公开(公告)号：US20240400119A1

公开(公告)日：2024-12-05

申请号：US18392173

申请日：2023-12-21

Applicant: BEIJING JIAOTONG UNIVERSITY

Inventor： Hongjie LIU ,  Xiaolin LUO ,  Tao TANG ,  Ming CHAI ,  Shuai SU ,  Jidong LV

IPC: B61L27/16 ,  B61L25/02

Abstract: A method and system for virtually coupled train set (VCTS) control is provided. The method includes following steps: determining whether to execute a backup control strategy based on an actual state for a current cycle of each train unit and a target state sequence for a first preset number of cycles before the current cycle to obtain a first determination result; if the first determination result is yes, executing the backup control strategy to control each train unit; if the first determination result is no, calculating the target state sequence for the current cycle of each train unit according to a position or calculating the target state sequence for the current cycle of each train unit by using a synchronization relationship; and controlling each train unit according to the target state sequence for the current cycle of each train unit, respectively.

公开(公告)号：US20240020959A1

公开(公告)日：2024-01-18

申请号：US18130169

申请日：2023-04-03

Applicant: Beijing Jiaotong University

Inventor： Ming CHAI ,  Dong XIE ,  Hongjie LIU ,  Shuai SU ,  Jidong LV

IPC: G06V10/776 ,  G06V10/774 ,  G06T7/70 ,  G06V10/764 ,  G06V10/74 ,  G06T5/50

CPC classification number: G06V10/776 ,  G06V10/774 ,  G06T7/70 ,  G06V10/764 ,  G06V10/761 ,  G06T5/50 ,  G06T2207/20081 ,  G06T2207/20221 ,  G06T2207/20084

Abstract: A method and system for generating test cases for visual train positioning are provided and relate to the technical field of train positioning. The method includes: first obtaining real environment images around a train, and classifying the real environment images based on blur types; training a generative adversarial network (GAN)-based image generation network with each non-blurred training image, each blurred training image of any blur type in a same scenario and a preset blur type as inputs, and a reconstructed blurred image and a corresponding blur type as outputs, to obtain an image generation model; then inputting each real non-blurred image and target reference data into the image generation model to generate a target reconstructed blurred image, and then deleting target reconstructed blurred images with structural similarities lower than a set threshold.