Singular Value Decomposition Analysis Research Articles

Adaptive Kalman filter based on multiple fading factors for fast in-motion initial alignment with rotation modulation technique

In this paper, a novel fast fine alignment method is proposed on the basis of micro electro mechanical systems (MEMS) for unmanned aerial vehicle (UAV) under mooring conditions. Firstly, the model of single-axis rotation modulation with extended angular rate measurement is built to improve the speed of convergence, especially the azimuth misalignment angle and the constant error of gyroscope along the rotation axis. Both model mismatch and noise uncertainty of the extended measurement will arise for the difficulty to obtain the exact angular velocity of the carrier without auxiliary sensors. To address this problem, the adaptive Kalman filter based on multiple fading factors is proposed. Besides, the meta-heuristic beetle antennae search (BAS) algorithm is applied for the first time to optimize the fading factors in real time. Then, the effectiveness of the proposed method is verified by means of singular value decomposition and error covariance matrix analysis. Finally, the result of comparative simulations and experiments show that the observability degree and convergence speed of misalignment angles improve significantly compared to those conventional methods and the estimated values can satisfy the requirements of fine alignment. More importantly, the constant error of gyroscope along the rotation axis is estimated accurately and can be compensated in the subsequent navigation process.

Effects of Subseasonal Variation in the East Asian Monsoon System on the Summertime Heat Wave in Western North America in 2021

AbstractIn the early summer (late June–early July) of 2021, a prolonged and extremely high temperature event was observed in the Northwest United States and Southwest Canada. In this study, a connection was found between the subseasonal variations of the East Asian monsoon system and the anomalous heat dome over western North America (WNA). Around 2–3 pentads prior to the period of maximum heat, a northward‐propagating rain belt and abnormally intensified jet stream in East Asia excited a Rossby wave train, causing the high‐pressure anomaly over WNA that was responsible for the occurrence of this heat wave in 2021. Multivariate singular value decomposition analysis results using historical data and a series of numerical experiments further supported the finding of this East Asia–western North America teleconnection. The results provide precursor signals for better monitoring and predicting heat waves over WNA.

Has substantial sea ice loss along the Siberian coast contributed to the 2020/2021 winter cold wave in China?

In winter 2020/2021, several episodes of cold air outbreaks have wreaked havoc across much of China, resulting in exceptionally low temperatures over northern-to-southeastern China. Our analysis shows that the anomalous circulation leading to the cold wave in China was characterized as: (a) a strengthening of the Siberian high, (b) a deepening of the East Asian trough, and (c) a weakening of the stratospheric polar vortex. Prior to the winter, in summer and autumn, the largest sea ice loss along the Siberian coast was observed. We therefore address the question whether the substantial sea ice loss has contributed to the cold wave. First, applying singular value decomposition (SVD) analysis, we find strong coherence between the autumn Arctic sea ice coverage and temperatures over the central-to-east Asian region with the sea ice component (SVD1_SIC) reaching a record high in 2020 and displaying a decadal shift around 2005. Associated with both the interannual and decadal variations of the SVD1_SIC, there are similar circulation anomalies as described in (a), (b) and (c) above. Second, applying self-organizing maps, wintertime daily air temperatures are clustered into nine nodes with Node-1 mostly resembling “warm Arctic, cold Eurasia” pattern. The frequency of Node-1 shows a similar decadal shift as observed in SVD_SIC1 above, with a correlation of about 0.56. Associated with Node-1, the circulation anomalies are consistent with features highlighted in (a), (b) and (c) above. Thus, taking all the evidence into account, we conclude that the substantial sea ice loss along the Siberian coast contributed significantly to the 2020/2021 winter cold wave in China. The mechanism behind the contribution in this study highlights that the Arctic sea ice loss is favourable for certain circulation patterns on climate timescale, reinforcing disruptive chain of reactions on weather timescale, and leading to severe cold events.

Cluster Analysis of Combined EDS and EBSD Data to Solve Ambiguous Phase Identifications.

A common problem in analytical scanning electron microscopy (SEM) using electron backscatter diffraction (EBSD) is the differentiation of phases with distinct chemistry but the same or very similar crystal structure. X-ray energy dispersive spectroscopy (EDS) is useful to help differentiate these phases of similar crystal structures but different elemental makeups. However, open, automated, and unbiased methods of differentiating phases of similar EBSD responses based on their EDS response are lacking. This paper describes a simple data analytics-based method, using a combination of singular value decomposition and cluster analysis, to merge simultaneously acquired EDS + EBSD information and automatically determine phases from both their crystal and elemental data. I use hexagonal TiB2 ceramic contaminated with multiple crystallographically ambiguous but chemically distinct cubic phases to illustrate the method. Code, in the form of a Python 3 Jupyter Notebook, and the necessary data to replicate the analysis are provided as Supplementary material.

Improvements of the Sub-Seasonal Precipitation Predicted by the BCC_S2S Forecast System Over Eastern China in Summer Using the Singular Value Decomposition Bias Correction Method

A bias correction scheme has been developed based on the singular value decomposition (SVD) analysis in this study, and it is further validated and used to improve the skill of sub-seasonal precipitation over Eastern China in summer predicted by the Beijing Climate Center sub-seasonal to seasonal forecast system (BCC_S2SFS). Results show that the BCC_S2SFS prediction skill of summer precipitation over Eastern China at the sub-seasonal scale is up to 1 days in advance and exhibits clear regional and inter-annual differences. Further adopting the bias correction scheme can significantly improve the skill of BCC_S2SFS in predicting the sub-seasonal precipitation over Eastern China in summer with different lead time especially longer than 10 days. Compared to the original prediction of BCC_S2SFS, the temporal (spatial) correlation coefficient between the bias corrected predictions and observations over Eastern China can be increased by 0.15, 0.55, and 0.56 (0.14, 0.17, and 0.19) during the forecast lead time of 0–10, 11–20, and 21–30 days, respectively. The bias correction scheme developed in this study shows large potential application prospects in the operational forecast.

IOD, ENSO, and seasonal precipitation variation over Eastern China

This study investigates seasonal precipitation variation over eastern China associated with Indian Ocean Dipole (IOD) forcing and emphasizes the distinction of such responses to preceding IOD events compared with responses to El Niño–Southern Oscillation (ENSO). Precipitation evolution patterns in response to the IOD from autumn to the ensuing summer are derived from singular value decomposition analysis, revealing that the IOD causes a large portion of the seasonal variation in precipitation over eastern China in the simultaneous autumn and time-lagged summer. The difference in the impacts associated with the IOD and ENSO revealed by multiple linear regression and partial correlation analysis is that the IOD contributes mainly to abnormal precipitation in South China during autumn and in the region between the Yangtze River and Yellow River during the ensuing summer; while ENSO primarily boosts precipitation over eastern China during winter and spring. The distinctive effects of the IOD on the ensuing summer precipitation contrast with the less significant signals related to ENSO during the ensuing summer. Such precipitation responses correspond to an anomalous anticyclonic circulation pattern around the South China Sea, which is sustained by direct IOD forcing during autumn and winter and an SST cooling pattern triggered by the IOD over the central equatorial Pacific during the ensuing spring and summer. The calculation of wave activity flux and anomalous AGCM model experiments further confirm the importance of the direct heating of the IOD during autumn and winter and the indirect heating sink over the central Pacific during the ensuing spring and summer for modulating the seasonal variation in precipitation over eastern China.

Spatial autocorrelation and temporal variation of contaminants of emerging concern in a typical urbanizing river

The distribution and fate of contaminants of emerging concern (CECs) was studied in relation to hydrological conditions, land use characteristics, and spatial contiguity in Houxi River. Thirty-four CECs were detected in the surface water during a three-year sampling campaign. Caffeine was most prevalent (99% frequency), while bisphenol A had the highest median concentration (78.2 ng/L) among the detected CECs. Caffeine and the other prevalent CECs lincomycin and bisphenol A, with median concentrations of 3.89 ng/L, 0.26 ng/L, and 78.2 ng/L, respectively, were positively correlated with land use types related to anthropogenic activities (grass, barren, built up, and cropland areas and landscape indexes for human activities). The analysis of similarities revealed significant annual variations, with increasing trends in both the concentrations and detection frequencies of CECs. Spatial variations were demonstrated by higher concentrations and detection frequencies downstream compared to upstream. The singular value decomposition analysis revealed that the downstream sites were the major contributors (55.6%–100%) to the spatial variability of most CECs. Moran's I analysis based on downstream contiguity indicated strong spatial autocorrelation among the connected sites for most CECs. This was further supported by longer correlation lengths for 18 CECs than the average distance between the sampling sites. The spatial autocorrelation can be attributed to the physicochemical properties of CECs and local hydrological dynamics, including temperature, wind speed, and sunshine hours. For most CECs, local contribution predominated over neighbor influence with an average value of 75.5%. The results of this study provide new insight to evaluate CEC distributions, which will be beneficial to policymakers for the management and prioritization of CEC contaminants in the Houxi watershed.

Time-Resolved Infrared Spectroscopy with Multivariate Analysis on Photoinduced Proton Transfer in Aromatic Urea-Acetate Anion Complexes.

1-Anthracen-2-yl-3-phenylurea (2PUA) is an aromatic urea compound, which forms a hydrogen-bonded complex with an acetate anion (AcO-), 2PUA-AcO- complex. We investigated the photoinduced reaction of the 2PUA-AcO- complex in dimethyl sulfoxide (DMSO) by nanosecond time-resolved infrared (TR-IR) spectroscopy. TR-IR spectra obtained after the photoexcitation of 2PUA with the equal concentration of AcO- were consistently explained by a photoinduced proton transfer model. The spectral and temporal profiles of the TR-IR spectra largely depended on concentration conditions of 2PUA and AcO-. Under the condition where excessive amounts of AcO- existed, the TR-IR spectra contained an unexpected signal whose amplitude was related to the concentration of free AcO- in the solution. Using singular value decomposition analysis of the concentration-dependent TR-IR spectra, we extracted the spectral component that reflects the photoinduced reaction of the 2PUA-AcO- complex. The extracted spectrum resembled the TR-IR spectrum obtained under the equal concentration condition, indicating that the same proton transfer occurs during the photoinduced reaction of the 2PUA-AcO- complex irrespective of the concentration conditions. Comparing the steady-state and transient IR spectra of the 2PUA with AcO- in DMSO with density functional theory calculations suggests that both 2PUA-AcO- complex and tautomer species interact with solvent DMSO molecules in their electronic ground states to a large extent.

Formation of β-Strand Oligomers of Antimicrobial Peptide Magainin 2 Contributes to Disruption of Phospholipid Membrane.

The antimicrobial peptide magainin 2 (M2) interacts with and induces structural damage in bacterial cell membranes. Although extensive biophysical studies have revealed the interaction mechanism between M2 and membranes, the mechanism of membrane-mediated oligomerization of M2 is controversial. Here, we measured the synchrotron-radiation circular dichroism and linear dichroism (LD) spectra of M2 in dipalmitoyl-phosphatidylglycerol lipid membranes in lipid-to-peptide (L/P) molar ratios from 0–26 to characterize the conformation and orientation of M2 on the membrane. The results showed that M2 changed from random coil to α-helix structures via an intermediate state with increasing L/P ratio. Singular value decomposition analysis supported the presence of the intermediate state, and global fitting analysis revealed that M2 monomers with an α-helix structure assembled and transformed into M2 oligomers with a β-strand-rich structure in the intermediate state. In addition, LD spectra showed the presence of β-strand structures in the intermediate state, disclosing their orientations on the membrane surface. Furthermore, fluorescence spectroscopy showed that the formation of β-strand oligomers destabilized the membrane structure and induced the leakage of calcein molecules entrapped in the membrane. These results suggest that the formation of β-strand oligomers of M2 plays a crucial role in the disruption of the cell membrane.

The Characteristics of Water Vapor Transport and Its Linkage with Summer Precipitation over the Source Region of the Three-River

Abstract Precipitation is one of the most important meteorological factors affecting the water cycle and ecological system over the Source Region of the Three-River (SRTR), where the Yangtze River, Yellow River, and Lantsang River originated. The characteristics of water vapor transport and budget in annual and summer over the SRTR are analyzed using monthly observational and reanalysis datasets during 1980-2019. The linkage between water vapor transport and summer precipitation is also explored in this study. The results show that the Global Precipitation Climatology Project (GPCP) data are in agreement with the measured precipitation well. The SRTR is a sink region for water vapor, where the water vapor content shows an increasing trend with a rate of 0.2 mm/10a in annual and 0.3 mm/10a in summer. The water vapor mainly flows into the SRTR from the lower (521.2×106 kg s−1) and the middle (195.7×106 kg s−1) layers of the southern boundary in summer, while it exports from the middle (208.1×106 kg s−1) layer of the eastern boundary. The abnormal wind convergence and the low-pressure system, combining with the effects of the Western Pacific Subtropical High and the Mongolian High, provide conditions for the transport of water vapor and precipitation over the SRTR. A close relationship is found between water vapor flux and precipitation from the Singular Value Decomposition (SVD) analysis. The Brahmaputra River basin is the key region of water vapor transport over the SRTR, which contributes to further understanding the mechanisms of water vapor transport and the regional water cycle.

Artifact Removal from EEG signals using Regenerative Multi-Dimensional Singular Value Decomposition and Independent Component Analysis

The EEG signals are regularly blended with sources like Electrooculogram, Electromyogram and few other artifacts caused by physical or signal interferences. The presence of artifacts induces inaccuracy in the examination of the signals acquired. Independent Component Analysis has been predominantly utilized towards these discrepancies by isolating the artifacts from the EEG signals. Direct utilization of ICA isn't conceivable with the frameworks that are outfitted with single or few EEG channels. Distinctly using ICA to eliminate artifacts on a single channel is harder. Therefore, we combine ICA with a proposed decomposition method called Regenerative Multi-Dimensional Singular Value Decomposition (RMD-SVD) which maps the acquired signals into multivariate data after which ICA is applied on it. In our proposed scheme, the pattern of a source signal is mimicked with frequency, phase and amplitude value of the input signal using EEG sigmoid function. Both the input signal and the constructed regenerative reference signals are decomposed and the most significant singular values can be observed with the help of ICA which are the values of the pure input signal. Performance measures such as SNR, PSNR, MSE etc., in our proposed systems are analyzed under different filters and it is noticed that our proposed method of RMD-SVD indicates increased noise omitting efficiency.

A novel dictionary learning-based approach for Ultrasound Elastography denoising.

Ultrasound Elastography is a late-model Ultrasound imaging technique mainly used to diagnose tumors and diffusion diseases that can't be detected by traditional Ultrasound imaging. However, artifact noise, speckle noise, low contrast and low signal-to-noise ratio in images make disease diagnosing a challenging task. Medical images denoising, as the first step in the follow-up processing of medical images, has been concerned by many people. With the widespread use of deep learning technique in the research field, dictionary learning method are once again receiving attention. Dictionary learning, as a traditional machine learning method, requires less sample size, has high training efficiency, and can describe images well. In this work, we present a novel strategy based on K-clustering with singular value decomposition (K-SVD) and principal component analysis (PCA) to reduce noise in Ultrasound Elastography images. At this stage of dictionary training, we implement a PCA method to transform the way dictionary atoms are updated in K-SVD. Finally, we reconstructed the image based on the dictionary atoms and sparse coefficients to obtain the denoised image. We applied the presented method on datasets of clinical Ultrasound Elastography images of lung cancer from Nanjing First Hospital, and compared the results of the presented method and the original method. The experimental results of subjective and objective evaluation demonstrated that presented approach reached a satisfactory denoising effect and this research provides a new technical reference for computer aided diagnosis.

A Subspace Projection Approach for Clutter Mitigation in Holographic Subsurface Imaging

The holographic subsurface radar (HSR) is recognized as an effective remote sensing modality for the detection of shallowly buried objects with a high-resolution image in plain view. However, subsurface detection with HSR is prone to be impaired by clutter contamination, which often obscures the target response. In this letter, a novel clutter mitigation method combining singular value decomposition (SVD) and response cross correlation analysis is presented. The proposed method first applies SVD to decompose the radar data matrix to a number of singular components. Furthermore, the signal cross correlation characteristics are analyzed to demonstrate that the variance of left singular vectors is directly proportional to the target proportion in radar data. Then, target and clutter subspaces can be identified by maximizing the defined weighted target-to-clutter ratio (WTCR). Results of numerical simulation and laboratory experiments corroborate the effectiveness of the proposed method in reducing clutter while preserving the target image.

Singular value decomposition analysis of the electron density changes occurring upon electrostatic polarization of water.

In-depth elucidation of how molecules are electrically polarized would be one key factor for understanding the properties of those molecules under various thermodynamic and/or spatial conditions. Here this problem is tackled for the case of hydrogen-bonded water by conducting singular value decomposition of the electron density changes that occur upon electrostatic polarization. It is shown that all those electron density changes are approximately described as linear combinations of ten orthonormal basis “vectors”. One main component is the interatomic charge transfer through each OH bond, while some others are characterized as the atomic dipolar polarizations, meaning that both of these components are important for the electrostatic polarization of water. The interaction parameters that reasonably well reproduce the induced dipole moments are derived, which indicate the extent of mixing of the two components in electrostatic polarization.

Electronic Circular Dichroism Imaging (ECDi) Casts a New Light on the Origin of Solid-State Chiroptical Properties.

Solid-state ECD (ss-ECD) spectra of a model microcrystalline solid, finasteride, dispersed into a KCl pellet were recorded by using the synchrotron radiation source at the Diamond B23 beamline. Scanning a surface of 36 mm2 with a step of 0.5 mm, we measured a set of ECD imaging (ECDi) spectra very different from each other and from the ss-ECD recorded with a bench-top instrument (1 cm2 area). This is due to the anisotropic part of the ECD (ACD), which averages to zero in solution or on a large number of randomly oriented crystallites, but can otherwise be extremely large. Two-way singular value decomposition (SVD) analysis, through experimental and simulated TDDFT spectra, disclosed that the measured and theoretical principal components are in line with each other. This finding demonstrates that the observed isotropic ss-ECD spectrum is governed by the anisotropy of locally oriented crystals. It also introduces a new quality for ss-ECD measurements and opens a new future for probing and mapping chiral materials in the solid state such as active pharmaceutical ingredients (APIs).

Logistic and multinomial-logit models: A brief review on their modifications and extensions

The work presents various techniques of the logistic and multinomial-logit modeling with their modifications. These methods are useful for regression modeling with a binary or categorical outcome, structuring in regression and clustering, singular value decomposition and principal component analysis with positive loadings, and numerous other applications. Particularly, these models are employed in the discrete choice modeling and the best-worst scaling known in applied psychology and socio-economics studies.

Singular value decomposition analysis of the secondary structure features contributing to the circular dichroism spectra of model proteins

Amyloid fibril formation occurs in restricted environment, such as the interface between intercellular fluids and bio-membranes. Conformational interconversion from α-helix to β-structure does not progress in fluids; however, it can occur after sedimentary aggregation during amyloid fibril formation induced by heat treatment of hen egg white lysozyme (HEWL). Secondary structures of various proteins and denatured proteins titrated with 2,2,2-trifluoroethanol (TFE) were examined using their CD spectra. Gaussian peak/trough and singular value decompositions (SVD) showed that the spectral pattern of the α-helix comprised a sharp trough at wavelength 207 nm and a broad trough at 220 nm. Conversely, we distinguished two patterns for β-sheet—a spread barrel type, corresponding to ConA, and a tightly weaved type, corresponding to the soybean trypsin inhibitor. Herein, we confirmed that the spectral/conformational interconversion of the heat-treated HEWL was not observed in the dissolved fluid.

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.

Unsupervised Machine Learning for Robust Bridge Damage Detection: Full-Scale Experimental Validation

This study focused on the development of damage detection indices using unsupervised Machine Learning with data obtained from tests of a full-scale bridge deck mock-up. Measured structural response to different live loads at various damage levels was analyzed and a detection tool, termed a novelty index, was developed. Three levels of damage were induced into the structure: crash-induced damage to the concrete barrier; “sawcutting” the entire height of the barrier; and extending the sawcut through the deck. Bridge response to passage of vehicles at various speeds was recorded for the undamaged structure and after introduction of each damage level. The main novelty of this work is development of the novelty index, its application and validation against measured field data from a full-scale field test of a mock-up bridge subjected to varying levels of damage. Data was incorporated into health monitoring frameworks previously developed by the authors (1, 2) and the viability of using Singular Value Decomposition (SVD) and Independent Component Analysis (ICA) as damage sensitive features were explored. Novelty indices were subsequently developed using two approaches: Left Singular Vectors (LSVs) of measured response, commonly known as Proper Orthogonal Modes (POMs); and Independent Component Modes (ICMs). It was shown that a novelty index based on POMs was relatively insensitive to vehicle load variability and speed when compared to ICMs. It was also shown that novelty indices based on POMs could detect the lowest amount of induced damage, which was caused by a crash into a bridge barrier. Damage induced by the collision did not lead to visible barrier cracks; however, a quantitative assessment indicated damage levels like damage caused by a “sawcut” of the barrier. This study shows that the proposed method has the potential to be a robust damage detection tool; however, its application for detecting damage types other than the one studies herein needs to be further investigated due to statistical nature of SVD.

Analisis Curah Hujan di Indonesia untuk Memetakan Daerah Potensi Banjir dan Tanah Longsor dengan Metode Cluster Fuzzy C-Means dan Singular Value Decompotition (SVD)

One of the climate changes impact is extreme rainfall. Extreme rainfall can cause some hydrometeorological disasters, especially floods and landslides. In 2016, BNPB recorded that there were 2,342 disaster events in Indonesia, 92% of which were disasters dominated by floods, landslides, and cyclones. There were 766 floods, 612 landslides, and 74 combinations of both. In this case, clustering is one of the important method to identify groups of areas with similar characteristics of high or low rainfall. Another analysis that can be applied is the identification of the extreme rainfall and the determination of dominant rainfall pattern using Singular Value Decomposition (SVD) for each region in Indonesia. The data was taken from Indonesia’s rainfall data in January 1998 - March 2017, obtained from the Tropical Rainfall Measuring Mission (TRMM) meteorological satellite, with 12,834 observation units. The highest average rainfall in Indonesia in December is 290,26 mm/month. Rainy season is predicted from November to April because they have the highest average rainfall. The SVD analysis formed four dominant rainfall patterns in Indonesia with a variance of 25.59%, where Papua and West Papua are the regions with the highest rainfall. The area around the Indian Ocean is the area with the most extreme rainfall events compared to other regions. Using Fuzzy C-means, three clusters can be formed, with criteria for areas of high, medium, and low rainfall. Based on the results of the analysis, the area has the potential for flooding and landslides due to high rainfall, so that preventive and mitigation efforts against the risk of flooding and landslides can be treated better.