405 research outputs found
Extend Wave Function Collapse to Large-Scale Content Generation
Wave Function Collapse (WFC) is a widely used tile-based algorithm in
procedural content generation, including textures, objects, and scenes.
However, the current WFC algorithm and related research lack the ability to
generate commercialized large-scale or infinite content due to constraint
conflict and time complexity costs. This paper proposes a Nested WFC (N-WFC)
algorithm framework to reduce time complexity. To avoid conflict and
backtracking problems, we offer a complete and sub-complete tileset preparation
strategy, which requires only a small number of tiles to generate aperiodic and
deterministic infinite content. We also introduce the weight-brush system that
combines N-WFC and sub-complete tileset, proving its suitability for game
design. Our contribution addresses WFC's challenge in massive content
generation and provides a theoretical basis for implementing concrete games.Comment: This paper is accepted by IEEE Conference on Games 2023 (nomination
of the Best Paper Award
Three-dimensional topology optimization of auxetic metamaterial using isogeometric analysis and model order reduction
In this work, we present an efficiently computational approach for designing
material micro-structures by means of topology optimization. The central idea
relies on using the isogeometric analysis integrated with the parameterized
level set function for numerical homogenization, sensitivity calculation and
optimization of the effective elastic properties. Design variables, which are
level set values associated with control points, are updated from the optimizer
and represent the geometry of the unit cell. We further improve the
computational efficiency in each iteration by employing reduced order modeling
when solving linear systems of the equilibrium equations. We construct a
reduced basis by reusing computed solutions from previous optimization steps,
and a much smaller linear system of equations is solved on the reduced basis.
Two- and three-dimensional numerical results show the effectiveness of the
topology optimization algorithm coupled with the reduced basis approach in
designing metamaterials
NeAI: A Pre-convoluted Representation for Plug-and-Play Neural Ambient Illumination
Recent advances in implicit neural representation have demonstrated the
ability to recover detailed geometry and material from multi-view images.
However, the use of simplified lighting models such as environment maps to
represent non-distant illumination, or using a network to fit indirect light
modeling without a solid basis, can lead to an undesirable decomposition
between lighting and material. To address this, we propose a fully
differentiable framework named neural ambient illumination (NeAI) that uses
Neural Radiance Fields (NeRF) as a lighting model to handle complex lighting in
a physically based way. Together with integral lobe encoding for
roughness-adaptive specular lobe and leveraging the pre-convoluted background
for accurate decomposition, the proposed method represents a significant step
towards integrating physically based rendering into the NeRF representation.
The experiments demonstrate the superior performance of novel-view rendering
compared to previous works, and the capability to re-render objects under
arbitrary NeRF-style environments opens up exciting possibilities for bridging
the gap between virtual and real-world scenes. The project and supplementary
materials are available at https://yiyuzhuang.github.io/NeAI/.Comment: Project page: <a class="link-external link-https"
href="https://yiyuzhuang.github.io/NeAI/" rel="external noopener
nofollow">https://yiyuzhuang.github.io/NeAI/</a
A phantom-node method with edge-based strain smoothing for linear elastic fracture mechanics
This paper presents a novel numerical procedure based on the combination of an edge-based smoothed finite element (ES-FEM) with a phantom-node method for 2D linear elastic fracture mechanics. In the standard phantom-node method, the cracks are formulated by adding phantom nodes, and the cracked element is replaced by two new superimposed elements. This approach is quite simple to implement into existing explicit finite element programs. The shape functions associated with discontinuous elements are similar to those of the standard finite elements, which leads to certain simplification with implementing in the existing codes. The phantom-node method allows modeling discontinuities at an arbitrary location in the mesh. The ES-FEM model owns a close-to-exact stiffness that is much softer than lower-order finite element methods (FEM). Taking advantage of both the ES-FEM and the phantom-node method, we introduce an edge-based strain smoothing technique for the phantom-node method. Numerical results show that the proposed method achieves high accuracy compared with the extended finite element method (XFEM) and other reference solutions
Hydroelastic investigation on a pile breakwater integrated with a flexible tail for long-wave attenuation
A novel concept of wave attenuator is proposed for the defense of long waves, through integrating a flexible tail to the lee-side surface of a pile breakwater. The flexible tail works as a floating blanket made up of hinged blocks, whose scale and stiffness can be easily adjusted. A two-phase-flow numerical model is established based on the open-source computational fluid dynamics (CFD) code OpenFOAM to investigate its wave attenuation performance. Incompressible Navier—Stokes equations are solved in the fluid domain, where an additional computational solid mechanics (CSM) solver is embedded to describe the elastic deformation of the floating tail. The coupling of fluid dynamics and structural mechanics is solved in a full manner to allow assess of wave variation along the deforming body. The accuracy of the numerical model is validated through comparison with experimental data. Effects of the flexible tail on performance of the pile breakwater are investigated systematically. Dynamic behaviours of the tail are examined, and characteristics of its natural frequency are identified. For safety reasons, the wave loads impacting on the main body of the pile breakwater and the stress distribution over the tail are specially examined. It is found that both the length and stiffness of the tail can affect the wave-attenuation performance of the breakwater. A proper choice of the length and stiffness of the tail can greatly improve the long-wave defending capability of the pile breakwater. The maximum stress over the flexible tail can be restrained through optimising the deformation and stiffness of the tail
GABNet: global attention block for retinal OCT disease classification
IntroductionThe retina represents a critical ocular structure. Of the various ophthalmic afflictions, retinal pathologies have garnered considerable scientific interest, owing to their elevated prevalence and propensity to induce blindness. Among clinical evaluation techniques employed in ophthalmology, optical coherence tomography (OCT) is the most commonly utilized, as it permits non-invasive, rapid acquisition of high-resolution, cross-sectional images of the retina. Timely detection and intervention can significantly abate the risk of blindness and effectively mitigate the national incidence rate of visual impairments.MethodsThis study introduces a novel, efficient global attention block (GAB) for feed forward convolutional neural networks (CNNs). The GAB generates an attention map along three dimensions (height, width, and channel) for any intermediate feature map, which it then uses to compute adaptive feature weights by multiplying it with the input feature map. This GAB is a versatile module that can seamlessly integrate with any CNN, significantly improving its classification performance. Based on the GAB, we propose a lightweight classification network model, GABNet, which we develop on a UCSD general retinal OCT dataset comprising 108,312 OCT images from 4686 patients, including choroidal neovascularization (CNV), diabetic macular edema (DME), drusen, and normal cases.ResultsNotably, our approach improves the classification accuracy by 3.7% over the EfficientNetV2B3 network model. We further employ gradient-weighted class activation mapping (Grad-CAM) to highlight regions of interest on retinal OCT images for each class, enabling doctors to easily interpret model predictions and improve their efficiency in evaluating relevant models.DiscussionWith the increasing use and application of OCT technology in the clinical diagnosis of retinal images, our approach offers an additional diagnostic tool to enhance the diagnostic efficiency of clinical OCT retinal images
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