Distributed Estimation With Cross-Verification Under False Data-Injection Attacks.

Under false data-injection (FDI) attacks, the data of some agents are tampered with by the FDI attackers, which causes that the distributed algorithm cannot estimate the ideal unknown parameter. Due to the concealment of the malicious data tampered with by the FDI attacks, many detection algorithms against FDI attacks often have poor detection results or low detection efficiencies. To solve these problems, a conveniently distributed diffusion least-mean-square (DLMS) algorithm with cross-verification (CV) is proposed against FDI attacks.

A transformer model with enhanced feature learning and its application in rotating machinery diagnosis.

Deep learning has become the prevailing trend of intelligent fault diagnosis for rotating machines. Compared to early-stage methods, deep learning methods use automatic feature extraction instead of manual feature design. However, conventional intelligent diagnosis models are trapped by a dilemma that simple models are unable to tackle difficult cases, while complicated models are likely to over-parameterize.

Adaptive filtering enhanced windowed correlated kurtosis for multiple faults diagnosis of locomotive bearings.

Compound faults diagnosis of locomotive bearings are still a challenge especially when the multi-fault impulses share the common resonant frequency. In this paper, an adaptive filtering enhanced windowed correlated kurtosis (WCK) method is proposed to isolate and identify each fault mode. A concept termed flexible analytical wavelet transform (FAWT) spectrum is defined to construct proper FAWT basis such that high signal-to-noise (SNR) is obtained in the filtered signal.

Complete synchronization of the global coupled dynamical network induced by Poisson noises.

The different Poisson noise-induced complete synchronization of the global coupled dynamical network is investigated. Based on the stability theory of stochastic differential equations driven by Poisson process, we can prove that Poisson noises can induce synchronization and sufficient conditions are established to achieve complete synchronization with probability 1. Furthermore, numerical examples are provided to show the agreement between theoretical and numerical analysis.