Signal Modeling Research Articles

Virtual inertia based hierarchical control scheme for distributed generations considering communication delay

The virtual inertia technology of DGs (Distributed Generations) can provide inertia and damping support for the power system by imitating the traditional synchronous machine. However, the inherent delay problems around the communication and process links of the virtual inertia control will make an impact on the fast support effect. In this paper, the virtual inertia based hierarchical control scheme considering communication delay for distributed generations integration systems is proposed. The hierarchical control architecture including the PQ primary control and the virtual inertia based secondary control method which can realize the optimal power utilization and certain frequency support under the situation of communication signal delay at the same time. Firstly, the basic hierarchical control scheme for distributed generations and corresponding small signal modeling considering communication delay are presented. Then to enhance the inertia support ability of the distributed generation integration system, an improved hierarchical control strategy considering communication delay is designed based on the robust passivity method to compensate the equivalent delay disturbance. Finally, the effectiveness verification of the proposed control is carried out with the simulation cases in PSCAD platform.

Signal processing algorithm and modeling of digital and analog filters for solving various technical problems

The article describes an algorithm for processing an audio signal in Matlab CAD, presents the results of filtering it using digital and analog filters and their comparison by specified characteristics. Practical significance of the work: studying the signal processing algorithm, writing digital filter code in Matlab and gaining skills in using Micro-Cap 12 CAD for modeling passive and active filters. This work can be used to teach students of technical universities in the study of software and principles of operation of analog and digital filters, for use as a methodological guide when performing calculations in homework or laboratory work using specialized software Matlab and Micro-Cap 12. An active low-pass filter is implemented and used as a stand for practical classes at the university.

Model-Based Approach for the Semi-Automatic Analysis of Collagen Birefringence in Polarized Light Microscopy

Collagen is a key determinant of physio-pathological processes in different tissues. Polarization light microscopy (PLM) of histological sections is the gold-standard for birefringence-based collagen quantification, but post-session image analysis can be time-consuming and subjective. We propose an efficient semi-automatic computational approach for the quantification of collagen content from the analysis of PLM images of birefringent histological sections. The method is based on a physical model of light-sample interaction and birefringence effect production. It combines the information of bright and dark-field PLM images to segment the luminal region and detect the birefringent signal associated with collagen in the tissue region. User input is limited to the selection of a threshold on an image subset and the supervision of the processing, enabling fast analysis of large datasets. Modeling of the birefringence signal compensates for variability factors related to sample processing and image acquisition, such as section thickness variability and nonuniform illumination and transmittance. As a proof-of-concept, the method was applied to human cardiac tissue PLM images, acquired in 14 cardiac surgery patients with different arrhythmic profiles. The method was able to detect a significantly larger amount and higher heterogeneity of fibrosis in the atrium of patients with as opposed to without atrial fibrillation (p < 0.05). The proposed method can be a valid aid to quicken and reinforce the analysis of large sets of PLM images for the quantification of collagen distribution in different tissues and pathologies.

METHOD OF SELF-DEFENSE OF GROUND (SURFACE) OBJECTS FROM HIGH-PRECISION RADAR MEANS OF AIR SURVEILLANCE

Context it is caused by the need to search for scientific and technical ways to ensure the effectiveness of protecting ground (surface) objects from high-precision guided missile weapons.
 Objective it is a necessity to ensure effective self-defense of objects from radar homing means.
 Method. Electrodynamic modeling of Echo signals from spatially distributed objects, taking into account the features of their design and related operational limitations.
 Results. Based on the analysis of the shortcomings of the well-known method of protecting stationary objects from radar surveillance and damage, based on the simulation of an effective reflection center outside the physical dimensions of the object, a new method of countering high-precision measurement of coordinates of stationary and mobile ground (surface) objects is proposed. The technique is based on the spatial deformation of the location of the effective target reflection center with dynamics that exceed the inertial capabilities of the auto-observation contour of the attacking missile (projectile). A structural and functional scheme of technical implementation of the methodology based on the first proposed relationship of simple design and technological solutions is proposed and justified.
 Conclusions. The analytical model of Echo signals of spatially distributed ground (surface) objects was further developed, which takes into account the specifics of their design, and on its basis, for the first time, a universal method of self-defense of objects from radar home-leading devices was developed, which is implemented in a patented method and complex to exclude damage to protected objects.

AMSCN: A Novel Dual-Task Model for Automatic Modulation Classification and Specific Emitter Identification

Specific emitter identification (SEI) and automatic modulation classification (AMC) are generally two separate tasks in the field of radio monitoring. Both tasks have similarities in terms of their application scenarios, signal modeling, feature engineering, and classifier design. It is feasible and promising to integrate these two tasks, with the benefit of reducing the overall computational complexity and improving the classification accuracy of each task. In this paper, we propose a dual-task neural network named AMSCN that simultaneously classifies the modulation and the transmitter of the received signal. In the AMSCN, we first use a combination of DenseNet and Transformer as the backbone network to extract the distinguishable features; then, we design a mask-based dual-head classifier (MDHC) to reinforce the joint learning of the two tasks. To train the AMSCN, a multitask cross-entropy loss is proposed, which is the sum of the cross-entropy loss of the AMC and the cross-entropy loss of the SEI. Experimental results show that our method achieves performance gains for the SEI task with the aid of additional information from the AMC task. Compared with the traditional single-task model, our classification accuracy of the AMC is generally consistent with the state-of-the-art performance, while the classification accuracy of the SEI is improved from 52.2% to 54.7%, which demonstrates the effectiveness of the AMSCN.

Biorthogonal harmonics for the decomposition of gravitational radiation. I. Angular modes, completeness, and the introduction of adjoint-spheroidal harmonics

The estimation of radiative modes is a central problem in gravitational wave theory, with essential applications in signal modeling and data analysis. This problem is complicated by most astrophysically relevant systems not having modes that are analytically tractable. A ubiquitous workaround is to use not modes, but multipole moments defined by spin-weighted spherical harmonics. However, spherical multipole moments are only related to the modes of systems without angular momentum. As a result, they can obscure the underlying physics of astrophysically relevant systems, such as binary black hole merger and ringdown. In such cases, spacetime angular momentum means that radiative modes are not spherical, but spheroidal in nature. Here, we work through various problems related to spheroidal harmonics. We show for the first time that spheroidal harmonics are not only capable of representing arbitrary gravitational wave signals, but that they also possess a kind of orthogonality not used before in general relativity theory. Along the way we present a new class of spin-weighted harmonic functions dubbed ``adjoint-spheroidal'' harmonics. These new functions may be used for the general estimation of spheroidal multipole moments via complete biorthogonal decomposition (in the angular domain). By construction, adjoint-spheroidal harmonics suppress mode-mixing effects known to plague spherical harmonic decomposition; as a result, they better approximate a system's true radiative modes. We discuss potential applications of these results. Lastly, we briefly comment on the challenges posed by the analogous problem with Teukolsky's radial functions.

Drell-Yan production in third-generation gauge vector leptoquark models at NLO+PS in QCD

Motivated by the long-standing hints of lepton-flavour non-universality in the b → cℓν and b → sℓ+ℓ− channels, we study Drell-Yan ditau production at the Large Hadron Collider (LHC). In the context of models with third-generation gauge vector leptoquarks (LQs), we calculate the complete mathcal{O} (αs) corrections to the pp → τ+τ− process, achieving next-to-leading order (NLO) plus parton shower (NLO+PS) accuracy using the POWHEG method. We provide a dedicated Monte Carlo code that evaluates the NLO QCD corrections on-the-fly in the event generation and use it to study the numerical impact of NLO+PS corrections on the kinematic distributions that enter the existing experimental searches for non-resonant ditau final states. Based on our phenomenological analysis we derive NLO accurate constraints on the masses and couplings of third-generation gauge vector LQs using the latest LHC ditau search results corresponding to an integrated luminosity of around 140 fb−1 of proton-proton collisions at sqrt{s} = 13 TeV. The presented NLO+PS generator allows for an improved signal modelling, making it an essential tool for future ATLAS and CMS searches for vector LQs in τ+τ− final states at LHC Run III and beyond.

Mathematical modeling of underwater signal anomaly perception based on multi-sensor data fusion

There is a lot of interference in underwater environment, which can affect underwater signal transmission. For this reason, an abnormal perception model of underwater signals based on multi-sensor data fusion is proposed. The underwater frequency hopping communication method is used to select the channel and construct the mathematical model of underwater communication signal. A signal modulation and recognition system for multi-sensor reception is established, and the multi-sensor data fusion model is obtained by calculating the support margin. The energy spectrum distribution is obtained by decomposition of abnormal features with wavelet detector in time domain. The deep learning algorithm is used to reorganize the signal with the minimum detection error, and the mathematical model of underwater signal anomaly perception is obtained. The experimental results show that the average accuracy of the method for locating abnormal underwater communication signals is 95.5%, the misrecognition rate of abnormal underwater signals is less than 2%, and the time-consuming for sensing abnormal signals of 420 MB underwater communication is less than 3 s.

Recovering fluorescence spectra hidden by scattering signal: In search of the best smoother

Interpolation of the scattering areas in fluorescence excitation-emission matrices is a useful preprocessing method in fluorescence spectroscopy and data modelling. Commonly used row-by-row interpolation using piecewise cubic Hermite interpolating polynomials smoother (PCHIP), however, frequently leads to artifacts because it does not make any use of the information in the other dimension. We have suggested the way of constructing the penalty matrices for Whittaker smoothing that removed one of the main sources of difference between the axis of multiparametric signal – the grid step size – thus making it possible to reduce the number of parameters to optimize. We have compared Whittaker smoother with various surface interpolation methods, including LOESS, Kriging, multilevel B-spline approximation, and PCHIP for the purpose of data preprocessing before PARAFAC modelling of fluorescence signal on a model dataset. The two leaders by signal reconstruction and reconstruction of PARAFAC loadings are LOESS and Whittaker smoothing; the latter is additionally shown to have fundamentally interpretable parameters, which are easier to optimise for the whole dataset. Moreover, Whittaker keeps the shape of the signal and is resistant to variations in data structure and noise level that is very important in numerous applications. We also tested smoothers performance for Åsmund Rinnan fluorescence dataset and the high performance of Whittaker was proved. We can recommend the Whittaker smoothing as a perfect tool for interpolation of scattering areas in florescence spectra of seawaters with low signal-to-noise ratio.

A Method For Autoregression Modeling of a Speech Signal

The problem of autoregressive modeling of a speech signal based on the data of the discrete Fourier transform in the mode of a sliding observation window of small duration (milliseconds) is considered. The problem of stability of the formed autoregressive model is investigated. To overcome it, it is proposed to use the envelope of the Schuster periodogram as a reference spectral sample. A new method of autoregressive modeling has been developed, in which the detection of the spectral envelope is carried out using a recirculator of a sequence of samples in the frequency domain. An example of its practical implementation is considered, a full-scale experiment is set up and carried out. Based on the results of the experiment, conclusions were drawn about achieving a significant gain in terms of not only stability, but also the accuracy of the autoregressive model of the speech signal.

Software JimenaE allows efficient dynamic simulations of Boolean networks, centrality and system state analysis

The signal modelling framework JimenaE simulates dynamically Boolean networks. In contrast to SQUAD, there is systematic and not just heuristic calculation of all system states. These specific features are not present in CellNetAnalyzer and BoolNet. JimenaE is an expert extension of Jimena, with new optimized code, network conversion into different formats, rapid convergence both for system state calculation as well as for all three network centralities. It allows higher accuracy in determining network states and allows to dissect networks and identification of network control type and amount for each protein with high accuracy. Biological examples demonstrate this: (i) High plasticity of mesenchymal stromal cells for differentiation into chondrocytes, osteoblasts and adipocytes and differentiation-specific network control focusses on wnt-, TGF-beta and PPAR-gamma signaling. JimenaE allows to study individual proteins, removal or adding interactions (or autocrine loops) and accurately quantifies effects as well as number of system states. (ii) Dynamical modelling of cell–cell interactions of plant Arapidopsis thaliana against Pseudomonas syringae DC3000: We analyze for the first time the pathogen perspective and its interaction with the host. We next provide a detailed analysis on how plant hormonal regulation stimulates specific proteins and who and which protein has which type and amount of network control including a detailed heatmap of the A.thaliana response distinguishing between two states of the immune response. (iii) In an immune response network of dendritic cells confronted with Aspergillus fumigatus, JimenaE calculates now accurately the specific values for centralities and protein-specific network control including chemokine and pattern recognition receptors.

Efficient analytical modeling for pulsed eddy current signals using adaptive interpolation-based Fourier transform

ABSTRACT In the analytical modelling for pulsed eddy current (PEC) signals based on the Fourier transform, there is a computational burden caused by calculating hundreds of harmonic impedance changes. In this paper, an adaptive interpolation method that takes inflection points as the initial interpolation nodes is proposed for calculating harmonic impedance changes. The inflection points of impedance change with frequency are deduced from the transformer model to divide the initial interval of interpolation. In the process of the adaptive interpolation method, it is necessary to divide the subintervals which fail to meet the requirements of interpolation accuracy, and then calculate the harmonic impedance changes by the cubic spline interpolation method. The adaptive interpolation method was verified by the simulation and experimental results. The results show that the adaptive interpolation method interpolates hundreds of harmonic impedance changes with dozens of interpolation nodes. There is a good agreement between the experimental PEC signals and the PEC signals obtained by the adaptive interpolation method, and the relative errors of the peak values are about 0.1%.

Design and experimental verification of a simplified controller for high‐order converters

SummaryThe modeling of high‐order converter systems and design of their closed‐loop controllers are difficult due to their high nonlinearity, hence high‐order systems, large number of zero‐poles, and complex frequency‐domain characteristics. To ease the design methodologies, this paper studies high‐order power converters and proposes a modeling and control method for such converter systems with order reduction. As an example, the 7th‐order non‐electrolytic capacitor‐I‐Z‐Source converter (NEC‐I‐ZSC) is employed to implement the proposed design method. Operational analysis and small signal modeling of the NEC‐I‐ZSC are conducted, and a reduced‐order controller is then devised. Experimental verification is conducted, which prove the feasibility and effectiveness of the proposed design method.

A Novel Adaptive Time-Frequency Filtering Approach to Enhance the Ultrasonic Inspection of Stainless Steel Structures.

Ultrasonic nondestructive testing (NDT) provides a valuable insight into the integrity of stainless steel structures, but the noise caused by the scattering of stainless steel microstructure often limits the effectiveness of inspection. This work presents a novel adaptive filtering approach to enhance the signal-to-noise ratio (SNR) of a measured ultrasonic signal from the inspection of a stainless steel component, enabling the detection of hidden flaws under strong noise. After the spectral modeling of the noisy ultrasonic NDT signal, the difference between the spectral characteristics of a flaw echo and that of grain noise is highlighted, and a reference spectrum model to estimate the frequency spectrum of the echo reflected by any possible flaw is developed. Then, the signal is segmented and the similarity between the spectra of data segments and the reference spectra is evaluated quantitatively by the spectral similarity index (SSI). Based on this index, an adaptive time-frequency filtering scheme is proposed. Each data segment is processed by the filtering to suppress the energy of noise. The processed data segments are recombined to generate the de-noised signal after multiplying weighting coefficients, which again is determined by the SSI. The performance of the proposed method for SNR enhancement is evaluated by both the simulated and experimental signal and the effectiveness has been successfully demonstrated.

Use of time dependent data in Bayesian global 21-cm foreground and signal modelling

ABSTRACT Global 21-cm cosmology aims to investigate the cosmic dawn and epoch of reionization by measuring the sky averaged H i absorption signal, which requires, accurate modelling of, or correction for, the bright radio foregrounds and distortions arising from chromaticity of the antenna beam. We investigate the effect of improving foreground modelling by fitting data sets from many observation times simultaneously in a single Bayesian analysis, fitting for the same parameter set by performing these fits on simulated data. We find that for a hexagonal dipole antenna, this simultaneous fitting produces a significant improvement in the accuracy of the recovered 21-cm signal, relative to fitting a time average of the data. Furthermore, the recovered models of the foreground are also seen to become more accurate by up to a factor of ∼2–3 relative to time averaged fitting. For a less chromatic log spiral antenna, no significant improvement in signal recovery was found by this process. However, the modelling of the foregrounds was still significantly improved. We also investigate extending this technique to fit multiple data sets from different antennas simultaneously for the same parameters. This is also found to improve both 21-cm signal and foreground modelling, to a higher degree than fitting data set from multiple times from the same antenna.

Instantaneous amplitude and phase signal modeling for harmonic removal in wind turbines

We present a novel approach to harmonic disturbance removal in single-channel wind turbine acceleration data by means of time-variant signal modeling. Harmonics are conceived as a set of quasi-stationary sinusoids whose instantaneous amplitude and phase vary slowly and continuously in a short-time analysis frame. These non-stationarities in the harmonics are modeled by low-degree time polynomials whose coefficients capture the instantaneous dynamics of the corresponding waveforms. The model is linear-in-parameters and is straightforwardly estimated by the linear least-squares algorithm. Estimates from contiguous analysis frames are further combined in the overlap-add fashion in order to yield overall harmonic disturbance waveform and its removal from the data. The algorithm performance analysis, in terms of input parameter sensitivity and comparison against three state-of-the-art methods, has been carried out with synthetic signals. Further model validation has been accomplished through real-world signals and stabilization diagrams, which are a standard tool for determining modal parameters in many time-domain modal identification algorithms. The results show that the proposed method exhibits a robust performance particularly when only the average rotational speed is known, as is often the case for stand-alone sensors which typically carry out data pre-processing for structural health monitoring. Moreover, for real-world analysis scenarios, we show that the proposed method delivers consistent vibration mode parameter estimates, which can straightforwardly be used for structural health monitoring.

Model and experimental research of the transient process for the improvement of the traveling wave fault location

The results of modeling of transient signals in a single-wire line are considered. The parameters of voltage and current signals are compared. Signal oscillation’s amplitude and period are given attention. The algorithm for determining the transient signal’s onset time (TSOT) under the conditions of noise and oscillations of the signal’s leading edge is presented. The results of experimentally recorded signals’ analyses are given.

Method of Elements Identification in the Multicomponent Signal

To measure concentrations of toxic elements in analytical systems of ecological monitoring, a generative model of a multicomponent differential signal of stripping chronopotentiometry is used in the coordinates of potentials in time (е, dt/dе) in the form of a model of the capacitive background discharge of electrodes (base curve) and the sum of the models of the stripping components of chemical elements, given by asymmetric normal distribution functions. Identification of elements with slight overlapping of component signals is carried out based on the maximum value and range in which potentials change.

Experimental characterization, modelling and simulation of ae signal due to crack propagation in PMMA

Acoustic Emission (AE) is a non-destructive instrumentation that allows the detection of cracking, which dissipates elastic waves resulting from local crack-induced displacement jumps within the material. However, based on the AE experiments, a link between the crack fracture mechanics and the AE features is difficult to establish quantitatively, especially under quasi-static and dynamic loading conditions. Numerical simulations have proved to be very promising for this purpose. In this study, Pencil-Lead Break (PLB) tests are carried out on PMMA plate specimens with different thicknesses in order to determine the damping characteristics of PMMA and experimentally validate the AE numerical simulations. The influence of the specimen thickness, the distance between the sensor/source and the type of sensors on the measured acoustic emission are also studied.

Дифракционная математическая модель лазерного спекл-интерферометра поперечных смещений рассеивающего объекта

On the basis of diffraction transformations of wave fields, a mathematical model of a speckle interferometer of transverse displacements of a scattering object has been developed and numerical modeling of speckle-modulated interference patterns and signals at the output of the interferometer has been performed. Numerical calculations of the spatial distribution of complex amplitudes of wave fields in an interferometer were used for modeling when the displaced scattering surface was illuminated by two obliquely incident laser Gaussian beams. A statistical numerical experiment was performed to determine the measurement error of the scattering surface displacement caused by the change of realizations of interfering speckle fields. The simulation results are in good agreement with the results of experimental studies of transverse displacements in the range up to 600 micrometers.