公开(公告)号：US20240211825A1

公开(公告)日：2024-06-27

申请号：US18537969

申请日：2023-12-13

申请人： Hangzhou Dianzi University

发明人： Yifan Wu ,  Zhiqiang Wang ,  Tingcong Ye ,  Yanping Xu ,  Hua Zhang ,  Guojun Dai

IPC分类号： G06Q10/0631 ,  G06N3/04 ,  G06N3/086

CPC分类号： G06Q10/06311 ,  G06N3/04 ,  G06N3/086

摘要： Disclosed in the present disclosure is a job-shop batch scheduling method based on a dueling double deep Q-network (D3QN) and a genetic algorithm. The present disclosure includes: constructing a mathematical model of a job-shop batch scheduling problem, where a goal of scheduling is to minimize a maximum completion time; determining a batch partition solution to the job-shop batch scheduling problem by the genetic algorithm; crossing and mutating chromosomes in a population; decoding the chromosomes, and obtaining a batch division solution of the workpieces to be machined; and expressing a procedure sequencing problem of the workpiece batches as a disjunctive graph model; performing representative learning on node feature information of a disjunctive graph by using a graph neural network, and extracting a feature state of the procedure sequencing problem; designing a D3QN model structure with priority experience replay, and training the D3QN model; and determining whether a termination condition is satisfied.

公开(公告)号：US20240193752A1

公开(公告)日：2024-06-13

申请号：US18483396

申请日：2023-10-09

申请人： Hangzhou Dianzi University

发明人： Lingjun Zhang ,  Hua Zhang ,  Yifan Wu ,  Yifei Wu

IPC分类号： G06T7/00

CPC分类号： G06T7/0004 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30124

摘要： The present disclosure provides a fabric defect detection method, including the following steps: constructing a data set; preprocessing the data set; constructing a region-based convolutional neural network (R-CNN) model for fabric defect detection; where the R-CNN model for fabric defect detection includes four convolutional layers, four max-pooling layers, and two fully connected layers; training the R-CNN model for fabric defect detection; and reducing a number of false negative (FN) samples by classification threshold reduction. The present disclosure provides a novel R-CNN model for fabric defect detection. The model provides a desirable feature detection accuracy, has a low running cost, and is easy to implement, such that the model can be better applicable to actual operations in an industrial environment.