咨询与建议

限定检索结果

文献类型

  • 54 篇 期刊文献
  • 27 篇 会议

馆藏范围

  • 81 篇 电子文献
  • 0 种 纸本馆藏

日期分布

学科分类号

  • 80 篇 工学
    • 53 篇 计算机科学与技术...
    • 31 篇 电气工程
    • 13 篇 控制科学与工程
    • 12 篇 软件工程
    • 9 篇 机械工程
    • 8 篇 信息与通信工程
    • 7 篇 仪器科学与技术
    • 3 篇 电子科学与技术(可...
    • 3 篇 测绘科学与技术
    • 2 篇 材料科学与工程(可...
    • 2 篇 生物医学工程(可授...
    • 1 篇 光学工程
    • 1 篇 动力工程及工程热...
    • 1 篇 化学工程与技术
    • 1 篇 石油与天然气工程
    • 1 篇 环境科学与工程(可...
    • 1 篇 网络空间安全
  • 13 篇 理学
    • 4 篇 物理学
    • 3 篇 数学
    • 3 篇 地球物理学
    • 3 篇 生物学
    • 3 篇 系统科学
    • 2 篇 化学
    • 1 篇 地理学
    • 1 篇 大气科学
  • 10 篇 医学
    • 7 篇 临床医学
    • 2 篇 基础医学(可授医学...
  • 7 篇 管理学
    • 7 篇 管理科学与工程(可...
  • 1 篇 经济学
    • 1 篇 应用经济学
  • 1 篇 法学
    • 1 篇 法学

主题

  • 81 篇 convolutional au...
  • 14 篇 deep learning
  • 8 篇 convolutional ne...
  • 6 篇 fault diagnosis
  • 6 篇 feature extracti...
  • 5 篇 anomaly detectio...
  • 4 篇 image reconstruc...
  • 4 篇 convolutional ne...
  • 2 篇 reinforcement le...
  • 2 篇 invariant featur...
  • 2 篇 image retrieval
  • 2 篇 layout analysis
  • 2 篇 cnn
  • 2 篇 specific feature
  • 2 篇 machine learning
  • 2 篇 neural network
  • 2 篇 multi-source dat...
  • 2 篇 image hashing
  • 2 篇 person re-recogn...
  • 2 篇 fault detection

机构

  • 2 篇 chinese acad sci...
  • 2 篇 shanghai jiao to...
  • 2 篇 tongji univ sch ...
  • 1 篇 loughborough uni...
  • 1 篇 northwestern pol...
  • 1 篇 yuan ze univ dep...
  • 1 篇 univ abderrahman...
  • 1 篇 csir cent inst m...
  • 1 篇 shahed univ elec...
  • 1 篇 naval univ engn ...
  • 1 篇 northeast normal...
  • 1 篇 jiangnan univ sc...
  • 1 篇 univ bergamo dep...
  • 1 篇 beijing jiaotong...
  • 1 篇 beijing jiaotong...
  • 1 篇 kings coll londo...
  • 1 篇 mcmaster univ el...
  • 1 篇 kings coll londo...
  • 1 篇 southwest jiaoto...
  • 1 篇 henan univ techn...

作者

  • 3 篇 yu jianbo
  • 2 篇 seuret mathias
  • 2 篇 liu xing
  • 2 篇 ingold rolf
  • 2 篇 liu shuo
  • 2 篇 zareapoor masoum...
  • 2 篇 yang jie
  • 2 篇 liwicki marcus
  • 1 篇 liu bowen
  • 1 篇 garz angelika
  • 1 篇 yan yongjie
  • 1 篇 khyam m. o.
  • 1 篇 lin chih-min
  • 1 篇 moattar mohammad...
  • 1 篇 wang lijun
  • 1 篇 wu siyuan
  • 1 篇 alanezi mohammed...
  • 1 篇 bouchekara houss...
  • 1 篇 hamdi slim
  • 1 篇 gu de

语言

  • 78 篇 英文
  • 1 篇 法文
  • 1 篇 其他
  • 1 篇 中文
检索条件"主题词=Convolutional Auto-Encoder"
81 条 记 录,以下是31-40 订阅
排序:
A Semantic Segmentation Method for Road Environment Images Based on Hybrid convolutional auto-encoder
收藏 引用
TRAITEMENT DU SIGNAL 2022年 第4期39卷 1235-1245页
作者: Song, Xiaona Liu, Haichao Wang, Lijun Wang, Song Cao, Yunyu Xu, Donglai Zhang, Shenfeng North China Univ Water Resources & Elect Power Sch Mech Engn Zhengzhou 450011 Peoples R China Teesside Univ Sch Comp Engn & Digital Technol Middlesbrough TS1 3BX England Suzhou Tianshuo Intelligent Software Co Ltd Suzhou 215011 Peoples R China
Deep convolutional neural networks (CNNs) have presented amazing performance in the task of semantic segmentation. However, the network model is complex, the training time is prolonged, the semantic segmentation accur... 详细信息
来源: 评论
Research on the Fault Diagnosis Method of an Internal Gear Pump Based on a convolutional auto-encoder and PSO-LSSVM
收藏 引用
SENSORS 2022年 第24期22卷 9841页
作者: Liao, Jian Zheng, Jianbo Chen, Zongbin Naval Univ Engn Inst Vibrat & Noise Wuhan 430033 Peoples R China Naval Univ Engn Naval Key Lab Ship Vibrat & Noise Wuhan 430033 Peoples R China
The raw signals produced by internal gear pumps are susceptible to noises brought on by mechanical vibrations and the surrounding environment, and the sample count collected during the various operating periods is not... 详细信息
来源: 评论
Image Geo-Site Estimation Using convolutional auto-encoder and Multi-Label Support Vector Machine
收藏 引用
INFORMATION 2023年 第1期14卷 29页
作者: Jain, Arpit Verma, Chaman Kumar, Neerendra Raboaca, Maria Simona Baliya, Jyoti Narayan Suciu, George Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Vaddeswaram 515001 Andhra Pradesh India Eotvos Lorand Univ Fac Informat Dept Media & Educ Informat H-1053 Budapest Hungary Cent Univ Jammu Dept Comp Sci & IT Jammu 181143 India Univ Politehn Bucuresti Doctoral Sch Splaiul Independentei St 313 Bucharest 060042 Romania Tech Univ Cluj Napoca Fac Bldg Serv Engn C-tin Daicoviciu St 15 Cluj Napoca 400020 Romania ICSI Energy Natl Res & Dev Inst Cryogen & Isotop T Ramnicu Valcea 240050 Romania Cent Univ Jammu Dept Educ Studies Jammu 181143 India R&D Dept Beia Consult Int Bucharest Bucharest 041386 Romania
The estimation of an image geo-site solely based on its contents is a promising task. Compelling image labelling relies heavily on contextual information, which is not as simple as recognizing a single object in an im... 详细信息
来源: 评论
Application of a Hybrid Model Based on a convolutional auto-encoder and convolutional Neural Network in Object-Oriented Remote Sensing Classification
收藏 引用
ALGORITHMS 2018年 第1期11卷 9-9页
作者: Cui, Wei Zhou, Qi Zheng, Zhendong Wuhan Univ Technol Resource & Environm Engn Coll Wuhan 430070 Hubei Peoples R China
Variation in the format and classification requirements for remote sensing data makes establishing a standard remote sensing sample dataset difficult. As a result, few remote sensing deep neural network models have be... 详细信息
来源: 评论
Multi-Branch convolutional auto-encoder Model For Cross-Domain Person Re-Recognition
Multi-Branch Convolutional Auto-Encoder Model For Cross-Doma...
收藏 引用
第35届中国控制与决策会议
作者: Tao Luo Songhao Zhu College of Automation and Artificial Intelligence Nanjing University of Posts and Telecommunications
Cross-domain person re-recognition is very important for the applications of intelligent video *** further reduce the cross-domain differences,a new multi-branch convolutional auto-encoder based cross-domain person re... 详细信息
来源: 评论
Target Classification Using convolutional Deep Learning and auto-encoder Models  4
Target Classification Using Convolutional Deep Learning and ...
收藏 引用
4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)
作者: Zaied, Sarra Toumi, Abdelmalek Khenchaf, Ali ENSTA Bretagne Lab STICC UMR CNRS 6285 2 Rue Francois Verny F-29806 Brest 9 France
Targets recognition in radar images presents an essential task for monitoring and surveillance of sensitive areas such as military zones. The fundamental problem in radar imaging is related to the recognition of objec... 详细信息
来源: 评论
A study of deep convolutional auto-encoders for anomaly detection in videos
收藏 引用
PATTERN RECOGNITION LETTERS 2018年 105卷 13-22页
作者: Ribeiro, Manasses Lazzaretti, Andre Eugenio Lopes, Heitor Silverio Catarinense Fed Inst Educ Sci & Technol Rod SC 135 Km 125 BR-89560000 Videira SC Brazil Fed Univ Technol Parana UTFPR Av Sete Setembro 3165 BR-80230901 Curitiba PR Brazil
The detection of anomalous behaviors in automated video surveillance is a recurrent topic in recent computer vision research. Depending on the application field, anomalies can present different characteristics and cha... 详细信息
来源: 评论
N-light-N: A Highly-Adaptable Java Library for Document Analysis with convolutional auto-encoders and Related Architectures  15
<i>N-light-N</i>: A Highly-Adaptable Java Library for Docume...
收藏 引用
15th International Conference on Frontiers in Handwriting Recognition (ICFHR)
作者: Seuret, Mathias Ingold, Rolf Liwicki, Marcus Univ Fribourg Document Image & Voice Anal Grp Fribourg Switzerland
This paper presents a novel, highly-adaptable Java framework N-light-N, for the work with deep neural networks, especially with convolutional auto-encoders (CAE). While the most popular deep learning libraries focus o... 详细信息
来源: 评论
Deep convolutional neural network based secure wireless voice communication for underground mines
收藏 引用
JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年 第10期12卷 9591-9610页
作者: Dey, Prasanjit Kumar, Chandan Mitra, Mitrabarun Mishra, Richa Chaulya, S. K. Prasad, G. M. Mandal, S. K. Banerjee, G. CSIR Cent Inst Min & Fuel Res Barwa Rd Dhanbad 826001 Bihar India
A secure wireless voice communication system for underground miners is an essential gadget for efficient and safe mining. Voice over internet protocol is a proven solution for wireless communication in underground min... 详细信息
来源: 评论
Topic driven multimodal similarity learning with multi-view voted convolutional features
收藏 引用
PATTERN RECOGNITION 2018年 75卷 223-234页
作者: Gao, Xinjian Mu, Tingting Goulermas, John Y. Wang, Meng Hefei Univ Technol Sch Comp & Informat Hefei Anhui Peoples R China Univ Manchester Sch Comp Sci Manchester M1 7DN Lancs England Univ Liverpool Dept Comp Sci Liverpool L69 3BX Merseyside England
Similarity (and distance metric) learning plays a very important role in many artificial intelligence tasks aiming at quantifying the relevance between objects. We address the challenge of learning complex relation pa... 详细信息
来源: 评论