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Significant progress has been made in incorporating fractional calculus into the projection and lag synchronization of complex networks. However, real-world networks are highly complex, making the fractional derivative used in complex dynamics more susceptible to changes over time. Therefore, it is essential to incorporate variable-order fractional calculus into the asymptotic hybrid projection lag synchronization of complex networks. Firstly, this approach considers nonidentical models with variable-order fractional characteristics, which is more general. Secondly, a class of variable-order fractional sliding mode surfaces is designed, and an accurate formula for calculating finite arriving time is provided, in contrast to traditional sliding mode control methods that use an inequality-based range. Thirdly, sufficient conditions for achieving asymptotic hybrid projection lag synchronization of nonidentical variable-order fractional complex networks are derived. Lastly, the feasibility and effectiveness of our approach are demonstrated through two illustrative examples. [ABSTRACT FROM AUTHOR]

The core of fault diagnosis of rolling bearing is to extract the narrowband sub-components containing fault feature information from the bearing fault signal. Variational mode extraction (VME), a novel single sub-component separation algorithm originated from variational mode decomposition (VMD), provides a promising solution to bearing fault detection. However, its performance is closely related to the hyperparameter selection, including the center frequency ω d and the penalty factor α. This paper proposes a non-recursive and adaptive signal decomposition algorithm termed spectral variational mode extraction (SVME). SVME can be seen as a spectral decomposition technique whose framework is composed of the adaptive spectral boundary division and boundary constrained VME. In the adaptive spectral boundary division, an adaptive iterative spectral envelope method referring to the continuous envelope correlation (CCE) index is developed to integrate with the parameterless scale-space division to adaptively locate the frequency band boundary. The presented adaptive spectral boundary division approach can effectively inhibit the spectral boundary over-division. In the boundary constrained VME, the dominant frequency of each frequency band determined by the optimal spectral division is distinguished as the preset center frequency. Meanwhile, the optimal penalty factor is determined based on the envelope spectral kurtosis (ESK) index and the boundary-constraint principle. The SVME method is utilized in the simulation and experimental case studies to investigate its capability. Furthermore, its superiority is highlighted through the comparison with the variational mode decomposition (VMD) and Autogram methods. [ABSTRACT FROM AUTHOR]

The fault feature signal of rolling bearing can be characterized as the narrow-band signal with a specific resonance frequency. Therefore, resonance demodulation analysis is a powerful damage detection technique of bearings. In addition to the fault feature signal, the measured vibration signals carry various interference components, and these interference components become a serious obstacle of fault feature extraction. Variational mode extraction is a novel signal analysis method designed to retrieve a specific signal component from the composite signal. Variational mode extraction is founded on a similar basis as variational mode decomposition, while it shows better accuracy and higher efficiency compared with variational mode decomposition. In this study, variational mode extraction is introduced to the resonance demodulation analysis of bearing fault. As the results of variational mode extraction analysis are greatly influenced by the choice of two parameters, that is, the balancing factor α and the initial guess of center frequency ωd, an optimized variational mode extraction method is further developed. First, a new fault information evaluation index for measuring the richness of fault characteristics of the signal, termed ensemble impulsiveness and cyclostationarity, is formulated. Second, the ensemble impulsiveness and cyclostationarity is used as the fitness function of particle swarm optimization to automatically determine the optimal values of α and ωd. Finally, the validity of optimized variational mode extraction method is verified by simulated and experimental analysis, and the superiority of optimized variational mode extraction method is highlighted through comparison with two other advanced resonance demodulation analysis approaches, that is, the improved kurtogram and infogram. The analysis results indicate that optimized variational mode extraction method has a powerful capability of resonance demodulation analysis. [ABSTRACT FROM AUTHOR]

With the rapid development of artificial intelligence technology, researchers have applied it to the diagnosis of various tumors in the urinary system in recent years, and have obtained many valuable research results. The article sorted the research status of artificial intelligence technology in the fields of renal tumors, bladder tumors and prostate tumors from three aspects: the number of papers, image data, and clinical tasks. The purpose is to summarize and analyze the research status and find new valuable research ideas in the future. The results show that the artificial intelligence model based on medical data such as digital imaging and pathological images is effective in completing basic diagnosis of urinary system tumors, image segmentation of tumor infiltration areas or specific organs, gene mutation prediction and prognostic effect prediction, but most of the models for the requirement of clinical application still need to be improved. On the one hand, it is necessary to further improve the detection, classification, segmentation and other performance of the core algorithm. On the other hand, it is necessary to integrate more standardized medical databases to effectively improve the diagnostic accuracy of artificial intelligence models and make it play greater clinical value.