Patents by Inventor Shiji SONG

Shiji SONG 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).

  • Publication number: 20220324014
    Abstract: A steelmaking-and-continuous-casting dispatching method and apparatus based on a distributed robust chance-constraint model. The method includes: according to parameters, an objective function and a constraint condition in steelmaking-and-continuous-casting dispatching, establishing the distributed robust chance-constraint model; by using a dual-approximation method or a linear-programming-approximation method, solving the distributed robust chance-constraint model, to obtain processing starting durations of cast batches in conticasters and processing starting durations of furnace batches in machines other than the conticasters; and by using a solved result of the distributed robust chance-constraint model as an evaluation criterion, by using a tabu-search algorithm, determining a furnace-batch sequence and a distribution theme in the steelmaking-and-continuous-casting dispatching.
    Type: Application
    Filed: March 30, 2022
    Publication date: October 13, 2022
    Applicant: Tsinghua University
    Inventors: Shiji SONG, Shengsheng NIU, Yali CHEN
  • Publication number: 20220245923
    Abstract: A method for detecting image information includes: acquiring at least one sample of image pair to be processed; calculating a reconstruction loss function of the second feature extraction model based on the first image samples and the first reconstructed image feature information; calculating an adversarial loss function of the third feature extraction model based on the second reconstructed image feature information and the first image samples; optimizing the first model parameters in the first feature extraction model based on the reconstruction and the adversarial loss function to generate the optimized first feature extraction model; inputting the acquired image pair to be processed into the optimized first feature extraction model to generate the difference information. The method reduces the first feature extraction model's dependence on the labeled data and improves the model's recognition efficiency and accuracy by using the samples without the labeled difference information.
    Type: Application
    Filed: December 27, 2021
    Publication date: August 4, 2022
    Applicant: Tsinghua University
    Inventors: Gao HUANG, Shiji SONG, Haojun JIANG, Le YANG, Yiming CHEN
  • Publication number: 20220237883
    Abstract: An image processing method and apparatus and a storage medium, wherein the method particularly includes firstly acquiring an image-to-be-trained sample and a label segmentation image corresponding to the image-to-be-trained sample; inputting the image-to-be-trained sample into an image segmentation model to be trained, obtaining a first image feature of a last one output layer in the image segmentation model and a second image feature of a second last output layer when the image-to-be-trained sample is being extracted by using the image segmentation model, outputting the corresponding segmented-image samples; based on the label segmentation image and the segmented-image samples, calculating the model loss function, optimizing the model parameter, and generating the image segmentation model that has been optimized; and inputting an acquired image to be processed into the image segmentation model that has been optimized, and generating segmented images corresponding to the image to be processed.
    Type: Application
    Filed: December 27, 2021
    Publication date: July 28, 2022
    Applicant: Tsinghua University
    Inventors: Gao HUANG, Shiji SONG, Chaoqun DU
  • Publication number: 20220214322
    Abstract: A method, apparatus and storage medium for forecasting air pollutant concentration, including: constructing a training set, a validation set and a test set based on a data set; the data set is obtained by collecting pollutant concentration data and meteorological data in a predetermined length of time in a target area; constructing an adjacent matrix A of a graph structure based on the spatial distribution of monitoring stations in the target area; establishing a neural network model F(x;?|A), where x is the input data of it, including pollutant concentration data and meteorological data within predetermined time period, training the neural network model using the data of the training set, adjusting the parameters ? of the neural network model using the data of the validation set and the data of the test set, and obtaining the modified neural network model; using the modified neural network model for air pollutant concentration forecasting.
    Type: Application
    Filed: December 27, 2021
    Publication date: July 7, 2022
    Applicant: Tsinghua University
    Inventors: Shiji SONG, Gao HUANG, Zhuofan XIA