Modeling, Control and Analysis of Mixed Traffic Flow with Autonomous Vehicles

Session Chair: Wenxing Zhu, Shandong University, China

Guanghan Peng, Guangxi Normal University, China

Key Words: Mixed Traffic Flow, Autonomous Vehicles, Stability Analysis, Controller Design, Energy Consumption Analysis 

Information: With the rapid development of the social economy and science technology, autonomous vehicles have become a research hotspot in the field of modern transportation, and the mixed operation of manual driving and autonomous vehicles will become an inevitable trend in the future. There are a lot of physicists and scholars interested in this field for many years. In addition, under the dual carbon goal in our country, the energy consumption of mixed traffic flow with vehicles driven by multiple energy sources is also a hot issue having attracted the attention of lots of experts and scholars. Through the organization of this session, we hope to attract experts in this field to share the latest research results and feasible solutions, and promote the solution of various complex problems in the field of mixed traffic flow.

Topics of interest include but are not limited to:

• Modeling on Mixed Traffic Flow 
• Energy Consumption Analysis on Various Vehicles
• Controller Design Method on Mixed Traffic Flow

Submission Deadline: June 30, 2026 (the second round)

Advances in Intelligent Perception, Decision-making and Control for Unmanned Systems

Session Chair: Panlong Wu, Nanjing University of Science and Technology, China

Baichun Gong, Nanjing University of Aeronautics and Astronautics, China

Information: The session will focus on the core aspects of unmanned systems, including target detection and recognition, situational awareness, autonomous decision-making, and collaborative control, with an emphasis on addressing critical challenges such as low efficiency in multimodal data fusion, delays in behavioral cognition and intent inference, poor interpretability of strategies, and insufficient stability in group consensus control. Furthermore, it will explore practical applications of machine learning algorithms—including deep learning, reinforcement learning, and large-scale models—in prediction, decision-making, and intelligent interaction. This session aims to bring together researchers from diverse disciplinary backgrounds to advance the theoretical development and practical implementation of unmanned system control.

Topics of interest include but are not limited to:

• Integration theory of intelligent perception and autonomous decision-making
• Autonomous target detection and recognition in open environments
• Task allocation and path planning
• Autonomous control of unmanned systems
• Machine learning algorithms and their trustworthy applications

Submission Deadline: June 30, 2026 (the second round)

Progress and Innovations in Multi-Source Information Fusion, Navigation, and Guidance Technologies

Session Chair: Shuai Chen, Nanjing University of Science and Technology, China

Key Words: Multi-source information fusion; filtering algorithms; cooperative navigation and guidance; intelligent navigation; positioning, navigation, and timing (PNT)

Information: Advances in the information era have imposed higher performance requirements on navigation systems, while also introducing new methods and challenges. This conference aims to discuss innovations and engineering practices in multi-source information fusion algorithms, data-driven intelligent navigation algorithms, and image-recognition- and matching-based localization methods, in order to improve the accuracy and robustness of position, attitude, and timing information. In addition, the conference will further explore integrated implementations of cooperative navigation, guidance, and control. We hope to jointly examine the latest research achievements and future development trends in navigation–control–guidance integration under complex conditions such as high dynamics, severe electromagnetic interference, and weak textures, thereby promoting solutions to high-precision position, attitude, and timing acquisition as well as navigation and guidance problems for aircraft, spacecraft, automobiles, robots, ships, underwater vehicles, and other platforms operating in diverse environments, including low-altitude, high-altitude, deep-space, urban canyons, indoor and underground spaces, sea surface, and underwater settings.

Topics of interest include but are not limited to:

• Innovations and applications in cooperative navigation and guidance
• Multi-source information fusion algorithms and engineering applications
• Image recognition and matching algorithms in complex environments
• Data-driven intelligent navigation methods and innovations
• Integrated PNT and micro-PNT technologies

Submission Deadline: June 30, 2026 (the second round)

Design, Control and Optimization of Green Hydrogen Integrated Energy Network System

Session Chair: Yunfeng Peng, Shanghai Jiao Tong University, China

Longze Wang, North China Electric Power University, China

Key Words: Green Hydrogen System, Industrial Control Network, Power Control, Energy Management, System Modeling

Information: Under the background of global decarbonization, as the main energy carrier, comprehensive energy system of green hydrogen has been paid widespread attention. Due to random change of weather, the output of photovoltaic and wind power system show significant randomness. Grid regulation, hydrogen energy vehicle scheduling and changing hydrogen supply demand of chemical plants make the problem of untimely and unreasonable hydrogen absorption increasingly prominent. Therefore, the imbalance on the supply and demand side leads to the low energy efficiency of the system. Digital modeling technology is considered to be an effective tool to guide system management. The construction of information perception, transmission and decision-making network of green hydrogen integrated energy network system based on digital model is expected to improve energy efficiency. Through the power control of hydrogen-to-electricity conversion, the management of multi-energy flow in the park and optimal scheduling of hydrogen industry chain, the maximum consumption of power generation can be realized, as well as reliable production, manufacturing, operation and maintenance. This topic will focus on the application of digital modeling and intelligent control methods to improve the efficiency of green hydrogen integrated energy network system.

Topics of interest include but are not limited to:

• Digital modeling of green hydrogen integrated energy network system
• Information perception, communication, control and calculation of green hydrogen integrated energy network system
• Power control of hydrogen-electric integration
• Management strategy of hydrogen, electricity and thermal energy in zero-carbon park
• Optimal scheduling of hydrogen production, storage, transport and hydrogen injection

Submission Deadline: June 30, 2026 (the second round)

Advanced Control Theory and Applications of Intelligent Systems

Session Chair: Jianqi Chen, Nanjing University, China

Di Zhao, Nanjing University, China

Key Words: Intelligent System, Adaptive Control, Multi-Agent Systems, Robust Control, Safety-Critical Control

Information: This session focuses on advanced control theory for intelligent systems and its cutting-edge applications. With the rapid development of artificial intelligence, machine learning, and big data technologies, the application of intelligent systems has expanded across various fields, including autonomous driving, smart manufacturing, robotics, and the Internet of Things. The session will concentrate on advanced control methods in intelligent systems, such as adaptive control, robust control, and coordination and optimization in multi-agent systems, while also addressing the challenges and real-world applications of these theories. We invite scholars and researchers worldwide to share their latest results on innovations in control theory for intelligent systems, application cases, and technical implementations, contributing to the theoretical development and widespread application of intelligent systems.

Topics of interest include but are not limited to:

• Adaptive Control and Collaborative Optimization for Intelligent Systems
• Robust Control in Multi-Agent Systems
• Control Theory and Practice in Smart Manufacturing
• Challenges and Solutions in Delay Systems and Networked Control
• Safety Verification and Control for Interconnected Systems

Submission Deadline: June 30, 2026 (the second round)

Learning Control and Games of Networked Systems

Session Chair: Bingchang Wang, Shandong University, China

Key Words: Stochastic games, Optimal control, Multiagent systems, Reinforcement learning, Stackelberg games, Mean field games

Information: This invited session focuses on learning control and games of networked systems, and includes topics of stochastic games, optimal control, multiagent systems, reinforcement learning, Stackelberg games and mean field games.

Topics of interest include but are not limited to:

• Stochastic Optimal Control
• Data-Driven Stochastic Differential Games
• Mean Field Stackelberg Games
• Optimal Control of Networked Dynamic Systems
• Stochastic Optimal Control of Networked Systems
• Mean Field Policy Iteration Algorithms
• Inverse Reinforcement Learning for Games
• Adaptive Distributed Learning Control
  

Submission Deadline: June 30, 2026 (the second round)

Secure State Estimation and Control for Networked Systems

Session Chair: Yong Xu, Guangdong University of Technology, China

Xiaoqiang Ren, Shanghai University, China

Key Words: Networked control systems, Secure state estimation, Resilient control, Adversarial attacks, Defensive transmission scheduling

Information: Networked systems such as cyber-physical systems, industrial control systems, intelligent transportation systems, and multi-agent systems are increasingly exposed to cyber threats due to the wireless communication networks. Malicious attacks, including false data injection, denial-of-service, eavesdropping and deception attacks, can severely degrade state estimation accuracy and compromise control performance. This session focuses on recent advances in secure and resilient state estimation and control for networked systems under adversarial and uncertain environments. The session aims to bring together researchers working on control theory, wireless communication, algorithm design, and practical implementations to enhance the robustness, security, and trustworthiness of networked control systems.

Topics of interest include but are not limited to: 

• Secure and resilient state estimation under cyber attacks
• Attack detection, isolation, and identification in networked systems
• False data injection, denial-of-service, eavesdropping and deception attacks
• Secure control and stabilization under adversarial environments
• Defensive transmission scheduling against cyber attacks
• Distributed and cooperative secure estimation in multi-agent systems
• Game-theoretic security analysis
• Privacy-preserving estimation and control
• Applications to smart grids, autonomous vehicles, robotics, and industrial CPS
  

Submission Deadline: June 30, 2026 (the second round)

Control, Learning, and Games of Complex Systems: Theory and Methods

Session Chair: Yuanhua Ni, Nankai University, China

Key Words: Optimal Control, Learning, Game Theory, Dynamical Systems

Information: This special session focuses on control, learning, and game-theoretic frameworks for complex dynamical systems, addressing fundamental problems in modeling, analysis, and methodology under structural complexity, information constraints, and interacting decision-makers. As modern systems grow in scale and coupling, control theory is increasingly integrated with learning methods and game theory, leading to new analytical paradigms and solution approaches.

The session welcomes theoretical contributions that explore unified perspectives of control, learning, and games, including stability and performance analysis, optimal and robust control, learning-based and adaptive decision-making, and coordination and competition in multi-agent systems. The goal is to advance the theoretical foundations of control and decision-making in complex systems.

Topics of interest include but are not limited to: 

• Optimal control theory and methods for dynamical systems
• Learning methods in control systems
• Game theory and decision-making in dynamical systems
• Control and decision-making in multi-agent systems
• Distributed and decentralized optimal control
• Intersections of control, learning, and games
• Interaction and decision-making in networked systems
• Optimization and decision-making in control systems

Submission Deadline: June 30, 2026 (the second round)

Advances in Stability Analysis and Intelligent Control for Stochastic Systems

Session Chair: Weihai Zhang, Shandong University of Science and Technology, China

Tianliang Zhang, Qingdao University of Technology, China

Key Words: Stochastic Systems, Intelligent Control, Stability Analysis, Machine Learning in Control

Information: The stability analysis of stochastic systems and intelligent control is a frontier field in contemporary control theory, especially with the high demands for system stability in complex and uncertain environments. With the rapid development of intelligent control technologies, ensuring the robustness of systems under stochastic disturbances has become an important challenge in both theory and practice. This special session is aimed at researchers, engineers, and students in the fields of control theory and intelligent control technologies, particularly those who focus on the stability analysis of stochastic systems and the application of intelligent control. The research objective of this session is to explore how advanced stability analysis and intelligent control techniques can address uncertainties and disturbances in stochastic systems. Through this research, the aim is to explore effective methods to improve system stability and optimize control strategies in practical applications, thereby advancing the theory and technology in this field.

Topics of interest include but are not limited to: 

• Development of adaptive learning control methods for stochastic systems
• Robustness analysis and performance guarantees in the presence of random disturbances
• Stochastic optimal control and adaptive learning control
• Lyapunov-based approaches for stochastic adaptive control
• Convergence and stability analysis in stochastic environments
• Exploration of deep learning and reinforcement learning for stochastic systems

Submission Deadline: June 30, 2026 (the second round)

Modeling, Control, and Optimization for Complex Systems

Session Chair: Baolin Zhang, Qingdao University of Science and Technology, China

Shiyuan Han, Shandong Women's University, China

Guangchen Zhang, North Minzu University, China

Key Words: Complex Systems, Hybrid and Data-driven Modeling, Intelligent and Learning-based Control, Real-time & Robust Optimization, Cyber-physical Systems

Information: Complex systems spanning smart manufacturing, intelligent energy networks, autonomous transportation, advanced robotics, and large-scale cyber-physical infrastructures present significant challenges in design, analysis, and operation. These systems are characterized by high dimensionality, strong nonlinearity, multi-scale dynamics, interconnected subsystems, and inherent uncertainties. Traditional methods often fall short in capturing their intricate behaviors and ensuring efficient, reliable, and adaptive performance. This special issue focuses on advanced methodologies and applications for modeling, control, and optimization of complex systems, and addresses theoretical foundations, algorithmic innovations, and practical implementations aimed at enhancing the understanding, autonomy, and performance of such systems in real-world settings.

Topics of interest include but are not limited to: 

• Data-driven, and hybrid modeling approaches of complex systems
• Multi-scale and network-based modeling of control systems
• Learning-based control and AI-driven autonomy
• Event-triggered control strategies
• Multi-objective and dynamic optimization
• Optimization under uncertainty and real-time decision-making
• Co-design of modeling, control, and optimization

Submission Deadline: June 30, 2026 (the second round)

Autonomous Multi-Agent Systems in Robotics: Perception, Decision-making, Control, Learning, and Optimization Methods

Session Chair: Xin Tong, Nanjing University, China

Jie Chen, University of Science and Technology of China, China

Key Words: Multi-Agent Systems, Autonomous Robots, Perception, Decision-Making, Control, Learning and Optimization

Information: This special session focuses on recent advances, challenges, and applications of autonomous multi-agent systems in robotics. It aims to bring together researchers and practitioners to discuss state-of-the-art theories, algorithms, and implementations related to multi-agent perception, cooperative decision-making, motion control, learning-based strategies, and optimization methods. It welcomes contributions covering distributed perception, collaborative planning, robust and safe control, multi-agent reinforcement learning, model predictive control, distributed optimization, task allocation, and real-world deployment in robotic systems. The session seeks to promote cross-disciplinary ideas and foster innovations for building reliable, scalable, and intelligent autonomous multi-agent systems.

Topics of interest include but are not limited to: 

• Distributed perception and environment cognition for autonomous multi-agent robotic systems
• Collaborative decision-making and task allocation in multi-robot systems
• Safe, robust and adaptive control of networked multi-agent robotic systems
• Learning-based strategies for multi-agent coordination and motion planning
• Distributed optimization and optimal control in multi-robot systems
• Integration of perception, decision-making, control and learning for autonomous multi-agent systems

Submission Deadline: June 30, 2026 (the second round)

Embodied Intelligent Robotic Joint Technologies: Innovative Design and Applications

Session Chair: Ming Lu, Hunan University of Science and Technology, China

Changzhong Pan, Hunan University of Science and Technology, China

Key Words: Embodied Intelligent Robotics, Joint Design and Optimization, Actuation and Control Technology, Sensor Integration and Feedback, Bio-inspired Structures and Materials, Human-Robot Collaboration and Interaction

Information: Embodied intelligent robotic joint technology is the core foundation for achieving dexterous motion, environmental adaptation, and intelligent interaction in robots. Recent breakthroughs in bionics, flexible actuation, high-precision sensing, and intelligent control algorithms have driven joint design toward lightweight, high dynamic response, and multimodal perception. However, challenges remain in energy efficiency optimization, dynamic stability, and human-robot collaboration safety. This session focuses on cutting-edge technologies in embodied robotic joints, including innovative actuation mechanisms (e.g., muscle-inspired actuators, magnetorheological fluid joints), multimodal sensor fusion, material-structure-control co-design, and dynamic modeling with real-time optimization. It aims to promote applications in industrial automation, medical rehabilitation, and service robotics.

Topics of interest include but are not limited to: 

• Intelligent Joint Actuation (Bio-inspired Actuators, Magnetorheological Materials, Soft Actuation)
• High-precision Joint Sensing and Multimodal Data Fusion
• Bio-inspired Joint Structures and Lightweight Material Design
• Dynamic Control Algorithms and Real-time Optimization for Joints
• Safe Interaction and Haptic Feedback in Human-Robot Collaboration
• Application Validation in Industrial, Medical, and Exoskeleton Scenarios

Submission Deadline: June 30, 2026 (the second round)

Aerial Launch and Recovery for Mother-Daughter UAV Systems

Session Chair: Jianwen Huo, Southwest University of Science and Technology, China

Key Words: Mother-Daughter UAV, Aerial Launch and Recovery, Docking Mechanism, Mission Planning, Cooperative Control

Information: Mother-daughter unmanned aerial vehicle (UAV) systems employ a collaborative architecture in which a carrier drone deploys multiple smaller daughter drones to execute tasks. This approach dramatically extends the operational radius for logistics and improves efficiency in wide-area inspection and emergency response. Despite these advantages, practical deployment still faces considerable hurdles: complex aerodynamic interference during the safe separation of daughter drones from the carrier platform, the demand for high-precision relative navigation and autonomous docking during midair recovery, and the real-time robustness required for multi-agent mission planning. This special session will address these critical bottlenecks by showcasing the latest advances in aerodynamic modeling, autonomous guidance, docking mechanisms, and swarm control. The goal is to advance engineering solutions tailored for civilian applications—including low-altitude logistics, forest monitoring, and maritime search and rescue—and to bridge the gap between technology demonstration and large-scale operational use.

Topics of interest include but are not limited to:

• Design of Lightweight / Compliant Docking and Capture Mechanisms and Rigid-Flexible Coupled Dynamics Modeling
• Cooperative Mission Planning and Dynamic Rescheduling Methods for Mother-Daughter UAVs
• Cooperative Control Strategies for Mother-Daughter UAV Formations
• High-Precision Relative Navigation, Guidance, and Control Technologies for Autonomous Aerial Recovery

Abstract Submission Deadline: July 16, 2026 (the second round)

Full paper Submission Deadline: July 31, 2026 (the second round)

Perception, Control, and Optimization of Intelligent Transportation Systems

Session Chair: Ge Guo, Northeastern University, China

Meng Han, Northeastern University, China

Key Words: Intelligent Transportation Systems, Connected and Autonomous Vehicles, State Perception, Traffic Control and Optimization

Information: With the rapid development of the Internet of Things, artificial intelligence, and big data technologies, transportation systems are evolving from traditional management paradigms toward real-time perception, autonomous decision-making, and coordinated optimization. This topic aims to explore the critical role of multidimensional sensing technologies in acquiring traffic state information. It also examines control methods that integrate data-driven and model-driven approaches, with applications in signal timing, congestion mitigation, and emergency response. In addition, attention is given to system-level optimization problems in complex transportation networks, including traffic flow assignment, travel demand management, and low-carbon transportation strategies. By combining theoretical advances with engineering practices, this topic seeks to promote the development of safe, efficient, green, and intelligent modern transportation systems.

Topics of interest include but are not limited to:

• Intersection Signal Control and Adaptive Scheduling
• Vehicle Perception and Decision-Making
• Data- and Mechanism-Driven Traffic Control
• Traffic State Prediction and Risk Identification
• Traffic Congestion Mitigation and Optimization Scheduling
• Multi-Source Traffic Data Perception and Fusion

Submission Deadline: June 30, 2026 (the second round)

New Advances in the Theory and Application of Optimization Algorithms

Session Chair: Yiying Zhang, Shandong University of Science and Technology, China

Key Words: Newton's Method, Gradient Descent Method, Conjugate Gradient Method, Particle Swarm Algorithm, Genetic Algorithm

Information: Optimization algorithms are core supporting technologies in computational mathematics, artificial intelligence, operations research, and control science. The theoretical research and engineering application of optimization algorithms have always been the focus of academic attention. Classical numerical optimization methods (such as Newton's method, gradient descent method, conjugate gradient method, etc.) and swarm intelligence optimization algorithms (such as particle swarm optimization algorithm, genetic algorithm, etc.) each have their own advantages in convergence analysis, complexity characterization, parameter self-adaptation, and handling high-dimensional non-convex problems. The cross-fusion of the two is becoming an effective paradigm for solving complex optimization problems. This special topic focuses on the latest theoretical breakthroughs and practical applications of optimization algorithms. We sincerely invite scholars and researchers worldwide to share their latest research achievements in classical numerical optimization methods and swarm intelligence optimization algorithms, and contribute their wisdom and strength to the continuous evolution and interdisciplinary integration of optimization algorithms.

Topics of interest include but are not limited to:

• Theoretical frontiers of classical optimization algorithms
• Theoretical progress of swarm intelligence optimization algorithms
• Optimization methods for high-dimensional non convex problems
• Cross fusion of classic and swarm intelligence optimization algorithms
• Optimization algorithms for complex engineering problems
• Application of Optimization Algorithms in Machine Learning

Submission Deadline: June 30, 2026 (the second round)

System Intelligence and Safety Supervision Technology for Low-altitude Aircraft

 

Session Chair: Xiao Zhang, Beihang University, China

Hao Luo, Harbin Institute of Technology, China

Qun Sun, Liaocheng University, China

 

Key Words: Low-altitude Aircraft, System intelligence, Safety supervision, Low-altitude Economy, Advanced AI system

Information: The national "15th Five-Year Plan" has explicitly included the low-altitude economy as a strategic emerging industry, outlining strategic directions and providing policy support for the development of the low-altitude aircraft industry, pushing it into a new phase of standardized and large-scale development. As the application scenarios of low-altitude aircraft continue to expand and flight frequencies significantly increase, system safety and supervision become increasingly crucial. Adhering to the principle of "safety first" and establishing a solid safety defense line are not only prerequisites for the high-quality development of the low-altitude economy but also essential requirements for addressing potential safety hazards in low-altitude flight. Therefore, the forum focuses on the core areas of intelligent and safe control of low-altitude aircraft systems, with a particular emphasis on discussing how to enhance real-time diagnostic and health management capabilities of equipment through the intelligent upgrade of the three major components of low-altitude aircraft power systems, integrated avionics systems, and airframe systems, integrating technologies such as intelligent sensing and big data, to prevent safety risks caused by equipment failures. At the same time, in response to the demand for coordinated operation of multiple low-altitude aircraft, the forum delves into multi-aircraft scheduling optimization and flight supervision technologies, striving to solve challenges such as airspace resource allocation and control of unlicensed flights, and further improving the low-altitude flight supervision system. This forum establishes a platform for industry exchange and innovative cooperation, aiming to forge technical consensus, break development bottlenecks, and is of great significance for promoting the intelligent upgrade of low-altitude aircraft, improving the safety control system, and facilitating the implementation of the low-altitude economy strategy.

 

Topics of interest include but are not limited to: 

 

• Drone intelligent power systems
• Drone intelligent avionics systems (including navigation and control)
• Flying cars
• Low-altitude safety regulation

Submission Deadline: June 30, 2026 (the second round)

 

 

Data-Driven Sparse Modeling and Intelligent Soft Sensing for Complex Industrial Processes

 

Session Chair: Yanjun Liu, Jiangnan University, China

Siyu Liu, Zhejiang Normal University, China

 

Key Words: Sparse Optimization, Data-Driven Identification, Soft Sensing, State Estimation, Process Monitoring

Information: The rapid advancement of industrial intelligence has generated massive datasets, yet extracting meaningful low-dimensional features from high-dimensional, noisy environments remains a significant challenge. This session focuses on the intersection of sparse learning, system identification, and soft sensor technology. We explore how sparsity-promoting techniques enhance the interpretability and robustness of system models. By leveraging sparse learning, researchers can identify the core structures of complex dynamical systems and develop efficient soft sensors for real-time estimation of hard-to-measure variables. The session discusses innovations in sparse optimization, nonlinear identification, and intelligent sensing deployment in volatile industrial settings. We invite contributions that bridge the gap between theoretical modeling and practical challenges to improve monitoring reliability across sectors like chemical processing, energy management, and advanced manufacturing.

 

Topics of interest include but are not limited to: 

 

• Identification of Nonlinear Dynamical Systems by Sparse learning
• Robust Soft Sensing in the Presence of Missing Data and Outliers
• Industrial Applications of Sparse Learning in Energy, Chemical, and Manufacturing Systems
• Soft Sensor-Based State Estimation and Feedback in Predictive Control
• Robust MPC Design under Sparse Uncertainty and Variable Selection
  

Submission Deadline: June 30, 2026 (the second round)

 

 

Optimal Operation and Control in Power Systems with Hydrogen Energy Collaborations

 

Session Chair: Yajian Zhang, Shanghai University, China

Enzhi Cao, China Jiliang University, China

 

Key Words: Electricity-Hydrogen Coupling System, Multi-Energy Complementarity, Optimal Operation, Cooperative Control

 

Information: The attention to carbon emissions has promoted the construction of low-carbon power systems in the world. Hydrogen energy, as an efficient and high calorific value secondary energy source, can effectively address the power supply and demand imbalance caused by the random fluctuations of high-proportional renewable generations, and plays an important supporting role in improving the stability of power systems. The purpose of this topic is to jointly explore the application scenarios of hydrogen energy collaborative participation in power system operation, and provide feasible technical routes for building low-carbon power systems.

 

Topics of interest include but are not limited to:

• Optimal Operation and Control in Power Systems with Hydrogen Energy Collaborations

Submission Deadline: August 31, 2026 (the second round)

 

Methods and Applications for Intelligent Industrial Alarm Optimization, Analytics, and Root Cause Tracing

Session Chair: Jiandong Wang, Shandong University of Science and Technology, China

Wenkai Hu, China University of Geosciences, Wuhan, China

Key Words: Industrial Alarm System, Alarm & Event Data, Alarm Flood, Early Fault Detection, Predictive Maintenance

Information: Industrial alarm systems are critical for safeguarding operational safety in complex facilities such as power generation and chemical processing. However, high false alarm rates and alarm floods severely degrade their effectiveness, potentially aggravating equipment faults and even leading to catastrophic incidents. In recent years, research on industrial alarm monitoring has gradually evolved from traditional rule-based paradigms toward the exploration and application of cutting-edge technologies, including deep learning, big data analytics, and large foundation models, aiming to build intelligent alarm monitoring frameworks. This special session focuses on frontier advances in this domain, with particular emphasis on alarm system design, alarm flood analysis, alarm root cause analysis, and alarm response decision-making, while also encompassing related topics such as early fault detection and predictive maintenance.

Topics of interest include but are not limited to: 

• Design and performance evaluation of univariate or multivariate alarm systems
• Pattern mining, similarity analysis, prediction, and classification of alarm floods
• Knowledge-based or data-driven causality modelling and root cause analysis
• Deep learning and pretrained models for process monitoring and fault diagnosis
• Real-time process monitoring, anomaly detection, and predictive maintenance

Submission Deadline: June 30, 2026 (the second round)

Advanced Control Techniques for Embodied Intelligent Robots Under Uncertain Working Conditions

Session Chair: Shaoxin Sun, Chongqing University, China

Key Words: Embodied Intelligent Robots, Uncertain Environment, Robust Control, Sim-to-Real Migration, Advanced Control, Multimodal Perception

Information: Diverse uncertain working conditions widely exist during the actual operation of embodied intelligent robots, which brings great difficulties to the stable operation of robot equipment. As robots are increasingly applied to diversified complex industrial and unmanned scenarios, dynamic interference, system switch and input saturation greatly restrict the operational reliability of robot systems. In recent years, adaptive Sim-to-Real based on neural networks and pre-training robust control have evolved into mainstream novel control technologies for robotic optimization. This invited session aims to gather relevant researchers and engineering practitioners to exchange frontier progress on theoretical modeling, algorithm development, hardware deployment and practical verification of robot control technologies, facilitate academic exchanges and advance the engineering transformation of advanced control theories for embodied intelligent robots across industrial automation and unmanned equipment fields.

Topics of interest include but are not limited to: 

• Latest Research Progress of Robot Control in Uncertain Scenarios
• Algorithm Mechanism Design of Neural Network Driven Sim-to-Real and Pre-trained Robust Control
• Engineering Realization and Experimental Verification of Advanced Robot Control Schemes
• Closed-Loop Control Construction Based on Multimodal Perception Fusion
• Key Bottlenecks of Zero-Shot Adaptation and Dynamic Path Planning for Robots

Submission Deadline: June 30, 2026 (the second round)