Patents by Inventor Mingbin Xu

Mingbin Xu 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: 12253257
    Abstract: A LED lighting device, comprising: a base which has a bottom plate and a side wall, a cavity being formed between the bottom plate and the side wall; an optical component which covers one side of the base in a light-emitting direction of the LED lighting device; and a light source which is provided in the cavity of the base and comprises a circuit board and several LED arrays, the LED arrays comprising LED lamp beads fixed on the circuit board. The optical component comprises an optical unit, and the optical unit comprises a plurality of first optical components and a plurality of second optical components which correspond to the first optical components.
    Type: Grant
    Filed: August 29, 2022
    Date of Patent: March 18, 2025
    Assignee: Jiaxing Super Lighting Electric Appliance Co.,Ltd.
    Inventors: Mingbin Wang, Zhichao Zhang, Dongmei Zhang, Jifeng Xu, Tao Jiang, Kuan Lin, Huan Wei, Heng Zhao, Zecheng Jing
  • Publication number: 20250035286
    Abstract: An LED lighting device includes a seat (1), a first optical member (3), a first light source (21) and a second light source (22). The seat (1) includes a bottom portion (11) and a side portion (12). The first optical member (3) is disposed on the seat (1) and covers the bottom portion (11). The first light source (21) is disposed between the bottom portion (11) of the seat (1) and the first optical member (3). The second light source (22) is disposed on the side portion (12) of the seat (1).
    Type: Application
    Filed: November 18, 2022
    Publication date: January 30, 2025
    Inventors: Mingbin Wang, Zecheng Jing, Jian Lu, Jifeng Xu
  • Patent number: 11030521
    Abstract: A database query comprising predicates may be received. Each predicate may operate on database columns. The database query may be determined to comprise strict operators. An upper bound neural network may be defined for calculating an adjacent upper bound and a lower bound neural network may be defined for calculating an adjacent lower bound. The upper bound neural network and the lower bound neural network may be trained using a selected value from data of a database table associated with the database query to be executed through the upper bound neural network and the lower bound neural network. The upper bound neural network and the lower bound neural network may be adjusted by passing in an expected value using an error found in expressions. The adjacent lower bound and the adjacent upper bound may be calculated in response to completion of initial training for the database columns.
    Type: Grant
    Filed: February 20, 2018
    Date of Patent: June 8, 2021
    Assignee: International Business Machines Corporation
    Inventors: Vincent Corvinelli, Huaxin Liu, Mingbin Xu, Ziting Yu, Calisto P. Zuzarte
  • Patent number: 10706354
    Abstract: A database query comprising predicates may be received. Each predicate may operate on database columns. The database query may be determined to comprise strict operators. An upper bound neural network may be defined for calculating an adjacent upper bound and a lower bound neural network may be defined for calculating an adjacent lower bound. The upper bound neural network and the lower bound neural network may be trained using a selected value from data of a database table associated with the database query to be executed through the upper bound neural network and the lower bound neural network. The upper bound neural network and the lower bound neural network may be adjusted by passing in an expected value using an error found in expressions. The adjacent lower bound and the adjacent upper bound may be calculated in response to completion of initial training for the database columns.
    Type: Grant
    Filed: May 6, 2016
    Date of Patent: July 7, 2020
    Assignee: International Business Machines Corporation
    Inventors: Vincent Corvinelli, Huaxin Liu, Mingbin Xu, Ziting Yu, Calisto P. Zuzarte
  • Patent number: 10643132
    Abstract: In an approach for generating a selectivity estimation, one or more processors generate an artificial neural network and receive a DBMS query comprising one or more predicates. One or more processors replace one or more predicates in the one or more predicates that have strict operators with one or more predicates that have non-strict operators. One or more processors generate a selectivity function from the one or more predicates that has one or more arguments that are each comprised of an upper bound and a lower bound for a value in a predicate. One or more processors generate a training data set from a data distribution in the database and train the artificial neural network on the training data set to compute the selectivity function. One or more processors generate a selectivity estimation with the artificial neural network for one or more predicates in the DBMS query.
    Type: Grant
    Filed: March 23, 2016
    Date of Patent: May 5, 2020
    Assignee: International Business Machines Corporation
    Inventors: Vincent Corvinelli, Huaxin Liu, Mingbin Xu, Ziting Yu, Calisto P. Zuzarte
  • Patent number: 10318866
    Abstract: In an approach for generating a selectivity estimation, one or more processors generate an artificial neural network and receive a DBMS query comprising one or more predicates. One or more processors replace one or more predicates in the one or more predicates that have strict operators with one or more predicates that have non-strict operators. One or more processors generate a selectivity function from the one or more predicates that has one or more arguments that are each comprised of an upper bound and a lower bound for a value in a predicate. One or more processors generate a training data set from a data distribution in the database and train the artificial neural network on the training data set to compute the selectivity function. One or more processors generate a selectivity estimation with the artificial neural network for one or more predicates in the DBMS query.
    Type: Grant
    Filed: March 5, 2015
    Date of Patent: June 11, 2019
    Assignee: International Business Machines Corporation
    Inventors: Vincent Corvinelli, Huaxin Liu, Mingbin Xu, Ziting Yu, Calisto P. Zuzarte
  • Publication number: 20180174048
    Abstract: A database query comprising predicates may be received. Each predicate may operate on database columns. The database query may be determined to comprise strict operators. An upper bound neural network may be defined for calculating an adjacent upper bound and a lower bound neural network may be defined for calculating an adjacent lower bound. The upper bound neural network and the lower bound neural network may be trained using a selected value from data of a database table associated with the database query to be executed through the upper bound neural network and the lower bound neural network. The upper bound neural network and the lower bound neural network may be adjusted by passing in an expected value using an error found in expressions. The adjacent lower bound and the adjacent upper bound may be calculated in response to completion of initial training for the database columns.
    Type: Application
    Filed: February 20, 2018
    Publication date: June 21, 2018
    Inventors: Vincent Corvinelli, Huaxin Liu, Mingbin Xu, Ziting Yu, Calisto P. Zuzarte
  • Publication number: 20170323200
    Abstract: A database query comprising predicates may be received. Each predicate may operate on database columns. The database query may be determined to comprise strict operators. An upper bound neural network may be defined for calculating an adjacent upper bound and a lower bound neural network may be defined for calculating an adjacent lower bound. The upper bound neural network and the lower bound neural network may be trained using a selected value from data of a database table associated with the database query to be executed through the upper bound neural network and the lower bound neural network. The upper bound neural network and the lower bound neural network may be adjusted by passing in an expected value using an error found in expressions. The adjacent lower bound and the adjacent upper bound may be calculated in response to completion of initial training for the database columns.
    Type: Application
    Filed: May 6, 2016
    Publication date: November 9, 2017
    Inventors: Vincent Corvinelli, Huaxin Liu, Mingbin Xu, Ziting Yu, Calisto P. Zuzarte
  • Publication number: 20160275398
    Abstract: In an approach for generating a selectivity estimation, one or more processors generate an artificial neural network and receive a DBMS query comprising one or more predicates. One or more processors replace one or more predicates in the one or more predicates that have strict operators with one or more predicates that have non-strict operators. One or more processors generate a selectivity function from the one or more predicates that has one or more arguments that are each comprised of an upper bound and a lower bound for a value in a predicate. One or more processors generate a training data set from a data distribution in the database and train the artificial neural network on the training data set to compute the selectivity function. One or more processors generate a selectivity estimation with the artificial neural network for one or more predicates in the DBMS query.
    Type: Application
    Filed: March 23, 2016
    Publication date: September 22, 2016
    Inventors: Vincent Corvinelli, Huaxin Liu, Mingbin Xu, Ziting Yu, Calisto P. Zuzarte
  • Publication number: 20160260011
    Abstract: In an approach for generating a selectivity estimation, one or more processors generate an artificial neural network and receive a DBMS query comprising one or more predicates. One or more processors replace one or more predicates in the one or more predicates that have strict operators with one or more predicates that have non-strict operators. One or more processors generate a selectivity function from the one or more predicates that has one or more arguments that are each comprised of an upper bound and a lower bound for a value in a predicate. One or more processors generate a training data set from a data distribution in the database and train the artificial neural network on the training data set to compute the selectivity function. One or more processors generate a selectivity estimation with the artificial neural network for one or more predicates in the DBMS query.
    Type: Application
    Filed: March 5, 2015
    Publication date: September 8, 2016
    Inventors: Vincent Corvinelli, Huaxin Liu, Mingbin Xu, Ziting Yu, Calisto P. Zuzarte