arXiv subjects

Zhigang Liu

Publications and source records attributed to Zhigang Liu.

17 recordsLinked to original sources

arXiv2026

Multiple Vehicles and Traction Network Interaction System Stability Analysis and Oscillation Responsibility Identification

The electrical incompatibility between vehicles and traction network in railway system can result in system instability and oscillation overvoltage issues. To analyze the system stability, impedance-based frequency-domain methods are commonly used. However, the current impedance-based modeling methods face challenges in practical implementation due to the requirement of precise analytical models and detailed internal parameters for all vehicles. Moreover, multiple vehicles operate simultaneously in railway systems, each with different operating conditions and internal parameters, thereby influencing system stability to different extents. Therefore, it is crucial to accurately identify the critical vehicles to prevent resonance accidents. To address these challenges, a component connection-based modeling approach for the railway vehicle-grid system is proposed, which only requires the measured impedance results without the internal information of vehicles. In addition, a multilevel sensitivity analysis method is introduced to quantitatively identify the critical vehicles and internal parameters that influence system stability, which outperforms traditional sensitivity analysis methods in computational complexity. Furthermore, a system-level electrical compatibility test process for the railway vehicle-grid system is provided, incorporating the proposed stability and sensitivity analysis methods. Finally, case studies based on the real-world train schedule of a multivehicle-accessed railway vehicle-grid system are designed to verify the correctness of the proposed method.

eess.SY↗

arXiv2026

Neural Network-Based Impedance Identification and Stability Analysis for Double-Sided Feeding Railway Systems

The double-sided power supply railway system increases the simultaneous operation of vehicles on the grid, potentially causing system instability and oscillation overvoltage issues. As vehicles frequently switch operating points during operation, it is essential to analyze system stability across a wide range of conditions. Therefore, accurately identifying the black-box impedance of vehicle converters at multiple operating points is crucial for studying railway vehicle-grid system stability. However, traditional impedance identification methods require extensive data and lack interpretability, leading to significant computational and data burdens. This study introduces an interpretable residual feedforward neural network (ResFNN) combined with SHapley Additive exPlanations for training vehicle impedance models, reducing data requirements while maintaining accuracy. Additionally, a component connection method is proposed for deriving the impedance matrix of a multivehicle railway system under the double-sided feeding mode. This method incorporates the dynamic mobility of vehicles and their positional distribution, and it utilizes the ResFNN to identify impedance for stability analysis. Real operational data from actual railway lines is used as case study to analyze the stability of the double-sided power supply railway system. The results demonstrate that this approach accurately assesses both lowfrequency and high-frequency instability issues.

eess.SY↗

arXiv2026

A Flow Model for the Electrified Railway-Power Grid Hybrid Asymmetric Coupled System and its Linearized Method

In mountainous regions where traction loads constitute a significant portion of a long-chain weak power grid (PG) with sustainable energy, the interaction between the traction power supply system and the PG becomes increasingly evident. The integrated power flow calculation (PFC) method and its linearized model are quite important for the PG - traction network (TN) joint planning. However, existing research on the port load characteristics of the EMUs and the connection angle characteristics of traction transformers is insufficient, and there is a lack of effective methods for PFC or linearized PFC in systems that couple the PG with the traction network. To fill this gap, this paper proposes an integrated PFC model for the AT TN - PG coupled system, along with a linearized method. Firstly, according to the relationship of the phases between the PG and the AT traction network, the node admittance matrix of the coupled system has been constructed. Then, the issue of power injection equations being unable to deal with the EMUs port load is resolved by merging the contact line node and the rail node. Subsequently, the integrated PFC equations for the coupling system are established. Next, a hybrid phase linear decoupled power flow model for the coupling system is developed, employing the correspondence between the phases of the PG and the TN, as well as the phase angle differences among various nodes and branches. Numerical simulations conducted in a specific region demonstrate the necessity of an integrated PFC for the coupled system and validate both the accuracy and efficiency of the linearized model.

eess.SY↗

arXiv2026

Dissipativity-Based Multiport Stability Root-Cause Identification and Mitigation for Solid-State Transformers

For solid-state transformers (SSTs) in high-power grid-connected applications, improperly designed control loops can excite strong inherent AC-DC port coupling, leading to low-frequency oscillation issues, especially under weak grid conditions. To address this problem, this article establishes a multiport admittance matrix for the SST, encompassing its AC dq axes and primary DC port, to characterize its inherent dynamics. Subsequently, a multiport dissipativity analysis is conducted to evaluate the robust stability of the SST. By leveraging the decomposition of passivity conditions into distinct self- and coupling-dissipativity indices, the specific root causes of instability are diagnosed. This framework reveals that a severe coupling-dissipativity failure, induced by the internal dynamics of the synchronization loop, is the dominant instability mechanism rather than a localized self-dissipativity issue. Guided by this diagnosis, a stabilizing controller featuring dynamics-free orthogonal signal reconstruction is designed to reshape the admittance characteristics of the SST. This enhancement specifically targets the identified coupling-dissipativity deficiencies, thereby resolving the root cause of the instability. Finally, the stability analysis and the effectiveness of the enhancement strategy are validated on a down-scaled SST prototype. Experimental results demonstrate that the criterion accurately predicts the coupling-induced oscillations and that the enhanced controller guarantees stable operation under challenging weak-grid conditions.

eess.SY↗

arXiv2026

A Multi-Frequency Input-Admittance Model of Locomotive Rectifier Considering PWM Sideband Harmonic Coupling in Electrical Railways

Electrical railway harmonic instability issues are common in the high-frequency range. The effective frequency of the traditional converter's small-signal averaging model is below 1/2 switching frequency since the pulse width modulation (PWM) sideband harmonic components are ignored. In this article, the dynamic propagations of perturbation frequency and the generated PWM sideband components are constructed first. Then the locomotive rectifier's multi-frequency input-admittance model is derived appropriately. Afterward, an admittance conversion approach is used to convert the multi-frequency model into the single-input-single-output (SISO) model whereas retaining the sideband frequency couplings. The proposed SISO model is more accurate than the traditional small-signal averaging model in the frequency range higher than 1 / 2 switching frequency. It is found that PWM sideband harmonics dominate the locomotive rectifier's input-admittance characteristic higher than 1 / 2 switching frequency. Finally, based on the proposed model, the influence of different switching frequencies, control bandwidths, and traction network impedance on system harmonic stability is revealed by the hardware-in-the-loop (HIL) results.

eess.SY↗

arXiv2026

An Improved Deep Reinforcement Learning Control Strategy for Traction Dual Rectifiers in EMUs

Due to the use of PI-based d q current decoupling in the pulse rectifier of CRH5 high-speed trains, the PI parameters directly affect the traction system's control performance. Linearized control may have issues with reference trajectory changes or model mismatches, leading to a decrease in system performance, while nonlinear control may have problems with jitter and poor steady-state accuracy. This paper proposes a new control strategy that replaces all PI in the d q current decoupling control with a single intelligent agent. This method based on Deep Reinforcement Learning (DRL) can avoid various drawbacks of linearization and nonlinear control and ensure the stability of intermediate DC voltage. However, when EMUs are in different working conditions and switching, the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm used in traction dual rectifiers does not have a good control effect. Focusing on the issue, Reward Shaping (RS) is added to re-design a nonlinear reward function, which can be combined with Prioritized Experience Replay (PER) to increase the convergence speed of the episode reward. The simulation results show that the improved control strategy can be effectively applied to EMUs working in multiple conditions. Finally, the stability analysis is carried out using Lyapunov's second method and the verification results of the hardware-in-the-loop (HIL) simulation platform show that the DRL control has a good effect.

eess.SY↗

arXiv2026

Adversarial Alignment: Ensuring Value Consistency in Large Language Models for Sensitive Domains

With the wide application of large language models (LLMs), the problems of bias and value inconsistency in sensitive domains have gradually emerged, especially in terms of race, society and politics. In this paper, we propose an adversarial alignment framework, which enhances the value consistency of the model in sensitive domains through continued pre-training, instruction fine-tuning and adversarial training. In adversarial training, we use the Attacker to generate controversial queries, the Actor to generate responses with value consistency, and the Critic to filter and ensure response quality. Furthermore, we train a Value-Consistent Large Language Model, VC-LLM, for sensitive domains, and construct a bilingual evaluation dataset in Chinese and English. The experimental results show that VC-LLM performs better than the existing mainstream models in both Chinese and English tests, verifying the effectiveness of the method. Warning: This paper contains examples of LLMs that are offensive or harmful in nature.

cs.CL↗

arXiv2025

Causal Heterogeneous Graph Learning Method for Chronic Obstructive Pulmonary Disease Prediction

Due to the insufficient diagnosis and treatment capabilities at the grassroots level, there are still deficiencies in the early identification and early warning of acute exacerbation of Chronic obstructive pulmonary disease (COPD), often resulting in a high prevalence rate and high burden, but the screening rate is relatively low. In order to gradually improve this situation. In this paper, this study develop a Causal Heterogeneous Graph Representation Learning (CHGRL) method for COPD comorbidity risk prediction method that: a) constructing a heterogeneous Our dataset includes the interaction between patients and diseases; b) A cause-aware heterogeneous graph learning architecture has been constructed, combining causal inference mechanisms with heterogeneous graph learning, which can support heterogeneous graph causal learning for different types of relationships; and c) Incorporate the causal loss function in the model design, and add counterfactual reasoning learning loss and causal regularization loss on the basis of the cross-entropy classification loss. We evaluate our method and compare its performance with strong GNN baselines. Following experimental evaluation, the proposed model demonstrates high detection accuracy.

cs.LG↗

arXiv2024

General Methods for Evaluating Collision Probability of Different Types of Theta-phi Positioners

In many modern astronomical facilities, multi-object telescopes are crucial instruments. Most of these telescopes have thousands of robotic fiber positioners(RFPs) installed on their focal plane, sharing an overlapping workspace. Collisions between RFPs during their movement can result in some targets becoming unreachable and cause structural damage. Therefore, it is necessary to reasonably assess and evaluate the collision probability of the RFPs. In this study, we propose a mathematical models of collision probability and validate its results using Monte Carlo simulations. In addition, a new collision calculation method is proposed for faster calculation(nearly 0.15% of original time). Simulation experiments have verified that our method can evaluate the collision probability between RFPs with both equal and unequal arm lengths. Additionally, we found that adopting a target distribution based on a Poisson distribution can reduce the collision probability by approximately 2.6% on average.

cs.RO↗

arXiv2024

Microscopic characteristics of SF6 partial discharge induced by a floating linear metal particle

Direct current (DC) gas insulated transmission lines (GILs) have been widely used in power transmission, but might be threatened by partial discharge due to the presence of floating impurities (e.g., dust and metal particles) inside the sealed chamber. In this letter, by using a 2D fluid model we characterize the microscopic properties of the partial discharge induced by a floating linear metal particle in SF6 (both the discharge propagation and interaction between space charge and metal particle) under negative high voltage direct current (HVDC) conditions. Due to the strong electronegativity of SF6, the spatiotemporal distributions of the charged species (electrons, positive and negative ions), space charge, and reduced electric field are rather different from those in air. Notably, a negative ion region is observed around the top tip of the metal particle, and it plays an important role in the generation and propagation of primary and secondary streamers in SF6, which may lead to severe motion characteristics of the particle and aliasing of partial discharge signals. Additionally, we analyze the charging process and electric force reversal phenomenon, which may provide a more precise understanding of the underlying mechanisms of the firefly motion previously reported for DC GILs.

physics.plasm-ph↗

arXiv2023

A Constraints Fusion-induced Symmetric Nonnegative Matrix Factorization Approach for Community Detection

Community is a fundamental and critical characteristic of an undirected social network, making community detection be a vital yet thorny issue in network representation learning. A symmetric and non-negative matrix factorization (SNMF) model is frequently adopted to address this issue owing to its great interpretability and scalability. However, it adopts a single latent factor matrix to represent an undirected network for precisely representing its symmetry, which leads to loss of representation learning ability due to the reduced latent space. Motivated by this discovery, this paper proposes a novel Constraints Fusion-induced Symmetric Nonnegative Matrix Factorization (CFS) model that adopts three-fold ideas: a) Representing a target undirected network with multiple latent factor matrices, thus preserving its representation learning capacity; b) Incorporating a symmetry-regularizer that preserves the symmetry of the learnt low-rank approximation to the adjacency matrix into the loss function, thus making the resultant detector well-aware of the target network's symmetry; and c) Introducing a graph-regularizer that preserves local invariance of the network's intrinsic geometry, thus making the achieved detector well-aware of community structure within the target network. Extensively empirical studies on eight real-world social networks from industrial applications demonstrate that the proposed CFS model significantly outperforms state-of-the-art models in achieving highly-accurate community detection results.

cs.SI↗

arXiv2023

A Symmetry and Graph Regularized Nonnegative Matrix Factorization Model for Community Detection

Community is a fundamental and critical characteristic of a Large-scale Undirected Network (LUN) like a social network, making community detection a vital yet thorny issue in LUN representation learning. Owing to its good scalability and interpretability, a Symmetric and Nonnegative Matrix Factorization (SNMF) model is commonly used to tackle this issue. However, it adopts a unique Latent Factor (LF) matrix for precisely representing an LUN's symmetry, which leads to a reduced LF space that impairs its representational learning ability. Motivated by this discovery, this study proposes a Symmetry and Graph-regularized Nonnegative Matrix Factorization (SGNMF) method that adopts three-fold ideas: a) leveraging multiple LF matrices to represent an LUN, thereby enhancing its representation learning ability; b) introducing a symmetry regularization term that implies the equality constraint between its multiple LF matrices, thereby illustrating an LUN's symmetry; and c) incorporating graph regularization into its learning objective, thereby illustrating an LUN's intrinsic geometry. A theoretical proof is provided to ensure SGNMF's convergence on an LUN. Extensive experiment results on ten LUNs from real applications demonstrate that our proposed SGNMF-based community detector significantly outperforms several baseline and state-of-the-art models in achieving highly-accurate results for community detection.

cs.SI↗

arXiv2022

High-order Order Proximity-Incorporated, Symmetry and Graph-Regularized Nonnegative Matrix Factorization for Community Detection

Community describes the functional mechanism of a network, making community detection serve as a fundamental graph tool for various real applications like discovery of social circle. To date, a Symmetric and Non-negative Matrix Factorization (SNMF) model has been frequently adopted to address this issue owing to its high interpretability and scalability. However, most existing SNMF-based community detection methods neglect the high-order connection patterns in a network. Motivated by this discovery, in this paper, we propose a High-Order Proximity (HOP)-incorporated, Symmetry and Graph-regularized NMF (HSGN) model that adopts the following three-fold ideas: a) adopting a weighted pointwise mutual information (PMI)-based approach to measure the HOP indices among nodes in a network; b) leveraging an iterative reconstruction scheme to encode the captured HOP into the network; and c) introducing a symmetry and graph-regularized NMF algorithm to detect communities accurately. Extensive empirical studies on eight real-world networks demonstrate that an HSGN-based community detector significantly outperforms both benchmark and state-of-the-art community detectors in providing highly-accurate community detection results.

cs.SI↗

arXiv2021

Doping: A technique for efficient compression of LSTM models using sparse structured additive matrices

Structured matrices, such as those derived from Kronecker products (KP), are effective at compressing neural networks, but can lead to unacceptable accuracy loss when applied to large models. In this paper, we propose the notion of doping -- addition of an extremely sparse matrix to a structured matrix. Doping facilitates additional degrees of freedom for a small number of parameters, allowing them to independently diverge from the fixed structure. To train LSTMs with doped structured matrices, we introduce the additional parameter matrix while slowly annealing its sparsity level. However, we find that performance degrades as we slowly sparsify the doping matrix, due to co-matrix adaptation (CMA) between the structured and the sparse matrices. We address this over dependence on the sparse matrix using a co-matrix dropout regularization (CMR) scheme. We provide empirical evidence to show that doping, CMA and CMR are concepts generally applicable to multiple structured matrices (Kronecker Product, LMF, Hybrid Matrix Decomposition). Additionally, results with doped kronecker product matrices demonstrate state-of-the-art accuracy at large compression factors (10 - 25x) across 4 natural language processing applications with minor loss in accuracy. Doped KP compression technique outperforms previous state-of-the art compression results by achieving 1.3 - 2.4x higher compression factor at a similar accuracy, while also beating strong alternatives like pruning and low-rank methods by a large margin (8% or more). Additionally, we show that doped KP can be deployed on commodity hardware using the current software stack and achieve 2.5 - 5.5x inference run-time speed-up over baseline.

cs.LG↗

arXiv2018

The Maunakea Spectroscopic Explorer Book 2018

(Abridged) This is the Maunakea Spectroscopic Explorer 2018 book. It is intended as a concise reference guide to all aspects of the scientific and technical design of MSE, for the international astronomy and engineering communities, and related agencies. The current version is a status report of MSE's science goals and their practical implementation, following the System Conceptual Design Review, held in January 2018. MSE is a planned 10-m class, wide-field, optical and near-infrared facility, designed to enable transformative science, while filling a critical missing gap in the emerging international network of large-scale astronomical facilities. MSE is completely dedicated to multi-object spectroscopy of samples of between thousands and millions of astrophysical objects. It will lead the world in this arena, due to its unique design capabilities: it will boast a large (11.25 m) aperture and wide (1.52 sq. degree) field of view; it will have the capabilities to observe at a wide range of spectral resolutions, from R2500 to R40,000, with massive multiplexing (4332 spectra per exposure, with all spectral resolutions available at all times), and an on-target observing efficiency of more than 80%. MSE will unveil the composition and dynamics of the faint Universe and is designed to excel at precision studies of faint astrophysical phenomena. It will also provide critical follow-up for multi-wavelength imaging surveys, such as those of the Large Synoptic Survey Telescope, Gaia, Euclid, the Wide Field Infrared Survey Telescope, the Square Kilometre Array, and the Next Generation Very Large Array.

astro-ph.IM↗

arXiv2017

Diffusion L0-norm constraint improved proportionate LMS algorithm for sparse distributed estimation

To exploit the sparsity of the considered system, the diffusion proportionate-type least mean square (PtLMS) algorithms assign different gains to each tap in the convergence stage while the diffusion sparsity-constrained LMS (ScLMS) algorithms pull the components towards zeros in the steady-state stage. In this paper, by minimizing a differentiable cost function that utilizes the Riemannian distance between the updated and previous weight vectors as well as the L0 norm of the weighted updated weight vector, we propose a diffusion L0-norm constraint improved proportionate LMS (L0-IPLMS) algorithm, which combines the benefits of the diffusion PtLMS and diffusion ScLMS algorithms and performs the best performance among them. Simulations in a system identification context confirm the improvement of the proposed algorithm.

cs.IT↗

arXiv2007

Spin wave dynamics and the determination of intrinsic Gilbert damping in locally-excited Permalloy thin films

Time-resolved scanning Kerr effect microscopy has been used to study magnetization dynamics in Permalloy thin films excited by transient magnetic pulses generated by a micrometer-scale transmission line structure. The results are consistent with magnetostatic spin wave theory and are supported by micromagnetic simulations. Magnetostatic volume and surface spin waves are measured for the same specimen using different bias field orientations and can be accurately calculated by k-space integrations over all excited plane wave components. A single damping constant of Gilbert form is sufficient to describe both scenarios. The nonuniform pulsed field plays a key role in the spin wave dynamics, with its Fourier transform serving as a weighting function for the participating modes. The intrinsic Gilbert damping parameter $α$ is most conveniently measured when the spin waves are effectively stationary.

cond-mat.other↗