Mechanical behavior of glass fiber-reinforced bosses: experiments and FE simulations

This paper, on the one, reports experimental data on injection molded polyamide 6 boss reinforced with 30 wt% of short glass fibers (PA6 GF30), on the other, presents an FE modelling technique for modeling viscoplastic material response. Before the tests all the specimens were conditioned to a moisture content of 1 wt%. The water concentration in the material was checked by weighting. All the mechanical tests have been carried out in compression mode (compression test, compression loading-unloading test, cyclic compression loading-unloading test, compression loading-relaxation-unloading-recovery test) by using a Zwick 1454 type tensile tester equipped with a temperature controlled chamber. The fiber orientation and the strain distribution in the boss was investigated by scanning electron microscopy (SEM) and optical grating technique. The modeling technique presented is based on the overlay method and the generalized Maxwell model. In order to take plastic deformations into consideration the linear viscoelast ic Maxwell-model was generalized to viscoplasticity by replacing the linear spring with an ``elastic-plastic´´ spring. The applicability of the model was proved by finite element simulation

Neural Space-filling Curves

We present Neural Space-filling Curves (SFCs), a data-driven approach to infer a context-based scan order for a set of images. Linear ordering of pixels forms the basis for many applications such as video scrambling, compression, and auto-regressive models that are used in generative modeling for images. Existing algorithms resort to a fixed scanning algorithm such as Raster scan or Hilbert scan. Instead, our work learns a spatially coherent linear ordering of pixels from the dataset of images using a graph-based neural network. The resulting Neural SFC is optimized for an objective suitable for the downstream task when the image is traversed along with the scan line order. We show the advantage of using Neural SFCs in downstream applications such as image compression. Code and additional results will be made available at https://hywang66.github.io/publication/neuralsfc

WARP: Wavelets with adaptive recursive partitioning for multi-dimensional data

Effective identification of asymmetric and local features in images and other data observed on multi-dimensional grids plays a critical role in a wide range of applications including biomedical and natural image processing. Moreover, the ever increasing amount of image data, in terms of both the resolution per image and the number of images processed per application, requires algorithms and methods for such applications to be computationally efficient. We develop a new probabilistic framework for multi-dimensional data to overcome these challenges through incorporating data adaptivity into discrete wavelet transforms, thereby allowing them to adapt to the geometric structure of the data while maintaining the linear computational scalability. By exploiting a connection between the local directionality of wavelet transforms and recursive dyadic partitioning on the grid points of the observation, we obtain the desired adaptivity through adding to the traditional Bayesian wavelet regression framework an additional layer of Bayesian modeling on the space of recursive partitions over the grid points. We derive the corresponding inference recipe in the form of a recursive representation of the exact posterior, and develop a class of efficient recursive message passing algorithms for achieving exact Bayesian inference with a computational complexity linear in the resolution and sample size of the images. While our framework is applicable to a range of problems including multi-dimensional signal processing, compression, and structural learning, we illustrate its work and evaluate its performance in the context of 2D and 3D image reconstruction using real images from the ImageNet database. We also apply the framework to analyze a data set from retinal optical coherence tomography

Plain concrete linearized stiffness diminution modeling subjected to different stresses-srain relationship models.

Linearized stiffness diminution, which is correlated with material damage characteristic, is the major parameters due to modeling of granular material behavior such as plain concrete subjected to cyclic loading. Many damage equations in tension and compression states are proposed in the literatures, however, they produces different damages considering the concepts of the equation's development without any capability of fitting and calibration of produced damages curves with any arbitrary test records. In the present paper, the new equations of concrete damages in the tension and compression state with calibration capability based on the two separated damage indices are developed based on linear interpolation hypothesis. In the result, it is shown that the present equations can be produced the damage parameters close to experimental data