arXiv subjects

Chang Shu

Publications and source records attributed to Chang Shu.

51 records · Page 3Linked to original sources

arXiv2021

On the evolution of fuel droplet evaporation zone and its interaction with the flame front in ignition of spray flames

Evolution of fuel droplet evaporation zone and its interaction with the propagating flame front are studied in this work. A general theory is developed to describe the evolutions of flame propagation speed, flame temperature, droplet evaporation onset and completion locations in ignition and propagation of spherical flames. The influences of liquid droplet mass loading, heat exchange coefficient (or evaporation rate) and Lewis number on spherical spray flame ignition are studied. Two flame regimes are considered, i.e., heterogeneous and homogeneous flames, based on the mixture condition near the flame front. The results indicate that the spray flame trajectories are considerably affected by the ignition energy addition. The critical condition for successful ignition for the fuel-rich mixture is coincidence of inner and outer flame balls from igniting kernel and propagating flame. The flame balls always exist in homogeneous mixtures, indicating that ignition failure and critical successful events occur only in purely gaseous mixture. The fuel droplets have limited effects on minimum ignition energy, which however increases monotonically with the Lewis number. Moreover, flame kernel originates from heterogeneous mixtures due to the initially dispersed droplets near the spark. The evaporative heat loss in the burned and unburned zones of homogeneous and heterogeneous spray flames is also evaluated, and the results show that for the failed flame kernels, evaporative heat loss behind and before the flame front first increases and then decreases. The evaporative heat loss before the flame front generally increases, although non-monotonicity exists, when the flame is successfully ignited and propagate outwardly. For heterogeneous flames, the ratio of the heat loss from the burned zone to the total one decreases as the flame expands.

physics.flu-dyn↗

arXiv2020

The smoothed complexity of Frank-Wolfe methods via conditioning of random matrices and polytopes

Frank-Wolfe methods are popular for optimization over a polytope. One of the reasons is because they do not need projection onto the polytope but only linear optimization over it. To understand its complexity, Lacoste-Julien and Jaggi introduced a condition number for polytopes and showed linear convergence for several variations of the method. The actual running time can still be exponential in the worst case (when the condition number is exponential). We study the smoothed complexity of the condition number, namely the condition number of small random perturbations of the input polytope and show that it is polynomial for any simplex and exponential for general polytopes. Our results also apply to other condition measures of polytopes that have been proposed for the analysis of Frank-Wolfe methods: vertex-facet distance (Beck and Shtern) and facial distance (Peña and Rodríguez). Our argument for polytopes is a refinement of an argument that we develop to study the conditioning of random matrices. The basic argument shows that for $c>1$ a $d$-by-$n$ random Gaussian matrix with $n \geq cd$ has a $d$-by-$d$ submatrix with minimum singular value that is exponentially small with high probability. This has consequences on results about the robust uniqueness of tensor decompositions.

cs.DS↗

arXiv2020

Propagation of weakly stretched premixed spherical spray flames in localized homogeneous and heterogeneous reactants

Propagation of weakly stretched spherical flames in partially pre-vaporized fuel sprays is theoretically investigated in this work. A general theory is developed to describe flame propagation speed, flame temperature, droplet evaporation onset and completion locations. The influences of liquid fuel and gas mixture properties on spherical spray flame propagation are studied. The results indicate that the spray flame propagation speed is enhanced with increased droplet mass loading and/or evaporation heat exchange coefficient (or evaporation rate). Opposite trends are found when the latent heat is high, due to strong evaporation heat absorption. Fuel vapor and temperature gradients are observed in the post-flame evaporation zone of heterogeneous flames. Evaporation completion front location considerably changes with flame radius, but the evaporation onset location varies little relative to the flame front when the flame propagates. For larger droplet loading and smaller evaporation rate, the fuel droplet tends to complete evaporation behind the flame front. Flame bifurcation occurs with high droplet mass loading under large latent heat, leading to multiplicity of flame propagation speed, droplet evaporation onset and completion fronts. The flame enhancement or weakening effects by the fuel droplet sprays are revealed by enhanced or suppressed heat and mass diffusion process in the pre-flame zone. Besides, for heterogeneous flames, heat and mass diffusion in the post-flame zone also exists. The mass diffusion for both homogeneous and heterogeneous flames is enhanced with decreased Lewis number. The magnitude of Markstein length is considerably reduced with increased droplet loading. Moreover, post-flame droplet burning behind heterogeneous flame influences the flame propagation speed and Markstein length when the liquid fuel loading is relatively low.

physics.flu-dyn↗

arXiv2020

Feature-metric Loss for Self-supervised Learning of Depth and Egomotion

Photometric loss is widely used for self-supervised depth and egomotion estimation. However, the loss landscapes induced by photometric differences are often problematic for optimization, caused by plateau landscapes for pixels in textureless regions or multiple local minima for less discriminative pixels. In this work, feature-metric loss is proposed and defined on feature representation, where the feature representation is also learned in a self-supervised manner and regularized by both first-order and second-order derivatives to constrain the loss landscapes to form proper convergence basins. Comprehensive experiments and detailed analysis via visualization demonstrate the effectiveness of the proposed feature-metric loss. In particular, our method improves state-of-the-art methods on KITTI from 0.885 to 0.925 measured by $δ_1$ for depth estimation, and significantly outperforms previous method for visual odometry.

cs.CV↗

arXiv2020

Non-iterative Simultaneous Rigid Registration Method for Serial Sections of Biological Tissue

In this paper, we propose a novel non-iterative algorithm to simultaneously estimate optimal rigid transformation for serial section images, which is a key component in volume reconstruction of serial sections of biological tissue. In order to avoid error accumulation and propagation caused by current algorithms, we add extra condition that the position of the first and the last section images should remain unchanged. This constrained simultaneous registration problem has not been solved before. Our algorithm method is non-iterative, it can simultaneously compute rigid transformation for a large number of serial section images in a short time. We prove that our algorithm gets optimal solution under ideal condition. And we test our algorithm with synthetic data and real data to verify our algorithm's effectiveness.

cs.CV↗

arXiv2020

How Furiously Can Colourless Green Ideas Sleep? Sentence Acceptability in Context

We study the influence of context on sentence acceptability. First we compare the acceptability ratings of sentences judged in isolation, with a relevant context, and with an irrelevant context. Our results show that context induces a cognitive load for humans, which compresses the distribution of ratings. Moreover, in relevant contexts we observe a discourse coherence effect which uniformly raises acceptability. Next, we test unidirectional and bidirectional language models in their ability to predict acceptability ratings. The bidirectional models show very promising results, with the best model achieving a new state-of-the-art for unsupervised acceptability prediction. The two sets of experiments provide insights into the cognitive aspects of sentence processing and central issues in the computational modelling of text and discourse.

cs.CL↗

arXiv2018

Abnormality Detection in Mammography using Deep Convolutional Neural Networks

Breast cancer is the most common cancer in women worldwide. The most common screening technology is mammography. To reduce the cost and workload of radiologists, we propose a computer aided detection approach for classifying and localizing calcifications and masses in mammogram images. To improve on conventional approaches, we apply deep convolutional neural networks (CNN) for automatic feature learning and classifier building. In computer-aided mammography, deep CNN classifiers cannot be trained directly on full mammogram images because of the loss of image details from resizing at input layers. Instead, our classifiers are trained on labelled image patches and then adapted to work on full mammogram images for localizing the abnormalities. State-of-the-art deep convolutional neural networks are compared on their performance of classifying the abnormalities. Experimental results indicate that VGGNet receives the best overall accuracy at 92.53\% in classifications. For localizing abnormalities, ResNet is selected for computing class activation maps because it is ready to be deployed without structural change or further training. Our approach demonstrates that deep convolutional neural network classifiers have remarkable localization capabilities despite no supervision on the location of abnormalities is provided.

cs.CV↗

arXiv2018

Hierarchical Spatial Transformer Network

Computer vision researchers have been expecting that neural networks have spatial transformation ability to eliminate the interference caused by geometric distortion for a long time. Emergence of spatial transformer network makes dream come true. Spatial transformer network and its variants can handle global displacement well, but lack the ability to deal with local spatial variance. Hence how to achieve a better manner of deformation in the neural network has become a pressing matter of the moment. To address this issue, we analyze the advantages and disadvantages of approximation theory and optical flow theory, then we combine them to propose a novel way to achieve image deformation and implement it with a hierarchical convolutional neural network. This new approach solves for a linear deformation along with an optical flow field to model image deformation. In the experiments of cluttered MNIST handwritten digits classification and image plane alignment, our method outperforms baseline methods by a large margin.

cs.CV↗

arXiv2017

A third-order discrete unified gas kinetic scheme for continuum and rarefied flows: low-speed isothermal case

An efficient third-order discrete unified gas kinetic scheme (DUGKS) with efficiency is presented in this work for simulating continuum and rarefied flows. By employing two-stage time-stepping scheme and the high-order DUGKS flux reconstruction strategy, third-order of accuracy in both time and space can be achieved in the present method. It is also analytically proven that the second-order DUGKS is a special case of the present method. Compared with the high-order lattice Boltzmann equation {LBE} based methods, the present method is capable to deal with the rarefied flows by adopting the Newton-Cotes quadrature to approximate the integrals of moments. Instead of being constrained by the second-order (or lower-order) of accuracy in time splitting scheme as in the conventional high-order Runge-Kutta (RK) based kinetic methods, the present method solves the original BE, which overcomes the limitation in time accuracy. Typical benchmark tests are carried out for comprehensive evaluation of the present method. It is observed in the tests that the present method is advantageous over the original DUGKS in accuracy and capturing delicate flow structures. Moreover, the efficiency of the present third-order method is also shown in simulating rarefied flows.

physics.flu-dyn↗

arXiv2014

Finite Element Based Tracking of Deforming Surfaces

We present an approach to robustly track the geometry of an object that deforms over time from a set of input point clouds captured from a single viewpoint. The deformations we consider are caused by applying forces to known locations on the object's surface. Our method combines the use of prior information on the geometry of the object modeled by a smooth template and the use of a linear finite element method to predict the deformation. This allows the accurate reconstruction of both the observed and the unobserved sides of the object. We present tracking results for noisy low-quality point clouds acquired by either a stereo camera or a depth camera, and simulations with point clouds corrupted by different error terms. We show that our method is also applicable to large non-linear deformations.

cs.CV↗

arXiv2014

Estimation of Human Body Shape and Posture Under Clothing

Estimating the body shape and posture of a dressed human subject in motion represented as a sequence of (possibly incomplete) 3D meshes is important for virtual change rooms and security. To solve this problem, statistical shape spaces encoding human body shape and posture variations are commonly used to constrain the search space for the shape estimate. In this work, we propose a novel method that uses a posture-invariant shape space to model body shape variation combined with a skeleton-based deformation to model posture variation. Our method can estimate the body shape and posture of both static scans and motion sequences of dressed human body scans. In case of motion sequences, our method takes advantage of motion cues to solve for a single body shape estimate along with a sequence of posture estimates. We apply our approach to both static scans and motion sequences and demonstrate that using our method, higher fitting accuracy is achieved than when using a variant of the popular SCAPE model as statistical model.

cs.CV↗

arXiv2013

Fully Automatic Expression-Invariant Face Correspondence

We consider the problem of computing accurate point-to-point correspondences among a set of human face scans with varying expressions. Our fully automatic approach does not require any manually placed markers on the scan. Instead, the approach learns the locations of a set of landmarks present in a database and uses this knowledge to automatically predict the locations of these landmarks on a newly available scan. The predicted landmarks are then used to compute point-to-point correspondences between a template model and the newly available scan. To accurately fit the expression of the template to the expression of the scan, we use as template a blendshape model. Our algorithm was tested on a database of human faces of different ethnic groups with strongly varying expressions. Experimental results show that the obtained point-to-point correspondence is both highly accurate and consistent for most of the tested 3D face models.

cs.CV↗

arXiv2012

Estimating 3D Human Shapes from Measurements

The recent advances in 3-D imaging technologies give rise to databases of human shapes, from which statistical shape models can be built. These statistical models represent prior knowledge of the human shape and enable us to solve shape reconstruction problems from partial information. Generating human shape from traditional anthropometric measurements is such a problem, since these 1-D measurements encode 3-D shape information. Combined with a statistical shape model, these easy-to-obtain measurements can be leveraged to create 3D human shapes. However, existing methods limit the creation of the shapes to the space spanned by the database and thus require a large amount of training data. In this paper, we introduce a technique that extrapolates the statistically inferred shape to fit the measurement data using nonlinear optimization. This method ensures that the generated shape is both human-like and satisfies the measurement conditions. We demonstrate the effectiveness of the method and compare it to existing approaches through extensive experiments, using both synthetic data and real human measurements.

cs.CV↗

arXiv2011

Automatically Creating Design Models from 3D Anthropometry Data

When designing a product that needs to fit the human shape, designers often use a small set of 3D models, called design models, either in physical or digital form, as representative shapes to cover the shape variabilities of the population for which the products are designed. Until recently, the process of creating these models has been an art involving manual interaction and empirical guesswork. The availability of the 3D anthropometric databases provides an opportunity to create design models optimally. In this paper, we propose a novel way to use 3D anthropometric databases to generate design models that represent a given population for design applications such as the sizing of garments and gear. We generate the representative shapes by solving a covering problem in a parameter space. Well-known techniques in computational geometry are used to solve this problem. We demonstrate the method using examples in designing glasses and helmets.

cs.CG↗

arXiv2008

Morphing of Triangular Meshes in Shape Space

We present a novel approach to morph between two isometric poses of the same non-rigid object given as triangular meshes. We model the morphs as linear interpolations in a suitable shape space $\mathcal{S}$. For triangulated 3D polygons, we prove that interpolating linearly in this shape space corresponds to the most isometric morph in $\mathbb{R}^3$. We then extend this shape space to arbitrary triangulations in 3D using a heuristic approach and show the practical use of the approach using experiments. Furthermore, we discuss a modified shape space that is useful for isometric skeleton morphing. All of the newly presented approaches solve the morphing problem without the need to solve a minimization problem.

cs.CG↗