272 research outputs found
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
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