Structural analysis of floating pipes of the fish cage in currents

A numerical model is developed to investigate the structural performance and stress distribution of floating pipes of fish cage subjected to the flow. The modeling approach is based on the joint use of the finite element method using the shell elements to simulate the floating pipes and the hydrodynamic force model improved from the Morison’s equation and lumped-mass method. The hydrodynamic response of the fish cage and forces on the floating pipes can be obtained by the Morison’s equation and lumped-mass method. The stress and deformation of the floating pipes can be evaluated using the finite element method. Using an appropriate iterative scheme, the stress distribution and maximum stress of the floating pipes can be obtained using the proposed model. To validate the numerical model, the numerical results were compared with the data obtained from corresponding physical model tests. The comparisons show the numerical results agree well with the experimental data. On that basis, the simulations of floating pipes in currents are performed to investigate the maximum stress and the critical locations. Simulations of the fish cage in different flow velocity are performed. The effect of the velocity on the deformations and stress of the floating pipes is analyzed. The simulations results show that the stress and deformations drastically increases with the increase of flow velocity. Comparing results of floating pipes with different mooring line arrangements indicates that increasing mooring lines can efficiently lower the stress of the floating pipes. The simulation of the SPM cage system with multiple net cages in current is preformed and the results show the middle cage is most dangerous for the tripartite-cage system

Towards Arbitrary Text-driven Image Manipulation via Space Alignment

The recent GAN inversion methods have been able to successfully invert the real image input to the corresponding editable latent code in StyleGAN. By combining with the language-vision model (CLIP), some text-driven image manipulation methods are proposed. However, these methods require extra costs to perform optimization for a certain image or a new attribute editing mode. To achieve a more efficient editing method, we propose a new Text-driven image Manipulation framework via Space Alignment (TMSA). The Space Alignment module aims to align the same semantic regions in CLIP and StyleGAN spaces. Then, the text input can be directly accessed into the StyleGAN space and be used to find the semantic shift according to the text description. The framework can support arbitrary image editing mode without additional cost. Our work provides the user with an interface to control the attributes of a given image according to text input and get the result in real time. Ex tensive experiments demonstrate our superior performance over prior works.Comment: 8 pages, 12 figure

Semantic-Sparse Colorization Network for Deep Exemplar-based Colorization

Exemplar-based colorization approaches rely on reference image to provide plausible colors for target gray-scale image. The key and difficulty of exemplar-based colorization is to establish an accurate correspondence between these two images. Previous approaches have attempted to construct such a correspondence but are faced with two obstacles. First, using luminance channels for the calculation of correspondence is inaccurate. Second, the dense correspondence they built introduces wrong matching results and increases the computation burden. To address these two problems, we propose Semantic-Sparse Colorization Network (SSCN) to transfer both the global image style and detailed semantic-related colors to the gray-scale image in a coarse-to-fine manner. Our network can perfectly balance the global and local colors while alleviating the ambiguous matching problem. Experiments show that our method outperforms existing methods in both quantitative and qualitative evaluation and achieves state-of-the-art performance.Comment: Accepted by ECCV2022; 14 pages, 10 figure