• 综合
  • 标题
  • 关键词
  • 摘要
  • 学者
  • 期刊-刊名
  • 期刊-ISSN
  • 会议名称
搜索

作者:

Pan, Xiaotong (Pan, Xiaotong.) | Tang, Jian (Tang, Jian.) | Xia, Heng (Xia, Heng.) | Cui, Canlin (Cui, Canlin.) | Hu, Yanhui (Hu, Yanhui.) | Wang, Tianzheng (Wang, Tianzheng.)

收录:

EI Scopus

摘要:

Accurately identifying flame combustion status during municipal solid waste incineration (MSWI) is a crucial prerequisite for intelligent combustion control. Previous research in constructing identification models often relied on expert knowledge and extensive repetitive experiments to determine hyperparameters and feature selection parameters. The process of parameter determination was resourceintensive, demanding significant computational power. To tackle these challenges, a method using parallel differential evolution (PDE) algorithms for optimizing the hyperparameters of combustion state recognition models is proposed. Initially, feature parameters and model hyperparameters are encoded as chromosome groups for the differential evolution algorithm, followed by random parameter initialization. Then, the population is randomly divided into several subpopulations, and each undergoing parallel evolution. Evolution halts upon meeting specific stagnation conditions. Finally, the hyperparameters enabling the recognition model based on vision transformer and improved deep forest classification to achieve optimal performance. The effective of the proposed method is validated by using the actual data of an MSWI plant. © 2024 IEEE.

关键词:

Waste incineration Image classification Feature extraction Image enhancement Municipal solid waste Evolutionary algorithms Parameter estimation Chromosomes Optimization

作者机构:

  • [ 1 ] [Pan, Xiaotong]Beijing University Of Technology, Faculty Of Information Technology, Beijing, China
  • [ 2 ] [Tang, Jian]Beijing University Of Technology, Faculty Of Information Technology, Beijing, China
  • [ 3 ] [Xia, Heng]Beijing University Of Technology, Faculty Of Information Technology, Beijing, China
  • [ 4 ] [Cui, Canlin]Beijing University Of Technology, Faculty Of Information Technology, Beijing, China
  • [ 5 ] [Hu, Yanhui]Beijing University Of Technology, Faculty Of Information Technology, Beijing, China
  • [ 6 ] [Wang, Tianzheng]Beijing University Of Technology, Faculty Of Information Technology, Beijing, China

通讯作者信息:

电子邮件地址:

查看成果更多字段

相关关键词:

来源 :

年份: 2024

页码: 3071-3074

语种: 英文

被引次数:

WoS核心集被引频次:

SCOPUS被引频次:

ESI高被引论文在榜: 0 展开所有

万方被引频次:

中文被引频次:

近30日浏览量: 1

归属院系:

在线人数/总访问数:448/4953705
地址:北京工业大学图书馆(北京市朝阳区平乐园100号 邮编:100124) 联系我们:010-67392185
版权所有:北京工业大学图书馆 站点建设与维护:北京爱琴海乐之技术有限公司