Multiresolution Method Research Articles

CMRFusion: A cross-domain multi-resolution fusion method for infrared and visible image fusion

Existing multi-resolution infrared and visible image fusion methods suffer from the weak ability of texture detail preservation, which restricts the practical application. In this paper, we proposed a cross-domain multi-resolution infrared and visible image fusion method, CMRFusion, based on auto-encoder networks and a cross-domain attention fusion strategy. Auto-encoder networks are adopted to extract deep multi-scale features with encoder networks and reconstruct images with decoder networks. The cross-domain attention fusion strategy is adopted to promote the preservation of texture detail from one of the source images. In the proposed method, low-resolution infrared images are firstly up-scaled by a simple bicubic strategy to match the resolution of source images. Then, an encoder network is adopted to extract features from infrared and visible images. The extracted features of the infrared image are served as the base and supplemented with details in the extracted features from the visible image through a cross-domain attention fusion strategy to obtain the fused features to reconstruct high-resolution infrared images with the first decoder network. Finally, the encoder network is adopted to extract features from visible and reconstructed infrared images. The extracted features of the visible image are served as the base and supplemented with details in the extracted features from the reconstructed high-resolution infrared image through a cross-domain attention fusion strategy to obtain the fused features to reconstruct the fusion result with the second decoder network. The qualitative and quantitative experiments conducted on the TNO, OSU, and MSRS datasets indicate that CMRFusion can balance the information from source images and well-retain texture detail from the visible image.

A Multi-Resolution MPS/FEM Coupling Method for Three-Dimensional Fluid–Structure Interaction Analysis

This work aims to propose an efficient MPS/FEM coupling method for the simulation of fluid–structure interaction (FSI), where the MPS and FEM are respectively employed to account for fluid flows and structural deformation. The main idea of our method is to develop a multi-scale multi-resolution MPS method for efficient fluid simulations in the context of MPS/FEM coupling. In the developed multi-scale MPS method, the fluid domain is discretized into particles of different resolutions before calculation, where particles close to the interest domain will be discretized into high resolution, while the rest are discretized into low resolution. A large particle interacting with small particles is divided into several small particles virtually, and weight functions are redefined to maintain the simulation stability. A bucket-sort-based algorithm is developed for the fast search of multi-resolution neighboring particles. The capacity of a newly proposed ghost cell boundary model is further enhanced, so as to accurately treat wall boundary problems with particles of different resolutions. On this basis, the multi-resolution MPS method is coupled with the FEM for FSI simulations. Finally, several numerical examples are conducted to demonstrate the accuracy and efficiency of the development method.

A multi-resolution method for internal partial discharge simulation

Partial discharges and the electrical treeing represent one of key aspects for the ageing of the insulating parts of electrical components. To predict the end of life of dielectric systems several different modelling approaches have been developed. One of the most promising is the modelling using first principles and a set of partial differential equations. In this field, one of the toughest challenges is to cope with the very different scales of the treeing (each branch is a few micrometers wide) and of electrical components (raging from a few centimetres to a few meters).In this paper we propose an approach, based on the finite element and finite volume methods, which uses two completely independent grids to describe internal defects and electrical components. Some interpolation algorithms have been developed to couple the solution on the two grids. We have also tested the accuracy of our approach by comparing it with previously published methods that use only one grid.

Multi-GPU multi-resolution SPH framework towards massive hydrodynamics simulations and its applications in high-speed water entry

We propose a multi-phase, multi-resolution SPH method for fluid/solid interaction with multi-GPU implementation and dynamic load balancing following the movement of the refinement regions. The primary design goal of this framework is to maintain the efficiency of the single-resolution SPH model running on a single GPU. To this end, a multi-background mesh is introduced, and the domain is regarded as a nested multi-domain with different resolutions. Validation using both a δ-SPH and Riemann SPH model is shown, and applications to the simulation of the water entry of a projectile with a high Froude number are considered, with comparisons to experimental data from three challenging test cases, showing the proposed model's ability to correctly reproduce the free surface evolution on water entry, the motion of the projectile, and the formation and evolution of multiple cavities depending on entry angle and velocity. An analysis of the computational performance and resolutions achieved (up to 120 million particles) is also provided across several test cases.

Wavelet transform for rotary machine fault diagnosis:10 years revisited

As a multi-resolution analysis method rooted rigorously in mathematics, wavelet transform (WT) has shown its great potential in rotary machine fault diagnosis, characterized by continued development and innovative new applications. In traditional fault diagnosis, WT has been widely used for fault feature extraction and extensively studied for performance improvement. With the emergence of data-driven intelligent fault diagnosis, especially deep learning techniques, WT has attracted renewed attention for its ability of adding interpretability into the intelligent diagnosis models. This paper aims to highlight the advancement of WT-based fault diagnosis research over the last decade. Toward this end, a comprehensive overview of WT method is given, followed by a summary of WT for fault diagnosis from two perspectives: traditional fault diagnosis and intelligent fault diagnosis. Finally, future research trends are discussed, including benchmarking, wavelet base design, integration with other methods, and enhancement through deep learning.

A consistent multi-resolution particle method for fluid-driven granular dynamics

Granular dynamics driven by fluid flow is ubiquitous in many industrial and natural processes, such as fluvial and coastal sediment transport. Yet, their complex multiphysics and multi-scale nature challenge numerical models’ accuracy and efficiency. Here, we develop a new multi-resolution mesh-free particle method, based on an enhanced weakly-compressible Moving Particle Semi-implicit (MPS) method, to study the dynamics of rapid fluid-driven granular erosion. We propose and validate a novel multi-resolution multiphase MPS formulation for the consistent and conservative form of the governing equations, including particle stabilization techniques. First, we discuss the numerical accuracy and convergence of the proposed approximation operators through two numerical benchmark cases: the multi-viscosity Poiseuille flow and the multi-density hydrostatic pressure. Then, coupling the developed model with a generalized rheology equation, we investigate the water dam-break waves over movable beds. The particle convergence study confirms that the proposed multi-resolution formulation predicts the analytical solutions with acceptable accuracy and order of convergence. Validating the multiphase granular flow reveals that the mechanical behavior of this fluid-driven problem is highly sensitive to the water-sediment density ratio; the bed with lighter grains experiences extreme erosion and interface deformations. For the bed with a heavier material but different geometrical setups, the surge speed, and the transport layer thickness remain almost identical (away from the gate). Furthermore, while the multi-resolution model accurately estimates the global sediment dynamics, the single-resolution model underestimates the flow evolution. Overall, the qualitative and quantitative analysis of results emphasizes the importance of multi-scale multi-density interactions in fluid-driven modeling.

CURVELET BASED U-NET FRAMEWORK FOR BUILDING FOOTPRINT IDENTIFICATION

Abstract. This paper proposes a multiresolution based U-net composite architecture for segmentation of remotely sensed images for building footprint identification. The features derived from curvelet decompositions at different scales are augmented to capture curvilinear discontinuities of the building footprint. This increases the contextual overview of the network as the same data on multiple scales is available for feature extraction and learning. This work further analyses the effects of different multiresolution methods on wavelets and curvelets for decomposition on segmentation performance. The performance is evaluated in terms of precision, recall, F-score, mean intersection over union, overall accuracy, local and global consistency errors. It is found that the proposed method has better class-discriminating power as compared to existing methods and has an overall classification accuracy of 92.4–95.22%. On comparison with the U-Net model performance, it is observed that the proposed network can identify the building areas with higher accuracy and mean intersection over union, the best performance being with curvelet basis of multiresolution analysis.

A Multiresolution Ensemble Hybrid 4DEnVar with Variable Ensemble Sizes to Improve Global and Tropical Cyclone Track Numerical Prediction

Abstract The current global operational four-dimensional ensemble-variational (4DEnVar) data assimilation (DA) system at NCEP adopts a background ensemble at a reduced resolution, which restricts the range of spatial scales that the ensemble background error covariance can resolve. A prior study developed a multiresolution ensemble 4DEnVar method and determined that this approach can provide a comparable forecast to an approach using solely high-resolution members, while substantially reducing the computational cost. This study further develops the multiresolution ensemble 4DEnVar approach to allow for a flexible number of low- and high-resolution ensemble members as well as varying localization length scales between the high- and low-resolution ensembles. Three 4DEnVar experiments with the same computational costs are compared. The first experiment has an 80-member high-resolution background ensemble with single-scale optimally tuned localization (SR-High). The second and third experiments utilize the multiresolution background ensembles. One has 130 low-resolution and 40 high-resolution members (MR170) while the other has 180 low-resolution members and 24 high-resolution members (MR204). Both multiresolution ensemble experiments utilize differing localization radii with ensemble resolution. Despite having the same costs, both MR170 and MR204 improves global forecasts and decreases tropical cyclone track errors for up to 5 days’ lead time compared to SR-High. Improvements are most apparent in larger-scale features, such as jet streams and the environmental steering flow of tropical cyclones. Additionally, MR170 outperforms MR204 in terms of global and tropical cyclone track forecasts, demonstrating the value of both increasing sampling at large scales and retaining substantial information at small scales.

Multi-resolution topology optimization using B-spline to represent the density field

This paper proposes a novel multi-resolution topology optimization method using B-spline to represent the density field, and overcomes the defects of tedious post-processing of element-based models and low computational efficiency of topology optimization for large-scale problems. The design domain embedded in the B-spline space is discretized with a coarser analysis mesh and a finer density mesh to reduce the computational cost of finite element analysis. As design variables, the coefficients of the control points control the shape of the B-spline. The optimized B-spline can be quickly and precisely converted into a CAD model. Sensitivity filtering is additionally applied to enhance B-spline's smoothness and suppress QR-patterns. Numerical examples, including 2D and 3D cases, are tested to demonstrate that the proposed method significantly saves computational time without sacrificing the performance of the optimized structure. Moreover, the post-processing procedures are streamlined, resulting in continuous, smooth, and editable models.

Topology optimization via implicit neural representations

Along with the rapid development of artificial intelligence (AI) technology, scientific research enters a new era of AI. Topology optimization (TO) and AI technology are recently showing a growing trend of cross development, which has received continuous attentions from relative researchers. In this paper, we introduce a concept of Implicit Neural Representations from AI into TO field and establish a novel TO framework which is named as TOINR. In TOINR, the Topology Description Function is the inherent combination factor, which is constructed by a Neural Network (NN) and determines the topology statuses of structural materials in the design domain. We adopt sine as a periodic activation function combined with MLP as the architecture of the NN. The inputs of NN are a predefined set of spatial points’ coordinates, while the outputs are the implicit representations of corresponding topological boundaries. Along with updating NN’s parameters (i.e., design variables), the structural topologies iteratively evolve according to the responses analysis results and optimization functions. A boundary-adaptive multi-resolution finite element analysis method is developed to improve the physical response accuracy. At each step of TOINR, we ensure the computational differentiability. Thus, the automatic differentiation is used in sensitivity analysis. The multi-penalty function approach is applied to handle objects with constraints. Besides, we design an adaptive adjustment scheme for learning rates to enhance the stability of the optimization process. Numerical examples illustrate that TOINR can stably obtain optimized structures for different problems with high performance and robustness.

Multiresolution method for bending of plates with complex shapes

Multiresolution method for bending of plates with complex shapes

A wavelet method for large-deflection bending of irregular plates

A very rare sixth-order multiresolution method based on wavelet theory for solving irregular domain problems is proposed and applied to the large-deflection bending of irregular plates. High accuracy approximation of multiple integrals of functions defined in the irregular domain is realized by suggesting a new method of boundary extension of functions, which is associated with a boundary node removal technique to avoid singular matrices in the integration process. Each derivative of the unknown function is defined as a new function in the solution of the governing differential equations of the problem. The original equations containing derivatives of different orders can then be transformed into a system of algebraic equations with only discrete nodal values of the highest-order derivative, based on integral relations between these functions and excellent accuracy features on the wavelet approximation of multiple integrals. Various boundary conditions for irregular domains can be automatically and almost exactly included in the integration operation. The proposed method is applied to the solution of large-deflection bending of various irregular plates. The displacement and stress results demonstrate that the accuracy of the proposed method is still as high as the rare sixth order even when the problems are strongly nonlinear and defined in irregular regions, showing a much better accuracy and efficiency than those of classical methods such as the finite element method.

Application of Wavelet Transform in The Construction of Short-term Memory EEG Information Transmission Model

Wavelet transform is an analysis method that combines time, frequency (or scale) domains. It has: (1) Multi-resolution; (2) The relative bandwidth is constant; (3) Proper selection of basic wavelet can make wavelet have the ability to represent the local characteristics of signals in both time and frequency domains, and it is known as "the microscope for analyzing signals". An analysis method of EEG signal based on autoregressive model (ARM) and wavelet transform is proposed, and it is used to eliminate noise interference in EEG signal. Wavelet transform is a multi-resolution time scale analysis method, which can divide the signal into sub-band signals of different frequency bands. According to this characteristic of wavelet transform, the EEG signals obtained by sampling are decomposed and denoised at various scales, and the results of decomposition and denoising at various scales are given. Wavelet transform can effectively remove noise interference from EEG signals. Wavelet transform is a multi resolution time scale analysis method that can divide signals into subband signals of different frequency bands. According to this characteristic of wavelet transform, the sampled EEG signal is decomposed and denoised at various scales, and the decomposition and denoising results at each scale are given. Wavelet transform can effectively remove noise interference from EEG signals.

A multiresolution layerwise method with intrinsic damage detection capabilities for the simulation of guided waves in composite strips

A high-order layerwise multiresolution method that utilizes Daubechies wavelet and scaling functions for the approximation of state variables is presented for the enhanced simulation of guided waves in composite strips. The multiresolution approximation yields a hierarchical set of equations of motion involving the coarse component of generalized displacements, and finer components can be added on the coarse one, forming improved predictions until the desired precision is accomplished. The multiresolution approach is combined with a high-order layerwise laminate theory, enabling the accurate prediction of both symmetric and antisymmetric wave modes, the modeling of surface traction, and localized intra-ply and delamination damage types. Numerical results for the simulation of guided waves in laminated composite strips are presented, exhibiting significant reduction in computing times and remarkable convergence rates compared to single-resolution approaches and traditional finite element methods. Moreover, it is shown that each resolution can model specific bandwidths of wavenumbers, thus providing unique inherent capabilities to localize and isolate coexisting wave modes and detect converted and reflected waves, induced by degraded material regions and delaminations.

Numerical analysis of cavity deformation of oblique water entry using a multi-resolution two-phase SPH method

Numerical analysis of cavity deformation of oblique water entry using a multi-resolution two-phase SPH method

Mapping abandoned cropland using Within-Year Sentinel-2 time series

Against the background of the COVID-19 pandemic and various armed conflicts, the world is experiencing an unprecedented food crisis. The reclamation of abandoned cropland with food production potential may increase the global food supply in a short period of time, ensuring food security. At present, the extraction of abandoned cropland is mainly based on low- and medium-resolution remote sensing image data, making it difficult to extract fragmented areas in mountainous regions and to distinguish between abandoned cropland and transitional classes (such as fallow cropland). We developed a change-detection method based on within-year Sentinel-2 time series to extract cropland abandoned from 2018 to 2021 and defined four types of croplands, namely spontaneously abandoned, induced abandoned, fallow, and lost cropland, using Linxia County in mountainous China as the study region. First, cropland objects were generated from multi-temporal Sentinel-2 images using the multi-resolution segmentation method, and the land use map of Linxia County from 2017 to 2021 was drawn using random forest classifier. Second, through defining and identifying different cropland types, the interannual dynamic changes in cropland from 2018 to 2021 were extracted by analyzing the annual land use change trajectory. Third, by analyzing the normalized difference vegetation index (NDVI) time series of cropland within-year, the active and cultivated cropland sites within-year were extracted by threshold segmentation. Finally, the changes in the four cropland types were extracted by intersecting the two result types. Our method captured the object level changes well (overall mapping accuracy = 93 ± 5 %), and the extraction accuracy of abandoned cropland reached 81 ± 2 %. Abandoned cropland was mostly located in areas of medium quality and with a moderate distance from rural settlements. Reclamation can potentially increase the grain production in Linxia County by at least 3.6 % and needs to be combined with the local natural geography and human activities. Our method is a robust method for extracting abandoned cropland and may be applied to other research related to land use change.

Adaptive Multi-Resolution Method for 3D Reactive Flows with Level Set Front Capturing

Adaptive Multi-Resolution Method for 3D Reactive Flows with Level Set Front Capturing

SAR and Optical Pixel Level Fusion Methods and Evaluations

ABSTRACT In the past years, many vital improvements in image fusion have been recognised, particularly in the fusion of Synthetic Aperture Radar (SAR) and optical sensors. Many kinds of research have been published focusing on pixel-level fusion of SAR and optical images. This paper focuses on pixel-level SAR-optical fusion methods, along with performance assessment and applications. Five categories (component substitution, numerical method, model-based, multi-resolution, and hybrid methods) of fusion are presented. Subjective, objective , and comprehensive methods are surveyed to assess fusion. Experimental findings utilising Sentinel-1A and Sentinel-2A data corroborate the assessment. Finally, paper suggests that there is still space for investigation into SAR-optical fusion.

Capturing the start point of the virus-cell interaction with high-speed 3D single-virus tracking.

The early stages of the virus-cell interaction have long evaded observation by existing microscopy methods due to the rapid diffusion of virions in the extracellular space and the large three-dimensional cellular structures involved. Here we present an active-feedback single-particle tracking method with simultaneous volumetric imaging of the live cell environment called 3D-TrIm to address this knowledge gap. 3D-TrIm captures the extracellular phase of the infectious cycle in what we believe is unprecedented detail. We report what are, to our knowledge, previously unobserved phenomena in the early stages of the virus-cell interaction, including skimming contact events at the millisecond timescale, orders of magnitude change in diffusion coefficient upon binding and cylindrical and linear diffusion modes along cellular protrusions. Finally, we demonstrate how this method can move single-particle tracking from simple monolayer culture toward more tissue-like conditions by tracking single virions in tightly packed epithelial cells. This multiresolution method presents opportunities for capturing fast, three-dimensional processes in biological systems.

Foveated Stochastic Lightcuts.

Foveated rendering provides an idea for accelerating rendering algorithms without sacrificing the perceived rendering quality in virtual reality applications. In this paper, we propose a foveated stochastic lightcuts method to render high-quality many-lights illumination effects in high perception-sensitive regions. First, we introduce a spatiotemporal-luminance based lightcuts generation method to generate lightcuts with different accuracy for different visual perception-sensitive regions. Then we propose a multi-resolution light samples selection method to select the light sample for each node in the lightcuts more efficiently. Our method supports full-dynamic scenes containing over 250k dynamic light sources and dynamic diffuse/specular/glossy objects. It provides frame rates up to 110fps for high-quality many-lights illumination effects in high perception-sensitive regions of the HVS in VR HMDs. Compared with the state-of-the-art stochastic lightcuts method using the same rendering time, our method achieves smaller mean squared errors in the fovea and periphery. We also conduct user studies to prove that the perceived quality of our method has a high visual similarity with the results of the ground truth rendered by using the stochastic lightcuts with 2048 light samples per pixel.