Patents by Inventor Zhiling LUO

Zhiling LUO 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: 11461581
    Abstract: Disclosed is system and method of connection information regularization, graph feature extraction and graph classification based on adjacency matrix. By concentrating the connection information elements in the adjacency matrix into a specific diagonal region of the adjacency matrix in order to reduce the non-connection information elements in advance. The subgraph structure of the graph is further extracted along the diagonal direction using the filter matrix. Then a stacked convolutional neural network is used to extract a larger subgraph structure. On the one hand, it greatly reduces the amount of computation and complexity, solving the limitations of the computational complexity and the limitations of window size. And on the other hand, it can capture large subgraph structure through a small window, as well as deep features from the implicit correlation structures at both vertex and edge level, which improves the accuracy and speed of the graph classification.
    Type: Grant
    Filed: December 26, 2019
    Date of Patent: October 4, 2022
    Assignee: ZHEJIANG UNIVERSITY
    Inventors: Zhiling Luo, Jianwei Yin, Zhaohui Wu, Shuiguang Deng, Ying Li, Jian Wu
  • Publication number: 20200134362
    Abstract: Disclosed is system and method of connection information regularization, graph feature extraction and graph classification based on adjacency matrix. By concentrating the connection information elements in the adjacency matrix into a specific diagonal region of the adjacency matrix in order to reduce the non-connection information elements in advance. The subgraph structure of the graph is further extracted along the diagonal direction using the filter matrix. Then a stacked convolutional neural network is used to extract a larger subgraph structure. On the one hand, it greatly reduces the amount of computation and complexity, solving the limitations of the computational complexity and the limitations of window size. And on the other hand, it can capture large subgraph structure through a small window, as well as deep features from the implicit correlation structures at both vertex and edge level, which improves the accuracy and speed of the graph classification.
    Type: Application
    Filed: December 26, 2019
    Publication date: April 30, 2020
    Inventors: Zhiling LUO, Jianwei YIN, Zhaohui WU, Shuiguang DENG, Ying LI, Jian WU
  • Publication number: 20200110777
    Abstract: A method and system of graph feature extraction and graph classification based on adjacency matrix is provided. The invention first concentrates the connection information elements in the adjacency matrix into a specific diagonal region of the adjacency matrix which reduces the non-connection information elements in advance. Then the subgraph structure of the graph is further extracted along the diagonal direction using the filter matrix. Further, it uses a stacked convolutional neural network to extract a larger subgraph structure. On one hand, it greatly reduces the amount of computation and complexity, getting rid of the limitations caused by computational complexity and window size. On the other hand, it can capture large subgraph structure through a small window, as well as deep features from the implicit correlation structures at both vertex and edge level, which improves speed and accuracy of graph classification.
    Type: Application
    Filed: November 26, 2019
    Publication date: April 9, 2020
    Inventors: Jianwei YIN, Zhiling LUO, Zhaohui WU, Shuiguang DENG, Ying LI, Jian WU
  • Publication number: 20190385105
    Abstract: Provided is a computer-based latent ability model construction method, a parameter calculation method for characteristic parameters of work ability, and a labor force assessment apparatus based on the latent ability model. The method constructs a latent ability model, and introduces characteristic parameters of work ability into the latent ability model to reveal the internal relations among the employee, the activity, and the service time. The characteristic parameters of work ability is calculated to obtain a final value, and labor force assessment can be carried out according to the final value. The labor force assessment comprises performance prediction, work ability comparison, and employee-activity matching evaluation.
    Type: Application
    Filed: June 13, 2019
    Publication date: December 19, 2019
    Inventors: Zhiling LUO, Jianwei YIN, Xiya LV, Ying LI, Shuiguang DENG, Zhaohui WU