arXiv subjects

Jiayang Song

Publications and source records attributed to Jiayang Song.

22 records · Page 2Linked to original sources

arXiv2023

Mosaic: Model-based Safety Analysis Framework for AI-enabled Cyber-Physical Systems

Cyber-physical systems (CPSs) are now widely deployed in many industrial domains, e.g., manufacturing systems and autonomous vehicles. To further enhance the capability and applicability of CPSs, there comes a recent trend from both academia and industry to utilize learning-based AI controllers for the system control process, resulting in an emerging class of AI-enabled cyber-physical systems (AI-CPSs). Although such AI-CPSs could achieve obvious performance enhancement from the lens of some key industrial requirement indicators, due to the random exploration nature and lack of systematic explanations for their behavior, such AI-based techniques also bring uncertainties and safety risks to the controlled system, posing an urgent need for effective safety analysis techniques for AI-CPSs. Hence in this work, we propose Mosaic, a model-based safety analysis framework for AI-CPSs. Mosaic first constructs a Markov decision process (MDP) model as an abstract model of the AI-CPS, which tries to characterize the behaviors of the original AI-CPS. Then, based on the derived abstract model, safety analysis is designed in two aspects: online safety monitoring and offline model-guided falsification. The usefulness of Mosaic is evaluated on diverse and representative industry-level AI-CPSs, the results of which demonstrate that Mosaic is effective in providing safety monitoring to AI-CPSs and enables to outperform the state-of-the-art falsification techniques, providing the basis for advanced safety analysis of AI-CPSs.

cs.SE↗

arXiv2023

AutoRepair: Automated Repair for AI-Enabled Cyber-Physical Systems under Safety-Critical Conditions

Cyber-Physical Systems (CPS) have been widely deployed in safety-critical domains such as transportation, power and energy. Recently, there comes an increasing demand in employing deep neural networks (DNNs) in CPS for more intelligent control and decision making in sophisticated industrial safety-critical conditions, giving birth to the class of DNN controllers. However, due to the inherent uncertainty and opaqueness of DNNs, concerns about the safety of DNN-enabled CPS are also surging. In this work, we propose an automated framework named AutoRepair that, given a safety requirement, identifies unsafe control behavior in a DNN controller and repairs them through an optimization-based method. Having an unsafe signal of system execution, AutoRepair iteratively explores the control decision space and searches for the optimal corrections for the DNN controller in order to satisfy the safety requirements. We conduct a comprehensive evaluation of AutoRepair on 6 instances of industry-level DNN-enabled CPS from different safety-critical domains. Evaluation results show that AutoRepair successfully repairs critical safety issues in the DNN controllers, and significantly improves the reliability of CPS.

cs.SE↗

arXiv2022

When Cyber-Physical Systems Meet AI: A Benchmark, an Evaluation, and a Way Forward

Cyber-physical systems (CPS) have been broadly deployed in safety-critical domains, such as automotive systems, avionics, medical devices, etc. In recent years, Artificial Intelligence (AI) has been increasingly adopted to control CPS. Despite the popularity of AI-enabled CPS, few benchmarks are publicly available. There is also a lack of deep understanding on the performance and reliability of AI-enabled CPS across different industrial domains. To bridge this gap, we initiate to create a public benchmark of industry-level CPS in seven domains and build AI controllers for them via state-of-the-art deep reinforcement learning (DRL) methods. Based on that, we further perform a systematic evaluation of these AI-enabled systems with their traditional counterparts to identify the current challenges and explore future opportunities. Our key findings include (1) AI controllers do not always outperform traditional controllers, (2) existing CPS testing techniques (falsification, specifically) fall short of analyzing AI-enabled CPS, and (3) building a hybrid system that strategically combines and switches between AI controllers and traditional controllers can achieve better performance across different domains. Our results highlight the need for new testing techniques for AI-enabled CPS and the need for more investigations into hybrid CPS systems to achieve optimal performance and reliability.

cs.SE↗

arXiv2018

A Performance Analysis Model of TCP over Multiple Heterogeneous Paths for 5G Mobile Services

Driven by the primary requirement of emerging 5G mobile services, the demand for concurrent multipath transfer (CMT) is still prominent. Yet, multipath transport protocols are not widely adopted and TCP-based CMT schemes will still be in dominant position in 5G. However, the performance of TCP flow transferred over multiple heterogeneous paths is prone to the link quality asymmetry, the extent of which was revealed to be significant by our field investigation. In this paper, we present a performance analysis model for TCP over multiple heterogeneous paths in 5G scenarios, where both bandwidth and delay asymmetry are taken into consideration. The evaluation adopting parameters from field investigation shows that the proposed model can achieve high accuracy in practical environments. Some interesting inferences can be drawn from the proposed model, such as the dominant factor that affect the performance of TCP over heterogeneous networks, and the criteria of determining the appropriate number of links to be used under different circumstances of path heterogeneity. Thus, the proposed model can provide a guidance to the design of TCP-based CMT solutions for 5G mobile services.

cs.NI↗