公开(公告)号：US20230025527A1

公开(公告)日：2023-01-26

申请号：US17681510

申请日：2022-02-25

申请人： CHONGQING UNIVERSITY ,  STAR INSTITUTE OF INTELLIGENT SYSTEMS ,  DB (CHONGQING) INTELLIGENT TECHNOLOGY RESEARCH INSTITUTE CO., LTD. ,  UNIVERSITY OF ELECTRONIC SCIENCE AND TECHNOLOGY OF CHINA

发明人： YONGDUAN SONG ,  FENG YANG ,  RUI LI ,  QIN CHEN ,  SHICHUN WANG ,  HONGYU XIA ,  CAISHI HE ,  SHIHAO PU

IPC分类号： G06T7/73 ,  G06V40/16 ,  G06V40/18 ,  G06V10/82 ,  G06V10/776

摘要： Embodiments of the present disclosure provide a quantitative method and system for attention based on a line-of-sight estimation neural network, which improves the stability and training efficiency of the line-of-sight estimation neural network. A few-sample learning method is applied to training of the line-of-sight estimation neural network, which improves generalization performance of the line-of-sight estimation neural network. A nonlinear division method for small intervals of angles of the line of sight is provided, which reduces an estimation error of the line-of-sight estimation neural network. Eye opening and closing detection is added to avoid the line-of-sight estimation error caused by an eye closing state. A method for solving a landing point of the line of sight is provided, which has high environmental adaptability and can be quickly used in actual deployment.

公开(公告)号：US20230070427A1

公开(公告)日：2023-03-09

申请号：US17681555

申请日：2022-02-25

申请人： CHONGQING UNIVERSITY ,  STAR INSTITUTE OF INTELLIGENT SYSTEMS ,  DB (CHONGQING) INTELLIGENT TECHNOLOGY RESEARCH INSTITUTE CO., LTD ,  UNIVERSITY OF ELECTRONIC SCIENCE AND TECHNOLOGY OF CHINA

发明人： YONGDUAN SONG ,  FENG YANG ,  RUI LI ,  HONGYU XIA ,  QIN CHEN ,  SHICHUN WANG ,  LIANGJIE LI ,  HAOYUAN ZHONG

IPC分类号： G06N20/00 ,  G09B19/00

摘要： The present disclosure provides a student performance evaluation method and system based on artificial intelligence (AI) identification data, and relates to the field of intelligent education. A lightweight network model suitable for student performance evaluation takes the AI identification data as an input and evaluation results as an output. A training data generation algorithm is provided, and multidimensional AI identification data and labels are uniformly processed into training data suitable for the network model through the above algorithm, which can solve the problems that dimensions between any AI identification data and various labels are not uniform, and original data cannot meet training of a multidimensional and cross-time prediction model. A simulated data generation algorithm and a simulated label generation algorithm are provided, and simulated training data is generated using these algorithms in conjunction with the training data generation algorithm.

公开(公告)号：US20220350329A1

公开(公告)日：2022-11-03

申请号：US17589179

申请日：2022-01-31

申请人： CHONGQING UNIVERSITY ,  STAR INSTITUTE OF INTELLIGENT SYSTEMS ,  DB (CHONGQING) INTELLIGENT TECHNOLOGY RESEARCH INSTITUTE CO., LTD.

发明人： YONGDUAN SONG ,  JIE ZHANG ,  JUNFENG LAI ,  HUAN LIU ,  ZIQIANG JIANG ,  LI HUANG

IPC分类号： G05D1/00 ,  G05D1/02 ,  H04B17/318 ,  G06N3/08

摘要： The present disclosure provides a neural network-based method for calibration and localization of an indoor inspection robot. The method includes the following steps: presetting positions for N label signal sources capable of transmitting radio frequency (RF) signals; computing an actual path of the robot according to numbers of signal labels received at different moments; computing positional information moved by the robot at a tth moment, and computing a predicted path at the tth moment according to the positional information; establishing an odometry error model with the neural network and training the odometry error model; and inputting the predicted path at the tth moment to a well-trained odometry error model to obtain an optimized predicted path. The present disclosure maximizes the localization accuracy for the indoor robot by minimizing the error of the odometer with the odometry calibration method.

公开(公告)号：US20220291276A1

公开(公告)日：2022-09-15

申请号：US17589237

申请日：2022-01-31

申请人： CHONGQING UNIVERSITY ,  STAR INSTITUTE OF INTELLIGENT SYSTEMS ,  DB (CHONGQING) INTELLIGENT TECHNOLOGY RESEARCH INSTITUTE CO., LTD.

发明人： YONGDUAN SONG ,  Shuaicheng Hou ,  Jiawei Chen ,  Mi Fang

IPC分类号： G01R31/26 ,  H03K17/60 ,  B60R16/02

摘要： A detection circuit for open, close and suspension states of a high and low level effective switch in a vehicle. The circuit includes an optocoupler circuit module, a low-level active path module, a high-level active path module, a filtering and debouncing module, a transient suppression module, and a wiring terminal. The optocoupler circuit module is connected to the low-level active path module, the high-level active path module and the low-level active path module are connected in parallel to the filtering and debouncing module, and the filtering and debouncing module is connected to the transient suppression module, and then connected to the external high-level active switch or low-level active switch through the wiring terminal. Whether it is a high-level active switch or a low-level active switch, the detection circuit can distinguish whether the switch is in the closed or suspended state, and the strong and weak voltages are isolated.