公开(公告)号：US20240256829A1

公开(公告)日：2024-08-01

申请号：US18243107

申请日：2023-09-07

Applicant: University of Science and Technology Beijing

Inventor： Tianyu HU ,  Huimin MA ,  Xiao ZHANG ,  Hao LIU ,  Kangsheng WANG

IPC: G06N3/042 ,  G06N3/084

CPC classification number: G06N3/042 ,  G06N3/084

Abstract: A wind power generation quantile prediction method based on machine mental model and self-attention includes: using human cognitive decision-making mechanism for reference to construct the machine mental model as the basic framework of WQPMMSA, and then the seasonal power generation rules and intraday power generation trend are encoded into WQPMMSA as the input information of the prediction method, using the self-attention layer to replace the recurrent neural network in the original machine mental model, and establishing the statistical relationship between the seasonal power generation rules and the intraday power generation trend effectively, reducing the long-range forgetting of the original machine mental model-convert the continuous rank probability score in the integral form into a summation form, and using it as a loss function to train WQPMMSA, so that WQPMMSA approaches the optimal quantile prediction result with the highest efficiency. Therefore, accurate quantile prediction of wind power generation is realized.

公开(公告)号：US20240161478A1

公开(公告)日：2024-05-16

申请号：US18130200

申请日：2023-04-03

Applicant: University of Science and Technology Beijing

Inventor： Huimin MA ,  Haizhuang LIU ,  Yilin WANG ,  Rongquan WANG

IPC: G06V10/80 ,  G06T5/00 ,  G06T7/73 ,  G06V10/77 ,  G06V10/774 ,  G06V20/58 ,  G06V20/64

CPC classification number: G06V10/803 ,  G06T5/002 ,  G06T7/73 ,  G06V10/7715 ,  G06V10/774 ,  G06V10/806 ,  G06V20/58 ,  G06V20/64 ,  G06T2207/10024 ,  G06T2207/10028 ,  G06T2207/20021 ,  G06T2207/20081 ,  G06T2207/30196 ,  G06T2207/30252

Abstract: Disclosed are a multimodal weakly-supervised three-dimensional (3D) object detection method and system, and a device. The method includes: shooting multiple two-dimensional (2D) red, green and blue (RGB) images with a camera, acquiring ground points by a vehicle LiDAR sensor and generating a 3D frustum based on 2D box labels on each of the 2D RGB images; filtering ground points in the 3D frustum and selecting a region with most 3D points; generating a 3D pseudo-labeling bounding box of an object according to the region with the most 3D points; training a multimodal superpixel dual-branch network with the 3D pseudo-labeling bounding boxes as labels and the 2D RGB image and the 3D point cloud as inputs; and inputting a 2D RGB image of a current frame and a 3D point cloud of a current scenario to a trained multimodal superpixel dual-branch network to generate an overall 3D point cloud.