Online Signal Denoising Using Adaptive Stochastic Resonance in Parallel Array and its Application to Acoustic Emission Signals

Signal denoising has been significantly explored in various engineering disciplines. In particular, structural health monitoring applications generally aim to detect weak anomaly responses (including acoustic emission) generated by incipient damage, which are easily buried in noise. Among various approaches, stochastic resonance (SR) has been widely adopted for weak signal detection. While many advancements have been focused on identifying useful information from the frequency domain by optimizing parameters in a post-processing environment to activate SR, it often requires detailed information about the original signal a priori, which is hardly assessed from signals overwhelmed by noise. This research presents a novel online signal denoising strategy by utilizing SR in a parallel array of bistable systems. The original noisy input with additionally applied noise is adaptively scaled, so that the total noise level matches the optimal level that is analytically predicted from a generalized model to robustly enhance signal denoising performance for a wide range of input amplitudes that are often not known in advance. Thus, without sophisticated post-processing procedures, the scaling factor is straightforwardly determined by the analytically estimated optimal noise level and the ambient noise level, which is one of the few quantities that can be reliably assessed from noisy signals in practice. Along with numerical investigations that demonstrate the operational principle and the effectiveness of the proposed strategy, experimental validation of denoising acoustic emission signals by employing a bistable Duffing circuit system exemplifies the promising potential of implementing the new approach for enhancing online signal denoising in practice.

Influence of Geometric Parameters On Mechanical Response of Concentric Cylinders With Centrosymmetric Lattice

A design method of centrosymmetric lattice structure is proposed. The centrosymmetric lattice is applied to the lightweight design of concentric cylindrical structures. Combining homogenization method and finite element method, the advantage of centrosymmetric lattice concentric cylinder in reducing the maximum stress is verified, and the stress distribution of centrosymmetric lattice concentric cylinder is more uniform along the circumference; two kinds of centrosymmetric lattice solids are selected, and the parametric method is used to study the influence of three parameters on the static response of a centrosymmetric lattice concentric cylinder under axisymmetric surface pressure.The results show that the maximum stress of the central symmetric concentric cylinder is less than that of the parallel array lattice cylinder under axisymmetric surface pressure;the stress distribution of the cylinder under axisymmetric load is periodic along the circumference, and the stress distribution of the concentric cylinder is more uniform than that of the parallel array lattice while the centrosymmetric lattice is applied to the lightweight design of concentric cylinder; the maximum stress increases with the increase of inside thickness and decreases with the increase of outside thickness; the increase of lattice section size will reduce the stress of outside and lattice of concentric cylinder, while the maximum stress of inside is less affected by the size of lattice section.

Image Restoration Based on Stochastic Resonance in a Parallel Array of Fitzhugh–Nagumo Neuron

The poor denoising effect for noisy grayscale images with traditional processing methods would be obtained under strong noise condition, and some image details would be lost. In this paper, a parallel array model of Fitzhugh–Nagumo (FHN) neurons was proposed, which can restore noisy grayscale images well with low peak signal-to-noise ratio (PSNR) conditions and the image details are better preserved. Firstly, the row-column scanning method was used to convert the 2D grayscale image into a 1D signal, and then the 1D signal was converted into a binary pulse amplitude modulation (BPAM) signal by signal modulation. The modulated signal was input to an FHN parallel array for stochastic resonance (SR). Finally, the array output signal was restored to a 2D gray image, and the image restoration effect was analyzed based on the PSNR and Structural SIMilarity (SSIM) index. It is shown that the SR effect can be exhibited in an array of FHN neuron nonlinearities by increasing the array size, and the image restoration effect is significantly better than the traditional image restoration method, and larger PSNR and SSIM can be obtained. It provides a new idea for grayscale image restoration in a low PSNR environment.