Time-domain System Research Articles

Evaluation of power system instability probability considering output randomness of grid-connected converter

Abstract In recent years, there has been a notable increase in the installed capacity of new energy sources. Nevertheless, the random nature of the output power of G-CCs may potentially result in power system instability. This paper presents a closed-loop interconnection model comprising a G-CC as a feedback subsystem and a VSC-HVDC system as a feedforward subsystem. The instability condition of the closed-loop system is derived from the open-loop mode resonance theory. Subsequently, a mathematical model is developed to represent the random output of the G-CC. This approach allows the probability of open-loop mode resonant instability of the power system to be evaluated, as well as the risk of sub-synchronous resonant instability caused by the output randomness of the G-CC. Finally, validation is performed by building a simulation example system and a time-domain simulation system in MATLAB.

Investigating cortical hypoxia in multiple sclerosis via time-domain near-infrared spectroscopy.

Hypoperfusion and tissue hypoxia have been implicated as contributory mechanisms in the neuropathology of multiple sclerosis (MS). Our objective has been to study cortical oxygenation in vivo in patients with MS and age-matched controls. A custom, multiwavelength time-domain near-infrared spectroscopy system was developed for assessing tissue hypoxia from the prefrontal cortex. A cross-sectional case-control study was undertaken assessing patients with secondary progressive MS (SPMS) and age-matched controls. Co-registered magnetic resonance imaging was used to verify the location from which near-infrared spectroscopy data were obtained through Monte Carlo simulations of photon propagation. Additional clinical assessments of MS disease severity were carried out by trained neurologists. Linear mixed effect models were used to compare cortical oxygenation between cases and controls, and against measures of MS severity. Thirty-three patients with secondary progressive MS (median expanded disability status scale 6 [IQR: 5-6.5]; median age 53.0 [IQR: 49-58]) and 20 age-matched controls were recruited. Modeling of photon propagation confirmed spectroscopy data were obtained from the prefrontal cortex. Patients with SPMS had significantly lower cortical hemoglobin oxygenation compared with controls (-6.0% [95% CI: -10.0 to -1.9], P = 0.004). There were no significant associations between cortical oxygenation and MS severity. Using an advanced, multiwavelength time-domain near-infrared spectroscopy system, we demonstrate that patients with SPMS have lower cortical oxygenation compared with controls.

Understanding Electric Current Effects on Tribological Behaviors of Instantaneous Current-Carrying Pair with Recurrence Plot

Abstract Armature-rail instantaneous current-carrying friction in electromagnetic launchers refers to a sliding electric-mechanical impact friction and transition-induced arc erosion on a millisecond time scale. To reveal the electric current (50 to 300 A) effects on friction behavior and wear mechanism, the instantaneous current-carrying friction tests were performed with Al 1060 and Brass H62. Given the short nonlinear friction-induced signals, the friction behavior, including the time-domain information and system state, was comprehensively analyzed via frictional sound pressure (FSP), recurrence plot (RP), and recurrence quantification analysis (RQA). The wear topography was observed, and characterized by the multifractal spectrum. Recurrence analyses demonstrate that as the current increases, the nonstationarity of the system state weakens, and the complexity and unpredictability enhance. Higher currents reduce the FSP amplitude, i.e., enhance the interfacial lubrication effect, but intensify electrical wear and surface roughness. This signifies a wear mechanism transition from abrasive wear and slight adhesive wear to arc ablation, fatigue wear, and severe adhesive wear. The widening spectrum width implies that the irregularity and fluctuation of the topography are enhanced with the current. RP patterns and RQA quantifiers correlate with the wear damage state. The results provide a reference for anti-wear design and online degradation tracking of the rail.

Data-driven adjoint-based calibration of port-Hamiltonian systems in time domain

We present a gradient-based calibration algorithm to identify the system matrices of a linear port-Hamiltonian system from given input–output time data. Aiming for a direct structure-preserving approach, we employ techniques from optimal control with ordinary differential equations and define a constrained optimization problem. The input-to-state stability is discussed which is the key step towards the existence of optimal controls. Further, we derive the first-order optimality system taking into account the port-Hamiltonian structure. Indeed, the proposed method preserves the skew symmetry and positive (semi)-definiteness of the system matrices throughout the optimization iterations. Numerical results with perturbed and unperturbed synthetic data, as well as an example from the PHS benchmark collection [17] demonstrate the feasibility of the approach.

Research on Fault Diagnosis Method of CNC Machine Tools Based on Integrated MPA Optimised Random Forests

INTRODUCTION: Intelligent diagnosis of CNC machine tool faults can not only early detection and troubleshooting to improve the reliability of machine tool operation and work efficiency, but also in advance of the station short maintenance to extend the life of the machine tool to ensure that the production line of normal production.OBJECTIVES: For the current research on CNC machine tool fault diagnosis, there are problems such as poorly considered feature selection and insufficiently precise methods.METHODS: This paper proposes a CNC machine tool fault diagnosis method based on improving random forest by intelligent optimisation algorithm with integrated learning as the framework. Firstly, the CNC machine tool fault diagnosis process is analysed to extract the CNC machine tool fault features and construct the time domain, frequency domain and time-frequency domain feature system; then, the random forest is improved by the marine predator optimization algorithm with integrated learning as the framework to construct the CNC machine tool fault diagnosis model; finally, the validity and superiority of the proposed method is verified by simulation experiment analysis.RESULTS: The results show that the proposed method meets the real-time requirements while improving the diagnosis accuracy.CONCLUSION: Solve the problem of poor accuracy of fault diagnosis of CNC machine tools and unsound feature system.

Accurate measurement of a THz beam radius through a knife-edge technique with a photoconductive antenna detector

In a terahertz (THz) time-domain spectroscopic system, the THz beam waist radius can be measured by eclipsing the beam waist with a semi-infinite mask while monitoring signal variation (knife-edge technique). However, the obtained beam waist radius is affected by the spatial distribution of the detector sensitivity. This effect was confirmed by calculating the refractive-index corrections required when inserting a sample into the beam waist with and without consideration of the detector sensitivity distribution and comparing them to the experimental results. The real beam waist radius was 2 times larger than that obtained by simply differentiating the signal variation in a system comprising optics symmetrical about the beam waist. This effect must be considered in obtaining the correct THz beam profile by the knife-edge technique.

Direct Numerical Modeling as a Tool for Optical Coherence Tomography Development: SNR (Sensitivity) and Lateral Resolution Test Target Interpretation

Optical Coherence Tomography (OCT) is a growing family of biophotonic imaging techniques, but in the literature there is a lack of easy-to-use tools to universally directly evaluate a device’s theoretical performance for a given metric. Modern computing tools mean that direct numerical modeling can, from first principles, simulate the performance metrics of a specific device directly without relying on analytical approximations and/or complexities. Here, we present two different direct numerical models, along with the example MATLAB code for the reader to adapt to their own systems. The first model is of photo-electron shot noise at the detector, the primary noise source for OCT. We use this firstly to evaluate the amount of additional noise present (1.5 dB) for an experimental setup. Secondly, we demonstrate how to use it to precisely quantify the expected shot noise SNR limit difference between time-domain and Fourier-domain OCT systems in a given hypothetical experiment. The second model is used to demonstrate how USAF 1951 test chart images should be interpreted for a given lateral PSF shape. Direct numerical modeling is an easy and powerful basic tool for researchers and developers, the wider use of which may improve the rigor of the OCT literature.

Empirical study of robust/developed PID control for nonlinear time-delayed dynamical system in discrete time domain

In addition to the high nonlinearity of liquid dynamics inside a tank, a study was conducted to overcome loading/unloading liquid storage tank control problems. In this study, the author developed a nonlinear control system to conquer both nonlinearity and the significant time delay arising from using a pressure-difference-based level sensor. To this end, this study proposes the implementation of nonlinear state dependent (SDP-PID+) control using the SDP transfer function model as a class of nonlinear descriptions of dynamical systems. By incorporating additional robust (plus) compensators alongside the traditional P-, I-, and D-compensators, the robust SDP-PID controller utilizes the complete state feedback to create a time-varying state variable feedback (SDP-SVF) control law. This approach effectively mitigates the effects of the discrete-time SDP-TF. It introduces the pole placement tuning approach, which renders significant performance using the laboratory test rig TPS8.2.2.4, for automatic liquid level control.

Cellular Imaging and Time-Domain FLIM Studies of Meso-Tetraphenylporphine Disulfonate as a Photosensitising Agent in 2D and 3D Models.

Fluorescence lifetime imaging (FLIM) and confocal fluorescence studies of a porphyrin-based photosensitiser (meso-tetraphenylporphine disulfonate: TPPS2a) were evaluated in 2D monolayer cultures and 3D compressed collagen constructs of a human ovarian cancer cell line (HEY). TPPS2a is known to be an effective model photosensitiser for both Photodynamic Therapy (PDT) and Photochemical Internalisation (PCI). This microspectrofluorimetric study aimed firstly to investigate the uptake and subcellular localisation of TPPS2a, and evaluate the photo-oxidative mechanism using reactive oxygen species (ROS) and lipid peroxidation probes combined with appropriate ROS scavengers. Light-induced intracellular redistribution of TPPS2a was observed, consistent with rupture of endolysosomes where the porphyrin localises. Using the same range of light doses, time-lapse confocal imaging permitted observation of PDT-induced generation of ROS in both 2D and 3D cancer models using fluorescence-based ROS together with specific ROS inhibitors. In addition, the use of red light excitation of the photosensitiser to minimise auto-oxidation of the probes was investigated. In the second part of the study, the photophysical properties of TPPS2a in cells were studied using a time-domain FLIM system with time-correlated single photon counting detection. Owing to the high sensitivity and spatial resolution of this system, we acquired FLIM images that enabled the fluorescence lifetime determination of the porphyrin within the endolysosomal vesicles. Changes in the lifetime dynamics upon prolonged illumination were revealed as the vesicles degraded within the cells.

Building Depth Maps Using an Active-Pulse Television Measuring System in Real Time Domain

The paper presents the results of software development for building depth maps based on video data from a television camera of an active-pulse television measuring system (AP TMS) in real time domain. The development of such software is required to conduct various scientific studies, as well as to improve the methods and techniques for building depth maps and remote measurement of the characteristics of objects of interest. The software was implemented using the Python programming language with additional libraries installed. According to the results of testing the implemented algorithm, it was found that the calculation speed using the graphics processing unit (GPU) is on average 3.5 times higher than the speed of the algorithm using only the central processing unit (CPU). It has been established that with the help of CUDA cores it is possible to build depth maps in real time domain at the maximum possible resolution of video frames of the system (1544x2064 pixels), while when using the central processor, real-time operation is possible only at a reduced resolution of video frames (772x1032 pixels).

Improving the spatial resolution and signal-to-noise ratio of ultrasound switchable fluorescence imaging.

Ultrasound switchable fluorescence (USF) imaging, a hybrid imaging technology that combines the advantages of both fluorescence sensitivity and acoustic resolution in centimeter-deep tissue, has great potential for biomedical different applications. A camera-based USF imaging system reveals its capability of capturing both spatial and temporal dynamics of the USF signal in tissue. In this study, various algorithms were explored to enhance the spatial resolution and signal-to-noise ratio (SNR) of USF images, utilizing temporal and spatial information from a camera-based time-domain USF imaging system. The correlation method proved effective in boosting SNR, while the ascending-slope-weighted method enhanced spatial resolution. Additionally, the spatially back-projection method significantly improved spatial resolution in silicone phantoms. The results underscore the advantages of incorporating temporal and spatial information from USF signals.

Identification of flutter boundary of a bridge deck section using CFD and flutter margin analysis

Aeroelastic flutter identification of a two-dimensional bridge deck section using time domain techniques is studied. Aeroelastic simulations are performed using the aeroelastic solver available in open source OpenFOAM 5.0, wherein aerodynamic loads are computed using Unsteady Reynolds Average Naiver–Stokes (URANS) solver. The bridge deck section, modelled as a rigid body, exhibits pitch and heave degrees of freedom. Time domain system identification techniques, which include VAR and ERA (Vector auto-regressive and Eigensystem realization algorithm), are used to extract frequency and damping parameters from the aeroelastic response for various wind speeds.Two approaches are used for identification of the flutter boundary. The first consists of tracking the behaviour of damping and identifying the speed at which damping vanishes. The second approach is based on the flutter margin for discrete-time system (FMDS), which exhibits a linear behaviour with dynamic pressure in the vicinity of flutter.Results presented indicate that the ERA yielded slightly better estimates of damping compared to VAR. Furthermore, the FMDS parameter provided a reasonable estimate of the flutter boundary using only two data points, suggesting that this is an efficient strategy (as compared to tracking the damping) for flutter identification when the cost per aeroelastic response data point is high.

CDRGN-SDE: Cross-Dimensional Recurrent Graph Network with neural Stochastic Differential Equation for temporal knowledge graph embedding

The temporal knowledge graph builds upon the static knowledge graph by introducing the time dimension and finds extensive applications in real artificial intelligence scenarios. However, the temporal knowledge graph’s incompleteness restricts the applicability of numerous temporal knowledge graph tasks. Temporal knowledge graph embedding (TKGE) is a representation learning technique that effectively addresses the temporal knowledge graph’s incompleteness. These methods aim to map entities and relations in the knowledge graph to a low-dimensional vector space while capturing features over time. However, the current TKGE methods encounter three main challenges: (1) how to resolve the continuity problem of dynamic features in the continuous time domain; (2) how to simultaneously model the dependence of structural knowledge representation on time and dimension; (3) how to address the heterogeneity of time representation in a temporal knowledge graph. We propose the CDRGN-SDE, a Cross-Dimensional Recurrent Graph Network with a neural Stochastic Differential Equation framework, to tackle these challenges. The CDRGN-SDE model effectively addresses the above challenges and establishes a unified framework: (1) we employ a neural ordinary differential equation with stochastic noise to simulate the continuity of dynamic systems in the continuous time domain; (2) we propose a Dimension-Segment-Wise (DSW) embedding method for TKGE, which effectively integrates time and dimensional information; (3) we introduce a simple and effective time representation method that integrates complex time features into the model. Experimental evaluations on benchmark datasets demonstrate that the CDRGN-SDE model outperforms the state-of-the-art temporal knowledge graph reasoning models.

Generalized sequential state equation method for moving subsystem-induced structural parametric resonance

Parametric excitation is a unique type of excitation that arises from the time-varying parameters of a dynamical system, typically induced by the periodic movements of the system components. However, the structure-moving subsystems problem creates a new type of parametric excitation where the subsystems do not have exact periodic movements. Under the circumstances, dimension of the mathematical model of such a system exhibits time dependency, which prohibits the employment of conventional analytical methods to predict its stability. In this paper, we propose a novel Generalized Sequential State Equation Method that provides a distinct solution framework for analysis of the subsystem-induced parametric resonance. With the time-domain system partition and state mapping, the proposed method reconfigures the coupled system with a sequence of state space equations and solves them chronologically. The proposed method relaxes the single-subsystem-assumption in its original form and applies to a general scenario where the subsystems travel with arbitrary characteristic period. With the proposed method, occurrences of the subsystem-induced parametric resonance are precisely predicted via a set of analytical stability criteria and the steady state response is determined in an exact analytical form.

Magnetic-field-induced spin reorientation in TmFeO<sub>3</sub> single crystals

TmFeO<sub>3</sub> exhibits rich physical properties such as magneto-optical effect, multiferroicity, and spin reorientation, making it possess significant research value in condensed matter physics and materials science. In this study, we utilize a time-domain terahertz magneto-optical spectroscopy system to investigate the changes in spin resonance frequency of TmFeO<sub>3</sub> single crystal at <i>T</i> = 1.6 K under external magnetic fields in a range of 0–7 T. The TmFeO<sub>3</sub> sample is grown in an optical floating zone furnace and its crystallographic orientation is determined by using back-reflection Laue X-ray photography with a tungsten target. The measurement setup is a self-built time-domain terahertz magneto-optical spectroscopy system, with magnetic fields in a range of 0–7 T, temperatures in a range of 1.6–300 K, and a spectral range of 0.2–2.0 THz. A pair of 1 mm-thick ZnTe nonlinear crystals is used to generate and detect terahertz signals through optical rectification and electro-optic sampling technique. The system variable temperature and magnetic field are controlled by a superconducting magnet. In experiments, a linearly polarized terahertz wave is vertically incident on the sample surface, and its magnetic component <i>H</i><sub>THz</sub> is parallel to the sample surface. By rotating the sample, the angle (<i>θ</i>) between macroscopic magnetic moment <i> <b>M</b> </i> and <i>H</i><sub>THz</sub> can be tuned, achieving selective excitations of the two modes, that is, <i>θ</i> = 0 for q-AFM mode and 90° for q-FM mode. Terahertz absorption spectrum results indicate that as the magnetic field increases, the quasi-ferromagnetic resonance (q-FM) of TmFeO<sub>3</sub> single crystal shifts towards high frequencies, and quasi-antiferromagnetic resonance (q-AFM) transits to q-FM under low critical magnetic fields (2.2–3.6 T). Through magnetic structure analysis and theoretical fitting, it is confirmed that the magnetic moment of the single crystal undergoes magnetic field induced spin reorientation. This study is helpful in better understanding of the regulation mechanism of the internal magnetic structure of rare earth ferrite under the combined action of external magnetic field and temperature field, and also in developing related spin electronic devices.

Time Domain Extremum Phase Shift Laser Ranging Method and System Based on Ergodic Under-Sampling

Time Domain Extremum Phase Shift Laser Ranging Method and System Based on Ergodic Under-Sampling

Time-domain versus frequency-domain system identification of lithium-ion batteries using fractional models

This study presents a comparison between time-domain and frequency-domain approaches for system identification of Lithium-ion batteries using fractional models. As reported in the literature, time-domain approaches have the advantage of collecting data on smaller time-intervals (few seconds) as compared to frequency-domain data (several minutes). The model is derived using a fractional-order equivalent circuit model (FO-ECM) which exhibit long memory diffusion phenomena, and its impedance parameters are determined through the application of Levenberg-Marquardt optimization algorithm both in time and frequency domains. Comparisons are conducted on experimental data generated from three commercial batterie cells with different chemistries and cross-validation results are shown between the obtained time-domain and frequency-domain models. Similar models found with both methods which affirm the reliability and applicability of the proposed identification methods both in time and frequency domains.

A non-dimensional time-domain lumped model for externally DC biased capacitive microphones with two electrodes

Microphones that utilize externally biased capacitive transduction (condenser microphones) are typically being modeled in lumped parameter networks where the mechanical, acoustical, and electrical elements are all represented as passive electronic circuit components. Those models have been shown to be insufficient to fully describe crucial aspects of this type of transducer, such as the pull-in phenomenon in which, after a certain distance and for a certain bias voltage, the moving electrode, typically a flexible membrane, attaches itself to the stationary. In this paper, we account for several non-linearities present in an extended simplified model of such a transducer in the time domain and identify the different factors related to its nonlinear response. We derive a time-domain non-linear non-dimensional system of equations for the coupled lumped model where we also take into account parasitic capacitances that are usually present and can significantly affect the overall electroacoustic performance as well as the fringing fields due to the nonhomogeneous electric field between the electrodes and nonlinearities related to damping due to the thin-film of air between the electrodes. We present the nondimensionalization method we used that allows for the identification of a novel set of nondimensional parameters that characterize the non-linear behavior of our system in the time-domain. A designer can use these parameters to optimize for linearity in the voltage response of the transducer. We post-process our time-domain solution to calculate the response to the fundamental excitation frequency and discuss the harmonic distortion. It is shown that coupling the electrical nonlinearities to the mechanical can significantly contribute to the nonlinear voltage response of the transducer. Our model agrees well with nonlinear measurements of analog microelectromechanical (MEMS) microphones for a set of physical values of the nondimensional parameters.

Simulation Analysis Based on COMSOL Helicopter Time-domain Aeromagnetic Method

This paper takes the time-domain aeromagnetic system as the research object and uses the finite element software COMSOL Multiphysics 6.0 to establish the three-dimensional system model. By comparing the COMSOL Multiphysics forward simulation solution with the one-dimensional numerical solution, it verifies that the COMSOL software meets the simulation accuracy requirements of the three-dimensional time-domain aeromagnetic method, and focuses on the analysis of the transient response at different flight altitudes is also analyzed. Based on the analysis of this paper, a set of aeromagnetic parameters with high detection capability is determined for engineering practice, and a test of the system is completed.

An efficient watermarking algorithm for digital audio data in security applications

Transform-domain audio watermarking systems are more robust than time-domain systems. However, the main weakness of these systems is their high computational cost, especially for long-duration audio signals. Therefore, they are not desirable for real-time security applications where speed is a critical factor. In this paper, we propose a fast watermarking system for audio signals operating in the hybrid transform domain formed by the fractional Charlier transform (FrCT) and the dual-tree complex wavelet transform (DTCWT). The central idea of the proposed algorithm is to parallelize the intensive and repetitive steps in the audio watermarking system and then implement them simultaneously on the available physical cores on an embedded systems cluster. In order to have a low power consumption and a low-cost cluster with a large number of physical cores, four Raspberry Pis 4B are used where the communication between them is ensured using the Message Passing Interface (MPI). The adopted Raspberry Pi cluster is also characterized by its portability and mobility, which are required in watermarking-based smart city applications. In addition to its resistance to any possible manipulation (intentional or unintentional), high payload capacity, and high imperceptibility, the proposed parallel system presents a temporal improvement of about 70%, 80%, and 90% using 4, 8, and 16 physical cores of the adopted cluster, respectively.