期刊文献+
共找到5篇文章
< 1 >
每页显示 20 50 100
应用质量控制方法完善复材壁板设计 被引量:1
1
作者 支晗 郑双 朱照泽 《中国工程机械学报》 北大核心 2018年第5期436-442,共7页
应用质量控制(QC)方法,开展以完善飞机机身复材壁板设计工作.运用创新型QC方法、统计技术工具提出创新方案,有理有据得出非线性屈曲法的最优方案,提出了有效对策并加以实施.结果表明:应用QC方法,结合统计技术工具,复材壁板重量控制良好... 应用质量控制(QC)方法,开展以完善飞机机身复材壁板设计工作.运用创新型QC方法、统计技术工具提出创新方案,有理有据得出非线性屈曲法的最优方案,提出了有效对策并加以实施.结果表明:应用QC方法,结合统计技术工具,复材壁板重量控制良好,提高了飞机的经济性和竞争力;QC方法是完善复材壁板设计工作的有效方法. 展开更多
关键词 质量控制(QC) 复材壁板 非线性屈曲 统计技术工具
下载PDF
篦冷机换代改造的效果分析 被引量:1
2
作者 郑波 韩智 《新世纪水泥导报》 CAS 2020年第5期31-33,共3页
5 000 t/d生产线第三代篦冷机经过10多年的运行,设备老化,故障率逐渐增高,维护量加大;提产时出篦冷机熟料温度高;二、三次风温低,热回收效率低。将原第三代篦冷机升级改造为CB型第四代篦冷机后,性能上能够满足回转窑系统提产后的熟料冷... 5 000 t/d生产线第三代篦冷机经过10多年的运行,设备老化,故障率逐渐增高,维护量加大;提产时出篦冷机熟料温度高;二、三次风温低,热回收效率低。将原第三代篦冷机升级改造为CB型第四代篦冷机后,性能上能够满足回转窑系统提产后的熟料冷却要求,热回收效率较原篦冷机有所提高,二三次风温和窑头锅炉进口烟温都有所提高并且更加稳定,对于整个回转窑和余热发电系统都带来更好的运行效果。 展开更多
关键词 篦冷机 第三代 第四代 故障率 维护量 熟料温度 热回收效率
下载PDF
A Domain-Guided Model for Facial Cartoonlization
3
作者 Nan Yang Bingjie Xia +1 位作者 zhi han Tianran Wang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2022年第10期1886-1888,共3页
Dear Editor,This work investigates the issue of facial cartoonlization under the condition of lacking training data.We propose a domain-guided model(DGM)to realize facial cartoonlization for different kinds of faces.I... Dear Editor,This work investigates the issue of facial cartoonlization under the condition of lacking training data.We propose a domain-guided model(DGM)to realize facial cartoonlization for different kinds of faces.It includes two parts:1)a domain-guided model that contains four different interface networks and can embed an image from a facial domain to a cartoon domain independently;and 2)a one-to-one tutoring strategy that uses a sub-model as a teacher to train other interface networks and can yield fine-grained cartoon faces. 展开更多
关键词 NETWORKS INTERFACE DOMAIN
下载PDF
TSUNAMI:Translational Bioinformatics Tool Suite for Network Analysis and Mining
4
作者 zhi Huang zhi han +6 位作者 Tongxin Wang Wei Shao Shunian Xiang Paul Salama Maher Rizkalla Kun Huang Jie Zhang 《Genomics, Proteomics & Bioinformatics》 SCIE CAS CSCD 2021年第6期1023-1031,共9页
Gene co-expression network(GCN)mining identifies gene modules with highly correlated expression profiles across samples/conditions.It enables researchers to discover latent gene/molecule interactions,identify novel ge... Gene co-expression network(GCN)mining identifies gene modules with highly correlated expression profiles across samples/conditions.It enables researchers to discover latent gene/molecule interactions,identify novel gene functions,and extract molecular features from certain disease/condition groups,thus helping to identify disease bio-markers.However,there lacks an easy-to-use tool package for users to mine GCN modules that are relatively small in size with tightly connected genes that can be convenient for downstream gene set enrichment analysis,as well as modules that may share common members.To address this need,we developed an online GCN mining tool package:TSUNAMI(Tools SUite for Network Analysis and MIning).TSUNAMI incorporates our state-of-the-art lmQCM algorithm to mine GCN modules for both public and user-input data(microarray,RNA-seq,or any other numerical omics data),and then performs downstream gene set enrichment analysis for the identified modules.It has several features and advantages:1)a user-friendly interface and real-time co-expression network mining through a web server;2)direct access and search of NCBI Gene Expression Omnibus(GEO)and The Cancer Genome Atlas(TCGA)databases,as well as user-input gene ex-pression matrices for GCN module mining;3)multiple co-expression analysis tools to choose from,all of which are highly flexible in regards to parameter selection options;4)identified GCN modules are summarized to eigengenes,which are convenient for users to check their correlation with other clinical traits;5)integrated downstream Enrichr enrichment analysis and links to other gene set enrichment tools;and 6)visualization of gene loci by Circos plot in any step of the process.The web service is freely accessible through URL:https://biolearns.medicine.iu.edu/.Source code is available at https://github.com/huangzhii/TSUNAMI/. 展开更多
关键词 Network mining Gene co-expression network Transcriptomic data analysis lmQCM Web server Survival analysis
原文传递
BrcaSeg:A Deep Learning Approach for Tissue Quantification and Genomic Correlations of Histopathological Images
5
作者 Zixiao Lu Xiaohui Zhan +7 位作者 Yi Wu Jun Cheng Wei Shao Dong Ni zhi han Jie Zhang Qianjin Feng Kun Huang 《Genomics, Proteomics & Bioinformatics》 SCIE CAS CSCD 2021年第6期1032-1042,共11页
Epithelial and stromal tissues are components of the tumor microenvironment and play a major role in tumor initiation and progression.Distinguishing stroma from epithelial tissues is critically important for spatial c... Epithelial and stromal tissues are components of the tumor microenvironment and play a major role in tumor initiation and progression.Distinguishing stroma from epithelial tissues is critically important for spatial characterization of the tumor microenvironment.Here,we propose BrcaSeg,an image analysis pipeline based on a convolutional neural network(CNN)model to classify epithelial and stromal regions in whole-slide hematoxylin and eosin(H&E)stained histopathological images.The CNN model is trained using well-annotated breast cancer tissue microarrays and validated with images from The Cancer Genome Atlas(TCGA)Program.BrcaSeg achieves a classification accuracy of 91.02%,which outperforms other state-of-the-art methods.Using this model,we generate pixel-level epithelial/stromal tissue maps for 1000 TCGA breast cancer slide images that are paired with gene expression data.We subsequently estimate the epithelial and stromal ratios and perform correlation analysis to model the relationship between gene expression and tissue ratios.Gene Ontology(GO)enrichment analyses of genes that are highly correlated with tissue ratios suggest that the same tissue is associated with similar biological processes in different breast cancer subtypes,whereas each subtype also has its own idiosyncratic biological processes governing the development of these tissues.Taken all together,our approach can lead to new insights in exploring relationships between image-based phenotypes and their underlying genomic events and biological processes for all types of solid tumors.BrcaSeg can be accessed at https://github.com/Serian1992/ImgBio. 展开更多
关键词 Whole-slide tissue image Computational pathology Deep learning Integrative genomics Breast cancer
原文传递
上一页 1 下一页 到第
使用帮助 返回顶部