公开(公告)号：US20240260885A1

公开(公告)日：2024-08-08

申请号：US18565501

申请日：2023-04-04

Applicant: SOUTHEAST UNIVERSITY

Inventor： Zhe LI ,  Liya WANG ,  Xiao HAN ,  Jie LI ,  Qixin ZHANG ,  Mingjing DONG ,  Mingchen XU ,  Shuang WU ,  Yi SHI ,  Haini CHEN ,  Qiaochu WANG

IPC: A61B5/378 ,  A61B5/0205 ,  A61B5/0533 ,  A61B5/352 ,  A61B5/397 ,  G06N20/00 ,  G06Q50/26 ,  G06V10/26 ,  G06V10/764 ,  G06V20/00

CPC classification number: A61B5/378 ,  A61B5/0205 ,  A61B5/0533 ,  A61B5/352 ,  A61B5/397 ,  G06N20/00 ,  G06V10/26 ,  G06V10/764 ,  G06V20/39 ,  A61B2503/12 ,  G06Q50/26

Abstract: A street greening quality detection method based on physiological activation recognition is provided. The street greening quality detection method includes establishing a greening quality factor index system, and obtaining and uniformly processing street greening images; collecting raw data, and performing reclassification and differential wave processing on the raw data to obtain valid physiological data that can be used for activation feature recognition of greening quality factors; calculating physiological activation feature parameters, training the physiological activation feature parameters by transfer learning fusion to determine importance of physiological activation features, and recognizing weighted average greening activation indexes of the greening quality factors; analyzing weighted average greening activation index data of the greening quality factors to form a street greening quality detection model; and inputting annotated street samples to be analyzed into the street greening quality detection model to obtain annotated results of street greening quality grading detection target data.

公开(公告)号：US20240335156A1

公开(公告)日：2024-10-10

申请号：US18039286

申请日：2022-11-07

Applicant: SOUTHEAST UNIVERSITY

Inventor： Zhe LI ,  Liya WANG ,  Xiao HAN ,  Futian YUAN ,  Tongyi ZHU ,  Ruoxuan HUANG ,  Ying GAO ,  Yinyin CAO ,  Zheng ZHOU ,  Hengyi ZHAO ,  Jie LI

IPC: A61B5/372 ,  A61B5/00 ,  G06T7/11 ,  G06T7/90 ,  G06V10/56 ,  G06V10/762

CPC classification number: A61B5/372 ,  A61B5/7257 ,  G06T7/11 ,  G06T7/90 ,  G06V10/56 ,  G06V10/762 ,  G06T2207/10024

Abstract: The present disclosure provides a measurement method and system based on image electroencephalogram sensitivity data for a built environment dominant color, and relates to the field of urban quality measurement. The measurement method based on image electroencephalogram sensitivity data for a built environment dominant color includes acquiring electroencephalogram data corresponding to a built environment image sample; calculating an environment dominant color sensitivity on the basis of the electroencephalogram data; extracting a dominant color feature parameter according to the built environment image sample; constructing a built environment dominant color measurement model, and training same by taking sensitivity data and a dominant color feature as an input; and inputting an environment image to be analyzed into a trained model, so as to obtain a predicted dominant color sensitivity result. Therefore, the problems that a prediction effect of a nonlinear model integrating an image color feature and an environment quality is improved.

公开(公告)号：US20240394430A1

公开(公告)日：2024-11-28

申请号：US18265260

申请日：2022-12-08

Applicant: SOUTHEAST UNIVERSITY

Inventor： Xiao HAN ,  Zhe LI ,  Liya WANG ,  Jie LI ,  Qixin ZHANG ,  Mingjing DONG ,  Shuang WU ,  Mingchen XU ,  Haini CHEN ,  Yi SHI ,  Qiaochu WANG ,  Mengyao YU

IPC: G06F30/20 ,  G06F17/16 ,  G06V20/10

Abstract: Disclosed are a method, an apparatus and a storage medium for measuring the quality of a built environment. The method includes the following steps: identifying key influencing factors determining environmental quality, and establishing an index system of environmental quality influencing factors, wherein the key influencing factors include non-observation elements and observation elements; analyzing the relationship between the environmental quality and the key influencing factors to form a theoretical model; acquiring observation elements to form a large sample database; calculating path coefficients of each key influencing factor of environmental quality according to the distribution of sample data in the large sample database, and converting the path coefficients into weights; dividing distribution intervals of all observation elements dynamically according to the distribution of sample data, and defining quality assignments of all observation elements; and performing an environmental quality measurement of samples in combination with the weights and the quality assignments.