The late start of environmental protection in Hong Kong was discussed in the light of problems encountered during the development of environmental protection legislation in Hong Kong for the past 20 years. The collabo...The late start of environmental protection in Hong Kong was discussed in the light of problems encountered during the development of environmental protection legislation in Hong Kong for the past 20 years. The collaboration in monitoring and assessment of environmental pollutants between the University of Hong Kong and various governments were descrbed in parallel with the progress in environmental protection in Hong Kong. The developments of new analytical techniques for environmental monitoring and analysis is given and their application in environmental control described. The joint projects in assessment and control of environmental pollutants carried out in collaboration with local industries and other organizations within and without the university are given and discussed. The problems and possible solution facing Hong Kong in development control equipment for small scale industries are discussed and areas of development identified. The development and experience in the monitoring assessment and control of environmental pollutants in Hong Kong are summarized and areas of difficulties are illustrated.展开更多
Modern manufacturing aims to reduce downtime and track process anomalies to make profitable business decisions.This ideology is strengthened by Industry 4.0,which aims to continuously monitor high-value manufacturing ...Modern manufacturing aims to reduce downtime and track process anomalies to make profitable business decisions.This ideology is strengthened by Industry 4.0,which aims to continuously monitor high-value manufacturing assets.This article builds upon the Industry 4.0 concept to improve the efficiency of manufacturing systems.The major contribution is a framework for continuous monitoring and feedback-based control in the friction stir welding(FSW)process.It consists of a CNC manufacturing machine,sensors,edge,cloud systems,and deep neural networks,all working cohesively in real time.The edge device,located near the FSW machine,consists of a neural network that receives sensory information and predicts weld quality in real time.It addresses time-critical manufacturing decisions.Cloud receives the sensory data if weld quality is poor,and a second neural network predicts the new set of welding parameters that are sent as feedback to the welding machine.Several experiments are conducted for training the neural networks.The framework successfully tracks process quality and improves the welding by controlling it in real time.The system enables faster monitoring and control achieved in less than 1 s.The framework is validated through several experiments.展开更多
Control -net网络是一个开放的、高速的、确定性的工业局域网,用于传输对时间有苛刻要求的信息,为对等通信提供实时控制和报文传送。可实现PC机、控制器、操作界面设备、I/O模块等不同设备间的联网通信。网络成功地应用在多种工业自动...Control -net网络是一个开放的、高速的、确定性的工业局域网,用于传输对时间有苛刻要求的信息,为对等通信提供实时控制和报文传送。可实现PC机、控制器、操作界面设备、I/O模块等不同设备间的联网通信。网络成功地应用在多种工业自动控制系统上。展开更多
燃煤锅炉是一种重要的能量转换设备,对其燃烧过程的稳定控制是燃料充分燃烧的必要条件。为提高锅炉系统的环保性,采用工控机(Industrial Personal Computer,IPC)与可编程逻辑控制器(Programmable Logic Controller,PLC)相结合的方式,设...燃煤锅炉是一种重要的能量转换设备,对其燃烧过程的稳定控制是燃料充分燃烧的必要条件。为提高锅炉系统的环保性,采用工控机(Industrial Personal Computer,IPC)与可编程逻辑控制器(Programmable Logic Controller,PLC)相结合的方式,设计了燃煤锅炉的分布式控制系统。以给煤量控制为例,阐述了控制方法的设计过程。依据燃煤锅炉的工艺过程,采用WinCC组态软件设计监控系统,以实现锅炉的远程交互控制,提高锅炉运行的安全性和稳定性。展开更多
The reliability of the Controller Area Network（CAN） is critical to the performance and safety of the system. However, direct bus-off time assessment tools are lacking in practice due to inaccessibility of the node i...The reliability of the Controller Area Network（CAN） is critical to the performance and safety of the system. However, direct bus-off time assessment tools are lacking in practice due to inaccessibility of the node information and the complexity of the node interactions upon errors. In order to measure the mean time to bus-off（MTTB） of all the nodes, a novel data driven node bus-off time assessment method for CAN network is proposed by directly using network error information. First, the corresponding network error event sequence for each node is constructed using multiple-layer network error information. Then, the generalized zero inflated Poisson process（GZIP） model is established for each node based on the error event sequence. Finally, the stochastic model is constructed to predict the MTTB of the node. The accelerated case studies with different error injection rates are conducted on a laboratory network to demonstrate the proposed method, where the network errors are generated by a computer controlled error injection system. Experiment results show that the MTTB of nodes predicted by the proposed method agree well with observations in the case studies. The proposed data driven node time to bus-off assessment method for CAN networks can successfully predict the MTTB of nodes by directly using network error event data.展开更多
文摘The late start of environmental protection in Hong Kong was discussed in the light of problems encountered during the development of environmental protection legislation in Hong Kong for the past 20 years. The collaboration in monitoring and assessment of environmental pollutants between the University of Hong Kong and various governments were descrbed in parallel with the progress in environmental protection in Hong Kong. The developments of new analytical techniques for environmental monitoring and analysis is given and their application in environmental control described. The joint projects in assessment and control of environmental pollutants carried out in collaboration with local industries and other organizations within and without the university are given and discussed. The problems and possible solution facing Hong Kong in development control equipment for small scale industries are discussed and areas of development identified. The development and experience in the monitoring assessment and control of environmental pollutants in Hong Kong are summarized and areas of difficulties are illustrated.
文摘Modern manufacturing aims to reduce downtime and track process anomalies to make profitable business decisions.This ideology is strengthened by Industry 4.0,which aims to continuously monitor high-value manufacturing assets.This article builds upon the Industry 4.0 concept to improve the efficiency of manufacturing systems.The major contribution is a framework for continuous monitoring and feedback-based control in the friction stir welding(FSW)process.It consists of a CNC manufacturing machine,sensors,edge,cloud systems,and deep neural networks,all working cohesively in real time.The edge device,located near the FSW machine,consists of a neural network that receives sensory information and predicts weld quality in real time.It addresses time-critical manufacturing decisions.Cloud receives the sensory data if weld quality is poor,and a second neural network predicts the new set of welding parameters that are sent as feedback to the welding machine.Several experiments are conducted for training the neural networks.The framework successfully tracks process quality and improves the welding by controlling it in real time.The system enables faster monitoring and control achieved in less than 1 s.The framework is validated through several experiments.
文摘燃煤锅炉是一种重要的能量转换设备,对其燃烧过程的稳定控制是燃料充分燃烧的必要条件。为提高锅炉系统的环保性,采用工控机(Industrial Personal Computer,IPC)与可编程逻辑控制器(Programmable Logic Controller,PLC)相结合的方式,设计了燃煤锅炉的分布式控制系统。以给煤量控制为例,阐述了控制方法的设计过程。依据燃煤锅炉的工艺过程,采用WinCC组态软件设计监控系统,以实现锅炉的远程交互控制,提高锅炉运行的安全性和稳定性。
基金Supported by National Natural Science Foundation of China(Grant No.51475422)Science Fund for Creative Research Groups of National Natural Science Foundation of China(Grant No.51521064)+1 种基金National Basic Research Program of China(973 Program,Grant No.2013CB-035405)Open Foundation of State Key Laboratory of Automotive Safety and Energy,Tsinghua University,China(Grant No.KF13011)
文摘The reliability of the Controller Area Network（CAN） is critical to the performance and safety of the system. However, direct bus-off time assessment tools are lacking in practice due to inaccessibility of the node information and the complexity of the node interactions upon errors. In order to measure the mean time to bus-off（MTTB） of all the nodes, a novel data driven node bus-off time assessment method for CAN network is proposed by directly using network error information. First, the corresponding network error event sequence for each node is constructed using multiple-layer network error information. Then, the generalized zero inflated Poisson process（GZIP） model is established for each node based on the error event sequence. Finally, the stochastic model is constructed to predict the MTTB of the node. The accelerated case studies with different error injection rates are conducted on a laboratory network to demonstrate the proposed method, where the network errors are generated by a computer controlled error injection system. Experiment results show that the MTTB of nodes predicted by the proposed method agree well with observations in the case studies. The proposed data driven node time to bus-off assessment method for CAN networks can successfully predict the MTTB of nodes by directly using network error event data.
