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Gang Zheng

Publications and source records attributed to Gang Zheng.

At least 19 recordsLinked to original sources

Classical Mechanics Exactly Yields the Full Bound-State Spectrum of the Two-Dimensional Coulomb Problem

High-lying Rydberg excitons in two-dimensional semiconductors universally exhibit a characteristic odd-integer energy scaling distinct from three-dimensional systems. While this hallmark of two-dimensional Coulomb interaction is well known from quantum mechanical solutions, its deeper classical geometric origin remains unclarified. Here we show that the complete bound-state spectral structure of the two-dimensional Coulomb problem---a central model for two-dimensional exciton physics---follows as an exact theorem from classical mechanics augmented by a single phase-scale parameter $\alpha$ with dimensions of action. We derive an amplitude-closure criterion as a necessary and sufficient condition for a classical propagator kernel to satisfy a linear evolution equation, and demonstrate that the singular Coulomb potential can be mapped shell-by-shell via Levi-Civita regularization into the class of quadratic Hamiltonians that obey this criterion exactly. The resulting spectrum bears odd-integer modal numbers, $1/N^2$ energy ratios and $N$-fold degeneracies, all independent of $\alpha$ and consistent with experimental observations of high-lying Rydberg excitons. This work provides a pure classical-geometry benchmark for two-dimensional exciton spectral analysis, allowing quantitative disentanglement of universal Coulomb effects from material-specific screening effects. No semiclassical, short-wavelength or $\hbar \to 0$ approximation is invoked at any stage. Our results invert the usual logical hierarchy for this integrable system: the wave equation emerges as a representation of the underlying classical geometry, rather than as an independent first principle.

cond-mat.mes-hall

Moving-Horizon Estimation and Nonlinear Model Predictive Control of Cable-Driven Soft Manipulators

Precise control of soft manipulators remains challenging due to the difficulty of developing accurate yet computationally tractable models for model-based estimation and control. Reduced Cosserat-rod models provide a physics-based and control-oriented description of soft-robot dynamics, offering an explicit alternative to purely data-driven input-output representations. In this paper, we propose a moving-horizon estimation (MHE) and nonlinear model predictive control (NMPC) framework for cable-driven soft manipulators based on reduced Cosserat dynamics. A smooth cable-length-driven modeling formulation is developed by approximating the complementarity relationship between cable tension and cable slackness, enabling cable-length control without direct tension sensing. Based on this formulation, an MHE method is introduced to estimate the reduced state and reconstruct the manipulator configuration from end-effector pose measurements and cable-length information. An NMPC controller is then formulated to achieve task-space control under cable-length and cable-rate constraints. The proposed framework is validated through numerical simulations and experiments. Simulation results demonstrate the effectiveness of the estimator and controller for pose and strain-related regulation on a multi-cable soft manipulator. Experimental results on a four-cable prototype further show that the proposed MHE-NMPC scheme can be implemented in real time and enables accurate end-effector position tracking through cable-length control.

cs.RO

CT-Enabled Patient-Specific Simulation and Contact-Aware Robotic Planning for Cochlear Implantation

Robotic cochlear-implant (CI) insertion requires precise prediction and regulation of contact forces to minimize intracochlear trauma and prevent failure modes such as locking and buckling. Aligned with the integration of advanced medical imaging and robotics for autonomous, precision interventions, this paper presents a unified CT-to-simulation pipeline for contact-aware insertion planning and validation. We develop a low-dimensional, differentiable Cosserat-rod model of the electrode array coupled with frictional contact and pseudo-dynamics regularization to ensure continuous stick-slip transitions. Patient-specific cochlear anatomy is reconstructed from CT imaging and encoded via an analytic parametrization of the scala-tympani lumen, enabling efficient and differentiable contact queries through closest-point projection. Based on a differentiated equilibrium-constraint formulation, we derive an online direction-update law under an RCM-like constraint that suppresses lateral insertion forces while maintaining axial advancement. Simulations and benchtop experiments validate deformation and force trends, demonstrating reduced locking/buckling risk and improved insertion depth. The study highlights how CT-based imaging enhances modeling, planning, and safety capabilities in robot-assisted inner-ear procedures.

cs.RO

Discrete Homogeneity and Quantizer Design for Nonlinear Homogeneous Control Systems

This paper proposes a framework for analysis of generalized homogeneous control systems under state quantization. In particular, it addresses the challenge of maintaining finite/fixed-time stability of nonlinear systems in the presence of quantized measurements. To analyze the behavior of quantized control system, we introduce a new type of discrete homogeneity, where the dilation is defined by a discrete group. The converse Lyapunov function theorem is established for homogeneous systems with respect to discrete dilations. By extending the notion of sector-boundedness to a homogeneous vector space, we derive a generalized homogeneous sector-boundedness condition that guarantees finite/fixed-time stability of nonlinear control system under quantized measurements. A geometry-aware homogeneous static vector quantizer is then designed using generalized homogeneous coordinates, enabling an efficient quantization scheme. The resulting homogeneous control system with the proposed quantizer is proven to be homogeneous with respect to discrete dilation and globally finite-time, nearly fixed-time, or exponentially stable, depending on the homogeneity degree. Numerical examples validate the effectiveness of the proposed approach.

eess.SY

Collaborative Continuum Robots: A Survey

Continuum robots (CRs), owing to their compact structure, inherent compliance, and flexible deformation, have been widely applied in various fields. By coordinating multiple CRs to form collaborative continuum robots (CCRs), task adaptability, workspace, flexibility, load capacity, and operational stability can be further improved, thus offering significant advantages. In recent years, interest in this emerging field has grown steadily within the continuum-robotics community, accompanied by a consistent rise in related publications. By presenting a comprehensive overview of recent progress from different system-architecture levels, this survey provides a clear framework for research on CCRs. First, CCRs are classified into the three collaboration modes of separated collaboration, assistance collaboration, and parallel collaboration, with definitions provided. Next, advances in structural design, modeling, motion planning, and control for each mode are systematically summarized. Finally, current challenges and future opportunities for CCRs are discussed.

cs.RO

Lightweight Kinematic and Static Modeling of Cable-Driven Continuum Robots via Actuation-Space Energy Formulation

Continuum robots, inspired by octopus arms and elephant trunks, combine dexterity with intrinsic compliance, making them well suited for unstructured and confined environments. Yet their continuously deformable morphology poses challenges for motion planning and control, calling for accurate but lightweight models. We propose the Lightweight Actuation Space Energy Modeling (LASEM) framework for cable driven continuum robots, which formulates actuation potential energy directly in actuation space. LASEM yields an analytical forward model derived from geometrically nonlinear beam and rod theories via Hamilton's principle, while avoiding explicit modeling of cable backbone contact. It accepts both force and displacement inputs, thereby unifying kinematic and static formulations. Assuming the friction is neglected, the framework generalizes to nonuniform geometries, arbitrary cable routings, distributed loading and axial extensibility, while remaining computationally efficient for real-time use. Numerical simulations validate its accuracy, and a semi-analytical iterative scheme is developed for inverse kinematics. To address discretization in practical robots, LASEM further reformulates the functional minimization as a numerical optimization, which also naturally incorporates cable potential energy without explicit contact modeling.

cs.RO

EZhouNet:A framework based on graph neural network and anchor interval for the respiratory sound event detection

Auscultation is a key method for early diagnosis of respiratory and pulmonary diseases, relying on skilled healthcare professionals. However, the process is often subjective, with variability between experts. As a result, numerous deep learning-based automatic classification methods have emerged, most of which focus on respiratory sound classification. In contrast, research on respiratory sound event detection remains limited. Existing sound event detection methods typically rely on frame-level predictions followed by post-processing to generate event-level outputs, making interval boundaries challenging to learn directly. Furthermore, many approaches can only handle fixed-length audio, limiting their applicability to variable-length respiratory sounds. Additionally, the impact of respiratory sound location information on detection performance has not been extensively explored. To address these issues, we propose a graph neural network-based framework with anchor intervals, capable of handling variable-length audio and providing more precise temporal localization for abnormal respiratory sound events. Our method improves both the flexibility and applicability of respiratory sound detection. Experiments on the SPRSound 2024 and HF Lung V1 datasets demonstrate the effectiveness of the proposed approach, and incorporating respiratory position information enhances the discrimination between abnormal sounds. The reference implementation is available at https://github.com/chumingqian/EzhouNet.

cs.SD

STEPC: A Multi-energy Nonuniform Response Calibration Framework for Photon-Counting Micro-CT in Multi-material Imaging

Photon-counting computed tomography has demonstrated significant advancements in recent years; however, micro photon-counting CT (Micro-PCCT) systems are still limited by pixel-wise detector response nonuniformity, which degrades measurement uniformity across detector pixels and commonly produces ring artifacts in reconstructed images. Existing calibration methods exhibit limited generalizability in complex multi-material scenarios, such as contrast-enhanced imaging. This study introduces a Signal-to-Nonuniformity Error Polynomial Calibration (STEPC) framework based on measurement nonuniformity error modeling to address this issue. STEPC first fits multi-energy projections using a 2D polynomial surface to generate ideal references, then applies a nonlinear multi-energy polynomial model to predict and correct pixel-wise nonuniformity errors. The model is calibrated using homogeneous slab phantoms of different materials, including PMMA, aluminum, and iodinated contrast agents, enabling correction for both non-contrast and contrast-enhanced imaging. Experiments were performed on a custom Micro-PCCT system with phantoms and mouse. Correction performance of STEPC was evaluated using the mean local standard deviation (MLSD) in the projection domain and the ring artifact deviation (RAD) on the reconstructed images. Compared with existing methods, STEPC achieved an average MLSD reduction of at least 21.58% and reduced RAD by at least 14.18%, consistently yielding the best performance in both non-contrast and contrast-enhanced scenarios. Furthermore, STEPC can be readily extended to compensate for beam hardening effects within the same calibration framework. Quantitative material decomposition results indicate that the proposed method preserves measurement accuracy across different basis materials...

physics.med-ph

Quantum Spin Correlation Amplification Enables Macroscopic Detection of Atomic-Level Fatigue in Ferromagnetic Metals

Structural fatigue failures account for most of catastrophic metal component failures, annually causing thousands of accidents, tens of thousands of casualties, and $100 billion in global economic losses. Current detection methods struggle to identify early-stage fatigue damage characterized by sub-nanometer atomic displacements and localized bond rupture. Here we present a quantum-enhanced monitoring framework leveraging the fundamental symbiosis between metallic bonding forces and magnetic interactions. Through magnetic excitation of quantum spin correlation in metallic structures, we establish a macroscopic quantum spin correlation amplification technology that visualizes fatigue-induced magnetic flux variations corresponding to bond strength degradation. Our multi-scale analysis integrates fatigue life prediction with quantum mechanical parameters (bonding force constants, crystal orbital overlap population) and ferromagnetic element dynamics, achieving unprecedented prediction accuracy (R^2>0.9, p<0.0001). In comprehensive fatigue trials encompassing 193 ferromagnetic metal specimens across 3,700 testing hours, this quantum magnetic signature consistently provided macroscopic fracture warnings prior to failure - a critical advance enabling 100% early detection success. This transformative framework establishes the first operational platform for preemptive fatigue mitigation in critical infrastructure, offering a paradigm shift from post-failure analysis to quantum-enabled predictive maintenance.

cond-mat.mtrl-sci

CycleGuardian: A Framework for Automatic RespiratorySound classification Based on Improved Deep clustering and Contrastive Learning

Auscultation plays a pivotal role in early respiratory and pulmonary disease diagnosis. Despite the emergence of deep learning-based methods for automatic respiratory sound classification post-Covid-19, limited datasets impede performance enhancement. Distinguishing between normal and abnormal respiratory sounds poses challenges due to the coexistence of normal respiratory components and noise components in both types. Moreover, different abnormal respiratory sounds exhibit similar anomalous features, hindering their differentiation. Besides, existing state-of-the-art models suffer from excessive parameter size, impeding deployment on resource-constrained mobile platforms. To address these issues, we design a lightweight network CycleGuardian and propose a framework based on an improved deep clustering and contrastive learning. We first generate a hybrid spectrogram for feature diversity and grouping spectrograms to facilitating intermittent abnormal sound capture.Then, CycleGuardian integrates a deep clustering module with a similarity-constrained clustering component to improve the ability to capture abnormal features and a contrastive learning module with group mixing for enhanced abnormal feature discernment. Multi-objective optimization enhances overall performance during training. In experiments we use the ICBHI2017 dataset, following the official split method and without any pre-trained weights, our method achieves Sp: 82.06 $\%$, Se: 44.47$\%$, and Score: 63.26$\%$ with a network model size of 38M, comparing to the current model, our method leads by nearly 7$\%$, achieving the current best performances. Additionally, we deploy the network on Android devices, showcasing a comprehensive intelligent respiratory sound auscultation system.

cs.SD

Finite-time Non-overshooting Leader-following Consensus Control for Multi-Agent Systems

This paper addresses the finite-time non-overshooting leader-following consensus problem for multi-agent systems, whose agents are modeled by a dynamical system topologically equivalent to the integrator chain. Based on the weighted homogeneity, a nonlinear consensus control protocol is designed. A tuning scheme ensures the finite-time stability of the consensus error such that the agents do not have overshoots in the first component of the state vector. Simulations are presented to demonstrate the effectiveness of the proposed design.

math.OC

Homogeneous Distributed Observers for Quasilinear Systems

The problem of finite/fixed-time cooperative state estimation is considered for a class of quasilinear systems with nonlinearities satisfying a Hölder condition. A strongly connected nonlinear distributed observer is designed under the assumption of global observability. By proper parameter tuning with linear matrix inequalities, the observer error equation possesses finite/fixed-time stability in the perturbation-free case and input-to-state stability with respect to bounded perturbations. Numerical simulations are performed to validate this design.

math.OC

A proof-of-concept online metadata catalogue service of Earth observation datasets for human health research in exposomics

This article describes research carried out during 2023 under an International Society for Photogrammetry and Remote Sensing (ISPRS)-funded project to develop and disseminate a metadata catalogue of Earth observation data sources/products and types that are relevant to human health research in exposomics, as a free service to interested researchers worldwide. The proof-of-concept catalogue was informed by input from existing research literature on the subject (desk research), as well as online communications with, and relevant research publications collected from, a small panel (n = 5) of select experts from the academia in three countries (China, UK and USA). It has 90 metadata records of relevant Earth observation datasets (n = 40) and associated health-focused research publications (n = 50). The project's online portal offers a searchable version of the catalogue featuring a number of search modes and filtering options. It is hoped future, more comprehensive versions of this service will enable more researchers and studies to discover and use remote sensing data about population-level exposures to disease determinants (exposomic determinants of disease) in combination with other relevant data to reveal fresh insights that could improve our understanding of relevant diseases, and hence contribute to the development of better-optimized prevention and management plans to tackle them.

cs.DL

Robust Finite-time Stabilization of Linear Systems with Limited State Quantization

This paper investigates the robust asymptotic stabilization of a linear time-invariant (LTI) system by a static feedback with a static state quantization. It is shown that the controllable LTI system can be stabilized to zero in a finite time by means of a nonlinear feedback with a quantizer having a limited (finite) number of values (quantization seeds) even when all parameters of the controller and the quantizer are time-invariant. The control design is based on generalized homogeneity. A homogeneous spherical quantizer is introduced. The static homogeneous feedback is shown to be local (or global) finite-time stabilizer for the linear system (dependently of the system matrix). The tuning rules for both the quantizer and the feedback law are obtained in the form of Linear Matrix Inequalities (LMIs). The closed-loop system is proven to be robust with respect to some bounded matched and vanishing mismatched perturbations. Theoretical results are supported by numerical simulations. \

math.OC

Optimal Rejection of Bounded Perturbations in Linear Leader-Following Consensus Protocol: Method Invariant Ellipsoid

The objective of the invariant ellipsoid method is to minimize the smallest invariant and attractive set of a linear control system operating under the influence of bounded external disturbances. In this paper, this method is extended into the leader-following consensus problem. Initially, a linear control protocol is designed for the Multi-agent System without disturbances. Subsequently, in the presence of bounded disturbances, by employing a similar linear control protocol, a necessary and sufficient condition is introduced to derive the optimal control parameters for the MAS such that the state of followers converge and remain in an minimal invariant ellipsoid around the state of the leader.

math.OC

Homogeneous control design using invariant ellipsoid method

The invariant ellipsoid method is aimed at minimization of the smallest invariant and attractive set of a linear control system operating under bounded external disturbances. This paper extends this technique to a class of the so-called generalized homogeneous system by defining the $\dn-$homogeneous invariant/attractive ellipsoid. The generalized homogeneous optimal (in the sense of invariant ellipsoid) controller allows further improvement of the control system providing a faster convergence and better precision. Theoretical results are supported by numerical simulations and experiments.

math.OC

Finite/fixed-time Stabilization of Linear Systems with States Quantization

This paper develops a homogeneity-based approach to finite/fixed-time stabilization of linear time-invariant (LTI) system with quantized measurements. A sufficient condition for finite/fixed-time stabilization of multi-input LTI system under states quantization is derived. It is shown that a homogeneous quantized state feedback with logarithmic quantizer can guarantee finite/fixed-time stability of the closed-loop system provided that the quantization is sufficiently dense. Theoretical results are supported with numerical simulations.

math.OC

Cosserat-Rod Based Dynamic Modeling of Soft Slender Robot Interacting with Environment

Soft slender robots have attracted more and more research attentions in these years due to their continuity and compliance natures. However, mechanics modeling for soft robots interacting with environment is still an academic challenge because of the non-linearity of deformation and the non-smooth property of the contacts. In this work, starting from a piece-wise local strain field assumption, we propose a nonlinear dynamic model for soft robot via Cosserat rod theory using Newtonian mechanics which handles the frictional contact with environment and transfer them into the nonlinear complementary constraint (NCP) formulation. Moreover, we smooth both the contact and friction constraints in order to convert the inequality equations of NCP to the smooth equality equations. The proposed model allows us to compute the dynamic deformation and frictional contact force under common optimization framework in real time when the soft slender robot interacts with other rigid or soft bodies. In the end, the corresponding experiments are carried out which valid our proposed dynamic model.

cs.RO