no code implementations • 28 Apr 2024 • Bai Xue
This paper studies finite-time safety and reach-avoid verification for stochastic discrete-time dynamical systems.
no code implementations • 13 Apr 2024 • Taoran Wu, Yiqing Yu, Bican Xia, Ji Wang, Bai Xue
Ensuring safety through set invariance has proven to be a valuable method in various robotics and control applications.
no code implementations • 27 Feb 2024 • Yonghan Li, Chenyu Wu, Taoran Wu, Shijie Wang, Bai Xue
In this paper, we investigate the problem of verifying the finite-time safety of continuous-time perturbed deterministic systems represented by ordinary differential equations in the presence of measurable disturbances.
no code implementations • 23 Jan 2024 • Zhen Liang, Taoran Wu, Ran Zhao, Bai Xue, Ji Wang, Wenjing Yang, Shaojun Deng, Wanwei Liu
However, these strategies face challenges in addressing the "unknown dilemma" concerning whether the exact output region or the introduced approximation error violates the property in question.
no code implementations • 26 Dec 2023 • Bai Xue
This manuscript presents an innovative framework for constructing barrier functions to bound reachability probabilities for continuous-time stochastic systems described by stochastic differential equations (SDEs).
no code implementations • 8 Oct 2023 • Bai Xue
To begin, we present a sufficient condition for establishing lower bounds on the exit probability in the first case.
no code implementations • 8 Oct 2023 • Taoran Wu, Dejin Ren, Shuyuan Zhang, Lei Wang, Bai Xue
Digital control has become increasingly prevalent in modern systems, making continuous-time plants controlled by discrete-time (digital) controllers ubiquitous and crucial across industries, including aerospace, automotive, and manufacturing.
no code implementations • 12 Sep 2023 • Jiang Liu, Han Su, Yunjun Bai, Bin Gu, Bai Xue, Mengfei Yang, Naijun Zhan
Controller synthesis, including reset controller, feedback controller, and switching logic controller, provides an essential mechanism to guarantee the correctness and reliability of hybrid systems in a correct-by-construction manner.
1 code implementation • 27 Jun 2023 • Zhen Liang, Dejin Ren, Bai Xue, Ji Wang, Wenjing Yang, Wanwei Liu
Moreover, for NNs that do not feature these properties with respect to the input set, we explore subsets of the input set for establishing the local homeomorphism property and then abandon these subsets for reachability computations.
1 code implementation • 5 May 2023 • Zhen Liang, Taoran Wu, Changyuan Zhao, Wanwei Liu, Bai Xue, Wenjing Yang, Ji Wang
For the fine-tuning repair process, BIRDNN analyzes the behavior differences of neurons on positive and negative samples to identify the most responsible neurons for the erroneous behaviors.
no code implementations • 23 Apr 2023 • Jianqiang Ding, Taoran Wu, Yuping Qian, Lijun Zhang, Bai Xue
In this paper, we propose an approach for synthesizing provable reach-avoid controllers, which drive a deterministic system operating in an unknown environment to safely reach a desired target set.
no code implementations • 28 Feb 2023 • Bai Xue
Consequently, a lax control guidance-barrier function is further developed such that only the safe set set is an invariance before the system enters the target set, expanding the space of admissible control inputs.
no code implementations • 20 Feb 2023 • Bai Xue
In this paper we study reachability verification problems of stochastic discrete-time dynamical systems over the infinite time horizon.
no code implementations • 6 Feb 2023 • Bai Xue
We propose two novel sufficient conditions using Lyapunov densities for the weak reach-avoid verification.
no code implementations • 2 Dec 2022 • Zhen Liang, Changyuan Zhao, Wanwei Liu, Bai Xue, Wenjing Yang, Zhengbin Pang
Based on Koopman operator theory, this paper presents an alternative perspective of linear dynamics on dealing with the credit assignment problem for trained neural networks.
1 code implementation • 9 Oct 2022 • Zhen Liang, Dejin Ren, Wanwei Liu, Ji Wang, Wenjing Yang, Bai Xue
The homeomorphism property exists in some widely used NNs such as invertible NNs.
no code implementations • 5 Jun 2021 • Renjue Li, Hanwei Zhang, Pengfei Yang, Cheng-Chao Huang, Aimin Zhou, Bai Xue, Lijun Zhang
In this paper, we propose a framework of filter-based ensemble of deep neuralnetworks (DNNs) to defend against adversarial attacks.
no code implementations • 22 Mar 2021 • Yunjun Bai, Ting Gan, Li Jiao, Bican Xia, Bai Xue, Naijun Zhan
Delays are ubiquitous in modern hybrid systems, which exhibit both continuous and discrete dynamical behaviors.
1 code implementation • 25 Jan 2021 • Renjue Li, Pengfei Yang, Cheng-Chao Huang, Youcheng Sun, Bai Xue, Lijun Zhang
It is shown that DeepPAC outperforms the state-of-the-art statistical method PROVERO, and it achieves more practical robustness analysis than the formal verification tool ERAN.
1 code implementation • 15 Oct 2020 • Pengfei Yang, Renjue Li, Jianlin Li, Cheng-Chao Huang, Jingyi Wang, Jun Sun, Bai Xue, Lijun Zhang
The core idea is to make use of the obtained constraints of the abstraction to infer new bounds for the neurons.
no code implementations • 17 Jul 2020 • Bai Xue, Miaomiao Zhang, Arvind Easwaran, Qin Li
In this paper we present a novel model checking approach to finite-time safety verification of black-box continuous-time dynamical systems within the framework of probably approximately correct (PAC) learning.
Systems and Control Formal Languages and Automata Theory Systems and Control