Patents by Inventor Fuhui ZHOU

Fuhui ZHOU 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: 11948092
    Abstract: A brain-inspired cognitive learning method can obtain good learning results in various environments and tasks by selecting the most suitable algorithm models and parameters based on the environments and tasks, and can correct wrong behavior. The framework includes four main modules: a cognitive feature extraction module, a cognitive control module, a learning network module, and a memory module. The memory module includes a data base, a cognitive case base, and an algorithm and hyper-parameter base, which store data of dynamic environments and tasks, cognitive cases, and concrete algorithms and hyper-parameter values, respectively. For dynamic environments and tasks, the most suitable algorithm model and hyper-parameter combination can be flexibly selected. In addition, with “good money drives out bad”, mislabeled data is corrected using correctly labeled data, to achieve robustness of training data.
    Type: Grant
    Filed: November 8, 2021
    Date of Patent: April 2, 2024
    Assignee: Nanjing University of Aeronautics and Astronautics
    Inventors: Qihui Wu, Tianchen Ruan, Shijin Zhao, Fuhui Zhou, Yang Huang
  • Patent number: 11700156
    Abstract: An intelligent data and knowledge-driven method for modulation recognition includes the following steps: collecting spectrum data; constructing corresponding attribute vector labels for different modulation schemes; constructing and pre-training an attribute learning model based on the attribute vector labels for different modulation schemes; constructing and pre-training a visual model for modulation recognition; constructing a feature space transformation model, and constructing an intelligent data and knowledge-driven model for modulation recognition based on the attribute learning model and the visual model; transferring parameters of the pre-trained visual model and the pre-trained attribute learning model and retraining the transformation model; and determining whether training on a network is completed and outputting a classification result.
    Type: Grant
    Filed: September 2, 2022
    Date of Patent: July 11, 2023
    Assignee: Nanjing University of Aeronautics and Astronautics
    Inventors: Fuhui Zhou, Rui Ding, Ming Xu, Hao Zhang, Lu Yuan, Qihui Wu, Chao Dong
  • Publication number: 20230133628
    Abstract: A brain-inspired cognitive learning method can obtain good learning results in various environments and tasks by selecting the most suitable algorithm models and parameters based on the environments and tasks, and can correct wrong behavior. The framework includes four main modules: a cognitive feature extraction module, a cognitive control module, a learning network module, and a memory module. The memory module includes a data base, a cognitive case base, and an algorithm and hyper-parameter base, which store data of dynamic environments and tasks, cognitive cases, and concrete algorithms and hyper-parameter values, respectively. For dynamic environments and tasks, the most suitable algorithm model and hyper-parameter combination can be flexibly selected. In addition, with “good money drives out bad”, mislabeled data is corrected using correctly labeled data, to achieve robustness of training data.
    Type: Application
    Filed: November 8, 2021
    Publication date: May 4, 2023
    Applicant: Nanjing University of Aeronautics and Astronautics
    Inventors: Qihui WU, Tianchen RUAN, Shijin ZHAO, Fuhui ZHOU, Yang HUANG