91 citations · 280 across the 22 of their papers we have counts for
23 papers
Energy Efficient Robust Beamforming for Vehicular ISAC with Imperfect Channel Estimation
Hanwen Zhang, Haijian Sun, Tianyi He +2
This paper investigates robust beamforming for system-centric energy efficiency (EE) optimization in the vehicular integrated sensing and communication (ISAC) system, where the mob…
Guaranteed Quantization Error Computation for Neural Network Model Compression
Wesley Cooke, Zihao Mo, Weiming Xiang
Neural network model compression techniques can address the computation issue of deep neural networks on embedded devices in industrial systems. The guaranteed output error computa…
A Data-Driven Hybrid Automaton Framework to Modeling Complex Dynamical Systems
Yejiang Yang, Zihao Mo, Weiming Xiang
In this paper, a computationally efficient data-driven hybrid automaton model is proposed to capture unknown complex dynamical system behaviors using multiple neural networks. The…
Safety Verification of Neural Network Control Systems Using Guaranteed Neural Network Model Reduction
Weiming Xiang, Zhongzhu Shao
This paper aims to enhance the computational efficiency of safety verification of neural network control systems by developing a guaranteed neural network model reduction method. F…
Approximate Bisimulation Relations for Neural Networks and Application to Assured Neural Network Compression
Weiming Xiang, Zhongzhu Shao
In this paper, we propose a concept of approximate bisimulation relation for feedforward neural networks. In the framework of approximate bisimulation relation, a novel neural netw…
Robust Optimization Framework for Training Shallow Neural Networks Using Reachability Method
Yejiang Yang, Weiming Xiang
In this paper, a robust optimization framework is developed to train shallow neural networks based on reachability analysis of neural networks. To characterize noises of input data…
Necessary and Sufficient Conditions for Stability of Discrete-Time Switched Linear Systems with Ranged Dwell Time
Weiming Xiang
This paper deals with the stability analysis problem of discrete-time switched linear systems with ranged dwell time. A novel concept called L-switching-cycle is proposed, which co…
A Data-Driven Modeling Framework of Time-Dependent Switched Dynamical Systems via Extreme Learning Machine
Weiming Xiang
In this work, a data-driven modeling framework of switched dynamical systems under time-dependent switching is proposed. The learning technique utilized to model system dynamics is…
Run-Time Safety Monitoring of Neural-Network-Enabled Dynamical Systems
Weiming Xiang
Complex dynamical systems rely on the correct deployment and operation of numerous components, with state-of-the-art methods relying on learning-enabled components in various stage…
Verification of Deep Convolutional Neural Networks Using ImageStars
Hoang-Dung Tran, Stanley Bak, Weiming Xiang +1
Convolutional Neural Networks (CNN) have redefined the state-of-the-art in many real-world applications, such as facial recognition, image classification, human pose estimation, an…
Reachable Set Estimation for Neural Network Control Systems: A Simulation-Guided Approach
Weiming Xiang, Hoang-Dung Tran, Xiaodong Yang +1
The vulnerability of artificial intelligence (AI) and machine learning (ML) against adversarial disturbances and attacks significantly restricts their applicability in safety-criti…
NNV: The Neural Network Verification Tool for Deep Neural Networks and Learning-Enabled Cyber-Physical Systems
Hoang-Dung Tran, Xiaodong Yang, Diego Manzanas Lopez +5
This paper presents the Neural Network Verification (NNV) software tool, a set-based verification framework for deep neural networks (DNNs) and learning-enabled cyber-physical syst…
Reachability Analysis for Feed-Forward Neural Networks using Face Lattices
Xiaodong Yang, Hoang-Dung Tran, Weiming Xiang +1
The paper introduces a parallelizable method that uses face lattices to compute exact reachable sets for feed‑forward ReLU neural networks, enabling more efficient safety verificat…
Real-Time Verification for Distributed Cyber-Physical Systems
Hoang-Dung Tran, Luan Viet Nguyen, Patrick Musau +2
Safety-critical distributed cyber-physical systems (CPSs) have been found in a wide range of applications. Notably, they have displayed a great deal of utility in intelligent trans…
Specification-Guided Safety Verification for Feedforward Neural Networks
Weiming Xiang, Hoang-Dung Tran, Taylor T. Johnson
This paper presents a specification-guided safety verification method for feedforward neural networks with general activation functions. As such feedforward networks are memoryless…
Verification for Machine Learning, Autonomy, and Neural Networks Survey
Weiming Xiang, Patrick Musau, Ayana A. Wild +5
This survey presents an overview of verification techniques for autonomous systems, with a focus on safety-critical autonomous cyber-physical systems (CPS) and subcomponents thereo…
Reachability Analysis and Safety Verification for Neural Network Control Systems
Weiming Xiang, Taylor T. Johnson
Autonomous cyber-physical systems (CPS) rely on the correct operation of numerous components, with state-of-the-art methods relying on machine learning (ML) and artificial intellig…
Simulation-Based Reachability Analysis for High-Index Large Linear Differential Algebraic Equations
Hoang-Dung Tran, Weiming Xiang, Nathaniel Hamilton +1
Reachability analysis is a fundamental problem for safety verification and falsification of Cyber-Physical Systems (CPS) whose dynamics follow physical laws usually represented as…
Reachable Set Estimation and Safety Verification for Piecewise Linear Systems with Neural Network Controllers
Weiming Xiang, Hoang-Dung Tran, Joel A. Rosenfeld +1
In this work, the reachable set estimation and safety verification problems for a class of piecewise linear systems equipped with neural network controllers are addressed. The neur…
Reachable Set Estimation and Verification for Neural Network Models of Nonlinear Dynamic Systems
Weiming Xiang, Diego Manzanas Lopez, Patrick Musau +1
Neural networks have been widely used to solve complex real-world problems. Due to the complicate, nonlinear, non-convex nature of neural networks, formal safety guarantees for the…
Reachable Set Computation and Safety Verification for Neural Networks with ReLU Activations
Weiming Xiang, Hoang-Dung Tran, Taylor T. Johnson
Neural networks have been widely used to solve complex real-world problems. Due to the complicate, nonlinear, non-convex nature of neural networks, formal safety guarantees for the…
Output Reachable Set Estimation and Verification for Multi-Layer Neural Networks
Weiming Xiang, Hoang-Dung Tran, Taylor T. Johnson
In this paper, the output reachable estimation and safety verification problems for multi-layer perceptron neural networks are addressed. First, a conception called maximum sensiti…
Order-Reduction Abstractions for Safety Verification of High-Dimensional Linear Systems
Hoang-Dung Tran, Luan Viet Nguyen, Weiming Xiang +1
Order-reduction is a standard automated approximation technique for computer-aided design, analysis, and simulation of many classes of systems, from circuits to buildings. For a gi…