arXiv subjects

Min Zhong

Publications and source records attributed to Min Zhong.

12 recordsLinked to original sources

arXiv2026

Adaptive Nesterov Momentum Method for Electrical Impedance Tomography with the Complete Electrode Model

We apply the adaptive Nesterov momentum (ANM) method [30] to electrical impedance tomography under the complete electrode model. The forward problem is formulated in the variational CEM setting, accounting for finite electrode size, contact impedance, insulating gaps, and the mean-free voltage gauge. The resulting nonlinear inverse problem is treated within a unified dual-to-primal framework using three classes of strongly convex structural penalties: an L2-type penalty, an L1-type penalty promoting sparse deviations from a calibrated homogeneous background, and a TV-type penalty favoring approximately piecewise-constant conductivities with sharp interfaces. The TV class is implemented using both smoothed TV and Huber-TV formulations. The data- misfit gradient is computed through CEM adjoint equations and stabilized by Sobolev smoothing. The method is evaluated on the publicly available KIT4 tank measurement data after calibration of the background conductivity and contact impedance from no-object measurements. The experiments include single, multiple, mixed-conductivity, and geometrically challenging phantom configurations. The L2-type penalty generally produces smooth but diffuse reconstructions, whereas the L1-type penalty yields cleaner backgrounds with occasional geometric distortion. The smoothed TV and Huber-TV penalties provide more spatially coherent localization and exhibit similar reconstruction behavior across most tested configurations. These results demonstrate the practical applicability of the adaptive Nesterov framework to measured CEM-EIT data.

math.NA↗

arXiv2024

Acousto-electric tomography by the convergence of Kaczamrz two-point gradient-$Θ$ method

We study the numerical reconstruction problem in acousto-electric tomography (AET) of recovering the conductivity distribution in a bounded domain from multiple interior power density data. The Two-Point-Gradient-$Θ$ (TPG-$Θ$) in Kaczmarz type is proposed, with a general convex penalty term $Θ$, the algorithm can be utilized in AET problem for recovering sparse and discontinuous conductivity distributions. We establish the convergence of such iterative regularized method. Extensive numerical experiments are presented to illustrate the feasibility and effectiveness of the proposed approach.

math.NA↗

arXiv2023

Low Energy Electronic Recoils and Single Electron Detection with a Liquid Xenon Proportional Scintillation Counter

Liquid xenon (LXe) is a well-studied detector medium to search for rare events in dark matter and neutrino physics. Two-phase xenon time projection chambers (TPCs) can detect electronic and nuclear recoils with energy down to kilo-electron volts (keV). In this paper, we characterize the response of a single-phase liquid xenon proportional scintillation counter (LXePSC), which produces electroluminescence directly in the liquid, to detect electronic recoils at low energies. Our design uses a thin (10 - 25 $μ$m diameter), central anode wire in a cylindrical LXe target where ionization electrons, created from radiation particles, drift radially towards the anode, and electroluminescence is produced. Both the primary scintillation (S1) and electroluminescence (S2) are detected by photomultiplier tubes (PMTs) surrounding the LXe target. Up to 17 photons are produced per electron, obtained with a 10 $μ$m diameter anode wire, allowing for the highly efficient detection of electronic recoils from beta decays of a tritium source down to roughly 1 keV. Single electrons, from photo-emission of the cathode wires, are observed at a gain of 1.8 photoelectrons (PE) per electron. The delayed signals following the S2 signals are dominated by single-photon-like hits, without evidence for electron signals observed in the two-phase xenon TPCs. We discuss the potential application of such a LXePSC for reactor neutrino detection via Coherent Elastic Neutrino Nucleus Scattering (CE$ν$NS).

physics.ins-det↗

arXiv2022

MaskGroup: Hierarchical Point Grouping and Masking for 3D Instance Segmentation

This paper studies the 3D instance segmentation problem, which has a variety of real-world applications such as robotics and augmented reality. Since the surroundings of 3D objects are of high complexity, the separating of different objects is very difficult. To address this challenging problem, we propose a novel framework to group and refine the 3D instances. In practice, we first learn an offset vector for each point and shift it to its predicted instance center. To better group these points, we propose a Hierarchical Point Grouping algorithm to merge the centrally aggregated points progressively. All points are grouped into small clusters, which further gradually undergo another clustering procedure to merge into larger groups. These multi-scale groups are exploited for instance prediction, which is beneficial for predicting instances with different scales. In addition, a novel MaskScoreNet is developed to produce binary point masks of these groups for further refining the segmentation results. Extensive experiments conducted on the ScanNetV2 and S3DIS benchmarks demonstrate the effectiveness of the proposed method. For instance, our approach achieves a 66.4\% mAP with the 0.5 IoU threshold on the ScanNetV2 test set, which is 1.9\% higher than the state-of-the-art method.

cs.CV↗

arXiv2021

On the asymptotical regularization with convex constraints for inverse problems

In this paper, we consider the asymptotical regularization with convex constraints for nonlinear ill-posed problems. The method allows to use non-smooth penalty terms, including the L1-like and the total variation-like penalty functionals, which are significant in reconstructing special features of solutions such as sparsity and piecewise constancy. Under certain conditions we give convergence properties of the methods. Moreover, we propose Runge-Kutta type methods to discrete the initial value problems to construct new type iterative regularization methods.

math.NA↗

arXiv2021

A Tikhonov Regularization Based Algorithm for Scattered Data with Random Noise

With the rapid growth of data, how to extract effective information from data is one of the most fundamental problems. In this paper, based on Tikhonov regularization, we propose an effective method for reconstructing the function and its derivative from scattered data with random noise. Since the noise level is not assumed small, we will use the amount of data for reducing the random error, and use a relatively small number of knots for interpolation. An indicator function for our algorithm is constructed. It indicates where the numerical results are good or may not be good. The corresponding error estimates are obtained. We show how to choose the number of interpolation knots in the reconstruction process for balancing the random errors and interpolation errors. Numerical examples show the effectiveness and rapidity of our method. It should be remarked that the algorithm in this paper can be used for on-line data.

math.NA↗

arXiv2021

Revisiting the Light Time Correction in Gravimetric Missions Like GRACE and GRACE Follow-On

The gravity field maps of the satellite gravimetry missions GRACE (Gravity Recovery and Climate Experiment) and GRACE Follow-On are derived by means of precise orbit determination. The key observation is the biased inter-satellite range, which is measured primarily by a K-Band Ranging system (KBR) in GRACE and GRACE Follow-On. The GRACE Follow-On satellites are additionally equipped with a Laser Ranging Interferometer (LRI), which provides measurements with lower noise compared to the KBR. The biased range of KBR and LRI needs to be converted for gravity field recovery into an instantaneous range, i.e. the biased Euclidean distance between the satellites' center-of-mass at the same time. One contributor to the difference between measured and instantaneous range arises due to the non-zero travel time of electro-magnetic waves between the spacecraft. We revisit the calculation of the light time correction (LTC) from first principles considering general relativistic effects and state-of-the-art models of Earth's potential field. The novel analytical expressions for the LTC of KBR and LRI can circumvent numerical limitations of the classical approach. The dependency of the LTC on geopotential models and on the parameterization is studied, and afterwards the results are compared against the LTC provided in the official datasets of GRACE and GRACE Follow-On. It is shown that the new approach has a significantly lower noise, well below the instrument noise of current instruments, especially relevant for the LRI, and even if used with kinematic orbit products. This allows calculating the LTC accurate enough even for the next generation of gravimetric missions.

astro-ph.IM↗

arXiv2021

Physics potential for the H$\rightarrow$ZZ$^{*}$ decay at the CEPC

The precision of the yield measurement of the Higgs boson decaying into a pair of $Z$ bosons process at the Circular Electron Positron Collider (CEPC) is evaluated. Including the recoil $Z$ boson associated with the Higgs production (Higgsstrahlung) total three $Z$ bosons are involved for this channel, from which final states characterized by the presence of a pair of leptons, quarks, and neutrinos are chosen for the signal. Two analysis approaches are compared and the final precision of $σ_{\mathrm{ZH}}{\cdot}$BR($H \rightarrow ZZ^{*}$) is estimated to be 7.9% using a multivariate analysis technique, based on boosted decision trees. The relative precision of the Higgs boson width, using this $H \rightarrow ZZ^{*}$ decay topology, is estimated by combining the obtained result with the precision of the inclusive \textit{ZH} cross section measurement.

hep-ex↗

arXiv2021

Extract the information from the big data with randomly distributed noise

In this manuscript, a purely data driven statistical regularization method is proposed for extracting the information from big data with randomly distributed noise. Since the variance of the noise maybe large, the method can be regarded as a general data preprocessing method in ill-posed problems, which is able to overcome the difficulty that the traditional regularization method unable to solve, and has superior advantage in computing efficiency. The unique solvability of the method is proved and a number of conditions are given to characterize the solution. The regularization parameter strategy is discussed and the rigorous upper bound estimation of confidence interval of the error in $L^2$ norm is established. Some numerical examples are provided to illustrate the appropriateness and effectiveness of the method.

math.NA↗

arXiv2018

Regularization of inverse problems by two-point gradient methods with convex constraints

In this paper, we propose and analyze a two-point gradient method for solving inverse problems in Banach spaces which is based on the Landweber iteration and an extrapolation strategy. The method allows to use non-smooth penalty terms, including the L^1 and the total variation-like penalty functionals, which are significant in reconstructing special features of solutions such as sparsity and piecewise constancy in practical applications. The design of the method involves the choices of the step sizes and the combination parameters which are carefully discussed. Numerical simulations are presented to illustrate the effectiveness of the proposed method.

math.NA↗

arXiv2014

Nonstationary iterated Tikhonov regularization in Banach spaces with uniformly convex penalty terms

We consider the nonstationary iterated Tikhonov regularization in Banach spaces which defines the iterates via minimization problems with uniformly convex penalty term. The penalty term is allowed to be non-smooth to include $L^1$ and total variation (TV) like penalty functionals, which are significant in reconstructing special features of solutions such as sparsity and discontinuities in practical applications. We present the detailed convergence analysis and obtain the regularization property when the method is terminated by the discrepancy principle. In particular we establish the strong convergence and the convergence in Bregman distance which sharply contrast with the known results that only provide weak convergence for a subsequence of the iterative solutions. Some numerical experiments on linear integral equations of first kind and parameter identification in differential equations are reported.

math.NA↗

arXiv2013

On the iteratively regularized Gauss--Newton method in Banach spaces with applications to parameter identification problems

In this paper we propose an extension of the iteratively regularized Gauss--Newton method to the Banach space setting by defining the iterates via convex optimization problems. We consider some a posteriori stopping rules to terminate the iteration and present the detailed convergence analysis. The remarkable point is that in each convex optimization problem we allow non-smooth penalty terms including $L^1$ and total variation (TV) like penalty functionals. This enables us to reconstruct special features of solutions such as sparsity and discontinuities in practical applications. Some numerical experiments on parameter identification in partial differential equations are reported to test the performance of our method.

math.NA↗