In high-risk industrial environments like nuclear power plants,precise defect identification and localization are essential for maintaining production stability and safety.However,the complexity of such a harsh enviro...In high-risk industrial environments like nuclear power plants,precise defect identification and localization are essential for maintaining production stability and safety.However,the complexity of such a harsh environment leads to significant variations in the shape and size of the defects.To address this challenge,we propose the multivariate time series segmentation network(MSSN),which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates.To tackle the classification difficulty caused by structural signal variance,MSSN employs logarithmic normalization to adjust instance distributions.Furthermore,it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences.Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95%localization and demonstrates the capture capability on the synthetic dataset.In a nuclear plant's heat transfer tube dataset,it captures 90%of defect instances with75%middle localization F1 score.展开更多
With the rapid development of ubiquitous networks and smart cities,the connection and communication of Internet of Everything(IoE)have drawn great attention from both academia and industry.The main challenge for const...With the rapid development of ubiquitous networks and smart cities,the connection and communication of Internet of Everything(IoE)have drawn great attention from both academia and industry.The main challenge for constructing IoE is to enable real-time communication and high-efficiency computing among mobile devices.Mobile fog computing is promising to lower communication delay and offload network traffic.However,how to realize fog-enabled communication and computing in IoE with high-dynamic and heterogeneous network characters has not been fully investigated.Furthermore,deployment and reliable communications among fog nodes are also challenging.展开更多
Digital twin self-evolution means that digital twin can fuse online sensor data from physical entity to evolve itself,hence improve credibility of the model and represent the physical entity faithfully.There is an urg...Digital twin self-evolution means that digital twin can fuse online sensor data from physical entity to evolve itself,hence improve credibility of the model and represent the physical entity faithfully.There is an urgent need to address the fundamental theories and techniques on how to build such a self-evolving digital twin system for complex equipment optimization and control.Focused on this problem,integrating Bayesian optimization theory and deep reinforcement learning(DRL),this paper proposes a method to build dynamic self-evolving equipment digital twin system for optimal control.First,considering the complexity of current equipment and real-time requirement of dynamic self-evolution scenario,we design digital twin dynamic self-evolution engine using Bayesian optimization theory,which can continuously integrate real-time sensing data,adapt to the dynamic uncertainty changes of physical equipment,so as to improve the credibility of digital twin.Then,a decision-making agent based on DRL algorithm soft actor-critic is designed,which can interact with equipment digital twin in virtual space.When the digital twin model evolves,the agent follows and continues to learn and update itself through online fine-tuning strategy,so as to continuously improve the equipment optimization control performance.Finally,the feasibility and effectiveness of the proposed method are verified by two simulation cases.展开更多
基金supported by the National Science and Technology Major Project of the Ministry of Science and Technology of China(2024ZD0608100)the National Natural Science Foundation of China(62332017,U22A2022)
文摘In high-risk industrial environments like nuclear power plants,precise defect identification and localization are essential for maintaining production stability and safety.However,the complexity of such a harsh environment leads to significant variations in the shape and size of the defects.To address this challenge,we propose the multivariate time series segmentation network(MSSN),which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates.To tackle the classification difficulty caused by structural signal variance,MSSN employs logarithmic normalization to adjust instance distributions.Furthermore,it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences.Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95%localization and demonstrates the capture capability on the synthetic dataset.In a nuclear plant's heat transfer tube dataset,it captures 90%of defect instances with75%middle localization F1 score.
文摘With the rapid development of ubiquitous networks and smart cities,the connection and communication of Internet of Everything(IoE)have drawn great attention from both academia and industry.The main challenge for constructing IoE is to enable real-time communication and high-efficiency computing among mobile devices.Mobile fog computing is promising to lower communication delay and offload network traffic.However,how to realize fog-enabled communication and computing in IoE with high-dynamic and heterogeneous network characters has not been fully investigated.Furthermore,deployment and reliable communications among fog nodes are also challenging.
基金supported by the National Natural Science Foundation of China under Grant No.62373026。
文摘Digital twin self-evolution means that digital twin can fuse online sensor data from physical entity to evolve itself,hence improve credibility of the model and represent the physical entity faithfully.There is an urgent need to address the fundamental theories and techniques on how to build such a self-evolving digital twin system for complex equipment optimization and control.Focused on this problem,integrating Bayesian optimization theory and deep reinforcement learning(DRL),this paper proposes a method to build dynamic self-evolving equipment digital twin system for optimal control.First,considering the complexity of current equipment and real-time requirement of dynamic self-evolution scenario,we design digital twin dynamic self-evolution engine using Bayesian optimization theory,which can continuously integrate real-time sensing data,adapt to the dynamic uncertainty changes of physical equipment,so as to improve the credibility of digital twin.Then,a decision-making agent based on DRL algorithm soft actor-critic is designed,which can interact with equipment digital twin in virtual space.When the digital twin model evolves,the agent follows and continues to learn and update itself through online fine-tuning strategy,so as to continuously improve the equipment optimization control performance.Finally,the feasibility and effectiveness of the proposed method are verified by two simulation cases.
