arXiv subjects

Wei Guo

Publications and source records attributed to Wei Guo.

At least 289 records · Page 16Linked to original sources

arXiv2020

A cryogenic-helium pipe flow facility with unique double-line molecular tagging velocimetry capability

Cryogenic helium-4 has extremely small kinetic viscosity, which makes it a promising material for high Reynolds ($Re$) number turbulence research in compact laboratory apparatuses. In its superfluid phase (He II), helium has an extraordinary heat transfer capability and has been utilized in various scientific and engineering applications. In order to unlock the full potential of helium in turbulence research and to improve our understanding of the heat transfer mechanism in He II, a flow facility that allows quantitative study of helium heat-and-mass transfer processes is needed. Here we report our work in assembling and testing a unique helium pipe flow facility that incorporates a novel double-line molecular tracking velocimetry (DL-MTV) system. This flow facility allows us to generate turbulent pipe flows with $Re$ above $10^7$, and it can also be adapted to produce heat-induced counterflow in He II. The DL-MTV system, which is based on the generation and tracking of two parallel thin He$^*_2$ molecular tracer lines with an adjustable separation distance, allows us to measure not only the velocity profile but also both the transverse and longitudinal spatial velocity structure functions. We have also installed a deferential pressure sensor to the flow pipe for pressure drop measurement. The testing results of the flow facility and the measurement devices are presented. We discuss how this facility will allow us to solve some outstanding problems in the helium heat-and-mass transfer topic area.

cond-mat.other↗

arXiv2020

Molecular Tagging Velocimetry in Superfluid Helium-4: Progress, Issues, and Future Development

Helium-4 in the superfluid phase (He II) is a two-fluid system that exhibits fascinating quantum hydrodynamics with important scientific and engineering applications. However, the lack of high-precision flow measurement tools in He II has impeded the progress in understanding and utilizing its hydrodynamics. In recent years, there have been extensive efforts in developing quantitative flow visualization techniques applicable to He II. In particular, a powerful molecular tagging velocimetry (MTV) technique, based on tracking thin lines of He$^*_2$ excimer molecules created via femtosecond laser-field ionization in helium, has been developed in our lab. This technique allows unambiguous measurement of the normal-fluid velocity field in the two-fluid system. Nevertheless, there are two limitations of this technique: 1) only the velocity component perpendicular to the tracer line can be measured; and 2) there is an inherent error in determining the perpendicular velocity. In this paper, we discuss how these issues can be resolved by advancing the MTV technique. We also discuss two novel schemes for tagging and producing He$^*_2$ tracers. The first method allows the creation of a tagged He$^*_2$ tracer line without the use of an expensive femtosecond laser. The second method enables full-space velocity field measurement through tracking small clouds of He$^*_2$ molecules created via neutron-$^3$He absorption reactions in He II.

cond-mat.other↗

arXiv2020

A semi-Lagrangian discontinuous Galerkin (DG) -- local DG method for solving convection-diffusion equations

In this paper, we propose an efficient high order semi-Lagrangian (SL) discontinuous Galerkin (DG) method for solving linear convection-diffusion equations. The method generalizes our previous work on developing the SLDG method for transport equations (J. Sci. Comput. 73: 514-542, 2017), making it capable of handling additional diffusion and source terms. Within the DG framework, the solution is evolved along the characteristics; while the diffusion term is discretized by the local DG (LDG) method and integrated along characteristics by implicit Runge-Kutta methods together with source terms. The proposed method is named the `SLDG-LDG' method and enjoys many attractive features of the DG and SL methods. These include the uniformly high order accuracy (e.g. third order) in space and in time, compact, mass conservative, and stability under large time stepping size. An $L^2$ stability analysis is provided when the method is coupled with the first order backward Euler discretization. Effectiveness of the method are demonstrated by a group of numerical tests in one and two dimensions.

math.NA↗

arXiv2020

Shape fluctuations and optical transition of He$_{2}^{*}$ excimer tracers in superfluid $^4$He

Metastable He$_{2}^{*}$ excimer molecules have been utilized as tracer particles of the normal component in superfluid $^4$He (He II) which can be imaged via laser-induced fluorescence. These excimer molecules form tiny bubbles in He II and can bind to quantized vortices at sufficiently low temperatures, thereby allowing for direct visualization of vortex dynamics in an inviscid superfluid. However, the $a^{3}Σ^+_{u}$${\rightarrow}$$c^{3}Σ^+_{g}$ optical absorption line, which is responsible for the fluorescence imaging of the He$_{2}^{*}$ molecules, is controlled by fluctuations on the bubble shape, and its exact line profile is not known at low temperatures. In this paper, we present a bubble model for evaluating the surface fluctuation eigenmodes of the excimers in He II. The line profile of the $a^{3}Σ^+_{u}{\rightarrow}c^{3}Σ^+_{g}$ transition is calculated at different temperatures by considering both the zero-point and thermal fluctuations on the bubble shape. We show that, as the temperature drops from 2~K to 20 mK, the peak absorption strength is enhanced by a factor of about five, accompanying a blueshift of the peak location by about 2 nm. A double-peak line profile due to the rotational levels of the molecular core can be resolved. This bubble model also allows us to evaluate the stiffness of the He$_{2}^{*}$ bubbles and hence their diffusion constant in He II due to scattering off thermal phonons. Our results will aid the design of future experiments on imaging quantized vortices in He II using He$_{2}^{*}$ tracers.

cond-mat.mes-hall↗

arXiv2019

Semantic-Aware Label Placement for Augmented Reality in Street View

In an augmented reality (AR) application, placing labels in a manner that is clear and readable without occluding the critical information from the real-world can be a challenging problem. This paper introduces a label placement technique for AR used in street view scenarios. We propose a semantic-aware task-specific label placement method by identifying potentially important image regions through a novel feature map, which we refer to as guidance map. Given an input image, its saliency information, semantic information and the task-specific importance prior are integrated into the guidance map for our labeling task. To learn the task prior, we created a label placement dataset with the users' labeling preferences, as well as use it for evaluation. Our solution encodes the constraints for placing labels in an optimization problem to obtain the final label layout, and the labels will be placed in appropriate positions to reduce the chances of overlaying important real-world objects in street view AR scenarios. The experimental validation shows clearly the benefits of our method over previous solutions in the AR street view navigation and similar applications.

cs.CV↗

arXiv2019

An Adaptive Supervision Framework for Active Learning in Object Detection

Active learning approaches in computer vision generally involve querying strong labels for data. However, previous works have shown that weak supervision can be effective in training models for vision tasks while greatly reducing annotation costs. Using this knowledge, we propose an adaptive supervision framework for active learning and demonstrate its effectiveness on the task of object detection. Instead of directly querying bounding box annotations (strong labels) for the most informative samples, we first query weak labels and optimize the model. Using a switching condition, the required supervision level can be increased. Our framework requires little to no change in model architecture. Our extensive experiments show that the proposed framework can be used to train good generalizable models with much lesser annotation costs than the state of the art active learning approaches for object detection.

cs.CV↗

arXiv2019

Torquing the Condensate: Angular Momentum Transport in Bose-Einstein Condensates by Solitonic "Corkscrew"

When rotating classical fluid drops merge together, angular momentum can be advected from one to another due to the viscous shear flow at the drop interface. It remains elusive what the corresponding mechanism is in inviscid quantum fluids such as Bose-Einstein condensates (BECs). Here we report our theoretical study of an initially static BEC merging with a rotating BEC in three-dimensional space along the rotational axis. We show that a soliton sheet resembling a "corkscrew" spontaneously emerges at the interface. Rapid angular momentum transfer at a constant rate universally proportional to the initial angular momentum density is observed. Strikingly, this transfer does not necessarily involve fluid advection or drifting of the quantized vortices. We reveal that the solitonic corkscrew can exert a torque that directly creates angular momentum in the static BEC and annihilates angular momentum in the rotating BEC. Uncovering this intriguing angular momentum transport mechanism may benefit our understanding of various coherent matter-wave systems, spanning from atomtronics on chips to dark matter BECs at cosmic scales.

cond-mat.quant-gas↗

arXiv2019

Deep Learning-Based Semantic Segmentation of Microscale Objects

Accurate estimation of the positions and shapes of microscale objects is crucial for automated imaging-guided manipulation using a non-contact technique such as optical tweezers. Perception methods that use traditional computer vision algorithms tend to fail when the manipulation environments are crowded. In this paper, we present a deep learning model for semantic segmentation of the images representing such environments. Our model successfully performs segmentation with a high mean Intersection Over Union score of 0.91.

eess.IV↗

arXiv2019

A sparse grid discontinuous Galerkin method for the high-dimensional Helmholtz equation with variable coefficients

The simulation of high-dimensional problems with manageable computational resource represents a long-standing challenge. In a series of our recent work [25, 17, 18, 24], a class of sparse grid DG methods has been formulated for solving various types of partial differential equations in high dimensions. By making use of the multiwavelet tensor-product bases on sparse grids in conjunction with the standard DG weak formulation, such a novel method is able to significantly reduce the computation and storage cost compared with full grid DG counterpart, while not compromising accuracy much for sufficiently smooth solutions. In this paper, we consider the high-dimensional Helmholtz equation with variable coefficients and demonstrate that for such a problem the efficiency of the sparse grid DG method can be further enhanced by exploring a semi-orthogonality property associated with the multiwavelet bases, motivated by the work [21, 22, 19]. The detailed convergence analysis shows that the modified sparse grid DG method attains the same order accuracy, but the resulting stiffness matrix is much sparser than that by the original method, leading to extra computational savings. Numerical tests up to six dimensions are provided to verify the analysis.

math.NA↗

arXiv2019

Automatic estimation of heading date of paddy rice using deep learning

Accurate estimation of heading date of paddy rice greatly helps the breeders to understand the adaptability of different crop varieties in a given location. The heading date also plays a vital role in determining grain yield for research experiments. Visual examination of the crop is laborious and time consuming. Therefore, quick and precise estimation of heading date of paddy rice is highly essential. In this work, we propose a simple pipeline to detect regions containing flowering panicles from ground level RGB images of paddy rice. Given a fixed region size for an image, the number of regions containing flowering panicles is directly proportional to the number of flowering panicles present. Consequently, we use the flowering panicle region counts to estimate the heading date of the crop. The method is based on image classification using Convolutional Neural Networks (CNNs). We evaluated the performance of our algorithm on five time series image sequences of three different varieties of rice crops. When compared to the previous work on this dataset, the accuracy and general versatility of the method has been improved and heading date has been estimated with a mean absolute error of less than 1 day.

q-bio.QM↗

arXiv2019

Comparison of semi-Lagrangian discontinuous Galerkin schemes for linear and nonlinear transport simulations

Transport problems arise across diverse fields of science and engineering. Semi-Lagrangian (SL) discontinuous Galerkin (DG) methods are a class of high order deterministic transport solvers that enjoy advantages of both SL approach and DG spatial discretization. In this paper, we review existing SLDG methods to date and compare numerical their performances. In particular, we make a comparison between the splitting and non-splitting SLDG methods for multi-dimensional transport simulations. Through extensive numerical results, we offer a practical guide for choosing optimal SLDG solvers for linear and nonlinear transport simulations.

math.NA↗

arXiv2019

Weyl nodal point-line Fermion in ferromagnetic Eu$_5$Bi$_3$

Based on $ab$ $initio$ calculations and low-energy effective $k{\cdot}p$ model, we propose a type of Weyl nodal point-line fermion, composed of 0D Weyl points and 1D Weyl nodal line, in ferromagnetic material Eu$_5$Bi$_3$. In the absence of spin-orbital coupling (SOC), the spin-up bands host a pair of triply degenerate points together with a unique bird-cage like node structure. In the presence of SOC with (001) magnetization, each triplet point splits into a double Weyl point and a single Weyl point accompanied by two nodal rings, forming two sets of Weyl nodal point-line fermions near the Fermi level. The novel properties of Weyl nodal point-line fermion are explored by revealing the unusual Berry curvature field and demonstrating the pinned chiral surface states with exotic Fermi arcs at different planes. Moreover, a large anomalous Hall conductivity of -260 ($\hbar$/$e$)($Ω$cm)$^{-1}$ parallel to [001] direction is predicted. Our work offers a new perspective for exploring novel topological semimetal states with diverse band-crossing dimensions, and provides an ideal material candidate for future experimental realiztion.

cond-mat.mtrl-sci↗

arXiv2019

Towards Personalized Management of Type B Aortic Dissection Using STENT: a STandard cta database with annotation of the ENtire aorta and True-false lumen

Type B Aortic Dissection(TBAD) is a rare aortic disease with a high 5-year mortality.Personalized and precise management of TBAD has been increasingly desired in clinic which requires the geometric parameters of TBAD specific to the patient be measured accurately.This remains to be a challenging task for vascular surgeons as manual measurement is highly subjective and imprecise. To solve this problem,we introduce STENT-a STandard cta database with annotation of the ENtire aorta and True-false lumen. The database contains 274 CT angiography (CTA) scans from 274 unique TBAD patients and is split into a training set(254 cases including 210 preoperative and 44 postoperative scans ) and a test set(20 cases).Based on STENT,we develop a series of methods including automated TBAD segmentation and automated measurement of TBAD parameters that facilitate personalized and precise management of the disease. In this work, the database and the proposed methods are thoroughly introduced and evaluated and the results of our study shows the feasibility and effectiveness of our approach to easing the decision-making process for vascular surgeons during personalized TBAD management.

physics.med-ph↗

arXiv2019

Sparse Grid Discontinuous Galerkin Methods for the Vlasov-Maxwell System

In this paper, we develop sparse grid discontinuous Galerkin (DG) schemes for the Vlasov-Maxwell (VM) equations. The VM system is a fundamental kinetic model in plasma physics, and its numerical computations are quite demanding, due to its intrinsic high-dimensionality and the need to retain many properties of the physical solutions. To break the curse of dimensionality, we consider the sparse grid DG methods that were recently developed in \cite{guo2016sparse,guo2017adaptive} for transport equations. Such methods are based on multiwavelets on tensorized nested grids and can significantly reduce the numbers of degrees of freedom. We formulate two versions of the schemes: sparse grid DG and adaptive sparse grid DG methods for the VM system. Their key properties and implementation details are discussed. Accuracy and robustness are demonstrated by numerical tests, with emphasis on comparison of the performance of the two methods, as well as with their full grid counterparts.

math.NA↗

arXiv2018

Quench spot detection for superconducting accelerator cavities via flow visualization in superfluid helium-4

Superconducting ratio-frequency (SRF) cavities, cooled by superfluid helium-4 (He II), are key components in modern particle accelerators. Quenches in SRF cavities caused by Joule heating from local surface defects can severely limit the maximum achievable accelerating field. Existing methods for quench spot detection include temperature mapping and second-sound triangulation. These methods are useful but all have known limitations. Here we describe a new method for surface quench spot detection by visualizing the heat transfer in He II via tracking He$_2^*$ molecular tracer lines. A proof-of-concept experiment has been conducted, in which a miniature heater mounted on a plate was pulsed on to simulate a surface quench spot. A He$_2^*$ tracer line created nearby the heater deforms due to the counterflow heat transfer in He II. By analyzing the tracer-line deformation, we can well reproduce the heater location within a few hundred microns, which clearly demonstrates the feasibility of this new technology. Our analysis also reveals that the heat content transported in He II is only a small fraction of the total input heat energy. We show that the remaining energy is essentially consumed in the formation of a cavitation zone near the heater. By estimating the size of this cavitation zone, we discuss how the existence of the cavitation zone may explain a decades-long puzzle observed in many second-sound triangulation experiments.

physics.acc-ph↗

arXiv2018

On spinodal decomposition in alnico---a transmission electron microscopy and atom probe tomography study

Alnico is a prime example of a finely tuned nanostructure whose magnetic properties are intimately connected to magnetic annealing (MA) during spinodal transformation and subsequent lower temperature annealing (draw) cycles. Using a combination of transmission electron microscopy and atom probe tomography, we show how these critical processing steps affect the local composition and nanostructure evolution with impact on magnetic properties. The nearly 2-fold increase of intrinsic coercivity ($H_\text{ci}$) during the draw cycle is not adequately explained by chemical refinement of the spinodal phases. Instead, increased Fe-Co phase ($α_1$) isolation, development of Cu-rich spheres/rods/blades and additional $α_1$ rod precipitation that occurs during the MA and draw, likely play a key role in $H_\text{ci}$ enhancement. Chemical ordering of the Al-Ni-phase ($α_2$) and formation of Ni-rich ($α_3$) may also contribute. Unraveling of the subtle effect of these nano-scaled features is crucial to understanding on how to improve shape anisotropy in alnico magnets.

cond-mat.mtrl-sci↗

arXiv2018

Characterizing vortex tangle properties in steady-state He II counterflow using particle tracking velocimetry

Historically, there is little faith in particle tracking velocimetry (PTV) as a tool to make quantitative measurements of thermal counterflow in He II, since tracer particle motion is complicated by influences from the normal fluid, superfluid, and quantized vortex lines, or a combination thereof. Recently, we introduced a scheme for differentiating particles trapped on vortices (G1) from particles entrained by the normal fluid (G2). In this paper, we apply this scheme to demonstrate the utility of PTV for quantitative measurements of vortex dynamics in He II counterflow. We estimate $\ell$, the mean vortex line spacing, using G2 velocity data, and $c_2$, a parameter related to the mean curvature radius of vortices and energy dissipation in quantum turbulence, using G1 velocity data. We find that both estimations show good agreement with existing measurements that were obtained using traditional experimental methods. This is of particular consequence since these parameters likely vary in space, and PTV offers the advantage of spatial resolution. We also show a direct link between power-law tails in transverse particle velocity probability density functions (PDFs) and reconnection of vortex lines on which G1 particles are trapped.

cond-mat.other↗

arXiv2018

A Study of Deep Feature Fusion based Methods for Classifying Multi-lead ECG

An automatic classification method has been studied to effectively detect and recognize Electrocardiogram (ECG). Based on the synchronizing and orthogonal relationships of multiple leads, we propose a Multi-branch Convolution and Residual Network (MBCRNet) with three kinds of feature fusion methods for automatic detection of normal and abnormal ECG signals. Experiments are conducted on the Chinese Cardiovascular Disease Database (CCDD). Through 10-fold cross-validation, we achieve an average accuracy of 87.04% and a sensitivity of 89.93%, which outperforms previous methods under the same database. It is also shown that the multi-lead feature fusion network can improve the classification accuracy over the network only with the single lead features.

cs.CV↗