Patents by Inventor Runsu ZHU

Runsu ZHU has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Patent number: 11512864
    Abstract: The present disclosure discloses a deep spatial-temporal similarity method for air quality prediction, and belongs to the technical field of environmental protection. When the method predicts air quality-related indexes of a target site, a temporal change of air pollution and a spatial diffusion relationship are effectively combined, and then spatial-temporal similarity sites of the target site are selected; air quality monitoring data collected by the target site, the spatial-temporal similarity site of the target site and geographical neighbour sites of the target site and meteorological data are respectively taken as inputs of a long short term memory network (LSTM) model to obtain uncorrelated output results, and then predicted values of air quality-related index data of the target site are obtained in a mode of support vector regression (SVR) integration.
    Type: Grant
    Filed: June 16, 2022
    Date of Patent: November 29, 2022
    Assignee: JIANGNAN UNIVERSITY
    Inventors: Wei Fang, Runsu Zhu, Hengyang Lu, Xin Zhang, Jun Sun, Xiaojun Wu
  • Publication number: 20220316734
    Abstract: The present disclosure discloses a deep spatial-temporal similarity method for air quality prediction, and belongs to the technical field of environmental protection. When the method predicts air quality-related indexes of a target site, a temporal change of air pollution and a spatial diffusion relationship are effectively combined, and then spatial-temporal similarity sites of the target site are selected; air quality monitoring data collected by the target site, the spatial-temporal similarity site of the target site and geographical neighbour sites of the target site and meteorological data are respectively taken as inputs of a long short term memory network (LSTM) model to obtain uncorrelated output results, and then predicted values of air quality-related index data of the target site are obtained in a mode of support vector regression (SVR) integration.
    Type: Application
    Filed: June 16, 2022
    Publication date: October 6, 2022
    Inventors: Wei FANG, Runsu ZHU, Hengyang LU, Xin ZHANG, Jun SUN, Xiaojun WU