Decomposition Method Research Articles

Research on New Energy Transmission Line Fault Localization Method Based on APSVMD-NTEO

Abstract Given the new energy transmission line fault, traveling wave head detection difficulties, wave speed uncertainty, positioning errors, and other problems, this paper proposes a new Teager energy operator based on adaptive parameter selection variational mode decomposition (APSVMD) and new energy transmission line fault location method (NTEO). The envelope fuzzy entropy is introduced to select the key parameters of SVM, and the sampling range of the Teager energy operator (TEO) is improved to effectively improve the capture accuracy of the wave head. In terms of fault location, by improving the double-ended traveling wave method, the uncertainty of wave speed and the effect of actual line length error is eliminated, and the ranging error is reduced.

Spin thermoelectric and spin transport in YIG films fabricated by chemical method

Spin transport in heterojunctions between magnetic insulators and heavy metals has garnered significant attention due to its potential in spintronics applications. In this study, we employed the metal-organic decomposition (MOD) method to fabricate high-quality yttrium iron garnet (Y3Fe5O12, YIG)films on (111)-oriented gadolinium gallium iron garnet (Gd3Ga5O12, GGG) substrate. We conducted a thorough characterization of the crystallinity, surface morphology, and magnetic properties of the YIG films at various thicknesses. The obtained samples exhibited smooth surfaces with roughness mean-square (RMS) below 0.6 nm. Epitaxial growth was maintained for films up to 116 nm in thickness but deteriorated beyond that. The magnetic anisotropy demonstrated an easy axis in the in-plane direction, accompanied by a low coercivity of 4.8 ± 0.4 Oe. The spin Seebeck effect voltage measurement shows the highest signal for 366 nm thick film and is reduced if the thickness increases. Furthermore, we investigated spin transport across the YIG/Pt interface using spin Hall magnetoresistance. The average spin-mixing conductance (Gr) was determined to be 5.79 ± 0.54 × 1014 Ω−1m−2, a value comparable to those reported in previous studies.

Создание функциональной подсистемы по предупреждению и ликвидации последствий чрезвычайных ситуаций на водном транспорте

The relevance of the study is based on the need to improve the Unified State System of Prevention and Elimination of Emergencies in order to increase the safety level in case of accidents of natural and anthropogenic origins in water transport industry facilities. In the field of functional subsystems of water transport, the focus is made on eliminating marine and internal waterway oil and petroleum product spills as well as the search and rescue operations during marine emergencies. At the same time, the scope of emergencies in water transport that can be environmentally hazardous is more extensive and includes fires and explosions, discharges of non-petroleum dangerous goods (for example, coal, sulfur, chemical goods, etc.) The Unified State System is regulated in conditions of insufficient scientific substantiation of the principles of construction, organization, and use of functional subsystems. Therefore, the study aims to substantiate the necessity to establish and develop structural schemes of a new functional subsystem that includes functioning modes, management bodies, responsibility levels, logistical and financial support. For this purpose, the existing functional subsystems have been analyzed, and the development of an integral functional subsystem to prevent and eliminate emergencies in water transport has been proposed. Methods of decomposition of emergencies and generalization of information on the operation of other transports as well as the analysis of regulatory legal acts outlining certain issues of the operation of these functional subsystems have been used. The characteristics to determine the extent of emergency danger have been formulated; the detailed characteristics of levels of a functional subsystem to prevent and eliminate a wide range of various emergencies have been provided based on the example of marine water transport; the subsystem tasks for various levels of emergency have been formulated.

Data-driven modeling of unsteady flow based on deep operator network

Time-dependent flow fields are typically generated by a computational fluid dynamics method, which is an extremely time-consuming process. However, the latent relationship between the flow fields is governed by the Navier–Stokes equations and can be described by an operator. We therefore train a deep operator network (DeepONet) to learn the temporal evolution between flow snapshots. Once properly trained, given a few consecutive snapshots as input, the network has a great potential to generate the next snapshot accurately and quickly. Using the output as a new input, the network iterates the process, generating a series of successive snapshots with little wall time. Specifically, we consider two-dimensional flow around a circular cylinder at Reynolds number 1000 and prepare a set of high-fidelity data using a high-order spectral/hp element method as ground truth. Although the flow fields are periodic, there are many small-scale features in the wake flow that are difficult to generate accurately. Furthermore, any discrepancy between the prediction and the ground truth for the first snapshots can easily accumulate during the iterative process, which eventually amplifies the overall deviations. Therefore, we propose two alternative techniques to improve the training of DeepONet. The first one enhances the feature extraction of the network by harnessing the “multi-head non-local block.” The second one refines the network parameters by leveraging the local smooth optimization technique. Both techniques prove to be highly effective in reducing the cumulative errors, and our results outperform those of the dynamic mode decomposition method.

Microstructure and magnetic properties of the FeSiBCuNb nanocrystalline soft magnetic composites coated with oil-phase single Fe3O4 nanoparticles by thermal decomposition method

Microstructure and magnetic properties of the FeSiBCuNb nanocrystalline soft magnetic composites coated with oil-phase single Fe3O4 nanoparticles by thermal decomposition method

Improved Group Sparse Modal Decomposition Methods With Applications to Fault Diagnosis of Rotating Machinery

Improved Group Sparse Modal Decomposition Methods With Applications to Fault Diagnosis of Rotating Machinery

Visual and Quantitative Evaluation of Low-Concentration Bismuth in Dual-Contrast Imaging of Iodine and Bismuth Using Clinical Photon-Counting CT.

Simultaneous dual-contrast imaging of iodine and bismuth has shown promise in prior phantom and animal studies utilizing spectral CT. However, it is noted that in previous studies, Pepto-Bismol has frequently been employed as the source of bismuth, exceeding the recommended levels for human subjects. This investigation sought to assess the feasibility of visually differentiating and precisely quantifying low-concentration bismuth using clinical dual-source photon-counting CT (PCCT) in a scenario involving both iodinated and bismuth-based contrast materials. Four bismuth samples (0.6, 1.3, 2.5, and 5.1 mg/mL) were prepared using Pepto-Bismol, alongside three iodine rods (1, 2, and 5 mg/mL), inserted into multi-energy CT phantoms with three different sizes, and scanned on a PCCT system at three tube potentials (120, 140, and Sn140 kV). A generic image-based three-material decomposition method generated iodine and bismuth maps, with mean mass concentrations and noise levels measured. The root-mean-square errors for iodine and bismuth determined the optimal tube potential. The tube potential of 140 kV demonstrated optimal quantification performance when both iodine and bismuth were considered. Distinct differentiation of iodine rods with all three concentrations and bismuth samples with mass concentrations ≥ 1.3 mg/mL was observed across all phantom sizes at the optimal kV setting.

Domain Decomposition Methods for Diffusion Problems with Discontinuous Coefficients Revisited

Domain Decomposition Methods for Diffusion Problems with Discontinuous Coefficients Revisited

Direct numerical simulation of shock wave/boundary layer interaction controlled by steady microjet in a compression ramp

Shock wave/boundary layer interaction in a 24° turning angle of the compression ramp at Mach number 2.9 controlled by steady microjet is investigated using direct numerical simulation. Three different jet spacings which are termed as sparse, moderate and dense are considered, and the induced vortex system and shock structures are compared. A moderate jet spacing configuration is found to generate counter-rotating vortex pairs that transport high-momentum fluid towards the vicinity of wall and strengthen the boundary layer to resist separation, reducing the separation region. The dense jet spacing configuration creates a larger momentum deficit region, reducing the friction downstream of the corner. Analysis of pressure and pressure gradient reveals that dense jet spacing configuration reduces the intensity of separation shock. The impact of varying jet spacings on the turbulent kinetic energy transport mechanism is also investigated by decomposing the budget terms in the transport equation. Furthermore, the spectral characteristics of the separation region are studied using power spectral density and dynamic mode decomposition methods, revealing that moderate jet spacing configuration suppresses low-frequency fluctuations in the separation region.

Synergic effect of rare earth doped Sm doped WO3 nanoparticles for enhanced MB dye photocatalytic activities of waste water treatment and antibacterial activities against Staphylococcus aureus

One of the most effective ways to reduce environmental pollution on the earth's surface is the catalytic decomposition method. In this paper, the co-precipitation method was successfully used to synthesize pure and Sm (3, 5, and 7 wt.%) doped WO3 nanoparticles by various weight percentage. The structural and optical properties of prepared NPs were characterized using XRD, SEM, EDS, HR-TEM, XPS, FTIR and UV-Vis. From XRD analysis confirmed the monoclinic phase of WO3 as well as the substitutional incorporation of Sm in WO3 lattice. The surface morphology of the obtained samples was studied by SEM and HR-TEM, which shows nanoplate morphology. EDS and XPS analysis element confirmation for W, O and Sm. The systematic decrease of bandgap (Eg) from 2.86 to 2.82 eV with increasing Sm doping concentration from 0 - 7 wt.% was strong evidenced from UV-Vis analysis. Furthermore, the extensive surface area of the active CV curves and EIS indicating the good to improve photocatalytic performance and photo-generated carriers’ separation efficiency. The photodegradation of Methyl Blue (MB) aqueous solution in 120 minutes under the visible light irradiation is used to assess the photocatalytic activity of the prepared samples. Among the four samples, 7 wt.% doped one shows higher degradation efficiency of 94.2%. Finally, the antibacterial properties of Sm:WO3 (0, 3, 5 and 7 wt.%) NPs against Staphylococcus aureus (MTCC 8708) were investigated with the disc diffusion method and zone of inhibition in mm were recorded. Based on the above analysis, the superior antibacterial activity against Staphylococcus aureus (18 mm) is rather obtained with Sm:WO3 (7 wt.%) sample.

Output-feedback path-following control of underactuated AUVs via singular perturbation and interconnected-system technique

This paper focuses on the output-feedback control for path-following of underactuated autonomous underwater vehicles subject to multiple uncertainties and unmeasured velocities. First, a novel extended state observer is proposed to estimate the mismatched lumped disturbance and recover the unmeasured velocities. Based on this premise, to overcome the limitation of relying solely on the accurate kinematic model, a disturbance observer-based stabilizing controller is developed. The difference in bandwidths between the observer and the vehicle dynamics allows for a mathematical setup amenable to standard singular perturbation theory. In the fast mode, a kinematic observer is designed to reject system uncertainty caused by unknown attack angular velocity and prohibitive path-tangential angular velocity, using a novel physical perspective. In the slow mode, an interconnected-system control law is proposed by integrating the backstepping technique with the time scale decomposition method. Furthermore, the stability of the overall closed-loop system is established. Finally, simulation results are presented to demonstrate the effectiveness of the proposed method for path-following of underactuated autonomous underwater vehicles in the vertical plane.

EEG tensor decomposition delineates neurophysiological principles underlying conflict-modulated action restraint and action cancellation

Executive functions are essential for adaptive behavior. One executive function is the so-called ‘interference control’ or conflict monitoring another one is inhibitory control (i.e., action restraint and action cancelation). Recent evidence suggests an interplay of these processes, which is conceptually relevant given that newer conceptual frameworks imply that nominally different action/response control processes are explainable by a small set of cognitive and neurophysiological processes. The existence of such overarching neural principles has as yet not directly been examined. In the current study, we therefore use EEG tensor decomposition methods, to look into possible common neurophysiological signatures underlying conflict-modulated action restraint and action cancelation as mechanism underlying response inhibition. We show how conflicts differentially modulate action restraint and action cancelation processes and delineate common and distinct neural processes underlying this interplay. Concerning the spatial information modulations are similar in terms of an importance of processes reflected by parieto-occipital electrodes, suggesting that attentional selection processes play a role. Especially theta and alpha activity seem to play important roles. The data also show that tensor decomposition is sensitive to the manner of task implementation, thereby suggesting that switch probability/transitional probabilities should be taken into consideration when choosing tensor decomposition as analysis method. The study provides a blueprint of how to use tensor decomposition methods to delineate common and distinct neural mechanisms underlying action control functions using EEG data.

A triangle decomposition method for the mobility control of mecanum wheel-based robots

Mobile robots are used in a variety of applications including research, education, healthcare, customer service, security and so on. Based upon the application, the robots employ different locomotion systems for their mobility. When it comes to rolling locomotion, the wheels used to provide mobility to robots can be categorized as: tracks, omnidirectional wheels, and unidirectional wheels with a steering system. The ability of omnidirectional wheels to drive machines in small spaces makes them interesting to use. Among the types of omnidirectional wheels, mecanum wheels are widely used due to their inherent benefits. With the right control strategy, robots equipped with mecanum wheels can move freely, in all possible directions. In this study, a triangle decomposition approach is employed for controlling omnidirectional mecanum wheel-based robots. The method consists of breaking down any path into a set of linear motions that can be horizontal, vertical, or oblique. Furthermore, the oblique paths are divided into smaller segments that can be resolved into a horizontal and vertical component in a right-angle triangle. The suggested control method is tested and proved on a simple scenario using Webots simulation software.

Real-time reconstruction of 3D transient non-uniform temperature field for thermal protection system based on machine learning

Reconstructed temperature field will provide critical input for monitoring the aerodynamic heating on the thermal protection system (TPS). In this paper, a novel data-driven method is proposed that allows real-time reconstruction of the 3D transient non-uniform temperature field in a TPS ceramic tile. The measured temperature is obtained by embedding sparse sensors at different layers of the tile. The spatial feature bases and time projection coefficients of the non-uniform temperature fields are extracted using the multi-parameter proper orthogonal decomposition (POD) method. The Fully-connected Long Short-Term Memory (FC-LSTM) networks are used to establish the prediction relationship between the measurements and the full-field temperatures. The problem to estimate the projection coefficients of the spatial feature bases is solved by optimizing the loss function using Adam algorithm. The numerical examples show an approximation error less than 5% and the reconstruction time-consuming less than 1 s in 3D temperature field reconstruction (TFR). The proposed reconstruction method is robust at different measurement noise levels and tested point arrangements.

RPCA-based thermoacoustic imaging for microwave ablation monitoring

Microwave ablation (MWA) is a potent cancer treatment tool, but its effectiveness can be hindered by the lack of visual feedback. This paper validates the feasibility of using microwave-induced thermoacoustic imaging (TAI) technique to monitor the MWA process. A feasibility analysis was conducted at the principle level and a high-performance real-time TAI system was introduced. To address the interference caused by MWA, a robust principal component analysis (RPCA)-based method for TAI was proposed. This method leverages the correlation between multiple signal frames to eliminate interference. RPCA’s effectiveness in TAI was demonstrated through three sets of different experiments. Experiments demonstrated that TAI can effectively monitors the MWA process. This work represents the first application of RPCA-related matrix decomposition methods in TAI, paving the way for the application of TAI in more complex clinical scenarios. By providing rapid and accurate visual feedback, this research advances MWA technology.

Application of Multi-Temporal and Multisource Satellite Imagery in the Study of Irrigated Landscapes in Arid Climates

The study of ancient irrigation is crucial in the archaeological research of arid regions. It covers a wide range of topics, with the Near East being the focus for decades. However, political instability and limited data have posed challenges to these studies. The primary objective is to establish a standardised method applicable to different arid environments using the Google Earth Engine platform, considering local relief of terrain and seasonal differences in vegetation. This study integrates multispectral data from LANDSAT 5, Sentinel-2, SAR imagery from Sentinel 1, and TanDEM-X (12 m and 30 m) DSMs. Using these datasets, calculations of selected vegetation indices such as the SMTVI and NDVSI, spectral decomposition methods such as TCT and PCA, and topography-based methods such as the MSRM contribute to a comprehensive understanding of landscape irrigation. This paper investigates the influence of modern environmental conditions on the visibility of features like levees and palaeo-channels by testing different methods and parameters. This study aims to identify the most effective approach for each case study and explore the possibility of applying a consistent method across all areas. Optimal results are achieved by combining several methods, adjusting seasonal parameters, and conducting a comparative analysis of visible features.

The crucial role of casein on crystal structure, size, morphology, and magnetic properties of carbon supported Ni nanoparticles

Carbon supported nonprecious metal nanoparticles have gained significant attention for their low cost and excellent properties. However, immobilizing them with good dispersion on carbon supports is still a great challenge. Herein, we report a simple and low cost thermal decomposition method to prepare a high quality carbon supported nickel nanoparticles in presence of casein as carbon source. By optimizing the concentration of carbon source, engineered the particle size from 50 nm to 17 nm and morphology from pyramid to spherical. Further, the impacts of morphology and particle size on the magnetic properties of these nanoparticles are studied. Moreover, thermally stable, biocompatible, low cost with unique magnetic properties makes these materials as potential candidate for various applications including chemisorbers, sensor, and electrochemical uses.

Anomalous Warm Temperatures Recorded using Tree Rings in the Headwater of the Jinsha River during the Little Ice Age

As a feature of global warming, climate change has been a severe issue in the 21st century. A more comprehensive reconstruction is necessary in the climate assessment process, considering the heterogeneity of climate change scenarios across various meteorological elements and seasons. To better comprehend the change in minimum temperature in winter in the Jinsha River Basin (China), we built a standard tree-ring chronology from Picea likiangensis var. balfouri and reconstructed the regional mean minimum temperature of the winter half-years from 1606 to 2016. This reconstruction provides a comprehensive overview of the changes in winter temperature over multiple centuries. During the last 411 years, the regional climate has undergone seven warm periods and six cold periods. The reconstructed temperature sensitively captures the climate warming that emerged at the end of the 20th century. Surprisingly, during 1650–1750, the lowest winter temperature within the research area was about 0.44 °C higher than that in the 20th century, which differs significantly from the concept of the “cooler” Little Ice Age during this period. This result is validated by the temperature results reconstructed from other tree-ring data from nearby areas, confirming the credibility of the reconstruction. The Ensemble Empirical Mode Decomposition method (EEMD) was adopted to decompose the reconstructed sequence into oscillations of different frequency domains. The decomposition results indicate that the temperature variations in this region exhibit significant periodic changes with quasi-3a, quasi-7a, 15.5-16.8a, 29.4-32.9a, and quasi-82a cycles. Factors like El Niño–Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), and solar activity, along with Atlantic Multidecadal Oscillation (AMO), may be important driving forces. To reconstruct this climate, this study integrates the results of three machine learning algorithms and traditional linear regression methods. This novel reconstruction method can provide valuable insights for related research endeavors. Furthermore, other global climate change scenarios can be explored through additional proxy reconstructions.

A New Order Tracking Method for Fault Diagnosis of Gearbox under Non-Stationary Working Conditions Based on In Situ Gravity Acceleration Decomposition

Rotational speed measuring is important in order tracking under non-stational working conditions. However, sometimes, encoders or coded discs are not easy to mount due to the limited measurement environment. In this paper, a new in situ gravity acceleration decomposition method (GAD) is proposed for rotational speed estimation, and it is applied in the order tracking scene for fault diagnosis of a gearbox under non-stationary working conditions. In the proposed method, a MEMS accelerometer is locally embedded on the rotating shaft or disc in the tangential direction. The time-varying gravity acceleration component is sensed by the in situ accelerometer during the rotation of the shaft or disc. The GAD method is established to exploit the gravity acceleration component based on the linear-phase finite impulse response (FIR) filter and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) methods. Then, the phase signal of time-varying gravity acceleration is derived for rotational speed estimations. A motor–shaft–disc experimental setup is established to verify the correctness and effectiveness of the proposed method in comparison to a mounted encoder. The results show that both the estimated average and instantaneous rotational speed agree well with the mounted encoder. Furthermore, both the proposed GAD method and the traditional vibration-based tacholess speed estimation methods are applied in the context of order tracking for fault diagnosis of a gearbox. The results demonstrate the superiority of the proposed method in the detection of tooth spalling faults under non-stationary working conditions.

Global Coronal Plasma Diagnostics Based on Multislit Extreme-ultraviolet Spectroscopy

Full-disk spectroscopic observations of the solar corona are highly desired to forecast solar eruptions and their impact on planets and to uncover the origin of solar wind. In this paper, we introduce a new multislit design (five slits) to obtain extreme-ultraviolet (EUV) spectra simultaneously. The selected spectrometer wavelength range (184–197 Å) contains several bright EUV lines that can be used for spectral diagnostics. The multislit approach offers an unprecedented way to efficiently obtain the global spectral data but the ambiguity from different slits should be resolved. Using a numerical simulation of the global corona, we primarily concentrate on the optimization of the disambiguation process, with the objective of extracting decomposed spectral information of six primary lines. This subsequently facilitates a comprehensive series of plasma diagnostics, including density (Fe xii 195.12/186.89 Å), Doppler velocity (Fe xii 193.51 Å), line width (Fe xii 193.51 Å), and temperature diagnostics (Fe viii 185.21 Å, Fe x 184.54 Å, Fe xi 188.22 Å, and Fe xii 193.51 Å). We find a good agreement between the forward modeling parameters and the inverted results at the initial eruption stage of a coronal mass ejection, indicating the robustness of the decomposition method and its immense potential for global monitoring of the solar corona.