Reconstruction Scheme Research Articles

High order recovery of geometric interfaces from cell-average data

We consider the problem of recovering multivariate characteristic functions u := χΩ from cell-average data on a coarse grid, motivated in particular by the accurate treatment of interfaces in finite volume schemes. While linear recovery methods are known to perform poorly, nonlinear strategies based on local reconstructions of the jump interface Γ := ∂Ω by geometrically simpler interfaces may offer significant improvements. We study two main families of local reconstruction schemes the first one based on nonlinear least-squares fitting, the second one based on the explicit computation of a polynomial-shaped curve fitting the data, which yields simpler numerical computations and high order geometric fitting. For each of them, we derive a general theoretical framework which allows us to control the recovery error by the error of best approximation up to a fixed multiplicative constant. Numerical tests in 2d illustrate the expected approximation order of these strategies. Several extensions are discussed, in particular the treatment of piecewise smooth interfaces with corners.

Improving approximation accuracy in Godunov-type smoothed particle hydrodynamics methods

The study examines the origin of errors resulting from the approximation of the right hand sides of the Euler equations using the Godunov type contact method of smoothed particle hydrodynamics (CSPH). The analytical expression for the numerical shear viscosity in CSPH method is obtained. In our recent study the numerical viscosity was determined by comparing the numerical solution of momentum diffusion in the shear flow with theoretical one. In this study we deduce the analytical expression for the numerical viscosity which is found to be similar to numerical one, confirming the obtained results. To reduce numerical diffusion, diffusion limiters are typically applied to expressions for contact values of velocity and pressure, as well as higher-order reconstruction schemes. Based on the performed theoretical analysis, we propose a new method for correcting quantities at interparticle contacts in CSPH method, which can be easily extended to the MUSCL-type (Monotonic Upstream-centered Scheme for Conservation Laws) method. Original CSPH and MUSCL-SPH approaches and ones with aforementioned correction are compared.

Reconstruction of directional sources using physics-informed neural networks

In the context of augmented/virtual reality applications, replicating the directivity of sound sources is a critical step to ensure a high-quality immersive experience. However, accurate directivity measurements require complex experimental procedures involving a high number of microphones. In these scenarios, spatial interpolation allows to reconstruct the sound field with fewer sensors, which can be sparsely distributed around the source. The literature offers several interpolation schemes for sound field reconstruction, although they exhibit limited accuracy due to the lack of knowledge on the underlying physics. Recently, Physics-Informed Neural Networks (PINN) have been used to directly solve partial differential equations and thus better predict the evolution of dynamical systems. In this manuscript, we propose a spatial interpolation scheme based on PINN for the reconstruction of sound source directivity. PINN are used to solve the Helmholtz equation starting from acquisitions of the directivity over a limited set of sparsely distributed points. Results are compared with the outcomes of well-established methods based on Spherical Harmonics Decomposition (SH), Thin Plate Pseudo-Splines Interpolation (SPLI) and Piece-wise Linear Spherical Triangular Interpolation (TRI). PINN prove to provide better reconstruction of the directivity with respect to the proposed comparison methods, even when dealing with highly sparse sampling grids.

Flux reconstruction approach for long-time calculations of the linearized Euler equation

The Linearized Euler Equations (LEE) are one of the fundamental governing equations in acoustics research. Due to the relatively small magnitude of acoustic disturbances compared to the physical quantities of the background flow, ensuring highly precise solutions without dissipation and dispersion over time is essential. The Flux Reconstruction (FR) method, recognized as a high-order numerical method with flexible construction capabilities, has been extensively utilized in recent years for solving partial differential equations numerically. This paper introduces an offsetting numerical flux for one-dimensional LEE. Subsequently, it develops a flux reconstruction scheme with energy-conserving characteristics, referred to as the ECFR scheme, aimed at achieving high-precision numerical solutions for prolonged computations. The energy-conserving feature of the scheme is substantiated through theoretical derivation and further corroborated by numerical validation. Moreover, the impact of Mach numbers and the parameter of the offsetting flux on the scheme's performance is discussed. The stability of the scheme is also analyzed, with the limit of the CFL number being provided. The novel ECFR scheme facilitates obtaining high-precision acoustic numerical solutions, thereby providing strong support for the study of complex engineering problems such as aircraft, submarine, and automobile noise.

Deep-learning based single-shot 3D reconstruction with simulated color-crosstalk and randomized extrinsics

In recent years, many single-frame 3D reconstruction schemes based on fringe projection profilometry (FPP) have been proposed. However, most single-frame reconstruction schemes still face the following three issues: (1) obtaining large datasets is very time-consuming, (2) focusing only on achieving single-frame reconstruction for white objects, and (3) requiring fixing the camera-projector positional relationship, increasing operational difficulty. By building a virtual FPP simulation system, our method can quickly render the required datasets, avoiding cumbersome manual operations. When rendering the datasets, we simulate adverse factors such as color channel crosstalk, system extrinsic parameter variations, and object surface colors to guide the training of the neural network. Ultimately, from a single three-frequency color image, the corresponding three-frequency three-step phase-shift images are predicted, achieving single-frame 3D reconstruction of colored objects and allowing some variation in system extrinsic parameters. Real-world experiments demonstrate that the network trained with the diverse data generated by our virtual system has good accuracy, providing valuable guidance for the practical application of deep learning methods.

Improving mimetic gravity with non-trivial scalar potential: Cosmology, black holes, shadow and photon sphere

It is not easy to treat the spacetime with horizon(s) in the standard mimetic gravity. The solution to this problem has been presented in Nojiri and Nashed (2022), where it was suggested to modify the Lagrange multiplier constraint.In this paper, by using the improved formulation, we investigate the cosmology and black holes in mimetic gravity with scalar potential and in the scalar mimetic F(R) gravity. The inflationary era and dark energy epoch for the above theories are presented as specific examples from the general reconstruction scheme which permits to realize any universe expansion history via the choice of the corresponding scalar potential or function F(R). Two black hole solutions including the Schwarzschild and Hayward ones are constructed. The shadow and the radius of the photon sphere for the above black holes are found. The explicit confrontation of the black hole shadow radius with the observational bounds from M87∗ and Sgr A∗ objects is done. It is demonstrated that they do not conflict with Event Horizon Telescope observations.

Surface pressure reconstruction from LPT data with boundary conforming grids

Abstract The coupling between fluid-structure interactions is governed by the pressure distribution over the interaction surface between the fluid and solid domains. The capabilities of non-intrusive optical techniques, such as particle image velocimetry and Lagrangian particle tracking (LPT), have been proven to provide accurate velocity and acceleration information within the flow field while simultaneously tracking the corresponding structural deformations. However, scattered data from LPT measurements are typically mapped onto Cartesian grids, independently of the shape of the solid objects in the measurement domain. The use of Cartesian grids poses challenges for the determination of the surface pressure because the velocity gradients close to the object’s surface are not captured accurately. Therefore, an alternative surface pressure reconstruction scheme utilizing LPT data based on the arbitrary Lagrangian–Eulerian approach is proposed to mitigate the error propagation associated with the use of uniform grids. The introduced method provides an exact surface conformation utilizing boundary fitted coordinate systems and radial basis function based mesh deformations, which eliminates the need to use extrapolations to obtain surface pressure distributions. The introduced approach is assessed by means of a synthetic hill surface probing a three-dimensional analytical flow field; its practical applicability is demonstrated through an experimental characterization of turbulent boundary layer interactions with a steadily and unsteadily deforming elastic membrane.

CoSeg: Cognitively Inspired Unsupervised Generic Event Segmentation.

Some cognitive research has discovered that humans accomplish event segmentation as a side effect of event anticipation. Inspired by this discovery, we propose a simple yet effective end-to-end self-supervised learning framework for event segmentation/boundary detection. Unlike the mainstream clustering-based methods, our framework exploits a transformer-based feature reconstruction scheme to detect event boundaries by reconstruction errors. This is consistent with the fact that humans spot new events by leveraging the deviation between their prediction and what is perceived. Thanks to their heterogeneity in semantics, the frames at boundaries are difficult to be reconstructed (generally with large reconstruction errors), which is favorable for event boundary detection. In addition, since the reconstruction occurs on the semantic feature level instead of the pixel level, we develop a temporal contrastive feature embedding (TCFE) module to learn the semantic visual representation for frame feature reconstruction (FFR). This procedure is like humans building up experiences with "long-term memory." The goal of our work is to segment generic events rather than localize some specific ones. We focus on achieving accurate event boundaries. As a result, we adopt the F1 score (Precision/Recall) as our primary evaluation metric for a fair comparison with previous approaches. Meanwhile, we also calculate the conventional frame-based mean over frames (MoF) and intersection over union (IoU) metric. We thoroughly benchmark our work on four publicly available datasets and demonstrate much better results. The source code is available at https://github.com/wang3702/CoSeg.

Thermal effect on the flow induced by a single-dielectric-barrier-discharge plasma actuator under steady actuation

The thermal effect of a single-dielectric-barrier-discharge plasma actuator under steady actuation is numerically investigated. A new actuator model is proposed and validated using experimental data. A discrete Galerkin method based on high-order flux reconstruction schemes is employed to solve the flow governing equations and the actuator model equations on unstructured quadrilateral grids. By comparing the induced heated and cold flow fields of the actuator with and without a plasma thermal source, its thermal effect is revealed. The actuator generates a thermal wall jet with rich vorticity, forming a monopolar starting vortex with a high-temperature and low-density core. Over time, the starting vortex becomes unstable and transforms into a dipole. Actuator heating enhances jet velocity and width, as well as vortex stability, while slowing down vorticity generation. The relative change in density and temperature fields due to actuator heating is four orders of magnitude greater than that without actuator heating. Additionally, the actuator heating causes the background thermodynamic fields to increase approximately linearly with time. Two stages in the actuator's thermal effect are distinguished due to time accumulation. Initially, the actuator heating minimally affects the monopolar starting vortex motion, and the temperature and density fields are treated as passive variables driven by the velocity field. During this stage, the momentum and thermal effects of the actuator can be studied separately. However, after the starting vortex becomes unstable, the actuator heating significantly impacts its motion and morphology, and these two effects are coupled with each other.

A family of TENOA-THINC-MOOD schemes based on diffuse-interface method for compressible multiphase flows

In this work, we develop a family of hybrid algorithms to simulate compressible multi-phase flows by solving the quasi-conservative five-equation model. The general numerical framework utilizes targeted essentially non-oscillatory schemes with adaptive dissipation (TENOA) to boost the resolution of high-frequency waves, whereas the Tangent of Hyperbola for INterface Capturing (THINC) scheme is activated near the shock and contact discontinuities as well as material interfaces to maintain their sharpness, based on a novel indicator. Also, the newly derived THINC reconstruction scheme is both simpler and better at preserving the flow symmetry. The robustness of our numerical approach is further fortified through the adaptation of a modified a posteriori multidimensional optimal order detection (MOOD) technique. Moreover, to mitigate carbuncle phenomenon encountered in two-dimensional simulations, a hybrid Riemann solver is also proposed based on the low Mach modification around shock for the Harten-Lax-van Leer contact (HLLC) approximate Riemann solver. Numerical results of the challenging one- and two-dimensional benchmark cases demonstrate the superiority of the proposed methods regarding oscillation-free, interface-sharpening, low-dissipation and robustness properties.

A virtual reconstruction method for corridor gable buildings based on the knowledge of structural dynamics: taking Leiyin Cave as an example

Virtual reconstruction of ancient buildings often has incomplete records of the original design and construction details, and can only be reconstructed based on limited data, drawings and photography, which is different from the actual conditions. The unique overhanging structure of the corridor gable building makes it vulnerable to damage in extreme weather conditions. In order to ensure that the virtual reconstruction results can not only reproduce the original appearance of history, but also ensure that the reconstructed model maintains structural stability in the long term. This paper proposes a reconstruction method of the original appearance of the corridor gable building remains based on structural dynamics analysis. This method comprehensively uses three-dimensional reconstruction, structural engineering, dynamic analysis, and computer simulation technology to ensure the structural accuracy and historical authenticity of the virtually reconstructed corridor gable building. First, through data collection and analysis, combined with ancient architectural construction techniques, a preliminary three-dimensional model was created, which included all structural elements and details. Several groups of reconstruction schemes are determined based on material properties. Then, using finite element analysis software, perform dynamic analysis on the three-dimensional model. Evaluate the stability of the reconstructed structure and optimize the material selection plan to ensure the feasibility and accuracy of the virtual reconstruction. Taking the virtual reconstruction of the eaves in front of Leiyin Cave as an example, it shows that this method is effective and feasible to achieve the virtual reconstruction of corridor gable buildings. It provides new ideas for virtual reconstruction of ancient buildings and has important practical application value.

A Hierarchical Point-spread Function Reconstruction Method

Reconstruction of the point-spread function (PSF) plays an important role in many areas of astronomy, including photometry, astrometry, galaxy morphology, and shear measurement. The atmospheric and instrumental effects are the two main contributors to the PSF, both of which may exhibit complex spatial features. Current PSF reconstruction schemes typically rely on individual exposures, and their ability to reproduce the complicated features of the PSF distribution is therefore limited by the number of stars. Interestingly, in conventional methods, after stacking the model residuals of the PSF ellipticities and (relative) sizes from a large number of exposures, one can often observe some stable and nontrivial spatial patterns on the entire focal plane, which could be quite detrimental to, e.g., weak-lensing measurements. These PSF residual patterns are caused by instrumental effects, as they consistently appear in different exposures. Taking this as an advantage, we propose a multilayer PSF reconstruction method to remove such PSF residuals, the second and third layers of which make use of all available exposures together. We test our method on the i-band data of the second release of the Hyper Suprime-Cam. Our method successfully eliminates most of the PSF residuals. Using the Fourier_Quad shear measurement method, we further test the performance of the resulting PSF fields on shear recovery using the field distortion effect. The PSF residuals have strong correlations with the shear residuals, and our new multilayer PSF reconstruction method can remove most of such systematic errors related to the PSF, leading to much smaller shear biases.

A Single-Frame and Multi-Frame Cascaded Image Super-Resolution Method.

The objective of image super-resolution is to reconstruct a high-resolution (HR) image with the prior knowledge from one or several low-resolution (LR) images. However, in the real world, due to the limited complementary information, the performance of both single-frame and multi-frame super-resolution reconstruction degrades rapidly as the magnification increases. In this paper, we propose a novel two-step image super resolution method concatenating multi-frame super-resolution (MFSR) with single-frame super-resolution (SFSR), to progressively upsample images to the desired resolution. The proposed method consisting of an L0-norm constrained reconstruction scheme and an enhanced residual back-projection network, integrating the flexibility of the variational model-based method and the feature learning capacity of the deep learning-based method. To verify the effectiveness of the proposed algorithm, extensive experiments with both simulated and real world sequences were implemented. The experimental results show that the proposed method yields superior performance in both objective and perceptual quality measurements. The average PSNRs of the cascade model in set5 and set14 are 33.413 dB and 29.658 dB respectively, which are 0.76 dB and 0.621 dB more than the baseline method. In addition, the experiment indicates that this cascade model can be robustly applied to different SFSR and MFSR methods.

Hybridized formulations of flux reconstruction schemes for advection-diffusion problems

We present the hybridization of flux reconstruction methods for advection-diffusion problems. Hybridization introduces a new variable into the problem so that it can be reduced via static condensation. This allows the solution of implicit discretizations to be done more efficiently. We derive an energy statement from a stability analysis considering a range of correction functions on hybridized and embedded flux reconstruction schemes. Then, we establish connections to standard formulations. We devise a post-processing scheme that leverages existing flux reconstruction operators to enhance accuracy for diffusion-dominated problems. Results show that the implicit convergence of these methods for advection-diffusion problems can result in performance benefits of over an order of magnitude. In addition, we observe that the superconvergence property of hybridized methods can be extended to the family of FR schemes for a range of correction functions.

One-shot structured light illumination based on shearlet transform

Balancing speed and accuracy has always been a challenge in 3D reconstruction. One-shot structured light illuminations are of perfect performance on real-time scanning, while the related 3D point clouds are typically of relatively poor quality, especially in regions with rapid height changes. To solve this problem, we propose a one-shot reconstruction scheme based on shearlet transform, which combines spatial and frequency domain information to enhance reconstruction accuracy. First, we apply the shearlet transform to the deformed fringe pattern to obtain the transform coefficients. Second, pixel-wise select the indices associated with the N largest coefficients in magnitude to obtain a new filter. Finally, we refocus globally to extract phase using these filters and generate a reliable quality map based on coefficient magnitudes to guide phase unwrapping. Simultaneously, we utilize the maximum coefficient value to generate a quality map for guiding the phase unwrapping process. Experimental results show that the proposed method is robust in discontinuous regions, resulting in more accurate 3D point clouds.

Human-in-the-Loop Consensus Control for Multiagent Systems With External Disturbances.

In this article, the human-in-the-loop leader-follower consensus control problem is addressed for multiagent systems (MASs) with unknown external disturbances. A human operator is deployed to monitor the MASs' team by transmitting an execution signal to a nonautonomous leader in response to any hazard detected, with the control input of the leader unknown to all followers. For each follower, a full-order observer, in which the observer error dynamic system decouples the unknown disturbance input, is designed for asymptotic state estimation. Then, an interval observer is constructed for the consensus error dynamic system, where the unknown disturbances and control inputs of its neighbors and its disturbance are treated as unknown inputs (UIs). To process the UIs, a new asymptotic algebraic UI reconstruction (UIR) scheme is proposed based on the interval observer, and one of the significant features of the UIR is the capacity to decouple the control input of the follower. The subsequent human-in-the-loop asymptotic convergence consensus protocol is developed by applying an observer-based distributed control strategy. Finally, the proposed control scheme is validated through two simulation examples.

Target reconstruction and process parameter decision-making for bolt intelligent assembly based on robot and multi-camera

Bolt assembly is widely used in modern manufacturing. And with the development of industrial automation, there is a growing demand for automatic assembly. To form a fully automatic and highly intelligent system for bolt assembly, a novel strategy based robot and multi-camera is proposed. In this strategy, a high assurance components reconstruction scheme is designed, wherein the Mask R-CNN is used for the identification and segmentation of the workpiece, then the shape, size and pose of the workpiece are determined by the local point cloud data and its envelope using basic geometries. The results show that the accuracy of workpiece recognition exceeds 96 %, the maximum positioning accuracy of the hole is 0.334 mm based on multi-camera system. Based on the historical fastening process data, an improved artificial jellyfish search optimizer (AJSO) BP neural network is proposed to automatically determine the optimal values for the bolt fastening speed and torque. The BP neural network optimized by AJSO reduces the maximum prediction error of process parameters from 0.117 mm/s to 0.051 mm/s for speed and from 0.052 N·m to 0.027 N·m for torque. Finally, the feasibility of the proposed strategy is demonstrated by an experimental case, which used a dual robot unit for the bearing seat assembly.

Study Of Mixing Enhancement Of The 3-D Acoustofluidic Mixer Using Digital In-Line Holographic Micro Particle Tracking Velocimetry

In this study, the 3-D flow patterns and mixing performances are investigated for an acoustofluidic Y-junction micromixer using Digitial In-line Holographic Micro-Particle Tracking Velocimetry (DIHμPTV) and epi-fluorescence flow visualization. Two parallel longitudinal spines were placed in the channel to induce sustaining solenoidal 3-D acoustic streaming vortices, which work as the secondary flow in the mixing channel to reduce the diffusion mixing length and in turn enhance the mixing performances. The two spines have a triangular cross-section, with a base width of 0.2mm and a height of 0.3mm, placed 1.2mm apart from each other in a 2.8mm width and 1mm height channel. The Y-junction has two configurations, one has the spines directly connected to the side wall at the Y-junction, and the other microchannel has a gap between the two spines and a flat junction shape. The driving mechanism is provided by two piezoelectric disks, operating at 12 kHz oscillation frequency and 20V peak-to-peak amplitude. The 3-D flow patterns were recorded by the DIHμPTV in two measurement volumes to fully cover the test section’s height. The holographic reconstruction scheme is based on the 1st Rayleigh-Sommerfeld solution with an extra deconvolution step to improve the accuracy of the reconstruction of the depth position. The deconvolution step utilizes a simulated hologram of the Point-Spread Function (PSF) and reduces the depth uncertainty during the deconvolution of the raw data. The mixing performances of the acoustofluidic Y-junction micro-mixers were evaluated by the epi-fluorescence experiments, and the mixing indices were calculated and compared to verify the improvements compared to the Yjunction micromixers without the acoustic streaming vortices. The results confirmed the improvement of the mixing and the sustaining 3-D flow patterns even with a high flow rate, which could not be achieved with 2-D sharp-edge obstructions tested previously. The novel configuration may have the potential to inspire designs of high-throughput and low-pressure loss active micro-mixers.

‘Help Shelter Mend a Broken Family’: Homes, Homelessness, and Crisis in 1960s Britain

Abstract In 1966, a new homelessness charity, Shelter, set out to redefine the homeless as impoverished families enduring poor or insecure housing rather than single men living on the street. Through a series of ‘hard-hitting’ reports and adverts, the group highlighted the destructiveness of poor housing on young families and gained a prominent national voice. This article examines Shelter’s campaigns in the 1960s to examine how debates about homes and homelessness can reframe the development of discourses of ‘crisis’ that defined the 1970s and provided a key narrative of Thatcherism. It focuses on how Shelter deployed ideas of childhood, family, and the family home, often echoing theories of leading child psychiatrists of the post-war period, to warn of the profound and acute danger that the housing crisis represented to British society. Shelter’s prominent and successful adverts and campaigns drew on growing anxieties about social and economic crisis, but did not discuss key issues of unemployment, racial discrimination, and deindustrialization. Instead, Shelter’s campaigns focused on the damage poor housing did to family life and the education and life chances of individual children. Discourses of crisis in this period are often associated with ‘inner cities’ and failing reconstruction schemes, but Shelter presented an image of social crisis that was centred in the family and the family home. By analysing how Shelter drew on fears about the coming social crisis, the article suggests that the family home and the life chances of individuals were central to popular understandings and perceptions of crisis before the 1970s.

Generalized sampling of multi-dimensional graph signals based on prior information

The prevalence of multi-dimensional (m-D) graph signals in various real-world applications, such as digital images and data with spatial and temporal dimensions, highlights their significance. However, efficiently sampling and reconstructing these m-D graph signals remains a significant challenge in the field of graph signal processing. In this paper, we study the generalized sampling framework of m-D graph signals based on prior knowledge. First, we consider the subspace priors of m-D graph signals, assuming that the signals are located in the periodic m-D graph spectral subspace. We design the reconstruction scheme through the subspace prior information. Then, we propose a generalized sampling framework for m-D graph signals based on smoothness prior information with lower constraints. It is still possible to recover the original m-D graph signal when the space where the m-D graph signal is located is unknown. In the above framework, sampling and reconstruction can be implemented efficiently, and there is no bandwidth limitation for the m-D graph signals. Finally, several experiments are performed to numerically validate the effectiveness of the proposed sampling framework.