检索结果-南通市图书馆

Numerical and experimental studies on unsupervised deep Lagrangian learning based rotor balancing method

Science China(Technological Sciences) 2023年第4期66卷 1050-1061页

作者： ZHONG Shun HOU Lei Department of Mechanics Tianjin UniversityTianjin 300000China School of Astronautics Harbin Institute of TechnologyHarbin 150001China

Rotor balancing is essential to rotor dynamic analysis.To make the balancing process convenient and costless,a balancing method using unsupervised deep Lagrangian network without weight trail is proposed.In the proposed network,a Lagrangian layer is applied to the network to introduce the physical prior knowledge.Compared to traditional balancing method,trail weight process is not necessary.Meanwhile,parameter sharing mechanics in baseline design or Lagrangian layer are applied to identify the unbalanced force without labeled data.Both numerical case study and corresponding experiment are conducted to validate the method.Both experimental and numerical results find that the proposed rotor balancing approach gives reasonable and comparative results with the considerations of both cost and accuracy.Compared with the baseline,to which no physical prior is applied,the balancing method with Lagrangian mechanism involved could achieve better performance.This proposed rotor dynamic balancing method gives out an alternative approach of rotor balancing methods.

关键词： rotor balancing unsupervised deep learning Lagrangian layer weight trial experimental rig balancing experiment

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Face recognition based on deep aggregated sparse autoencoder network

Face recognition based on deep aggregated sparse autoencoder...

引用

第37届中国控制会议

作者： Guofeng Zou Dingyi Lin Gui-xia Fu Jin Shen Mingliang Gao College of Electrical and Electronic Engineering Shandong University of Technology

Sparse autoencoder network is sensitive to face noise,and the learning process is easy to ignore the face structure information.Address this problem,we propose a face recognition approach fused sub-region LBP feature and deep aggregated sparse autoencoder network.Firstly,the face image is divided into different sub-regions,and the local binary pattern is used to pre-process the face image in order to obtain the LBP features of different sub-region faces.Then,the deep features for different sub-region LBP features are extracted with different sparse autoencoders.Finally,the output features of different sparse autoencoders are aggregated by full connection,and the face feature vectors are obtained for classification.We get the optimal aggregated network structure and network parameters through lots of experiments,while the face recognition accuracy is also improved.

关键词： unsupervised deep learning sparse autoencoder deep aggregated network LBP feature sub-region division face recognition

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：