arXiv subjects

Xiaowen Su

Publications and source records attributed to Xiaowen Su.

4 recordsLinked to original sources

arXiv2026

An Inner-Outer Iteration Algorithm with Optimal Parameters for Stochastic Lyapunov Matrix Equation

This paper proposes an inner--outer (IO) iterative algorithm with optimal parameters for solving stochastic Lyapunov matrix equation associated with discrete-time stochastic linear system. First, under the assumption that the underlying stochastic linear system is asymptotically mean-square stable, the monotonicity and boundedness of the iterative sequence generated by the proposed algorithm are analyzed. On this basis, a sufficient convergence result is established for the zero initial condition. Second, by deriving the spectral radius of the corresponding iteration matrix, several necessary and sufficient convergence conditions are obtained for arbitrary initial conditions. In addition, the optimal parameter-selection strategies are developed to improve the convergence performance of the algorithm. Finally, numerical examples are presented to verify the theoretical results and demonstrate the advantages of the proposed algorithm over several existing iterative methods.

math.NA↗

arXiv2025

Analysis and Mitigation of Cascading Failures Using a Stochastic Interaction Graph with Eigen-analysis

In studies on complex network systems using graph theory, eigen-analysis is typically performed on an undirected graph model of the network. However, when analyzing cascading failures in a power system, the interactions among failures suggest the need for a directed graph beyond the topology of the power system to model directions of failure propagation. To accurately quantify failure interactions for effective mitigation strategies, this paper proposes a stochastic interaction graph model and associated eigen-analysis. Different types of modes on failure propagations are defined and characterized by the eigenvalues of a stochastic interaction matrix, whose absolute values are unity, zero, or in between. Finding and interpreting these modes helps identify the probable patterns of failure propagation, either local or widespread, and the participating components based on eigenvectors. Then, by lowering the failure probabilities of critical components highly participating in a mode of widespread failures, cascading can be mitigated. The validity of the proposed stochastic interaction graph model, eigen-analysis and the resulting mitigation strategies is demonstrated using simulated cascading failure data on an NPCC 140-bus system.

eess.SY↗

arXiv2023

M3KE: A Massive Multi-Level Multi-Subject Knowledge Evaluation Benchmark for Chinese Large Language Models

Large language models have recently made tremendous progress in a variety of aspects, e.g., cross-task generalization, instruction following. Comprehensively evaluating the capability of large language models in multiple tasks is of great importance. In this paper, we propose M3KE, a Massive Multi-Level Multi-Subject Knowledge Evaluation benchmark, which is developed to measure knowledge acquired by Chinese large language models by testing their multitask accuracy in zero- and few-shot settings. We have collected 20,477 questions from 71 tasks. Our selection covers all major levels of Chinese education system, ranging from the primary school to college, as well as a wide variety of subjects, including humanities, history, politics, law, education, psychology, science, technology, art and religion. All questions are multiple-choice questions with four options, hence guaranteeing a standardized and unified assessment process. We've assessed a number of state-of-the-art open-source Chinese large language models on the proposed benchmark. The size of these models varies from 335M to 130B parameters. Experiment results demonstrate that they perform significantly worse than GPT-3.5 that reaches an accuracy of ~ 48% on M3KE. The dataset is available at https://github.com/tjunlp-lab/M3KE.

cs.CL↗

arXiv2021

Minimal mass design of a tensegrity tower for lunar electromagnetic launching

Lunar explorations have provided us with information about its abundant resources that can be utilized in orbiting-resource depots as lunar-derived commodities. To reduce the energy requirements of a launcher to send these commodities from the lunar surface to the space depots, this paper explores the application of the electromagnetic acceleration principle and provides an assessment of the actual technical characteristics of the launcher's installation to ensure the acceleration of a payload with a mass of 1,500 kg to a speed of 2,200 m/s (circumlunar orbit speed). To fulfill a lightweight (fewer materials and less energy) support structure for the electromagnetic launcher with strength requirements, the tensegrity structure minimum mass principle without global buckling has been developed and applied to support the electromagnetic acceleration device. Therefore, this paper proposes and develops a minimal mass electromagnetic tensegrity lunar launcher. We first demonstrate the mechanics of launcher and payload, how a payload can be accelerated to a specific velocity, and how a payload carrier can be recycled for another launch. Then, a detailed discussion on the lunar launch system, procedures of propulsion, the required mass, and energy of the launch barrel are given. The governing equations of tensegrity minimal mass tensegrity design algorithm with gravity and without global buckling. Finally, a case study is conducted to show a feasible structure design, the required mass, and energy. The principles developed in this paper are also applicable to the rocket launch system, space elevator, space train transportation, interstellar payload package delivery, etc.

physics.acc-ph↗