arXiv subjects

Ying Yang

Publications and source records attributed to Ying Yang.

At least 109 records · Page 6Linked to original sources

arXiv2021

Model-based iterative reconstruction for spectral-domain optical coherence tomography

Spectral domain optical coherence tomography (OCT) offers high resolution multidimensional imaging, but generally suffers from defocussing, intensity falloff and shot noise, causing artifacts and image degradation along the imaging depth. In this work, we develop an iterative statistical reconstruction technique, based upon the interferometric synthetic aperture microscopy (ISAM) model with additive noise, to actively compensate for these effects. For the ISAM re-sampling, we use a non uniform FFT with Kaiser-Bessel interpolation, offering efficiency and high accuracy. We then employ an accelerated gradient descent based algorithm, to minimize the negative log-likelihood of the model, and include spatial or wavelet sparsity based penalty functions, to provide appropriate regularization for given image structures. We evaluate our approach with titanium oxide micro-bead and cucumber samples with a commercial spectral domain OCT system, under various subsampling regimes, and demonstrate superior image quality over traditional reconstruction and ISAM methods.

eess.IV↗

arXiv2021

Unsupervised Spatial-spectral Network Learning for Hyperspectral Compressive Snapshot Reconstruction

Hyperspectral compressive imaging takes advantage of compressive sensing theory to achieve coded aperture snapshot measurement without temporal scanning, and the entire three-dimensional spatial-spectral data is captured by a two-dimensional projection during a single integration period. Its core issue is how to reconstruct the underlying hyperspectral image using compressive sensing reconstruction algorithms. Due to the diversity in the spectral response characteristics and wavelength range of different spectral imaging devices, previous works are often inadequate to capture complex spectral variations or lack the adaptive capacity to new hyperspectral imagers. In order to address these issues, we propose an unsupervised spatial-spectral network to reconstruct hyperspectral images only from the compressive snapshot measurement. The proposed network acts as a conditional generative model conditioned on the snapshot measurement, and it exploits the spatial-spectral attention module to capture the joint spatial-spectral correlation of hyperspectral images. The network parameters are optimized to make sure that the network output can closely match the given snapshot measurement according to the imaging model, thus the proposed network can adapt to different imaging settings, which can inherently enhance the applicability of the network. Extensive experiments upon multiple datasets demonstrate that our network can achieve better reconstruction results than the state-of-the-art methods.

eess.IV↗

arXiv2021

A Distributionally Robust Self-Scheduling Under Price Uncertainty Based on CVaR

To ensure a successful bid while maximizing of profits, generation companies (GENCOs) need a self-scheduling strategy that can cope with a variety of scenarios. So distributionally robust opti-mization (DRO) is a good choice because that it can provide an adjustable self-scheduling strategy for GENCOs in the uncertain environment, which can well balance robustness and economics compared to strategies derived from robust optimization (RO) and stochastic programming (SO). In this paper, a novel mo-ment-based DRO model with conditional value-at-risk (CVaR) is proposed to solve the self-scheduling problem under electricity price uncertainty. Such DRO models are usually translated into semi-definite programming (SDP) for solution, however, solving large-scale SDP needs a lot of computational time and resources. For this shortcoming, two effective approximate models are pro-posed: one approximate model based on vector splitting and an-other based on alternate direction multiplier method (ADMM), both can greatly reduce the calculation time and resources, and the second approximate model only needs the information of the current area in each step of the solution and thus information private is guaranteed. Simulations of three IEEE test systems are conducted to demonstrate the correctness and effectiveness of the proposed DRO model and two approximate models.

math.OC↗

arXiv2021

A new 2D auxetic CN2 nanostructure with high energy density and mechanical strength

The existence of a new two dimensional CN2 structure was predicted using ab-initio molecular dynamics (AIMD) and density-functional theory calculations. It consists tetragonal and hexagonal rings with C-N and N-N bonds arranged in a buckling plane, isostructural to tetrahex-carbon allotrope. It is thermodynamically and kinetically stable suggested by its phonon spectrum and AIMD. This nanosheet has high concentration of N and contains N-N single bonds with an energy density of 6.3 kJ/g, indicating potential applications as high energy density materials. It possesses exotic mechanical properties with negative Poisson's ratio and an anisotropic Young's modulus. The modulus in the zigzag direction is predicted to be 340 N/m, stiffer than h-BN and penta-CN2 sheets and comparable to graphene. Its ideal strength of 28.8 N/m outperforms that of penta-graphene. The material maintains phonon stability upon the application of uniaxial strain up to 10% (13%) in the zigzag (armchair) direction or biaxial strain up to 5%. It possesses a wide indirect HSE band gap of 4.57 eV which is tunable between 3.37 - 4.57 eV through strain. Double-layer structures are also explored. Such unique properties may have potential applications in high energy density materials, nanomechanics and electronics.

cond-mat.mtrl-sci↗

arXiv2020

New stable crystal structures of C and GeC2 predicted from first-principles calculations

Two novel three-dimensional (3D) crystal structures of carbon (C) and germanium carbide (GeC2) were predicted using first-principles density-functional theory (DFT) calculations. These newly discovered 3D carbon allotrope and GeC2 are in the space group of Fmmm (space group number 69). Their crystal structures have unique tetragonal/hexagonal rings formed by either C or Ge/C atoms. Both structures were proven to be thermodynamically stable through the phonon spectrum calculations. The C allotrope is a semiconductor with a wide band gap of 3.65 eV predicted by the hybrid density functional HSE06 method, while GeC2 is metallic. The new C allotrope has a low mass density of 2.84 g/cm3. More importantly, it is proven to be energetically stable with cohesive energy less than -7.5 eV/atom which is lower than many other carbon allotropes implying the possibility to be fabricated in lab. Such a carbon crystal structure with a low mass density and wide band gap once synthesized would have wide applications in gas adsorption sensors and photo-electronic devices.

cond-mat.mtrl-sci↗

arXiv2020

New stable two dimensional silicon carbide nanosheets

We predict the existence of new two dimensional silicon carbide nanostructure employing ab initio density-functional theory calculations. These structures are composed of tetragonal and hexagonal rings with C-C and Si-C bonds arranged in a buckling plane. They are proven to be thermodynamically and mechanically stable with relatively low formation energy, implying potential fabrication in lab. They exhibit strong ductility and anisotropicity from their strain-stress relation and directional dependence of mechanical moduli. The materials maintain phonon stability upon the application of mechanical strain up to 27% with fantastic ductile property. The SiC2 structure possesses a tiny direct band gap of 0.02 eV predicted using HSE06 functional and the band gap can be opened up through multiple approaches such as hydrogenation and strain application. The gap values can be strategically tuned in the range of 0.02 ~ 1.72 eV and the direct/indirect gap nature can be further manipulated. In contrast, a closely related structure of SiC shows an indirect HSE band gap of 1.80 eV and strain engineering its value between 0.0 ~ 1.95 eV. The unique properties in these newly proposed structures might have potential applications in future nanomechanics and electronics.

cond-mat.mtrl-sci↗

arXiv2020

An unnoticed side effect of electric vehicles

We illustrate that the electrification of our transport system might impose unnecessary extra congestion and delay for daily commuting passengers. By modelling travel behaviors of these passengers, it is found that more of them tend to depart at a narrower peak-hour time window. The occurrence of this shift is mainly caused by (1) the energy consumption of electric vehicles (EVs) is much lower than that of traditional vehicles and (2) the energy consumption of EVs is less sensitive to congestion than that of traditional vehicles. We further examine the role of congestion toll in minimizing the extra congestion and delay.

eess.SY↗

arXiv2020

High-order structure preserving graph neural network for few-shot learning

Few-shot learning can find the latent structure information between the prior knowledge and the queried data by the similarity metric of meta-learning to construct the discriminative model for recognizing the new categories with the rare labeled samples. Most existing methods try to model the similarity relationship of the samples in the intra tasks, and generalize the model to identify the new categories. However, the relationship of samples between the separated tasks is difficultly considered because of the different metric criterion in the respective tasks. In contrast, the proposed high-order structure preserving graph neural network(HOSP-GNN) can further explore the rich structure of the samples to predict the label of the queried data on graph that enables the structure evolution to explicitly discriminate the categories by iteratively updating the high-order structure relationship (the relative metric in multi-samples,instead of pairwise sample metric) with the manifold structure constraints. HOSP-GNN can not only mine the high-order structure for complementing the relevance between samples that may be divided into the different task in meta-learning, and but also generate the rule of the structure updating by manifold constraint. Furthermore, HOSP-GNN doesn't need retrain the learning model for recognizing the new classes, and HOSP-GNN has the well-generalizable high-order structure for model adaptability. Experiments show that HOSP-GNN outperforms the state-of-the-art methods on supervised and semi-supervised few-shot learning in three benchmark datasets that are miniImageNet, tieredImageNet and FC100.

cs.CV↗

arXiv2020

Local Averaging Type a Posteriori Error Estimates for the Nonlinear Steady-state Poisson-Nernst-Planck Equations

The a posteriori error estimates are studied for a class of nonlinear stead-state Poisson-Nernst-Planck equations, which are a coupled system consisting of the Nernst-Planck equation and the Poisson equation. Both the global upper bounds and the local lower bounds of the error estimators are obtained by using a local averaging operator. Numerical experiments are given to confirm the reliability and efficiency of the error estimators.

math.NA↗

arXiv2019

OntoPlot: A Novel Visualisation for Non-hierarchical Associations in Large Ontologies

Ontologies are formal representations of concepts and complex relationships among them. They have been widely used to capture comprehensive domain knowledge in areas such as biology and medicine, where large and complex ontologies can contain hundreds of thousands of concepts. Especially due to the large size of ontologies, visualisation is useful for authoring, exploring and understanding their underlying data. Existing ontology visualisation tools generally focus on the hierarchical structure, giving much less emphasis to non-hierarchical associations. In this paper we present OntoPlot, a novel visualisation specifically designed to facilitate the exploration of all concept associations whilst still showing an ontology's large hierarchical structure. This hybrid visualisation combines icicle plots, visual compression techniques and interactivity, improving space-efficiency and reducing visual structural complexity. We conducted a user study with domain experts to evaluate the usability of OntoPlot, comparing it with the de facto ontology editor Prot{é}g{é}. The results confirm that OntoPlot attains our design goals for association-related tasks and is strongly favoured by domain experts.

cs.HC↗

arXiv2019

The optimal system, similarity reduction and group-invariant solutions to the Fractional Porous Medium equation and Fractional Dual Porous Medium equation

In this paper, we developed the group analysis to the time Fractional Porous Medium Equation (FPME) and the time Fractional Dual Porous Medium Equation (FDPME). The symmetry groups and the corresponding optimal systems of these two equations are obtained. Based on the above results, similarity reductions are performed and some explicit group invariant solutions are constructed.

math.AP↗

arXiv2018

Engineering zero modes, Fano resonance and Tamm surface states of 'bound states in the gapped continuum'

In this article, we developed a generalized coupled-mode theory for mixing an isolated state with the gapped continuum to unify the engineering mechanism of gap-protected zero modes, Fano resonance and Tamm surface states, even though those phenomena are diverse in numerous branches of physics and optics. By tuning the on-site potential and coupling strength of/with the isolated state, we found the operating characteristics from zero modes, Fano resonance and to Tamm modes, with explicitly demonstrating their localization, transmission spectra and distinct evolution dynamics of the 'bound states in the gapped continuum' (BIGC). As an extent, we designed two sandwich-like structures in the BIGC framework: (1) the 'isolated-continuous-isolated' (ICI), which leads to adiabatic elimination technique and topologically-protected edge states, and (2) the 'continuous-isolated-continuous' (CIC), which results into the domain wall dynamics. Finally, we proposed the optical waveguide array (WA) modelling of the modified Su-Schrieffer-Heeger dimerized lattice to achieve those interesting engineering processes, and meanwhile to shed light on the generalization of bound states in the continuum into photonics and condensed matters.

physics.optics↗

arXiv2018

A Decoupling Two-grid Method for the Time-dependent Poisson-Nernst-Planck Equations

We study a two-grid strategy for decoupling the time-dependent Poisson-Nernst-Planck equations describing the mass concentration of ions and the electrostatic potential. The computational system is decoupled to smaller systems by using coarse space solutions at each time level, which can speed up the solution process compared with the finite element method combined with the Gummel iteration. We derive the optimal error estimates in $L^2$ norm for both semi- and fully discrete finite element approximations. Based on the a priori error estimates, the error estimates in $H^1$ norm are presented for the two-grid algorithm. The theoretical results indicate this decoupling method can retain the same accuracy as the finite element method. Numerical experiments including the Poisson-Nernst-Planck equations for an ion channel show the efficiency and effectiveness of the decoupling two-grid method.

math.NA↗

arXiv2018

An application of cascaded 3D fully convolutional networks for medical image segmentation

Recent advances in 3D fully convolutional networks (FCN) have made it feasible to produce dense voxel-wise predictions of volumetric images. In this work, we show that a multi-class 3D FCN trained on manually labeled CT scans of several anatomical structures (ranging from the large organs to thin vessels) can achieve competitive segmentation results, while avoiding the need for handcrafting features or training class-specific models. To this end, we propose a two-stage, coarse-to-fine approach that will first use a 3D FCN to roughly define a candidate region, which will then be used as input to a second 3D FCN. This reduces the number of voxels the second FCN has to classify to ~10% and allows it to focus on more detailed segmentation of the organs and vessels. We utilize training and validation sets consisting of 331 clinical CT images and test our models on a completely unseen data collection acquired at a different hospital that includes 150 CT scans, targeting three anatomical organs (liver, spleen, and pancreas). In challenging organs such as the pancreas, our cascaded approach improves the mean Dice score from 68.5 to 82.2%, achieving the highest reported average score on this dataset. We compare with a 2D FCN method on a separate dataset of 240 CT scans with 18 classes and achieve a significantly higher performance in small organs and vessels. Furthermore, we explore fine-tuning our models to different datasets. Our experiments illustrate the promise and robustness of current 3D FCN based semantic segmentation of medical images, achieving state-of-the-art results. Our code and trained models are available for download: https://github.com/holgerroth/3Dunet_abdomen_cascade.

cs.CV↗

arXiv2017

Integrated Modeling of Second Phase Precipitation in Cold-Worked 316 Stainless Steels under Irradiation

The current work combines the Cluster Dynamics (CD) technique and CALPHAD-based precipitation modeling to address the second phase precipitation in cold-worked (CW) 316 stainless steels (SS) under irradiation at 300-400 C. CD provides the radiation enhanced diffusion and dislocation evolution as inputs for the precipitation model. The CALPHAD-based precipitation model treats the nucleation, growth and coarsening of precipitation processes based on classical nucleation theory and evolution equations, and simulates the composition, size and size distribution of precipitate phases. We benchmark the model against available experimental data at fast reactor conditions (9.4 x 10^-7 dpa/s and 390 C) and then use the model to predict the phase instability of CW 316 SS under light water reactor (LWR) extended life conditions (7 x 10^-8 dpa/s and 275 C). The model accurately predicts the gamma-prime (Ni3Si) precipitation evolution under fast reactor conditions and that the formation of this phase is dominated by radiation enhanced segregation. The model also predicts a carbide volume fraction that agrees well with available experimental data from a PWR reactor but is much higher than the volume fraction observed in fast reactors. We propose that radiation enhanced dissolution and/or carbon depletion at sinks that occurs at high flux could be the main sources of this inconsistency. The integrated model predicts ~1.2% volume fraction for carbide and ~3.0% volume fraction for gamma-prime for typical CW 316 SS (with 0.054 wt.% carbon) under LWR extended life conditions. This work provides valuable insights into the magnitudes and mechanisms of precipitation in irradiated CW 316 SS for nuclear applications.

cond-mat.mtrl-sci↗

arXiv2017

A Two-grid Method for Linearizing and Symmetrizing the Steady-state Poisson-Nernst-Planck Equations

In this paper, a two-grid method is proposed to linearize and symmetrize the steady-state Poisson-Nernst-Planck equations. The computational system is decoupled to linearize and symmetrize equations by using this method, which can improve the computational efficiency compared with the finite element method. Error estimates are derived for the proposed method. The numerical results show that the two-grid method is effective.

math.NA↗

arXiv2016

Cross Section and Higgs Mass Measurement with Higgsstrahlung at the CEPC

The Circular Electron Positron Collider (CEPC) is a future Higgs factory proposed by the Chinese high energy physics community. It will operate at a center-of-mass energy of 240-250 GeV. The CEPC will accumulate an integrated luminosity of 5 ab$^{\rm{-1}}$ in ten years' operation, producing one million Higgs bosons via the Higgsstrahlung and vector boson fusion processes. This sample allows a percent or even sub-percent level determination of the Higgs boson couplings. With GEANT4-based full simulation and dedicated fast simulation tool, we evaluated the statistical precisions of the Higgstrahlung cross section $σ_{ZH}$ and the Higgs mass $m_{H}$ measurement at the CEPC in the $Z\rightarrowμ^+μ^-$ channel. The statistical precision of $σ_{ZH}$ ($m_{H}$) measurement could reach 0.97\% (6.9 MeV) in the model-independent analysis which uses only the information of Z boson decay. For the standard model Higgs boson, the $m_{H}$ precision could be improved to 5.4 MeV by including the information of Higgs decays. Impact of the TPC size to these measurements is investigated. In addition, we studied the prospect of measuring the Higgs boson decaying into invisible final states at the CEPC. With the standard model $ZH$ production rate, the upper limit of ${\cal B}(H\rightarrow \rm{inv.})$ could reach 1.2\% at 95\% confidence level.

hep-ex↗

arXiv2016

A Decoupling Two-Grid Method for the Steady-State Poisson-Nernst-Planck Equations

Poisson-Nernst-Planck equations are widely used to describe the electrodiffusion of ions in a solvated biomolecular system. Two kinds of two-grid finite element algorithms are proposed to decouple the steady-state Poisson-Nernst-Planck equations by coarse grid finite element approximations. Both theoretical analysis and numerical experiments show the efficiency and effectiveness of the two-grid algorithms for solving Poisson-Nernst-Planck equations.

math.NA↗