公开(公告)号：US20240177251A1

公开(公告)日：2024-05-30

申请号：US18156533

申请日：2023-01-19

Applicant: Henan University of Science and Technology

Inventor： Qingtao WU ,  Chenlu ZHANG ,  Mingchuan ZHANG ,  Ruijuan ZHENG ,  Xuhui ZHAO ,  Junlong ZHU ,  Zhihang JI ,  Muhua LIU

IPC: G06Q50/04 ,  G06F17/18 ,  G06Q10/04 ,  G06Q10/0631

CPC classification number: G06Q50/04 ,  G06F17/18 ,  G06Q10/04 ,  G06Q10/063116

Abstract: A production task scheduling method, system and device for a flexible assembly job shop is provided. The method includes: compiling production processing data in a double-layer integer coding manner to obtain a double-layer code scheme; sorting lower-layer codes in the double-layer code scheme to generate an initialized population; calculating a fitness value of each individual in the population, selecting a solution with an optimal fitness value as an elite individual, and replicating the elite individual to construct an elite matrix; constructing an external archive; selecting an excellent individual from all non-dominant solutions stored in the external archive as an optimal elite individual by using a simulated annealing algorithm, and updating the elite matrix through the optimal elite individual; determining a final optimal elite individual as an optimal scheduling scheme based on an updated elitist matrix through using a three-stage heuristic optimization algorithm with multi-search fusion in an iterative process.

公开(公告)号：US20230281806A1

公开(公告)日：2023-09-07

申请号：US18115726

申请日：2023-02-28

Applicant: Henan University of Science and Technology

Inventor： Mingchuan ZHANG ,  Mengjie GU ,  Lin WANG ,  Qingtao WU ,  Junlong ZHU ,  Zhihang JI

IPC: G06T7/00 ,  G06T5/00 ,  G06T5/50 ,  G06T7/11 ,  G06V10/25 ,  G06V10/44 ,  G06V10/764

CPC classification number: G06T7/0012 ,  G06T5/002 ,  G06T5/50 ,  G06T7/11 ,  G06V10/25 ,  G06V10/44 ,  G06V10/764 ,  G06T2207/10132 ,  G06T2207/20084 ,  G06T2207/20221 ,  G06T2207/30048 ,  G06T2207/30242

Abstract: A microbubble counting method for patent foramen ovale (PFO) based on deep learning is provided. The method includes: segmenting a target area of a left heart in an ultrasonic image; and generating a corresponding density map for a segmented target image using a convolutional neural network (CNN), and calculating a total number of the microbubbles in the segmented area by integration and summation. The method has the following beneficial effects: target segmentation is performed on the left atrium and left ventricular area of the heart using the neural network, and effective segmentation of the target area of the left heart is the key of obtaining parameters such as a size and form of the target area. The target area is quantitatively analyzed according to a segmentation result, and the number of the microbubbles in the target area is counted.