China has experienced rapid economic development and industrialization during the past four decades,leading to widespread distribution of various types of industrial facilities[1].With the understanding of soil contam...China has experienced rapid economic development and industrialization during the past four decades,leading to widespread distribution of various types of industrial facilities[1].With the understanding of soil contamination induced by industrial sites and rapid urbanization,abandoned industrial sites are generally reclassified for urban land[2].The remained brownfield sites(the sites of abandoned,idle,or under used industrial and commercial facilities where expansion or redevelopment is hindered by environmental contamination)exhibit great health risks to the residents due to that pollutants were continuously discharged intothe surrounding environments[3].展开更多
Purpose–This paper studies the lateral stability regulation of intelligent electric vehicle(EV)based on model predictive control(MPC)algorithm.Design/methodology/approach–Firstly,the bicycle model is adopted in the ...Purpose–This paper studies the lateral stability regulation of intelligent electric vehicle(EV)based on model predictive control(MPC)algorithm.Design/methodology/approach–Firstly,the bicycle model is adopted in the system modelling process.To improve the accuracy,the lateral stiffness of front and rear tire is estimated using the real-time yaw rate acceleration and lateral acceleration of the vehicle based on the vehicle dynamics.Then the constraint of input and output in the model predictive controller is designed.Soft constraints on the lateral speed of the vehicle are designed to guarantee the solved persistent feasibility and enforce the vehicle’s sideslip angle within a safety range.Findings–The simulation results show that the proposed lateral stability controller based on the MPC algorithm can improve the handling and stability performance of the vehicle under complex working conditions.Originality/value–The MPC schema and the objective function are established.The integrated active front steering/direct yaw moments control strategy is simultaneously adopted in the model.The vehicle’s sideslip angle is chosen as the constraint and is controlled in stable range.The online estimation of tire stiffness is performed.The vehicle’s lateral acceleration and the yaw rate acceleration are modelled into the two-degree-of-freedom equation to solve the tire cornering stiffness in real time.This can ensure the accuracy of model.展开更多
In recent years, many industrial enterprises located in the urban centers of China have been relocated owing to the rapid increase in urban development. At the sites abandoned by these enterprises, volatile organic co...In recent years, many industrial enterprises located in the urban centers of China have been relocated owing to the rapid increase in urban development. At the sites abandoned by these enterprises, volatile organic compounds have frequently been detected, sometimes at high concentrations, particularly at sites abandoned by chemical manufacturing enterprises. With the redevelopment of sites and changes in land-use tvpe associated with these sites, substantial amounts of contaminated soils now require remediation. ＂Since China is a developing country, soil remediation warrants the usage of techniques that are suitable for addressing the unique challenges faced in this country. Land shortage is a common problem in China; the large numbers of contaminated sites, tight development schedules, and limited financial resources necessitate the development of .cost-effective methods for land reclamation.Mechanical soil aeration is a simple, effective, and low-cost soil remediation tectm^que mat is particularly suitable for the remediation of large volatile organic compound-contaminated sites. Its effectiveness has been confirmed by conducting laboratory studies, pilot tests, and full-scale projects.This study reviews current engineei-ing practice and developmental trends of mechanical soil aeration and analyzes the advantages and disadvantages of this technology for application in China as an emerging soil remediation market. The findings of this study might aid technology development in China, as well as assist other developing countries in the assessment and implementation of costeffective hazardous waste site soil remediation programs.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
基金supported by the National Key Research and Development Program of China(2020YFC1806700)the National Natural Science Foundation of China(42230505 and 42206148)。
文摘China has experienced rapid economic development and industrialization during the past four decades,leading to widespread distribution of various types of industrial facilities[1].With the understanding of soil contamination induced by industrial sites and rapid urbanization,abandoned industrial sites are generally reclassified for urban land[2].The remained brownfield sites(the sites of abandoned,idle,or under used industrial and commercial facilities where expansion or redevelopment is hindered by environmental contamination)exhibit great health risks to the residents due to that pollutants were continuously discharged intothe surrounding environments[3].
基金supported by National Natural Science Foundation of China(51605108)Natural Science Foundation of Guangxi Province(2020GXNSFAA297031,2018GXNSFAA281271,Guike2018AD19065).
文摘Purpose–This paper studies the lateral stability regulation of intelligent electric vehicle(EV)based on model predictive control(MPC)algorithm.Design/methodology/approach–Firstly,the bicycle model is adopted in the system modelling process.To improve the accuracy,the lateral stiffness of front and rear tire is estimated using the real-time yaw rate acceleration and lateral acceleration of the vehicle based on the vehicle dynamics.Then the constraint of input and output in the model predictive controller is designed.Soft constraints on the lateral speed of the vehicle are designed to guarantee the solved persistent feasibility and enforce the vehicle’s sideslip angle within a safety range.Findings–The simulation results show that the proposed lateral stability controller based on the MPC algorithm can improve the handling and stability performance of the vehicle under complex working conditions.Originality/value–The MPC schema and the objective function are established.The integrated active front steering/direct yaw moments control strategy is simultaneously adopted in the model.The vehicle’s sideslip angle is chosen as the constraint and is controlled in stable range.The online estimation of tire stiffness is performed.The vehicle’s lateral acceleration and the yaw rate acceleration are modelled into the two-degree-of-freedom equation to solve the tire cornering stiffness in real time.This can ensure the accuracy of model.
文摘In recent years, many industrial enterprises located in the urban centers of China have been relocated owing to the rapid increase in urban development. At the sites abandoned by these enterprises, volatile organic compounds have frequently been detected, sometimes at high concentrations, particularly at sites abandoned by chemical manufacturing enterprises. With the redevelopment of sites and changes in land-use tvpe associated with these sites, substantial amounts of contaminated soils now require remediation. ＂Since China is a developing country, soil remediation warrants the usage of techniques that are suitable for addressing the unique challenges faced in this country. Land shortage is a common problem in China; the large numbers of contaminated sites, tight development schedules, and limited financial resources necessitate the development of .cost-effective methods for land reclamation.Mechanical soil aeration is a simple, effective, and low-cost soil remediation tectm^que mat is particularly suitable for the remediation of large volatile organic compound-contaminated sites. Its effectiveness has been confirmed by conducting laboratory studies, pilot tests, and full-scale projects.This study reviews current engineei-ing practice and developmental trends of mechanical soil aeration and analyzes the advantages and disadvantages of this technology for application in China as an emerging soil remediation market. The findings of this study might aid technology development in China, as well as assist other developing countries in the assessment and implementation of costeffective hazardous waste site soil remediation programs.
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.
