公开(公告)号：US20230186056A1

公开(公告)日：2023-06-15

申请号：US17762275

申请日：2021-06-02

Applicant: Qingdao University of Technology

Inventor： Jingbo Zhao ,  Tengfei Qiu ,  Jingxuhui Zhu ,  Xinchao Liu ,  Xiaohan Zhang ,  Wenbin Zhang

IPC: G06N3/0464 ,  G06N3/043 ,  G06N3/048 ,  G06V10/25 ,  G06V10/44 ,  G06V10/26

CPC classification number: G06N3/0464 ,  G06N3/043 ,  G06N3/048 ,  G06V10/25 ,  G06V10/454 ,  G06V10/267

Abstract: The present invention relates to a grabbing detection method based on an RP-ResNet, which method belongs to the field of computer vision, and in particular relates to recognition and positioning of a grabbing point of a mechanical arm. The method comprises: inputting a target object image; pre-processing data; performing data processing by means of an RP-ResNet model; and finally, generating a grabbing block diagram of a grabbing target. On the basis of a model ResNet 50, a region proposal network is used in the 30th layer of a network, fuzzy positioning is performed on the position of a grabbing point, feature information of high and low layers is fully fused to strengthen the utilization of information of low layers, and an SENet structure is added to the 40th layer of the network, thereby further increasing the detection accuracy of a grabbing point. By means of a grabbing detection framework based on ResNet-50, a residual network, a region proposal idea and SENet are combined, such that it is ensured that rapid target detection is realized, and the accuracy rate of target detection is further improved.

公开(公告)号：US11543787B2

公开(公告)日：2023-01-03

申请号：US16745581

申请日：2020-01-17

Applicant: Qingdao University of Technology

Inventor： Jingbo Zhao ,  Bingxin Xue ,  Zhong Wang ,  Jingxuhui Zhu ,  Tengfei Qiu ,  Xinchao Liu

IPC: G05B13/02 ,  G06F17/11 ,  G06N3/04 ,  G05B13/04

Abstract: The present invention discloses a networked control system (NCS) time-delay compensation method based on predictive control. The method comprises the following steps: (1) acquiring random time-delay data in an NCS, and preprocessing the data; (2) predicting the current time-delay by using a fuzzy neural network (FNN) optimized by a particle swarm optimization (PSO) algorithm; (3) compensating the predicted time-delay by using an implicit proportional-integral-based generalized predictive control (PIGPC) algorithm; (4) determining whether a preset work end time is up according to a clock in the NCS; if yes, ending the process; if no, returning to step (2). The method disclosed by the present invention can accurately predict and effectively compensate the NCS time-delay and has excellent development prospect.