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	Uncertainty Quantification in Remaining Useful Life Prediction under Covariate Shift 期刊论文 Neural Computing and Applications, 2023, 页码: 28 作者: Wang Huanjie; Bai Xiwei; Tan Jie; Liu Chengbao 收藏 \| 浏览/下载：7/0 \| 提交时间：2023/06/19 Remaining useful life Uncertainty quantification Bayesian convolutional neural network
	Deep Domain-Adversarial Anomaly Detection With One-Class Transfer Learning 期刊论文 IEEE/CAA Journal of Automatica Sinica, 2023, 卷号: 10, 期号: 2, 页码: 524-546 作者: Wentao Mao; Gangsheng Wang; Linlin Kou; Xihui Liang 收藏 \| 浏览/下载：17/0 \| 提交时间：2023/01/16 Anomaly detection domain adaptation domain-adversarial training one-class classification transfer learning
	Rolling Shutter Camera: Modeling, Optimization and Learning 期刊论文 Machine Intelligence Research, 2023, 卷号: 20, 期号: 6, 页码: 783-798 作者: Bin Fan, Yuchao Dai, Mingyi He 收藏 \| 浏览/下载：0/0 \| 提交时间：2023/11/23 Rolling shutter, motion modeling, image correction, temporal super-resolution, deep learning
	Microstructures, Thermal and Mechanical Properties of Pure Tungsten-A Comparison Between Selective Laser Melting and Hot Rolling 期刊论文 CHINESE JOURNAL OF MECHANICAL ENGINEERING, 2022, 卷号: 35 作者: Wang, Chong; Chen, Daobing; Zhou, Yan; Xie, Zhuoming; Fang, Qianfeng 收藏 \| 浏览/下载：19/0 \| 提交时间：2022/12/23 Tungsten (W) Selective laser melting (SLM) Hot rolling Microstructure Thermal conductivity Mechanical property
	Rolling bearing fault diagnosis by Markov transition field and multi-dimension convolutional neural network 期刊论文 Measurement Science and Technology, 2022, 卷号: 33, 期号: 11 作者: Lei, Chunli; Xue, Linlin; Jiao, Mengxuan; Zhang, Huqiang; Shi, Jiashuo 收藏 \| 浏览/下载：36/0 \| 提交时间：2022/09/22 Chemical activation Convolution Convolutional neural networks Fault detection Neural network models Roller bearings Activation functions Condition Convolutional neural network E-rectified linear unit activation function Faults diagnosis Linear units Markov transition field Multi dimensions Multi-dimension attention Transition fields
	A remaining useful life prediction method based on time–frequency images of the mechanical vibration signals 期刊论文 Measurement: Journal of the International Measurement Confederation, 2022, 卷号: 202 作者: Du, Xianjun; Jia, Wenchao; Yu, Ping; Shi, Yaoke; Cheng, Shengyi 收藏 \| 浏览/下载：32/0 \| 提交时间：2022/09/22 Convolution Forecasting Preventive maintenance Rotating machinery Vibrations (mechanical) Wavelet transforms Attention mechanisms Continuous Wavelet Transform Convolutional neural network Degradation trend Feature map Remaining useful life predictions Rolling bearings Spectral feature Time frequency Time–frequency spectral feature map
	A novel model for fatigue strength prediction based on the true stress-strain curves 期刊论文 FERROELECTRICS, 2022, 卷号: 596, 期号: 1, 页码: 137-151 作者: Duan, Hongyan; Pei, Lei; Sheng, Jie; Tang, Guoxin; Liu, Lin 收藏 \| 浏览/下载：21/0 \| 提交时间：2022/09/22 Fatigue strength prediction different strength levels tensile strength austenitic 304 stainless steel
	Blending Surface Segmentation and Editing for 3D Models 期刊论文 IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, 2022, 卷号: 28, 期号: 8, 页码: 2879-2894 作者: Zhang, Long; Guo, Jianwei; Xiao, Jun; Zhang, Xiaopeng; Yan, Dong-Ming 收藏 \| 浏览/下载：28/0 \| 提交时间：2022/07/25 Three-dimensional displays Solid modeling Shape Surface morphology Clustering algorithms Trajectory Partitioning algorithms Mesh segmentation structure recovery superfacets rolling-ball blending surface Markov random field
	Real-time identification of performance degradation stages of rolling element bearings by RVCFI 期刊论文 Measurement Science and Technology, 2022, 卷号: 33, 期号: 8 作者: Meng, Jiadong; Yan, Changfeng; Wen, Tao; Wang, Zonggang; Chen, Guangyi 收藏 \| 浏览/下载：14/0 \| 提交时间：2022/06/20 Condition based maintenance Condition monitoring Degradation Singular value decomposition Degradation stages Envelope spectra Fault time Initial faults Monitoring indicators Performance degradation Performance degradation stage Rate of variation coefficient of fused indicator Rolling Element Bearing Variation coefficient
	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

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