Singular Value Decomposition Approach Research Articles

Prediction of Heart Failure in Children with Congenital Heart Disease Based on Multichannel LSTM

Heart failure (HF) is a complicated clinical illness caused by a variety of primary and secondary causes, as well as increased infection pathways, that are associated with higher risk, illness, and costs. The overall incidence of congenital heart disease is approximately high and is the leading cause of death in infants and children. In this study, we present a novel computational model based on ECNN-LSTM for detecting congenital heart disease in real time and assessing its developing course objectively. The proposed model is a multiresolution singular value decomposition for congenital heart disease prediction in infants and children. Firstly, in the whole-life vibration time-domain signal of newborns and toddlers, the multiresolution singular value decomposition approach is employed to create approximation and detailed signals with different resolutions. Secondly, the health phases are split into the smooth running stage of newborns and toddlers, as well as the standard deviation of the earliest moments. Thirdly, the two-layer one-dimensional convolutional neural network structure divides the quick degradation stage, which can offer degradation information. Finally, the prediction of congenital heart disease is finished on LSTM utilizing the MSE loss function to unify the assessment scale. Validation with lifespan data demonstrates the feasibility and effectiveness of the proposed model. Moreover, the existing models have the insufficient ability for life span feature characterization in congenital heart disease prediction in infants and children.

False Data Injection Cyber-Attacks Detection for Multiple DC Microgrid Clusters

DC microgrids are considered as the next generation of power systems because of the possibility of connecting various renewable energy sources to different types of loads based on distributed networks. However, due to the strong reliance on communication networks, DC microgrids are vulnerable to intentional cyber-attacks. Therefore, in this paper, a robust cyber-attack detection scheme is proposed for DC microgrid systems. Utilizing the parity-based method, a multi-objective optimization problem is formulated to achieve robust detection against electrical parameter perturbations and unknown disturbances. An analytical solution is then provided using the singular value decomposition approach. With the disturbance decoupling scheme, the presented detection strategy can monitor the system with only local knowledge of the DC microgrid. The proposed method is easy to design and with less computation complexity. The performances of the provided scheme are validated by simulation tests and experimental results.

Enhanced Invisibility and Robustness of Digital Image Watermarking Based on DWT-SVD.

The propagation of digital media over the Internet has helped improve digitization, which has given an excessive lead to copyright issues. Digital watermarking techniques have been applied to address copyright issues. In research, a system is being developed to handle veracious types of watermarked attacks, for obtaining extreme security and an adequate level of visibility and robustness. The discrete wave transform (DWT) and singular value decomposition (SVD) approaches were applied to analyze veracious types of attacks. The DWT method was used to embed the host image in four levels; this level was processed using the SVD method. The peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) were applied to measure the invisibility, and the normalization correlation (NC) was used to examine the robustness of watermarked images. The empirical results showed that the proposed DWT-SVD achieved superior accuracy in identifying the various attacks. The proposed DWT-SVD performance was confirmed during the training process, and the proposed system was shown to have high invisibility and robustness against various types of attacks on watermarked images. Finally, the results of the proposed system were compared to existing systems, and it was shown that DWT-SVD achieved better performance in terms of pixel-value modification attacks.

Ground state of asymmetric tops with DMRG: Water in one dimension.

We propose an approach to compute the ground state properties of collections of interacting asymmetric top molecules based on the density matrix renormalization group method. Linear chains of rigid water molecules of varying sizes and density are used to illustrate the method. A primitive computational basis of asymmetric top eigenstates with nuclear spin symmetry is used, and the many-body wave function is represented as a matrix product state. We introduce a singular value decomposition approach in order to represent general interaction potentials as matrix product operators. The method can be used to describe linear chains containing up to 50 water molecules. Properties such as the ground state energy, the von-Neumann entanglement entropy, and orientational correlation functions are computed. The effect of basis set truncation on the convergence of ground state properties is assessed. It is shown that specific intermolecular distance regions can be grouped by their von-Neumann entanglement entropy, which in turn can be associated with electric dipole-dipole alignment and hydrogen bond formation. Additionally, by assuming conservation of local spin states, we present our approach to be capable of calculating chains with different arrangements of the para and ortho spin isomers of water and demonstrate that for the water dimer.

A new predictive energy management system: Deep learned type-2 fuzzy system based on singular value decommission

A new predictive frequency management system is designed for multi-area microgrids (MGs). The uncertainties are online modeled by a deep learned type-2 (T2) fuzzy-logic system (FLS) and singular-value decomposition (SVD) approach. The predictive controller is designed based on the SVD-T2FLS model. The robustness against perturbations is ensured by the adaptive learning laws and adaptive compensators. The learning rules are extracted from stability and robustness investigation. The effect of demand response (DR), multiple load changes, high dynamic perturbations and variation of wind speed and solar energies are studied. The simulations on two case study systems and comparisons with other fuzzy logic controllers (FLCs), demonstrate that the suggested approach is well effective and results in good regulation accuracy.

Output Feedback Sliding Mode Control of Markovian Jump Systems and Its Application to Switched Boost Converter

This paper presents a sliding mode dynamic output feedback controller design for Markovian jump systems under a communication network. For the inaccessible states and uncertain parts of Markovian jump systems, a novel integral-type sliding mode output feedback controller combined with system states and dynamic controller states is proposed. An event-triggered mechanism is introduced to reduce network bandwidth and resources requirements. The augmented dynamic system and complete control framework are presented together. The reachability of the novel integral-type sliding mode is proved. Then, based on delay-dependent Lyapunov functions, free-weighting matrices, and a singular value decomposition approach, a less conservative sufficient condition is proposed to ensure exponential stability with a weighted <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {L}_{2}$ </tex-math></inline-formula> - <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal{ L}_{\infty}$ </tex-math></inline-formula> disturbance performance. Finally, a numerical and a DC-DC switched boost converter circuit simulations that verify the feasibility and effectiveness of the proposed controller are presented.

Effect of the MJO on East Asian winter rainfall as revealed by an SVD analysis

AbstractThis paper studies the influences of the Madden Julian Oscillation (MJO) on East Asian (EA) winter rainfall using the Singular Value Decomposition (SVD) approach. This method uses two-dimensional instead of the latitudinally-averaged variables in the commonly used Real-time Multivariate MJO (RMM) index. A comparison of the two approaches is made using the same OLR and zonal wind data over 37 boreal winter seasons of December – March. The SVD composite reveals a more conspicuous and coherent variation throughout the MJO cycle, while the RMM composite is more ambiguous. In particular, the SVD analysis identifies the convection anomalies over the Maritime Continent and the subtropical western Pacific (MCWP) as a major cause of enhanced rainfall in EA at RMM phases 8 and 1. This is at least one-eighth cycle earlier than the phases of convection development over Indian Ocean (IO) that were emphasized by previous studies. A linearized global baroclinic model is used to demonstrate the mechanism of MJO forcing on EA rainfall during various phases, with a focus on the MCWP cooling. The result shows that the anomalous MCWP cooling and the resultant low-level anticyclonic flow interact with the East Asian Jet, leading to an overall weakened EA winter monsoon circulation. The associated anomalous overturning circulation, with ascending motion and low-level horizonal moisture convergence in EA, contributes to the enhanced rainfall. This model result supports the interpretation of the SVD analysis, in that the MCWP-cooling induced anomalous meridional circulation is a more direct cause of enhanced EA rainfall than the IO-heating (or the IO-MCWP heating dipole) induced Rossby wave teleconnection.

Guaranteed cost finite-time control of large-scale singular systems with interconnected delays

The guaranteed cost finite-time control problem of large-scale singular systems subjected to interconnected state delays is addressed in this article. A singular value decomposition approach combining with the Lyapunov function method is proposed to study the problem. Based on the method, delay-dependent sufficient conditions are established to design guaranteed cost controllers, which are presented in terms of tractable linear matrix inequalities. An example with simulation is given to demonstrate the validity and effectiveness of the theoretical results.

A singular value decomposition approach for testing the efficiency of Bitcoin and Ethereum markets

This letter revisits the informationally efficiency of the two major cryptocurrencies Bitcoin (2013–2021) and Ethereum (2016–2021). The analysis is based on the computation of the singular value decomposition (SVD) entropy of a matrix formed by lagged vectors of price returns. The computed entropy is compared with a reference obtained from uncorrelated time series to decide whether the rows of the lagged matrix are uncorrelated. The procedure was implemented over a sliding window to assess the time variations of the entropy. The results show that the markets are informationally efficient most of time over different scales, except for some short periods that are linked to the 2016–2017 price-boom period and the 2020 Covid-19 economic downturn.

Optimal distribution of piezoelectric patches for active vibration reduction of a thick plate using singular value decomposition approach

This paper uses the singular value decomposition approach to find the optimal distribution of a set of piezoelectric actuators and sensors in order to suppress the vibrations of a thick plate. The dynamic model of the system is derived using Mindlin plate theory and consequently, the finite difference method is employed to divide the thick plate to a finite number of nodes with appropriate horizontal and vertical distances. To compute the control force of piezoelectric actuators, the singular value decomposition approach for the column control matrix is supposed as the fitness function of an optimization problem. Through a genetic algorithm, the optimized solution is obtained. The results of numerical simulations indicate the optimal location achieved by the proposed method outperforms the previous results in suppressing the vibrations of a thick plate.

Physical-Layer Security for Frequency Diverse Array-Based Directional Modulation in Fluctuating Two-Ray Fading Channels

The frequency diverse array (FDA)-based directional modulation (DM) technology plays an important role in the physical-layer security (PLS) transmission of 5G and beyond communications. In order to meet the tremendous increase in mobile data traffic, a new memory-efficient design for the FDA-DM-based PLS transmission is urgently demanded. In this article, an analytical symmetrical multi-carrier FDA model is proposed in three dimensions, namely, range, azimuth angle, and elevation angle, differing from the conventional analytical approach with only range and azimuth angle considered. Then, a single-point (SP) artificial noise (AN) aided FDA-DM scheme is proposed, which reduces the memory consumption significantly compared with the conventional zero-forcing (ZF) and singular value decomposition (SVD) approaches. Moreover, the PLS performance of the proposed FDA-DM scheme is analyzed in fluctuating two-ray (FTR) fading channels for the first time, including the average secrecy capacity (ASC) and the secrecy outage probability (SOP). More importantly, the closed-form expressions for the lower bound on ASC and the upper bound on SOP are derived, respectively. The effectiveness of the analytical expressions is verified by numerical simulations. This work opens a way to lower the memory requirements for DM-based PLS transmission of 5G and beyond communications.

Lee-Carter method for forecasting mortality for Peruvian Population

In this article, we have modeled mortality rates of Peruvian female and male populations during the period&nbsp;of 1950-2017 using the two-factor Lee-Carter (LC) model. The stochastic mortality model was introduced&nbsp;by Lee and Carter (1992) and has been used by many authors for fitting and forecasting the human mortality&nbsp;rates. The Singular Value Decomposition (SVD) approach is used for estimation of the parameters of the&nbsp;LC model. Utilizing the best fitted auto regressive integrated moving average (ARIMA) model we forecast&nbsp;the values of the time dependent parameter of the LC model for the next thirty years. The forecasted values&nbsp;of life expectancy at different age group with 95% confidence intervals are also reported for the next thirty&nbsp; years. In this research we use the data, obtained from the Peruvian National Institute of Statistics (INEI). &nbsp

A Higher-Order Singular Value Decomposition Tensor Emulator for Spatiotemporal Simulators

We introduce methodology to construct an emulator for environmental and ecological spatiotemporal processes that uses the higher-order singular value decomposition (HOSVD) as an extension of singular value decomposition (SVD) approaches to emulation. Some important advantages of the method are that it allows for the use of a combination of supervised learning methods (e.g., random forests and Gaussian process regression) and also allows for the prediction of process values at spatial locations and time points that were not used in the training sample. The method is demonstrated with two applications: The first is a periodic solution to a shallow ice approximation partial differential equation from glaciology, and second is an agent-based model of collective animal movement. In both cases, we demonstrate the value of combining different machine learning models for accurate emulation. In addition, in the agent-based model case we demonstrate the ability of the tensor emulator to successfully capture individual behavior in space and time. We demonstrate via a real data example the ability to perform Bayesian inference in order to learn parameters governing collective animal behavior.

On the Optimal Field Sensing in Near-Field Characterization.

We deal with the problem of characterizing a source or scatterer from electromagnetic radiated or scattered field measurements. The problem refers to the amplitude and phase measurements which has applications also to interferometric approaches at optical frequencies. From low frequencies (microwaves) to high frequencies or optics, application examples are near-field/far-field transformations, object restoration from measurements within a pupil, near-field THz imaging, optical coherence tomography and ptychography. When analyzing the transmitting-sensing system, we can define “optimal virtual” sensors by using the Singular Value Decomposition (SVD) approach which has been, since long time, recognized as the “optimal” tool to manage linear algebraic problems. The problem however emerges of discretizing the relevant singular functions, thus defining the field sampling. To this end, we have recently developed an approach based on the Singular Value Optimization (SVO) technique. To make the “virtual” sensors physically realizable, in this paper, two approaches are considered: casting the “virtual” field sensors into arrays reaching the same performance of the “virtual” ones; operating a segmentation of the receiver. Concerning the array case, two ways are followed: synthesize the array by a generalized Gaussian quadrature discretizing the linear reception functionals and use elementary sensors according to SVO. We show that SVO is “optimal” in the sense that it leads to the use of elementary, non-uniformly located field sensors having the same performance of the “virtual” sensors and that generalized Gaussian quadrature has essentially the same performance.

Improved Skin Permeability after Topical Treatment with Serine Protease: Probing the Penetration of Rapamycin by Scanning Transmission X-ray Microscopy.

Drug penetration in human skin ex vivo following a modification of skin barrier permeability is systematically investigated by scanning transmission X-ray microscopy. Element-selective excitation is used in the O 1s regime for probing quantitatively the penetration of topically applied rapamycin in different formulations with a spatial resolution reaching <75 nm. The data were analyzed by a comparison of two methods: (i) two-photon energies employing the Beer–Lambert law and (ii) a singular value decomposition approach making use of the full spectral information in each pixel of the X-ray micrographs. The latter approach yields local drug concentrations more reliably and sensitively probed than the former. The present results from both approaches indicate that rapamycin is not observed within the stratum corneum of nontreated skin ex vivo, providing evidence for the observation that this high-molecular-weight drug inefficiently penetrates intact skin. However, rapamycin is observed to penetrate more efficiently the stratum corneum when modifications of the skin barrier are induced by the topical pretreatment with the serine protease trypsin for variable time periods ranging from 2 to 16 h. After the longest exposure time to serine protease, the drug is even found in the viable epidermis. High-resolution micrographs indicate that the lipophilic drug preferably associates with corneocytes, while signals found in the intercellular lipid compartment were less pronounced. This result is discussed in comparison to previous work obtained from low-molecular-weight lipophilic drugs as well as polymer nanocarriers, which were found to penetrate the intact stratum corneum exclusively via the lipid layers between the corneocytes. Also, the role of the tight junction barrier in the stratum granulosum is briefly discussed with respect to modifications of the skin barrier induced by enhanced serine protease activity, a phenomenon of clinical relevance in a range of inflammatory skin disorders.

A deep learned type-2 fuzzy neural network: Singular value decomposition approach

The main objective of this study is to present a novel dynamic fractional-order deep learned type-2 fuzzy logic system (FDT2-FLS) with improved estimation capability. The proposed FDT2-FLS is constructed based on the criteria of singular value decomposition and uncertainty bounds type-reduction. The upper and the lower singular values of the set of inputs are estimated by a simple filter and the output is obtained by fractional-order integral of the uncertainty bounds type-reduction. Using stability criteria of fractional-order systems, the adaptation rules of the consequent parameters are extracted such that the globally Mittag-Leffler stability is achieved. The proposed FDT2-FLS is employed for online dynamic identification of a hyperchaotic system, online prediction of chaotic time series and online prediction of glucose level in type-1 diabetes patients and its performance is compared with other well-known methods. It is shown that the proposed mechanism results in significantly better prediction and estimation performance with less tunable parameters in just one learning epoch.

Sparse principal component regression via singular value decomposition approach

Principal component regression (PCR) is a two-stage procedure: the first stage performs principal component analysis (PCA) and the second stage builds a regression model whose explanatory variables are the principal components obtained in the first stage. Since PCA is performed using only explanatory variables, the principal components have no information about the response variable. To address this problem, we present a one-stage procedure for PCR based on a singular value decomposition approach. Our approach is based upon two loss functions, which are a regression loss and a PCA loss from the singular value decomposition, with sparse regularization. The proposed method enables us to obtain principal component loadings that include information about both explanatory variables and a response variable. An estimation algorithm is developed by using the alternating direction method of multipliers. We conduct numerical studies to show the effectiveness of the proposed method.

A New Eigen-Structure Based DOA Estimation Method for Wideband Coherent Signals

A new Direction of arrival (DOA) estimation method for wideband coherent sources is presented in this paper. This method does not require any prior knowledge of the source number and initial estimation of source angles. In the proposed method, at first, upon calculating the focusing matrix, the covariance matrix of all frequency bins are transformed to the reference frequency. Then by forming a Hankel matrix for each signal subspace eigenvector (SSE), the de-correlation process is formed and the linear independent vectors are extracted from the Singular Value Decomposition (SVD) approach. The simulation results in different conditions shows that the proposed algorithm has better performance compare to other algorithms.

Full-Duplex mmWave Massive MIMO Systems: A Joint Hybrid Precoding/Combining and Self-Interference Cancellation Design

Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems have been considered as one of the primary candidates for the fifth generation (5G) and beyond 5G wireless communication networks to satisfy the ever-increasing capacity demands. Full-duplex technology can further enhance the advantages of mmWave massive MIMO systems. However, strong self-interference (SI) is the major limiting factor in full-duplex technology. Hence, this paper proposes a novel angular-based joint hybrid precoding/combining (AB-JHPC) technique for the full-duplex mmWave massive-MIMO systems. Our primary goals are listed as: (i) improving the self-interference cancellation (SIC), (ii) increasing the intended signal power, (iii) decreasing the channel estimation overhead, (iv) designing the massive MIMO systems with a low number of RF chains. First, the RF-stage of AB-JHPC is developed via slow time-varying angle-of-departure (AoD) and angle-of-arrival (AoA) information. A joint transmit/receive RF beamformer design is proposed for covering (excluding) the AoD/AoA support of intended (SI) channel. Second, the BB-stage of AB-JHPC is constructed via the reduced-size effective intended channel. After using the well-known singular value decomposition(SVD) approach at the BB-stage, we also propose a new semi-blind minimum mean square error (S-MMSE) technique to further suppress the residual SI power by using AoD/AoA parameters. The numerical results demonstrate that the SI signal is remarkably canceled via the proposed AB-JHPC technique. It is shown that AB-JHPC achieves 85.7 dB SIC and the total amount of SIC almost linearly increases via antenna isolation techniques. We observe that the proposed full-duplex mmWave massive MIMO systems double the achievable rate capacity compared to its half-duplex counterpart as the antenna array size increases and the transmit/receive antenna isolation improves.

Evaluation of Ionospheric Delays Based on Orthogonal Dimensionality Reduction Approach

The effect of space weather on Global Positioning System (GPS) signals transmitted through the ionosphere is a significant cause of range errors and can be vulnerable to GPS users. Accurate evaluation of ionospheric delays plays an essential role in improving the navigation service in the position domain for Category I (CAT I) operations. In this paper, an attempt is made to estimate the ionospheric delays based on the Spherical Harmonic Function (SHF) model using the Singular Value Decomposition (SVD) approach. Total Electron Content (TEC) data from 17 GPS stations were considered in this analysis and operate under the GPS–Aided Geo Augmented Navigation (GAGAN) network in the Indian region. The results show that the SVD–SHF approach is more efficient in estimating ionospheric delays than the Weighted Least Square (WLS) method. Due to the rapid convergence of the SVD, the first five singular values of the geometric matrix (H) can be used to represent the total variance. The average Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) deviations of (WLS–SHF) are 0.75 m and 0.61 m, and the corresponding deviations of SVD–SHF are 0.71 m and 0.59 m were recorded during the storm period 26 to 28 July 2004 (Dst = −170 nT). The computational load for modeling the ionosphere delays with the WLS–SHF model is 15.48 s and the corresponding load for SVD–SHF is 3.81 s. The proposed solution is numerically stable, requires less computation time, reduces the data dimensions, and round–off errors in initial data. The proposed algorithm can be implemented in Global Navigation Satellite System (GNSS) and surveying applications.