英文字典中文字典


英文字典中文字典51ZiDian.com



中文字典辞典   英文字典 a   b   c   d   e   f   g   h   i   j   k   l   m   n   o   p   q   r   s   t   u   v   w   x   y   z       







请输入英文单字，中文词皆可：


请选择你想看的字典辞典：
单词字典翻译
prohibits查看　prohibits　在百度字典中的解释百度英翻中〔查看〕
prohibits查看　prohibits　在Google字典中的解释Google英翻中〔查看〕
prohibits查看　prohibits　在Yahoo字典中的解释Yahoo英翻中〔查看〕

安装中文字典英文字典查询工具!


中文字典英文字典工具:
选择颜色:
输入中英文单字

































































英文字典中文字典相关资料:


  • Deep Transfer Learning for Bearing Fault Diagnosis: A Systematic Review . . .
    The traditional deep learning-based bearing fault diagnosis approaches assume that the training and test data follow the same distribution This assumption, however, is not always true for the bearing data collected in practical scenarios, leading to a significant decline in fault diagnosis performance In order to satisfy this assumption, the transfer learning concept is introduced in deep
  • A New Deep Transfer Learning Method for Bearing Fault Diagnosis Under . . .
    Moreover, since no sufficient labeled or even unlabeled data are available in target domain, collecting the labeled data and building the model from scratch is time-consuming and expensive Motivated by transfer learning (TL), we present a new fault diagnosis method, which generalizes convolutional neural network (CNN) to TL scenario
  • Deep Learning Algorithms for Bearing Fault Diagnostics—A Comprehensive . . .
    In this survey paper, we systematically summarize existing literature on bearing fault diagnostics with deep learning (DL) algorithms While conventional machine learning (ML) methods, including artificial neural network, principal component analysis, support vector machines, etc , have been successfully applied to the detection and categorization of bearing faults for decades, recent
  • A Novel Semi-Supervised Deep Transfer Learning Method With Improved . . .
    To address this gap, we propose a semi-supervised deep transfer method based on an improved extended isolation forest (EIF) for cross-domain bearing fault diagnosis First, nontarget task samples are scored using an EIF with an arithmetic mean aggregation mechanism for anomaly scores to identify latent fault patterns
  • Applications of Unsupervised Deep Transfer Learning to Intelligent . . .
    Recent progress on intelligent fault diagnosis (IFD) has greatly depended on deep representation learning and plenty of labeled data However, machines often operate with various working conditions, or the target task has different distributions with the collected data used for training (the domain shift problem) Besides, the newly collected test data in the target domain are usually
  • Transfer Learning-Motivated Intelligent Fault Diagnosis Designs: A . . .
    Over the last decade, transfer learning has attracted a great deal of attention as a new learning paradigm, based on which fault diagnosis (FD) approaches have been intensively developed to improve the safety and reliability of modern automation systems Because of inevitable factors such as the varying work environment, performance degradation of components, and heterogeneity among similar
  • Deep Learning Based Approach for Bearing Fault Diagnosis
    Effectively mining features from big data and accurately identifying the bearing health conditions with new advanced methods have become new issues This paper presents a deep learning-based approach for bearing fault diagnosis The presented approach preprocesses sensor signals using short-time Fourier transform (STFT)
  • An Approach to New Bearing Fault Diagnosis Utilizing Deep Transfer Learning
    Bearings, serving as crucial components in mechanical equipment, exert a direct influence on the overall performance of the system Deep transfer learning has garnered significant attention in the realm of intelligent fault diagnosis for machines Nevertheless, extant deep transfer learning algorithms encounter challenges when detecting newly emerging faults within the target domain To
  • Highly Accurate Machine Fault Diagnosis Using Deep Transfer Learning
    We develop a novel deep learning framework to achieve highly accurate machine fault diagnosis using transfer learning to enable and accelerate the training of deep neural network Compared with existing methods, the proposed method is faster to train and more accurate First, original sensor data are converted to images by conducting a Wavelet transformation to obtain time-frequency
  • A Balanced Deep Transfer Network for Bearing Fault Diagnosis
    In data-driven bearing fault diagnosis, it is unrealistic to obtain enough labeled data, and the data used for training and testing often have different distributions Existing methods typically address this issue by either marginal distribution adaptation or conditional distribution adaptation However, most studies fail to consider both distributions simultaneously and overlook the relative





中文字典-英文字典  2005-2009