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Variational mode decomposition provides a feasible method for non-stationary signal analysis, but the method is still not adaptive, which greatly limits the wide application of the method. Therefor...

A new fault diagnosis method based on adaptive spectrum mode extraction

Research and application of improved adaptive MOMEDA fault diagnosis method

Blind face restoration usually synthesizes degraded low-quality data with a pre-defined degradation model for training, while more complex cases could happen in the real world. This gap between the assumed and actual degradation hurts the restoration performance where artifacts are often observed in the output. However, it is expensive and infeasible to include every type of degradation to cover real-world cases in the training data. To tackle this robustness issue, we propose Diffusion-based Robust Degradation Remover (DR2) to first transform the degraded image to a coarse but degradation-invariant prediction, then employ an enhancement module to restore the coarse prediction to a high-quality image. By leveraging a well-performing denoising diffusion probabilistic model, our DR2 diffuses input images to a noisy status where various types of degradation give way to Gaussian noise, and then captures semantic information through iterative denoising steps. As a result, DR2 is robust against common degradation (e.g. blur, resize, noise and compression) and compatible with different designs of enhancement modules. Experiments in various settings show that our framework outperforms state-of-the-art methods on heavily degraded synthetic and real-world datasets.

DR2: Diffusion-based Robust Degradation Remover for Blind Face Restoration

The adaptive chirp mode decomposition method has a good effect on processing chirp signals. The parameter α controls the smoothness of the output signal. Too small an α will cause a smooth output signal. The parameter β controls the instantaneous frequency (IF). If too small a β value is used, the output IF will be very smooth. However, rapidly changing IFs require a relatively large β. However, the choice of α,β is artificially set, and there are errors in practical applications. Therefore, it employs the state transition algorithm to adaptively optimize α,β to improve the signal-to-noise ratio (SNR) and resolution of the signal. First, as the species number of the state transition algorithm method is set artificially and has a long running time, this paper proposes a Rastrigin optimization test equation to test the optimization time of different species and determine the number of optimal species; second, the state transition algorithm determined by the number of species is employed to adaptively find the α,β in the adaptive chirp mode decomposition algorithm; finally, the optimized adaptive chirp mode decomposition method is applied to the simulation signal and chirp signal from marine animals to verify the proposed method.

https://www.mdpi.com/2076-3417/10/11/3695/pdf

Ziying Zhang

Papers

Research and application of improved adaptive MOMEDA fault diagnosis method

DR2: Diffusion-based Robust Degradation Remover for Blind Face Restoration

Application of Optimized Adaptive Chirp Mode Decomposition Method in Chirp Signal