In this paper an event-based operational interleaving semantics is proposed for real-time processes, for which action refinement and a denotational true concurrency semantics are developed and defined in terms of time...In this paper an event-based operational interleaving semantics is proposed for real-time processes, for which action refinement and a denotational true concurrency semantics are developed and defined in terms of timed event structures. The authors characterize the timed event traces that are generated by the operational semantics in a denotational way, and show that this operational semantics is consistent with the denotational semantics in the sense that they generate the same set of timed event traces, thereby eliminating the gap between the true concurrency and interleaving semantics. Keywords action refinement - real-time process algebra - semantics - timed event structure - formal method This work was supported by the National Natural Science Foundation of China (Grant No. 60373113) and the “Hundred-Talent Program” of Chinese Academy of Sciences.Xiu-Li Sun was born in 1975. She received her B.S. degree in 1998 and M.S. degree in 2002 from Taiyuan University of Technology, Shanxi. She is studying in the Institute of Computer Application, Chengdu for her doctorate.Wen-Ying Zhang was born in 1972. Now he is a Ph.D. candidate of Computer Application, the Chinese Academy of Sciences. His current research interests include formal verification, digital watermarking and pattern recognition.Jin-Zhao Wu was born in 1965. He obtained his Ph.D. degree in 1994 from the Institute of System Science, CAS. From 1994 to 1999 he was a postdoctoral researcher. His research interests include formal specification and verification, automatic reasoning, logic programming.展开更多
Background:Intelligent monitoring of human action in production is an important step to help standardize production processes and construct a digital twin shop-floor rapidly.Human action has a significant impact on th...Background:Intelligent monitoring of human action in production is an important step to help standardize production processes and construct a digital twin shop-floor rapidly.Human action has a significant impact on the production safety and efficiency of a shop-floor,however,because of the high individual initiative of humans,it is difficult to realize real-time action detection in a digital twin shop-floor.Methods:We proposed a real-time detection approach for shop-floor production action.This approach used the sequence data of continuous human skeleton joints sequences as the input.We then reconstructed the Joint Classification-Regression Recurrent Neural Networks(JCR-RNN)based on Temporal Convolution Network(TCN)and Graph Convolution Network(GCN).We called this approach the Temporal Action Detection Net(TAD-Net),which realized real-time shop-floor production action detection.Results:The results of the verification experiment showed that our approach has achieved a high temporal positioning score,recognition speed,and accuracy when applied to the existing Online Action Detection(OAD)dataset and the Nanjing University of Science and Technology 3 Dimensions(NJUST3D)dataset.TAD-Net can meet the actual needs of the digital twin shop-floor.Conclusions:Our method has higher recognition accuracy,temporal positioning accuracy,and faster running speed than other mainstream network models,it can better meet actual application requirements,and has important research value and practical significance for standardizing shop-floor production processes,reducing production security risks,and contributing to the understanding of real-time production action.展开更多
文摘In this paper an event-based operational interleaving semantics is proposed for real-time processes, for which action refinement and a denotational true concurrency semantics are developed and defined in terms of timed event structures. The authors characterize the timed event traces that are generated by the operational semantics in a denotational way, and show that this operational semantics is consistent with the denotational semantics in the sense that they generate the same set of timed event traces, thereby eliminating the gap between the true concurrency and interleaving semantics. Keywords action refinement - real-time process algebra - semantics - timed event structure - formal method This work was supported by the National Natural Science Foundation of China (Grant No. 60373113) and the “Hundred-Talent Program” of Chinese Academy of Sciences.Xiu-Li Sun was born in 1975. She received her B.S. degree in 1998 and M.S. degree in 2002 from Taiyuan University of Technology, Shanxi. She is studying in the Institute of Computer Application, Chengdu for her doctorate.Wen-Ying Zhang was born in 1972. Now he is a Ph.D. candidate of Computer Application, the Chinese Academy of Sciences. His current research interests include formal verification, digital watermarking and pattern recognition.Jin-Zhao Wu was born in 1965. He obtained his Ph.D. degree in 1994 from the Institute of System Science, CAS. From 1994 to 1999 he was a postdoctoral researcher. His research interests include formal specification and verification, automatic reasoning, logic programming.
基金This work was supported by the National Key Research and Development Program,China(2020YFB1708400)the National Defense Fundamental Research Program,China(JCKY2020210B006,JCKY2017204B053)awarded to TL.
文摘Background:Intelligent monitoring of human action in production is an important step to help standardize production processes and construct a digital twin shop-floor rapidly.Human action has a significant impact on the production safety and efficiency of a shop-floor,however,because of the high individual initiative of humans,it is difficult to realize real-time action detection in a digital twin shop-floor.Methods:We proposed a real-time detection approach for shop-floor production action.This approach used the sequence data of continuous human skeleton joints sequences as the input.We then reconstructed the Joint Classification-Regression Recurrent Neural Networks(JCR-RNN)based on Temporal Convolution Network(TCN)and Graph Convolution Network(GCN).We called this approach the Temporal Action Detection Net(TAD-Net),which realized real-time shop-floor production action detection.Results:The results of the verification experiment showed that our approach has achieved a high temporal positioning score,recognition speed,and accuracy when applied to the existing Online Action Detection(OAD)dataset and the Nanjing University of Science and Technology 3 Dimensions(NJUST3D)dataset.TAD-Net can meet the actual needs of the digital twin shop-floor.Conclusions:Our method has higher recognition accuracy,temporal positioning accuracy,and faster running speed than other mainstream network models,it can better meet actual application requirements,and has important research value and practical significance for standardizing shop-floor production processes,reducing production security risks,and contributing to the understanding of real-time production action.
