Search

Original lidar return signals are covered by high levels of noise that seriously affect the accuracy of subsequent data processing and inversion. Therefore, it is important to separate the effective signal from the returned signal with noise interference. In this paper, an efficient denoising method based on the variational mode decomposition (VMD) algorithm optimized using the global search strategy-based whale algorithm and the total variational stationary wavelet transform (GSWOA-VMD-SWTTV) is proposed, and this method is applied to denoising of lidar return signals. First, the global search strategy-based whale optimization algorithm (GSWOA) is used to acquire the optimal parameters of the VMD algorithm adaptively, and the lidar return signal is then decomposed by global search strategy-based whale optimization algorithm (GSWOA)-VMD. The effective modal components are then determined using the cross-correlation coefficient method from the decomposed modal components, and total variation stationary wavelet denoising is performed on each effective mode. Finally, the effective modes are reconstructed to obtain a clean lidar return signal. Moreover, to provide further verification of the effectiveness of the proposed method, it is compared with the ensemble empirical mode decomposition (EEMD) method, the complete EEMD with adaptive noise (CEEMDAN) method, the singular value decomposition (SVD) method, and the wavelet threshold method under sunny, cloudy, and dusty weather conditions. The experimental results demonstrate the superior noise reduction performance of the proposed algorithm, which can filter out strong noise from the signal while retaining the complete signal details without distortion; additionally, the proposed method has the highest signal-to-noise ratio and lowest mean square error. [ABSTRACT FROM AUTHOR]

For automobile manufacturers, reducing vehicle interior noise is essential for increasing customer satisfaction and vehicle quality. Windshield wipers are one of the major components that generate such noises, and faulty wipers could negatively affect passengers' psychological and physiological perceptions while driving. Thus, identifying faulty wipers during the manufacturing process would improve the driving experience and vehicle and road safety as well as reduce driver distraction. However, the existing windshield wiper noise-detection process is entirely manual, relies upon human subjectivity, and is time-consuming. Accordingly, this paper develops a novel automated windshield wiper fault-detection system. First, a novel binarization approach is used to effectively binarize the transformed spectrograms of sound signals from windshield wiper operation to segment nAoisy regions. Then, a new matrix-factorisation approach called orthogonal binary singular value decomposition is proposed to decompose binarized mel spectrograms into uncorrelated binary eigenimages to extract meaningful features and identify faulty wipers. Then, the $ k $ k -nearest neighbour classifier is utilised to classify the extracted features into normal or faulty windshield wipers. Finally, to demonstrate the effectiveness of the proposed system, it was validated on real-life windshield wiper reversal and squeal noise datasets, where it outperformed existing methods and achieved accuracies of 95% and 94%, respectively. [ABSTRACT FROM AUTHOR]

Mouse tracking is an important source of data in cognitive science. Most contemporary mouse tracking studies use binary-choice tasks and analyze the curvature or velocity of an individual mouse movement during an experimental trial as participants select from one of the two options. However, there are many types of mouse tracking data available beyond what is produced in a binary-choice task, including naturalistic data from web users. In order to utilize these data, cognitive scientists need tools that are robust to the lack of trial-by-trial structure in most normal computer tasks. We use singular value decomposition (SVD) and detrended fluctuation analysis (DFA) to analyze whole time series of unstructured mouse movement data. We also introduce a new technique for describing two-dimensional mouse traces as complex-valued time series, which allows SVD and DFA to be applied in a straightforward way without losing important spatial information. We find that there is useful information at the level of whole time series, and we use this information to predict performance in an online task. We also discuss how the implications of these results can advance the use of mouse tracking research in cognitive science.

In recent years, all-inorganic perovskite materials have become an ideal choice for new thin film solar cells due to their excellent photophysical properties and have become a research hotspot. Studying the ultrafast dynamics of photo-generated carriers is of great significance for further improving the performance of such devices. In this work, we focus on the transient dynamic process of CsPbBr3/TiO2 composite systems with different Mn2+ doping contents using femtosecond transient absorption spectroscopy technology. We used singular value decomposition and global fitting to analyze the transient absorption spectra and obtained three components, which are classified as hot carrier cooling, charge transfer, and charge recombination processes, respectively. We found that the doping concentration of Mn2+ has an impact on all three processes. We think that the following two factors are responsible: one is the density of defect states and the other is the bandgap width of perovskite. As the concentration of doped Mn2+ increases, the charge transfer time constant shows a trend of initially increasing, followed by a subsequent decrease, reaching a turning point. This indicates that an appropriate amount of Mn2+ doping can effectively improve the photoelectric performance of solar cell systems. We proposed a possible charge transfer mechanism model and further elucidated the microscopic mechanism of the effect of Mn2+ doping on the interface charge transfer process of the CsPbBr3/TiO2 solar cell system. [ABSTRACT FROM AUTHOR]

Surface microseismic monitoring is widely used in hydraulic fracturing. Real‐time monitoring data collected during fracturing can be used to perform surface‐microseismic localization, which aids in assessing the effects of fracturing and provides guidance for the process. The accuracy of localization critically depends on the quality of monitoring data. However, the signal‐to‐noise ratio of the data is often low due to strong coherent and random noise, making denoising essential for processing surface monitoring data. To suppress noise more effectively, this paper introduces a novel denoising method that integrates the Ramanujan subspace with dynamic time warping and adaptive singular value decomposition. The new method consists of two steps: First, a Ramanujan subspace is constructed to suppress periodic noise. Then, dynamic time warping and adaptive singular value decomposition are applied to eliminate remaining coherent and random noise. The method has been evaluated using both synthetic and field data, and its performance is compared with traditional microseismic denoising techniques, including bandpass filtering and empirical mode decomposition. [ABSTRACT FROM AUTHOR]

The document is a guest editorial discussing the importance of multiresolution analytics in analyzing data with fractal, chaotic, or complex characteristics. The editorial highlights how these techniques reveal hidden patterns and dependencies in various fields such as financial markets, economic indices, environmental data, and medical imaging. By examining data at different resolutions, multiresolution analytics enhance our understanding of complex systems and phenomena exhibiting self-similarity or irregular behavior. The editorial emphasizes the need for advanced statistical and computational tools to handle the complexity of modern data and the importance of integrating multiresolution analytics with machine learning and artificial intelligence for comprehensive data analysis. [Extracted from the article]

Image steganography is an extremely rich and significant exploration region that gives productive answers to some genuine and modern issues. This paper deals with secret image transmission and securing it from different attacks. The proposed image steganography technique uses a combination of image segmentation, pre-processing, and scrambling methos. The proposed technique begins with the segmentation of secret and cover images using Rubik's cube. The segmentation process breaks the images into six parts, then after they are stored in a segmented image dataset. In the second step, each segmented secret image is processed by the image pre-processing method. The proposed image pre-processing method helps the system select the appropriate cover image from the segmented cover image dataset, which makes the technique more robust. In the third step, before the embedding process, secret images are scrambled using the proposed scrambling method. Finally, the cover and secret images are processed by discrete wavelet transforms (DWT) and singular value decomposition (SVD) to generate the stego image. The proposed technique is tested on a variety of grayscale images, with peak signal-to-noise ratio (PSNR) values ranging from 32.27 to 30.77 (dB) and structural similarity index measure (SSIM) values ranging from 0.93 to 0.90 for different alpha values. The extracted secret image is analyzed using normalized correlation (NC) and naturalness image quality evaluator (NIQE) parameters. The NC values are above 0.99, and the NIQE values ranged from 3.3264 to 3.8468 for various extracted images. To analyze the proposed technique, measured value of entropy and an elapse time are 7.7956 and 6.489 s, respectively. Comparative studies are conducted in four main areas. The first comparative study tested the reliability of the proposed scrambling method by calculating the overall and diagonal correlation values of the secret image and scrambled secret image. The second comparative study tested the impeccability and reliability of the stego image by comparing its PSNR value to other stego images produced by other researchers. The third comparative study tested the effect of false-positive tests on extracted secret images. The results showed that the extracted secret image could only be obtained from the original stego image, indicating that the proposed method was secure against false positives. The fourth or last comparative study compares the computational load of proposed technique with previously developed techniques. In general, the results of the comparative study show that the proposed technique is reliable and secure for image scrambling, stego image generation, and secret image extraction. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusions Substantial improvements in spatial resolution in brain positron emission tomography (PET) scanners have greatly reduced partial volume effect, making head movement the main source of image blur. To achieve high‐resolution PET neuroimaging, precise real‐time estimation of both head position and orientation is essential for accurate motion compensation.A high‐resolution electromagnetic motion tracking (EMMT) system with an event‐by‐event motion correction is developed for PET‐CT scanners.EMMT is comprised of a source, an array of sensors, and a readout electronic unit (REU). The source acts as a transmitter and emits an EM dipole field. It is placed in close proximity to the sensor array and detects changes in EM flux density due to sensor movement. The REU digitizes signals from each sensor and captures precise rotational and translational movements in real time. Tracked motion in the EMMT coordinate system is synchronized with the PET list‐mode data and transformed into the scanner coordinate system by locating paired positions in both systems. The optimal rigid motion is estimated using singular value decomposition. The rigid motion and depth‐of‐interaction (DOI) parallax effect are corrected by event‐by‐event rebinning of mispositioned lines‐of‐response (LORs). We integrated the EMMT with our recently developed ultra‐high resolution Prism‐PET prototype brain scanner and a commercial Siemens Biograph mCT PET‐CT scanner. We assessed the imaging performance of the Prism‐PET/EMMT system using multi‐frame motion of point sources and phantoms. The mCT/EMMT system was validated using a set of point sources attached to both a mannequin head and a human volunteer, for simulating multiframe and continuous motions, respectively. Additionally, a human subject for [18F]MK6240 PET imaging was included.The tracking accuracy of the Prism‐PET/EMMT system was quantified as a root‐mean‐square (RMS) error of 0.49∘$^{\circ }$ for 100∘$^{\circ }$ axial rotations, and an RMS error of 0.15 mm for 100 mm translations.The percent difference (%diff) in average full width at half maximum (FWHM) of point source between motion‐corrected and static images, within a motion range of ±20∘$\pm 20^\circ$ and ±$\pm$10 mm from the center of the scanner's field‐of‐view (FOV), was 3.9%. The measured recovery coefficients of the 2.5‐mm diameter sphere in the activity‐filled partial volume correction phantom were 23.9%, 70.8%, and 74.0% for the phantom with multi‐frame motion, with motion and motion compensation, and without motion, respectively. In the mCT/EMMT system, the %diff in average FWHM of point sources between motion‐corrected and static images, within a motion range of ±30∘$\pm 30^\circ$ and ±$\pm$10 mm from the center of the FOV, was 14%. Applying motion correction to the [18F]MK6240 PET imaging reduced the motion‐induced spill‐in artifact in the lateral ventricle region, lowering its standardized uptake value ratio (SUVR) from 0.70 to 0.34.The proposed EMMT system is a cost‐effective, high frame‐rate, and none‐line‐of‐sight alternative to infrared camera‐based tracking systems and is capable of achieving high rotational and translational tracking accuracies for mitigating motion‐induced blur in high‐resolution brain dedicated PET scanners. [ABSTRACT FROM AUTHOR]

Variable heat fluxes over the Tibetan Plateau (TP) interact thermally with the atmosphere, affecting weather in surrounding areas, particularly in the Middle and Lower Yangtze River (MLYR). However, the circulation patterns and time-lag effects between TP heat fluxes and MLYR precipitation remain unclear. This study identified 577 large-scale daily heavy precipitation events (LSDHPEs) in the MLYR from 1980 to 2022. We analyzed the weather causation and spatiotemporal correlations between the TP surface heat fluxes and MLYR LSDHPEs using self-organizing map clustering, singular value decomposition, and harmonic analysis of time series. The results found two dominant synoptic patterns of LSDHPEs at 500 hPa: one, driven by anticyclonic and cyclonic circulations coinciding with shifts in the West Pacific subtropical high and South Asian high, increased from 2000 to 2022; the other, influenced by MLYR cyclonic circulation, showed a significant decrease. For the first time, we revealed lagged effects of the latent heat anomalies (with a lag time of 1–10 d and 130–200 d) and sensible heat anomalies (with a lag time of 2–4 months) over the TP during LSDHPEs in the MLYR. The results may enhance our understanding of TP heat flux anomalies as precursor signals for early warning of heavy rainfall and flooding in the MLYR. [ABSTRACT FROM AUTHOR]

Considering that the cohort effect is added to the population mortality prediction model, the data information on the impact of birth year on mortality can be captured. In this article, the Linear Link model (LL) is extended to obtain the Extended Linear Link model (ELL) with cohort effect. Model fitting and forecasting employ a two-stage approach, first estimating the age term parameters of the extended model, and then forecasting the time parameters using an optimal combination ARIMA(p,d,q) of time series model. Using the mortality data of the Chinese population aged 0–89 from 1995 to 2018 to fit and predict the model, and to test the stability by rolling-window time frame. The results show that when the singular value decomposition method and the least square estimation method are adopted, the prediction effect of the Extended Linear Link model with cohort effect is significantly better than the previous Linear Link model, and the accuracy of fitting and predicting are improved. [ABSTRACT FROM AUTHOR]

The mining face under the close coal seam group is affected by the superposition of the concentrated stress of the overlying residual diagonally intersecting coal pillar and the mining stress, which can easily cause the instability and damage of the section coal pillars during the process of mining back to the downward face. Additionally, the traditional methods of monitoring such as numerical simulation, drilling peeping, and acoustic emission fail to realize the real-time and accurate deformation monitoring of the internal deformation of the section coal pillars. The introduction of the drill-hole-implanted fiber-optic grating monitoring method can realize real-time deformation monitoring for the whole area inside the coal pillar, which solves the short board problem of coal pillar deformation monitoring. However, fiber-optic monitoring is easily disturbed by the external environment, which is especially sensitive to the background noise of the complex underground mining environment. Therefore, taking the live chicken and rabbit well of Shaanxi Daliuta Coal Mine as the engineering background, the ensemble empirical modal decomposition (EEMD) is introduced for primary noise reduction and signal reconstruction by the threshold determination (DE) algorithm, and then the singular matrix decomposition (SVD) is introduced for secondary noise reduction. Finally, a machine learning algorithm is combined with the noise reduction algorithm for the prediction of the fiber grating strain signals of coal pillar in a zone, and DBO-LSTM-BP is constructed as the prediction model. The experimental results demonstrate that compared with the other two noise reduction prediction models, the SNR of the EEMD-DE-SVD-DBO-LSTM-BP model is improved by 0.8–2.3 dB on average, and the prediction accuracy is in the range of 88–99%, which realizes the over-advanced prediction of the deformation state of the coal column in the section. [ABSTRACT FROM AUTHOR]

As an important component of road traffic facilities, bridges play a crucial role in daily traffic operations, and changes in their status can have an impact on traffic operation. The existing research mainly focuses on monitoring the status of bridges themselves or analyzing the operation status of road traffic, and rarely considers the changes in traffic operation status caused by changes in bridge status. Therefore, in order to evaluate the impact relationship between the two, this article designs an algorithm that combines the Analytic Hierarchy Process (AHP), the Delphi method, and Singular Value Decomposition (SVD) based on the traditional evaluation of bridges and road traffic operation status, and establishes a bridge traffic operation status impact assessment model. Then, simulation analysis and actual data verification will be conducted based on the specific situation of Ma'anshi Bridge on the Chongqing Wuhan Expressway. The experimental results show that the evaluation model established in this paper conforms to the characteristics of traffic operation, can reflect the impact of bridge state changes on traffic operation status well, effectively promote the automation level of bridge traffic impact management, and has high reliability and accuracy. [ABSTRACT FROM AUTHOR]

In this study, we obtained results for the computation of eigen-pairs, singular value decomposition, pseudoinverse, and the least squares problem for elliptic quaternion matrices. Moreover, we established algorithms based on these results and provided illustrative numerical experiments to substantiate the accuracy of our conclusions. In the experiments, it was observed that the p-value in the algebra of elliptic quaternions directly affects the performance of the problem under consideration. Selecting the optimal p-value for problem-solving and the elliptic behavior of many physical systems make this number system advantageous in applied sciences. [ABSTRACT FROM AUTHOR]

In this paper, we perform a cryptanalysis of a previously published symmetric cryptosystem that utilizes the Arnold transform (AT), singular value decomposition (SVD), and the fractional Hartley transform (FrHT) domain. By performing an attack-analysis, it is shown that the symmetric cryptosystem is vulnerable to a Known-Plaintext Attack (KPA). If an attacker knows the cipher-text and the order of the FrHT, attacker can recover the correct combination of SVD and AT parameters used in the image encryption algorithm. To overcome the security weakness of the symmetric cryptosystem, we propose an asymmetric phase image encryption by employing the Arnold transform, Singular value decomposition, and Hessenberg decomposition (HD) in the FrHT domain. The scheme is validated using evaluation metrics; mean-square-error, peak signal-to-noise, entropy, 3D mesh-, correlation-, noise-attack-, occlusion-attack-, and linear-attack analyses. Keys generated by SVD and HD make the cryptosystem asymmetric, making it resistant to Known-Plaintext Attack (KPA) and Chosen-Plaintext Attack (CPA). [ABSTRACT FROM AUTHOR]

In a recent study, the eigenvalue numbers of Case eigenfunctions were investigated for the situation of anisotropic scattering. The results of this study is applied to the criticality problem. The investigation is performed with two different methods: the singular value decomposition (SVD) method and the H N method. The former was recently improved using the SVD to solve the matrix of the problem, and the latter is based on the usage of the Case method for proper boundary conditions. We have two different eigenvalues for the given number of secondary neutrons and the scattering parameter. Either these eigenvalues are both complex, or one is complex and one is real. The investigation is to show the effect of the second eigenvalues over the critical thickness values. [ABSTRACT FROM AUTHOR]

ABSTRACT The cancer atlas edited by several countries is the main resource for the analysis of the geographic variation of cancer risk. Correlating the observed spatial patterns with known or hypothesized risk factors is time‐consuming work for epidemiologists who need to deal with each cancer separately, breaking down the patterns according to sex and race. The recent literature has proposed to study more than one cancer simultaneously looking for common spatial risk factors. However, this previous work has two constraints: they consider only a very small (2–4) number of cancers previously known to share risk factors. In this article, we propose an exploratory method to search for latent spatial risk factors of a large number of supposedly unrelated cancers. The method is based on the singular value decomposition and nonnegative matrix factorization, it is computationally efficient, scaling easily with the number of regions and cancers. We carried out a simulation study to evaluate the method's performance and apply it to cancer atlas from the USA, England, France, Australia, Spain, and Brazil. We conclude that with very few latent maps, which can represent a reduction of up to 90% of atlas maps, most of the spatial variability is conserved. By concentrating on the epidemiological analysis of these few latent maps a substantial amount of work is saved and, at the same time, high‐level explanations affecting many cancers simultaneously can be reached. [ABSTRACT FROM AUTHOR]

Alfalfa (Medicago sativa L.) ‐ grass or alfalfa ‐ sainfoin (Onobrychis viciifolia Scop.) mixtures are commonly used for pastures in western Canada because of their high forage quality and their low risk of causing frothy bloat in grazing animals. However, the proportion of these two legumes declines in mixed forage stands over time. The objective of this study was to evaluate the effectiveness of selecting superior genotypes from clonally propagated alfalfa or sainfoin under plant competition in different growth environments. For this study, around 100 genotypes of each legume were cloned and transplanted into meadow bromegrass (Bromus riparius Relm.) or alfalfa swards at Saskatoon, SK and Lethbridge, AB, Canada in 2017. Genotype‐environment (G x E) interactions of alfalfa and sainfoin genotypes were analysed by an additive main‐effects and multiplicative interaction (AMMI) model. Significant variations in plant height, spring vigour and total dry matter yield (TDM) were observed for both species. Among the measured traits, plant height was a more highly heritable trait (H2 =.16 for alfalfa and H2 =.18 for sainfoin), while TDM was the least heritable (H2 =.08 for alfalfa and H2 =.04 for sainfoin). In the AMMI Analysis of variance for TDM, the genotype explained <12% of the variation for both species, suggesting a direct selection of yield would result in a low genetic gain. The biplot mean performance (Y) x weighted average of absolute scores from the singular value decomposition of the matrix of BLUPs stability index (WAASB) identified several promising genotypes with superior performance and stability across different environments that were selected for producing synthetic lines. However, the synthetic lines of both alfalfa and sainfoin did not consistently exhibit superior performance in mixtures compared to their respective check cultivars. [ABSTRACT FROM AUTHOR]

We consider the composition of operators with non-closed range in Hilbert spaces and how the nature of ill-posedness is affected by their composition. Specifically, we study the Hausdorff-, Cesàro-, integration operator, and their adjoints, as well as some combinations of those. For the composition of the Hausdorff- and the Cesàro-operator, we give estimates of the decay of the corresponding singular values. As a curiosity, this provides also an example of two practically relevant non-compact operators, for which their composition is compact. Furthermore, we characterize those operators for which a composition with a non-compact operator gives a compact one. [ABSTRACT FROM AUTHOR]

Resolvent analysis is a powerful tool that can reveal the linear amplification mechanisms between the forcing inputs and the response outputs about a base flow. These mechanisms can be revealed in terms of a pair of forcing and response modes and the associated energy gains (amplification magnitude) at a given frequency. The linear relationship that ties the forcing and the response is represented through the resolvent operator (transfer function), which is constructed through spatially discretizing the linearized Navier–Stokes operator. One of the unique strengths of resolvent analysis is its ability to analyze statistically stationary turbulent flows. In light of the increasing interest in using resolvent analysis to study a variety of flows, we offer this guide in hopes of removing the hurdle for students and researchers to initiate the development of a resolvent analysis code and its applications to their problems of interest. To achieve this goal, we discuss various aspects of resolvent analysis and its role in identifying dominant flow structures about the base flow. The discussion in this paper revolves around the compressible Navier–Stokes equations in the most general manner. We cover essential considerations ranging from selecting the base flow and appropriate energy norms to the intricacies of constructing the linear operator and performing eigenvalue and singular value decompositions. Throughout the paper, we offer details and know-how that may not be available to readers in a collective manner elsewhere. Towards the end of this paper, examples are offered to demonstrate the practical applicability of resolvent analysis, aiming to guide readers through its implementation and inspire further extensions. We invite readers to consider resolvent analysis as a companion for their research endeavors. [ABSTRACT FROM AUTHOR]

There is a large surface-groundwater exchange downstream of wastewater treatment plants (WWTPs), and antibiotics upstream may influence sites downstream of rivers. Thus, samples from 9 effluent-receiving urban rivers (ERURs) and 12 groundwater sites were collected in Shijiazhuang City in December 2020 and April 2021. For ERURs, 8 out of 13 target quinolone antibiotics (QNs) were detected, and the total concentration of QNs in December and April were 100.6-4,398 ng/L and 8.02–2,476 ng/L, respectively. For groundwater, all target QNs were detected, and the total QNs concentration was 1.09–23.03 ng/L for December and 4.54–170.3 ng/L for April. The distribution of QNs was dissimilar between ERURs and groundwater. Most QN concentrations were weakly correlated with land use types in the system. The results of a positive matrix factorization model (PMF) indicated four potential sources of QNs in both ERURs and groundwater, and WWTP effluents were the main source of QNs. From December to April, the contribution of WWTP effluents and agricultural emissions increased, while livestock activities decreased. Singular value decomposition (SVD) results showed that the spatial variation of most QNs was mainly contributed by sites downstream (7.09%-88.86%) of ERURs. Then, a new method that combined the results of SVD and PMF was developed for a specific-source-site risk quotient (SRQ), and the SRQ for QNs was at high level, especially for the sites downstream of WWTPs. Regarding temporal variation, the SRQ for WWTP effluents, aquaculture, and agricultural emissions increased. Therefore, in order to control the antibiotic pollution, more attention should be paid to WWTP effluents, aquaculture, and agricultural emission sources for the benefit of sites downstream of WWTPs. [ABSTRACT FROM AUTHOR]

In recent years, the non-invasive load monitoring (NILM) method based on sparse coding has shown promising research prospects. This type of method learns a sparse dictionary for each monitoring target device, and it expresses load decomposition as a problem of signal reconstruction using dictionaries and sparse vectors. The existing NILM methods based on sparse coding have problems such as inability to be applied to multi-state and time-varying devices, single-load characteristics, and poor recognition ability for similar devices in distributed manners. Using the analysis above, this paper focuses on devices with similar features in households and proposes a distributed non-invasive load monitoring method using Karhunen–Loeve (KL) feature extraction and an improved deep dictionary. Firstly, Karhunen–Loeve expansion (KLE) is used to perform subspace expansion on the power waveform of the target device, and a new load feature is extracted by combining singular value decomposition (SVD) dimensionality reduction. Afterwards, the states of all the target devices are modeled as super states, and an improved deep dictionary based on the distance separability measure function (DSM-DDL) is learned for each super state. Among them, the state transition probability matrix and observation probability matrix in the hidden Markov model (HMM) are introduced as the basis for selecting the dictionary order during load decomposition. The KL feature matrix of power observation values and improved depth dictionary are used to discriminate the current super state based on the minimum reconstruction error criterion. The test results based on the UK-DALE dataset show that the KL feature matrix can effectively reduce the load similarity of devices. Combined with DSM-DDL, KL has a certain information acquisition ability and acceptable computational complexity, which can effectively improve the load decomposition accuracy of similar devices, quickly and accurately estimating the working status and power demand of household appliances. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Magnetic Resonance Imaging (MRI) is a highly demanded medical imaging system due to high resolution, large volumetric coverage, and ability to capture the dynamic and functional information of body organs e.g. cardiac MRI is employed to assess cardiac structure and evaluate blood flow dynamics through the cardiac valves. Long scan time is the main drawback of MRI, which makes it difficult for the patients to remain still during the scanning process. Objective: By collecting fewer measurements, MRI scan time can be shortened, but this undersampling causes aliasing artifacts in the reconstructed images. Advanced image reconstruction algorithms have been used in literature to overcome these undersampling artifacts. These algorithms are computationally expensive and require a long time for reconstruction which makes them infeasible for real-time clinical applications e.g. cardiac MRI. However, exploiting the inherent parallelism in these algorithms can help to reduce their computation time. Methods: Low-rank plus sparse (L+S) matrix decomposition model is a technique used in literature to reconstruct the highly undersampled dynamic MRI (dMRI) data at the expense of long reconstruction time. In this paper, Compressed Singular Value Decomposition (cSVD) model is used in L+S decomposition model (instead of conventional SVD) to reduce the reconstruction time. The results provide improved quality of the reconstructed images. Furthermore, it has been observed that cSVD and other parts of the L+S model possess highly parallel operations; therefore, a customized GPU based parallel architecture of the modified L+S model has been presented to further reduce the reconstruction time. Results: Four cardiac MRI datasets (three different cardiac perfusion acquired from different patients and one cardiac cine data), each with different acceleration factors of 2, 6 and 8 are used for experiments in this paper. Experimental results demonstrate that using the proposed parallel architecture for the reconstruction of cardiac perfusion data provides a speed-up factor up to 19.15× (with memory latency) and 70.55× (without memory latency) in comparison to the conventional CPU reconstruction with no compromise on image quality. Conclusion: The proposed method is well-suited for real-time clinical applications, offering a substantial reduction in reconstruction time. [ABSTRACT FROM AUTHOR]

Objective: The primary aim of this investigation was to devise an intelligent approach for interpreting and measuring the spatial orientation of semicircular canals based on cranial MRI. The ultimate objective is to employ this intelligent method to construct a precise mathematical model that accurately represents the spatial orientation of the semicircular canals. Methods: Using a dataset of 115 cranial MRI scans, this study employed the nnDetection deep learning algorithm to perform automated segmentation of the semicircular canals and the eyeballs (left and right). The center points of each semicircular canal were organized into an ordered structure using point characteristic analysis. Subsequently, a point-by-point plane fit was performed along these centerlines, and the normal vector of the semicircular canals was computed using the singular value decomposition method and calibrated to a standard spatial coordinate system whose transverse planes were the top of the common crus and the bottom of the eyeballs. Results: The nnDetection target recognition segmentation algorithm achieved Dice values of 0.9585 and 0.9663. The direction angles of the unit normal vectors for the left anterior, lateral, and posterior semicircular canal planes were [80.19°, 124.32°, 36.08°], [169.88°, 100.04°, 91.32°], and [79.33°, 130.63°, 137.4°], respectively. For the right side, the angles were [79.03°, 125.41°, 142.42°], [171.45°, 98.53°, 89.43°], and [80.12°, 132.42°, 44.11°], respectively. Conclusion: This study successfully achieved real-time automated understanding and measurement of the spatial orientation of semicircular canals, providing a solid foundation for personalized diagnosis and treatment optimization of vestibular diseases. It also establishes essential tools and a theoretical basis for future research into vestibular function and related diseases. [ABSTRACT FROM AUTHOR]

Purpose: In this work, the use of joint Total Generalized Variation (TGV) regularization to improve Multipool‐Lorentzian fitting of chemical exchange saturation transfer (CEST) Spectra in terms of stability and parameter signal‐to‐noise ratio (SNR) was investigated. Theory and Methods: The joint TGV term was integrated into the nonlinear parameter fitting problem. To increase convergence and weight the gradients, preconditioning using a voxel‐wise singular value decomposition was applied to the problem, which was then solved using the iteratively regularized Gauss–Newton method combined with a Primal‐Dual splitting algorithm. The TGV method was evaluated on simulated numerical phantoms, 3T phantom data and 7T in vivo data with respect to systematic errors and robustness. Three reference methods were also implemented: The standard nonlinear fitting, a method using a nonlocal‐means filter for denoising and the pyramid scheme, which uses downsampled images to acquire accurate start values. Results: The proposed regularized fitting method showed significantly improved robustness (compared to the reference methods). In testing, over a range of SNR values the TGV fit outperformed the other methods and showed accurate results even for large amounts of added noise. Parameter values found were closer or comparable to the ground truth. For in vivo datasets, the added regularization increased the parameter map SNR and prevented instabilities. Conclusion: The proposed fitting method using TGV regularization leads to improved results over a range of different data‐sets and noise levels. Furthermore, it can be applied to all Z‐spectrum data, with different amounts of pools, where the improved SNR and stability can increase diagnostic confidence. [ABSTRACT FROM AUTHOR]

The traditional nonlinear aerostatic instability of long-span bridges is based on a two-layer iteration method that accurately predicts the structural equilibrium path before the critical buckling point. Due to strong nonlinearity after buckling, this traditional method cannot easily calculate the structural equilibrium and possible bifurcation using either Newton–Raphson or arc-length methods. In this study, a reduced order modeling (ROM) method for long-span bridge aerostatic deformation is proposed to approximate the bridge aerostatic equilibrium path after the critical point. The structural deformation mode shapes are extracted through singular value decomposition performed on the deformation matrix, and the nonlinear structural stiffness matrix is determined through the indirect displacement-based method on the finite-element method (FEM) platform. The ROM method is validated through comparison against the aerostatic deformation by the traditional two-layer iteration method based on FEM. By extending to higher wind speed, the ROM method can approximate the bridge deformation after initial buckling, and pitchfork bifurcation is observed after the structure undergoes rapid deformation growth. The stability of the equilibrium paths is examined through the Jacobian of restoring force vector, and the "snap-through" phenomenon exists for the equilibrium path before the bifurcation point. [ABSTRACT FROM AUTHOR]

The traditional method of computing singular value decomposition (SVD) of a data matrix is based on the least squares principle and is, therefore, very sensitive to the presence of outliers. Hence, the resulting inferences across different applications using the classical SVD are extremely degraded in the presence of data contamination. In particular, background modelling of video surveillance data in the presence of camera tampering cannot be reliably solved by the classical SVD. In this paper, we propose a novel robust singular value decomposition technique based on the popular minimum density power divergence estimator. We have established the theoretical properties of the proposed estimator such as convergence, equivariance and consistency under the high-dimensional regime where both the row and column dimensions of the data matrix approach infinity. We also propose a fast and scalable algorithm based on alternating weighted regression to obtain the estimate. Within the scope of our fairly extensive simulation studies, our method performs better than existing robust SVD algorithms. Finally, we present an application of the proposed method on the video surveillance background modelling problem. [ABSTRACT FROM AUTHOR]

An accurate characterization of the nonlinear behavior of stock markets is of prime importance for the proper design of investment portfolios and the pricing of financial instruments. Surrogate data analysis based on phase randomization and singular value decomposition (SVD) was used to test for multiresolution nonlinear dynamics in the US stock market. The study pointed to the presence of nonlinear dynamics acting at different time scales. Nonlinearity is stronger for larger time horizons and is directly linked to the diversity of patterns, quantified in terms of the SVD entropy, displayed by returns. [ABSTRACT FROM AUTHOR]

Summary: Matrix factorization (MF)‐based recommender systems (RSs) as black‐box models fail to provide explanations for the recommended items. While some models attain a degree of explainability by integrating neighborhood algorithms, which compute explainability based on the preferences of proximate users, they overlook the contribution of the subjective preferences of the target user to enhancing model explainability, resulting in suboptimal model explainability. To address this problem, an explainable RS directed by reconstructed explanatory factors and multi‐modal matrix factorization (ERS‐REFMMF) is proposed. By integrating users' subjective sentiment and preference features into the rating matrix to form a multi‐modal matrix, ERS‐REFMMF utilizes the Funk‐singular value decomposition method at the foundational layer to decompose the multi‐modal matrix and generate a candidate item set. At the upper layer, explainability is constructed based on the target user's subjective preferences and latent features derived from MF, and the final recommended list is optimized for accuracy, diversity, novelty, and explainability through multi‐objective optimization algorithms. ERS‐REFMMF models around users' explicit preferences and latent associations, reconstructs explainability with hybrid factors, and enhances overall performance through a many‐objective optimization algorithm. Experimental results on real datasets demonstrate that the proposed model is competitive in both phases compared to existing recommendation methods. [ABSTRACT FROM AUTHOR]

Single-image haze removal is an essential preprocessing phase in many object detection and segmentation approaches. Recently, end-to-end deep learning-based approaches have dominated the field of single-image dehazing because of their superiority in recovering clear images corrupted by different types of degradation. However, training an effective dehazing network remains challenging, particularly in the absence of high-quality realistic training datasets. In this paper, a novel approach called a subspace-based dehazing generative adversarial network (SuDGAN) is proposed. Traditional training methods attempt to apply changes to pixel intensities, whereas SuDGAN adopts a novel training approach using existing synthetic datasets to learn the adjustment of subspace components related to haze. This approach enables the network to learn more discriminative haze-aware features and focus on adjusting the components that are more affected by haze (luminance) while preserving those that are less influenced by haze (structure). The proposed SuDGAN, along with several state-of-the-art approaches, is evaluated on various challenging synthetic and realistic datasets using haze-related and traditional evaluation metrics. The experimental results demonstrate the efficiency of SuDGAN in removing haze and producing visually pleasing results. Furthermore, the results show that SuDGAN has clear quantitative and qualitative improvements over most state-of-the-art dehazing approaches. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusions Perfusion magnetic resonance imaging (MRI)s plays a central role in the diagnosis and monitoring of neurovascular or neurooncological disease. However, conventional processing techniques are limited in their ability to capture relevant characteristics of the perfusion dynamics and suffer from a lack of standardization.We propose a physics‐informed deep learning framework which is capable of analyzing dynamic susceptibility contrast perfusion MRI data and recovering the dynamic tissue response with high accuracy.The framework uses physics‐informed neural networks (PINNs) to learn the voxel‐wise TRF, which represents the dynamic response of the local vascular network to the contrast agent bolus. The network output is stabilized by total variation and elastic net regularization. Parameter maps of normalized cerebral blood flow (nCBF) and volume (nCBV) are then calculated from the predicted residue functions. The results are validated using extensive comparisons to values derived by conventional Tikhonov‐regularized singular value decomposition (TiSVD), in silico simulations and an in vivo dataset of perfusion MRI exams of patients with high‐grade gliomas.The simulation results demonstrate that PINN‐derived residue functions show a high concordance with the true functions and that the calculated values of nCBF and nCBV converge towards the true values for higher contrast‐to‐noise ratios. In the in vivo dataset, we find high correlations between conventionally derived and PINN‐predicted perfusion parameters (Pearson's rho for nCBF: 0.84±0.03$0.84 \pm 0.03$ and nCBV: 0.92±0.03$0.92 \pm 0.03$) and very high indices of image similarity (structural similarity index for nCBF: 0.91±0.03$0.91 \pm 0.03$ and for nCBV: 0.98±0.00$0.98 \pm 0.00$).PINNs can be used to analyze perfusion MRI data and stably recover the response functions of the local vasculature with high accuracy. [ABSTRACT FROM AUTHOR]

Solar-induced chlorophyll fluorescence (SIF) is highly correlated with photosynthesis and can be used for estimating gross primary productivity (GPP) and monitoring vegetation stress. The far-red band of the solar Fraunhofer lines (FLs) is close to the strongest SIF emission peak and is unaffected by chlorophyll absorption, making it suitable for SIF intensity retrieval. In this study, we propose a retrieval window for far-red SIF, significantly enhancing the sensitivity of data-driven methods to SIF signals near 757 nm. This window introduces a weak O2 absorption band based on the FLs window, allowing for better separation of SIF signals from satellite spectra by altering the shape of specific singular vectors. Additionally, a frequency shift correction algorithm based on standard non-shifted reference spectra is proposed to discuss and eliminate the influence of the Doppler effect. SIF intensity retrieval was achieved using data from the GOSAT satellite, and the retrieved SIF was validated using GPP, enhanced vegetation index (EVI) from the MODIS platform, and published GOSAT SIF products. The validation results indicate that the SIF products provided in this study exhibit higher fitting goodness with GPP and EVI at high spatiotemporal resolutions, with improvements ranging from 55% to 129%. At low spatiotemporal resolutions, the SIF product provided in this study shows higher consistency with EVI and GPP spatially. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel four-dimensional (4D) parameter estimation method to localize the mixed far-field (FF) and near-field (NF) targets using bistatic MIMO arrays and higher-order singular value decomposition (HOSVD). The estimated four parameters include the angle-of-departure (AOD), angle-of-arrival (AOA), range-of-departure (ROD), and range-of-arrival (ROA). In the method, we store array data in a tensor form to preserve the inherent multidimensional properties of the array data. First, the observation data are arranged into a third-order tensor and its covariance tensor is calculated. Then, the HOSVD of the covariance tensor is performed. From the left singular vector matrices of the corresponding module expansion of the covariance tensor, the subspaces with respect to transmit and receive arrays are obtained, respectively. The AOD and AOA of the mixed FF and NF targets are estimated with signal-subspace, and the ROD and ROA of the NF targets are achieved using noise-subspace. Finally, the estimated four parameters are matched via a pairing method. The Cramér–Rao lower bound (CRLB) of the mixed target parameters is also derived. The numerical simulations demonstrate the superiority of the tensor-based method. [ABSTRACT FROM AUTHOR]

The meniscus-confined electrodeposition (MCED) technique offers advantages such as low cost and wide applicability, making it a promising method in the field of micro/nanofabrication. However, unstable meniscal morphology and poor deposition quality during planar deposition in MCED necessitate the development of improved methods. Therefore, a planar adaptive micro-tuning deposition method (PAMTDM), which utilizes the positioning technology of scanning electrochemical cell microscopy (SECCM) and employs a singular value decomposition (SVD) planar fitting method to determine the flatness of the deposition plane, is proposed. An adaptive micro-tuning motion mode was proposed by analyzing the variation patterns of the meniscus. Moreover, a combination of multi-physics finite element simulations and orthogonal experimental methods was introduced to determine the optimal motion parameters. The experimental results demonstrate that the PAMTDM effectively addresses the issues encountered during planar growth. Compared to the point-by-point deposition method, the PAMTDM achieves a threefold increase in deposition speed for continuous deposition of 105-μm-long line segments in two-dimensional planes, with a deposition current error of less than 0.2 nA. In conclusion, the proposed method provides significant insights into the broad future applications of MCED. [ABSTRACT FROM AUTHOR]

Hyperthermia induces slight temperature increase of 4–8 °C inside the tumor, making it more responsive to radiation and drugs, thereby improving the outcome of the oncological treatment. To verify the level of heat in the tumor and to avoid damage of the healthy tissue, methods for non-invasive temperature monitoring are needed. Temperature estimation by means of microwave imaging is of great interest among the scientific community. In this paper, we present the results of experiments based on ultra-wideband (UWB) M-sequence technology. Our temperature estimation approach uses temperature dependency of tissue dielectric properties and relation of UWB images to the reflection coefficient on the boundary between tissue types. The realistic measurement setup for neck cancer hyperthermia considers three antenna arrangements. Data are processed with Delay and Sum beamforming and Truncated Singular Value Decomposition. Two types of experiments are presented in this paper. In the first experiment, relative permittivity of subsequently replaced tumor mimicking material is estimated, and in the second experiment, real temperature change in the tumor imitate is monitored. The results showed that the presented approach allows for qualitative as well as quantitative permittivity and temperature estimation. The frequency range for temperature estimation, preferable antenna configurations, and limitations of the method are indicated. [ABSTRACT FROM AUTHOR]

We show that the spectral theorem – which we understand to be a statement that every self-adjoint matrix admits a certain type of canonical form under unitary similarity – admits analogues over other $ * $ ∗ -algebras distinct from the complex numbers. If these $ * $ ∗ -algebras contain nilpotents, then it is shown that there is a consistent way in which many classic matrix decompositions – such as the Singular Value Decomposition, the Takagi decomposition, the skew-Takagi decomposition, and the Jordan decomposition, among others – are immediate consequences of these. If producing the relevant canonical form of a self-adjoint matrix were a subroutine in some programming language, then the corresponding classic matrix decomposition would be a 1-line invocation with no additional steps. We also suggest that by employing operator overloading in a programming language, a numerical algorithm for computing a unitary diagonalization of a complex self-adjoint matrix would generalize immediately to solving problems like SVD or Takagi. While algebras without nilpotents (like the quaternions) allow for similar unifying behaviour, the classic matrix decompositions which they unify are never obtained as easily. In the process of doing this, we develop some spectral theory over Clifford algebras of the form $ Cl_{p,q,0}(\mathbb R) $ C l p , q , 0 (R) and $ Cl_{p,q,1}(\mathbb R) $ C l p , q , 1 (R) where the former is admittedly quite easy. We propose a broad conjecture about spectral theorems. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the Cubature Kalman filter (CKF) algorithm has a non-positive definite of the covariance matrix in GNSS/SINS integrated navigation, which leads to the failure of Cholesky decomposition and the inability to estimate GNSS measurement noise, a backward smoothing adaptive CKF integrated navigation algorithm based on Singular Value Decomposition (SVD) is proposed. Backward smoothing adaptive CKF based on Singular Value Decomposition (BS-A-SVDCKF) algorithm replaces the Cholesky decomposition in standard CKF with SVD, constructs the test threshold for backward smoothing by using the relatively stable noise of the SINS system, and adaptively estimates based on different measurement characteristics when the GNSS measurement noise changes abruptly. Experimental results show that compared with adaptive SVDCKF (A-SVDCKF), backward smoothing SVDCKF (BS-SVDCKF) and SVDCKF, the average positioning accuracy of the proposed algorithm is improved by 32.12%, 57.07% and 68.66%, the average operating efficiency is reduced by 2.19% compared with SVDCKF, and 0.57% and 3.60% higher than that of BS-SVDCKF and A-SVDCKF, which improves the positioning accuracy of the integrated navigation system and ensures real-time. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As lithium-ion batteries are widely used in electric vehicles, safety accidents caused by battery failures emerge one after another. Nevertheless, failures caused by changes in the internal structure or characteristics of the battery, such as sudden and progressive failures, are still a serious problem for electric vehicles, challenging existing fault diagnosis methods. This paper first performs wavelet packet decomposition on the battery's raw voltage signal to obtain high-quality low-frequency and high-frequency characteristic signal components. Then performs singular value decomposition on the characteristic signal components to extract the corresponding singular value characteristic parameters, and introduces the Manhattan average distance algorithm to battery faults. Diagnosing and locating faulty battery units using the Laida criterion (3-σ criterion) outlier detection method. Finally, actual vehicle data were used to verify the reliability, stability, accuracy of the method, and compared with the traditional Manhattan distance, correlation coefficient, information entropy methods. The method in this paper has good fault detection effects on vehicles with sudden and progressive faults vehicles. [ABSTRACT FROM AUTHOR]

The use of process data in assessment has gained attention in recent years as more assessments are administered by computers. Process data, recorded in computer log files, capture the sequence of examinees' response activities, for example, timestamped keystrokes, during the assessment. Traditional measurement methods are often inadequate for handling this type of data. In this paper, we proposed a sequential reservoir method (SRM) based on a reservoir computing model using the echo state network, with the particle swarm optimization and singular value decomposition as optimization. Designed to regularize features from process data through a computational self‐learning algorithm, this method has been evaluated using both simulated and empirical data. Simulation results suggested that, on one hand, the model effectively transforms action sequences into standardized and meaningful features, and on the other hand, these features are instrumental in categorizing latent behavioral groups and predicting latent information. Empirical results further indicate that SRM can predict assessment efficiency. The features extracted by SRM have been verified as related to action sequence lengths through the correlation analysis. This proposed method enhances the extraction and accessibility of meaningful information from process data, presenting an alternative to existing process data technologies. [ABSTRACT FROM AUTHOR]

Medical image security is paramount in the digital era but remains a significant challenge. This paper introduces an innovative zero-watermarking methodology tailored for medical imaging, ensuring robust protection without compromising image quality. We utilize Sped-up Robust features for high-precision feature extraction and singular value decomposition (SVD) to embed watermarks into the frequency domain, preserving the original image's integrity. Our methodology uniquely encodes watermarks in a non-intrusive manner, leveraging the robustness of the extracted features and the resilience of the SVD approach. The embedded watermark is imperceptible, maintaining the diagnostic value of medical images. Extensive experiments under various attacks, including Gaussian noise, JPEG compression, and geometric distortions, demonstrate the methodology's superior performance. The results reveal exceptional robustness, with high Normalized Correlation (NC) and Peak Signal-to-noise ratio (PSNR) values, outperforming existing techniques. Specifically, under Gaussian noise and rotation attacks, the watermark retrieved from the encrypted domain maintained an NC value close to 1.00, signifying near-perfect resilience. Even under severe attacks such as 30% cropping, the methodology exhibited a significantly higher NC compared to current state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Due to their significant influence on large‐scale atmospheric circulation and climate anomalies, the variability of Arctic sea ice and Eurasian snow cover during late autumn and their combined effects have garnered increasing attention. This study aims to investigate the physical mechanism underlying the covariation among the Barents‐Kara Seas (BKS) sea ice concentration (SIC), Eurasian snow cover extent (SCE) and the ensuing winter Eurasian surface air temperature (SAT). The statistics results of singular value decomposition suggest a significant linkage between the decreased BKS SIC, zonal “negative–positive” dipole SCE anomalies over Eurasia in November and cold Eurasian SAT in January–February (JF). Observational diagnosis analyses about the meridional moisture, heat transport and surface heat flux demonstrate that subpolar Eurasian anticyclonic circulation plays a crucial role in connecting the predominant modes of SIC and SCE. Furthermore, the BKS SIC and Eurasian SCE anomalies can jointly excite upward‐propagating planetary waves into the stratosphere, while simultaneously reducing the subpolar meridional temperature gradient. This results in westerly wind deceleration and favours the continuous planetary wave propagation. Consequently, the stratospheric polar vortex is significantly weakened, along with negative Northern Annular Mode anomalies propagating downward from the stratosphere to troposphere. Negative‐phase Arctic Oscillation anomalies correspondingly develop during JF, resulting in widespread cold anomalies over the Eurasian continent. These results are further confirmed by numerical sensitivity experiments from the Community Atmosphere Model forced by the above mentioned SIC and SCE anomalies. The empirical hindcast model analyses further suggest that the prediction skill of JF Eurasian SAT is enhanced when both the November BKS SIC and Eurasian SCE signals are considered. [ABSTRACT FROM AUTHOR]

Drug-induced gene expression profiles can identify potential mechanisms of toxicity. We focus on obtaining signatures for cardiotoxicity of FDA-approved tyrosine kinase inhibitors (TKIs) in human induced-pluripotent-stem-cell-derived cardiomyocytes, using bulk transcriptomic profiles. We use singular value decomposition to identify drug-selective patterns across cell lines obtained from multiple healthy human subjects. Cellular pathways affected by cardiotoxic TKIs include energy metabolism, contractile, and extracellular matrix dynamics. Projecting these pathways to published single cell expression profiles indicates that TKI responses can be evoked in both cardiomyocytes and fibroblasts. Integration of transcriptomic outlier analysis with whole genomic sequencing of our six cell lines enables us to correctly reidentify a genomic variant causally linked to anthracycline-induced cardiotoxicity and predict genomic variants potentially associated with TKI-induced cardiotoxicity. We conclude that mRNA expression profiles when integrated with publicly available genomic, pathway, and single cell transcriptomic datasets, provide multiscale signatures for cardiotoxicity that could be used for drug development and patient stratification. Using a new computational pipeline for identification of drug-selective transcriptomic responses and FAERS data, the authors identified potential pathways and genomic variants indicative of cancer drug cardiotoxicity in iPSC-derived cardiomyocytes. [ABSTRACT FROM AUTHOR]

9% Ni steel is commonly utilised in liquefied natural gas storage tanks due to its strong resistance to corrosion and oxidation. However, the welds in these tanks are prone to serious defects, necessitating thorough non-destructive testing. Unfortunately, when inspecting nickel-based alloy welds using phased array ultrasonic testing (PAUT) technology the ultrasonic waves are scattered and attenuated because of the coarse columnar grains. To address this challenge, this paper introduces a based on SVD enhancement directivity corrected circular coherence factor weighted TFM imaging algorithm (SVD-DC-CCF). Initially, the CCF matrix is corrected by the directivity and energy attenuation compensation factors, then weighted with the TFM pixel point amplitude. Subsequently, the weighted amplitude matrix undergoes singular value decomposition (SVD), followed by the establishment of an enhancement factor to reconstruct the amplitude matrix. The proposed algorithm is evaluated through experiments on three types of defects in a 30.0 mm thick 9% Ni steel T-shaped fillet weld test block. Results demonstrate superior signal-to-noise ratio (SNR) and enhanced image resolution. Compared to TFM results, the SNR increases by 4.97 dB, 4.25 dB, and 4.96 dB, respectively, while the array performance index (API) decreases by 9.80%, 34.39%, and 22.22%, respectively. [ABSTRACT FROM AUTHOR]

Fault prediction ensures safe and stable production, and cuts maintenance costs. Due to the changing operating conditions that lead to the changes in the characteristics of industrial processes, there is a need to monitor the fault state of batch processes in real‐time and to accurately predict fault trends. An adaptive slow feature analysis‐neighborhood preserving embedding‐improved stochastic configuration network (SFA‐NPE‐ISCN) algorithm for batch process fault prediction is proposed. Firstly, SFA is used to extract the time‐varying features of process data and establish the update index of the NPE model. Then, to extract local nearest‐neighbor features and reconstruct them by the NPE model with adaptive update capability, square prediction error (SPE) statistics are constructed as fault state features based on the reconstructed error. Further, the hunter‐prey optimization (HPO) algorithm optimizes the weights and biases in the stochastic configuration network, and the singular value decomposition (SVD) and QR decomposition of column rotation are introduced to solve the ill‐posed problem of SCN and obtain the prediction model of ISCN. Finally, the obtained statistics SPE is formed into a time series, and the ISCN model is used to predict the process state trend. The effectiveness of the proposed algorithm is verified by case studies of industrial‐scale penicillin fermentation processes and the Hot strip mill process. We propose an adaptive slow feature analysis‐neighborhood preserving embedding‐improved stochastic configuration network (SFA‐NPE‐ISCN) algorithm for batch process fault prediction. The SFA is utilized to extract time‐varying features of process data, which are combined with NPE model reconstruction errors to construct squared prediction error (SPE) statistics as fault state features. Hunter‐prey optimization (HPO) algorithm is used to optimize the weights, and biases of SCN and SVD and QR decomposition are introduced to solve the ill‐posed problem of SCN. [ABSTRACT FROM AUTHOR]

Generally, bearing early failure-induced impulses is weak and easily submerged by strong background noise. How to extract weak impulses is a big challenge. In this paper, based on the K Singular Value Decomposition (KSVD) algorithm, an improved dictionary learning model (WID-MSKW-KSVD) is proposed, which integrates a special denoising method and a multi-scale transformation into a typical dictionary learning model and it successfully overcomes two major shortcomings of traditional KSVD model including the lack of multi-scale learning process and the ability of dictionary atoms to resist strong noise interference. Furthermore, the kurtosis of the envelope spectrum is weighted into the proposed model as a fault matrix to enhance the fault feature. Finally, the feasibility of the proposed model is demonstrated by numerical simulation and practical experiments, and the superiority and reliability of this model in suppressing noise interference and extracting fault features are verified by comparison with other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Accurate estimation of the State of Health (SOH) of batteries is important for intelligent battery management in energy storage systems. To solve the problems of poor quality of data features as well as the difficulty of model parameter adjustment, this study proposes a method for estimating the SOH of lithium batteries based on denoising battery health features and an improved Long Short-Term Memory (LSTM) neural network. First, in this study, three health features related to SOH decrease were selected from the battery charge/discharge data, and the singular value decomposition technique was applied to the noise reduction of the features to improve their correlation with the SOH. Then, the whale optimization algorithm is improved using cubic chaotic mapping to enhance its global optimization-seeking capability. Then, the Improved Whale Optimization Algorithm (IWOA) is used to optimize the model parameters of LSTM, and the IWOA-LSTM model is applied to the battery SOH estimation. Finally, the model proposed in this research is validated against the Center for Advanced Life Cycle Engineering (CALCE) battery dataset. The experimental results show that the prediction error of battery SOH by the method proposed in this study is less than 0.96%, and the prediction error is reduced by 49.42% compared to its baseline model. The method presented in the article achieves accurate estimation of the SOH, providing a reference for practical engineering applications. [ABSTRACT FROM AUTHOR]

Face recognition (FR) uses a passive approach to person authentication that avoids face-to-face contact. Among different FR techniques, most FR approaches place little emphasis on reducing powerful cryptography and instead concentrate on increasing recognition rates. In this paper, we have proposed the Hidden Markov Model (HMM) and convolutional Neural Network (CNN) models for FR by using ORL and Yale datasets. Facial images from the given data sets are divided into 3 portions, 4 portions, 5 portions, and 6 portions corresponding to their respective HMM hidden states being used in the HMM model. Quantized levels of eigenvalues and eigenvector coefficients of overlapping blocks of facial images define the observation states of the HMM model. For image selection and rejection, a threshold is calculated using singular value decomposition (SVD). After training HMM on 3 states HMM, 4 states HMM, 5 states HMM, and 6 states HMM, the recognition accuracies are 96.5%, 98.5%, 98.5%, and 99.5%, respectively, on the ORL database and 90.6667%, 94.6667%, 94.6667%, and 94.6667% on the Yale database. The CNN model uses convolutional layers, a max-pooling layer, a flattening layer, a dense layer, and a dropout layer. Relu is used as the activation function in all layers except in the last layer, where softmax is used as the activation function. Cross entropy is used as a loss function, and we have used the Adam optimizer in our proposed algorithm. The proposed CNN model has given 100% training and testing accuracy on the ORL data set. While using the Yale data set, the CNN model has a training accuracy of 100% and a testing accuracy of 85.71%. In this paper, our proposed model showed that the HMM model is cost-effective with lesser accuracy, while the CNN model is more accurate as compared to HMM but has a higher computational cost. [ABSTRACT FROM AUTHOR]