Rongxiao Wang
Biographic Data
| ID | 7867363 |
|---|---|
| NAME | Rongxiao Wang |
| GIVEN NAMES | Rongxiao |
| FAMILY NAME | Wang |
| SIGNATURE | WANG R |
| AFFILIATIONS | National University of Defense Technology |
| VERIFIED | No |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 4 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2018 |
| LATEST PUBLICATION YEAR | 2020 |
| H-INDEX | 0 |
Developing Industry 4.0 with systems perspectives
This paper analyses the definition and connotation of Industry 4.0 and proposes that intelligent manufacturing and innovative service are the two core elements of the Industry 4.0. The paper also discusses standardization, integrated information system, training and education, artificial intelligence applications, and the infrastructure issues for the development of Industry 4.0
Comparison of Machine Learning Models for Hazardous Gas Dispersion Prediction in Field Cases
Dispersion prediction plays a significant role in the management and emergency response to hazardous gas emissions and accidental leaks. Compared with conventional atmospheric dispersion models, machine leaning (ML) models have both high accuracy and efficiency in terms of prediction, especially in field cases. However, selection of model type and the inputs of the ML model are still essential problems. To address this issue, two ML models (i.e.,…
An Extended Chemical Plant Environmental Protection Game on Addressing Uncertainties of Human Adversaries
Chemical production activities in industrial districts pose great threats to the surrounding atmospheric environment and human health. Therefore, developing appropriate and intelligent pollution controlling strategies for the management team to monitor chemical production processes is significantly essential in a chemical industrial district. The literature shows that playing a chemical plant environmental protection (CPEP) game can force the che…
Data-Driven Hazardous Gas Dispersion Modeling Using the Integration of Particle Filtering and Error Propagation Detection
The accurate prediction of hazardous gas dispersion process is essential to air quality monitoring and the emergency management of contaminant gas leakage incidents in a chemical cluster. Conventional Gaussian-based dispersion models can seldom give accurate predictions due to inaccurate input parameters and the computational errors. In order to improve the prediction accuracy of a dispersion model, a data-driven air dispersion modeling method ba…
No prominent works on this page.
Comparison of Machine Learning Models for Hazardous Gas Dispersion Prediction in Field Cases
Dispersion prediction plays a significant role in the management and emergency response to hazardous gas emissions and accidental leaks. Compared with conventional atmospheric dispersion models, machine leaning (ML) models have both high accuracy and efficiency in terms of prediction, especially in field cases. However, selection of model type and the inputs of the ML model are still essential problems. To address this issue, two ML models (i.e.,…
An Extended Chemical Plant Environmental Protection Game on Addressing Uncertainties of Human Adversaries
Chemical production activities in industrial districts pose great threats to the surrounding atmospheric environment and human health. Therefore, developing appropriate and intelligent pollution controlling strategies for the management team to monitor chemical production processes is significantly essential in a chemical industrial district. The literature shows that playing a chemical plant environmental protection (CPEP) game can force the che…
Data-Driven Hazardous Gas Dispersion Modeling Using the Integration of Particle Filtering and Error Propagation Detection
The accurate prediction of hazardous gas dispersion process is essential to air quality monitoring and the emergency management of contaminant gas leakage incidents in a chemical cluster. Conventional Gaussian-based dispersion models can seldom give accurate predictions due to inaccurate input parameters and the computational errors. In order to improve the prediction accuracy of a dispersion model, a data-driven air dispersion modeling method ba…
Developing Industry 4.0 with systems perspectives
This paper analyses the definition and connotation of Industry 4.0 and proposes that intelligent manufacturing and innovative service are the two core elements of the Industry 4.0. The paper also discusses standardization, integrated information system, training and education, artificial intelligence applications, and the infrastructure issues for the development of Industry 4.0
Computer Science (4 works) · Data mining (3 works) · Air pollution (2 works) · Air Quality Monitoring and Forecasting (2 works) · Artificial Intelligence (2 works) · Atmospheric dispersion modeling (2 works) · Dispersion (optics (2 works) · Wind and Air Flow Studies (2 works) · Algorithm (1 work) · Atmospheric and Environmental Gas Dynamics (1 work)