Key technologies as well as their principles were discussed for a decentralized control platform capable of dynamic evolution. The primary content includes the automatic decision-making mechanism and the algorithm of ...Key technologies as well as their principles were discussed for a decentralized control platform capable of dynamic evolution. The primary content includes the automatic decision-making mechanism and the algorithm of the control center migration, the principle and technology of system self-monitoring, the principle and technology of the switch-mode of remote control station, the information transmission technology, and the data consistency technology. These key technologies have shown a series of advanced characteristics for decentralized control platform.展开更多
The volumetric flow rate of smoke generated from the fire in large space often reaches to hundreds of thousands CMH because of extended floor height and as it’s more difficult to isolate the smoke to the limited area...The volumetric flow rate of smoke generated from the fire in large space often reaches to hundreds of thousands CMH because of extended floor height and as it’s more difficult to isolate the smoke to the limited area, comparing to normal-scale building, design and operation of effective smoke control system for large space is more than important. In this study, with the analysis model for such a large space as exhibition hall or conference room in conventional center, design of mechanical smoke exhaust system was conducted based on currently-available design standard which was then followed by numerical analysis of the design using 3D numerical analysis method. For conference room at 2.0 MW heat release rate, 99,173 CMH flow rate is required, if smoke layer is maintained at 60% of the floor height and for exhibition hall at 8.8 MW with 80% of floor height, flow rate required is 219,802 CMH, which are incorporated into the design. In view of 3D numerical analysis, accuracy of the design according to algebraic expression is sufficient.展开更多
This paper describes a simulation-based intelligent decision support system (IDSS) for real time control of a flexible manufacturing system (FMS) with machine and tool flexibility. The manufacturing processes involved...This paper describes a simulation-based intelligent decision support system (IDSS) for real time control of a flexible manufacturing system (FMS) with machine and tool flexibility. The manufacturing processes involved in FMS are complicated since each operation may be done by several machining centers. The system design approach is built around the theory of dynamic supervisory control based on a rule-based expert system. The paper considers flexibility in operation assignment and scheduling of multi-purpose machining centers which have different tools with their own efficiency. The architecture of the proposed controller consists of a simulator module coordinated with an IDSS via a real time event handler for implementing inter-process synchronization. The controller’s performance is validated by benchmark test problem.展开更多
This work leveraged predictive modeling techniques in machine learning (ML) to predict heart disease using a dataset sourced from the Center for Disease Control and Prevention in the US. The dataset was preprocessed a...This work leveraged predictive modeling techniques in machine learning (ML) to predict heart disease using a dataset sourced from the Center for Disease Control and Prevention in the US. The dataset was preprocessed and used to train five machine learning models: random forest, support vector machine, logistic regression, extreme gradient boosting and light gradient boosting. The goal was to use the best performing model to develop a web application capable of reliably predicting heart disease based on user-provided data. The extreme gradient boosting classifier provided the most reliable results with precision, recall and F1-score of 97%, 72%, and 83% respectively for Class 0 (no heart disease) and 21% (precision), 81% (recall) and 34% (F1-score) for Class 1 (heart disease). The model was further deployed as a web application.展开更多
This paper proposes the concept and framework of smart operating system based on the artificial intelligence(AI)techniques. The demands and the potential applications of AI technologies in power system control centers...This paper proposes the concept and framework of smart operating system based on the artificial intelligence(AI)techniques. The demands and the potential applications of AI technologies in power system control centers is discussed in the beginning of the paper. The discussion is based on the results of a field study in the Tianjin Power System Control Center in China. According to the study, one problem in power systems is that the power system analysis system in the control center is not fast and powerful enough to help the operators in time to deal with the incidents in the power system. Another issue in current power system control center is that the operation tickets are compiled manually by the operators, so that it is less efficient and human errors cannot be avoided. Based on these problems, a framework of the smart operating robot is proposed in this paper, which includes an intelligent power system analysis system and a smart operation ticket compiling system to solve the two problems in power system control centers. The proposed framework is mainly based on the AI techniques, especially the neural network with deep learning, since it is faster and more capable of dealing with the highly nonlinear and complex power system.展开更多
基金The National Innovation Fund ( No.00C262251211336)
文摘Key technologies as well as their principles were discussed for a decentralized control platform capable of dynamic evolution. The primary content includes the automatic decision-making mechanism and the algorithm of the control center migration, the principle and technology of system self-monitoring, the principle and technology of the switch-mode of remote control station, the information transmission technology, and the data consistency technology. These key technologies have shown a series of advanced characteristics for decentralized control platform.
文摘The volumetric flow rate of smoke generated from the fire in large space often reaches to hundreds of thousands CMH because of extended floor height and as it’s more difficult to isolate the smoke to the limited area, comparing to normal-scale building, design and operation of effective smoke control system for large space is more than important. In this study, with the analysis model for such a large space as exhibition hall or conference room in conventional center, design of mechanical smoke exhaust system was conducted based on currently-available design standard which was then followed by numerical analysis of the design using 3D numerical analysis method. For conference room at 2.0 MW heat release rate, 99,173 CMH flow rate is required, if smoke layer is maintained at 60% of the floor height and for exhibition hall at 8.8 MW with 80% of floor height, flow rate required is 219,802 CMH, which are incorporated into the design. In view of 3D numerical analysis, accuracy of the design according to algebraic expression is sufficient.
文摘This paper describes a simulation-based intelligent decision support system (IDSS) for real time control of a flexible manufacturing system (FMS) with machine and tool flexibility. The manufacturing processes involved in FMS are complicated since each operation may be done by several machining centers. The system design approach is built around the theory of dynamic supervisory control based on a rule-based expert system. The paper considers flexibility in operation assignment and scheduling of multi-purpose machining centers which have different tools with their own efficiency. The architecture of the proposed controller consists of a simulator module coordinated with an IDSS via a real time event handler for implementing inter-process synchronization. The controller’s performance is validated by benchmark test problem.
文摘This work leveraged predictive modeling techniques in machine learning (ML) to predict heart disease using a dataset sourced from the Center for Disease Control and Prevention in the US. The dataset was preprocessed and used to train five machine learning models: random forest, support vector machine, logistic regression, extreme gradient boosting and light gradient boosting. The goal was to use the best performing model to develop a web application capable of reliably predicting heart disease based on user-provided data. The extreme gradient boosting classifier provided the most reliable results with precision, recall and F1-score of 97%, 72%, and 83% respectively for Class 0 (no heart disease) and 21% (precision), 81% (recall) and 34% (F1-score) for Class 1 (heart disease). The model was further deployed as a web application.
基金supported by State Grid Corporation of China(SGCC)Science and Technolgy Project(SGTJDK00DWJS1700060)
文摘This paper proposes the concept and framework of smart operating system based on the artificial intelligence(AI)techniques. The demands and the potential applications of AI technologies in power system control centers is discussed in the beginning of the paper. The discussion is based on the results of a field study in the Tianjin Power System Control Center in China. According to the study, one problem in power systems is that the power system analysis system in the control center is not fast and powerful enough to help the operators in time to deal with the incidents in the power system. Another issue in current power system control center is that the operation tickets are compiled manually by the operators, so that it is less efficient and human errors cannot be avoided. Based on these problems, a framework of the smart operating robot is proposed in this paper, which includes an intelligent power system analysis system and a smart operation ticket compiling system to solve the two problems in power system control centers. The proposed framework is mainly based on the AI techniques, especially the neural network with deep learning, since it is faster and more capable of dealing with the highly nonlinear and complex power system.
