objective of the study was to compare HRV parameters among medical students in three postures: Sitting erect, easy pose (cross-legged), and slouching (stooping head and shoulders). Materials and Methods: A comparative cross-sectional study enrolled 26 male medical students. Participants were instructed to maintain each posture for 5 min while HRV was recorded using a Polar V800. Time domain and frequency domain parameters among different postures were analyzed using Friedman’s test and post hoc tests for pairwise comparisons. P<0.05 was considered significant. Data were expressed as median interquartile range. Results: Significant differences were observed in HRV parameters between postures. The easy pose demonstrated a significantly higher median value of root mean square of differences of successive RR intervals (48.28 ms vs. 35.35 ms) and pNN50% (24.40% vs. 13.62%) compared to slouching. Frequency domain analysis revealed a significantly higher median value of high-frequency (HF) power in easy pose (626.56 ms² vs. 378.15 ms²) and HF normalized unit (33.78 vs. 22.55) compared to slouching. A lower low frequency (LF)/HF ratio and (1.96 vs. 3.43) lower LF normalized unit (66.18 vs. 77.30) was also seen in easy pose. Although statistically not significant, the parasympathetic indicators of HRV were higher in erect sitting compared to slouching posture. Conclusion: Among medical students, adopting the easy pose (cross-legged sitting) appeared to enhance HRV and thus increase parasympathetic activity, whereas adopting slouching postures decreased it.

De-noising magnetotelluric data based on machine learning

The magnetotelluric (MT) sounding is a common geophysical exploration technique, but it is highly polluted by various types of cultural noise. In the realm of MT data processing, traditional techniques often rely on the quality of the measured MT data. Conventional MT time domain denoising methods tend to eliminate valuable signals, potentially leading to unreliable resistivity estimates. To address this concern, we propose employing machine learning to effectively suppress strong noise interference in MT data, thereby preventing the loss of valuable signals. We augment this approach with mathematical morphological filtering (MMF) to capture low-frequency signals, preserving their integrity. We constructed a signal sample library based on a substantial volume of signal samples. Through consistent training, we establish a support vector machine (SVM) classification model that distinguishes high-quality signal fragments from noisy signals. Subsequently, we use adaptive K-singular value decomposition (K-SVD) dictionary learning to extract noise profiles and suppress noisy signals. To validate the feasibility of our method, we apply machine learning to measured data from two distinct observation areas. The measured data were analyzed and processed, and the results were compared with the robust results. This method can effectively eliminate large-scale strong interference in time domain sequences and preserve more low-frequency slow change information and high-quality signals in the reconstructed signals. The apparent resistivity phase curve of synthetic data is smoother and more continuous, and the data quality in the low-frequency range is significantly improved. The results can more accurately and reliably reflect underground electrical structure information.

Bi-Branching Feature Interaction Representation Learning for Multivariate Time Series

Representational learning of time series plays a crucial role in various fields. However, existing time-series models do not perform well in representation learning. These models usually focus only on the relationship between variables at the same timestamp or only consider the change of individual variables in time, while failing to effectively integrate the two, which limits their ability to capture complex time dependencies and multivariate interactions. We propose a Bi-Branching Feature Interaction Representation Learning for Multivariate Time Series (Bi-FI) to address these issues. Specifically, we elaborated a frequency domain analysis branch to address the complex associations between variables that are difficult to visualize in the time domain. In addition, to eliminate the time lag effect, another branch employs the mechanism of variable tokenization for attention to learning intra-variable representations. Ultimately, we interactively fuse the features learned from the two branches to obtain a more comprehensive time series representation. Bi-FI performs well in three time series analysis tasks: long sequence prediction, classification, and anomaly detection. Our code and dataset will be available at https://github.com/wwy8/Bi_FI.

Enhancing induction machine fault detection through machine learning: Time and frequency analysis of vibration signals

The integration of machine learning algorithms into fault diagnosis is regarded as an advanced and effective method for detecting electrical system faults. Vibration signals are identified as valuable and easily accessible data for fault identification in electrical machines. In this study, experiments were conducted to assemble a dataset consisting of 525 × 3 instances, each containing 200 three-axis vibration signal measurements. These measurements were obtained from a laboratory test bench equipped with a customized winding three-phase induction machine. Instead of directly analyzing the vibration signals, a feature extraction approach was employed, calculating a comprehensive set of statistical, harmonic, and impulsive features from the time domain (e.g., ShapeFactor, SINAD, ClearanceFactor, ImpulseFactor, CrestFactor, Kurtosis, Skewness, THD, Std, RMS, Mean, PeakValue, SNR), along with spectral features from the frequency domain (e.g., Peak Frequencies, Amplitudes, BandPower). To improve model efficiency, Analysis of Variance (ANOVA) was applied to select only the most significant features, with F-statistics used to rank feature importance. Features with higher F-statistics were prioritized for their ability to explain more variance in motor fault classification. This selective approach reduced model complexity, helping to minimize overfitting and focus on features that had a substantial impact on predictive accuracy. By concentrating on these high-impact features, a balance between simplicity and performance was achieved. These selected features were then used to train five machine learning classifiers: Ensemble (Bagged Trees), Quadratic Support Vector Machine, Weighted K-Nearest Neighbors, Efficient Logistic Regression, and Neural Networks. The performance of these classifiers was evaluated using various metrics, with validation accuracy rates ranging from 78.3% to 98.8%. A comparative study with similar works in the literature was conducted, demonstrating that high performances were obtained with the proposed approach while maintaining computational efficiency.

Decentralized Control in the Frequency Domain and in the Time Domain

Decentralized Control in the Frequency Domain and in the Time Domain