While it is usually not difficult to compute principal curvatures of a smooth surface of sufficient differentiability, it is a rather difficult task when only a polygonal approximation of the surface is available, because of the inherent ambiguity of such representation. A number of different approaches has been proposed in the past that tackle this problem using various techniques. Most papers tend to focus on a particular method, while an comprehensive comparison of the different approaches is usually missing.
We present results of a large experiment, involving both common and recently proposed curvature estimation techniques, applied to triangle meshes of varying properties. It turns out that none of the approaches provides reliable results under all circumstances. Motivated by this observation, we investigate mesh statistics, which can be computed from vertex positions and mesh connectivity information only, and which can help in deciding which estimator will work best for a particular case. Finally, we propose a meta‐estimator, which makes a choice between existing algorithms based on the value of the mesh statistics, and we demonstrate that such meta‐estimator, despite its simplicity, provides considerably more robust results than any existing approach.
The aim of this paper is to research the influence of a different heat treatment of duplex austenitic-ferritic stainless steel to a microstructure. First, the initial data for numerical simulation were obtained by tensile test. Numerical simulation serves to determine the state of the workpiece during open die forging. The second stage focused on the evaluation of the microstructure in state after dwell time at forging temperature (7-and 10-hour) and cooling (water, air). Metallographic analysis observed the influence on precipitation of secondary phase especially.
The main purpose of this work is to propose a modern one-dimensional convolutional neural network (1 D CNN) configurations for distinguishing separate PD impulses from different types of PD sources while the parameters of these sources are changed. Three PD sources were built for signal generation: corona discharge, discharge in a void, and surface discharge. The reason for using separate PD impulses for classification is to develop a universal tool with the ability to recognize an insulation defects by analysing very few events in the insulation in a short range of time. Additionally, we found the optimal sample rates for the data acquisition for these network configurations. The necessity of signal filtering was also tested. The following configurations of a neural network were proposed: configuration for classification raw PD impulses; configuration for classification of PD impulses represented by power spectral density, for both filtered and unfiltered variants.
Hairstyle is an important part of a human head as the hair can completely change the appearance of a person. Therefore hairstyle is important in the creation of identikits -human models for identification practices, namely for the police use. In this paper we propose a new method of hair modeling which allows modeling of common hairstyles in a reasonable time as needed by the police.
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