公开(公告)号：US20240296528A1

公开(公告)日：2024-09-05

申请号：US18366604

申请日：2023-08-07

Applicant: China Jiliang University ,  ZHEJIANG UNIVERSITY OF TECHNOLOGY

Inventor： Gang Xiao ,  Jiawei Lu ,  Qibing Wang ,  Chen Li ,  Hao HU ,  Junbo Yao

IPC: G06T5/00

CPC classification number: G06T5/70 ,  G06T2207/10028

Abstract: A denoising method based on a multiscale distribution score for a point cloud includes: constructing a two-layer network model based on multiscale perturbation and point cloud distribution, where the two-layer network model includes a feature extraction module for extracting a feature of the point cloud and a displacement prediction module for predicting a displacement of a noise point; constructing a point cloud noise model for improving a denoising effect and retaining a sharp feature and avoiding reducing quality of point cloud data; extracting a global feature h by inputting the point cloud data into the feature extraction module; iteratively learning the displacement of the noise point by the displacement prediction module according to a feature obtained by the feature extraction unit; and defining a loss function of network training, and completing convergence under the condition that the loss function reaches a set threshold or a maximum number of iterations.

公开(公告)号：US20240403599A1

公开(公告)日：2024-12-05

申请号：US18475200

申请日：2023-09-26

Applicant: ZHEJIANG UNIVERSITY OF TECHNOLOGY

Inventor： Gang Xiao ,  Jiacheng Huang ,  Yuanming Zhang ,  Zhenbo Cheng ,  Xuesong Xu ,  Jiawei Lu ,  Qibing Wang

IPC: G06N3/042 ,  G06N3/048 ,  G06N3/08

Abstract: Disclosed in the present invention is a health state assessment method for equipment based on a knowledge graph attention network, includes: steps: 1) constructing a graph data model which can comprehensively reflect change of a health state of the equipment by deeply integrating association relationships of equipment components, monitoring data dependence relationships and priori information, etc. by means of a knowledge graph and by combining with domain priori knowledge; 2) extracting feature information of the health state knowledge graph by using a graph attention network, and obtaining a target node vector representation which accurately reflects the health state of the equipment by means of learning; and 3) making a health state representation vector of the equipment pass through a fully connected layer to obtain a health state classification prediction probability, and performing training to reducing a loss value relative to a true label, thereby obtaining a health state assessment result.