5,265 research outputs found

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

    Get PDF
    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Curvature estimation for meshes via algebraic quadric fitting

    Full text link
    We introduce the novel method for estimation of mean and Gaussian curvature and several related quantities for polygonal meshes. The algebraic quadric fitting curvature (AQFC) is based on local approximation of the mesh vertices and associated normals by a quadratic surface. The quadric is computed as an implicit surface, so it minimizes algebraic distances and normal deviations from the approximated point-normal neighbourhood of the processed vertex. Its mean and Gaussian curvature estimate is then obtained as the respective curvature of its orthogonal projection onto the fitted quadratic surface. Experimental results for both sampled parametric surfaces and arbitrary meshes are provided. The proposed method AQFC approaches the true curvatures of the reference smooth surfaces with increasing density of sampling, regardless of its regularity. It is resilient to irregular sampling of the mesh, compared to the contemporary curvature estimators. In the case of arbitrary meshes, obtained from scanning, AQFC provides robust curvature estimation.Comment: 14 page

    A hybrid topology optimization method applied to reinforced concrete structures using polygonal finite elements

    Get PDF
    Abstract This work introduces a new alternative to obtain strut-and-tie models using the hybrid topology optimization method, which is already proposed in the technical literature and is refined here to use polygonal finite elements and accelerate the solution of the material nonlinearity problem. In this method, concrete is approached as a continuum, using polygonal two-dimensional finite elements, and steel bars as truss elements, using one-dimensional finite elements with two nodes. For a closer representation of reality, an orthotropic constitutive model for concrete was implemented considering different compression and tensile stiffness values, which is one of the advantages of the model. Further, the hybrid method limits the final layout of steel bars, thereby generating better structures from a constructive point of view, while allowing greater freedom for the shape and concrete strut slope. However, this method is more complex, and it increases the computational cost, which was substantially minimized through the implementation of an algorithm. Results obtained for some domains were very close to the results of other methodologies; however, small differences were noted that may be relevant to the final result. Other domains showed results with greater differences, thereby significantly changing the final strut-and-tie model and presenting a new structural design alternative

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

    Get PDF
    This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well

    Exploring Deep Learning for deformative operators in vector-based cartographic road generalization

    Full text link
    Cartographic generalisation is the process by which geographical data is simplified and abstracted to increase the legibility of maps at reduced scales. As map scales decrease, irrelevant map features are removed (selective generalisation), and relevant map features are deformed, eliminating unnec- essary details while preserving the general shapes (deformative generalisation). The automation of cartographic generalisation has been a tough nut to crack for years because it is governed not only by explicit rules but also by a large body of implicit cartographic knowledge that conven- tional automation approaches struggle to acquire and formalise. In recent years, the introduction of Deep Learning (DL) and its inductive capabilities has raised hope for further progress. This thesis explores the potential of three Deep Learning architectures — Graph Convolutional Neural Network (GCNN), Auto Encoder, and Recurrent Neural Network (RNN) — in their application on the deformative generalisation of roads using a vector-based approach. The generated small- scale representations of the input roads differ substantially across the architectures, not only in their included frequency spectra but also in their ability to apply certain generalisation operators. However, the most apparent learnt and applied generalisation operator by all architectures is the smoothing of the large-scale roads. The outcome of this thesis has been encouraging but suggests to pursue further research about the effect of the pre-processing of the input geometries and the inclusion of spatial context and the combination of map features (e.g. buildings) to better capture the implicit knowledge engrained in the products of mapping agencies used for training the DL models

    Nonlinear free-surface flows, waterfalls and related free-boundary problems

    Get PDF
    Many works have considered two-dimensional free-surface flow over the edge of a horizontal plate, forming a waterfall, and with uniform horizontal flow far upstream. The flow is assumed to be steady and irrotational, whilst the fluid is assumed to be inviscid and incompressible. Gravity is also taken into account. In particular, amongst these works, numerical solutions for supercritical flows have been computed, utilising conformal mappings as well as a series truncation and collocation method. Here, an extension to this work is presented where a more appropriate expression is taken for the assumed form of the complex velocity. The justification of this lies in the behaviour of the flows far downstream and the wish to better encapsulate the parabolic nature of such a free-falling jet. New numerical results will be presented, demonstrating the improved shape of the new free-surface profiles. Further adjustments to the method are presented which lead to enhanced coefficient decay. The aforementioned adjustments are also applied to other supercritical flows (such as weir flows) and similar improvements to the jet shape can be observed. Flows that are still horizontal upstream but instead negotiate a convex corner and then run along an angled supporting bed (i.e. spillway flows) are also surveyed. New spillway problems and results are presented, where the spillway’s angled wall is more complex than a linear path; and, again, series truncation and collocation are utilised. Finally, a wake model for potential flow past a finite plate, perpendicular to the oncoming flow and below a free surface, is pursued. The approach here is to adopt a closure model of horizontal flow far downstream and use the boundary integral equation method to obtain a solution numerically. Related free-boundary problems are included to progress from a case of zero-gravity, unbounded flow to the full problem

    Nitsche method for Navier-Stokes equations with slip boundary conditions: Convergence analysis and VMS-LES stabilization

    Full text link
    In this paper, we analyze the Nitsche's method for the stationary Navier-Stokes equations on Lipschitz domains under minimal regularity assumptions. Our analysis provides a robust formulation for implementing slip (i.e. Navier) boundary conditions in arbitrarily complex boundaries. The well-posedness of the discrete problem is established using the Banach Ne\v{c}as Babu\v{s}ka and the Banach fixed point theorems under standard small data assumptions, and we also provide optimal convergence rates for the approximation error. Furthermore, we propose a VMS-LES stabilized formulation, which allows the simulation of incompressible fluids at high Reynolds numbers. We validate our theory through numerous numerical tests in well established benchmark problems

    Dr. KID: Direct Remeshing and K-set Isometric Decomposition for Scalable Physicalization of Organic Shapes

    Full text link
    Dr. KID is an algorithm that uses isometric decomposition for the physicalization of potato-shaped organic models in a puzzle fashion. The algorithm begins with creating a simple, regular triangular surface mesh of organic shapes, followed by iterative k-means clustering and remeshing. For clustering, we need similarity between triangles (segments) which is defined as a distance function. The distance function maps each triangle's shape to a single point in the virtual 3D space. Thus, the distance between the triangles indicates their degree of dissimilarity. K-means clustering uses this distance and sorts of segments into k classes. After this, remeshing is applied to minimize the distance between triangles within the same cluster by making their shapes identical. Clustering and remeshing are repeated until the distance between triangles in the same cluster reaches an acceptable threshold. We adopt a curvature-aware strategy to determine the surface thickness and finalize puzzle pieces for 3D printing. Identical hinges and holes are created for assembling the puzzle components. For smoother outcomes, we use triangle subdivision along with curvature-aware clustering, generating curved triangular patches for 3D printing. Our algorithm was evaluated using various models, and the 3D-printed results were analyzed. Findings indicate that our algorithm performs reliably on target organic shapes with minimal loss of input geometry

    Beam scanning by liquid-crystal biasing in a modified SIW structure

    Get PDF
    A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium
    corecore