Literatura académica sobre el tema "Jump-like deformation"

In a cartoon, we often receive an animacy impression from a dynamic nonanimate object, such as a sponge or a flour sack, which does not have an animal-like shape. We hypothesize that the animacy impression of a nonanimal object could stem from dynamic patterns that are possibly fundamental for biological motion perception. Here we show that observers recognize the animacy of human jump actions from the combination of deformation and translation. We extracted vertical motion vectors from the uppermost and lowermost points in point-light jumper stimuli and assigned the vectors to a uniform rectangle. The participants’ task was to rate the animacy and jump impressions for the rectangle. Results showed that both animacy and jump impressions for the rectangle movements were comparable to those for the original point-light movements. The impressions decreased for stimuli having a deformation or translation component alone, which was extracted from the original motion vectors. By mathematically simulating deformation and translation in a human jump, we also found that the temporal relation between deformation and translation plays a critical role in the determination of jump impressions but only has a moderate effect for animacy impressions. On the basis of the results, we discuss how cartoon techniques take advantage of the properties of biological motion perception.

The parameters of the deformation of AMg6 alloy diagram under conditions of uniaxial tension were studied, taking into account the areas of material strengthening. The dependence of the change in magnitude of deformation occurring after jump-like increments in deformation caused by the destruction of dispersed phases in this alloy was revealed. A method of taking into account the revealed regularities in predicting the general deformation of AMg6 alloy based on the histogram of distribution of dispersed particles in the material is proposed.

Acute plastic deformation of long bones is more common in young children. We report a case of an acute plastic deformation of a pediatric radius via magnetic resonance imaging (MRI) evaluation. A 15-year-old boy fell on landing after a jump while practicing soccer, which injured his right forearm. He was diagnosed with a radial neck fracture and a medial epicondylar fracture of the humerus on the basis of plain radiograms. MRI was additionally performed and showed abnormal shadows indicating intramedullary bleeding at multiple bamboo-joint-like deformity sites of the radius. Surgery was performed and injury completely healed. Acute plastic deformation of long bones was often diagnosed by simple radiographic imaging. To our knowledge, there has been no previous reports of plastic deformation evaluated by MRI. If bone plastic deformation is missed, functional impairments such as limited range of motion remain; thus, an early diagnosis of acute bone plastic deformation by performing MRI is recommended.

Ce travail a été réalisé sous co-tutelle entre l’Université Technique Nationale de Ternopil (Ukraine) et l’Université Clermont Auvergne, CNRS, SIGMA Clermont, l’Institut Pascal à Clermont-Ferrand (France). La thèse porte sur la solution d’une tâche scientifique réelle d’évaluer la résistance et la durabilité des éléments responsables des structures. L’objectif de l’étude est d’évaluer la résistance et la durabilité résiduelle des éléments structurels par des méthodes d’apprentissage automatique. La plupart des parties des machines et des composants des structures pendant l'exploitation sont influencés par des charges de nature différente. Ces forces sont soit directement attachées à l’élément, soit transmises par des éléments adjacents qui y sont reliés. Pour le fonctionnement normal des parties responsables des structures,chaque élément doit être d’une taille et d’une forme qui lui permettent de résister aux charges. En particulier, il doit être solide, pas avoir de déformation significative sous tension, rigide et conserver sa forme d’origine. La durée de vie estimée des machines et des structures peut être prédite à l’aide des diagrammes de la croissance des fissures de fatigue des matériaux. Dans la plupart des cas, les données expérimentales présentent certaines variations dont il faut tenir compte. L’expérimentation prend souvent beaucoup de temps et de ressources humaines. Par conséquent, il est conseillé d'apprendre à calculer la durabilité par des méthodes d'apprentissage automatique, en particulier les réseaux de neurones, les arbres renforcés, les forêts aléatoires, les machines à vecteurs de support et les k-plus proches voisins
This work has been performed under co-tutelle supervision between Ternopil IvanPuluj National Technical University in Ternopil (Ukraine) and UniversityClermont Auvergne, CNRS, SIGMA Clermont, Institut Pascal in Clermont-Ferrand (France).This thesis solves the scientific task of responsible structural elements strength andlifetime evaluation. The aim of the thesis is to evaluate the strength and residuallifetime of structural elements by machine learning methods.Most parts of machines and structural elements while being in service are under theinfluence of loads of various nature. Such forces are applied either directly to theelement or transmitted through neighbor elements connected to it. For the normaloperation of the responsible structures parts, each element must have certain sizeand shape that will withstand the loads acting on it. In particular, it must haveappropriate strength properties, not deform significantly under the action ofstresses, be rigid, and preserve its original shape.The calculated residual lifetime of machines and structures can be predicted usingfatigue crack growth (FCG) diagrams. Often, the experimental data have a certainspread, which should be taken into account in their analysis. The experimentalmethod often takes a lot of time and human resources. Therefore, it is advisable tolearn how to calculate the residual lifetime using machine learning methods,particularly, neural networks, boosted trees, random forests, support-vectormachines and the method of k–nearest neighbors