公开(公告)号：US20250148173A1

公开(公告)日：2025-05-08

申请号：US18925255

申请日：2024-10-24

Applicant: ZHENGZHOU UNIVERSITY ,  CHINA UNIVERSITY OF MINING AND TECHNOLOGY

Inventor： Mingrui DU ,  Xupei YAO ,  Hongyuan FANG ,  Peng ZHAO ,  Haijian Su ,  Niannian Wang ,  Xueming Du ,  Xiaohua ZHAO ,  Binghan Xue

IPC: G06F30/27 ,  C04B28/04 ,  C04B40/00

Abstract: Provided is a method and system for predicting a hydration reaction degree of cement based on a cycle generative adversarial network (CycleGAN). The method includes the following steps: S1, acquiring a micro-structure image of a cement paste test specimen; S2, establishing a micro-pore structure image dataset; S3, establishing a cement micro-hydration prediction model based on a CycleGAN; and S4, completing prediction based on a final cement micro-hydration prediction model. A deep learning algorithm is applied to micro-hydration prediction of cement. A complex theoretical formula is replaced with a data driven mode. Dependence on ideal hypotheses is reduced, and the accuracy of prediction on micro-hydration of cement is thus improved.

公开(公告)号：US20210319265A1

公开(公告)日：2021-10-14

申请号：US17356533

申请日：2021-06-24

Applicant: Zhengzhou University ,  BeSTDR Infrastructure Hospital(Pingyu)

Inventor： Hongyuan Fang ,  Niannian Wang ,  Qunfang Hu ,  Binghan Xue ,  Xueming Du ,  Fan Huang

IPC: G06K9/62 ,  G06N3/08

Abstract: A method for segmentation of underground drainage pipeline defects based on full convolutional neural network includes steps of: collecting a data set of the underground drainage pipeline defects; processing the data set of the underground drainage pipeline defects; optimizing with a semantic segmentation algorithm; adjusting model hyperparameters; training a model; verifying the model; and testing the model. The method adopts a deep learning algorithm, optimizes the FCN full convolutional neural network, develops a semantic segmentation method suitable for complex and similar defect characteristics of underground drainage pipelines, and adopts real underground drainage pipeline defect detection big data, thereby realizing pixel-level segmentation of the underground drainage pipeline defects and providing better robustness and generality. The detection accuracy and efficiency of the underground drainage pipeline defects are effectively improved.