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In this paper, a unified method is proposed to predict the free and forced vibration behavior of the combined conical-cylindrical shell (CCS) structure in the steady-state thermal environment. The first-order shear deformation theory (FSDT) and the thermal strain are utilized to establish the energy equation of the combined structure, and the artificial virtual spring technique is introduced to realize the coupling between the substructures and the arbitrary boundary conditions of the CCS. The displacement function of the structure is constructed by the spectral-geometry method, and the vibration equation is solved by using the Rayleigh–Ritz method. The accuracy of the present model is verified by comparing the numerical results with the finite element method. The factors that may affect the vibration behavior of the CCS under thermal environment are analyzed specifically, and the results demonstrate that point constraints on the shell surface can effectively suppress shell vibration. This paper provides a compelling reference for vibration control of CCS in practical applications. [ABSTRACT FROM AUTHOR]

Existing methods have demonstrated effective performance on a single degradation type. In practical applications, however, the degradation is often unknown, and the mismatch between the model and the degradation will result in a severe performance drop. In this paper, we propose an all-in-one image restoration network that tackles multiple degradations. Due to the heterogeneous nature of different types of degradations, it is difficult to process multiple degradations in a single network. To this end, we propose to learn a neural degradation representation (NDR) that captures the underlying characteristics of various degradations. The learned NDR adaptively decomposes different types of degradations, similar to a neural dictionary that represents basic degradation components. Subsequently, we develop a degradation query module and a degradation injection module to effectively approximate and utilize the specific degradation based on NDR, enabling the all-in-one restoration ability for multiple degradations. Moreover, we propose a bidirectional optimization strategy to effectively drive NDR to learn the degradation representation by optimizing the degradation and restoration processes alternately. Comprehensive experiments on representative types of degradations (including noise, haze, rain, and downsampling) demonstrate the effectiveness and generalizability of our method. Code is available at https://github.com/mdyao/NDR-Restore.