Research on natural frequency of structure considering elastic joint with interval uncertainty

An efficient method, namely fixed interface mode synthesis-interval factor method (FIMS-IFM), is proposed to calculate the natural frequency of structure considering elastic joint with interval uncertainty. In this proposed method, the interval uncertain elastic joint is treated as spatial beam element with interval uncertain material parameters. Additionally, both the proposed method and Monte-Carlo simulation method are used to calculate the natural frequency of a specially designed structure with interval uncertain elastic joint. A meaningful conclusion can be acquired via comparing the calculation results of the two methods that, FIMS-IFM is correct and high-efficiency

Reformulation of Frequency Based Substructuring Method Considering Elastic Joints According to Sherman-Morrison-Woodbury Formula

The frequency based substructuring method considering elastic joints (FBSM-CEJ) is reformulated according to Sherman-Morrison-Woodbury Formula (SMWF) in this paper, the derivation process of which is more concise and the order of the matrix that requires inversion in the corresponding derivation result is lower comparing to the existing FBSM-CEJ. Meanwhile, the reformulated FBSM-CEJ possesses more applicability and operability that can be used to directly and efficiently calculate the frequency response function (FRF) matrix of complex structure no matter the impedance matrix of the elastic joints is singular or not. Last but not least, via using none-mass spatial beam element to simulate the dynamic properties of elastic joints, the performance of the reformulated FBSM-CEJ is verified through numerical simulation. All the achievements obtained from this work will provide a theoretical basis for analysing the dynamic properties of a complex structure considering elastic joints

Stage-by-stage Wavelet Optimization Refinement Diffusion Model for Sparse-View CT Reconstruction

Diffusion models have emerged as potential tools to tackle the challenge of sparse-view CT reconstruction, displaying superior performance compared to conventional methods. Nevertheless, these prevailing diffusion models predominantly focus on the sinogram or image domains, which can lead to instability during model training, potentially culminating in convergence towards local minimal solutions. The wavelet trans-form serves to disentangle image contents and features into distinct frequency-component bands at varying scales, adeptly capturing diverse directional structures. Employing the Wavelet transform as a guiding sparsity prior significantly enhances the robustness of diffusion models. In this study, we present an innovative approach named the Stage-by-stage Wavelet Optimization Refinement Diffusion (SWORD) model for sparse-view CT reconstruction. Specifically, we establish a unified mathematical model integrating low-frequency and high-frequency generative models, achieving the solution with optimization procedure. Furthermore, we perform the low-frequency and high-frequency generative models on wavelet's decomposed components rather than sinogram or image domains, ensuring the stability of model training. Our method rooted in established optimization theory, comprising three distinct stages, including low-frequency generation, high-frequency refinement and domain transform. Our experimental results demonstrate that the proposed method outperforms existing state-of-the-art methods both quantitatively and qualitatively