Patents by Inventor Weiping Ding

Weiping Ding 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: 11837329
    Abstract: A method for classifying multi-granularity breast cancer genes based on a double self-adaptive neighborhood radius includes large-scale gene locus data are read and normalized, and a data analysis is performed on the large-scale gene loci. An optimum value K is selected by adopting a combination of contour coefficients and a PCA dimensionality reduction visualization, and a model of information granulation is adjusted. A heuristic reduction algorithm is used to implement a multi-granularity attribute reduction of a self-adaptive neighborhood radius based on a cluster center distance and a multi-granularity attribute reduction of a neighborhood radius based on an attribute inclusion degree, and big data for breast cancer genes are classified and predicted by adopting a machine learning classification algorithm based on a SVM support vector machine.
    Type: Grant
    Filed: February 22, 2022
    Date of Patent: December 5, 2023
    Assignee: NANTONG UNIVERSITY
    Inventors: Weiping Ding, Yu Geng, Jialu Ding, Hengrong Ju, Jiashuang Huang, Chun Cheng, Ying Sun, Yi Zhang, Ming Li, Tingzhen Qin, Xinjie Shen, Haipeng Wang
  • Publication number: 20230197203
    Abstract: A method for classifying multi-granularity breast cancer genes based on a double self-adaptive neighborhood radius includes large-scale gene locus data are read and normalized, and a data analysis is performed on the large-scale gene loci. An optimum value K is selected by adopting a combination of contour coefficients and a PCA dimensionality reduction visualization, and a model of information granulation is adjusted. A heuristic reduction algorithm is used to implement a multi-granularity attribute reduction of a self-adaptive neighborhood radius based on a cluster center distance and a multi-granularity attribute reduction of a neighborhood radius based on an attribute inclusion degree, and big data for breast cancer genes are classified and predicted by adopting a machine learning classification algorithm based on a SVM support vector machine.
    Type: Application
    Filed: February 22, 2022
    Publication date: June 22, 2023
    Inventors: Weiping DING, Yu GENG, Hengrong JU, Jiashuang HUANG, Chun CHENG, Ying SUN, Yi ZHANG, Ming LI, Tingzhen QIN, Xinjie SHEN, Haipeng WANG
  • Patent number: 11593942
    Abstract: Disclosed is a fully convolutional genetic neural network method for segmentation of infant brain record images. First, infant brain record image data is input and preprocessed, and genetic coding initialization is performed for parameters according to the length of a DMPGA-FCN network weight. Then, m individuals are randomly grouped into genetic native subpopulations and corresponding twin subpopulations are derived, where respective crossover probability and mutation probability pm of all the subpopulations are determined from disjoint intervals; and an optimal initialization value fa is searched for by using a genetic operator. Afterwards, fa is used as a forward propagation calculation parameter and a weighting operation is performed on the feature address featuremap.
    Type: Grant
    Filed: April 12, 2021
    Date of Patent: February 28, 2023
    Assignee: NANTONG UNIVERSITY
    Inventors: Weiping Ding, Zhihao Feng, Ming Li, Ying Sun, Yi Zhang, Hengrong Ju, Jinxin Cao
  • Publication number: 20220327705
    Abstract: Disclosed is a fully convolutional genetic neural network method for segmentation of infant brain record images. First, infant brain record image data is input and preprocessed, and genetic coding initialization is performed for parameters according to the length of a DMPGA-FCN network weight. Then, m individuals are randomly grouped into genetic native subpopulations and corresponding twin subpopulations are derived, where respective crossover probability and mutation probability pm of all the subpopulations are determined from disjoint intervals; and an optimal initialization value fa is searched for by using a genetic operator. Afterwards, fa is used as a forward propagation calculation parameter and a weighting operation is performed on the feature address featuremap.
    Type: Application
    Filed: April 12, 2021
    Publication date: October 13, 2022
    Applicant: NANTONG UNIVERSITY
    Inventors: Weiping DING, Zhihao FENG, Ming LI, Ying SUN, Yi ZHANG, Hengrong JU, Jinxin CAO
  • Publication number: 20210202050
    Abstract: A method for calculating a thickness of an oxide film of a martensite heat-resistant steel under supercritical high-temperature steam is disclosed, which includes following steps: the martensite heat-resistant steel is a 9% Cr martensite heat-resistant steel; and a formula for calculating the thickness of the oxide film is X = A ? ? exp ? ( - Q R ? T ) ? t n , which X is the thickness of the oxide film (?m), A is a constant coefficient, Q is an activation energy (J·mol?1), R is a gas constant, T is temperature (° C.), and t is time (h).
    Type: Application
    Filed: December 29, 2020
    Publication date: July 1, 2021
    Inventors: Yalin ZHANG, Xue WANG, Kai ZHANG, Dejun REN, Zhixiong ZUO, Shengli LIU, Weiping DING, Qiaosheng HUANG
  • Patent number: 6740764
    Abstract: Oxide comprising at least the elements Si and Ti, at least noncrystalline silicon dioxide and at least one crystalline silicate phase which has at least one zeolite structure, with noncrystalline silicon dioxide being applied to at least one crystalline silicate phase having at least one zeolite structure, wherein the oxide has no silicon-carbon bonds.
    Type: Grant
    Filed: May 9, 2002
    Date of Patent: May 25, 2004
    Assignee: BASF Aktiengesellschaft
    Inventors: Yi Chen, Weiping Ding, Yuan Chun, Ma Lian, Ulrich Müller