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科研机构
兰州理工大学 [23]
内容类型
期刊论文 [17]
会议论文 [6]
发表日期
2022 [6]
2021 [4]
2020 [7]
2019 [6]
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An intelligent diagnosis method of rolling bearing based on multi-scale residual shrinkage convolutional neural network
期刊论文
Measurement Science and Technology, 2022, 卷号: 33, 期号: 8
作者:
Zhao, Xiaoqiang
;
Zhang, Yazhou
收藏
  |  
浏览/下载:15/0
  |  
提交时间:2022/06/20
Convolutional neural networks
Deep learning
Failure (mechanical)
Failure analysis
Fault detection
Roller bearings
Shrinkage
Bearing fault diagnosis
Convolutional neural network
Intelligent diagnosis methods
Multi-scale residual shrinkage convolutional neural network
Multi-scales
Noise environments
Rolling bearings
Separable convolution
Variable operating condition
Vibration signal
Improved YOLO v5 Wheat Ear Detection Algorithm Based on Attention Mechanism
期刊论文
ELECTRONICS, 2022, 卷号: 11, 期号: 11
作者:
Li, Rui
;
Wu, Yanpeng
收藏
  |  
浏览/下载:15/0
  |  
提交时间:2022/06/21
wheat ear
deep learning
CBAM
YOLO v5
detection and counting
Adversarial domain adaptation convolutional neural network for intelligent recognition of bearing faults
期刊论文
Measurement: Journal of the International Measurement Confederation, 2022, 卷号: 195
作者:
Wu, Yaochun
;
Zhao, Rongzhen
;
Ma, Hongru
;
He, Qiang
;
Du, Shaohua
收藏
  |  
浏览/下载:35/0
  |  
提交时间:2022/04/21
Convolution
Machinery
Bearing
Bearing fault
Convolutional neural network
Domain adaptation
Fault recognition
Intelligent fault recognition
Intelligent recognition
Recognition methods
Target domain
Varying working conditions
Content-based encrypted speech retrieval scheme with deep hashing
期刊论文
Multimedia Tools and Applications, 2022, 卷号: 81, 期号: 7, 页码: 10221-10242
作者:
Zhang, Qiu-yu
;
Zhao, Xue-jiao
;
Zhang, Qi-wen
;
Li, Yu-zhou
收藏
  |  
浏览/下载:17/0
  |  
提交时间:2022/04/21
Binary codes
Convolutional neural networks
Cryptography
Deep neural networks
Efficiency
Extraction
Feature extraction
Hamming distance
Information retrieval
Mapping
Semantic Web
Semantics
Spectrographs
Speech
Content-based
Convolutional neural network
Deep hashing
Deep semantic feature
Encrypted speech
Encrypted speech retrieval
Semantic features
Spectrograms
Speech retrieval
Multi-scale dynamic adaptive residual network for fault diagnosis
期刊论文
MEASUREMENT, 2022, 卷号: 188
作者:
Liang, Haopeng
;
Cao, Jie
;
Zhao, Xiaoqiang
收藏
  |  
浏览/下载:27/0
  |  
提交时间:2022/03/01
Fault detection and classification
Vibration Signals
Bearing Defects
Residual network
Multi-scale dynamic adaptive residual network
Average Descent Rate Singular Value Decomposition and Two-Dimensional Residual Neural Network for Fault Diagnosis of Rotating Machinery
期刊论文
IEEE Transactions on Instrumentation and Measurement, 2022, 卷号: 71
作者:
Liang, Haopeng
;
Cao, Jie
;
Zhao, Xiaoqiang
收藏
  |  
浏览/下载:11/0
  |  
提交时间:2022/06/20
Deep neural networks
Failure analysis
Fault detection
Feature extraction
Gears
Rotating machinery
Average descent rate singular value decomposition
Convolutional neural network
Deep learning
Faults diagnosis
Features extraction
Gramian angular difference field
Gramians
Neural-networks
Noise measurements
Two-dimensional
Two-dimensional residual neural network
Vibration
Deep Learning-Based Framework for the Detection of Cyberattack Using Feature Engineering
期刊论文
SECURITY AND COMMUNICATION NETWORKS, 2021, 卷号: 2021
作者:
Akhtar, Muhammad Shoaib
;
Feng, Tao
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  |  
浏览/下载:15/0
  |  
提交时间:2022/03/01
Evaluation and Design Method for Product Form Aesthetics Based on Deep Learning
期刊论文
IEEE ACCESS, 2021, 卷号: 9, 页码: 108992-109003
作者:
Zhou, Aimin
;
Liu, Hongbin
;
Zhang, Shutao
;
Ouyang, Jinyan
收藏
  |  
浏览/下载:10/0
  |  
提交时间:2021/10/14
Licenses
Convolution
Predictive models
Generative adversarial networks
Training
Task analysis
Kernel
Aesthetic evaluation
aesthetic design
product form
deep learning
Rolling Bearing Fault Diagnosis Based on One-Dimensional Dilated Convolution Network With Residual Connection
期刊论文
IEEE ACCESS, 2021, 卷号: 9, 页码: 31078-31091
作者:
Liang, Haopeng
;
Zhao, Xiaoqiang
收藏
  |  
浏览/下载:21/0
  |  
提交时间:2021/03/12
Convolution
Fault diagnosis
Feature extraction
Rolling bearings
Vibrations
Kernel
Load modeling
Different load domains
different noisy environments
dilated convolution
one-dimensional convolution neural network
rolling bearing fault diagnosis
residual connection
Rolling Bearing Fault Diagnosis Based on One-Dimensional Dilated Convolution Network with Residual Connection
期刊论文
IEEE Access, 2021, 卷号: 9, 页码: 31078-31091
作者:
Liang, Haopeng
;
Zhao, Xiaoqiang
收藏
  |  
浏览/下载:63/0
  |  
提交时间:2021/04/12
Convolution
Failure analysis
Fault detection
Multilayer neural networks
Time domain analysis
Connection structures
Convolution neural network
Feature learning
Noisy environment
Residual structure
Rolling bearings
Time-domain signal
Weight coefficients
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