首页 > 最新文献

北京航空航天大学学报最新文献

英文 中文
Topological relation detection technology of substation wiring diagram in electric power system 电力系统变电站接线图拓扑关系检测技术
Q3 Engineering
北京航空航天大学学报
Pub Date : 2021-04-08 DOI: 10.13700/J.BH.1001-5965.2020.0476
Li Hao, Guan Ti, W. Shan, shinhwan wei, Liu Zixin, L. Xiaochuan
{"title":"Topological relation detection technology of substation wiring diagram in electric power system","authors":"Li Hao, Guan Ti, W. Shan, shinhwan wei, Liu Zixin, L. Xiaochuan","doi":"10.13700/J.BH.1001-5965.2020.0476","DOIUrl":"https://doi.org/10.13700/J.BH.1001-5965.2020.0476","url":null,"abstract":"","PeriodicalId":39840,"journal":{"name":"北京航空航天大学学报","volume":"47 1","pages":"531"},"PeriodicalIF":0.0,"publicationDate":"2021-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46819741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Fast, low-power and high-precision 3D reconstruction of UAV images based on FPGA 基于FPGA的无人机图像快速、低功耗、高精度三维重建
Q3 Engineering
北京航空航天大学学报
Pub Date : 2021-04-08 DOI: 10.13700/J.BH.1001-5965.2020.0452
Li Jie, L. Yixuan, WU Tiansheng, Wang Haorong, Liang Min
{"title":"Fast, low-power and high-precision 3D reconstruction of UAV images based on FPGA","authors":"Li Jie, L. Yixuan, WU Tiansheng, Wang Haorong, Liang Min","doi":"10.13700/J.BH.1001-5965.2020.0452","DOIUrl":"https://doi.org/10.13700/J.BH.1001-5965.2020.0452","url":null,"abstract":"","PeriodicalId":39840,"journal":{"name":"北京航空航天大学学报","volume":"47 1","pages":"486"},"PeriodicalIF":0.0,"publicationDate":"2021-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44020341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Block-diagonal projective representation for face recognition 人脸识别的块对角投影表示
Q3 Engineering
北京航空航天大学学报
Pub Date : 2021-04-08 DOI: 10.13700/J.BH.1001-5965.2020.0460
Liu Baolong, Wang Yong, Liao Danping, Wang Lei
{"title":"Block-diagonal projective representation for face recognition","authors":"Liu Baolong, Wang Yong, Liao Danping, Wang Lei","doi":"10.13700/J.BH.1001-5965.2020.0460","DOIUrl":"https://doi.org/10.13700/J.BH.1001-5965.2020.0460","url":null,"abstract":"","PeriodicalId":39840,"journal":{"name":"北京航空航天大学学报","volume":"47 1","pages":"623"},"PeriodicalIF":0.0,"publicationDate":"2021-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43307916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Heterogeneous remote sensing image change detection based on hybrid network 基于混合网络的异构遥感图像变化检测
Q3 Engineering
北京航空航天大学学报
Pub Date : 2021-04-08 DOI: 10.13700/J.BH.1001-5965.2020.0455
Zhou Yuan, L. Xiangrui, Y. Jing
{"title":"Heterogeneous remote sensing image change detection based on hybrid network","authors":"Zhou Yuan, L. Xiangrui, Y. Jing","doi":"10.13700/J.BH.1001-5965.2020.0455","DOIUrl":"https://doi.org/10.13700/J.BH.1001-5965.2020.0455","url":null,"abstract":"","PeriodicalId":39840,"journal":{"name":"北京航空航天大学学报","volume":"47 1","pages":"451"},"PeriodicalIF":0.0,"publicationDate":"2021-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41394196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Social image tag refinement and annotation based on noise Cauchy distribution 基于噪声柯西分布的社会图像标签细化与标注
Q3 Engineering
北京航空航天大学学报
Pub Date : 2021-04-08 DOI: 10.13700/J.BH.1001-5965.2020.0454
Li Lianrong, Xiang Xinguang
{"title":"Social image tag refinement and annotation based on noise Cauchy distribution","authors":"Li Lianrong, Xiang Xinguang","doi":"10.13700/J.BH.1001-5965.2020.0454","DOIUrl":"https://doi.org/10.13700/J.BH.1001-5965.2020.0454","url":null,"abstract":"","PeriodicalId":39840,"journal":{"name":"北京航空航天大学学报","volume":"47 1","pages":"632"},"PeriodicalIF":0.0,"publicationDate":"2021-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41516001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Low-latency video coding techniques 低延迟视频编码技术
Q3 Engineering
北京航空航天大学学报
Pub Date : 2021-04-08 DOI: 10.13700/J.BH.1001-5965.2020.0463
Song Li, Liu Xiaoyong, Wu Guoqing, Zhu Chen, Huang Yan, Xie Rong, Z. Wenjun
{"title":"Low-latency video coding techniques","authors":"Song Li, Liu Xiaoyong, Wu Guoqing, Zhu Chen, Huang Yan, Xie Rong, Z. Wenjun","doi":"10.13700/J.BH.1001-5965.2020.0463","DOIUrl":"https://doi.org/10.13700/J.BH.1001-5965.2020.0463","url":null,"abstract":"","PeriodicalId":39840,"journal":{"name":"北京航空航天大学学报","volume":"47 1","pages":"558"},"PeriodicalIF":0.0,"publicationDate":"2021-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41597764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Multimodal deformable registration based on unsupervised learning 基于无监督学习的多模态可变形配准
Q3 Engineering
北京航空航天大学学报
Pub Date : 2021-04-08 DOI: 10.13700/J.BH.1001-5965.2020.0449
T. Ma, Z. Li, R. Liu, X. Fan, Z. Luo
Multimodal deformable registration is designed to solve dense spatial transformations and is used to align images of two different modalities It is a key issue in many medical image analysis applications Multimodal image registration based on traditional methods aims to solve the optimization problem of each pair of images, and usually achieves excellent registration performance, but the calculation cost is high and the running time is long The deep learning method greatly reduces the running time by learning the network used to perform registration These learning-based methods are very effective for single-modality registration However, the intensity distribution of different modal images is unknown and complex Most existing methods rely heavily on label data Faced with these challenges, this paper proposes a deep multimodal registration framework based on unsupervised learning Specifically, the framework consists of feature learning based on matching amount and deformation field learning based on maximum posterior probability, and realizes unsupervised training by means of spatial conversion function and differentiable mutual information loss function In the 3D image registration tasks of MRI T1, MRI T2 and CT, the proposed method is compared with the existing advanced multi-modal registration methods In addition, the registration performance of the proposed method is demonstrated on the latest COVID-19 CT data A large number of results show that the proposed method has a competitive advantage in registration accuracy compared with other methods, and greatly reduces the calculation time © 2021, Editorial Board of JBUAA All right reserved
多模态可变形配准是为了解决密集的空间变换而设计的，用于对齐两种不同模态的图像。这是许多医学图像分析应用中的一个关键问题。基于传统方法的多模态图像配准旨在解决每对图像的优化问题，通常会获得优异的配准性能，但是计算成本高且运行时间长。深度学习方法通过学习用于执行注册的网络大大减少了运行时间。这些基于学习的方法对于单模态注册非常有效。然而，不同模态图像的强度分布是未知和复杂的。现有的方法大多严重依赖于标签数据。面对这些挑战，本文提出了一种基于无监督学习的深度多模态配准框架。具体而言，该框架由基于匹配量的特征学习和基于最大后验概率的变形场学习组成，并通过空间转换函数和可微互信息损失函数实现了无监督训练。在MRI T1、MRI T2和CT的三维图像配准任务中，将所提出的方法与现有的先进多模态配准方法进行了比较。此外，在最新的新冠肺炎CT数据上演示了所提出的方法的配准性能。大量结果表明，与其他方法相比，所提出的算法在配准精度方面具有竞争优势，并大大减少了计算时间©2021，JBUAA编委会保留所有权利
{"title":"Multimodal deformable registration based on unsupervised learning","authors":"T. Ma, Z. Li, R. Liu, X. Fan, Z. Luo","doi":"10.13700/J.BH.1001-5965.2020.0449","DOIUrl":"https://doi.org/10.13700/J.BH.1001-5965.2020.0449","url":null,"abstract":"Multimodal deformable registration is designed to solve dense spatial transformations and is used to align images of two different modalities It is a key issue in many medical image analysis applications Multimodal image registration based on traditional methods aims to solve the optimization problem of each pair of images, and usually achieves excellent registration performance, but the calculation cost is high and the running time is long The deep learning method greatly reduces the running time by learning the network used to perform registration These learning-based methods are very effective for single-modality registration However, the intensity distribution of different modal images is unknown and complex Most existing methods rely heavily on label data Faced with these challenges, this paper proposes a deep multimodal registration framework based on unsupervised learning Specifically, the framework consists of feature learning based on matching amount and deformation field learning based on maximum posterior probability, and realizes unsupervised training by means of spatial conversion function and differentiable mutual information loss function In the 3D image registration tasks of MRI T1, MRI T2 and CT, the proposed method is compared with the existing advanced multi-modal registration methods In addition, the registration performance of the proposed method is demonstrated on the latest COVID-19 CT data A large number of results show that the proposed method has a competitive advantage in registration accuracy compared with other methods, and greatly reduces the calculation time © 2021, Editorial Board of JBUAA All right reserved","PeriodicalId":39840,"journal":{"name":"北京航空航天大学学报","volume":"47 1","pages":"658-664"},"PeriodicalIF":0.0,"publicationDate":"2021-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43734181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Many-to-many voice conversion with sentence embedding based on VAACGAN 基于VAACGAN的句子嵌入多对多语音转换
Q3 Engineering
北京航空航天大学学报
Pub Date : 2021-04-08 DOI: 10.13700/J.BH.1001-5965.2020.0475
Li Yanping, Cao Pan, Shih-Chien Yang, Zhang Yan
{"title":"Many-to-many voice conversion with sentence embedding based on VAACGAN","authors":"Li Yanping, Cao Pan, Shih-Chien Yang, Zhang Yan","doi":"10.13700/J.BH.1001-5965.2020.0475","DOIUrl":"https://doi.org/10.13700/J.BH.1001-5965.2020.0475","url":null,"abstract":"","PeriodicalId":39840,"journal":{"name":"北京航空航天大学学报","volume":"47 1","pages":"500"},"PeriodicalIF":0.0,"publicationDate":"2021-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49241619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Multi-granularity hazard detection method for electrical power system 电力系统多粒度危险检测方法
Q3 Engineering
北京航空航天大学学报
Pub Date : 2021-04-08 DOI: 10.13700/J.BH.1001-5965.2020.0491
Xu Xiaohua, Qian Ping, Wang Yida, Zhou Xinyue, Xu Hanlin, Xue Libing
{"title":"Multi-granularity hazard detection method for electrical power system","authors":"Xu Xiaohua, Qian Ping, Wang Yida, Zhou Xinyue, Xu Hanlin, Xue Libing","doi":"10.13700/J.BH.1001-5965.2020.0491","DOIUrl":"https://doi.org/10.13700/J.BH.1001-5965.2020.0491","url":null,"abstract":"","PeriodicalId":39840,"journal":{"name":"北京航空航天大学学报","volume":"47 1","pages":"520"},"PeriodicalIF":0.0,"publicationDate":"2021-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45820648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Classification of satellite cloud images of disaster weather based on adversarial and transfer learning 基于对抗和迁移学习的灾害天气卫星云图分类
Q3 Engineering
北京航空航天大学学报
Pub Date : 2021-04-08 DOI: 10.13700/J.BH.1001-5965.2020.0459
Zhang Minjing, Bai Cong, Zhang Jinglin, Zheng Jianwei
{"title":"Classification of satellite cloud images of disaster weather based on adversarial and transfer learning","authors":"Zhang Minjing, Bai Cong, Zhang Jinglin, Zheng Jianwei","doi":"10.13700/J.BH.1001-5965.2020.0459","DOIUrl":"https://doi.org/10.13700/J.BH.1001-5965.2020.0459","url":null,"abstract":"","PeriodicalId":39840,"journal":{"name":"北京航空航天大学学报","volume":"47 1","pages":"585"},"PeriodicalIF":0.0,"publicationDate":"2021-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45824588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
首页 上一页 下一页 尾页
类型
全部 化学•材料 生命科学 医学 物理 工程技术 环境•农林 材料科学 地球科学 法学 管理学 化学 环境科学与生态学 计算机科学 教育学 经济学 农林科学 人文科学 生物学 数学 物理与天体物理 心理学 综合性期刊 其他 工业工程 理学 历史学 农学 文学 信息工程
数据库
全部 ACS Publications Elsevier ieeexplore Springer The Royal Society of Chemistry Wiley
期刊
北京航空航天大学学报
全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.
﹀
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号：481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们：info@booksci.cn
Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1