A combined generalized Warblet transform and second order synchroextracting transform for analyzing nonstationary signals of rotating machinery.

In recent years, considerable attention has been paid in time-frequency analysis (TFA) methods, which is an effective technology in processing the vibration signal of rotating machinery. However, TFA techniques are not sufficient to handle signals having a strong non-stationary characteristic. To overcome this drawback, taking short-time Fourier transform as a link, a TFA methods that using the generalized Warblet transform (GWT) in combination with the second order synchroextracting transform (SSET) is proposed in this study.

Modeling and prediction for diesel performance based on deep neural network combined with virtual sample.

The performance models are the critical step for condition monitoring and fault diagnosis of diesel engines, and are an important bridge to describe the link between input parameters and targets. Large-scale experimental methods with higher economic costs are often adopted to construct accurate performance models. To ensure the accuracy of the model and reduce the cost of the test, a novel method for modeling the performances of marine diesel engine is proposed based on deep neural network method coupled with virtual sample generation technology.

Characterization of the deubiquitination activity and substrate specificity of the chicken ubiquitin-specific protease 1/USP associated factor 1 complex.

Deubiquitinases (DUBs) are essential regulators of intracellular processes involving ubiquitin (Ub) modification. The human DUB ubiquitin-specific protease 1 (hUSP1) interacts with human USP-associated factor 1 (hUAF1), and helps to regulate processes such as DNA damage repair. Previously, we identified a chicken USP1 homologue (chUSP1) during an investigation into the properties of Marek's disease virus (MDV).