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    Microscopic non-destructive measurement method of microstructure linewidth based on translation difference
    3.
    发明授权
    Microscopic non-destructive measurement method of microstructure linewidth based on translation difference 有权

    公开(公告)号：US12190495B2

    公开(公告)日：2025-01-07

    申请号：US18681461

    申请日：2023-09-22

    Applicant: NANJING UNIVERSITY OF SCIENCE AND TECHNOLOGY

    Inventor： Zhishan Gao ,  Jianqiu Ma ,  Qun Yuan ,  Yifeng Sun ,  Xiao Huo ,  Shumin Wang ,  Jiale Zhang ,  Xiaoxin Fan

    IPC: G06T7/00 ,  G01N21/88

    Abstract: The present disclosure discloses a microscopic non-destructive measurement method of a microstructure linewidth based on a translation difference, based on a conventional microscopic imaging method, a high-precision displacement platform is used to move a to-be-measured sample, one microscopic image of the sample is acquired before and after the displacement separately, subtraction is performed on the two image to obtain a differential image, a light intensity distribution function of the differential image is derived, data fitting is performed on the differential image, and a high-precision sample linewidth measurement result is obtained by using the characteristic of a high differential pulse positioning resolution. The linewidth measurement method of the present disclosure retains the advantages of intuitiveness, quickness, and non-destructive measurement of the microscopic imaging method, breaks through the microscopic imaging diffraction limit, and reducing the impact of uneven illumination and imaging system noise, thereby improving the linewidth measurement accuracy

    MULTI-TASK PANOPTIC DRIVING PERCEPTION METHOD AND SYSTEM BASED ON IMPROVED YOU ONLY LOOK ONCE VERSION 5 (YOLOv5)
    4.
    发明申请
    MULTI-TASK PANOPTIC DRIVING PERCEPTION METHOD AND SYSTEM BASED ON IMPROVED YOU ONLY LOOK ONCE VERSION 5 (YOLOv5) 有权

    公开(公告)号：US20250005914A1

    公开(公告)日：2025-01-02

    申请号：US18711367

    申请日：2023-04-21

    Applicant: NANJING UNIVERSITY OF SCIENCE AND TECHNOLOGY

    Inventor： Yong QI ,  Xin ZENG

    IPC: G06V10/80 ,  G06V10/77 ,  G06V10/82 ,  G06V20/54 ,  G06V20/56

    Abstract: The present disclosure provides a multi-task panoptic driving perception method and system based on improved You Only Look Once version 5 (YOLOv5). The method in the present disclosure includes: performing image preprocessing on an image in a dataset to obtain an input image; extracting a feature of the input image by using a backbone network of improved YOLOv5, to obtain a feature map, where the backbone network is obtained by replacing a C3 module in a backbone network of YOLOv5 with an inverted residual bottleneck module; inputting the feature map into a neck network to obtain a feature map, and fusing the obtained feature map and the feature map obtained by the backbone network; inputting the fused feature map into a detection head to perform traffic target detection; and inputting the feature map of the neck network into a branch network to perform lane line detection and drivable area segmentation.

    IOV INTRUSION DETECTION METHOD AND DEVICE BASED ON IMPROVED CONVOLUTIONAL NEURAL NETWORK
    5.
    发明公开
    IOV INTRUSION DETECTION METHOD AND DEVICE BASED ON IMPROVED CONVOLUTIONAL NEURAL NETWORK 审中-公开

    公开(公告)号：US20240244066A1

    公开(公告)日：2024-07-18

    申请号：US18579856

    申请日：2023-02-07

    Applicant: NANJINGH UNIVERSITY OF SCIENCE AND TECHNOLOGY

    Inventor： Yong QI ,  Mingjun LIU ,  Jianye YU

    IPC: H04L9/40 ,  G06N3/045 ,  G06N3/0464 ,  G06N3/084

    CPC classification number: H04L63/1416 ,  G06N3/045 ,  G06N3/0464 ,  G06N3/084

    Abstract: The present disclosure belongs to the technical field of Internet of vehicles (IoV) security and provides an IoV intrusion detection method and device based on an improved convolutional neural network. The method of the present disclosure includes: collecting original data of data traffic during IoV communication, and inputting the original data to a data dimension reduction algorithm model for IoV intrusion detection for preprocessing to obtain standardized data for IoV data analysis; inputting the standardized data for IoV data analysis to an improved convolutional neural network model for calculation, including: performing convolutional calculation and nonlinear activation on the input data for layering; performing two convolutional operations, two pooling operations and one full connection operation on each layer of data; and classifying a data set output by the improved convolutional neural network model through a SoftMax layer.

    DOMAIN ADAPTATION METHOD AND SYSTEM FOR GESTURE RECOGNITION
    6.
    发明公开
    DOMAIN ADAPTATION METHOD AND SYSTEM FOR GESTURE RECOGNITION 审中-公开

    公开(公告)号：US20240168554A1

    公开(公告)日：2024-05-23

    申请号：US18515592

    申请日：2023-11-21

    Applicant: Nanjing University of Science and Technology

    Inventor： Wentao WEI ,  Linyan REN ,  Bowen ZHOU

    IPC: G06F3/01 ,  G06N3/08

    CPC classification number: G06F3/015 ,  G06F3/017 ,  G06N3/08

    Abstract: An objective of the present disclosure is to provide a domain adaptation method and system for gesture recognition, which relates to the field of gesture recognition technologies. The domain adaptation method for gesture recognition includes: obtaining a to-be-recognized target domain surface electromyography signal of a user; separately inputting the to-be-recognized target domain surface electromyography signal into multiple target domain gesture recognition models, to obtain target domain gesture recognition results under multiple source-specific views, where source domains of training data used by different target domain gesture recognition models are different; and determining a gesture category of the to-be-recognized target domain surface electromyography signal according to the gesture recognition results under multiple source-specific views and a weight under each source-specific view.

    THREE-DIMENSIONAL TOWERED CHECKERBOARD FOR MULTI-SENSOR CALIBRATION
    8.
    发明公开
    THREE-DIMENSIONAL TOWERED CHECKERBOARD FOR MULTI-SENSOR CALIBRATION 审中-公开

    公开(公告)号：US20240134024A1

    公开(公告)日：2024-04-25

    申请号：US18403731

    申请日：2024-01-04

    Applicant: NANJING UNIVERSITY OF SCIENCE AND TECHNOLOGY

    Inventor： Mingwu REN ,  Dexin REN

    IPC: G01S7/497 ,  G01S17/86 ,  G06T7/33 ,  G06T7/80

    CPC classification number: G01S7/497 ,  G01S17/86 ,  G06T7/33 ,  G06T7/80 ,  G06T2207/10028 ,  G06T2207/20164 ,  G06T2207/30244

    Abstract: The disclosure is a three-dimensional towered checkerboard for multi-sensor calibration, and a LiDAR and camera joint calibration method based on the checkerboard. The joint calibration method includes: establishing a modeling coordinate system taking the three-dimensional towered checkerboard as a basis, and generating a point cloud of the three-dimensional towered checkerboard; denoising a three-dimensional point cloud obtained by LiDAR, and obtaining an actual point cloud of the three-dimensional towered checkerboard under a LiDAR coordinate system; determining a transformation relationship between the LiDAR coordinate system and the modeling coordinate system; generating a corner point set of two-dimensional checkerboards under the modeling coordinate system in sequence according to actual positions of corners of the two-dimensional checkerboards, and transforming into the LiDAR coordinate system; obtaining a corner point set of the two-dimensional checkerboards on a photo; and calculating a transformation relationship between the camera coordinate system and the LiDAR coordinate system.

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