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兰州理工大学 [30]

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限定条件	专题：兰州理工大学第一署名单位第一作者单位通讯作者单位

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	ResLNet: deep residual LSTM network with longer input for action recognition 期刊论文 FRONTIERS OF COMPUTER SCIENCE, 2022, 卷号: 16, 期号: 6 作者: Wang, Tian; Li, Jiakun; Wu, Huai-Ning; Li, Ce; Snoussi, Hichem 收藏 \| 浏览/下载：16/0 \| 提交时间：2022/03/01 action recognition deep learning neural network
	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
	Inverted N-Type Lightweight Network Based on Back Projection for Image Super-Resolution Reconstruction 期刊论文 Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics, 2022, 卷号: 34, 期号: 6, 页码: 923-932 作者: Song, Zhaoyang; Zhao, Xiaoqiang; Hui, Yongyong; Jiang, Hongmei 收藏 \| 浏览/下载：11/0 \| 提交时间：2022/07/20 Deep learning Image reconstruction Optical resolving power Signal to noise ratio Backprojections Deep learning Image super-resolution reconstruction Inverted N-type network Low resolution images Network-based Reconstruction algorithms Resolution images Super-resolution reconstruction Superresolution
	Milling residual stress of macro-forecast based on RBF and micro-analysis based on molecular dynamics 期刊论文 Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS, 2022, 卷号: 28, 期号: 5, 页码: 1385-1392 作者: Jin, Lan; Zhang, Xuefeng; Xie, Liming 收藏 \| 浏览/下载：11/0 \| 提交时间：2022/06/20 Forecasting Functions Milling (machining) Molecular dynamics Residual stresses Surface stress Turning Cutting parameters Function analysis Micro analysis Neural network predictions Performance Prediction accuracy Radial basis function neural networks (RBF) Simulation Surface residual stress Workpiece
	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：24/0 \| 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：9/0 \| 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
	Progressive back-projection network for COVID-CT super-resolution 期刊论文 COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2021, 卷号: 208 作者: Song, Zhaoyang; Zhao, Xiaoqiang; Hui, Yongyong; Jiang, Hongmei 收藏 \| 浏览/下载：4/0 \| 提交时间：2021/10/14 COVID-CT Super-resolution Progressive back-projection network Residual attention module Up-projection and down-projection residual module
	Gradual deep residual network for super-resolution 期刊论文 Multimedia Tools and Applications, 2021, 卷号: 80, 期号: 7, 页码: 9765-9778 作者: Song, Zhaoyang; Zhao, Xiaoqiang; Jiang, Hongmei 收藏 \| 浏览/下载：2/0 \| 提交时间：2021/06/03 Deep neural networks Image enhancement Optical resolving power Benchmark datasets High resolution image Low resolution images Quantitative evaluation Reconstruction networks Reconstruction process State-of-the-art methods Visual evaluation
	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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