Secure and Trustworthy Industrial Intelligent Monitoring (安全与可信的工业智能监测)
Chair: 
Xiaoyu Jiang, Beihang University, China
Co-chairs: 
Zhihuan Song, Guangdong University of Petrochemical Technology, China 
Yang Hu, Beihang University, China
Xiangyin Kong, National University of Singapore, Singapore
Keywords: | Topics: |
·Secure Intelligent Monitoring Models (安全的智能监测模型) ·Trustworthy AI Systems (可信赖的AI系统) ·Robust Control System (鲁棒控制系统) ·Fault Detection and Diagnosis (故障检测与诊断) ·Explainable AI (可解释AI) ·Key Performance Indicator Prediction (关键性能指标预测) ·AI Reliability Assessment (AI可靠性评估) | ·AI-based Intelligent Monitoring Modeling (基于AI的智能监测建模) ·Fault Detection and Diagnosis in Safety-Critical Industrial Systems (安全关键工业系统中的故障检测与异常诊断) ·Prediction and Monitoring of Key Performance Indicators (关键性能指标的预测与监测) ·Robust and Explainable AI for Industrial Applications (面向工业应用的鲁棒与可解释AI) ·Adversarial attack and defense in Industrial Control Systems (工业控制系统中的对抗攻防) ·Uncertainty Quantification and Reliability Assessment of AI Models (AI模型的不确定性量化与可靠性评估) |
Summary:
·The widespread applications of Artificial Intelligence (AI) in industrial systems brings new challenges in ensuring security, trustworthiness, and reliability for industrial intelligent monitoring. This special session focuses on the development of robust, explainable, and secure AI solutions for safety-critical industrial environments. Key topics include adversarial attacks and defenses, model uncertainty, and system robustness, all of which are crucial for maintaining reliable operation. We invite contributions on AI-based monitoring and modeling, fault detection and diagnosis, key performance indicator (KPI) prediction, and AI reliability assessment under uncertainty. Special attention is given to research that enhances the resilience and interpretability of industrial AI systems. By bridging advanced AI techniques with real-world engineering needs, this session aims to foster the development of secure and trustworthy intelligent monitoring in complex industrial scenarios.
·人工智能技术在工业系统中的快速应用，带来了保障智能监测系统安全性、可信性与可靠性的新挑战。本专题聚焦于面向安全关键工业环境的鲁棒、可解释与安全可信的AI解决方案，涵盖对抗攻击与防御、模型不确定性、系统鲁棒性等关键问题。本专题欢迎在AI监测建模、故障检测与诊断、关键性能指标预测以及AI可靠性评估等方向的研究成果，特别关注提升工业AI系统在不确定环境下的稳健性与可解释性。通过融合前沿AI技术与工业工程实践，专题旨在推动复杂工业场景中安全可信的智能监测系统的构建与应用。