Cyber-physical systems (CPS) are connecting our physical world and cyberspace through real-time sensing, situational awareness, and intelligent control. The calculation paradigm of CPS perception data is changing from traditional manual control to automatic intelligent decision-making. With the emergence of these new paradigms, the concept of CPS is also evolving by integrating humans into the traditional interaction between the network and physical space. Therefore, the concept of CPHS (Cyber-Physical-Human System) was proposed by placing people in the cycle of traditional CPS. CPHS integrates human factors, and realizes real-time, efficient and reliable data intelligence through calculation, physical interaction, human participation and feedback loops in the depth of fusion perception, calculation, communication and control. The core problem to achieve this goal is how to make real-time, accurate, and intelligent decisions based on the interaction of the three elements of man-machine-object and using massive sensor data.
The traditional CPS decision-making model and framework are generally based on two-factor interaction of cyber and physical factors, without considering the human factors. They cannot automatically generate decision-making knowledge based on decision feedback and historical decisions and actions to formulate iterative learning. For CPHS, when we add human factors to CPS, how to formalize the relationship between different elements to guide the design of new decision-making models is the first challenge we face. Moreover, most of the existing CPS data intelligence and decision-making technologies, including sensor information fusion, time-series data management, complex event processing, etc., are directed to certain data characteristics, processing the single-dimensional observations of the real world, and applying them to limited application scenarios. In addition, many machine learning methods only support offline learning and require supervised training and processing, so they cannot provide real-time, online, and unsupervised features for CPS data and decision intelligence.
Robots are also used in the cyber-physical-human system, which provides the integration of sensing, computation, and actuation as the interaction with the physical world. Although significant progress is being made in robotic research and enabling technologies for practical applications, new methods must be developed for the design, modeling, and control of robotic systems that can work safely with humans in a cyber-physical system.
In conclusion, to build a human-in-the-loop cyber-physical system, both artificial intelligence, human-computer interaction and intelligent robotic techniques should be taken into consideration. The purpose of this Research Topic is to promote outstanding research concerning human-in-the-loop cyber-physical systems, focusing on state-of-the-art progress, developments, and new trends.
Potential topics of interest include, but are not limited to the following:
• Human-in-the-loop Cyber-Physical Systems
• Cyber-Physical-Human System
• Artificial Intelligence
• Human-Computer Interaction
• Intelligent Robots
• Computer-Supported Cooperative Work
• Iterative Learning Paradigms
• Unsupervised Learning
• Online Learning and Reinforcement Learning
Cyber-physical systems (CPS) are connecting our physical world and cyberspace through real-time sensing, situational awareness, and intelligent control. The calculation paradigm of CPS perception data is changing from traditional manual control to automatic intelligent decision-making. With the emergence of these new paradigms, the concept of CPS is also evolving by integrating humans into the traditional interaction between the network and physical space. Therefore, the concept of CPHS (Cyber-Physical-Human System) was proposed by placing people in the cycle of traditional CPS. CPHS integrates human factors, and realizes real-time, efficient and reliable data intelligence through calculation, physical interaction, human participation and feedback loops in the depth of fusion perception, calculation, communication and control. The core problem to achieve this goal is how to make real-time, accurate, and intelligent decisions based on the interaction of the three elements of man-machine-object and using massive sensor data.
The traditional CPS decision-making model and framework are generally based on two-factor interaction of cyber and physical factors, without considering the human factors. They cannot automatically generate decision-making knowledge based on decision feedback and historical decisions and actions to formulate iterative learning. For CPHS, when we add human factors to CPS, how to formalize the relationship between different elements to guide the design of new decision-making models is the first challenge we face. Moreover, most of the existing CPS data intelligence and decision-making technologies, including sensor information fusion, time-series data management, complex event processing, etc., are directed to certain data characteristics, processing the single-dimensional observations of the real world, and applying them to limited application scenarios. In addition, many machine learning methods only support offline learning and require supervised training and processing, so they cannot provide real-time, online, and unsupervised features for CPS data and decision intelligence.
Robots are also used in the cyber-physical-human system, which provides the integration of sensing, computation, and actuation as the interaction with the physical world. Although significant progress is being made in robotic research and enabling technologies for practical applications, new methods must be developed for the design, modeling, and control of robotic systems that can work safely with humans in a cyber-physical system.
In conclusion, to build a human-in-the-loop cyber-physical system, both artificial intelligence, human-computer interaction and intelligent robotic techniques should be taken into consideration. The purpose of this Research Topic is to promote outstanding research concerning human-in-the-loop cyber-physical systems, focusing on state-of-the-art progress, developments, and new trends.
Potential topics of interest include, but are not limited to the following:
• Human-in-the-loop Cyber-Physical Systems
• Cyber-Physical-Human System
• Artificial Intelligence
• Human-Computer Interaction
• Intelligent Robots
• Computer-Supported Cooperative Work
• Iterative Learning Paradigms
• Unsupervised Learning
• Online Learning and Reinforcement Learning
