Multiple Time Instants Research Articles

Real‐time implementation for vulnerability of power components under switching attack based on sliding mode

AbstractIn recent years, cyber security‐related studies in the power grid have drawn wide attention, with much focus on its detection, mainly for data injection type of attacks. The vulnerability of power components as a result of attack and their impact on generator dynamics have been largely ignored so far. With the aim of addressing some of these issues, the authors propose a novel approach using real‐time sliding surface‐based switching attack (SA) construction. This approach targets the circuit breaker, excitation system, and governor system of the generator. The vulnerability of these power components to cyber‐physical attacks and assessment of their potential impact on the stability of generator are discussed. The study is presented to show the progression of cascading generator dynamics on account of single or multiple time instants of SA launched on these power components. The results are discussed according to criteria in terms of deviations in rotor speed of the generator and identify some of possible combinations of power components that are most critical to grid stability. The proposed study is implemented on standard IEEE 3‐machine, 9‐bus network in real‐time digital simulator via transmission control protocol/internet protocol (TCP/IP) communication network established as cyber‐physical system. The sliding surface‐based SA algorithm developed in MATLAB is launched from another computer.

Signal area estimation based on deep learning

In many practical application scenarios, radio communication signals are commonly represented as a spectrogram, which represents the signal strength measured at multiple discrete time instants and frequency points within a specific time interval and frequency band, respectively. In the context of spectrum occupancy measurements, the notion of Signal Area (SA) is defined as the rectangular region in the time–frequency domain where a signal is assumed to be present. Signal Area Estimation (SAE) is an important functionality in spectrum-aware wireless systems where spectrum usage monitoring is required. However, the conventional approaches to SAE have a limited estimation accuracy, in particular at low SNR. In this work, a novel technique for SAE is proposed using Deep Learning based on Artificial Neural Network (DL-ANN) for enhanced extraction of SA information from radio spectrograms. The performance of the proposed DL-ANN method is evaluated both with software simulations and hardware experiments, and the results are compared with several conventional methods from the literature, showing significant performance improvements. A key feature of the proposed method is the improvement in the SAE accuracy compared to other existing methods (in particular in the low SNR regime) and the capability to extract the location of the detected SAs automatically. Overall, the proposed technique is a promising solution for the automatic processing of radio spectrograms in spectrum-aware wireless systems.

Convolutional Neural Network Predictions for Unsteady Reynolds-Averaged Navier–Stokes-Based Numerical Simulations

The application of computational fluid dynamics (CFD) to turbulent flow has been a considerable topic of research for many years. Nonetheless, using CFD tools results in a large computational cost, which implies that, for some applications, CFD may be unviable. To date, several authors have carried out research applying deep learning (DL) techniques to CFD-based simulations. One of the main applications of DL with CFD is in the use of convolutional neural networks (CNNs) to predict which samples will have the desired magnitude. In this study, a CNN which predicts the streamwise and vertical velocities and the pressure fields downstream of a circular cylinder for a series of time instants is presented. The CNN was trained using a signed distance function (SDF), a flow region channel (FRC) and the t-1 sample as inputs, and the ground-truth CFD data as the output. The results showed that the CNN was able to predict multiple time instants with low error rates for turbulent flows with variable input velocities to the domain.

A k‐nearest neighbor‐based averaging model for probabilistic PV generation forecasting

AbstractProbabilistic forecasting of PV generation is crucial in uncertainty management to reinforce PV‐integrated power systems for long‐term planning. In this context, developing a reliable probabilistic forecast model is challenging due to weather conditions' stochastic nature and varying daily PV production patterns at multiple time instants. Due to varying probability distribution patterns, a nonparametric approach, such as quantile regression, is challenging to approximate the forecast error distribution. A forecast combination concept that yields impressive probabilistic forecasting results has rarely been utilized for PV generation forecasting. The use of complementary and sensible point forecasters characterizing different aspects of intricate PV production patterns in a k‐nearest neighbor quantile regression averaging framework is the proposed probabilistic forecasting model. The proposed forecasting model's accuracy is assessed with real‐world PV generation data from the USA for multi‐time instants. The probabilistic forecast performance of the proposed quantile k‐nearest neighbors regression averaging model is evaluated against the traditional quantile regression averaging, quantile regression forests, quantile k‐nearest neighbors, basic quantile regression, and regression bootstrapping in terms of the forecast (quantiles and prediction intervals) accuracy, using relevant error measures and scores. The proposed model has superior performance compared to the other models, as indicated by the lowest forecast errors and scores.

3-D Bi-directional LSTM for Satellite Soil Moisture Downscaling

Soil moisture is a crucial parameter of hydrological processes as it affects the exchange of water and heat at the land/atmosphere interface. Regional hydrological applications (floods and modeling of small basins) and agricultural applications (irrigation and agricultural land mapping) require daily soil moisture (SM) values having a spatial resolution of at least 1-km. This requirement is currently unmet by existing satellite missions. Notably, SM has variability over three dimensions. As such, accurate prediction of satellite SM requires multiple bidirectional spectra-spatiotemporal analyses. However, current state-of-the-art SM downscaling models can not yet fulfill this requirement. This paper proposes a new bi-directional LSTM model dubbed the three-dimensional bi-directional LSTM (3D-Bi-LSTM), which downscales the Soil Moisture Active Passive (SMAP) global daily 9-km SM to daily 1-km SM. In the proposed downscaling model, the region-specific soil moisture indices (SMIs) are first extracted using a covariance-adaptive convolutional neural network (CNN) to support the extraction of important distinctive information from multispectral data. Next, the CNN output is provided to the 3D-Bi-LSTM to perform the bi-directional analysis of spatial correlation within a feature and spectral correlation between features over multiple time instants. Experimental results demonstrate the proposed model outperforms state-of-the-art networks. An ablation study, transferability assessment, and feature importance study further demonstrate the proposed 3D-Bi-LSTM’s efficiency.

Comparison of approximation schemes for three-dimensional thermal stress analysis and applications to cracks with fluid flowing

In this paper, we first present an approximate scheme for thermal problem with heat sources varying with time (time-varying). It is different from one that the authors used previously. Both schemes use series of stepwise constant heat sources to approximate the continuously varying heat source. But the constant sources of the two schemes are different. The previous scheme divides the time period from the initial time to the solution time, at which the solutions are sought, into some time-steps and assumes that each of the series of constant sources is applied at the start time of the time-steps and all the constant sources last to the solution time. The new scheme assumes that each of the constant sources starts at the start time of a time-step but lasts only for one step of time. When solutions for temperature, displacement and stress are sought at multiple time instants or the boundary conditions are obtained step by step from other problems, it is easier and computing efficient to use the new scheme. If the solution is required at just one time instant, then computations of the two schemes are basically the same. The schemes are compared for the thermal effects caused by time-varying temperature on surface of a spherical cavity in an infinite body and the results of the two schemes agree very well. Comparison of the two schemes is also done for the temperature change in rock due to fluid flowing through a fracture inside the rock.

From sparse data to high-resolution fields: ensemble particle modes as a basis for high-resolution flow characterization

In this work, we present an approach to reconstruct high-resolution flow velocity or scalar fields from sparse particle-based measurements such as particle tracking velocimetry, thermographic phosphors or pressure-sensitive particles. The proposed approach can be applied to any of those fields; without leading its generality, it is hereby assessed for flow velocity measurements. Particles allow probing physical quantities at multiple time instants in randomly located points in the investigated region. In previous works, it has been shown that high-resolution time-averaged fields can be estimated by an ensemble average of the particles contained into spatial bins whose size can be reduced almost ad libitum. In this work, high-resolution ensemble particle modes are estimated from the ensemble average of particles, weighted with Proper Orthogonal Decomposition time coefficients which are estimated from low-resolution spatially-averaged fields. These modes represent a self-tunable compressed-sensing library for the reconstruction of high-resolution fields. High-resolution instantaneous fields are then obtained from a linear combination of these modes times their respective time coefficients. This data-enhanced particle approach is assessed employing two DNS datasets: the wake of a cylinder and a fluidic pinball. It is shown here that it is possible to reconstruct phenomena whose characteristic wavelength is smaller than the mean particle spacing whenever such events are correlated with any other flow phenomenon with a wavelength large enough to be sampled. The proposed approach is also applied to experimental wind-tunnel data, again showing excellent performances in presence of realistic measurement-noise conditions.

Model-Free State Estimation Using Low-Rank Canonical Polyadic Decomposition

As electric grids experience high penetration levels of renewable generation, fundamental changes are required to address real-time situational awareness. This letter utilizes unique traits of tensors to devise a model-free situational awareness and energy forecasting framework for distribution networks. This letter formulates the state of the network at multiple time instants as a three-way tensor; hence, recovering full state information of the network is tantamount to estimating all the values of the tensor. Given measurements received from μphasor measurement units and/or smart meters, the recovery of unobserved quantities is carried out using the low-rank canonical polyadic decomposition of the state tensor-that is, the state estimation task is posed as a tensor imputation problem utilizing observed patterns in measured (sampled) quantities. Two structured sampling schemes are considered, namely, asynchronous slab and fiber sampling. For both schemes, we present sufficient conditions on the number of sampled slabs and fibers that guarantee identifiability of the factors of the state tensor. Numerical results demonstrate the ability of the proposed framework to achieve high estimation accuracy in multiple sampling scenarios.

MT-BCS-Based DoA and Bandwidth Estimation of Unknown Signals through Multiple Snapshots Data

The Direction-of-Arrival (DoA) and bandwidth (BW) estimation strategy impinging on a linear array using multiple snapshots data is addressed within the multitask Bayesian Compressive Sensing (MT-BCS). The DoA estimation is used as the reconstruction of sparse signal constrained by the Laplace prior through multitask Bayesian Compressive Sensing. Receiving wideband signal data through linear array, the space is divided into I parts according to the equal interval. The data of interest are assumed to be represented as I-dimensional vector, and the wideband signal can be reconstructed accurately using only a small number M. The receiving antenna operates in the frequency range fmin,fmax. Starting from the voltages measured at the output of the array elements at a multiple time instants at fp=fmin+Δf,p=1,…,P, the retrieval of the DoAs is addressed by means of a customized strategy based on MT-BCS in order to correlate the solutions obtained over different frequency samples. The bandwidth of the signals is obtained as a byproduct by identifying at which frequencies the MT-BCS estimations include a signal along the ith (i = 1,…, I) sampling direction. From the outputs of different frequencies, we can know the DoA and BW of signals. A preliminary numerical result is reported to show the behavior of the proposed approach in multiple snapshots data.

Kinetic Euclidean Distance Matrices

Euclidean distance matrices (EDMs) are a major tool for localization from distances, with applications ranging from protein structure determination to global positioning and manifold learning. They are, however, static objects which serve to localize points from a snapshot of distances. If the objects move, one expects to do better by modeling the motion. In this paper, we introduce Kinetic Euclidean Distance Matrices (KEDMs)—a new kind of time-dependent distance matrices that incorporate motion. The entries of KEDMs become functions of time, the squared time-varying distances. We study two smooth trajectory models—polynomial and bandlimited trajectories—and show that these trajectories can be reconstructed from incomplete, noisy distance observations, scattered over multiple time instants. Our main contribution is a semidefinite relaxation, inspired by similar strategies for static EDMs. Similarly to the static case, the relaxation is followed by a spectral factorization step; however, because spectral factorization of polynomial matrices is more challenging than for constant matrices, we propose a new factorization method that uses anchor measurements. Extensive numerical experiments show that KEDMs and the new semidefinite relaxation accurately reconstruct trajectories from noisy, incomplete distance data and that, in fact, motion improves rather than degrades localization if properly modeled. This makes KEDMs a promising tool for problems in geometry of dynamic points sets.

Distributed Signal Compressive Quantization and Parallel Interference Cancellation for Cloud Radio Access Network

The explosive growth of wireless traffic requires revolutionary wireless communication techniques. The cloud radio access network (C-RAN) is a solution to leverage the spatial multiplexing gain by utilizing a number of antennas distributed in a certain area. However, in most of the current literature, due to the limited front-haul capacity, each remote radio head (RRH) can use only a single antenna, and thus the total number of antennas can be utilized are limited. In this work, we propose a distributed baseband signal compressive quantization scheme for the uplink of a multi-antenna C-RAN where each RRH uses multiple antennas. At each RRH, the baseband signals of multiple time instants are embedded into a vector with low dimension using delay-and-add so that more bits can be allocated to each value, and the quantization noise power caused by the front-haul link capacity deficit is reduced. The delay-and-add operation is low complexity that can be realized using basic buffering and adding, and it does not require channel information in the RRHs. Therefore, the low deployment cost feature of C-RAN is preserved. As a result, a large number of antennas can be utilized by deploying a lot of multi-antenna RRHs, which provide rich spatial diversity that assists the detection which happens in the baseband units. In the symbol detection phase, the corresponding weight vectors are designed to detect the symbols from the compressive quantized baseband signal. A parallel interference cancellation algorithm is proposed to further improve the accuracy of the symbol detection. Numerical results show that the proposed scheme is efficient in tackling the front-haul capacity challenge. We also apply the proposed scheme to orthogonal frequency-division multiplexing-based multi-antenna C-RAN, where we find that the system can utilize larger bandwidth with limited front-haul capacity. It facilitates the deployment of C-RAN based on both 4G and 5G wireless communications.

Regularised estimation for ARMAX process with measurements subject to outliers

ARMAX models are widely used in control engineering for both system description and control design. They can accurately describe a large class of real processes with relatively low complexity, but do not take into account observation errors, which can be particularly important in applications like filtering and fault diagnosis. Due to the intrinsic dependence in ARMAX process output, a single outlier may contaminate multiple data entries and completely spoil the conventional least-squares estimate. In this study, the authors develop a novel Moving Horizon Estimator that is not only robust to outliers but also able to reveal outliers. By utilising the fact that outliers are extreme errors that occur infrequently, the estimation problem is formulated as a least-squares optimisation problem with outliers explicitly modelled and regularised with l 1 -norm to induce sparsity. A coordinate descent-based solver is developed to obtain iterative algorithms with guaranteed convergence and closed-form solution available to each coordinate sub-problem per iteration. Due to the explicit modelling of outlier vectors, the impact of an outlier on multiple time instants can be estimated and mitigated. Simulation tests demonstrate that the proposed algorithm can effectively cope with outliers, even for the case when the commonly used Huber's M-estimation approach breaks down.

Assessment of Quantitative and Qualitative Characteristics of Ultrasonic Guided Wave Phase Velocity Measurement Technique

Various time-of-flight measurement techniques are widely used in ultrasonic nondestructive testing (NDT) and structural health monitoring (SHM). On the basis of changes in the amplitude or the velocity of propagating guided wave signal, defects and delamination in objects can be detected. However, ultrasonic guided waves possess a dispersion phenomenon therefore an accurate determination of the signal amplitude or velocity is complicated. In this paper, a technique assessing a dispersion phenomenon using one of the time-of-flight measurement methods is proposed. This method deals with measured phase velocity values from different frequency components of the signal based on measurement of multiple zero-crossing time instants. Then, it identifies fundamental Lamb wave modes, allows to express evaluation of presence of dispersion effect, estimates if low or high dispersion is appearing within frequency range of interest and reconstructs a segment of the dispersion curve using only a few different spatial positions of the receiving transducer. The proposed measurement algorithm was tested using Lamb wave signals propagating in aluminium plate. To assess the quantitative and qualitative characteristics of phase velocity measurement technique, simulated and experimental signals were used. Systematic errors for given conditions and the expanded uncertainty of the measurement process have been calculated. The errors and uncertainties depending on Lamb wave phase velocity and for the A $$_{0}$$ mode are equal to approximately 1.2% and 3% while for the S $$_{0}$$ mode −0.65 and 1.1% respectively.

Performance Evaluation of Practical Passive Source Localization Using Two Software Defined Radios

Passive source localization has been a hot topic for many years. It is traditionally implemented using multiple spatially distributed stationary sensors. In this letter, a passive localization solution using two sensors is proposed. To demonstrate the solution in the real-world environment, a localization system is developed using commercial-off-the-shelf software defined radios. One stationary sensor and one mobile sensor are developed to take time difference of arrival and frequency difference of arrival measurements at multiple time instants to localize a non-cooperative signal tower. To improve the localization accuracy, the measurement error is systematically analyzed. Moreover, the methods to select appropriate measurements are discussed. The localization performance of the system is evaluated via the experiments implemented in a large outdoor area with a sensor-target range of several kilometers.

Underwater Inertial Navigation With Long Baseline Transceivers: A Near-Real-Time Approach

Inertial navigation systems for underwater vehicles are often aided by acoustic time-of-flight positioning schemes. One widely implemented long baseline (LBL) approach uses a ping-response protocol resulting in asynchronous measurements that depend on the state of the vehicle at two time instants. Such aiding measurements that depend on the state at multiple time instants do not fit the model format of the standard extended Kalman filter (EKF) framework. This paper proposes a near-real-time (NRT) Bayesian smoothing framework for the LBL-aided inertial navigation system application. Within this NRT framework, the time interval between LBL cycles is divided into two intervals defined by the ping-response cycles of LBL. The time instant when all the LBL responses are received will be referred to as the NRT point . Between the LBL ping and the NRT point, a traditional real-time EKF is implemented using the inertial measurement unit and all aiding measurements. After the NRT point, when all the LBL responses have been received, an optimal Bayesian trajectory estimator executes. This maximum a posteriori (MAP) estimation includes all the measurement information already collected up to the NRT point of the current LBL cycle. The MAP output is a smoothed trajectory estimate for the period of time between the LBL ping and the NRT point. At the conclusion of this smoothing process, the current EKF estimate is corrected to the optimal MAP solution and propagated to the current time, and then the EKF estimation process continues to the NRT point of the next LBL cycle. At all times, the approach maintains a real-time state estimate suitable for use by the control system. This paper presents the theoretical solution, discusses the implementation, and presents implementation results to illustrate the accuracy and reliability of this NRT approach.

Adaptive Quantization on a Grassmann-Manifold for Limited Feedback Beamforming Systems

In this paper we examine delay limited adaptive quantization on the Grassmann manifold of 1-dimensional subspaces in n-dimensional space. Due to strict delay limits, vector quantization over multiple time instants cannot be applied to exploit the temporal correlation of the source signal. Instead, a vector predictive quantizer is proposed that combines prediction and differential quantization algorithms to achieve an efficient quantization of the correlated Grassmannian source. The proposed predictor is based on adaptive finite impulse response filters to adjust to the temporal statistics of the source signal. It is shown that the prediction error in the tangent space associated with the Grassmann manifold behaves approximately Gaussian, provided its norm is sufficiently small. The proposed quantization algorithm is applied to channel state information quantization in multi-user beamforming wireless communication systems. Large throughput gains are demonstrated in comparison to memoryless quantization, due to reduced multi-user interference.

Directions-of-Arrival Estimation Through Bayesian Compressive Sensing Strategies

The estimation of the directions of arrival (DoAs) of narrow-band signals impinging on a linear antenna array is addressed within the Bayesian compressive sensing (BCS) framework. Unlike several state-of-the-art approaches, the voltages at the output of the receiving sensors are directly used to determine the DoAs of the signals thus avoiding the computation of the correlation matrix. Towards this end, the estimation problem is properly formulated to enforce the sparsity of the solution in the linear relationships between output voltages (i.e., the problem data) and the unknown DoAs. Customized implementations exploiting the measurements collected at a unique time instant (single-snapshot) and multiple time instants (multiple-snapshots) are presented and discussed. The effectiveness of the proposed approaches is assessed through an extensive numerical analysis addressing different scenarios, signal configurations, and noise conditions. Comparisons with state-of-the-art methods are reported, as well.

Generalized High-Order Phase Function for Parameter Estimation of Polynomial Phase Signal

The high-order phase function (HPF) has been introduced recently to estimate the parameters of a polynomial phase signal (PPS). In this correspondence, we generalize the standard HPF by introducing multiple time instants. Thus, the standard HPF can be treated as a special example of the generalized HPF with identical time instants. We propose a procedure for finding time instants minimizing the mean-square error (MSE). The proposed method achieves better performances than the high-order ambiguity function (HAF) and polynomial Wigner-Ville distribution (PWVD). The theoretical analysis as well as the Monte Carlo simulations verify the advantages such as lower MSE and lower SNR threshold for the PPS.

Identifying the mechanical properties of tissue by ultrasound strain imaging

A new elastography method is presented that images the mechanical properties of soft tissue. The tissue is externally vibrated over a range of frequencies simultaneously and the resulting displacement is recorded at multiple locations and time instants with a sequence of ultrasound images. Two methods are proposed for estimating the mechanical properties from the recorded data. In the first method, equations of motion are written for a tissue model of masses, springs and dampers. The equations are solved to identify the model parameters and construct an image. The second method performs a transfer function analysis of the tissue motion. The tissue properties are identified from the shape the of transfer function. Simulations show good performance of the new method compared with static elastography. Initial experimental results from homogeneous and layered tissue phantoms also demonstrate the ability quantitatively to image tissue stiffness. Preliminary results are obtained for viscosity and density estimation. Further work is needed to extend the formulation to 3-D and improve robustness of the viscosity and density estimates. (E-mail: tims@ece.ubc.ca)

Influence of measurement noise and electrode mislocalisation on EEG dipole-source localisation.

Measurement noise in the electro-encephalogram (EEG) and inaccurate information about the locations of the EEG electrodes on the head induce localisation errors in the results of EEG dipole source analysis. These errors are studied by performing dipole source localisation for simulated electrode potentials in a spherical head model, for a range of different dipole locations and for two different numbers (27 and 148) of electrodes. Dipole source localisation is performed by iteratively minimising the residual energy (RE), using the simplex algorithm. The ratio of the dipole localisation error (cm) to the noise level (%) of Gaussian measurement noise amounts to 0.15 cm/% and 0.047 cm/% for the 27 and 148 electrode configurations, respectively, for a radial dipole with 40% eccentricity The localisation error due to noise can be reduced by taking into account multiple time instants of the measured potentials. In the case of random displacements of the EEG electrodes, the ratio of dipole localisation errors to electrode location errors amounts to 0.78 cm-1 cm and 0.27 cm-1 cm for the 27 and 148 electrode configurations, respectively. It is concluded that it is important to reduce the measurement noise, and particularly the electrode mislocalisation, as the influence of the latter is not reduced by taking into account multiple time instants.